the population in a research study is

Unraveling Research Population and Sample: Understanding their role in statistical inference

Research population and sample serve as the cornerstones of any scientific inquiry. They hold the power to unlock the mysteries hidden within data. Understanding the dynamics between the research population and sample is crucial for researchers. It ensures the validity, reliability, and generalizability of their findings. In this article, we uncover the profound role of the research population and sample, unveiling their differences and importance that reshapes our understanding of complex phenomena. Ultimately, this empowers researchers to make informed conclusions and drive meaningful advancements in our respective fields.

Table of Contents

What Is Population?

The research population, also known as the target population, refers to the entire group or set of individuals, objects, or events that possess specific characteristics and are of interest to the researcher. It represents the larger population from which a sample is drawn. The research population is defined based on the research objectives and the specific parameters or attributes under investigation. For example, in a study on the effects of a new drug, the research population would encompass all individuals who could potentially benefit from or be affected by the medication.

When Is Data Collection From a Population Preferred?

In certain scenarios where a comprehensive understanding of the entire group is required, it becomes necessary to collect data from a population. Here are a few situations when one prefers to collect data from a population:

1. Small or Accessible Population

When the research population is small or easily accessible, it may be feasible to collect data from the entire population. This is often the case in studies conducted within specific organizations, small communities, or well-defined groups where the population size is manageable.

2. Census or Complete Enumeration

In some cases, such as government surveys or official statistics, a census or complete enumeration of the population is necessary. This approach aims to gather data from every individual or entity within the population. This is typically done to ensure accurate representation and eliminate sampling errors.

3. Unique or Critical Characteristics

If the research focuses on a specific characteristic or trait that is rare and critical to the study, collecting data from the entire population may be necessary. This could be the case in studies related to rare diseases, endangered species, or specific genetic markers.

4. Legal or Regulatory Requirements

Certain legal or regulatory frameworks may require data collection from the entire population. For instance, government agencies might need comprehensive data on income levels, demographic characteristics, or healthcare utilization for policy-making or resource allocation purposes.

5. Precision or Accuracy Requirements

In situations where a high level of precision or accuracy is necessary, researchers may opt for population-level data collection. By doing so, they mitigate the potential for sampling error and obtain more reliable estimates of population parameters.

What Is a Sample?

A sample is a subset of the research population that is carefully selected to represent its characteristics. Researchers study this smaller, manageable group to draw inferences that they can generalize to the larger population. The selection of the sample must be conducted in a manner that ensures it accurately reflects the diversity and pertinent attributes of the research population. By studying a sample, researchers can gather data more efficiently and cost-effectively compared to studying the entire population. The findings from the sample are then extrapolated to make conclusions about the larger research population.

What Is Sampling and Why Is It Important?

Sampling refers to the process of selecting a sample from a larger group or population of interest in order to gather data and make inferences. The goal of sampling is to obtain a sample that is representative of the population, meaning that the sample accurately reflects the key attributes, variations, and proportions present in the population. By studying the sample, researchers can draw conclusions or make predictions about the larger population with a certain level of confidence.

Collecting data from a sample, rather than the entire population, offers several advantages and is often necessary due to practical constraints. Here are some reasons to collect data from a sample:

1. Cost and Resource Efficiency

Collecting data from an entire population can be expensive and time-consuming. Sampling allows researchers to gather information from a smaller subset of the population, reducing costs and resource requirements. It is often more practical and feasible to collect data from a sample, especially when the population size is large or geographically dispersed.

2. Time Constraints

Conducting research with a sample allows for quicker data collection and analysis compared to studying the entire population. It saves time by focusing efforts on a smaller group, enabling researchers to obtain results more efficiently. This is particularly beneficial in time-sensitive research projects or situations that necessitate prompt decision-making.

3. Manageable Data Collection

Working with a sample makes data collection more manageable . Researchers can concentrate their efforts on a smaller group, allowing for more detailed and thorough data collection methods. Furthermore, it is more convenient and reliable to store and conduct statistical analyses on smaller datasets. This also facilitates in-depth insights and a more comprehensive understanding of the research topic.

4. Statistical Inference

Collecting data from a well-selected and representative sample enables valid statistical inference. By using appropriate statistical techniques, researchers can generalize the findings from the sample to the larger population. This allows for meaningful inferences, predictions, and estimation of population parameters, thus providing insights beyond the specific individuals or elements in the sample.

5. Ethical Considerations

In certain cases, collecting data from an entire population may pose ethical challenges, such as invasion of privacy or burdening participants. Sampling helps protect the privacy and well-being of individuals by reducing the burden of data collection. It allows researchers to obtain valuable information while ensuring ethical standards are maintained .

Key Steps Involved in the Sampling Process

Sampling is a valuable tool in research; however, it is important to carefully consider the sampling method, sample size, and potential biases to ensure that the findings accurately represent the larger population and are valid for making conclusions and generalizations. While the specific steps may vary depending on the research context, here is a general outline of the sampling process:

1. Define the Population

Clearly define the target population for your research study. The population should encompass the group of individuals, elements, or units that you want to draw conclusions about.

2. Define the Sampling Frame

Create a sampling frame, which is a list or representation of the individuals or elements in the target population. The sampling frame should be comprehensive and accurately reflect the population you want to study.

3. Determine the Sampling Method

Select an appropriate sampling method based on your research objectives, available resources, and the characteristics of the population. You can perform sampling by either utilizing probability-based or non-probability-based techniques. Common sampling methods include random sampling, stratified sampling, cluster sampling, and convenience sampling.

4. Determine Sample Size

Determine the desired sample size based on statistical considerations, such as the level of precision required, desired confidence level, and expected variability within the population. Larger sample sizes generally reduce sampling error but may be constrained by practical limitations.

5. Collect Data

Once the sample is selected using the appropriate technique, collect the necessary data according to the research design and data collection methods . Ensure that you use standardized and consistent data collection process that is also appropriate for your research objectives.

6. Analyze the Data

Perform the necessary statistical analyses on the collected data to derive meaningful insights. Use appropriate statistical techniques to make inferences, estimate population parameters, test hypotheses, or identify patterns and relationships within the data.

Population vs Sample — Differences and examples

While the population provides a comprehensive overview of the entire group under study, the sample, on the other hand, allows researchers to draw inferences and make generalizations about the population. Researchers should employ careful sampling techniques to ensure that the sample is representative and accurately reflects the characteristics and variability of the population.

Research Study: Investigating the prevalence of stress among high school students in a specific city and its impact on academic performance.

Population: All high school students in a particular city

Sampling Frame: The sampling frame would involve obtaining a comprehensive list of all high schools in the specific city. A random selection of schools would be made from this list to ensure representation from different areas and demographics of the city.

Sample: Randomly selected 500 high school students from different schools in the city

The sample represents a subset of the entire population of high school students in the city.

Research Study: Assessing the effectiveness of a new medication in managing symptoms and improving quality of life in patients with the specific medical condition.

