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• Published: 06 December 2017

Healthy food choices are happy food choices: Evidence from a real life sample using smartphone based assessments

• Deborah R. Wahl 1   na1 ,
• Karoline Villinger 1   na1 ,
• Laura M. König   ORCID: orcid.org/0000-0003-3655-8842 1 ,
• Katrin Ziesemer 1 ,
• Harald T. Schupp 1 &
• Britta Renner 1  

Scientific Reports volume  7 , Article number:  17069 ( 2017 ) Cite this article

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Research suggests that “healthy” food choices such as eating fruits and vegetables have not only physical but also mental health benefits and might be a long-term investment in future well-being. This view contrasts with the belief that high-caloric foods taste better, make us happy, and alleviate a negative mood. To provide a more comprehensive assessment of food choice and well-being, we investigated in-the-moment eating happiness by assessing complete, real life dietary behaviour across eight days using smartphone-based ecological momentary assessment. Three main findings emerged: First, of 14 different main food categories, vegetables consumption contributed the largest share to eating happiness measured across eight days. Second, sweets on average provided comparable induced eating happiness to “healthy” food choices such as fruits or vegetables. Third, dinner elicited comparable eating happiness to snacking. These findings are discussed within the “food as health” and “food as well-being” perspectives on eating behaviour.

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Introduction.

When it comes to eating, researchers, the media, and policy makers mainly focus on negative aspects of eating behaviour, like restricting certain foods, counting calories, and dieting. Likewise, health intervention efforts, including primary prevention campaigns, typically encourage consumers to trade off the expected enjoyment of hedonic and comfort foods against health benefits 1 . However, research has shown that diets and restrained eating are often counterproductive and may even enhance the risk of long-term weight gain and eating disorders 2 , 3 . A promising new perspective entails a shift from food as pure nourishment towards a more positive and well-being centred perspective of human eating behaviour 1 , 4 , 5 . In this context, Block et al . 4 have advocated a paradigm shift from “food as health” to “food as well-being” (p. 848).

Supporting this perspective of “food as well-being”, recent research suggests that “healthy” food choices, such as eating more fruits and vegetables, have not only physical but also mental health benefits 6 , 7 and might be a long-term investment in future well-being 8 . For example, in a nationally representative panel survey of over 12,000 adults from Australia, Mujcic and Oswald 8 showed that fruit and vegetable consumption predicted increases in happiness, life satisfaction, and well-being over two years. Similarly, using lagged analyses, White and colleagues 9 showed that fruit and vegetable consumption predicted improvements in positive affect on the subsequent day but not vice versa. Also, cross-sectional evidence reported by Blanchflower et al . 10 shows that eating fruits and vegetables is positively associated with well-being after adjusting for demographic variables including age, sex, or race 11 . Of note, previous research includes a wide range of time lags between actual eating occasion and well-being assessment, ranging from 24 hours 9 , 12 to 14 days 6 , to 24 months 8 . Thus, the findings support the notion that fruit and vegetable consumption has beneficial effects on different indicators of well-being, such as happiness or general life satisfaction, across a broad range of time spans.

The contention that healthy food choices such as a higher fruit and vegetable consumption is associated with greater happiness and well-being clearly contrasts with the common belief that in particular high-fat, high-sugar, or high-caloric foods taste better and make us happy while we are eating them. When it comes to eating, people usually have a spontaneous “unhealthy = tasty” association 13 and assume that chocolate is a better mood booster than an apple. According to this in-the-moment well-being perspective, consumers have to trade off the expected enjoyment of eating against the health costs of eating unhealthy foods 1 , 4 .

A wealth of research shows that the experience of negative emotions and stress leads to increased consumption in a substantial number of individuals (“emotional eating”) of unhealthy food (“comfort food”) 14 , 15 , 16 , 17 . However, this research stream focuses on emotional eating to “smooth” unpleasant experiences in response to stress or negative mood states, and the mood-boosting effect of eating is typically not assessed 18 . One of the few studies testing the effectiveness of comfort food in improving mood showed that the consumption of “unhealthy” comfort food had a mood boosting effect after a negative mood induction but not to a greater extent than non-comfort or neutral food 19 . Hence, even though people may believe that snacking on “unhealthy” foods like ice cream or chocolate provides greater pleasure and psychological benefits, the consumption of “unhealthy” foods might not actually be more psychologically beneficial than other foods.

However, both streams of research have either focused on a single food category (fruit and vegetable consumption), a single type of meal (snacking), or a single eating occasion (after negative/neutral mood induction). Accordingly, it is unknown whether the boosting effect of eating is specific to certain types of food choices and categories or whether eating has a more general boosting effect that is observable after the consumption of both “healthy” and “unhealthy” foods and across eating occasions. Accordingly, in the present study, we investigated the psychological benefits of eating that varied by food categories and meal types by assessing complete dietary behaviour across eight days in real life.

Furthermore, previous research on the impact of eating on well-being tended to rely on retrospective assessments such as food frequency questionnaires 8 , 10 and written food diaries 9 . Such retrospective self-report methods rely on the challenging task of accurately estimating average intake or remembering individual eating episodes and may lead to under-reporting food intake, particularly unhealthy food choices such as snacks 7 , 20 . To avoid memory and bias problems in the present study we used ecological momentary assessment (EMA) 21 to obtain ecologically valid and comprehensive real life data on eating behaviour and happiness as experienced in-the-moment.

In the present study, we examined the eating happiness and satisfaction experienced in-the-moment, in real time and in real life, using a smartphone based EMA approach. Specifically, healthy participants were asked to record each eating occasion, including main meals and snacks, for eight consecutive days and rate how tasty their meal/snack was, how much they enjoyed it, and how pleased they were with their meal/snack immediately after each eating episode. This intense recording of every eating episode allows assessing eating behaviour on the level of different meal types and food categories to compare experienced eating happiness across meals and categories. Following the two different research streams, we expected on a food category level that not only “unhealthy” foods like sweets would be associated with high experienced eating happiness but also “healthy” food choices such as fruits and vegetables. On a meal type level, we hypothesised that the happiness of meals differs as a function of meal type. According to previous contention, snacking in particular should be accompanied by greater happiness.

Eating episodes

Overall, during the study period, a total of 1,044 completed eating episodes were reported (see also Table  1 ). On average, participants rated their eating happiness with M  = 77.59 which suggests that overall eating occasions were generally positive. However, experienced eating happiness also varied considerably between eating occasions as indicated by a range from 7.00 to 100.00 and a standard deviation of SD  = 16.41.

Food categories and experienced eating happiness

All eating episodes were categorised according to their food category based on the German Nutrient Database (German: Bundeslebensmittelschlüssel), which covers the average nutritional values of approximately 10,000 foods available on the German market and is a validated standard instrument for the assessment of nutritional surveys in Germany. As shown in Table  1 , eating happiness differed significantly across all 14 food categories, F (13, 2131) = 1.78, p  = 0.04. On average, experienced eating happiness varied from 71.82 ( SD  = 18.65) for fish to 83.62 ( SD  = 11.61) for meat substitutes. Post hoc analysis, however, did not yield significant differences in experienced eating happiness between food categories, p  ≥ 0.22. Hence, on average, “unhealthy” food choices such as sweets ( M  = 78.93, SD  = 15.27) did not differ in experienced happiness from “healthy” food choices such as fruits ( M  = 78.29, SD  = 16.13) or vegetables ( M  = 77.57, SD  = 17.17). In addition, an intraclass correlation (ICC) of ρ = 0.22 for happiness indicated that less than a quarter of the observed variation in experienced eating happiness was due to differences between food categories, while 78% of the variation was due to differences within food categories.

However, as Figure  1 (left side) depicts, consumption frequency differed greatly across food categories. Frequently consumed food categories encompassed vegetables which were consumed at 38% of all eating occasions ( n  = 400), followed by dairy products with 35% ( n  = 366), and sweets with 34% ( n  = 356). Conversely, rarely consumed food categories included meat substitutes, which were consumed in 2.2% of all eating occasions ( n  = 23), salty extras (1.5%, n  = 16), and pastries (1.3%, n  = 14).

Left side: Average experienced eating happiness (colour intensity: darker colours indicate greater happiness) and consumption frequency (size of the cycle) for the 14 food categories. Right side: Absolute share of the 14 food categories in total experienced eating happiness.

Amount of experienced eating happiness by food category

To account for the frequency of consumption, we calculated and scaled the absolute experienced eating happiness according to the total sum score. As shown in Figure  1 (right side), vegetables contributed the biggest share to the total happiness followed by sweets, dairy products, and bread. Clustering food categories shows that fruits and vegetables accounted for nearly one quarter of total eating happiness score and thus, contributed to a large part of eating related happiness. Grain products such as bread, pasta, and cereals, which are main sources of carbohydrates including starch and fibre, were the second main source for eating happiness. However, “unhealthy” snacks including sweets, salty extras, and pastries represented the third biggest source of eating related happiness.

Experienced eating happiness by meal type

To further elucidate the contribution of snacks to eating happiness, analysis on the meal type level was conducted. Experienced in-the-moment eating happiness significantly varied by meal type consumed, F (4, 1039) = 11.75, p  < 0.001. Frequencies of meal type consumption ranged from snacks being the most frequently logged meal type ( n  = 332; see also Table  1 ) to afternoon tea being the least logged meal type ( n  = 27). Figure  2 illustrates the wide dispersion within as well as between different meal types. Afternoon tea ( M  = 82.41, SD  = 15.26), dinner ( M  = 81.47, SD  = 14.73), and snacks ( M  = 79.45, SD  = 14.94) showed eating happiness values above the grand mean, whereas breakfast ( M  = 74.28, SD  = 16.35) and lunch ( M  = 73.09, SD  = 18.99) were below the eating happiness mean. Comparisons between meal types showed that eating happiness for snacks was significantly higher than for lunch t (533) = −4.44, p  = 0.001, d  = −0.38 and breakfast, t (567) = −3.78, p  = 0.001, d  = −0.33. However, this was also true for dinner, which induced greater eating happiness than lunch t (446) = −5.48, p  < 0.001, d  = −0.50 and breakfast, t (480) = −4.90, p  < 0.001, d  = −0.46. Finally, eating happiness for afternoon tea was greater than for lunch t (228) = −2.83, p  = 0.047, d  = −0.50. All other comparisons did not reach significance, t  ≤ 2.49, p  ≥ 0.093.

Experienced eating happiness per meal type. Small dots represent single eating events, big circles indicate average eating happiness, and the horizontal line indicates the grand mean. Boxes indicate the middle 50% (interquartile range) and median (darker/lighter shade). The whiskers above and below represent 1.5 of the interquartile range.

Control Analyses

In order to test for a potential confounding effect between experienced eating happiness, food categories, and meal type, additional control analyses within meal types were conducted. Comparing experienced eating happiness for dinner and lunch suggested that dinner did not trigger a happiness spill-over effect specific to vegetables since the foods consumed at dinner were generally associated with greater happiness than those consumed at other eating occasions (Supplementary Table  S1 ). Moreover, the relative frequency of vegetables consumed at dinner (73%, n  = 180 out of 245) and at lunch were comparable (69%, n  = 140 out of 203), indicating that the observed happiness-vegetables link does not seem to be mainly a meal type confounding effect.

Since the present study focuses on “food effects” (Level 1) rather than “person effects” (Level 2), we analysed the data at the food item level. However, participants who were generally overall happier with their eating could have inflated the observed happiness scores for certain food categories. In order to account for person-level effects, happiness scores were person-mean centred and thereby adjusted for mean level differences in happiness. The person-mean centred happiness scores ( M cwc ) represent the difference between the individual’s average happiness score (across all single in-the-moment happiness scores per food category) and the single happiness scores of the individual within the respective food category. The centred scores indicate whether the single in-the-moment happiness score was above (indicated by positive values) or below (indicated by negative values) the individual person-mean. As Table  1 depicts, the control analyses with centred values yielded highly similar results. Vegetables were again associated on average with more happiness than other food categories (although people might differ in their general eating happiness). An additional conducted ANOVA with person-centred happiness values as dependent variables and food categories as independent variables provided also a highly similar pattern of results. Replicating the previously reported analysis, eating happiness differed significantly across all 14 food categories, F (13, 2129) = 1.94, p  = 0.023, and post hoc analysis did not yield significant differences in experienced eating happiness between food categories, p  ≥ 0.14. Moreover, fruits and vegetables were associated with high happiness values, and “unhealthy” food choices such as sweets did not differ in experienced happiness from “healthy” food choices such as fruits or vegetables. The only difference between the previous and control analysis was that vegetables ( M cwc  = 1.16, SD  = 15.14) gained slightly in importance for eating-related happiness, whereas fruits ( M cwc  = −0.65, SD  = 13.21), salty extras ( M cwc  = −0.07, SD  = 8.01), and pastries ( M cwc  = −2.39, SD  = 18.26) became slightly less important.

This study is the first, to our knowledge, that investigated in-the-moment experienced eating happiness in real time and real life using EMA based self-report and imagery covering the complete diversity of food intake. The present results add to and extend previous findings by suggesting that fruit and vegetable consumption has immediate beneficial psychological effects. Overall, of 14 different main food categories, vegetables consumption contributed the largest share to eating happiness measured across eight days. Thus, in addition to the investment in future well-being indicated by previous research 8 , “healthy” food choices seem to be an investment in the in-the moment well-being.

Importantly, although many cultures convey the belief that eating certain foods has a greater hedonic and mood boosting effect, the present results suggest that this might not reflect actual in-the-moment experiences accurately. Even though people often have a spontaneous “unhealthy = tasty” intuition 13 , thus indicating that a stronger happiness boosting effect of “unhealthy” food is to be expected, the induced eating happiness of sweets did not differ on average from “healthy” food choices such as fruits or vegetables. This was also true for other stereotypically “unhealthy” foods such as pastries and salty extras, which did not show the expected greater boosting effect on happiness. Moreover, analyses on the meal type level support this notion, since snacks, despite their overall positive effect, were not the most psychologically beneficial meal type, i.e., dinner had a comparable “happiness” signature to snacking. Taken together, “healthy choices” seem to be also “happy choices” and at least comparable to or even higher in their hedonic value as compared to stereotypical “unhealthy” food choices.

In general, eating happiness was high, which concurs with previous research from field studies with generally healthy participants. De Castro, Bellisle, and Dalix 22 examined weekly food diaries from 54 French subjects and found that most of the meals were rated as appealing. Also, the observed differences in average eating happiness for the 14 different food categories, albeit statistically significant, were comparable small. One could argue that this simply indicates that participants avoided selecting bad food 22 . Alternatively, this might suggest that the type of food or food categories are less decisive for experienced eating happiness than often assumed. This relates to recent findings in the field of comfort and emotional eating. Many people believe that specific types of food have greater comforting value. Also in research, the foods eaten as response to negative emotional strain, are typically characterised as being high-caloric because such foods are assumed to provide immediate psycho-physical benefits 18 . However, comparing different food types did not provide evidence for the notion that they differed in their provided comfort; rather, eating in general led to significant improvements in mood 19 . This is mirrored in the present findings. Comparing the eating happiness of “healthy” food choices such as fruits and vegetables to that of “unhealthy” food choices such as sweets shows remarkably similar patterns as, on average, they were associated with high eating happiness and their range of experiences ranged from very negative to very positive.

This raises the question of why the idea that we can eat indulgent food to compensate for life’s mishaps is so prevailing. In an innovative experimental study, Adriaanse, Prinsen, de Witt Huberts, de Ridder, and Evers 23 led participants believe that they overate. Those who characterised themselves as emotional eaters falsely attributed their over-consumption to negative emotions, demonstrating a “confabulation”-effect. This indicates that people might have restricted self-knowledge and that recalled eating episodes suffer from systematic recall biases 24 . Moreover, Boelsma, Brink, Stafleu, and Hendriks 25 examined postprandial subjective wellness and objective parameters (e.g., ghrelin, insulin, glucose) after standardised breakfast intakes and did not find direct correlations. This suggests that the impact of different food categories on wellness might not be directly related to biological effects but rather due to conditioning as food is often paired with other positive experienced situations (e.g., social interactions) or to placebo effects 18 . Moreover, experimental and field studies indicate that not only negative, but also positive, emotions trigger eating 15 , 26 . One may speculate that selective attention might contribute to the “myth” of comfort food 19 in that people attend to the consumption effect of “comfort” food in negative situation but neglect the effect in positive ones.

The present data also show that eating behaviour in the real world is a complex behaviour with many different aspects. People make more than 200 food decisions a day 27 which poses a great challenge for the measurement of eating behaviour. Studies often assess specific food categories such as fruit and vegetable consumption using Food Frequency Questionnaires, which has clear advantages in terms of cost-effectiveness. However, focusing on selective aspects of eating and food choices might provide only a selective part of the picture 15 , 17 , 22 . It is important to note that focusing solely on the “unhealthy” food choices such as sweets would have led to the conclusion that they have a high “indulgent” value. To be able to draw conclusions about which foods make people happy, the relation of different food categories needs to be considered. The more comprehensive view, considering the whole dietary behaviour across eating occasions, reveals that “healthy” food choices actually contributed the biggest share to the total experienced eating happiness. Thus, for a more comprehensive understanding of how eating behaviours are regulated, more complete and sensitive measures of the behaviour are necessary. Developments in mobile technologies hold great promise for feasible dietary assessment based on image-assisted methods 28 .

As fruits and vegetables evoked high in-the-moment happiness experiences, one could speculate that these cumulate and have spill-over effects on subsequent general well-being, including life satisfaction across time. Combing in-the-moment measures with longitudinal perspectives might be a promising avenue for future studies for understanding the pathways from eating certain food types to subjective well-being. In the literature different pathways are discussed, including physiological and biochemical aspects of specific food elements or nutrients 7 .

The present EMA based data also revealed that eating happiness varied greatly within the 14 food categories and meal types. As within food category variance represented more than two third of the total observed variance, happiness varied according to nutritional characteristics and meal type; however, a myriad of factors present in the natural environment can affect each and every meal. Thus, widening the “nourishment” perspective by including how much, when, where, how long, and with whom people eat might tell us more about experienced eating happiness. Again, mobile, in-the-moment assessment opens the possibility of assessing the behavioural signature of eating in real life. Moreover, individual factors such as eating motives, habitual eating styles, convenience, and social norms are likely to contribute to eating happiness variance 5 , 29 .

A key strength of this study is that it was the first to examine experienced eating happiness in non-clinical participants using EMA technology and imagery to assess food intake. Despite this strength, there are some limitations to this study that affect the interpretation of the results. In the present study, eating happiness was examined on a food based level. This neglects differences on the individual level and might be examined in future multilevel studies. Furthermore, as a main aim of this study was to assess real life eating behaviour, the “natural” observation level is the meal, the psychological/ecological unit of eating 30 , rather than food categories or nutrients. Therefore, we cannot exclude that specific food categories may have had a comparably higher impact on the experienced happiness of the whole meal. Sample size and therefore Type I and Type II error rates are of concern. Although the total number of observations was higher than in previous studies (see for example, Boushey et al . 28 for a review), the number of participants was small but comparable to previous studies in this field 20 , 31 , 32 , 33 . Small sample sizes can increase error rates because the number of persons is more decisive than the number of nested observations 34 . Specially, nested data can seriously increase Type I error rates, which is rather unlikely to be the case in the present study. Concerning Type II error rates, Aarts et al . 35 illustrated for lower ICCs that adding extra observations per participant also increases power, particularly in the lower observation range. Considering the ICC and the number of observations per participant, one could argue that the power in the present study is likely to be sufficient to render the observed null-differences meaningful. Finally, the predominately white and well-educated sample does limit the degree to which the results can be generalised to the wider community; these results warrant replication with a more representative sample.

Despite these limitations, we think that our study has implications for both theory and practice. The cumulative evidence of psychological benefits from healthy food choices might offer new perspectives for health promotion and public-policy programs 8 . Making people aware of the “healthy = happy” association supported by empirical evidence provides a distinct and novel perspective to the prevailing “unhealthy = tasty” folk intuition and could foster eating choices that increase both in-the-moment happiness and future well-being. Furthermore, the present research lends support to the advocated paradigm shift from “food as health” to “food as well-being” which entails a supporting and encouraging rather constraining and limiting view on eating behaviour.

The study conformed with the Declaration of Helsinki. All study protocols were approved by University of Konstanz’s Institutional Review Board and were conducted in accordance with guidelines and regulations. Upon arrival, all participants signed a written informed consent.

Participants

Thirty-eight participants (28 females: average age = 24.47, SD  = 5.88, range = 18–48 years) from the University of Konstanz assessed their eating behaviour in close to real time and in their natural environment using an event-based ambulatory assessment method (EMA). No participant dropped out or had to be excluded. Thirty-three participants were students, with 52.6% studying psychology. As compensation, participants could choose between taking part in a lottery (4 × 25€) or receiving course credits (2 hours).

Participants were recruited through leaflets distributed at the university and postings on Facebook groups. Prior to participation, all participants gave written informed consent. Participants were invited to the laboratory for individual introductory sessions. During this first session, participants installed the application movisensXS (version 0.8.4203) on their own smartphones and downloaded the study survey (movisensXS Library v4065). In addition, they completed a short baseline questionnaire, including demographic variables like age, gender, education, and eating principles. Participants were instructed to log every eating occasion immediately before eating by using the smartphone to indicate the type of meal, take pictures of the food, and describe its main components using a free input field. Fluid intake was not assessed. Participants were asked to record their food intake on eight consecutive days. After finishing the study, participants were invited back to the laboratory for individual final interviews.

Immediately before eating participants were asked to indicate the type of meal with the following five options: breakfast, lunch, afternoon tea, dinner, snack. In Germany, “afternoon tea” is called “Kaffee & Kuchen” which directly translates as “coffee & cake”. It is similar to the idea of a traditional “afternoon tea” meal in UK. Specifically, in Germany, people have “Kaffee & Kuchen” in the afternoon (between 4–5 pm) and typically coffee (or tea) is served with some cake or cookies. Dinner in Germany is a main meal with mainly savoury food.