Population: Patients diagnosed with a specific medical condition

Sampling Frame: The sampling frame for this study would involve accessing medical records or databases that include information on patients diagnosed with the specific medical condition. Researchers would select a convenient sample of patients who meet the inclusion criteria from the sampling frame.

Sample: Convenient sample of 100 patients from a local clinic who meet the inclusion criteria for the study

The sample consists of patients from the larger population of individuals diagnosed with the medical condition.

Research Study: Investigating community perceptions of safety and satisfaction with local amenities in the neighborhood.

Population: Residents of a specific neighborhood

Sampling Frame: The sampling frame for this study would involve obtaining a list of residential addresses within the specific neighborhood. Various sources such as census data, voter registration records, or community databases offer the means to obtain this information. From the sampling frame, researchers would randomly select a cluster sample of households to ensure representation from different areas within the neighborhood.

Sample: Cluster sample of 50 households randomly selected from different blocks within the neighborhood

The sample represents a subset of the entire population of residents living in the neighborhood.

To summarize, sampling allows for cost-effective data collection, easier statistical analysis, and increased practicality compared to studying the entire population. However, despite these advantages, sampling is subject to various challenges. These challenges include sampling bias, non-response bias, and the potential for sampling errors.

To minimize bias and enhance the validity of research findings , researchers should employ appropriate sampling techniques, clearly define the population, establish a comprehensive sampling frame, and monitor the sampling process for potential biases. Validating findings by comparing them to known population characteristics can also help evaluate the generalizability of the results. Properly understanding and implementing sampling techniques ensure that research findings are accurate, reliable, and representative of the larger population. By carefully considering the choice of population and sample, researchers can draw meaningful conclusions and, consequently, make valuable contributions to their respective fields of study.

Now, it’s your turn! Take a moment to think about a research question that interests you. Consider the population that would be relevant to your inquiry. Who would you include in your sample? How would you go about selecting them? Reflecting on these aspects will help you appreciate the intricacies involved in designing a research study. Let us know about it in the comment section below or reach out to us using  #AskEnago  and tag  @EnagoAcademy  on  Twitter ,  Facebook , and  Quora .

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Methodology

• Population vs. Sample | Definitions, Differences & Examples

Population vs. Sample | Definitions, Differences & Examples

Published on May 14, 2020 by Pritha Bhandari . Revised on June 21, 2023.

A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc.

Table of contents

Collecting data from a population, collecting data from a sample, population parameter vs. sample statistic, practice questions : populations vs. samples, other interesting articles, frequently asked questions about samples and populations.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.

For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.

However, historically, marginalized and low-income groups have been difficult to contact, locate and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.

In cases like this, sampling can be used to make more precise inferences about the population.

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When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.

Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .

For practical reasons, researchers often use non-probability sampling methods. Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.

Reasons for sampling

• Necessity : Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
• Practicality : It’s easier and more efficient to collect data from a sample.
• Cost-effectiveness : There are fewer participant, laboratory, equipment, and researcher costs involved.
• Manageability : Storing and running statistical analyses on smaller datasets is easier and reliable.

When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.

You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

Sampling error

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.

Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .

Because the aim of scientific research is to generalize findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

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Study Population: Characteristics & Sampling Techniques

How do you define a study population?  Research studies require specific groups to draw conclusions and make decisions based on their results. This group of interest is known as a sample. The method used to select respondents is known as sampling.

What is a Study Population?

A study population is a group considered for a study or statistical reasoning. The study population is not limited to the human population only. It is a set of aspects that have something in common. They can be objects, animals, measurements, etc., with many characteristics within a group.

For example, suppose you are interested in the average time a person between the ages of 30 and 35 takes to recover from a particular condition after consuming a specific type of medication. In that case, the study population will be all people between the ages of 30 and 35.

A medical study examines the spread of a specific disease in stray dogs in a city. Here, the stray dogs belonging to that city are the study population. This population or sample represents the entire population you want to conclude about.

How to establish a study population?

Sampling is a powerful technique for collecting opinions from a wide range of people, chosen from a particular group, to learn more about the whole group in general.

For any research study to be effective, it is necessary to select the study population that truly represents the entire population. Before starting your study, the target population must be identified and agreed upon. By appointing and knowing your sample well in advance, any feedback deemed useless to the study will be largely eliminated.

If your survey aims to understand a product’s or service’s effectiveness, then the study population should be the customers who have used it or are best suited to their needs and who will use the product/service.

It would be costly and time-consuming to collect data from the entire population of your target market. By accurately sampling your study population, it is possible to build a true picture of the target market using the trends in the results.

LEARN ABOUT: Survey Sampling

Choosing an accurate sample from the study population

The decision on an appropriate sample depends on several key factors.

• First, you decide which population parameters you want to estimate.
• Don’t expect estimates from a sample to be exact. Always expect a margin of error when making assumptions based on the results of a sample.
• Understanding the cost of sampling helps us determine how precise our estimates need to be.
• Know how variable the population you want to measure is. It is not necessary to assume that a large sample is required if the study population is large.
• Take into account the response rate of your population. A 20% response rate is considered “good” for an online research study.

Sampling characteristics in the study population

• Sampling is a mechanism to collect data without surveying the entire target population.
• The study population is the entire unit of people you consider for your research. A sample is a subset of this group that represents the population.
• Sampling reduces survey fatigue as it is used to prevent pollsters from conducting too many surveys, thereby increasing response rates.
• Also, it is much cheaper and saves more time than measuring the entire group.
• Tracking the response rate patterns of different groups will help determine how many respondents to select.
• The study is not only limited to the selected part, but is applied to the entire target population.

Sampling techniques for your study population

Now that you understand that you cannot survey the entire study population due to various factors, you should adopt one of the sample selection methodologies that best suits your research study.

In general terms, two methodologies can be applied: probability sampling and non-probability sampling .

Sampling Techniques: Probability Sampling

This method is used to select sample objects from a population based on probability theory. Everyone is included in the sample and has an equal chance of being selected. There is no bias in this type of sample. Every person in the population has the opportunity to be part of the research.

Probability sampling can be categorized into four types:

• Simple Random Sampling : Simple random sampling is the easiest way to select a sample. Here, each member has an equal chance of being part of the sample. The objects in this sample are chosen at random, and each member has exactly the same probability of being selected.
• Cluster sampling : Cluster sampling is a method in which respondents are grouped into clusters. These groups can be defined based on age, gender, location, and demographic parameters.
• Systematic Sampling : In systematic sampling, individuals are chosen at equal intervals from the population. A starting point is selected, and then respondents are chosen at predefined sample intervals.
• Stratified Sampling: S tratified random sampling is a process of dividing respondents into distinct but predefined parameters. In this method, respondents do not overlap but collectively represent the entire population.

Sampling techniques: Non-probabilistic sampling

The non-probability sampling method uses the researcher’s preference regarding sample selection bias . This sampling method derives primarily from the researcher’s ability to access this sample. Here the population members do not have the same opportunities to be part of the sample.