After each meal, participants were asked to rate their meal on three dimensions. They rated (1) how much they enjoyed the meal, (2) how pleased they were with their meal, and (3) how tasty their meal was. Ratings were given on a scale of one to 100. For reliability analysis, Cronbach’s Alpha was calculated to assess the internal consistency of the three items. Overall Cronbach’s alpha was calculated with α = 0.87. In addition, the average of the 38 Cronbach’s alpha scores calculated at the person level also yielded a satisfactory value with α = 0.83 ( SD  = 0.24). Thirty-two of 38 participants showed a Cronbach’s alpha value above 0.70 (range = 0.42–0.97). An overall score of experienced happiness of eating was computed using the average of the three questions concerning the meals’ enjoyment, pleasure, and tastiness.

Analytical procedure

The food pictures and descriptions of their main components provided by the participants were subsequently coded by independent and trained raters. Following a standardised manual, additional components displayed in the picture were added to the description by the raters. All consumed foods were categorised into 14 different food categories (see Table  1 ) derived from the food classification system designed by the German Nutrition Society (DGE) and based on the existing food categories of the German Nutrient Database (Max Rubner Institut). Liquid intake and preparation method were not assessed. Therefore, fats and additional recipe ingredients were not included in further analyses, because they do not represent main elements of food intake. Further, salty extras were added to the categorisation.

No participant dropped out or had to be excluded due to high missing rates. Missing values were below 5% for all variables. The compliance rate at the meal level cannot be directly assessed since the numbers of meals and snacks can vary between as well as within persons (between days). As a rough compliance estimate, the numbers of meals that are expected from a “normative” perspective during the eight observation days can be used as a comparison standard (8 x breakfast, 8 × lunch, 8 × dinner = 24 meals). On average, the participants reported M  = 6.3 breakfasts ( SD  = 2.3), M  = 5.3 lunches ( SD  = 1.8), and M  = 6.5 dinners ( SD  = 2.0). In comparison to the “normative” expected 24 meals, these numbers indicate a good compliance (approx. 75%) with a tendency to miss six meals during the study period (approx. 25%). However, the “normative” expected 24 meals for the study period might be too high since participants might also have skipped meals (e.g. breakfast). Also, the present compliance rates are comparable to other studies. For example, Elliston et al . 36 recorded 3.3 meal/snack reports per day in an Australian adult sample and Casperson et al . 37 recorded 2.2 meal reports per day in a sample of adolescents. In the present study, on average, M  = 3.4 ( SD  = 1.35) meals or snacks were reported per day. These data indicate overall a satisfactory compliance rate and did not indicate selective reporting of certain food items.

To graphically visualise data, Tableau (version 10.1) was used and for further statistical analyses, IBM SPSS Statistics (version 24 for Windows).

Data availability

The dataset generated and analysed during the current study is available from the corresponding authors on reasonable request.

Cornil, Y. & Chandon, P. Pleasure as an ally of healthy eating? Contrasting visceral and epicurean eating pleasure and their association with portion size preferences and wellbeing. Appetite 104 , 52–59 (2016).

Article   PubMed   Google Scholar  

Mann, T. et al . Medicare’s search for effective obesity treatments: Diets are not the answer. American Psychologist 62 , 220–233 (2007).

van Strien, T., Herman, C. P. & Verheijden, M. W. Dietary restraint and body mass change. A 3-year follow up study in a representative Dutch sample. Appetite 76 , 44–49 (2014).

Block, L. G. et al . From nutrients to nurturance: A conceptual introduction to food well-being. Journal of Public Policy & Marketing 30 , 5–13 (2011).

Article   Google Scholar  

Renner, B., Sproesser, G., Strohbach, S. & Schupp, H. T. Why we eat what we eat. The eating motivation survey (TEMS). Appetite 59 , 117–128 (2012).

Conner, T. S., Brookie, K. L., Carr, A. C., Mainvil, L. A. & Vissers, M. C. Let them eat fruit! The effect of fruit and vegetable consumption on psychological well-being in young adults: A randomized controlled trial. PloS one 12 , e0171206 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Rooney, C., McKinley, M. C. & Woodside, J. V. The potential role of fruit and vegetables in aspects of psychological well-being: a review of the literature and future directions. Proceedings of the Nutrition Society 72 , 420–432 (2013).

Mujcic, R. & Oswald, A. J. Evolution of well-being and happiness after increases in consumption of fruit and vegetables. American Journal of Public Health 106 , 1504–1510 (2016).

White, B. A., Horwath, C. C. & Conner, T. S. Many apples a day keep the blues away – Daily experiences of negative and positive affect and food consumption in young adults. British Journal of Health Psychology 18 , 782–798 (2013).

Blanchflower, D. G., Oswald, A. J. & Stewart-Brown, S. Is psychological well-being linked to the consumption of fruit and vegetables? Social Indicators Research 114 , 785–801 (2013).

Grant, N., Wardle, J. & Steptoe, A. The relationship between life satisfaction and health behavior: A Cross-cultural analysis of young adults. International Journal of Behavioral Medicine 16 , 259–268 (2009).

Conner, T. S., Brookie, K. L., Richardson, A. C. & Polak, M. A. On carrots and curiosity: Eating fruit and vegetables is associated with greater flourishing in daily life. British Journal of Health Psychology 20 , 413–427 (2015).

Raghunathan, R., Naylor, R. W. & Hoyer, W. D. The unhealthy = tasty intuition and its effects on taste inferences, enjoyment, and choice of food products. Journal of Marketing 70 , 170–184 (2006).

Evers, C., Stok, F. M. & de Ridder, D. T. Feeding your feelings: Emotion regulation strategies and emotional eating. Personality and Social Psychology Bulletin 36 , 792–804 (2010).

Sproesser, G., Schupp, H. T. & Renner, B. The bright side of stress-induced eating: eating more when stressed but less when pleased. Psychological Science 25 , 58–65 (2013).

Wansink, B., Cheney, M. M. & Chan, N. Exploring comfort food preferences across age and gender. Physiology & Behavior 79 , 739–747 (2003).

Article   CAS   Google Scholar  

Taut, D., Renner, B. & Baban, A. Reappraise the situation but express your emotions: impact of emotion regulation strategies on ad libitum food intake. Frontiers in Psychology 3 , 359 (2012).

Tomiyama, J. A., Finch, L. E. & Cummings, J. R. Did that brownie do its job? Stress, eating, and the biobehavioral effects of comfort food. Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource (2015).

Wagner, H. S., Ahlstrom, B., Redden, J. P., Vickers, Z. & Mann, T. The myth of comfort food. Health Psychology 33 , 1552–1557 (2014).

Schüz, B., Bower, J. & Ferguson, S. G. Stimulus control and affect in dietary behaviours. An intensive longitudinal study. Appetite 87 , 310–317 (2015).

Shiffman, S. Conceptualizing analyses of ecological momentary assessment data. Nicotine & Tobacco Research 16 , S76–S87 (2014).

de Castro, J. M., Bellisle, F. & Dalix, A.-M. Palatability and intake relationships in free-living humans: measurement and characterization in the French. Physiology & Behavior 68 , 271–277 (2000).

Adriaanse, M. A., Prinsen, S., de Witt Huberts, J. C., de Ridder, D. T. & Evers, C. ‘I ate too much so I must have been sad’: Emotions as a confabulated reason for overeating. Appetite 103 , 318–323 (2016).

Robinson, E. Relationships between expected, online and remembered enjoyment for food products. Appetite 74 , 55–60 (2014).

Boelsma, E., Brink, E. J., Stafleu, A. & Hendriks, H. F. Measures of postprandial wellness after single intake of two protein–carbohydrate meals. Appetite 54 , 456–464 (2010).

Article   CAS   PubMed   Google Scholar  

Boh, B. et al . Indulgent thinking? Ecological momentary assessment of overweight and healthy-weight participants’ cognitions and emotions. Behaviour Research and Therapy 87 , 196–206 (2016).

Wansink, B. & Sobal, J. Mindless eating: The 200 daily food decisions we overlook. Environment and Behavior 39 , 106–123 (2007).

Boushey, C., Spoden, M., Zhu, F., Delp, E. & Kerr, D. New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods. Proceedings of the Nutrition Society , 1–12 (2016).

Stok, F. M. et al . The DONE framework: Creation, evaluation, and updating of an interdisciplinary, dynamic framework 2.0 of determinants of nutrition and eating. PLoS ONE 12 , e0171077 (2017).

Pliner, P. & Rozin, P. In Dimensions of the meal: The science, culture, business, and art of eating (ed H Meiselman) 19–46 (Aspen Publishers, 2000).

Inauen, J., Shrout, P. E., Bolger, N., Stadler, G. & Scholz, U. Mind the gap? Anintensive longitudinal study of between-person and within-person intention-behaviorrelations. Annals of Behavioral Medicine 50 , 516–522 (2016).

Zepeda, L. & Deal, D. Think before you eat: photographic food diaries asintervention tools to change dietary decision making and attitudes. InternationalJournal of Consumer Studies 32 , 692–698 (2008).

Stein, K. F. & Corte, C. M. Ecologic momentary assessment of eating‐disordered behaviors. International Journal of Eating Disorders 34 , 349–360 (2003).

Bolger, N., Stadler, G. & Laurenceau, J. P. Power analysis for intensive longitudinal studies in Handbook of research methods for studying daily life (ed . Mehl, M. R. & Conner, T. S.) 285–301 (New York: The Guilford Press, 2012).

Aarts, E., Verhage, M., Veenvliet, J. V., Dolan, C. V. & Van Der Sluis, S. A solutionto dependency: using multilevel analysis to accommodate nested data. Natureneuroscience 17 , 491–496 (2014).

Elliston, K. G., Ferguson, S. G., Schüz, N. & Schüz, B. Situational cues andmomentary food environment predict everyday eating behavior in adults withoverweight and obesity. Health Psychology 36 , 337–345 (2017).

Casperson, S. L. et al . A mobile phone food record app to digitally capture dietary intake for adolescents in afree-living environment: usability study. JMIR mHealth and uHealth 3 , e30 (2015).

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Acknowledgements

This research was supported by the Federal Ministry of Education and Research within the project SmartAct (Grant 01EL1420A, granted to B.R. & H.S.). The funding source had no involvement in the study’s design; the collection, analysis, and interpretation of data; the writing of the report; or the decision to submit this article for publication. We thank Gudrun Sproesser, Helge Giese, and Angela Whale for their valuable support.

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Deborah R. Wahl and Karoline Villinger contributed equally to this work.

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Department of Psychology, University of Konstanz, Konstanz, Germany

Deborah R. Wahl, Karoline Villinger, Laura M. König, Katrin Ziesemer, Harald T. Schupp & Britta Renner

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B.R. & H.S. developed the study concept. All authors participated in the generation of the study design. D.W., K.V., L.K. & K.Z. conducted the study, including participant recruitment and data collection, under the supervision of B.R. & H.S.; D.W. & K.V. conducted data analyses. D.W. & K.V. prepared the first manuscript draft, and B.R. & H.S. provided critical revisions. All authors approved the final version of the manuscript for submission.

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Wahl, D.R., Villinger, K., König, L.M. et al. Healthy food choices are happy food choices: Evidence from a real life sample using smartphone based assessments. Sci Rep 7 , 17069 (2017). https://doi.org/10.1038/s41598-017-17262-9

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A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing

• Pauline Hautekiet   ORCID: orcid.org/0000-0003-3805-3004 1 , 2 ,
• Nelly D. Saenen 1 , 2 ,
• Dries S. Martens 2 ,
• Margot Debay 2 ,
• Johan Van der Heyden 3 ,
• Tim S. Nawrot 2 , 4 &
• Eva M. De Clercq 1  

BMC Medicine volume  20 , Article number:  328 ( 2022 ) Cite this article

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Studies often evaluate mental health and well-being in association with individual health behaviours although evaluating multiple health behaviours that co-occur in real life may reveal important insights into the overall association. Also, the underlying pathways of how lifestyle might affect our health are still under debate. Here, we studied the mediation of different health behaviours or lifestyle factors on mental health and its effect on core markers of ageing: telomere length (TL) and mitochondrial DNA content (mtDNAc).

In this study, 6054 adults from the 2018 Belgian Health Interview Survey (BHIS) were included. Mental health and well-being outcomes included psychological and severe psychological distress, vitality, life satisfaction, self-perceived health, depressive and generalised anxiety disorder and suicidal ideation. A lifestyle score integrating diet, physical activity, smoking status, alcohol consumption and BMI was created and validated. On a subset of 739 participants, leucocyte TL and mtDNAc were assessed using qPCR. Generalised linear mixed models were used while adjusting for a priori chosen covariates.

The average age (SD) of the study population was 49.9 (17.5) years, and 48.8% were men. A one-point increment in the lifestyle score was associated with lower odds (ranging from 0.56 to 0.74) for all studied mental health outcomes and with a 1.74% (95% CI: 0.11, 3.40%) longer TL and 4.07% (95% CI: 2.01, 6.17%) higher mtDNAc. Psychological distress and suicidal ideation were associated with a lower mtDNAc of − 4.62% (95% CI: − 8.85, − 0.20%) and − 7.83% (95% CI: − 14.77, − 0.34%), respectively. No associations were found between mental health and TL.

Conclusions

In this large-scale study, we showed the positive association between a healthy lifestyle and both biological ageing and different dimensions of mental health and well-being. We also indicated that living a healthy lifestyle contributes to more favourable biological ageing.

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According to the World Health Organization (WHO), a healthy lifestyle is defined as “a way of living that lowers the risk of being seriously ill or dying early” [ 1 ]. Public health authorities emphasise the importance of a healthy lifestyle, but despite this, many individuals worldwide still live an unhealthy lifestyle [ 2 ]. In Europe, 26% of adults smoke [ 3 ], nearly half (46%) never exercise [ 4 ], 8.4% drink alcohol on a daily basis [ 5 ] and over half (51%) are overweight [ 5 ]. These unhealthy behaviours have been associated with adverse health outcomes like cardiovascular diseases [ 6 , 7 , 8 ], respiratory diseases [ 9 ], musculoskeletal diseases [ 10 ] and, to a lesser extent, mental disorders [ 11 , 12 ].

Even though the association between lifestyle and health outcomes has been extensively investigated, biological mechanisms explaining these observed associations are not yet fully understood. One potential mechanism that can be suggested is biological ageing. Both telomere length (TL) and mitochondrial DNA content (mtDNAc) are known biomarkers of ageing. Telomeres are the end caps of chromosomes and consist of multiple TTAGGG sequence repeats. They protect chromosomes from degradation and shorten with every cell division because of the “end-replication problem” [ 13 ]. Mitochondria are crucial to the cell as they are responsible for apoptosis, the control of cytosolic calcium levels and cell signalling [ 14 ]. Living a healthy lifestyle can be linked with healthy ageing as both TL and mtDNAc have been associated with health behaviours like obesity [ 15 ], diet [ 16 ], smoking [ 17 ] and alcohol abuse [ 18 ]. Furthermore, as biomarkers of ageing, both TL and mtDNAc have been associated with age-related diseases like Parkinson’s disease [ 19 ], coronary heart disease [ 20 ], atherosclerosis [ 21 ] and early mortality [ 22 ]. Also, early mortality and higher risks for the aforementioned age-related diseases are observed in psychiatric illnesses, and it is suggested that advanced biological ageing underlies these observations [ 23 ].

Multiple studies evaluated individual health behaviours, but research on the combination of these health behaviours is limited. As they often co-occur and may cause synergistic effects, assessing them in combination with each other rather than independently might better reflect the real-life situation [ 24 , 25 ]. Therefore, in a general adult population, we combined five commonly studied health behaviours including diet, smoking status, alcohol consumption, BMI and physical activity into one healthy lifestyle score to evaluate its association with mental health and well-being and biological ageing. Furthermore, we evaluated the association between the markers of biological ageing and mental health and well-being. We hypothesise that individuals living a healthy lifestyle have a better mental health status, a longer TL and a higher mtDNAc and that these biomarkers are positively associated with mental health and well-being.

Study population

In 2018, 11611 Belgian residents participated in the 2018 Belgian Health Interview Survey (BHIS). The sampling frame of the BHIS was the Belgian National Register, and participants were selected based on a multistage stratified sampling design including a geographical stratification and a selection of municipalities within provinces, of households within municipalities and of respondents within households [ 26 ]. The study population for this cross-sectional study included 6054 BHIS participants (see flowchart in Additional file 1 : Fig. S1) [ 27 , 28 , 29 , 30 , 31 ]. Minors (< 18 years) and participants not eligible to complete the mental health modules (participants who participated through a proxy respondent, i.e. a person of confidence filled out the survey) were excluded ( n  = 2172 and n  = 846, respectively). Furthermore, of the 8593 eligible participants, those with missing information to create the mental health indicators, the lifestyle score or the covariates used in this study were excluded ( n  = 1642, 788 and 109, respectively).

For the first time in 2018, a subset of 1184 BHIS participants contributed to the 2018 Belgian Health Examination Survey (BELHES). All BHIS participants were invited to participate except for minors (< 18 years), BHIS participants who participated through a proxy respondent and residents of the German Community of Belgium, the latter representing 1% of the Belgian population. Participants were recruited on a voluntary basis until the regional quotas were reached (450, 300 and 350 in respectively Flanders, Brussels Capital Region and Wallonia). These participants underwent a health examination, including anthropological measurements and completed an additional questionnaire. Also, blood and urine samples were collected. Of the 6054 included BHIS participants, 909 participated in the BELHES. Participants for whom we could not calculate both TL and mtDNAc were excluded ( n  = 170). More specifically, participants were excluded because they did not provide a blood sample ( n  = 91) or because they did not provide permission for DNA research ( n  = 32). Twenty samples were excluded from DNA extraction because either total blood volume was too low ( n  = 7), samples were clothed ( n  = 1) or tubes were broken due to freezing conditions ( n  = 12). Twenty-seven samples were excluded because they did not meet the biomarker quality control criteria (high technical variation in qPCR triplicates). This was not met for 3 TL samples, 20 mtDNAc samples and 4 samples for both biomarkers. For this subset, we ended up with a final number of 739 participants. Further in this paper, we refer to “the BHIS subset” for the BHIS participants ( n  = 6054) and the “BELHES subset” for the BELHES participants ( n  = 739).

As part of the BELHES, this project was approved by the Medical Ethics Committee of the University Hospital Ghent (registration number B670201834895). The project was carried out in line with the recommendations of the Belgian Privacy Commission. All participants have signed a consent form that was approved by the Medical Ethics Committee.

Health interview survey

The BHIS is a comprehensive survey which aims to gain insight into the health status of the Belgian population. The questions on the different dimensions of mental health and well-being were based on international standardised and validated questionnaires [ 32 ], and this resulted in eight mental health outcomes that were used in this study. Detailed information on each indicator score and its use is addressed in Additional file 1 : Table. S1. Firstly, the General Health Questionnaire (GHQ-12) provides the prevalence of psychological and severe psychological distress in the population [ 27 ]. On the total GHQ score, cut-off points of + 2 and + 4 were used to identify respectively psychological and severe psychological distress.

Secondly, we used two indicators for the positive dimensions of mental health: vitality and life satisfaction. Four questions of the short form health survey (SF-36) indicate the participant’s vital energy level [ 28 , 33 ]. We used a cut-off point to identify participants with an optimal vitality score, which is a score equal to or above one standard deviation above the mean, as used in previous studies [ 34 , 35 ]. Life satisfaction was measured by the Cantril Scale, which ranges from 0 to 10 [ 29 ]. A cut-off point of + 6 was used to indicate participants with high or medium life satisfaction versus low life satisfaction.

Thirdly, the question “How is your health in general? Is it very good, good, fair, bad or very bad?” was used to assess self-perceived health, also known as self-rated health. Based on WHO recommendations [ 36 ], the answer categories were dichotomised into “good to very good self-perceived health” and “very bad to fair self-perceived health”.

Fourthly, depressive and generalised anxiety disorders were defined using respectively the Patient Health Questionnaire (PHQ-9) and the Generalised Anxiety Disorder Questionnaire (GAD-7). We identified individuals who suffer from major depressive syndrome or any other type of depressive syndrome according to the criteria of the PHQ-9 [ 37 ]. A cut-off point of + 10 on the total sum of the GAD-7 score was used to indicate generalised anxiety disorder [ 31 ]. Additionally, a dichotomous question on suicidal ideation was used: “Have you ever seriously thought of ending your life?”; “If yes, did you have such thoughts in the past 12 months?”. Finally, the BHIS also includes personal, socio-economic and lifestyle information. The standardised Cronbach’s alpha coefficients for the PHQ-9, GHQ-12, GAD-7 and questions on vitality of the SF-36 ranged between 0.80 and 0.90.

Healthy lifestyle score

We developed a healthy lifestyle score based on five different health behaviours: body mass index (BMI), smoking status, physical activity, alcohol consumption and diet (Table 1 ). These health behaviours were defined as much as possible according to the existing guidelines for healthy living issued by the Belgian Superior Health Council [ 38 ] and the World Health Organisation [ 39 , 40 , 41 ]. Firstly, BMI was calculated as a person’s self-reported weight in kilogrammes divided by the square of the person’s self-reported height in metres (kg/m 2 ). BMI was classified into four categories: underweight (BMI < 18.5 kg/m 2 ), normal weight (BMI 18.5–24.9 kg/m 2 ), overweight (BMI 25.0–29.9 kg/m 2 ) and obese (BMI ≥ 30.0 kg/m 2 ). Due to a J-shaped association of BMI with the overall mortality and multiple specific causes of death, obesity and underweight were both classified as least healthy [ 42 ]. BMI was scored as follows: obese and underweight = 0, overweight = 1 and normal weight = 2.

Secondly, smoking status was divided into four categories. Participants were categorised as regular smokers if they smoked a minimum of 4 days per week or if they quit smoking less than 1 month before participation (= 0). Occasional smokers were defined as smoking more than once per month up to 3 days per week (= 1). Participants were classified as former smokers if they quit smoking at least 1 month before the questionnaire or if they smoked less than once a month (= 2). The final category included people who never smoked (= 3).

Thirdly, physical activity was assessed by the question: “What describes best your leisure time activities during the last year?”. Four categories were established and scored as follows: sedentary activities (= 0), light activities less than 4 h/week (= 1), light activities more than 4 h/week or recreational sport less than 4 h/week (= 2) and recreational sport more than 4 h or intense training (= 3). Fourthly, information on the number of alcoholic drinks per week was used to categorise alcohol consumption. The different categories were set from high to low alcohol consumption: 22 drinks or more/week (= 0), 15–21 drinks/week (= 1), 8–14 drinks/week (= 2), 1–7 drinks/week (= 3)and less than 1 drink/week (= 4).

Finally, in line with the research by Benetou et al., a diet score was calculated using the frequency of consuming fruit, vegetables, snacks and sodas [ 43 ]. For fruit as well as vegetable consumption, the frequency was scored as follows: never (= 0), < 1/week (= 1), 1–3/week (= 2), 4–6/week (= 3) and ≥ 1/day (= 4). The frequency of consuming snacks and sodas was scored as follows: never (= 4), < 1/week (= 3), 1–3/week (= 2), 4–6/week (= 1) and ≥ 1/day (= 0). The diet score was then divided into tertiles, in line with the research by Benetou et al. [ 43 ]. A diet score of 0–9 points was classified as the least healthy behaviour (= 0). A diet score ranging from 10 to 12 made up the middle category (= 1), and a score from 13 to 16 was classified as the healthiest behaviour (= 2).

All five previously described health behaviours were combined into one healthy lifestyle score (Table 1 ). The sum of the scores obtained for each health behaviour indicated the absolute lifestyle score. To calculate the relative lifestyle score, each absolute scored health behaviour was given equal weight by recalculating its maximum absolute score to a relative score of 1. The relative lifestyle scores were then summed up to achieve a final continuous lifestyle score, ranging from 0 to 5, with a higher score representing a healthier lifestyle.

Telomere length and mitochondrial DNA content assay

Blood samples were collected during the BELHES and centrifuged for 15 min at 3000 rpm before storage at − 80 °C. After extracting the buffy coat from the blood sample, DNA was isolated using the QIAgen Mini Kit (Qiagen, N.V.V Venlo, The Netherlands). The purity and quantity of the sample were measured with a NanoDrop spectrophotometer (ND-2000; Thermo Fisher Scientific, Wilmington, DE, USA). DNA integrity was assessed by agarose gel electrophoresis. To ensure a uniform DNA input of 6 ng for each qPCR reaction, samples were diluted and checked using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Life Technologies, Europe).

Relative TL and mtDNAc were measured in triplicate using a previously described quantitative real-time PCR (qPCR) assay with minor modifications [ 44 , 45 ]. All reactions were performed on a 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) in a 384-well format. Used telomere, mtDNAc and single copy-gene reaction mixtures and PCR cycles are given in Additional file 1 : Text. S1. Reaction efficiency was assessed on each plate by using a 6-point serial dilution of pooled DNA. Efficiencies ranged from 90 to 100% for single-copy gene runs, 100 to 110% for telomere runs and 95 to 105% for mitochondrial DNA runs. Six inter-run calibrators (IRCs) were used to account for inter-run variability. Also, non-template controls were used in each run. Raw data were processed and normalised to the reference gene using the qBase plus software (Biogazelle, Zwijnaarde, Belgium), taking into account the run-to-run differences.

Leucocyte telomere length was expressed as the ratio of telomere copy number to single-copy gene number (T/S) relative to the mean T/S ratio of the entire study population. Leucocyte mtDNAc was expressed as the ratio of mtDNA copy number to single-copy gene number (M/S) relative to the mean M/S ratio of the entire study population. The reliability of our assay was assessed by calculating the interclass correlation coefficient (ICC) of the triplicate measures (T/S and M/S ratios and T, M and S separately) as proposed by the Telomere Research Network, using RStudio version 1.1.463 (RStudio PBC, Boston, MA, USA). The intra-plate ICCs of T/S ratios, TL runs, M/S ratios, mtDNAc runs and single-copy runs were respectively 0.804 ( p  < 0.0001), 0.907 ( p  < 0.0001), 0.815 ( p  < 0.0001), 0.916 ( p  < 0.0001) and 0.781 ( p  < 0.0001). Based on the IRCs, the inter-plate ICC was 0.714 ( p  < 0.0001) for TL and 0.762 ( p  < 0.0001) for mtDNAc.

Statistical analysis

Statistical analyses were performed using the SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA). We performed a log(10) transformation of the TL and mtDNAc data to reduce skewness and to better approximate a normal distribution. Three analyses were done: (1) In the BHIS subset ( n  = 6054), we evaluated the association between the lifestyle score and the mental health and well-being outcomes (separately). These results are presented as the odds ratio (95% CI) of having a mental health condition or disorder for a one-point increment in the lifestyle score. (2) In the BELHES subset ( n  = 739), we evaluated the association between the lifestyle score and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) for a one-point increment in the lifestyle score. (3) In the BELHES subset ( n  = 739), we evaluated the association between the mental health and well-being outcomes and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) when having a mental health condition or disorder compared with the healthy group.

For all three analyses, we performed multivariable linear mixed models (GLIMMIX; unstructured covariance matrix) taking into account a priori selected covariates including age (continuous), sex (male, female), region (Flanders, Brussels Capital Region, Wallonia), highest educational level of the household (up to lower secondary, higher secondary, college or university), country of birth (Belgium, EU, non-EU) and household type (single, one parent with child, couple without child, couple with child, others). To capture the non-linear effect of age, we included a quadratic term when the result of the analysis showed that both the linear and quadratic terms had a p -value < 0.1. For the two analyses on TL and mtDNAc, we additionally adjusted for the date of participation in the BELHES. As multiple members of one household participated, we added household numbers in the random statement.

Bivariate analyses evaluating the associations between the characteristics and TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health and well-being are evaluated based on the same model. The chi-squared tests (categorical data) and t -tests (continuous data) were used to evaluate the characteristics of included and excluded participants. The lifestyle score was validated by creating a ROC curve and calculating the area under the curve (AUC) of the adjusted association between the lifestyle score and self-perceived health. Adjustments were made for age, sex, region, highest educational level of the household, country of birth and household type.

In a sensitivity analysis, to evaluate the robustness of our findings, we additionally adjusted our main models separately for perceived quality of social support (poor, moderate, strong) and chronic disease (suffering from any chronic disease or condition: yes, no). The third model, evaluating the biomarkers with the mental health outcomes, was also additionally adjusted for the lifestyle score.

Population characteristics

The characteristics of the BHIS and BELHES subset are presented in Table 2 . In the BHIS subset, 48.8% of the participants were men. The average age (SD) was 49.9 (17.5) years, and most participants were born in Belgium (79.5%). The highest educational level in the household was most often college or university degree (53.3%), and the most common household composition was couple with child(ren) (37.7%). The proportion of participants in different regions of Belgium, i.e. Flanders, Brussels Capital Region and Wallonia, was respectively 41.1%, 23.3% and 35.6%. For the BELHES subset, we found similar results except for region and education. We noticed more participants from Flanders and more participants with a high educational level in the household. The mean (SD) relative TL and mtDNAc were respectively 1.04 (0.23) and 1.03 (0.24). TL and mtDNAc were positively correlated (Spearman’s correlation = 0.21, p  < 0.0001).

We compared (1) the characteristics of the 6054 eligible BHIS participants that were included in the BHIS subset with the 2539 eligible participants that were excluded from the BHIS subset (Additional file 1 : Table S2) and (2) the 739 participants from the BHIS subset that were included in the BELHES subset with the 5315 participants that were excluded from the BELHES subset (Additional file 1 : Table S3). Except for sex and nationality in the latter, all other covariates showed differences between the included and excluded groups. On the other hand, population data from 2018 indicates that the average age (SD) of the adult Belgian population was 49.5 (18.9) with a distribution over Flanders, Brussels Capital Region and Wallonia of respectively 58.2%, 10.2% and 31.6% and that 48.7% were men. The distribution of our sample according to age and sex thus largely corresponds to the age and sex distribution of the adult Belgian population figures. The large difference in the regional distribution is due to the oversampling of the Brussels Capital Region in the BHIS.

Bivariate associations evaluating the characteristics with TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health are presented in Additional file 1 : Table S4. Briefly, men had a − 6.41% (95% CI: − 9.10 to − 3.65%, p  < 0.0001) shorter TL, a − 8.03% (95% CI: − 11.00 to − 4.96%, p  < 0.0001) lower mtDNAc, lower odds of psychological distress (OR = 0.59, 95% CI: 0.53 to 0.66, p  < 0.0001) and a lifestyle score of − 0.28 (95% CI: − 0.32 to − 0.24, p  < 0.0001) points less compared with women. Furthermore, a 1-year increment in age was associated with a − 0.64% (− 0.73 to − 0.55%, p  < 0.0001) shorter TL and a − 0.19% (95% CI: − 0.31 to − 0.08%, p  = 0.00074) lower mtDNAc.

Mental health prevalence and lifestyle characteristics

Within the BHIS subset, 32.3% and 18.0% of the participants had respectively psychological and severe psychological distress. 86.7% had suboptimal vitality, 12.0% indicated low life satisfaction and 22.0% had very bad to fair self-perceived health. The prevalence of depressive and generalised anxiety disorders was respectively 9.0% and 10.8%, respectively. 4.4% of the participants indicated to have had suicidal thoughts in the past 12 months. Similar results were found for the BELHES subset (Table 3 ).

Within the BHIS subset, the average lifestyle score (SD) was 3.1 (0.9) (Table 4 ). A histogram of the lifestyle score is shown in Additional file 1 : Fig. S2. 16.6% were regular smokers, and 4.9% reported 22 alcoholic drinks per week or more. 29.7% reported that their main leisure time included mainly sedentary activities, and 18.6% were underweight or obese. 29.2% were classified as having an unhealthy diet score. The participants of the BELHES subset were slightly more active, but no other dissimilarities were found (Table 4 ). The ROC curve shows an area under the curve (AUC) of 0.74, indicating a 74% predictive accuracy for the lifestyle score as a self-perceived health predictor (Additional file 1 : Fig. S3).

Healthy lifestyle and mental health and well-being

Living a healthier lifestyle, indicated by having a higher lifestyle score, was associated with lower odds of all mental health and well-being outcomes (Table 5 ). After adjustment, a one-point increment in the lifestyle score was associated with lower odds of psychological (OR = 0.74, 95% CI: 0.69, 0.79) and severe psychological distress (OR = 0.69, 95% CI: 0.64, 0.75). Similarly, for the same increment, the odds of suboptimal vitality, low life satisfaction and very bad to fair self-perceived health were respectively 0.62 (95% CI: 0.56, 0.68), 0.62 (95% CI: 0.56, 0.68) and 0.56 (95% CI: 0.52, 0.61). Finally, the odds of having depressive disorder, generalised anxiety disorder or suicidal ideation were respectively 0.57 (95% CI: 0.51, 0.63), 0.63 (95% CI: 0.57, 0.69) and 0.63 (95% CI: 0.55, 0.72) for a one-point increment in the lifestyle score.

The biomarkers of ageing

After adjustment, living a healthy lifestyle was positively associated with both TL and mtDNAc (Table 6 ). A one-point increment in the lifestyle score was associated with a 1.74 (95% CI: 0.11, 3.40%, p  = 0.037) higher TL and a 4.07 (95% CI: 2.01, 6.17%, p  = 0.00012) higher mtDNAc.

People suffering from severe psychological distress had a − 4.62% (95% CI: − 8.85, − 0.20%, p  = 0.041) lower mtDNAc compared with those who did not suffer from severe psychological distress. Similarly, people with suicidal ideation had a − 7.83% (95% CI: − 14.77, − 0.34%, p  = 0.041) lower mtDNAc compared with those without suicidal ideation. No associations were found for the other mental health and well-being outcomes, and no associations were found between mental health and TL (Table 6 ).

Sensitivity analysis

Additional adjustment of the main analyses for perceived quality of social support, chronic disease or lifestyle score (in the association between the mental health outcomes and the biomarkers of ageing) did not strongly change the effect of our observations (Additional file 1 : Tables S5-S7). However, we noticed that most of the associations between severe psychological distress or suicidal ideation and mtDNAc showed marginally significant results.

In this study, we evaluated the associations between eight mental health and well-being outcomes, a healthy lifestyle score and 2 biomarkers of biological ageing: telomere length and mitochondrial DNA content. Having a healthy lifestyle was positively associated with all mental health and well-being indicators and the markers of biological ageing. Furthermore, having had suicidal ideation or suffering from severe psychological distress was associated with a lower mtDNAc. However, no association was found between mental health and TL.

In the first part of this research, we evaluated the association between lifestyle and mental health and well-being and showed that living a healthy lifestyle was positively associated with better mental health and well-being outcomes. Similar trends were found in previous studies for each of the health behaviours separately [ 11 , 12 , 46 , 47 , 48 ]. Although evaluating these health behaviours separately provides valuable information, assessing them in combination with each other rather than independently might better reflect the real-life situation as they often co-occur and may exert a synergistic effect on each other [ 24 , 25 , 49 ]. For example, 68% of the adults in England engaged in two or more unhealthy behaviours [ 25 ]. Especially, smoking status and alcohol consumption co-occurred, but half of the studies in the review by Noble et al. indicated clustering of all included health behaviours [ 24 ].

To date, the number of studies evaluating the combination of multiple health behaviours and mental health and well-being in adults is limited, and most of them use a different methodology to assess this association [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ]. Firstly, differences are found between the included health behaviours. Most studies included the four “SNAP” risk factors, i.e. smoking, poor nutrition, excess alcohol consumption and physical inactivity. Other health behaviours that were sometimes included were BMI/obesity, sleep duration/quality and psychological distress [ 50 , 53 , 54 , 56 ]. Secondly, differences are found in the scoring of the health behaviours and the use of the lifestyle score. Whereas in this study the health behaviours were scored categorically, studies often dichotomised the health behaviours and/or the final lifestyle score [ 50 , 52 , 53 , 56 ]. Also, two studies performed clustering [ 54 , 55 ]. Health behaviours can cluster together at both ends of the risk spectrum, but less is known about the middle categories. This is avoided by using the cluster method where participants are clustered based on similar behaviours. On the other hand, a lifestyle score can be of better use and more easily be interpreted when aiming to compare healthy versus unhealthy lifestyles, as was the case for this study.

Despite these different methods, all previously mentioned studies show similar results. Together with our findings, which also support these results, this provides clear evidence that an unhealthy lifestyle is associated with poor mental health and well-being outcomes. Important to notice is that, like our research, most studies in this field have a cross-sectional design and are therefore not able to assume causality. Therefore, mental health might be the cause or the consequence of an unhealthy lifestyle. Further prospective and longitudinal studies are warranted to confirm the direction of the association.

Healthy lifestyle and biomarkers of ageing

How lifestyle affects our health is not yet fully understood. One possible pathway is through oxidative stress and biological ageing. An unhealthy lifestyle has been associated with an increase in oxidative stress [ 57 , 58 , 59 ], and in turn, higher concentrations of oxidative stress are known to negatively affect TL and mtDNAc [ 60 ]. In this study, we showed that living a healthy lifestyle was associated with a longer TL and a higher mtDNAc. Our results showed a stronger association of lifestyle with mtDNAc compared with TL. TL is strongly determined by TL at birth [ 61 ]. On the other hand, mtDNAc might be more variable in shorter time periods. Although mtDNAc and TL were strongly correlated, this could explain why lifestyle is more strongly associated with mtDNAc. However, we can only speculate about this, and further research is necessary to confirm our results.

Similar as for the association with mental health, in previous studies, the biomarkers have been associated with health behaviours separately rather than combined [ 62 , 63 , 64 , 65 ]. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc. Our results are in line with our expectations. As TL and mtDNAc are known to be correlated [ 60 ], we would expect similar trends for both biomarkers. In the case of TL, few studies included a combined lifestyle score in association with this biomarker. Consistent with our results, in a study population of 1661 men, the sum score of a healthier lifestyle was correlated with a longer TL [ 66 ]. Similar results were found by Sun et al. where a combination of healthy lifestyles in a female study population was associated with a longer TL compared with the least healthy group [ 67 ]. Also, improvement in lifestyle has been associated with TL maintenance in the elderly at risk for dementia [ 68 ], and a lifestyle intervention programme was positively associated with leucocyte telomere length in children and adolescents [ 69 ]. These results suggest that on a biological level, a healthy lifestyle is associated with healthy ageing. Within this context, a study on adults aged 60 and older showed that maintaining a normal weight, not smoking and performing regular physical activity were associated with slower development of disability and a reduction in mortality [ 70 ]. Similarly, midlife lifestyle factors like non-smoking, higher levels of physical activity, non-obesity and good social support have been associated with successful ageing, 22 years later [ 71 ].

Mental health and well-being and biomarkers of ageing

Finally, we evaluated the association between the biomarkers of ageing and the mental health and well-being outcomes. The hypothesis that biological ageing is associated with mental health has been supported by observations showing that chronically stressed or psychiatrically ill persons have a higher risk for age-related diseases like dementia, diabetes and hypertension [ 23 , 72 , 73 ]. Important to notice is that, like our research, the majority of studies on this topic have a cross-sectional design and therefore are unable to identify causality. Therefore, it is currently unknown whether psychological diseases accelerate biological ageing or whether biological ageing precedes the onset of these diseases [ 74 ].

Our results showed a lower mtDNAc for individuals with suicidal ideation or severe psychological distress but not for any of the other mental health outcomes. Evidence on the association between mtDNAc and mental health is inconsistent. Women above 60 years old with depression had a significantly lower mtDNAc compared with the control group [ 75 ]. Furthermore, individuals with a low mtDNAc had poorer outcomes in terms of self-rated health [ 76 ]. In contrast, Otsuka et al. showed a higher peripheral blood mtDNAc in suicide completers [ 77 ], and studies on major depressive syndrome [ 78 ] and self-rated health [ 79 ] showed the same trend. Finally, Vyas et al. showed no significant association between mtDNAc and depression status in mid-life and older adults [ 80 ]. These differences might be due to the differences in study population and methods. For example, the two studies indicating lower mtDNAc in association with poor mental health both had an elderly study population, and one study population consisted of psychiatrically ill patients. Next to that, differences were found in the type of samples, mtDNAc assays and questionnaires or diagnostics. The inconsistency of these studies and our results calls for further research on this association and for standardisation of methods within studies to enable clear comparisons.

As for TL, we did not find an association with any of the mental health and well-being outcomes. Previous studies in adults showed a lower TL in association with current but not remitted anxiety disorder [ 81 ], depressive [ 82 ] and major depressive disorder [ 73 , 83 ], childhood trauma [ 84 ] suicide [ 77 , 85 ], depressive symptoms in younger adults [ 86 ] and acculturative stress and postpartum depression in Latinx women [ 87 ]. Also, in a meta-analysis, psychiatric disorders overall were associated with a shorter leucocyte TL [ 88 ]. However, other studies failed to demonstrate an association between TL and mental health outcomes like major depressive disorder [ 89 ], late-life depression [ 90 ] and anxiety disorder [ 91 ]. Again, this could be due to a different method to assess the mental health outcomes, a different study design, uncontrolled confounding factors and the type of telomere assay. For example, a meta-analysis showed stronger associations with depression when using southern blot or FISH assay compared with qPCR to measure telomere length [ 92 ].

Strengths and limitations

An important strength of this study is the use of a validated lifestyle score that can easily be reproduced and used for other research on lifestyle. Secondly, we were able to use a large sample size for our analyses in the BHIS subset. Thirdly, by assessing multiple dimensions of mental health and well-being, we were able to give a comprehensive overview of the mental health status. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc.

Our results should however be interpreted with consideration for some limitations. As mentioned before, the study has a cross-sectional design, and therefore, we cannot assume causality. Secondly, for the lifestyle score, we used self-reported data, which might not always represent the actual situation. For example, BMI values tend to be underestimated due to the overestimation of height and underestimation of weight [ 93 ], and also, smoking behaviour is often underestimated [ 94 ]. Also, equal weights were used for each of the health behaviours as no objective information was available on which weight should be given to a specific health behaviour. Thirdly, there is a distinct time lag between the completion of the BHIS questionnaire and the collection of the BELHES samples. The mean (SD) number of days is 52 (35). This is less than the period for suicidal ideation, assessed over the 12 previous months, but there might be a more limited overlap with the period for assessment of the other mental health variables, such as vitality and psychological distress, assessed over the last few weeks, and depressive and generalised anxiety disorders, assessed over the last 2 weeks. Fourthly, due to a non-response bias, the lowest socio-economic classes are less represented in our study population. This will not affect our dose–response associations but might affect the generalisability of our findings to the overall population. Finally, we do not have data on blood cell counts, which has been associated with mtDNAc [ 95 ].

In this large-scale study, we showed that living a healthy lifestyle was positively associated with mental health and well-being and, on a biological level, with a higher TL and mtDNAc, indicating healthy ageing. Furthermore, individuals with suicidal ideation or suffering from severe psychological distress had a lower mtDNAc. Our findings suggest that implementing strategies to incorporate healthy lifestyle changes in the public’s daily life could be beneficial for public health, and might offset the negative impact of environmental stressors. However, further studies are necessary to confirm our results and especially prospective and longitudinal studies are essential to determine causality of the associations.