Non-probability sampling can be further classified into four distinct types:

• Convenience Sampling: As the name implies, convenience sampling represents the convenience with which the researcher can reach the respondent. The researchers do not have the authority to select the samples and they are done solely for reasons of proximity and not representativeness.
• Deliberate, critical, or judgmental sampling: In this type of sampling the researcher judges and develops his sample on the nature of the study and the understanding of his target audience. Only people who meet the research criteria and the final objective are selected.
• Snowball Sampling: As a snowball speeds up, it accumulates more snow around itself. Similarly, with snowball sampling, respondents are tasked with providing references or recruiting samples for the study once their participation ends.
• Quota Sampling: Quota sampling is a method where the researcher has the privilege to select a sample based on its strata. In this method, two people cannot exist under two different conditions.

LEARN ABOUT: Theoretical Research

Advantages and disadvantages of sampling in a study population

In most cases, of the total study population, perceptions can only be obtained from predefined samples. This comes with its own advantages and disadvantages. Some of them are listed below.

• Highly accurate – low probability of sampling errors (if sampled well)
• Economically feasible by nature, highly reliable
• High fitness ratio to different surveys Takes less time compared to surveying the entire population Reduced resource deployment
• Data-intensive and comprehensive Properties are applied to a larger population wideIdeal when the study population is vast.

Disadvantages

• Insufficient samples
• Possibility of bias
• Precision problems (if sampling is poor)
• Difficulty obtaining the typical sample
• Lack of quality sources
• Possibility of making mistakes.

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Selecting the Study Participants

Defining the target population is an essential part of protocol development to ensure that the study participants are well suited to the research question ( Hulley et al., 2013 ). The target population is the entire group of people who share a common condition (disease process) or characteristic the researcher is interested in studying ( Elfil & Negada, 2017 ). While the ' accessible population' is the geographically and temporally classified subset that is available for the study participant recruitment ( Hulley et al., 2013 ). The clinical and demographic characteristics of the accessible population are determined using a ruled based eligibility criterion in which all study participants must have to join a study. The choice of study participants must also consider the cost in time and money to obtain the required number of study participants. This paper in the Step by Step Research series provides an introduction to designing the eligibility criteria and recruitment considerations to select the study participants. Future articles will discuss sampling (random and non-random) approaches and sample size determination.

Introduction to Designing the Eligibility Criteria

All clinical research protocols must have an Eligibility Criteria Section , which includes both an inclusion and exclusion criteria. Common elements used to determine suitability for a study are age, gender, medical condition, specific disease examined, and medications. The development of the study criteria must also consider participant safety and review potential interactions with an intervention such as a study medication or a study diet that might interfere with a participant's current medication or concurrent health condition. The eligibility criteria should be representative of the target population to improve understanding of the disease processes, new treatment options, and socio-demographic trends ( Hulley et al., 2013 ). To illustrate the development of the eligibility criteria, see table 1 : Designing the Eligibility Criteria: Clinical Trial for High Triglycerides Among Individuals with HIV Using Fish Oil Versus Placebo. Discussed below is the rationale for the selection of the inclusion and exclusion criteria for this study.

Designing the Eligibility Criteria: * Clinical Trial for High Triglycerides Among Individuals with HIV Using Fish Oil Versus Placebo

Adapted with permission:(Cummings, 2013)

Inclusion Criteria Considerations

The inclusion criteria describe the study participants that fulfill the needs of the clinical research question. Commonly used elements for the inclusion criteria include demographic characteristics, clinical attributes, geographic factors, and temporal considerations ( Panacek & Thompson, 2007 ) ( Hulley et al., 2013 ). Defining the inclusion criteria helps to maintain a degree of homogeneity among the accessible population and narrows the available pool of eligible study participants that reflect the study question ( Panacek & Thompson, 2007 ). The inclusion criteria should also address the capacity of the participant to understand the study goals, the study requirements (i.e., attending study visits, completing questionnaires, blood draws, taking study medications), and the potential risks associated with joining a study.

For example, the study exploring the efficacy of over-the-counter fish oil supplementation among HIV+ individuals with high triglyceride considers an age limitation since fish oil can reduce the CD4+ level, and advanced ages are associated with the accelerated loss of the CD4+ level. A triglyceride parameter (fasting serum triglyceride levels between 150-500 mg/dl) is also required to capture participants with the condition, yet limits those with extremely elevated levels and likely eligible for prescription fish-oil treatment. Additionally, parameters for the CD4+ level (CD4+ T cell count ≥ 300 cells/mm 3 ) is necessary to ensure the participants' immune systems are stable. Travel to the study sight is another vital consideration. Geographic limitations are essential if virtual study visits (i.e., web-based interventions) are not possible, and face-to-face interactions are necessary. Using geographic boundaries may reduce missed study visits due to the distance of the study sight from participants' homes. An example of a temporal characteristic using the sample study is the requirement to wait for a minimum of six months or more after starting a new HIV drug regimen. The waiting period reduces the likelihood that the elevated triglyceride level was a temporary elevation and returns to normal levels once the proper dosage achieved.

The informed consent process is the final consideration for the inclusion criteria. A future article in this series will discuss the ethics, including concerns relating to the potential for coercion or undue influence, and the ability of the study participant to provide verbal and written consent.

Exclusion Criteria Considerations

The exclusion criteria contain the characteristics of the potential study participant who meet the inclusion criteria but have conditions that could affect the outcome of the study in terms of the success or the increased risks of possible adverse effects ( Patino & Ferreira, 2018 ). Therefore, the exclusion criteria is a mechanism to reduce potential study risks and reduce potential confounding variables ( Panacek & Thompson, 2007 ). Confounding variables may affect the outcome of interest (dependent variable), as in the sample study, the triglyceride level is the dependent variable. Therefore, potential participants using fish oil are not eligible to join.

In formulating the exclusion criteria, Hulley and colleagues (2013) recommend considering factors that increase the likelihood of loss to follow-up, the inability to provide necessary data, and the high risk of possible adverse effects related to co-morbidities or current treatments.

In continuing the HIV and high triglyceride example, participants actively abusing alcohol and illicit substances are not eligible to join this study. The chronic use of some substances can lead to changes in the brain and linked with the development of mental health issues such as paranoia, hallucinations, and other problems (National Institute on Drug Abuse (NIDA) ( NIDA, 2017 )). The use of illicit substances can potentially limit a participant's ability to comprehend and comply with the study requirements. The use of a measurement tool that examines attention, recall, and calculation and excluding participants who score below a required screening threshold (less than 23 on the Mini-Mental State Examination ( Folstein et al., 1975 )) is one method to screen for cognitive impairment and hopefully increase the likelihood for obtaining the required data. Participants with diabetes mellitus are excluded from the study to prevent possible adverse effects since fish oil can raise blood glucose levels and not safe for such participants.