Availability of data and materials

The dataset used for this study is available through a request to the Health Committee of the Data Protection Authority.

Abbreviations

Area under the curve

Body mass index

Confidence intervals

Generalised Anxiety Disorder Questionnaire

General Health Questionnaire

Inter-run calibrator

• Mitochondrial DNA content

Patient Health Questionnaire

Relative operating characteristic curve

Short Form Health Survey

• Telomere length

World Health Organization. Healthy living: what is a healthy lifestyle? Copenhagen: WHO Regional Office for Europe; 1999.

Google Scholar  

World Health Organization. Tackling NCDs: ‘best buys’ and other recommended interventions for the prevention and control of noncommunicable diseases. Geneva: World Health Organization; 2017.

World Health Organization. WHO global report on trends in prevalence of tobacco smoking 2000–2025. 3rd ed. Geneva: World Health Organisation; 2019.

World Health Organization. 2021 Physical Activity Factsheets for the European Union Member States in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2021.

Eurostat. Database on health determinants 2021 [Available from: https://ec.europa.eu/eurostat/web/health/data/database?node_code=hlth_det .

Whitman IR, Agarwal V, Nah G, Dukes JW, Vittinghoff E, Dewland TA, et al. Alcohol abuse and cardiac disease. J Am Coll Cardiol. 2017;69(1):13–24. https://doi.org/10.1016/j.jacc.2016.10.048 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Koliaki C, Liatis S, Kokkinos A. Obesity and cardiovascular disease: revisiting an old relationship. Metabolism. 2019;92:98–107. https://doi.org/10.1016/j.metabol.2018.10.011 .

Article   CAS   PubMed   Google Scholar  

Freisling H, Viallon V, Lennon H, Bagnardi V, Ricci C, Butterworth AS, et al. Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. BMC Med. 2020;18(1):5. https://doi.org/10.1186/s12916-019-1474-7 .

Liu Y, Pleasants RA, Croft JB, Wheaton AG, Heidari K, Malarcher AM, et al. Smoking duration, respiratory symptoms, and COPD in adults aged ≥ 45 years with a smoking history. Int J Chron Obstruct Pulmon Dis. 2015;10:1409. https://doi.org/10.2147/COPD.S82259 .

Kirsch Micheletti J, Bláfoss R, Sundstrup E, Bay H, Pastre CM, Andersen LL. Association between lifestyle and musculoskeletal pain: cross-sectional study among 10,000 adults from the general working population. BMC Musculoskelet Disord. 2019;20(1):609. https://doi.org/10.1186/s12891-019-3002-5 .

Bowe AK, Owens M, Codd MB, Lawlor BA, Glynn RW. Physical activity and mental health in an Irish population. Ir J Med Sci. 2019;188(2):625–31. https://doi.org/10.1007/s11845-018-1863-5 .

Article   PubMed   Google Scholar  

Richardson S, McNeill A, Brose LS. Smoking and quitting behaviours by mental health conditions in Great Britain (1993–2014). Addict Behav. 2019;90:14–9. https://doi.org/10.1016/j.addbeh.2018.10.011 .

Article   PubMed   PubMed Central   Google Scholar  

Levy MZ, Allsopp RC, Futcher AB, Greider CW, Harley CB. Telomere end-replication problem and cell aging. J Mol Biol. 1992;225(4):951–60. https://doi.org/10.1016/0022-2836(92)90096-3 .

Shaughnessy DT, McAllister K, Worth L, Haugen AC, Meyer JN, Domann FE, et al. Mitochondria, energetics, epigenetics, and cellular responses to stress. Environ Health Perspect. 2014;122(12):1271–8. https://doi.org/10.1289/ehp.1408418 .

Cui Y, Gao YT, Cai Q, Qu S, Cai H, Li HL, et al. Associations of leukocyte telomere length with body anthropometric indices and weight change in Chinese women. Obesity. 2013;21(12):2582–8. https://doi.org/10.1002/oby.20321 .

Crous-Bou M, Fung TT, Prescott J, Julin B, Du M, Sun Q, et al. Mediterranean diet and telomere length in Nurses’ Health Study: population based cohort study. BMJ. 2014;349:g6674. https://doi.org/10.1136/bmj.g6674 .

Janssen BG, Gyselaers W, Byun H-M, Roels HA, Cuypers A, Baccarelli AA, et al. Placental mitochondrial DNA and CYP1A1 gene methylation as molecular signatures for tobacco smoke exposure in pregnant women and the relevance for birth weight. J Transl Med. 2017;15(1):5. https://doi.org/10.1186/s12967-016-1113-4 .

Navarro-Mateu F, Husky M, Cayuela-Fuentes P, Álvarez FJ, Roca-Vega A, Rubio-Aparicio M, et al. The association of telomere length with substance use disorders: a systematic review and meta-analysis of observational studies. Addiction. 2020;116(8):1954–72. https://doi.org/10.1111/add.15312 .

Pyle A, Anugrha H, Kurzawa-Akanbi M, Yarnall A, Burn D, Hudson G. Reduced mitochondrial DNA copy number is a biomarker of Parkinson’s disease. Neurobiol Aging. 2016;38:216.e7-.e10. https://doi.org/10.1016/j.neurobiolaging.2015.10.033 .

Article   CAS   Google Scholar  

Ashar FN, Zhang Y, Longchamps RJ, Lane J, Moes A, Grove ML, et al. Association of mitochondrial DNA copy number with cardiovascular disease. JAMA Cardiol. 2017;2(11):1247–55. https://doi.org/10.1001/jamacardio.2017.3683 .

Chen S, Lin J, Matsuguchi T, Blackburn E, Yeh F, Best LG, et al. Short leukocyte telomere length predicts incidence and progression of carotid atherosclerosis in American Indians: the Strong Heart Family Study. Aging. 2014;6(5):414–27. https://doi.org/10.18632/aging.100671 .

Mons U, Müezzinler A, Schöttker B, Dieffenbach AK, Butterbach K, Schick M, et al. Leukocyte telomere length and all-cause, cardiovascular disease, and cancer mortality: results from individual-participant-data meta-analysis of 2 large prospective cohort studies. Am J Epidemiol. 2017;185(12):1317–26. https://doi.org/10.1093/aje/kww210 .

Lindqvist D, Epel ES, Mellon SH, Penninx BW, Révész D, Verhoeven JE, et al. Psychiatric disorders and leukocyte telomere length: underlying mechanisms linking mental illness with cellular aging. Neurosci Biobehav Rev. 2015;55:333–64. https://doi.org/10.1016/j.neubiorev.2015.05.007 .

Noble N, Paul C, Turon H, Oldmeadow C. Which modifiable health risk behaviours are related? A systematic review of the clustering of Smoking, Nutrition, Alcohol and Physical activity (‘SNAP’) health risk factors. Prev Med. 2015;81:16–41. https://doi.org/10.1016/j.ypmed.2015.07.003 .

Poortinga W. The prevalence and clustering of four major lifestyle risk factors in an English adult population. Prev Med. 2007;44(2):124–8. https://doi.org/10.1016/j.ypmed.2006.10.006 .

Demarest S, Van der Heyden J, Charafeddine R, Drieskens S, Gisle L, Tafforeau J. Methodological basics and evolution of the Belgian Health Interview Survey 1997–2008. Arch Public Health. 2013;71(1):24. https://doi.org/10.1186/0778-7367-71-24 .

Goldberg DP. User’s guide to the General Health Questionnaire. Windsor: NFER-Nelson; 1988.

Ware JE Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220–33. https://doi.org/10.1097/00005650-199603000-00003 .

Cantril H. Pattern of human concerns. New Brunswick: Rutgers University Press; 1965.

Kroenke K, Spitzer RL, Williams JB, Löwe B. The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. Gen Hosp Psychiatry. 2010;32(4):345–59. https://doi.org/10.1016/j.genhosppsych.2010.03.006 .

Löwe B, Decker O, Müller S, Brähler E, Schellberg D, Herzog W, et al. Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Med Care. 2008;46(3):266–74. https://doi.org/10.1097/MLR.0b013e318160d093 .

Gisle L, Drieskens S, Demarest S, Van der Heyden J. Enquête de santé 2018: Santé mentale. Bruxelles: Sciensano; 2018. ( https://www.enquetesante.be . Available from. Numéro de rapport: D/2020/14.440/3).

Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care. 1992;30(6):473–83.

Article   Google Scholar  

Braunholtz S, Davidson S, Myant K, O’Connor R. Well? What do you think?: the Third National Scottish Survey of Public Attitudes to Mental Health, Mental Wellbeing and Mental Health Problems. Scotland: Scottish Government Edinburgh; 2007.

Van Lente E, Barry MM, Molcho M, Morgan K, Watson D, Harrington J, et al. Measuring population mental health and social well-being. Int J Public Health. 2012;57(2):421–30. https://doi.org/10.1007/s00038-011-0317-x .

de Bruin A, Picavet HSJ, Nossikov A. Health interview surveys: towards international harmonization of methods and instruments. Copenhagen: World Health Organization. Regional Office for Europe; 1996.

Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

Superior Health Council. Dietary guidelines for the Belgian adult population. Report 9284. Brussels: Superior Health Council; 2019.

World Health Organization. WHO guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020.

World Health Organization. HEARTS Technical package for cardiovascular disease management in primary health care: healthy-lifestyle counselling. Geneva: World Health Organization; 2018.

World Health Organization. Country profiles on nutrition, physical activity and obesity in the 28 European Union Member States of the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2013.

Bhaskaran K, dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3.6 million adults in the UK. Lancet Diabetes Endocrinol. 2018;6(12):944–53. https://doi.org/10.1016/S2213-8587(18)30288-2 .

Benetou V, Kanellopoulou A, Kanavou E, Fotiou A, Stavrou M, Richardson C, et al. Diet-related behaviors and diet quality among school-aged adolescents living in Greece. Nutrients. 2020;12(12):3804. https://doi.org/10.3390/nu12123804 .

Article   PubMed Central   Google Scholar  

Martens DS, Plusquin M, Gyselaers W, De Vivo I, Nawrot TS. Maternal pre-pregnancy body mass index and newborn telomere length. BMC Med. 2016;14(1):148. https://doi.org/10.1186/s12916-016-0689-0 .

Janssen BG, Munters E, Pieters N, Smeets K, Cox B, Cuypers A, et al. Placental mitochondrial DNA content and particulate air pollution during in utero life. Environ Health Perspect. 2012;120(9):1346–52. https://doi.org/10.1289/ehp.1104458 .

Jacka FN, O’Neil A, Opie R, Itsiopoulos C, Cotton S, Mohebbi M, et al. A randomised controlled trial of dietary improvement for adults with major depression (the ‘SMILES’ trial). BMC Med. 2017;15(1):23. https://doi.org/10.1186/s12916-017-0791-y .

Pavkovic B, Zaric M, Markovic M, Klacar M, Huljic A, Caricic A. Double screening for dual disorder, alcoholism and depression. Psychiatry Res. 2018;270:483–9. https://doi.org/10.1016/j.psychres.2018.10.013 .

De Wit LM, Van Straten A, Van Herten M, Penninx BW, Cuijpers P. Depression and body mass index, a u-shaped association. BMC Public Health. 2009;9:14. https://doi.org/10.1186/1471-2458-9-14 .

Meader N, King K, Moe-Byrne T, Wright K, Graham H, Petticrew M, et al. A systematic review on the clustering and co-occurrence of multiple risk behaviours. BMC Public Health. 2016;16:657. https://doi.org/10.1186/s12889-016-3373-6 .

Saneei P, Esmaillzadeh A, Hassanzadeh Keshteli A, Reza Roohafza H, Afshar H, Feizi A, et al. Combined healthy lifestyle is inversely associated with psychological disorders among adults. PLoS ONE. 2016;11(1):e0146888. https://doi.org/10.1371/journal.pone.0146888 .

Bonnet F, Irving K, Terra J-L, Nony P, Berthezène F, Moulin P. Anxiety and depression are associated with unhealthy lifestyle in patients at risk of cardiovascular disease. Atherosclerosis. 2005;178(2):339–44. https://doi.org/10.1016/j.atherosclerosis.2004.08.035 .

Loprinzi PD, Mahoney S. Concurrent occurrence of multiple positive lifestyle behaviors and depression among adults in the United States. J Affect Disord. 2014;165:126–30. https://doi.org/10.1016/j.jad.2014.04.073 .

Yang H, Gao J, Wang T, Yang L, Liu Y, Shen Y, et al. Association between adverse mental health and an unhealthy lifestyle in rural-to-urban migrant workers in Shanghai. J Formos Med Assoc. 2017;116(2):90–8. https://doi.org/10.1016/j.jfma.2016.03.004 .

Oftedal S, Kolt GS, Holliday EG, Stamatakis E, Vandelanotte C, Brown WJ, et al. Associations of health-behavior patterns, mental health and self-rated health. Prev Med. 2019;118:295–303. https://doi.org/10.1016/j.ypmed.2018.11.017 .

Conry MC, Morgan K, Curry P, McGee H, Harrington J, Ward M, et al. The clustering of health behaviours in Ireland and their relationship with mental health, self-rated health and quality of life. BMC Public Health. 2011;11(1):692. https://doi.org/10.1186/1471-2458-11-692 .

Buttery AK, Mensink GB, Busch MA. Healthy behaviours and mental health: findings from the German Health Update (GEDA). The European Journal of Public Health. 2015;25(2):219–25. https://doi.org/10.1093/eurpub/cku094 .

Ahmed NJ, Husen AZ, Khoshnaw N, Getta HA, Hussein ZS, Yassin AK, et al. The effects of smoking on IgE, oxidative stress and haemoglobin concentration. Asian Pac J Cancer Prev. 2020;21(4):1069–72. https://doi.org/10.31557/APJCP.2020.21.4.1069 .

Langley MR, Yoon H, Kim HN, Choi C-I, Simon W, Kleppe L, et al. High fat diet consumption results in mitochondrial dysfunction, oxidative stress, and oligodendrocyte loss in the central nervous system. Biochim Biophys Acta Mol Basis Dis. 2020;1866(3):165630. https://doi.org/10.1016/j.bbadis.2019.165630 .

Tan HK, Yates E, Lilly K, Dhanda AD. Oxidative stress in alcohol-related liver disease. World J Hepatol. 2020;12(7):332–49. https://doi.org/10.4254/wjh.v12.i7.332 .

Martens DS, Nawrot TS. Air pollution stress and the aging phenotype: the telomere connection. Curr Environ Health Rep. 2016;3(3):258–69. https://doi.org/10.1007/s40572-016-0098-8 .

Martens DS, Van Der Stukken C, Derom C, Thiery E, Bijnens EM, Nawrot TS. Newborn telomere length predicts later life telomere length: tracking telomere length from birth to child- and adulthood. EBioMedicine. 2021;63:103164. https://doi.org/10.1016/j.ebiom.2020.103164 .

Hang D, Nan H, Kværner AS, De Vivo I, Chan AT, Hu Z, et al. Longitudinal associations of lifetime adiposity with leukocyte telomere length and mitochondrial DNA copy number. Eur J Epidemiol. 2018;33(5):485–95. https://doi.org/10.1007/s10654-018-0382-z .

Gu Y, Honig LS, Schupf N, Lee JH, Luchsinger JA, Stern Y, et al. Mediterranean diet and leukocyte telomere length in a multi-ethnic elderly population. Age. 2015;37(2):24. https://doi.org/10.1007/s11357-015-9758-0 .

Sellami M, Al-Muraikhy S, Al-Jaber H, Al-Amri H, Al-Mansoori L, Mazloum NA, et al. Age and sport intensity-dependent changes in cytokines and telomere length in elite athletes. Antioxidants. 2021;10(7):1035. https://doi.org/10.3390/antiox10071035 .

Savela S, Saijonmaa O, Strandberg TE, Koistinen P, Strandberg AY, Tilvis RS, et al. Physical activity in midlife and telomere length measured in old age. Exp Gerontol. 2013;48(1):81–4. https://doi.org/10.1016/j.exger.2012.02.003 .

Mirabello L, Huang WY, Wong JY, Chatterjee N, Reding D, David Crawford E, et al. The association between leukocyte telomere length and cigarette smoking, dietary and physical variables, and risk of prostate cancer. Aging Cell. 2009;8(4):405–13. https://doi.org/10.1111/j.1474-9726.2009.00485.x .

Sun Q, Shi L, Prescott J, Chiuve SE, Hu FB, De Vivo I, et al. Healthy lifestyle and leukocyte telomere length in US women. PLoS ONE. 2012;7(5):e38374. https://doi.org/10.1371/journal.pone.0038374 .

Sindi S, Solomon A, Kåreholt I, Hovatta I, Antikainen R, Hänninen T, et al. Telomere length change in a multidomain lifestyle intervention to prevent cognitive decline: a randomized clinical trial. J Gerontol A. 2021;76(3):491–8. https://doi.org/10.1093/gerona/glaa279 .

Paltoglou G, Raftopoulou C, Nicolaides NC, Genitsaridi SM, Karampatsou SI, Papadopoulou M, et al. A comprehensive, multidisciplinary, personalized, lifestyle intervention program is associated with increased leukocyte telomere length in children and adolescents with overweight and obesity. Nutrients. 2021;13(8):2682. https://doi.org/10.3390/nu13082682 .

Chakravarty EF, Hubert HB, Krishnan E, Bruce BB, Lingala VB, Fries JF. Lifestyle risk factors predict disability and death in healthy aging adults. Am J Med. 2012;125(2):190–7. https://doi.org/10.1016/j.amjmed.2011.08.006 .

Bosnes I, Nordahl HM, Stordal E, Bosnes O, Myklebust TÅ, Almkvist O. Lifestyle predictors of successful aging: a 20-year prospective HUNT study. PLoS ONE. 2019;14(7):e0219200. https://doi.org/10.1371/journal.pone.0219200 .

Epel ES, Prather AA. Stress, telomeres, and psychopathology: toward a deeper understanding of a triad of early aging. Annu Rev Clin Psychol. 2018;14:371–97. https://doi.org/10.1146/annurev-clinpsy-032816-045054 .

Verhoeven JE, Révész D, Epel ES, Lin J, Wolkowitz OM, Penninx BW. Major depressive disorder and accelerated cellular aging: results from a large psychiatric cohort study. Mol Psychiatry. 2014;19(8):895–901. https://doi.org/10.1038/mp.2013.151 .

Han LK, Verhoeven JE, Tyrka AR, Penninx BW, Wolkowitz OM, Månsson KN, et al. Accelerating research on biological aging and mental health: current challenges and future directions. Psychoneuroendocrinology. 2019;106:293–311. https://doi.org/10.1016/j.psyneuen.2019.04.004 .

Kim MY, Lee JW, Kang HC, Kim E, Lee DC. Leukocyte mitochondrial DNA (mtDNA) content is associated with depression in old women. Arch Gerontol Geriatr. 2011;53(2):e218–21. https://doi.org/10.1016/j.archger.2010.11.019 .

Mengel-From J, Thinggaard M, Dalgard C, Kyvik KO, Christensen K, Christiansen L. Mitochondrial DNA copy number in peripheral blood cells declines with age and is associated with general health among elderly. Hum Genet. 2014;133(9):1149–59. https://doi.org/10.1007/s00439-014-1458-9 .

Otsuka I, Izumi T, Boku S, Kimura A, Zhang Y, Mouri K, et al. Aberrant telomere length and mitochondrial DNA copy number in suicide completers. Sci Rep. 2017;7(1):3176.

Chung JK, Lee SY, Park M, Joo E-J, Kim SA. Investigation of mitochondrial DNA copy number in patients with major depressive disorder. Psychiatry Res. 2019;282:112616. https://doi.org/10.1016/j.psychres.2019.112616 .

Takahashi PY, Jenkins GD, Welkie BP, McDonnell SK, Evans JM, Cerhan JR, et al. Association of mitochondrial DNA copy number with self-rated health status. Appl Clin Genet. 2018;11:121–7. https://doi.org/10.2147/TACG.S167640 .

Vyas CM, Ogata S, Reynolds CF 3rd, Mischoulon D, Chang G, Cook NR, et al. Lifestyle and behavioral factors and mitochondrial DNA copy number in a diverse cohort of mid-life and older adults. PLoS ONE. 2020;15(8):e0237235. https://doi.org/10.1371/journal.pone.0237235 .

Verhoeven JE, Révész D, van Oppen P, Epel ES, Wolkowitz OM, Penninx BW. Anxiety disorders and accelerated cellular ageing. Br J Psychiatry. 2015;206(5):371–8. https://doi.org/10.1192/bjp.bp.114.151027 .

Pisanu C, Vitali E, Meloni A, Congiu D, Severino G, Ardau R, et al. Investigating the role of leukocyte telomere length in treatment-resistant depression and in response to electroconvulsive therapy. J Pers Med. 2021;11(11):1100. https://doi.org/10.3390/jpm11111100 .

da Silva RS, de Moraes LS, da Rocha CAM, Ferreira-Fernandes H, Yoshioka FKN, Rey JA, et al. Telomere length and telomerase activity of leukocytes as biomarkers of selective serotonin reuptake inhibitor responses in patients with major depressive disorder. Psychiatr Genet. 2022;32(1):34–6. https://doi.org/10.1097/ypg.0000000000000305 .

Aas M, Elvsåshagen T, Westlye LT, Kaufmann T, Athanasiu L, Djurovic S, et al. Telomere length is associated with childhood trauma in patients with severe mental disorders. Transl Psychiatry. 2019;9(1):97. https://doi.org/10.1038/s41398-019-0432-7 .

Birkenæs V, Elvsåshagen T, Westlye LT, Høegh MC, Haram M, Werner MCF, et al. Telomeres are shorter and associated with number of suicide attempts in affective disorders. J Affect Disord. 2021;295:1032–9. https://doi.org/10.1016/j.jad.2021.08.135 .

Phillips AC, Robertson T, Carroll D, Der G, Shiels PG, McGlynn L, et al. Do symptoms of depression predict telomere length? Evidence from the West of Scotland Twenty-07 Study. Psychosom Med. 2013;75(3):288–96. https://doi.org/10.1097/PSY.0b013e318289e6b5 .

Incollingo Rodriguez AC, Polcari JJ, Nephew BC, Harris R, Zhang C, Murgatroyd C, et al. Acculturative stress, telomere length, and postpartum depression in Latinx mothers. J Psychiatr Res. 2022;147:301–6. https://doi.org/10.1016/j.jpsychires.2022.01.063 .

Darrow SM, Verhoeven JE, Révész D, Lindqvist D, Penninx BW, Delucchi KL, et al. The association between psychiatric disorders and telomere length: a meta-analysis involving 14,827 persons. Psychosom Med. 2016;78(7):776–87. https://doi.org/10.1097/PSY.0000000000000356 .

Simon NM, Walton ZE, Bui E, Prescott J, Hoge E, Keshaviah A, et al. Telomere length and telomerase in a well-characterized sample of individuals with major depressive disorder compared to controls. Psychoneuroendocrinology. 2015;58:9–22. https://doi.org/10.1016/j.psyneuen.2015.04.004 .

Schaakxs R, Verhoeven JE, Voshaar RCO, Comijs HC, Penninx BW. Leukocyte telomere length and late-life depression. Am J Geriatr Psychiatry. 2015;23(4):423–32. https://doi.org/10.1016/j.jagp.2014.06.003 .

de Baumont AC, Hoffmann MS, Bortoluzzi A, Fries GR, Lavandoski P, Grun LK, et al. Telomere length and epigenetic age acceleration in adolescents with anxiety disorders. Sci Rep. 2021;11(1):7716. https://doi.org/10.1038/s41598-021-87045-w .

Schutte NS, Malouff JM. The association between depression and leukocyte telomere length: a meta-analysis. Depress Anxiety. 2015;32(4):229–38. https://doi.org/10.1002/da.22351 .

Drieskens S, Demarest S, Bel S, De Ridder K, Tafforeau J. Correction of self-reported BMI based on objective measurements: a Belgian experience. Archives of Public Health. 2018;76:10. https://doi.org/10.1186/s13690-018-0255-7 .

Gorber SC, Schofield-Hurwitz S, Hardt J, Levasseur G, Tremblay M. The accuracy of self-reported smoking: a systematic review of the relationship between self-reported and cotinine-assessed smoking status. Nicotine Tob Res. 2009;11(1):12–24. https://doi.org/10.1093/ntr/ntn010 .

Knez J, Winckelmans E, Plusquin M, Thijs L, Cauwenberghs N, Gu Y, et al. Correlates of peripheral blood mitochondrial DNA content in a general population. Am J Epidemiol. 2016;183(2):138–46. https://doi.org/10.1093/aje/kwv175 .

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Acknowledgements

We are grateful to all BHIS and BELHES participants for contributing to this study.

The HuBiHIS project is financed by Sciensano (PJ) N°: 1179–101. Dries Martens is a postdoctoral fellow of the Research Foundation—Flanders (FWO 12X9620N).

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PH drafted the paper. PH, NS, MD and ED set up the design of the study. NS, DM, JvdH, TN and ED reviewed and commented on the manuscript. All authors read and approved the final manuscript.

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Additional file 1: text s1..

TL, mtDNAc and single copy-gene reaction mixture and PCR cycling conditions. Table S1. The mental health indicators with their scores and uses. Table S2. Comparison of the characteristics of the 6,054 eligible BHIS participants that were included in the BHIS subset compared to the 1,838 eligible participants that were excluded from the BHIS subset. Table S3. Comparison of the characteristics of the 739 participants from the BHIS subset that were included in the BELHES subset compared to the 5,315 participants that were excluded from the BELHES subset. Table S4. Bivariate associations between the characteristics and telomere length (TL), mitochondrial DNA content (mtDNAc), the lifestyle score or psychological distress. Table S5. Results of the sensitivity analysis of the association between lifestyle and mental health. Table S6. Results of the sensitivity analysis of the association between lifestyle and the biomarkers of ageing. Table S7. Results of the sensitivity analysis of the association between mental health and the biomarkers of ageing. Fig. S1. Exclusion criteria. The BHIS subset consisted of 6,055 BHIS participants and the BELHES subset consisted of 739 BELHES participants. Fig. S2. Histogram of the lifestyle score. Fig. S3. Validation of the lifestyle score. ROC curve for the lifestyle score as a predictor for good to very good self-perceived health. The model was adjusted for age, sex, region, highest educational level in the household, household composition and country of birth.

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Hautekiet, P., Saenen, N.D., Martens, D.S. et al. A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing. BMC Med 20 , 328 (2022). https://doi.org/10.1186/s12916-022-02524-9

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Association between healthy lifestyle practices and life purpose among a highly health-literate cohort: a cross-sectional study

• Nobutaka Hirooka 1 ,
• Takeru Kusano 1 ,
• Shunsuke Kinoshita 1 ,
• Ryutaro Aoyagi 1 &
• Nakamoto Hidetomo 1  

BMC Public Health volume  21 , Article number:  820 ( 2021 ) Cite this article

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The national health promotion program in the twenty-first century Japan (HJ21) correlates life purpose with disease prevention, facilitating the adoption of healthy lifestyles. However, the influence of clustered healthy lifestyle practices on life purpose, within the context of this national health campaign remains uninvestigated. This study assessed the association between such practices and life purpose, in line with the HJ21.

We performed a nationwide cross-sectional survey on certified specialists in health management. Participants’ demographic information, lifestyle, and purpose in life were measured using a validated tool. The cohort was median-split into two groups based on their clustered health-related lifestyle score. The values for health-related lifestyle and purpose were compared between the two groups and the correlation between health-related lifestyle and purpose in life was measured.

Data from 4820 participants were analyzed. The higher-scoring health-related lifestyle group showed a significantly higher life purpose than the lower group (35.3 vs 31.4; t  = 23.6, p  < 0.001). There was a significant association between the scores of clustered healthy lifestyle practices and life purpose ( r  = 0.401, p  < 0.001). The higher-scoring health-related lifestyle group achieved a higher life purpose than the lower-scoring group. This association between healthy lifestyle practices and life purpose denotes a positive and linear relationship.

Conclusions

Our results suggest that individuals who have a better health-related lifestyle gain a higher sense of life purpose. In other words, a healthy lifestyle predicts a purpose in life. Our findings posit that examining the causal relationship between healthy lifestyle and purpose in life may be a more efficient approach toward health promotion.

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Several studies have investigated the implications of life purpose, and literature has shown that a strong sense of purpose in life was positively associated with positive health outcomes [ 1 , 2 , 3 , 4 , 5 , 6 ]. Thus, having a sense of purpose in life is a vital component of human life. Due to a rapidly aging society in Japan, a national health promotion program in the twenty-first century—Health Japan twenty-first century (HJ21)—considers purpose in life as one of the major target goals of health promotion [ 7 ].

Purpose in life is defined as “a self-organizing life aim that stimulates goals” [ 1 ] and is known to promote healthy behaviors and give life meaning [ 8 , 9 ]. Ikigai is a Japanese word for what is considered an important factor for achieving better health and a fulfilling life [ 10 ]. Ikigai is defined as something to live for, exemplifying the joy and the goal of living [ 11 ]. Although Ikigai may not be fully comparable to purpose in life, it does contain the respective concept and plays a cardinal role in yielding positive health-related outcomes [ 12 ].

Notably, health outcomes associated with life purpose or Ikigai include physical [ 1 , 12 , 13 ] and mental health [ 3 , 13 ], quality of life [ 4 ], disease mortality [ 1 , 12 ], and longevity [ 12 ]. Possessing a strong sense of purpose in life has been associated with a lower risk of mortality and cardiovascular diseases [ 1 ] (relative risk: 0.83 and 0.83, respectively). The study concluded that purpose in life tends to yield health benefits. One of the mechanisms considered in the literature was the benefits associated with a healthy lifestyle. People who have adopted a higher purpose in life tend to follow healthier lifestyle practices, which may decrease the incidence of non-communicable chronic diseases, such as cardiovascular diseases or cancer.

Healthcare personnel are responsible for the health of their patients. Studies have shown that healthcare personnel are more likely to encourage healthy lifestyle behaviors among their patients if they engage in such behaviors themselves. Our study population comprises certified specialists in health management who routinely provide advice on health to individuals in their community. Investigating the relationship between lifestyle and purpose in life among healthcare personnel, our target population, is therefore of great scientific interest.

There is a hierarchy of causality among chronic diseases. Non-communicable diseases, such as diabetes, stroke, cancer, and coronary artery disease, have risk factors. In the case of risk factors, such as hypertension, smoking, dyslipidemia, hyperglycemia, studies typically signified proximal causes [ 14 , 15 ]. A healthy lifestyle is a central causality for these risk factors and thus basic lifestyle should be considered a fundamental and proximal risk factor for the aforementioned non-communicable diseases. Studies also highlight that healthy lifestyle practices prevent many similar chronic diseases [ 16 , 17 ], and that intervening to promote healthier lifestyle decreases mortality due to non-communicable diseases [ 18 , 19 ]. Hence, the notion that health benefits are brought through a healthy lifestyle may be supported if the lifestyle strongly correlates with purpose in life.

In this context, however, research exploring the association between purpose in life and healthy lifestyle practices remain scarce. Moreover, existing literature usually considers a single health behavior in relation to purpose in life. To determine the relationship between purpose in life and clustered health-related lifestyle—the fundamental and proximal cause of many health outcomes—the potential benefits of purpose in life towards disease prevention and health must be deciphered.

This study aimed to investigate the association between health-related lifestyles, in line with the HJ21, and purpose in life, measured with a validated tool to better understand the relational mechanisms.

Study design

The design was a cross-sectional study on a cohort of nationwide certified specialists in health management. We surveyed health-related lifestyles similar to those in the questionnaire used for the HJ21. Our questionnaire is based on the one of the oldest national health surveys around the world, the National Health and Nutrition Survey conducted by Japanese Government [ 20 ]. This survey is the oldest of all national health examination surveys currently conducted worldwide and serves as a comprehensive database for risk factors related to non-communicable diseases in Japan. The survey includes questions on demographic data and health-related habits, such as physical activity and exercise, nutrition and diet, smoking, stress, and alcohol intake. Purpose in life was measured with a validated tool in Japanese using the purposeful life scale (Ikigai-9) [ 21 ]. The ethics committee of the Saitama Medical University approved the study (ID: 896, 2018).

Participants

Study participants were certified specialists in health management who actively pursued professional growth provided by the Japanese Association of Preventive Medicine for Adult Disease [ 22 ]. This certification is sponsored by the Ministry of Education, Culture, Sports, Science and Technology, Japan. We excluded specialists who did not actively engage in continuing education or health promotion activities. These specialists are expected to engage the community and the society they live in to promote health and wellbeing. Specialists in health management are certified in multiple processes of study. Candidates study various aspects within the course, including health promotion, lifestyle-related diseases, mental health, nutrition, environment and health, physical activity and exercise, emergency medicine, life support, and health care system. To register, candidates must pass the final written examination. The Japanese Association of Preventive Medicine for Adult Disease encourages specialists to participate in numerous activities by facilitating health promotion workshops, speeches, and activities after registration. Among these individuals who met our inclusion criteria ( N  = 9149), 4820 agreed to participate in the survey.

Variables and measurements

Variables measured in this study were demographic characteristics; health-related habits, including physical activity and exercise, nutrition and diet, smoking, stress, and alcohol intake; and purpose in life. There were eleven health-related lifestyle questions, of which five were two-scaled (“Intention to maintain ideal weight,” “Exercise,” “Alcohol intake,” “Manage lifestyle to prevent disease,” and “Smoking”). For these items, a score of “1” was assigned for an unhealthy lifestyle and a score of “4” was assigned for a healthy lifestyle. The rest of the six health-related habits (“Reading nutritional information labels,” “Maintaining a balanced diet in daily life,” “Intention for exercise,” “Stress,” “Rest,” and “Sleep”) were to be answered on a four-point scale, from “4” (most favorable) to “1” (least favorable). Finally, we added the values of each answer to the questions on the health-related lifestyle of the participants as their clustered health-related lifestyle scores. To measure purpose in life, we used the Ikigai-9 scale, a validated tool to quantify purpose in life. The Ikigai-9 is a psychometric tool that measures across the dimensions of (1) optimistic and positive emotions toward life, (2) active and positive attitudes towards one’s life, and (3) acknowledgement of the meaning of one’s existence [ 23 ]. The Ikigai-9 scale consists of nine questions on various aspects of life purpose and each question must be answered on a five-point scale, from “1” (Strongly disagree) to “5” (Strongly agree). These variables and measurements were previously described elsewhere [ 24 ]. Considering the variables, age, weight, height, BMI, volume of alcohol intake, and purpose in life scores were numeric. Sex, healthy lifestyle, smoking, alcohol intake, and stress comprised either binary or ordinal data.

Descriptive statistics (i.e., mean, standard deviation, range) were used to describe participants’ characteristics. The cohort was divided into two groups (i.e., a higher and lower group, with a cut-off using the median score) based on the clustered health-related lifestyle scores. The correlations between age and lifestyle score and between age and purpose in life score were analyzed. The difference in the Ikigai-9 score between the two clustered health-related lifestyle score groups was investigated. Further, the effect size of the difference in Ikigai-9 score between the two groups was calculated with using Cohen’s d . The association between the clustered health-related lifestyle score and the Ikigai-9 score was also analyzed as a bivariate correlation and a correlation coefficient was calculated to see whether the health-related lifestyles accounted for life purpose. A multiple regression analysis was performed to determine the association between the clustered health-related lifestyle score and the purpose in life score, after controlling for age. All statistical tests were two-tailed and the software IBM SPSS Statistics (Version 26.0. Armonk, NY) was used for the analysis.

The demographic and health-related lifestyle characteristics of the study participants are shown in Table  1 . In total, 4820 certified specialists in health management were included in the analysis. There were 3190 women (66.2%) and 1630 men (33.8%). The mean ( SD ) age of all study participants was 55.4 (±12.2) years. The majority of the participants (85.0%) were non-obese and “intended to keep ideal weight” and “maintain a healthy lifestyle (82.6% and 89.2%, respectively) to prevent lifestyle-related disease,” such as obesity, metabolic syndrome, and cardiovascular disease. We also found that more than 80% of the study participants “read nutritional information labels” and more than 90% “maintained a balanced diet in daily life.” Regarding exercise and physical activity, more than 80% of the study participants “intended to exercise” and approximately 64% of them achieved the recommended levels. These findings reflected a low rate of obesity among the participants, which was 15.0% in the study. While most of the participants reported resting and sleeping adequately, the rate of taking on stress was high (74.4%). The descriptive analysis of the Ikigai-9 scores confirmed that it was normally distributed, based on the histogram and P-P plot.

Table  2 shows the demographics and healthy lifestyle practices for both the higher and lower clustered health-related lifestyle score groups. We found consistent favorable results in all measured health-related habits in the higher clustered health-related lifestyle score group. There was a significant difference in the scores of purpose in life between the higher group and the lower clustered health-related lifestyle score group ( t  = 23.6, p  < .0001). In the higher group, the average score of purpose in life was 35.3 (95% CI; [35.1–35.5]), while for the lower group, the average score for purpose in life was 31.4 (95% CI; [31.2–31.7]). The differences in the Ikigai-9 purpose in life scores of the two groups and its effect sizes (Cohen’s d) were 3.8 (95% CI; [3.5–4.2]) and 0.68, respectively. Moreover, there was a significant association between the clustered health-related lifestyle score and purpose in life score, r  = .401, p  < .001. The significance remained after controlling for age. Correlation between age and both lifestyle and purpose in life were significant (Pearson r  = 0.29 and 0.15, respectively; both p  < .05).

We found that the higher-scoring clustered health-related lifestyle group showed a statistically significant higher purpose in life than the lower-scoring clustered health-related lifestyle group. The study also highlighted a significant positive association between the clustered health-related lifestyle score and the Ikigai-9 score. To the best of our knowledge, this study was the first to show that a strong sense of purpose in life correlates with clustered health-related lifestyles in the context of a national health campaign. Several studies indicate a positive relationship between purpose in life and health-related lifestyles [ 1 , 25 , 26 , 27 ]. Furthermore, many publications reveal a correlation between a single healthy habit and purpose in life. Therefore, our findings—that affirm a positive relationship between purpose in life and clustered health-related lifestyle—are consistent with previously reported results and help broaden the evidence of this association.

Exploring the mechanistic link of purpose in life with a healthy lifestyle may help us understand this relationship. While studies highlight the positive relationship between purpose in life and health-related lifestyle, a few studies’ results are inconsistent with our findings. For example, an existing prospective study did not observe a positive association between purpose in life and healthy sleep patterns [ 28 ]. In other studies, the purpose of life was not associated with smoking [ 29 , 30 ]. Notably, the mechanistic link between health-related lifestyle and purpose in life has not been well examined. Hooker et al. proposed a hypothesized model linking purpose in life with health [ 31 ]. They summarized the relationship between life purpose and health outcomes by utilizing the concept of self-regulation. In the model, they proposed that life purpose influenced health through three self-regulatory processes and skills: stress-buffering, adaptive coping, and health behaviors. Health-related lifestyle, one of the self-regulatory processes, is the result of individuals setting goals, monitoring their progress, and using feedback to modify their lifestyle [ 31 ]. Thus, a purpose provides the foundation and motivation for engaging in a healthy lifestyle. Kim et al. also suggested that sense of purpose in life enhances the likelihood for engagement in restorative health-related lifestyle practices (e.g., physical activity, healthy sleep quality, use of preventive health care services) from cardiovascular disease to the indirect effect of behavior [ 32 ].

There is an alternative explanation for the mechanistic link between purpose in life and health-related lifestyle. A reverse causality model suggested that engaging in healthy lifestyle practices could predict a greater purpose in life [ 31 , 33 ]. Our results denoted that the group with a higher score in purpose in life performed healthier lifestyle practices and behaviors (Table 2 ), which can be supported by either of the hypothesized models. Age statistically significantly influenced both lifestyle and purpose in life in this study, while gender did not. However, age did not change overall relation between lifestyle and purpose in life. This infers that age may act as a moderator on the association. Further research is needed to clarify the mechanism and the directionality of the association, including any modifying factors. The mechanism to explain the causal relationship between life purpose and healthy lifestyle practices helped prepare for healthy aging by preventing diseases, increasing health longevity, and imbuing a health-oriented drive, which are the major goals of the HJ21.

Additionally, the difference in life purpose scores between the two groups (35.3 vs 31.4), shown in Table 2 , should be further explored, whilst we found a statistically significant difference and a correlation between healthy lifestyle practices and purpose in life. Rather than being a single concept, purpose in life has several elements and a more comprehensive construct. The majority of measurement tools concerned with meaning in life assess two distinct concepts: coherence and purpose [ 34 ]. Coherence is a sense of comprehensibility, or one’s life “making sense,” which is descriptive and value-neutral. Purpose means a sense of core goals, aims, and direction in one’s life, which is more evaluative and value-laden in concept. Ikigai is the Japanese concept meaning a sense of life worth living. The Ikigai-9 scale used in this study has three constructs for measuring the purpose in life; (1) optimistic and positive emotions toward life, (2) active and positive attitudes towards one’s life, and (3) acknowledgement of the meaning of one’s existence. The scale seems to measure more similarly to the purpose; however, the total score does not distinguish between the association of specific constructs and healthy lifestyle practices. Thus, further methodological sophistication regarding the evaluation of a specific concept encompassed within life purpose needs to be reached. This aspect broadens our understanding of purpose in life and its relation to health. This particular cohort of certified specialists shared many features of high health literacy through the process of professional development and certification, combined with life-long learning and activities related to their role as health management specialists. Further, health-related lifestyle practices mean that the certified specialists were far healthier than the national average. These characteristics represent an individual’s health literacy. Health literacy is considered to be an individuals’ capacity to obtain and understand basic health information and services and to make appropriate health-related decisions based on this information [ 35 ]. Therefore, health literacy is directly associated with disease mortality [ 36 ], overall health status [ 37 ], disease prevention [ 38 , 39 ], and health behaviors. These can be attributed to purpose in life [ 2 ].

Thus, health literacy and health-related lifestyle appear to have a similar relationship with disease prevention and better health outcomes. The mediating effect of health literacy on the relationship between healthy lifestyle and life purpose should be investigated. Such inquiries in a prospective cohort study can better explain the mechanism of the causal link between purpose in life, health-related lifestyle, and health literacy.

Limitations

There are several limitations to this study. First, all the measurements were self-reported, which can be a source of bias. Second, while the survey questionnaires are widely used in national health promotion, they have not been fully validated. Third, the real-life meaning of purpose in life has not been determined yet. The Ikigai-9 score, one of the tools used to measure the life purpose score, was validated in a small and limited population; however, the instrument may not capture it holistically. This limitation was implicated by the previously reported systematic review. Furthermore, Zheng et al. found variability in the strength of correlation among the questionnaire for quality of life, part of which included questions regarding a purposeful life [ 40 ]. Lastly, the correlational analysis did not include an adjustment for confounding factors other than age. Hence, little is known about factors influencing the relationship between a healthy lifestyle and purpose in life. We need to establish other potential influencing factors and determine which variables have mediating, moderating, and confounding effects on purpose in life to understand the causal relationship between healthy lifestyle practices and life purpose [ 41 ]. This exploration proposes a promising model for future intervention programs.