Practical Considerations for Designing the Eligibility Criteria

For a new clinical research study, reviewing published studies with similar study participants and objectives can assist in formulating the eligibility criteria. Examine the approaches used in such investigations. Assess the data on the retention of the participants and evaluate the reasons for lost-to-follow-up. Much can be learned from the experiences of previously conducted studies to assess if methods were successful and allow investigators to apply useful strategies to their new study.

Defining the eligibility criteria requires a delicate balance between restrictive and less restrictive. A restrictive criterion can make participant recruitment and enrollment more difficult since the study requirements can reduce the eligible participant pool. Additionally, the restrictive criteria might impact the time-frame to complete the study, as well as the workforce needed to recruit and to enroll study participants, requiring more time and staff to complete. On the other hand, a less stringent eligibility criterion is often easier to recruit and to enroll participants and can increase the generalizability of a study, but can leave it open to more confounding variables and a heterogeneous participant pool ( Gallin et al., 2018 ).

During the development of the eligibility criteria, review the Code of Federal Regulations (CFR) 45 , which focuses on the protection of human subjects. Since institutional review board (IRB) approval is mandatory for all clinical research studies, IRBs will assess study protocols to ensure it meets the CFR 45 requirements before approving. CFR 45 focuses on the unbiased selection of study participants centered around gender, race, ethnicity, religion, nationality, and other factors ( Gallin et al., 2018 ). CFR 45 stipulates that all eligible study participants are considered unless there is a compelling safety or scientific rationale for excluding them ( Gallin et al., 2018 ). For example, a prostate cancer study can reasonably exclude women since the disease under study is gender-specific. A case to prohibit pregnant women might involve studies with known risks to maternal and fetal health.

In writing the eligibility criteria, use bullets or a numbered list ( Gallin et al., 2018 ). This format makes it easier for IRB review and implementation. A printed or an electronic version of the eligibility criteria placed near the telephone, computer, or participant screening area can provide an excellent guide for study team members to review the study criteria and reduce the chances for incorrectly enrolling ineligible participants.

Study Recruitment

Formulating the recruitment process occurs after the development of the eligibility criteria. The study protocol provides the details of the procedures the research team undergoes to recruit and to enroll study participants. In designing the recruitment plan, consider the anticipated participant accrual rate, which includes the number of participants enrolled per month/year and how long it will take to complete the recruitment goals. Additionally, other critical components of the recruitment process include the identification and the recruitment of participants, and the methods to advertise the study and the evaluation of the recruitment plan ( Gallin et al., 2018 ; University of Rochester, 2018 ).

Describing the specifics of how and where to obtain eligible study participants are also essential components of the study recruitment section. Details include the methods to approach the participants and the setting (i.e., clinic setting, research site). For example, in recruiting for the HIV+ adults with high triglycerides, flyers, and brochures were developed and sent to local HIV clinics in the New York City (NYC) area to promote the availability of the study. Potential participants called the research center to inquire about the HIV study; hence the participants initiated the process to join the research study.

Recruitment materials should include the title of the protocol, IRB study number, the sponsoring institution, contact information, purpose of the research (i.e., HIV+ adults with high triglycerides) and indicate whether compensation for time and effort is given to the study participants ( University of Rochester, 2018 ; Gallin et al., 2018 ). All methods to advertise or promote the study requires IRB approval before implementation. Some examples of recruitment materials include the telephone script used to discuss the study, and flyers, posters, postcards, newspaper advertisements, press releases, website advertisements, electronic mailings and social media blogs, and tweets ( Gelinas et al., 2017 ; University of Rochester, 2018 ). Each IRB may require specific information for recruitment materials to obtain approval.

Most institutions publish their IRB requirements online; therefore, to ensure a smoother review process, read the guidelines associated with each IRB institution. Participant recruitment is often one of the most challenging aspects of conducting clinical research. Delays in participant enrollment account for up to 60% ( Gelinas et al., 2017 ) of issues encountered in clinical research; therefore, a well-thought-out and feasible recruitment plan is vital.

The development of the eligibility criteria is a complex process that can affect participant recruitment, enrollment, and study completion. Reviewing the literature for research studies similar to the proposed target population is an excellent method to identify appropriate eligibility criteria. Developing a feasible recruitment plan in terms of selecting the suitable participant pool and approaches to recruit and enroll the eligible participants is at the cornerstone for implementing a successful study.

Acknowledgments

This manuscript is supported in part by grant # UL1TR001866 from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program.

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• Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

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The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

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Acknowledgements

This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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What’s driving five-year movement itch in regional Australia?

Key points:

• Regional cities with higher numbers of graduates in the workforce and increased lifestyle activity grew faster
• Proximity to the coast, major metropolitan areas, and the availability of air services, positively influenced growth
• Cheaper housing was a common attraction across all three local government areas (LGAs)
• Respondents highlighted 'lifestyle', 'personal reasons', and 'employment' as the top factors influencing their decision to relocate
• Respondents however were more likely to move again within five years due to gaps in health and education services, unaffordable housing or lack of rental availability, crime rates, and climate
• Survey respondents indicated a preference to move to other regional cities (46 percent) over capital cities (30 percent), countering the broader urbanisation trend in Australia.

The nations obsession with regional Australia continues, finds research led by the Sydney School of Architecture, Design and Planning . Undertaken for the  Australian Housing and Urban Research Institute  (AHURI), The paper ‘Place-based drivers and effective management of population growth and change in regional Australia’ reveals the factors and trends influencing population change across regional Australia.

The study highlights the critical importance of aligning policy responses with population changes to effectively manage regional growth.

Despite the strong appeal of these regional areas, the potential for population turnover remains high. Survey data revealed that 44 percent of respondents in Broken Hill, 35 percent in Ballarat, and 30 percent in Port Macquarie Hastings are likely to move within the next five years.

Interviews revealed that inadequate secondary and tertiary education, limited health and disability services, crime rates, climate concerns, and rising housing costs were significant reasons for leaving these areas.

To foster population growth and retention, policies aimed at enhancing liveability are critical. These include improving housing affordability and availability, bolstering local health and education services, upgrading local transport, water and road infrastructure, and increasing funding for regional airports, and tertiary campuses.

"Both federal, state and local governments have a role to play in improving liveability and working with the community and industry to provide improved services that encourage thriving populations in regional centres," said Dr Werner.

Potential policy solutions include limiting short-term rental accommodations (STRA), increasing government support for social and affordable housing, and attracting trade workers to boost housing construction.

A significant challenge in keeping pace with population change is the complexity of addressing areas that involve multiple public and private sector entities beyond local government control such as health, housing, transport, and education, underscoring the need for cooperation between all levels of government, as well as industry.

Over the past five years, the appeal of more affordable housing and an enhanced lifestyle has been the major driver for people moving from metropolitan to regional areas while higher regional housing prices discourage that movement.

"While some people move to regional cities for housing and rental affordability, it can also prompt existing residents to leave if prices in those regions rise as a result. Housing solutions for key workers are as important in regional centres as they are in metropolitan regions," said Dr Werner.