Despite these limitations, this study has several strengths. First, the study sample size, N  = 4820, was large and distributed throughout Japan. This aspect of the study increases generalizability. According to the previous review, numerous studies on purpose in life focused on older adults [ 42 ], whereas only a few were concerned with younger or middle-aged adults. In the present study, the majority of the participants were younger and middle-aged adults. Second, previous studies used relatively simple questions or did not employ validated tools to measure purpose in life. However, we used a validated tool, Ikigai-9, in this study. This aspect allows the study results to increase the reliability and validity of the measurement of purpose in life and also hold applicability in other studies. Lastly, study participants were certified specialists in health management who have shown high health literacy. This inclusion criterion provides guidance on improving healthy lifestyle practices through health literacy as an approach to health promotion.

In conclusion, a healthy lifestyle was found to be positively associated with purpose in life among a cohort of highly health-literate professionals. Healthcare personnel who receive specific training for health management may play important roles in promoting a population’s health and wellbeing. However, the mechanism to explain the relationship between purpose in life and health-related lifestyle remains unknown. Therefore, causal relations between improving healthier lifestyles and increasing purpose in life should be tested.

Availability of data and materials

The datasets used in the current study are available from the corresponding author upon reasonable request.

Cohen R, Bavishi C, Rozanski A. Purpose in life and its relationship to all-cause mortality and cardiovascular events: a meta-analysis. Psychosom Med. 2016;78(2):122–33. https://doi.org/10.1097/PSY.0000000000000274 .

Article   PubMed   Google Scholar  

Roepke AM, Jayawickreme E, Riffle QM. Meaning and health: a systematic review. Appl Res Qual of Life. 2014;9(4):1055–79. https://doi.org/10.1007/s11482-013-9288-9 .

Article   Google Scholar  

Wood AM, Joseph S. The absence of positive psychological (eudemonic) well-being as a risk factor for depression: a ten year cohort study. J Affect Disord. 2010;122(3):213–7. https://doi.org/10.1016/j.jad.2009.06.032 .

Park CL, Malone MR, Suresh DP, Bliss D, Rosen RI. Coping, meaning in life, and quality of life in congestive heart failure patients. Qual Life Res. 2008;17(1):21–6. https://doi.org/10.1007/s11136-007-9279-0 .

Sherman AC, Simonton S. Effects of personal meaning among patients in primary and specialized care: associations with psychosocial and physical outcomes. Psychol Health. 2012;27(4):475–90. https://doi.org/10.1080/08870446.2011.592983 .

Kraus N. Meaning in life and mortality. J Gerontol B, Psycol Sci Soc Sci. 2009;64B(4):517–27. https://doi.org/10.1093/geronb/gbp047 .

Ministry of Health, Labour, and Welfare. Health Japan 21, 2 nd phase. https://www.mhlw.go.jp/file/06-Seisakujouhou-10900000-Kenkoukyoku/0000047330.pdf . Accessed 31 October 2020.

McKnight PE, Kashdan TB. Purpose in life as a system creates and sustains health and well-being: an integrative, testable theory. Rev Gen Psychol. 2009;13(3):242–51. https://doi.org/10.1037/a0017152 .

Ryff CD, Keyes CM. The structure of psychological well-being revisited. J Pers Soc Psychol. 1995;69(4):719–27. https://doi.org/10.1037/0022-3514.69.4.719 .

Article   CAS   PubMed   Google Scholar  

Nakanishi N. “Ikigai” in older Japanese people. Age Ageing. 1999;28(3):323–4. https://doi.org/10.1093/ageing/28.3.323 .

Sone T, Nakaya N, Ohmori K, Shimazu T, Higashiguchi M, Kakizaki M, et al. Sense of life worth living (Ikigai) and mortality in Japan: Ohsaki study. Psychosom Med. 2008;70(6):709–15. https://doi.org/10.1097/PSY.0b013e31817e7e64 .

Tanno K, Sakata K, Ohsawa M, Onoda T, Itai K, Yaegashi Y, et al. Association of Ikigai as a positive psychological factor with all-cause mortality and cause-specific mortality among middle-aged and elderly Japanese people: findings from the Japan collaborative cohort study. J Psychosom Res. 2009;67(1):67–75. https://doi.org/10.1016/j.jpsychores.2008.10.018 .

Nakanishi N, Fukuda H, Tatara K. Changes in psychosocial conditions and eventual mortality in community-residing elderly people. J Epidemiol. 2003;13(2):72–9. https://doi.org/10.2188/jea.13.72 .

Mozaffarian D, Wilson PF, Kannel WB. Beyond established and novel risk factors: lifestyle factors for cardiovascular disease. Circulation. 2008;117(23):3031–8. https://doi.org/10.1161/CIRCULATIONAHA.107.738732 .

Egger G, Dixon J. Beyond obesity and lifestyle: a review of 21st century chronic disease determinants. Biomed Res Int. 2014;2014:731685.

Cecchini M, Sassi F, Lauer JA, Lee YY, Guajardo-Barron V, Chisholm D. Chronic diseases: chronic diseases and development 3 tackling of unhealthy diets, physical inactivity, and obesity: health effects and cost-effectiveness. Lancet. 2010;376(9754):1775–84. https://doi.org/10.1016/S0140-6736(10)61514-0 .

Bodai BI, Nakata TE, Wong WT, Clark DR, Lawenda S, Tsou C, et al. Lifestyle medicine: a brief review of its dramatic impact on health and survival. Perm J. 2018;22:17–25. https://doi.org/10.7812/TPP/17-025 .

Anderson E, Durstine JL. Physical activity, exercise, and chronic diseases: a brief review. Sports Med Health Sci. 2019;1(1):3–10. https://doi.org/10.1016/j.smhs.2019.08.006 .

Achieving global targets: healthy lifestyles to prevent and control non-communicable diseases. World Health Organization, 2013 https://www.who.int/china/news/detail/12-11-2013-achieving-global-targets-healthy-lifestyles-to-prevent-and-control-non-communicable-diseases . Accessed 31 October 2020.

Katanoda K, Matsumura Y. National Nutrition Survey in Japan—its methodological transition and current findings. J Nutr Sci Vitaminol. 2002;48(5):423–32. https://doi.org/10.3177/jnsv.48.423 .

Imai T, Osada H, Nishimura Y. The reliability and validity of a new scale for measuring the concept of Ikigai (Ikigai-9). Nihon Koshu Eisei Zassi. 2012;59:433–40.

Google Scholar  

Japanese Association of Preventive Medicine for Adult Disease. Kenko kanrisi [Specialists in health management]. https://www.healthcare.or.jp . Accessed 31 October 2020).

Fido D, Kotera Y, Asano K. English translation and validation of the Ikigai-9 in a UK sample. Int J Ment Health Addict. 2020;18(5):1352–9. https://doi.org/10.1007/s11469-019-00150-w .

Kinoshita S, Hirooka N, Kusano T, Saito K, Nakamoto H. Does improvement in health-related lifestyle habits increase purpose in life among a health literate cohort? Int J Environ Res Pub Health. 2020;17(23):E8878.

Morimoto Y, Yamasaki S, Ando S, Koike S, Fujikawa S, Kanata S, et al. Purpose in life and tobacco use among community-dwelling mothers of early adolescents. BMJ Open. 2018;8(4):e020586. https://doi.org/10.1136/bmjopen-2017-020586 .

Article   PubMed   PubMed Central   Google Scholar  

Hooker SA, Masters KS. Purpose in life is associated with physical activity measured by accelerometer. J Health Psychol. 2016;21(6):962–71. https://doi.org/10.1177/1359105314542822 .

Conner TS, Brookie KL, Richardson AC, Polak MA. On carrots and curiosity: eating fruit and vegetables is associated with greater flourishing in daily life. Br J Health Psychol. 2015;20(2):413–27. https://doi.org/10.1111/bjhp.12113 .

Ryff CD, Singer B, Love GD. Positive health: connecting well-being with biology. Philos Trans R Soc Lond Ser B Biol Sci. 2004;359(1449):1383–94. https://doi.org/10.1098/rstb.2004.1521 .

Steptoe A, Fancourt D. Leading a meaningful life at older ages and its relation with social engagement, prosperity, health, biology, and time use. Proc Natl Acad Sci U S A. 2019;116(4):1207–12. https://doi.org/10.1073/pnas.1814723116 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Chen Y, Kim ES, Koh HK, Fraizer AL, Van der Weele TJ. Sense of mission and subsequent health and well-being among young adults: an outcome-wide analysis. Am J Epidemiol. 2019;188(4):664–73. https://doi.org/10.1093/aje/kwz009 .

Hooker SA, Master KS, Park CL. A meaningful life is a healthy life: a conceptual model liking meaning and meaning salience to health. Rev Gen Psychol. 2018;22(1):11–24. https://doi.org/10.1037/gpr0000115 .

Kim ES, Delaney SW, Kubzansky LD. Sense of purpose in life and cardiovascular disease: underlying mechanisms and future directions. Cur Cardiol Rep. 2019;21(11):135. https://doi.org/10.1007/s11886-019-1222-9 .

Kahana E, Lawrence RH, Kahana B, Kercher K, Wisniewski A, Stoller E, et al. Long-term impact of preventive proactivity on the quality of life of the old-old. Psychosom Med. 2002;64(3):382–94. https://doi.org/10.1097/00006842-200205000-00003 .

Martela F, Sterger MF. The three meanings of meaning in life: distinguishing coherence, purpose, and significance. J Posit Psychol. 2016;11(5):531–45. https://doi.org/10.1080/17439760.2015.1137623 .

Nutbeam D. Health literacy as a public goal: a challenge for contemporary health education and communication strategies into the 21 st century. Health Prom Int. 2000;15(3):259–67. https://doi.org/10.1093/heapro/15.3.259 .

Baker DW, Wolf MS, Feinglass J, Thompson JA, Gazmararian JA, Huang J. Health literacy and mortality among elderly persons. Arch Intern Med. 2007;167(14):1503–9. https://doi.org/10.1001/archinte.167.14.1503 .

Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97–107. https://doi.org/10.7326/0003-4819-155-2-201107190-00005 .

Fernandez DM, Larson JL, Zikmund-Fisher BJ. Associations between health literacy and preventive health behaviors among older adults: findings from the health and retirement study. BMC Public Health. 2016;16(1):596. https://doi.org/10.1186/s12889-016-3267-7 .

Santos P, Sá L, Couto L, Hespanhol A. Health literacy as a key for effective preventive medicine. Cogent Soc Sci. 2017;3(1):1407522. https://doi.org/10.1080/23311886.2017.1407522 .

Zheng M, Jin H, Shi N, Duan C, Wang D, Yu X, et al. The relationship between health literacy and quality of life: a systematic review and meta-analysis. Health Qual Life Outcomes. 2018;16(1):1–10.

Alimujiang A, Wiensch A, Boss J, Fleischer NL, Mondul AM, McLean K, et al. Association between life purpose and mortality among US adults older than 50 years. JAMA Netw Open. 2019;2(5):e194270. https://doi.org/10.1001/jamanetworkopen.2019.4270 .

Hill PL, Turiano NA. Purpose in life as a predictor of mortality across adulthood. Psychol Sci. 2014;25(7):1482–6. https://doi.org/10.1177/0956797614531799 .

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All authors contributed to the study conception and design. Material preparation and data analysis were performed by Nobutaka Hirooka, Takeru Kusano, and Shunsuke Kinoshita. Nobutaka Hirooka, Shunsuke Kinoshita, and Ryutaro Aoyagi collected the data. Nobutaka Hirooka, Takeru Kusano, and Hidetomo Nakamoto interpreted the analysis. The first draft of the manuscript was written by Nobutaka Hirooka and all authors commented on drafted versions of the manuscript. All authors read and approved the final version of the manuscript.

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Hirooka, N., Kusano, T., Kinoshita, S. et al. Association between healthy lifestyle practices and life purpose among a highly health-literate cohort: a cross-sectional study. BMC Public Health 21 , 820 (2021). https://doi.org/10.1186/s12889-021-10905-7

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Narrative review and analysis of the use of “lifestyle” in health psychology.

1. Introduction

2. materials and methods, 3.1. the concepts of lifestyle.

• Internal dimension: Lifestyle as a synonym for personality style, an expression of cognitive styles, or a set of attitudes, interests, and values. The focus is placed on the subject and on the internal processes that guide behaviour and action;
• External dimension: lifestyle as an expression of the individual’s status and social position within a given context or as an expression of behavioural patterns;
• Temporal dimension: lifestyle as a stable dimension that is expressed within daily practices; this dimension is found transversally in some sociological and psychological perspectives.

3.1.1. Internal Dimension

3.1.2. external dimension, social positioning, practice and behaviour, 3.1.3. temporal dimension, 3.2. lifestyle in the field of health psychology, 4. discussion, 4.1. toward a perspective of definition and research on lifestyle, 4.2. system of meanings, attitudes, and values within which subject acts, 4.3. define individual and collective models of health practices within social, historical, and cultural contexts, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Buffon, L.G.L. Discours Prononcé a l’Académie Française Par M. De Buffon Le Jour de Sa Réception Le 25 Août 1753 ; J. Lecoffre: Paris, France, 1872. [ Google Scholar ]
• Bartlett, J. Familiar Quotations: A Collection of Passages, Phrases and Proverbs, Traced to Their Sources in Ancient and Modern Literature , 12th ed.; Morley, C., Everett, L.D., Eds.; Little, Brown: Boston, MA, USA, 1951. [ Google Scholar ]
• Ansbacher, H.L. Life Style: A Historical and Systematic Review. J. Individ. Psychol. 1967 , 23 , 191–212. [ Google Scholar ] [ PubMed ]
• Weber, M. Class, Status and Party. In Class, Status, and Power ; Bendix, R., Lipset, S., Eds.; Wiley: New York, NY, USA, 1966; pp. 21–28. [ Google Scholar ]
• Bourdieu, P. Distinction: A Social Critique of the Judgement of Taste ; Harvard University Press: Cambridge, MA, USA, 1984; ISBN 978-0-674-21277-0. [ Google Scholar ]
• Giddens, A. Modernity and Self-Identity: Self and Society in the Late Modern Age ; Stanford University Press: Stanford, CA, USA, 1991; ISBN 978-0-8047-1944-5. [ Google Scholar ]
• Stebbins, R.A. Lifestyle as a Generic Concept in Ethnographic Research. Qual. Quant. 1997 , 31 , 347–360. [ Google Scholar ] [ CrossRef ]
• Mitchell, A. Way of Life ; SRI International: Stanford, CA, USA, 1982. [ Google Scholar ]
• Mitchell, A. The Nine American Lifestyles. Who We Are and Where We’re Going ; Macmillan: New York, NY, USA, 1983. [ Google Scholar ]
• Rokeach, M. The Nature of Human Values ; Free Press: New York, NY, USA, 1973. [ Google Scholar ]
• Schutz, H.G.; Baird, P.C.; Hawkes, G.R. Lifestyles and Consumer Behavior of Older Americans ; Praeger: New York, NY, USA, 1979. [ Google Scholar ]
• Adler, A. Social Interest ; Capricorn Books: New York, NY, USA, 1933. [ Google Scholar ]
• Allport, G.W. Pattern and Growth in Personality ; American Psychological Association: Washington, DC, USA, 1961; ISBN 978-0-03-010810-5. [ Google Scholar ]
• Gochman, D.S. Handbook of Health Behavior Research II: Provider Determinants ; American Psychological Association: Washington, DC, USA, 1997; ISBN 978-1-4899-1760-7. [ Google Scholar ]
• Frohlich, K.; Corin, E.; Potvin, L. A Theoretical Proposal for the Relationship Between Context and Disease. Sociol. Health Illn. 2001 , 23 , 776–797. [ Google Scholar ] [ CrossRef ]
• Lomas, J. Social Capital and Health: Implications for Public Health and Epidemiology. Soc. Sci. Med. 1998 , 47 , 1181–1188. [ Google Scholar ] [ CrossRef ]
• Sweat, M.D.; Denison, J.A. Reducing HIV Incidence in Developing Countries with Structural and Environmental Interventions. AIDS 1995 , 9 , S251–S257. [ Google Scholar ]
• Cockerham, W.C.; Wolfe, J.D.; Bauldry, S. Health Lifestyles in Late Middle Age. Res. Aging 2020 , 42 , 34–46. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Daw, J.; Margolis, R.; Wright, L. Emerging Adulthood, Emergent Health Lifestyles: Sociodemographic Determinants of Trajectories of Smoking, Binge Drinking, Obesity, and Sedentary Behavior. J. Health Soc. Behav. 2017 , 58 , 181–197. [ Google Scholar ] [ CrossRef ]
• Mize, T.D. Profiles in Health: Multiple Roles and Health Lifestyles in Early Adulthood. Soc. Sci. Med. 2017 , 178 , 196–205. [ Google Scholar ] [ CrossRef ]
• Beck, U. Risk Society: Towards a New Modernity ; Theory, Culture & Society; Sage Publications: London, UK; Newbury Park, CA, USA, 1992; ISBN 978-0-8039-8345-8. [ Google Scholar ]
• Horrocks, C.; Johnson, S. A Socially Situated Approach to Inform Ways to Improve Health and Wellbeing. Sociol. Health Illn. 2014 , 36 , 175–186. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• World Health Organization. Noncommunicable Diseases: Progress Monitor 2020 ; WHO: Geneva, Switzerland, 2020.
• Hubert, H.B.; Bloch, D.A.; Oehlert, J.W.; Fries, J.F. Lifestyle Habits and Compression of Morbidity. J. Gerontol. A Biol. Sci. Med. Sci. 2002 , 57 , M347–M351. [ Google Scholar ] [ CrossRef ]
• King, D.E.; Mainous, A.G.; Carnemolla, M.; Everett, C.J. Adherence to Healthy Lifestyle Habits in US Adults, 1988–2006. Am. J. Med. 2009 , 122 , 528–534. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Siegler, I.; Kaplan, B.; Von Dras, D.; Mark, D. Cardiovascular Health: A Challenge for Midlife. In Life in the middle: Psychological and Social Development in Middle Age ; Willis, S., Reid, J., Eds.; Academic Press: San Diego, CA, USA, 1999; pp. 147–157. [ Google Scholar ]
• Matarazzo, J.D. Behavioral Health: A 1990 Challenge for the Health Sciences Professions. In Behavioral Health: A Handbook of Health Enhancement and Disease Prevention ; Matarazzo, J.D., Miller, N.E., Weiss, S.M., Herd, J.A., Eds.; Wiley: New York, NY, USA, 1983. [ Google Scholar ]
• World Health Organization. Constitution of the World Health Organization ; WHO: Geneva, Switzerland, 1948.
• Huber, M.; Knottnerus, J.A.; Green, L.; van der Horst, H.; Jadad, A.R.; Kromhout, D.; Leonard, B.; Lorig, K.; Loureiro, M.I.; van der Meer, J.W.M.; et al. How Should We Define Health? BMJ 2011 , 343 , d4163. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Diener, E. Guidelines for National Indicators of Subjective Well-Being and Ill-Being. J. Happiness Stud. 2006 , 7 , 397–404. [ Google Scholar ] [ CrossRef ]
• Engel, G.L. The Need for a New Medical Model: A Challenge for Biomedicine. Science 1977 , 196 , 129–136. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bronfenbrenner, U. Toward an Experimental Ecology of Human Development. Am. Psychol. 1977 , 32 , 513–531. [ Google Scholar ] [ CrossRef ]
• Antonovsky, A. Health, Stress, and Coping ; Jossey-Bass: San Francisco, CA, USA, 1979; ISBN 978-0-87589-412-6. [ Google Scholar ]
• Antonovsky, A. The Salutogenic Model as a Theory to Guide Health Promotion1. Health Promot. Int. 1996 , 11 , 11–18. [ Google Scholar ] [ CrossRef ]
• Canguilhem, G. Le normal et le pathologique ; Presses Universitaires de France: Paris, France, 1966. [ Google Scholar ]
• Murray, M. (Ed.) Critical Health Psychology ; Palgrave Macmillan: London, UK; New York, NY, USA, 2004; ISBN 978-0-333-99033-9. [ Google Scholar ]
• Greenhalgh, T.; Thorne, S.; Malterud, K. Time to Challenge the Spurious Hierarchy of Systematic over Narrative Reviews? Eur. J. Clin. Investig. 2018 , 48 , e12931. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Veal, A.J. The Concept of Lifestyle: A Review. Leis. Stud. 1993 , 12 , 233–252. [ Google Scholar ] [ CrossRef ]
• Jensen, M. Defining Lifestyle. Environ. Sci. 2007 , 4 , 63–73. [ Google Scholar ] [ CrossRef ]
• Sobel, M.E. Lifestyle and Social Structure: Concepts, Definitions, Analyses ; Quantitative Studies in Social Relations; Academic Press: New York, NY, USA, 2013; ISBN 978-0-12-654280-6. [ Google Scholar ]
• Coleman, J.C. Abnormal Psychology and Modern Life ; Scott, Foresman: Chicago, IL, USA, 1964. [ Google Scholar ]
• World Health Organization. Ottawa Charter for Health Promotion ; WHO: Geneva, Switzerland, 1986.
• Cockerham, W.C. The Social Determinants of the Decline of Life Expectancy in Russia and Eastern Europe: A Lifestyle Explanation. J. Health Soc. Behav. 1997 , 38 , 117–130. [ Google Scholar ] [ CrossRef ]
• Jensen, M. Lifestyle: Suggesting Mechanisms and a Definition from a Cognitive Science Perspective. Environ. Dev. Sustain. 2009 , 11 , 215–228. [ Google Scholar ] [ CrossRef ]
• Adler, A. Problems of Neurosis: A Book of Case Histories ; Harper & Row: New York, NY, USA, 1929. [ Google Scholar ]
• Sperry, J.; Sperry, L. Individual Psychology (Adler). In Encyclopedia of Personality and Individual Differences ; Zeigler-Hill, V., Shackelford, T.K., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 2227–2231. ISBN 978-3-319-24612-3. [ Google Scholar ]
• Coleman, J.C. Personality Dynamics and Effective Behavior ; Scott, Foresman: Chicago, IL, USA, 1960. [ Google Scholar ]
• Rokeach, M. Beliefs, Attitudes, and Values: A Theory of Organization and Change ; Jossey-Bass: San Francisco, CA, USA, 1968; ISBN 978-0-87589-013-5. [ Google Scholar ]
• Mitchell, A. Life Way and Life Styles, Business Intelligence Program ; SRI International: Stanford, CA, USA, 1973. [ Google Scholar ]
• Mitchell, A. Consumer Values. A Tipology, Value and Lifestyle Program ; SRI International: Stanford, CA, USA, 1978. [ Google Scholar ]
• Maslow, A.H. A Theory of Human Motivation. Psychol. Rev. 1943 , 50 , 370–396. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Maslow, A.H. Motivation and Personality ; Harper & Row: New York, NY, USA, 1954. [ Google Scholar ]
• Maslow, A.H. Toward a Psychology of Being ; American Psychological Association: Washington, DC, USA, 1962. [ Google Scholar ]
• Anderson, W.T.; Golden, L.L. Lifestyle and Psychographics: A Critical Review and Recommendation. In Advances in Consumer Research ; Kinnear, T.C., Ed.; Association for Consumer Research: Provo, UT, USA, 1984; Volume 11, pp. 405–411. [ Google Scholar ]
• Pessemier, E.A.; Tigert, D.J. Personality, Activity and Attitude Predictors of Consumer Behavior. In New Ideas for Successful Marketing, American Marketing ; Whright, J.S., Goldstucker, J.L., Eds.; American Marketing Association: Chicago, IL, USA, 1966; pp. 332–347. [ Google Scholar ]
• Wells, W.D. Backward Segmentation. In Insights into Consumer Behavior ; Arndt, J., Ed.; Allyn and Bacon: New York, NY, USA, 1968; pp. 85–100. [ Google Scholar ]
• Wells, W.D. Comment on the Meaning of LifeStyle. In Advances in Consumer Research ; Anderson, B.B., Ed.; Association for Consumer Research: Ann Arbor, MI, USA, 1975; p. 498. [ Google Scholar ]
• Wilson, C.C. Homemaker Living Patterns and Marketplace Behavior—A Psychometric Approach. In New Ideas for Successful Marketing ; Wright, J.S., Goldstucker, J.L., Eds.; American Marketing Association: Chicago, IL, USA, 1966; pp. 305–347. [ Google Scholar ]
• Wells, W.D. Life Style and Psychographics ; American Marketing Association: Chicago, IL, USA, 1974. [ Google Scholar ]
• Valette-Florence, P. Les Styles de vie: Bilan Critique et Perspectives; du Mythe à la Réalité; Connaître et Pratiquer la Gestion ; Nathan: Paris, France, 1994; ISBN 978-2-09-192127-3. [ Google Scholar ]
• Cathelat, B. Les Styles de Vie des Français: 1978–1998 ; Au-Delà du Miroir; Stanké: Ottawa, ON, Canada, 1977; ISBN 978-2-86242-003-5. [ Google Scholar ]
• Cathelat, B. Styles de Vie ; Ed. d’Organisation: Paris, France, 1985; ISBN 978-2-7081-0625-3. [ Google Scholar ]
• Cathelat, B. Socio-Styles-Système: Les Styles de Vie, Théorie, Méthodes, Applications ; Ed. d’Organisation: Paris, France, 1990; ISBN 978-2-7081-1176-9. [ Google Scholar ]
• Berzano, L.; Genova, C. Sociologia dei Lifestyles , Studi Superiori, 1st ed.; Carocci: Rome, Italy, 2011; ISBN 978-88-430-6030-6. [ Google Scholar ]
• Weber, M. Wirtshaft und Gesellsschaft, Mohr, Tubingen as: Economy and Society ; Roth, G., Wittich, C., Eds.; University of California: Berkeley, CA, USA, 1922; Volume 2. [ Google Scholar ]
• Horley, J. A Longitudinal Examination of Lifestyles. Soc. Indic. Res. 1992 , 26 , 205–219. [ Google Scholar ] [ CrossRef ]
• Jones, I.R.; Higgs, P.; Ekerdt, D.J. Consumption and Generational Change: The Rise of Consumer Lifestyles. In Consumption and Generational Change: The Rise of Consumer Lifestyles ; Transaction Publishers: Piscataway, NJ, USA, 2009; pp. 1–20. ISBN 978-1-4128-0857-6. [ Google Scholar ]
• Dean, K.; Colomer, C.; Pérez-Hoyos, S. Research on Lifestyles and Health: Searching for Meaning. Soc. Sci. Med. 1995 , 41 , 845–855. [ Google Scholar ] [ CrossRef ]
• Berkman, H.W.; Gilson, C.C. Consumer Behavior: Concepts and Strategies ; Dickenson: Los Angeles, CA, USA, 1978. [ Google Scholar ]
• Chaney, D.C. Lifestyles ; Key Ideas; Routledge: London, UK; New York, NY, USA, 1996; ISBN 978-0-415-11718-0. [ Google Scholar ]
• Dumazedier, J. Vers une Civilisation du Loisir? Points Série Civilisation; Ed. du Seuil: Paris, France, 1972; ISBN 978-2-02-000604-0. [ Google Scholar ]
• Kroeber, A.L. Style and Civilisation ; Cornell University Press: Ithaca, NY, USA, 1957. [ Google Scholar ]
• Starr, M. Lifestyle Conformity and Lifecycle Saving: A Veblenian Perspective ; Social Science Research Network: Rochester, NY, USA, 2009. [ Google Scholar ]
• Baldwin, D.A.; Baird, J.A. Discerning Intentions in Dynamic Human Action. Trends Cogn. Sci. 2001 , 5 , 171–178. [ Google Scholar ] [ CrossRef ]
• Jonker, C.M.; Treur, J.; Wijngaards, W.C.A. A Temporal Modelling Environment for Internally Grounded Beliefs, Desires and Intentions. Cogn. Syst. Res. 2003 , 4 , 191–210. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Tomasello, M. The Cultural Origins of Human Cognition ; Harvard University Press: Cambridge, MA, USA, 2003; ISBN 978-0-674-00582-2. [ Google Scholar ]
• Tomasello, M.; Carpenter, M.; Call, J.; Behne, T.; Moll, H. Understanding and Sharing Intentions: The Origins of Cultural Cognition. Behav. Brain Sci. 2005 , 28 , 675–691; discussion 691–735. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hooker, K.; McAdams, D.P. Personality Reconsidered: A New Agenda for Aging Research. J. Gerontol. Ser. B 2003 , 58 , P296–P304. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Pulkkinen, L.; Caspi, A. Paths to Successful Development: Personality in the Life Course ; Cambridge University Press: New York, NY, USA, 2002; ISBN 978-0-521-80048-8. [ Google Scholar ]
• Costa, P.T., Jr.; McCrae, R.R. Set like Plaster? Evidence for the Stability of Adult Personality. In Can Personality Change? Heatherton, T.F., Weinberger, J.L., Eds.; American Psychological Association: Washington, DC, USA, 1994; pp. 21–40. ISBN 978-1-55798-213-1. [ Google Scholar ]
• Wrzus, C.; Roberts, B.W. Processes of Personality Development in Adulthood: The TESSERA Framework. Personal. Soc. Psychol. Rev. 2017 , 21 , 253–277. [ Google Scholar ] [ CrossRef ]
• Roberts, B.W.; DelVecchio, W.F. The Rank-Order Consistency of Personality Traits from Childhood to Old Age: A Quantitative Review of Longitudinal Studies. Psychol. Bull. 2000 , 126 , 3–25. [ Google Scholar ] [ CrossRef ]
• Cockerham, W.C. Health Lifestyle Theory and the Convergence of Agency and Structure. J. Health Soc. Behav. 2005 , 46 , 51–67. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Weber, M. Economy and Society, Volume 2 ; Roth, G., Wittich, C., Eds.; University of California: Berkeley, CA, USA, 1978. [ Google Scholar ]
• CSDH. Closing the Gap in a Generation: Health Equity through Action on the Social Determinants of Health. Final Report of the Commission on Social Determinants of Health ; WHO: Geneva, Switzerland, 2008.
• Alcántara, C.; Diaz, S.V.; Cosenzo, L.G.; Loucks, E.B.; Penedo, F.J.; Williams, N.J. Social Determinants as Moderators of the Effectiveness of Health Behavior Change Interventions: Scientific Gaps and Opportunities. Health Psychol. Rev. 2020 , 14 , 132–144. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Braveman, P. Health Disparities and Health Equity: Concepts and Measurement. Annu. Rev. Public Health 2006 , 27 , 167–194. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Braveman, P.; Egerter, S.; Williams, D.R. The Social Determinants of Health: Coming of Age. Annu. Rev. Public Health 2011 , 32 , 381–398. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Marmot, M. Social Determinants of Health Inequalities. Lancet 2005 , 365 , 1099–1104. [ Google Scholar ] [ CrossRef ]
• Solar, O.; Irwin, A. A Conceptual Framework for Action on the Social Determinants of Health ; WHO: Geneva, Switzerland, 2010.
• Korp, P. Problems of the Healthy Lifestyle Discourse. Sociol. Compass 2010 , 4 , 800–810. [ Google Scholar ] [ CrossRef ]
• Elder, G.H. Time, Human Agency, and Social Change: Perspectives on the Life Course. Soc. Psychol. Q. 1994 , 57 , 4–15. [ Google Scholar ] [ CrossRef ]
• Mirowsky, J.; Ross, C.E. Education, Social Status, and Health ; Social Institutions and Social Change; A. de Gruyter: New York, NY, USA, 2003; ISBN 978-0-202-30706-0. [ Google Scholar ]
• Stainton Rogers, W. Changing Behaviour: Can Critical Psychology Influence Policy and Practice? In Advances in Health Psychology: Critical Approaches ; Horrocks, C., Johnson, S., Eds.; Palgrave Macmillan: Basingstoke/Hampshire, UK; New York, NY, USA, 2012; ISBN 978-0-230-27538-6. [ Google Scholar ]
• Crawford, R. Health as a Meaningful Social Practice. Health 2006 , 10 , 401–420. [ Google Scholar ] [ CrossRef ]
• Davies, B. The (Im)Possibility of Intellectual Work in Neoliberal Regimes. Discourse Stud. Cult. Politics Educ. 2005 , 26 , 1–14. [ Google Scholar ] [ CrossRef ]
• Crawford, R. Healthism and the Medicalization of Everyday Life. Int. J. Health Serv. 1980 , 10 , 365–388. [ Google Scholar ] [ CrossRef ]
• Glassner, B. Fitness and the Postmodern Self. J. Health Soc. Behav. 1989 , 30 , 180–191. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hodgetts, D.; Bolam, B.; Stephens, C. Mediation and the Construction of Contemporary Understandings of Health and Lifestyle. J. Health Psychol. 2005 , 10 , 123–136. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pakholok, O. The Idea of Healthy Lifestyle and Its Transformation Into Health-Oriented Lifestyle in Contemporary Society. SAGE Open 2013 , 3 , 2158244013500281. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mielewczyk, F.; Willig, C. Old Clothes and an Older Look: The Case for a Radical Makeover in Health Behaviour Research. Theory Psychol. 2007 , 17 , 811–837. [ Google Scholar ] [ CrossRef ]
• Dodds, A.; Chamberlain, K. The Problematic Messages of Nutritional Discourse: A Case-Based Critical Media Analysis. Appetite 2017 , 108 , 42–50. [ Google Scholar ] [ CrossRef ]
• Crossley, M. Critical Health Psychology: Developing and Refining the Approach. Soc. Personal. Psychol. Compass 2007 , 2 , 21–33. [ Google Scholar ] [ CrossRef ]
• Murray, M.; Poland, B. Health Psychology and Social Action. J. Health Psychol. 2006 , 11 , 379–384, author reply 401. [ Google Scholar ] [ CrossRef ]
• Bowleg, L. The Problem with the Phrase Women and Minorities: Intersectionality—An Important Theoretical Framework for Public Health. Am. J. Public Health 2012 , 102 , 1267–1273. [ Google Scholar ] [ CrossRef ]
• Cole, E.R. Intersectionality and Research in Psychology. Am. Psychol. 2009 , 64 , 170–180. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Evans, C.R.; Williams, D.R.; Onnela, J.-P.; Subramanian, S.V. A Multilevel Approach to Modeling Health Inequalities at the Intersection of Multiple Social Identities. Soc. Sci. Med. 2018 , 203 , 64–73. [ Google Scholar ] [ CrossRef ]
• Mistry, R.; McCarthy, W.J.; Yancey, A.K.; Lu, Y.; Patel, M. Resilience and Patterns of Health Risk Behaviors in California Adolescents. Prev. Med. 2009 , 48 , 291–297. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Mollborn, S.; James-Hawkins, L.; Lawrence, E.; Fomby, P. Health Lifestyles in Early Childhood. J. Health Soc. Behav. 2014 , 55 , 386–402. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mollborn, S.; Lawrence, E. Family, Peer, and School Influences on Children’s Developing Health Lifestyles. J. Health Soc. Behav. 2018 , 59 , 133–150. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Saint Onge, J.M.; Krueger, P.M. Health Lifestyle Behaviors among U. S. Adults. SSM Popul. Health 2017 , 3 , 89–98. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Alvaro, C.; Jackson, L.A.; Kirk, S.; McHugh, T.L.; Hughes, J.; Chircop, A.; Lyons, R.F. Moving Canadian Governmental Policies beyond a Focus on Individual Lifestyle: Some Insights from Complexity and Critical Theories. Health Promot. Int. 2011 , 26 , 91–99. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Baum, F.; Fisher, M. Why Behavioural Health Promotion Endures despite Its Failure to Reduce Health Inequities. Sociol. Health Illn. 2014 , 36 , 213–225. [ Google Scholar ] [ CrossRef ]
• Layte, R.; Whelan, C.T. Explaining Social Class Inequalities in Smoking: The Role of Education, Self-Efficacy, and Deprivation. Eur. Sociol. Rev. 2009 , 25 , 399–410. [ Google Scholar ] [ CrossRef ]
• Baum, F. Cracking the Nut of Health Equity: Top down and Bottom up Pressure for Action on the Social Determinants of Health. Promot. Educ. 2007 , 14 , 90–95. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Slama, K. Tobacco Control and Health Equality. Glob. Health Promot. 2010 , 17 , 3–6. [ Google Scholar ] [ CrossRef ]
• Lyons, A.C.; Chamberlain, K. Health Psychology: A Critical Introduction ; Cambridge University Press: Cambridge, UK, 2006; ISBN 978-0-521-00526-5. [ Google Scholar ]
• Kwasnicka, D.; Dombrowski, S.U.; White, M.; Sniehotta, F. Theoretical Explanations for Maintenance of Behaviour Change: A Systematic Review of Behaviour Theories. Health Psychol. Rev. 2016 , 10 , 277–296. [ Google Scholar ] [ CrossRef ]
• Mark, D.; Murray, M.; Evans, B.; Willig, C.; Woodall, C.; Sykes, C.M. (Eds.) Health Psychology: Theory, Research and Practice , 2nd ed.; SAGE: London, UK; Thousand Oaks, CA, USA, 2005; ISBN 978-1-4129-0336-3. [ Google Scholar ]
• Wittgenstein, L. Philosophische Untersuchungen ; Suhrkamp: Frankfurt am Main, Germany, 1953. [ Google Scholar ]
• Reimer, B. Youth and Modern Lifestyle. In Youth Culture in Late Modernity ; Fornas, J., Bolin, G., Eds.; SAGE: London, UK, 1995. [ Google Scholar ]
• Ajzen, I. Nature and Operation of Attitudes. Annu. Rev. Psychol. 2001 , 52 , 27–58. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Schwartz, S.H. Universals in the Content and Structure of Values: Theoretical Advances and Empirical Tests in 20 Countries. In Advances in Experimental Social Psychology ; Zanna, M.P., Ed.; Academic Press: Cambridge, MA, USA, 1992; Volume 25, pp. 1–65. [ Google Scholar ]
• Schwartz, S.H. Are There Universal Aspects in the Structure and Contents of Human Values? J. Soc. Issues 1994 , 50 , 19–45. [ Google Scholar ] [ CrossRef ]
• Marks, D.F. The Case for a Pluralist Health Psychology. J. Health Psychol. 2006 , 11 , 367–372. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Braibanti, P. Ripensare la Salute: Per un Riposizionamento Critico nella Psicologia della Salute ; Psicologia Textbook; F. Angeli: Milano, Italy, 2015; ISBN 978-88-568-3563-2. [ Google Scholar ]

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Brivio, F.; Viganò, A.; Paterna, A.; Palena, N.; Greco, A. Narrative Review and Analysis of the Use of “Lifestyle” in Health Psychology. Int. J. Environ. Res. Public Health 2023 , 20 , 4427. https://doi.org/10.3390/ijerph20054427

Brivio F, Viganò A, Paterna A, Palena N, Greco A. Narrative Review and Analysis of the Use of “Lifestyle” in Health Psychology. International Journal of Environmental Research and Public Health . 2023; 20(5):4427. https://doi.org/10.3390/ijerph20054427

Brivio, Francesca, Anna Viganò, Annalisa Paterna, Nicola Palena, and Andrea Greco. 2023. "Narrative Review and Analysis of the Use of “Lifestyle” in Health Psychology" International Journal of Environmental Research and Public Health 20, no. 5: 4427. https://doi.org/10.3390/ijerph20054427

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Home — Essay Samples — Life — Lifestyle & Interests — Healthy Lifestyle

Healthy Lifestyle Essay Titles

The importance of choosing healthy lifestyle essay topics.

When it comes to writing an essay on healthy lifestyle topics, the choices are endless. The importance of selecting the right topic cannot be overstated. A well-chosen topic not only showcases your knowledge and understanding of healthy living but also makes your essay engaging and informative for the reader. Whether you are writing for a school assignment or for personal interest, the right topic can make all the difference in the impact of your essay.

Why Healthy Lifestyle Topics Matter

Healthy lifestyle topics are essential because they promote awareness and understanding of the importance of maintaining a healthy lifestyle. By writing on these topics, you can educate others about the benefits of healthy living, such as physical and mental well-being, disease prevention, and overall quality of life. Additionally, discussing these topics can inspire readers to make positive changes in their own lives, leading to a healthier and happier society as a whole.

Choosing the Right Topic

When choosing a healthy lifestyle essay topic, consider your interests, knowledge, and the target audience. It's important to select a topic that you are passionate about and have substantial knowledge of, as this will make the writing process more enjoyable and the content more compelling. Additionally, consider the interests and concerns of your audience to ensure that your essay resonates with them and provides valuable information.

Recommended Healthy Lifestyle Essay Topics

Nutrition and diet.

• Benefits of a balanced diet
• Impact of fast food on health
• Role of nutrition in preventing diseases
• Importance of drinking water for health
• Vegetarianism vs. non-vegetarianism

Physical Activity and Exercise

• Benefits of regular exercise
• Effects of sedentary lifestyle on health
• Role of exercise in stress management
• Importance of physical education in schools
• Comparison of different workout routines

Mental Health and Well-being

• Effects of stress on overall health
• Importance of sleep for mental well-being
• Role of mindfulness and meditation in promoting mental health
• Impact of social media on mental well-being
• Ways to cope with anxiety and depression

Substance Abuse and Addiction

• Impact of alcohol consumption on health
• Consequences of tobacco use on the body
• Prevention and treatment of drug addiction
• Effects of caffeine on the nervous system
• Role of education in preventing substance abuse

Healthcare and Prevention

• Importance of regular health check-ups
• Impact of vaccination on public health
• Role of healthcare policies in promoting a healthy lifestyle
• Preventive measures for common diseases
• Challenges in accessing healthcare in underserved communities

Choosing a healthy lifestyle essay topic is an important decision that can significantly impact the quality and effectiveness of your essay. By selecting a topic that aligns with your interests and provides valuable information to the reader, you can create a compelling and impactful piece of writing. The recommended topics provided above cover a wide range of aspects related to healthy living, ensuring that there is something for everyone to explore and discuss.

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Encouraging Healthy Lifestyles in Children and Teens

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Healthcare Research Paper Topics

In this page, we provide a comprehensive list of healthcare research paper topics , expert advice on selecting compelling topics, guidance on writing an impactful research paper, and information about iResearchNet’s writing services. By exploring these resources, students in the health sciences field can choose relevant and significant healthcare research paper topics, develop their papers effectively, and access professional writing assistance to excel in their academic endeavors.

100 Healthcare Research Paper Topics

The field of healthcare research encompasses a vast array of topics that are crucial for understanding, improving, and transforming healthcare practices. As students in the health sciences, you have the opportunity to explore these diverse areas and contribute to the knowledge base of healthcare research. This comprehensive list aims to inspire and guide you in selecting healthcare research paper topics that align with your interests and academic goals. The topics are divided into ten distinct categories, each containing ten thought-provoking and relevant research ideas. Let this list serve as a springboard for your exploration and a catalyst for impactful research in the dynamic field of healthcare.