These findings underscore the importance of place-based attributes in influencing growth, decline, and population turnover in regional cities. Supporting the development of local businesses, especially in sectors like tourism, hospitality, tertiary education, and renewable energy, could attract new residents, retain local youth, and diversify regional economies.

“Employment remains a crucial driver for migration to regional Australia, making policies that foster economic growth and local job creation vital for sustaining and expanding regional populations,” said Dr Werner.

The study was authored by Dr Caitlin Buckle, Dr Greta Werner , Professor Nicole Gurran , Dr Glen Searle , Associate Professor Somwrita Sarkar , Associate Professor Nick Osbaldiston and Durba Kundu.

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Essential workers face ever greater challenges, the push and pull affecting population growth, affordable rentals out of reach for low-income workers.

Billions worldwide consume inadequate levels of micronutrients critical to human health

For immediate release: August 29, 2024

Boston, MA—More than half of the global population consumes inadequate levels of several micronutrients essential to health, including calcium , iron, and vitamins C and E, according to a new study by researchers at Harvard T.H. Chan School of Public Health, UC Santa Barbara (UCSB), and the Global Alliance for Improved Nutrition (GAIN). It is the first study to provide global estimates of inadequate consumption of 15 micronutrients critical to human health.

The study was published in The Lancet Global Health on August 29.

Micronutrient deficiencies are one of the most common forms of malnutrition globally, and each deficiency carries its own health consequences, from adverse pregnancy outcomes , to blindness, to increased susceptibility to infectious diseases . Previous research has estimated the amounts of micronutrients available to and consumed by people; this study evaluates whether these intakes meet requirements recommended for human health and looks at the inadequacies specifically facing males and females across their lifespans.

“Our study is a big step forward,” said co-lead author Chris Free, research professor at UCSB. “Not only because it is the first to estimate inadequate micronutrient intakes for 34 age-sex groups in nearly every country, but also because it makes these methods and results easily accessible to researchers and practitioners.”

The researchers used data from the Global Dietary Database, the World Bank, and dietary recall surveys in 31 countries to compare nutritional requirements with nutritional intake among the populations of 185 countries. (They have made these data, as well as code for analysis,  freely available .) They divided populations into males and females belonging to17 age groups: zero to 80 in five-year spans, as well as an 80+ group. The assessment studied fifteen vitamins and minerals: calcium, iodine, iron, riboflavin, folate, zinc, magnesium, selenium, thiamin, niacin, and vitamins A, B6, B12, C, and E.

The study found significant intake inadequacies for nearly all of the evaluated micronutrients, excluding fortification as a potential source of additional nutrients. Inadequate intake was especially prevalent for iodine (68% of the global population), vitamin E (67%), calcium (66%), and iron (65%). More than half of people consumed inadequate levels of riboflavin, folate, and vitamins C and B6. Intake of niacin was closest to sufficient, with 22% of the global population consuming inadequate levels, followed by thiamin (30%) and selenium (37%).

Estimated inadequate intakes were higher for women than men for iodine, vitamin B12, iron, and selenium within the same country and age groups. Conversely, more men consumed inadequate levels of calcium, niacin, thiamin, zinc, magnesium, and vitamins A, C, and B6 compared to women. While patterns of micronutrient inadequacy emerged more clearly on the basis of sex, the researchers also observed that males and females ages 10-30 were most prone to low levels of calcium intake, especially in South and East Asia and sub-Saharan Africa. Calcium intake was also low across North America, Europe, and Central Asia.

“These results are alarming,” said Ty Beal, senior technical specialist at GAIN. “Most people—even more than previously thought, across all regions and countries of all incomes—are not consuming enough of multiple essential micronutrients. These gaps compromise health outcomes and limit human potential on a global scale.”

“The public health challenge facing us is immense, but practitioners and policymakers have the opportunity to identify the most effective dietary interventions and target them to the populations most in need,” added senior author Christopher Golden , associate professor of nutrition and planetary health at Harvard Chan School.

The researchers noted that a lack of available data, especially on individual dietary intake worldwide, may have limited their findings.

Simone Passarelli, former doctoral student and postdoctoral research fellow in the Department of Nutrition at Harvard Chan School, served as co-lead author. She received funding from the National Institutes of Health (training grant 2T32DK007703-26).

“Global estimation of dietary micronutrient inadequacies: a modeling analysis,” Simone Passarelli, Christopher M. Free, Alon Shepon, Ty Beal, Carolina Batis, Christopher D. Golden, The Lancet Global Health, August 29, 2024, doi: 10.1016/S2214-109X(24)00276-6

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Harvard T.H. Chan School of Public Health  brings together dedicated experts from many disciplines to educate new generations of global health leaders and produce powerful ideas that improve the lives and health of people everywhere. As a community of leading scientists, educators, and students, we work together to take innovative ideas from the laboratory to people’s lives—not only making scientific breakthroughs, but also working to change individual behaviors, public policies, and health care practices. Each year, more than 400 faculty members at Harvard Chan School teach 1,000-plus full-time students from around the world and train thousands more through online and executive education courses. Founded in 1913 as the Harvard-MIT School of Health Officers, the School is recognized as America’s oldest professional training program in public health.

The University of California, Santa Barbara is a leading research institution that also provides a comprehensive liberal arts learning experience. Teaching and research go hand-in-hand at UC Santa Barbara. Our students are full participants in an educational journey of discovery that stimulates independent thought, critical reasoning, and creativity. Our academic community of faculty, students, and staff is characterized by a culture of interdisciplinary collaboration that is responsive to the needs of our multicultural and global society. All of this takes place within a living and learning environment like no other, as we draw inspiration from the beauty and resources of our extraordinary location at the edge of the Pacific Ocean.

The Global Alliance for Improved Nutrition (GAIN) is a Swiss-based foundation launched at the United Nations in 2002 to tackle the human suffering caused by malnutrition. Working with governments, businesses, and civil society, we aim to transform food systems so that they deliver more nutritious foods for all people, especially the most vulnerable.

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Yoga: Effectiveness and Safety

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What is yoga and how does it work?

Yoga is an ancient and complex practice, rooted in Indian philosophy. It began as a spiritual practice but has become popular as a way of promoting physical and mental well-being.

Although classical yoga also includes other elements, yoga as practiced in the United States typically emphasizes physical postures (asanas), breathing techniques (pranayama), and meditation (dyana). 

There are many different yoga styles, ranging from gentle practices to physically demanding ones. Differences in the types of yoga used in research studies may affect study results. This makes it challenging to evaluate research on the health effects of yoga.

Yoga and two practices of Chinese origin— tai chi and qigong —are sometimes called “meditative movement” practices. All three practices include both meditative elements and physical ones.

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Research suggests that yoga may:

• Help improve general wellness by relieving stress, supporting good health habits, and improving mental/emotional health, sleep, and balance.
• Relieve neck pain, migraine or tension-type headaches, and pain associated with knee osteoarthritis. It may also have a small benefit for low-back pain.
• Help people with overweight or obesity lose weight.
• Help people quit smoking.
• Help people manage anxiety symptoms or depression.
• Relieve menopause symptoms.
• Be a helpful addition to treatment programs for substance use disorders.
• Help people with chronic diseases manage their symptoms and improve their quality of life.

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Studies have suggested possible benefits of yoga for several aspects of wellness, including stress management, mental/emotional health, promoting healthy eating/activity habits, sleep, and balance. 

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• A 2020 review of 12 recent studies (672 total participants) of a variety of types of yoga for stress management in healthy adults found beneficial effects of yoga on measures of perceived stress in all the studies.
• Of 17 older studies (1,070 total participants) of yoga for stress management included in a 2014 review, 12 showed improvements in physical or psychological measures related to stress.
• Mental/emotional health. In a 2018 review of 14 studies (involving 1,084 total participants) that assessed the effects of yoga on positive aspects of mental health, most found evidence of benefits, such as improvements in resilience or general mental well-being.
• In a 2021 study in which 60 women with obesity were randomly assigned to 12 yoga sessions or a waiting list, the beneficial effect of yoga on body mass index (BMI, an estimate of body fat based on height and weight) was found to depend on changes in physical activity and daily fruit and vegetable intake. 
• A 2018 survey of young adults (involving 1,820 participants) showed that practicing yoga regularly was associated with better eating and physical activity habits. In interviews, survey respondents said they thought yoga encouraged greater mindfulness and motivated them to participate in other forms of activity and to eat healthier. In addition, they saw the yoga community as a social circle that encourages connection, where healthy eating is commonplace.
• In questionnaires and interviews, participants in a 2022 British study of a yoga intervention for people who were at risk for certain health conditions said that they had made changes in their lifestyles in response to the yoga program. They reported reducing consumption of unhealthy foods, increasing fruit and vegetable intake, and increasing their overall levels of physical activity.
• Sleep. Yoga has been shown to be helpful for sleep in multiple studies of cancer patients, women with sleep problems, and older adults. Individual studies of population groups including health care workers, people with arthritis, and women with menopause symptoms have also reported improved sleep from yoga. 
• Balance. In a 2014 review, 11 of 15 studies (688 total participants) that looked at the effect of yoga on balance in healthy people showed improvements in at least one outcome related to balance.   Several newer studies have provided additional evidence supporting a beneficial effect of yoga on balance in community-dwelling older adults.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Can yoga help with pain management?

Research has been done on yoga for several conditions that involve pain, including low-back pain, neck pain, headaches, and knee osteoarthritis. For low-back pain, a large amount of research has been done, and the evidence suggests a slight benefit. For the other conditions, the evidence looks promising, but the amount of research is relatively small.

• A 2022 review of 21 studies (2,223 total participants) of yoga interventions for low-back pain found that yoga is slightly better than no exercise, but the small difference may not be important to patients. There was evidence that participating in yoga was associated with slight improvements in physical function (ability to be active) and mental quality of life (emotional problems) in people with low-back pain. It was unclear whether there was any difference between the effects of yoga and those of other types of exercise.
• A 2020 report by the Agency for Healthcare Research and Quality evaluated 10 studies of yoga for low-back pain (involving 1,520 total participants) and found that yoga improved pain and function in both the short term (1 to 6 months) and intermediate term (6 to 12 months). The effects of yoga were similar to those of exercise and massage.
• A clinical practice guideline issued by the American College of Physicians in 2017 recommends using nondrug methods for the initial treatment of chronic low-back pain. Yoga is one of several suggested nondrug approaches. 
• Neck pain. A 2019 review of 10 studies (686 total participants) found that practicing yoga reduced the intensity of neck pain, decreased disability related to neck pain, and improved range of motion in the neck.
• A 2020 review of 6 studies (240 participants) of yoga for chronic or episodic headaches (tension headaches or migraines) found evidence of reductions in headache frequency, headache duration, and pain intensity, with effects seen mostly in people with tension headaches. Because of the small numbers of studies and participants, as well as limitations in the quality of the studies, these results should be considered preliminary.
• A 2022 review of 6 studies (445 participants) of yoga for migraine suggested that yoga was associated with decreases in pain intensity, headache frequency, and headache duration, and reduced the impact of migraine on daily life. However, most of the studies included small numbers of people, and the types of yoga therapy varied among studies, so the results are not conclusive. Also, most of the studies were done in Asia, and their findings might not apply to other populations.
• A 2019 review of 9 studies (640 total participants) showed that yoga may be helpful for improving pain, function, and stiffness in people with osteoarthritis of the knee. However, the number of studies was small, and the research was not of high quality.
• A 2019 guideline from the American College of Rheumatology and the Arthritis Foundation conditionally recommends yoga for people with knee osteoarthritis based on similarities to tai chi, which has been better studied and is strongly recommended by the same guideline.

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There’s evidence that yoga may help people lose weight.

• A 2022 review of 22 studies (1,178 participants) of yoga interventions for people with overweight or obesity showed reductions in body weight, BMI, body fat, and waist size.
• Longer and more frequent yoga sessions (at least 75 to 90 minutes, at least 3 times per week).
• A longer duration of the overall program (3 months or more).
• A yoga-based dietary component.
• A residential component (such as a full weekend to start the program).
• A larger number of elements of yoga.
• Home practice.

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There’s evidence that yoga may help people stop smoking. 

• A 2019 NCCIH-funded study with 227 participants compared yoga classes with general wellness classes as additions to a conventional once-weekly counseling program. The people in the yoga group were 37 percent more likely to have quit smoking by the end of the 8-week program. However, 6 months after treatment, there was no difference between the groups in the proportion of people who were still not smoking.
• A study published in 2020 showed a reduction in cigarette cravings after a single yoga session, as compared with a wellness education session. The study participants were people who were trying to cut back or stop smoking.

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Yoga can be a helpful addition to treatment for depression. It may also be helpful for anxiety symptoms in a variety of populations, but there’s little evidence of a benefit for people with anxiety disorders. Yoga might have benefits for people with post-traumatic stress disorder (PTSD).

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• In a 2017 review of 23 studies (involving 1,272 participants) of people with depressive symptoms (although not necessarily diagnosed with depression), yoga was helpful in reducing symptoms in 14 of the studies.
• A 2020 review of 7 studies (260 participants) of yoga interventions for people who had been diagnosed with major depressive disorder concluded that yoga may have small additional benefits for depression symptoms when used along with other forms of treatment.

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• A 2019 review of 38 studies (2,295 participants) of yoga for anxiety symptoms found that yoga had a substantial beneficial effect, with the greatest effects seen in studies performed in India. The studies included a variety of different groups of people, including healthy people such as students and military personnel, patients with various physical or mental health conditions, and caregivers.
• A 2021 review looked at the evidence on yoga for people who have been diagnosed with anxiety disorders. The reviewers identified some promising results, but they were unable to reach conclusions about whether yoga is helpful because not enough rigorous studies have been done.
• A 2021 study of Kundalini yoga for generalized anxiety disorder (226 participants, 155 of whom completed the study), supported by NCCIH, found that Kundalini yoga improved symptoms but was less helpful than cognitive behavioral therapy, an established first-line treatment for this condition.