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1. Healthcare Policy and Management

• The Impact of Health Policies on Access to Care
• Assessing the Effectiveness of Health Insurance Programs
• Analyzing the Role of Healthcare Leadership in Quality Improvement
• Exploring Strategies for Healthcare Cost Containment
• Investigating the Relationship Between Healthcare Regulations and Patient Outcomes
• Evaluating the Impact of Electronic Health Records on Healthcare Delivery
• Examining the Role of Public-Private Partnerships in Healthcare
• Analyzing the Influence of Political Factors on Healthcare Decision-Making
• Assessing the Ethical Implications of Resource Allocation in Healthcare
• Investigating the Effectiveness of Health Promotion Programs in Primary Care Settings

2. Healthcare Ethics and Legal Issues

• Analyzing the Ethical Challenges of Healthcare Research Involving Human Subjects
• Exploring the Impact of Cultural and Religious Beliefs on Healthcare Decision-Making
• Examining Legal Issues in End-of-Life Care and Advance Directives
• Investigating the Ethical Implications of Genetic Testing and Personalized Medicine
• Assessing the Ethical Dilemmas in Access to Experimental Treatments
• Exploring the Role of Ethics Committees in Healthcare Organizations
• Analyzing the Intersection of Healthcare Ethics and Artificial Intelligence
• Evaluating the Legal and Ethical Implications of Telemedicine
• Investigating the Ethics of Healthcare Resource Allocation during Public Health Emergencies
• Examining the Legal and Ethical Issues of Patient Privacy in the Digital Age

3. Healthcare Technology and Innovation

• Assessing the Impact of Artificial Intelligence in Healthcare Diagnostics
• Exploring the Potential of Wearable Devices for Remote Patient Monitoring
• Investigating the Role of Big Data Analytics in Healthcare Decision-Making
• Analyzing the Use of Robotics in Surgery and Patient Care
• Examining the Impact of Telehealth on Healthcare Access and Delivery
• Evaluating the Benefits and Challenges of Electronic Health Records Implementation
• Exploring the Applications of Virtual Reality in Healthcare Education and Training
• Investigating the Role of Mobile Health Applications in Health Behavior Change
• Assessing the Potential of Blockchain Technology in Healthcare Data Security
• Analyzing the Ethical and Social Implications of Genetic Engineering in Healthcare

4. Healthcare Quality and Patient Safety

• Evaluating the Impact of Patient-Centered Care on Health Outcomes
• Analyzing the Role of Quality Improvement Initiatives in Reducing Medical Errors
• Assessing the Effectiveness of Medication Safety Practices in Healthcare Settings
• Exploring Strategies to Improve Healthcare Communication and Interprofessional Collaboration
• Investigating the Relationship Between Nursing Workforce and Patient Safety
• Examining the Impact of Clinical Practice Guidelines on Healthcare Quality
• Analyzing the Role of Patient Engagement in Enhancing Healthcare Quality
• Evaluating the Effectiveness of Lean Six Sigma in Healthcare Process Improvement
• Exploring the Role of Health Information Technology in Enhancing Patient Safety
• Investigating the Influence of Organizational Culture on Healthcare Quality and Safety

5. Mental Health and Psychological Well-being

• Analyzing the Impact of Stigma on Mental Health Help-Seeking Behavior
• Exploring the Effectiveness of Psychotherapy Approaches in Treating Mental Health Disorders
• Assessing the Role of Early Intervention in Preventing Mental Health Disorders
• Investigating the Relationship Between Adverse Childhood Experiences and Mental Health Outcomes
• Examining the Intersection of Mental Health and Substance Abuse Disorders
• Evaluating the Impact of Mindfulness-Based Interventions on Psychological Well-being
• Exploring the Role of Social Support in Mental Health Recovery
• Analyzing the Effectiveness of Mental Health Awareness Campaigns
• Investigating the Influence of Cultural Factors on Mental Health Help-Seeking Behavior
• Examining the Mental Health Needs and Challenges among Specific Populations (e.g., LGBTQ+, Veterans, Refugees)

6. Chronic Diseases and their Management

• Assessing the Impact of Lifestyle Factors on Chronic Disease Prevention and Management
• Exploring the Role of Community-Based Interventions in Chronic Disease Control
• Investigating the Relationship Between Social Determinants of Health and Chronic Disease Burden
• Analyzing the Use of Digital Health Technologies in Chronic Disease Management
• Examining the Impact of Health Literacy on Chronic Disease Outcomes
• Evaluating the Effectiveness of Self-Management Programs for Chronic Conditions
• Exploring the Role of Healthcare Providers in Chronic Disease Prevention and Management
• Analyzing the Impact of Health Policies on Chronic Disease Prevention Efforts
• Investigating the Relationship Between Mental Health and Chronic Disease Management
• Examining the Disparities in Access to Chronic Disease Care and Treatment

7. Healthcare Disparities and Access to Care

• Analyzing Racial and Ethnic Disparities in Healthcare Access and Quality
• Exploring the Role of Socioeconomic Factors in Healthcare Disparities
• Assessing the Impact of Geographic Location on Healthcare Access and Health Outcomes
• Investigating Gender Disparities in Healthcare Utilization and Treatment
• Examining the Influence of Health Insurance Status on Healthcare Disparities
• Evaluating the Effectiveness of Culturally Competent Care in Reducing Disparities
• Exploring the Relationship Between Language Barriers and Healthcare Access
• Analyzing the Impact of Implicit Bias on Healthcare Disparities
• Investigating the Role of Health Literacy in Healthcare Disparities
• Examining the Disparities in Mental Health Services and Access to Mental Healthcare

8. Healthcare Education and Training

• Assessing the Effectiveness of Simulation-Based Training in Healthcare Education
• Exploring the Role of Interprofessional Education in Improving Collaborative Practice
• Investigating the Impact of Technology-Enhanced Learning in Healthcare Education
• Analyzing the Use of Gamification in Healthcare Training and Skill Development
• Examining the Role of Continuing Education in Enhancing Healthcare Providers’ Competence
• Evaluating the Effectiveness of Mentorship Programs in Healthcare Education
• Exploring Strategies to Address Cultural Competence in Healthcare Education
• Analyzing the Role of Reflective Practice in Healthcare Professional Development
• Investigating the Use of Team-Based Learning in Healthcare Education
• Examining the Impact of Experiential Learning in Healthcare Training Programs

9. Public Health and Preventive Medicine

• Assessing the Impact of Vaccination Programs on Public Health Outcomes
• Exploring the Role of Health Promotion Campaigns in Preventing Non-communicable Diseases
• Investigating the Effectiveness of Community-Based Interventions in Disease Prevention
• Analyzing the Impact of Environmental Factors on Public Health
• Examining the Role of Social Determinants of Health in Health Disparities
• Evaluating the Effectiveness of Public Health Policies in Tobacco Control
• Exploring Strategies for Preventing and Managing Infectious Diseases
• Analyzing the Role of Health Education in Promoting Healthy Lifestyles
• Investigating the Influence of Media on Public Health Perceptions and Behaviors
• Examining the Challenges and Opportunities in Global Health Initiatives

10. Emerging Topics in Healthcare Research

• Assessing the Implications of Artificial Intelligence in Healthcare
• Exploring the Role of Precision Medicine in Personalized Healthcare
• Investigating the Impact of Genomic Research on Healthcare Delivery
• Analyzing the Use of Telemedicine in Rural and Underserved Areas
• Examining the Integration of Traditional and Complementary Medicine in Healthcare
• Evaluating the Potential of Digital Therapeutics in Disease Management
• Exploring the Ethical Considerations of Gene Editing Technologies in Healthcare
• Analyzing the Influence of Social Media on Healthcare Decision-Making
• Investigating the Role of Health Information Exchange in Coordinated Care
• Examining the Implications of Health Equity in Healthcare Research and Practice

This comprehensive list of healthcare research paper topics encompasses a wide range of areas within the healthcare field. Each category offers diverse research ideas that can inspire students in the health sciences to explore pressing issues, propose innovative solutions, and contribute to the advancement of healthcare knowledge. Whether you are interested in healthcare policy, ethics, technology, mental health, chronic diseases, healthcare disparities, education, public health, or emerging healthcare research paper topics, this list serves as a valuable resource to kickstart your research journey. Choose a topic that resonates with you, aligns with your academic goals, and enables you to make a meaningful impact in the field of healthcare research. Remember, the pursuit of knowledge and the drive to improve healthcare practices are at the heart of your journey as a student in the health sciences.

Choosing Healthcare Research Paper Topics

Choosing the right healthcare research paper topic is a crucial step in conducting a successful and impactful study. With the vast array of healthcare issues and areas to explore, it can be challenging to narrow down your focus. To help you navigate this process effectively, we have compiled expert advice and ten essential tips for selecting compelling healthcare research paper topics. Consider these insights as you embark on your research journey in the dynamic field of healthcare:

• Follow Your Passion : Choose a topic that genuinely interests you. Passion and enthusiasm will drive your motivation, ensuring that you remain engaged throughout the research process.
• Stay Informed : Keep up with the latest healthcare trends, emerging issues, and ongoing debates. Stay informed through reputable sources, academic journals, conferences, and professional networks to identify current and relevant research gaps.
• Identify a Research Gap : Conduct a thorough literature review to identify areas where there is a need for further research. Look for unanswered questions, controversies, or gaps in knowledge that you can address in your study.
• Consider Relevance and Significance : Choose a topic that is relevant to current healthcare challenges or contributes to improving healthcare practices, policies, or patient outcomes. Aim for a topic that has real-world implications and societal impact.
• Delve into Specific Areas : Narrow down your focus by selecting a specific aspect or subtopic within the broad field of healthcare. This allows for a more focused and in-depth analysis of the chosen area.
• Consult with Your Advisor or Faculty : Seek guidance from your research advisor or faculty members who specialize in healthcare research. They can provide valuable insights, help you refine your topic, and direct you to relevant literature and resources.
• Brainstorm with Peers : Engage in discussions with your peers and classmates to explore different perspectives and gain inspiration. Collaborative brainstorming sessions can generate new ideas and offer fresh insights.
• Consider Ethical Considerations : Take ethical considerations into account when selecting a healthcare research topic. Ensure that your research adheres to ethical guidelines and respects the rights and privacy of participants, especially in studies involving human subjects.
• Think Interdisciplinary : Consider interdisciplinary approaches to healthcare research. Explore how other disciplines, such as sociology, psychology, economics, or technology, intersect with healthcare, providing a broader perspective and enhancing the depth of your research.
• Feasibility and Available Resources : Assess the feasibility of your chosen topic, considering the resources, time, and data availability required for your research. Ensure that you have access to relevant data sources, research tools, and necessary support to carry out your study effectively.

By following these expert tips, you will be equipped to choose a healthcare research paper topic that aligns with your interests, is relevant to current healthcare challenges, and has the potential to make a meaningful impact in the field. Remember, selecting the right topic sets the foundation for a successful research endeavor, allowing you to contribute to the advancement of healthcare knowledge and practices.

How to Write a Healthcare Research Paper

Writing a healthcare research paper requires careful planning, organization, and attention to detail. To help you navigate the intricacies of the writing process, we have compiled ten essential tips to guide you towards crafting a well-written and impactful healthcare research paper. Follow these expert recommendations to enhance the quality and effectiveness of your research paper:

• Develop a Clear Research Question : Start by formulating a clear and concise research question that will serve as the central focus of your paper. Ensure that your question is specific, measurable, achievable, relevant, and time-bound (SMART).
• Conduct a Thorough Literature Review : Before diving into your research, conduct a comprehensive literature review to familiarize yourself with existing knowledge on the topic. Identify key theories, concepts, methodologies, and gaps in the literature that your research aims to address.
• Create a Solid Research Design : Design a robust research plan that aligns with your research question. Define your study population, sampling strategy, data collection methods, and statistical analyses. A well-designed research plan enhances the validity and reliability of your findings.
• Collect and Analyze Data : Implement your data collection methods, ensuring ethical considerations and adherence to research protocols. Once collected, analyze the data using appropriate statistical techniques and tools. Provide a clear description of your analytical methods.
• Structure your Paper Effectively : Organize your research paper into logical sections, including an introduction, literature review, methodology, results, discussion, and conclusion. Use headings and subheadings to enhance readability and guide the reader through your paper.
• Write a Compelling Introduction : Start your paper with a strong introduction that captures the reader’s attention and provides a concise overview of the research topic, objectives, and significance. Clearly state your research question and the rationale for your study.
• Present Clear and Concise Results : Present your research findings in a clear and concise manner. Use tables, graphs, and figures where appropriate to enhance the readability of your results. Provide a comprehensive interpretation of the results, highlighting key findings and their implications.
• Engage in Critical Analysis and Discussion : Analyze and interpret your findings in the context of existing literature. Discuss the strengths and limitations of your study, addressing potential biases or confounders. Consider alternative explanations and provide a thoughtful discussion of the implications of your findings.
• Follow Proper Citation and Referencing Guidelines : Adhere to the appropriate citation style (such as APA, MLA, or Chicago) consistently throughout your paper. Cite all sources accurately and include a comprehensive list of references at the end of your paper.
• Revise and Edit : Before finalizing your research paper, revise and edit it thoroughly. Pay attention to clarity, coherence, grammar, spelling, and punctuation. Ensure that your arguments flow logically and that your paper is well-structured and cohesive.

By following these tips, you will be well-equipped to write a high-quality healthcare research paper that effectively communicates your findings, contributes to the existing knowledge in the field, and engages readers with your insights and conclusions. Remember to seek feedback from your peers, professors, or research advisors to further refine your paper and ensure its overall excellence.

iResearchNet’s Custom Writing Services

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• In-Depth Research : Our writers are skilled in conducting extensive research using reputable sources. They delve deep into the literature to gather the most relevant and up-to-date information, providing a solid foundation for your research paper.
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At iResearchNet, we are dedicated to your success. We strive to exceed your expectations and provide you with a seamless and exceptional experience. Trust us with your healthcare research paper and let our expert writers bring your ideas to life with professionalism, accuracy, and academic excellence.

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September National Health Observances: Healthy Aging, Sickle Cell Disease, and More

Each month, we feature select National Health Observances (NHOs) that align with our priorities for improving health across the nation. In September, we’re raising awareness about healthy aging, sickle cell disease, substance use recovery, and HIV/AIDS. 

Below, you’ll find resources to help you spread the word about these NHOs with your audiences. 

• Healthy Aging Month Each September, we celebrate Healthy Aging Month to promote ways people can stay healthy as they age. Explore our healthy aging resources , bookmark the Healthy People 2030 and Older Adults page , share our Move Your Way® materials for older adults , and check out the Physical Activity Guidelines for Americans Midcourse Report . You can also share resources related to healthy aging from the National Institute on Aging — and register for the 2024 National Healthy Aging Symposium to hear from experts on innovations to improve the health and well-being of older adults.
• National Recovery Month The Substance Abuse and Mental Health Services Administration (SAMHSA) sponsors National Recovery Month to raise awareness about mental health and addiction recovery. Share our MyHealthfinder resources on substance use and misuse — and be sure to check out Healthy People 2030’s evidence-based resources related to drug and alcohol use . 
• National Sickle Cell Awareness Month National Sickle Cell Awareness Month is a time to raise awareness and support people living with sickle cell disease. Help your community learn about sickle cell disease by sharing these resources from the National Heart, Lung, and Blood Institute (NHLBI) . You can also encourage new and expecting parents to learn about screening their newborn baby for sickle cell . And be sure to view our Healthy People 2030 objectives on improving health for people who have blood disorders .
• National HIV/AIDS and Aging Awareness Day (September 18) On September 18, we celebrate HIV/AIDS and Aging Awareness Day to encourage older adults to get tested for HIV. Share CDC’s Let’s Stop HIV Together campaign to help promote HIV testing, prevention, and treatment. MyHealthfinder also has information for consumers about getting tested for HIV and actionable questions for the doctor about HIV testing . Finally, share these evidence-based resources on sexually transmitted infections from Healthy People 2030.
• National Gay Men’s HIV/AIDS Awareness Day (September 27) National Gay Men’s HIV/AIDS Awareness Day on September 27 highlights the impact of HIV on gay and bisexual men and promotes strategies to encourage testing. Get involved by sharing CDC’s social media toolkit and HIV information to encourage men to get tested — and share our MyHealthfinder resources to help people get tested for HIV and talk with their doctor about testing .

We hope you’ll join us in promoting these important NHOs with your networks to help improve health across the nation!

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• Iran J Public Health
• v.44(11); 2015 Nov

Impact of Lifestyle on Health

Dariush d. farhud.

1. School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

2. Dept. of Basic Sciences, Iranian Academy of Medical Sciences,Tehran, Iran

Introduction

Lifestyle is a way used by people, groups and nations and is formed in specific geographical, economic, political, cultural and religious text. Lifestyle is referred to the characteristics of inhabitants of a region in special time and place. It includes day to day behaviors and functions of individuals in job, activities, fun and diet.

In recent decades, life style as an important factor of health is more interested by researchers. According to WHO, 60% of related factors to individual health and quality of life are correlated to lifestyle ( 1 ). Millions of people follow an unhealthy lifestyle. Hence, they encounter illness, disability and even death. Problems like metabolic diseases, joint and skeletal problems, cardio-vascular diseases, hypertension, overweight, violence and so on, can be caused by an unhealthy lifestyle. The relationship of lifestyle and health should be highly considered.

Today, wide changes have occurred in life of all people. Malnutrition, unhealthy diet, smoking, alcohol consuming, drug abuse, stress and so on, are the presentations of unhealthy life style that they are used as dominant form of lifestyle. Besides, the lives of citizens face with new challenges. For instance, emerging new technologies within IT such as the internet and virtual communication networks, lead our world to a major challenge that threatens the physical and mental health of individuals. The challenge is the overuse and misuse of the technology.

Therefore, according to the existing studies, it can be said that: lifestyle has a significant influence on physical and mental health of human being. There are different forms of such influences. Consanguinity in some ethnicity is a dominant form of life style that it leads to the genetic disorders. Reformation of this unhealthy life style is a preventing factor for decreasing the rate of genetic diseases ( 2 ). In some countries, the overuse of drugs is a major unhealthy life style. Iran is one of the 20 countries using the most medications. They prefer medication to other intervention. Furthermore, in 15–40% of cases they use medications about without prescription ( 3 ). Pain relievers, eye drops and antibiotics have the most usage in Iran. While self-medications such as antibiotics have a negative effect on the immune system, if the individual would be affected by infection, antibiotics will not be effective in treatment. Overall, 10 percent of those who are self-medicated will experience severe complications such as drug resistance. Sometimes drug allergy is so severe that it can cause death ( 4 ).

Finally, variables of lifestyle that influence on health can be categorized in some items:

• Diet and Body Mass Index (BMI) : Diet is the greatest factor in lifestyle and has a direct and positive relation with health. Poor diet and its consequences like obesity is the common healthy problem in urban societies. Unhealthy lifestyle can be measured by BMI. Urban lifestyle leads to the nutrition problems like using fast foods and poor foods, increasing problems like cardiovascular ( 5 ).
• Exercise: For treating general health problems, the exercise is included in life style ( 6 ). The continuous exercise along with a healthy diet increases the health. Some studies stress on the relation of active life style with happiness ( 7 , 8 ).
• Sleep: One of the bases of healthy life is the sleep. Sleep cannot be apart from life. Sleep disorders have several social, psychological, economical and healthy consequences. Lifestyle may effect on sleep and sleep has a clear influence on mental and physical health ( 9 ).
• Sexual behavior: Normal sex relation is necessary in healthy life. Dysfunction of sex relation is the problem of most of societies and it has a significant effect on mental and physical health. It can be said that dysfunctional sex relation may result in various family problems or sex related illnesses like; AIDS
• Substance abuse: Addiction is considered as an unhealthy life style. Smoking and using other substance may result in various problems; cardiovascular disease, asthma, cancer, brain injury. According to the resent studies in Iran, 43% of females and 64% of males experience the use of hubble-bubble ( 10 ). A longitudinal study shows that 30% of people between 18–65 years old smoke cigarette permanently ( 11 ).
• Medication abuse: It is a common form of using medication in Iran and it is considered as an unhealthy life style. Unhealthy behaviors in using medication are as followed: self-treatment, sharing medication, using medications without prescription, prescribing too many drugs, prescribing the large number of each drug, unnecessary drugs, bad handwriting in prescription, disregard to the contradictory drugs, disregard to harmful effects of drugs, not explaining the effects of drugs.
• Application of modern technologies: Advanced technology facilitates the life of human beings. Misuse of technology may result in unpleasant consequences. For example, using of computer and other devices up to midnight, may effect on the pattern of sleep and it may disturb sleep. Addiction to use mobile phone is related to depression symptoms ( 12 ).
• Recreation: Leisure pass time is a sub factor of life style. Neglecting leisure can bring negative consequences. With disorganized planning and unhealthy leisure, people endanger their health.
• Study: Study is the exercise of soul. Placing study as a factor in lifestyle may lead to more physical and mental health. For example, prevalence of dementia, such as Alzheimer's disease is lowerin educated people. Study could slow process of dementia.

With a look at existing studies in health domain, 9 key factors can be suggested for healthy life style ( Fig. 1 ). In regard to each factor, the systematic planning in micro and macro level can be established. It can provide a social and individual healthy lifestyle.

Acknowledgements

The authors declare that there is no conflict of interests.

Mental Health, Substance Use, and Child Maltreatment

Child maltreatment is a pressing concern in the United States, with more than four million children referred to child protective services in 2022. Reducing child maltreatment is a national health objective given the substantial, negative consequences for children who experience maltreatment, both in the short- and long-term. Parental mental health and substance use disorders are strongly associated with child maltreatment. In this study, we use administrative data over the period 2004 to 2021 to study the relationship between the number of mental health and substance use treatment centers per county and child maltreatment reports. Our findings provide evidence that better access to mental health and substance use treatment reduces child maltreatment reports. In particular, an 8% increase in the supply of treatment would reduce maltreatment reports by 1%. These findings suggest that recent and ongoing efforts by the federal government to expand mental health and substance use treatment availability may lead to reduced child maltreatment.

All authors contributed equally to this study. Authors are listed in alphabetical order. Research reported in this publication was supported by the National Institute on Mental Health of the National Institutes of Health under Award Number 1R01MH132552 (PI: Johanna Catherine Maclean). Dr. Meinhofer acknowledges support from the Foundation for Opioid Response Efforts GR00015582 and the National Institute on Drug Abuse K01DA051777. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Institutes of Health or the Foundation for Opioid Response Efforts. We thank Douglas Webber and Jiaxin Wei for excellent comments. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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