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• A 2018 evaluation of 7 studies (284 participants) of yoga for people with post-traumatic stress disorder (PTSD) found only low-quality evidence of a possible benefit. 

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Can yoga help with menopause symptoms?

Yoga seems to be at least as effective as other types of exercise in relieving menopause symptoms. A 2018 evaluation of 13 studies (more than 1,300 participants) of yoga for menopause symptoms found that yoga reduced physical symptoms such as hot flashes as well as psychological symptoms such as anxiety or depression.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Is yoga helpful for substance use disorders?

A small amount of research has looked at the possible benefits of incorporating yoga into treatment programs for various types of substance use disorders (opioid, alcohol, or tobacco use disorders or others). In a 2021 review of 8 studies (1,889 participants), 7 studies showed evidence of beneficial effects in terms of reduced use of the substance or reduction in symptoms such as pain, stress, or anxiety.

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There’s promising evidence that yoga may help people with some chronic diseases manage their symptoms and improve their quality of life. Thus, yoga could be a helpful addition to treatment programs. 

• In a 2018 evaluation of 138 studies on the use of yoga in patients with various types of cancer (10,660 total participants), most of the studies found that yoga improved patients’ physical and psychological symptoms and quality of life. 
• A 2021 review looked at 26 studies of yoga for depressive symptoms (1,486 participants) and 16 studies of yoga for anxiety symptoms (977 participants) in people with cancer. Small-to-moderate beneficial effects were seen for both types of symptoms. 
• Many yoga studies have focused on women who have or have had breast cancer. A 2022 review examined 23 studies that looked at the effects of yoga interventions on various symptoms in women with breast cancer during active cancer treatment. The majority of the studies showed significant benefits of yoga on quality of life, fatigue, nausea/vomiting, sleep quality, anxiety, depression, stress, or wound healing, suggesting that yoga may be helpful for symptom management.
• A review of 8 studies (92 participants) suggested that yoga may have benefits for sleep, anxiety, fatigue, and quality of life in children and adolescents with cancer.
• Chronic obstructive pulmonary disease (COPD). A 2019 review of 11 studies (586 participants) of breathing-focused yoga interventions for people with Parkinson’s disease found beneficial effects of these interventions on exercise capacity, lung function, and quality of life.
• HIV/AIDS. A 2019 review of 7 studies (396 participants) of yoga interventions for people with HIV/AIDS found that yoga was a promising intervention for stress management.
• A 2016 review of 15 studies of yoga for asthma (involving 1,048 total participants, most of whom were adults) concluded that yoga probably leads to small improvements in quality of life and symptoms.
• A 2020 review of 9 studies (1,230 participants) of yoga-based interventions for children or adolescents with asthma found that the use of yoga was associated with improvements in lung function, stress/anxiety, and quality of life. However, because of wide variation in both the populations who were studied and the yoga interventions that were tested, it was unclear which components of yoga and how much yoga are needed to provide benefits.
• Multiple sclerosis. Two recent reviews on yoga for people with multiple sclerosis showed little evidence of benefits. One review found a significant benefit only for fatigue (comparable to the effect of other types of exercise), and the other found no benefits for any aspect of quality of life. 
• Parkinson’s disease.   A 2022 review (14 studies, 444 participants) suggests that yoga may have benefits for mobility, balance, and quality of life for people with mild-to-moderate Parkinson’s disease. The studies that were reviewed also suggest that yoga interventions are safe and acceptable for people with this condition.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What does research show about practicing yoga during pregnancy?

Physical activities such as yoga are safe and desirable for most pregnant women as long as appropriate precautions are taken. Yoga may have health benefits for pregnant women, such as decreasing stress, anxiety, and depression.

• If you are pregnant, you should be evaluated by your health care provider to make sure there’s no medical reason why you shouldn’t exercise.
• You may need to modify some activities, including yoga, during pregnancy. You should avoid “hot yoga” while you are pregnant because it can cause overheating. You also need to avoid activities, including yoga poses, that involve long periods of being still or lying on your back. Talk with your health care provider about how to adjust your physical activity during pregnancy.  
• A 2022 analysis of 29 studies of pregnancy yoga interventions (2,217 participants) found that these programs reduced anxiety, depression, perceived stress, and duration of labor and increased the likelihood of a normal vaginal birth. However, because the yoga programs varied widely and because some of the studies had weaknesses in their methods, additional rigorous research is needed to better understand the effects of yoga during pregnancy and to find out what types of yoga programs are best in terms of both effectiveness and safety.

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Research suggests that yoga may have several potential benefits for children.

• A 2020 review of 27 studies (1,805 total participants) of yoga interventions in children or adolescents found reductions in anxiety or depression in 70 percent of the studies, with more promising results for anxiety. Some of the studies involved children who had or were at risk for various physical or mental health disorders and others involved groups of children in schools. The quality of the studies was relatively weak, and the results cannot be considered conclusive. 
• A 2021 review evaluated 9 studies (289 total participants) of yoga interventions for weight loss in children or adolescents with obesity or overweight. Some of the studies evaluated yoga alone; others evaluated yoga in combination with other interventions such as changes in diet. The majority of the yoga interventions had beneficial effects on weight loss and related behavior changes. The studies were small, and some did not use the most rigorous study designs.
• A 2022 review of 21 studies (2,227 participants) of school-based yoga interventions in students age 5 to 15 showed promising results suggesting that yoga may enhance mental health among children and adolescents.
• Yoga interventions in educational settings have also been studied in preschool-aged children (age 3 to 5). A 2021 review of studies of yoga and mindfulness practices in this age group suggested that these practices may have benefits for social-emotional functioning, although more research is needed before definite conclusions can be reached.
• A small amount of evidence suggests that school-based yoga programs may have academic and psychological benefits for neurodiverse children.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What are the risks of yoga?

Yoga is generally considered a safe form of physical activity for healthy people when performed properly, under the guidance of a qualified instructor. However, as with other forms of physical activity, injuries can occur. The most common injuries are sprains and strains, and the parts of the body most commonly injured are the knee or lower leg. Serious injuries are rare. The risk of injury associated with yoga is lower than that for higher impact physical activities.

Older adults may need to be particularly cautious when practicing yoga. The rate of yoga-related injuries treated in emergency departments is higher in people age 65 and older than in younger adults.

To reduce your chances of getting hurt while doing yoga:

• Practice yoga under the guidance of a qualified instructor. Learning yoga on your own without supervision has been associated with increased risks.
• If you’re new to yoga, avoid extreme practices such as headstands, shoulder stands, the lotus position, and forceful breathing.
• Be aware that hot yoga has special risks related to overheating and dehydration.
• Pregnant women, older adults, and people with health conditions should talk with their health care providers and the yoga instructor about their individual needs. They may need to avoid or modify some yoga poses and practices. Some of the health conditions that may call for modifications in yoga include preexisting injuries, such as knee or hip injuries, lumbar spine disease, severe high blood pressure, balance issues, and glaucoma.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} How popular is yoga in the United States?

According to a national survey, the percentage of U.S. adults who practiced yoga increased from 5.0 percent in 2002 to 15.8 percent in 2022.

For children, there are data from 2017; in that year, 8.4 percent of U.S. children age 4 to 17 practiced yoga.

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National survey data from 2012 showed that 94 percent of adults who practiced yoga did it for wellness-related reasons, while 17.5 percent did it to treat a specific health condition. Some people reported doing both. 

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Do different groups of people have different experiences with yoga?

Much of the research on yoga in the United States has been conducted in predominantly female, non-Hispanic White, well-educated people with relatively high incomes. Other people—particularly members of minority groups and those with lower incomes—have been underrepresented in yoga studies.

Different groups of people may have different yoga-related experiences, and the results of studies that did not examine a diverse population may not apply to everyone.

• Differences related to age. In one survey, people age 40 to 54 were more likely to be motivated to practice yoga to increase muscle strength or lose weight, while those age 55 or older were more likely to be motivated by age-related chronic health issues. People age 65 and older may be more likely to need treatment for yoga-related injuries.
• Differences related to gender. A study found evidence for differences between men and women in the effects of specific yoga poses on muscles. A study in veterans found preliminary evidence that women might benefit more than men from yoga interventions for chronic back pain.
• Differences related to Hispanic ethnicity. U.S. national survey data show lower participation in yoga among Hispanic adults, compared to non-Hispanic White adults (8.0 percent vs. 17.1 percent of adults in 2017). A small 2021 survey of U.S. Hispanic adults with low incomes showed that cost was the most common barrier to participation in yoga. Other perceived barriers included concern about the need for physical flexibility (especially among men and those with no prior experience with yoga), thinking that they would feel like outsiders in a yoga class (among those with no prior experience), and considering yoga boring (among young adults).

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NCCIH is sponsoring a variety of yoga studies, including:

• An evaluation of emotion regulation as a mechanism of action in yoga interventions for chronic low-back pain.
• A study of yoga for chronic pain in people who are being treated for opioid use disorder.
• A study of the effects of yoga postures and slow, deep breathing in people with hypertension (high blood pressure).

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• Don’t use yoga to postpone seeing a health care provider about a medical problem.
• Ask about the training and experience of the yoga instructor you’re considering.
• Take charge of your health—talk with your health care providers about any complementary health approaches you use. Together, you can make shared, well-informed  decisions.

For More Information

Nccih clearinghouse.

The NCCIH Clearinghouse provides information on NCCIH and complementary and integrative health approaches, including publications and searches of Federal databases of scientific and medical literature. The Clearinghouse does not provide medical advice, treatment recommendations, or referrals to practitioners.

Toll-free in the U.S.: 1-888-644-6226

Telecommunications relay service (TRS): 7-1-1

Website: https://www.nccih.nih.gov

Email: [email protected] (link sends email)

Know the Science

NCCIH and the National Institutes of Health (NIH) provide tools to help you understand the basics and terminology of scientific research so you can make well-informed decisions about your health. Know the Science features a variety of materials, including interactive modules, quizzes, and videos, as well as links to informative content from Federal resources designed to help consumers make sense of health information.

Explaining How Research Works (NIH)

Know the Science: How To Make Sense of a Scientific Journal Article

Understanding Clinical Studies (NIH)

A service of the National Library of Medicine, PubMed® contains publication information and (in most cases) brief summaries of articles from scientific and medical journals. For guidance from NCCIH on using PubMed, see How To Find Information About Complementary Health Approaches on PubMed .

Yoga for Health—Systematic Reviews/Reviews/Meta-analyses

Yoga for Health—Randomized Controlled Trials

Website: https://pubmed.ncbi.nlm.nih.gov/

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• American College of Obstetricians and Gynecologists. FAQs: Exercise During Pregnancy. Accessed at acog.org/womens-health/faqs/exercise-during-pregnancy on January 3, 2023.
• Anheyer D, Klose P, Lauche R, et al.  Yoga for treating headaches: a systematic review and meta-analysis. Journal of General Internal Medicine. 2020;35(3):846-854.
• Batrakoulis A. Psychophysiological adaptations to yoga practice in overweight and obese individuals: a topical review . Diseases. 2022;10(4):107.
• Black LI, Barnes PM, Clarke TC, Stussman BJ, Nahin RL.  Use of yoga, meditation, and chiropractors among U.S. children aged 4–17 years. NCHS Data Brief, no 324. Hyattsville, MD: National Center for Health Statistics. 2018.
• Bock BC, Dunsiger SI, Rosen RK, et al.  Yoga as a complementary therapy for smoking cessation: results from BreathEasy, a randomized clinical trial. Nicotine and Tobacco Research.  2019;21(11):1517-1523.
• Clarke TC, Barnes PM, Black LI, Stussman BJ, Nahin RL.  Use of yoga, meditation, and chiropractors among U.S. adults aged 18 and older. NCHS Data Brief, no 325. Hyattsville, MD: National Center for Health Statistics. 2018.
• Corrigan L, Moran P, McGrath N, et al. The characteristics and effectiveness of pregnancy yoga interventions: a systematic review and meta-analysis . BMC Pregnancy and Childbirth. 2022;22(1):250.
• Cramer H, Krucoff C, Dobos G.  Adverse events associated with yoga: a systematic review of published case reports and case series. PLoS One. 2013;8(10):e75515.
• Cramer H, Lauche R, Klose P, et al.  Yoga for improving health-related quality of life, mental health and cancer-related symptoms in women diagnosed with breast cancer. Cochrane Database of Systematic Reviews. 2017;(1):CD010802. Accessed at  cochranelibrary.com  on March 17, 2023.
• Cramer H, Ostermann T, Dobos G.  Injuries and other adverse events associated with yoga practice: a systematic review of epidemiological studies. Journal of Science and Medicine in Sport. 2018;21(2):147-154.
• Cramer H, Ward L, Saper R, et al.  The safety of yoga: a systematic review and meta-analysis of randomized controlled trials. American Journal of Epidemiology. 2015;182(4):281-293.
• Domingues RB.  Modern postural yoga as a mental health promoting tool: a systematic review. Complementary Therapies in Clinical Practice . 2018;31:248-255.
• Gonzalez M, Pascoe MC, Yang G, et al. Yoga for depression and anxiety symptoms in people with cancer: a systematic review and meta-analysis . Psychooncology. 2021;30(8):1196-1208.
• James-Palmer A, Anderson EZ, Zucker L, et al.  Yoga as an intervention for the reduction of symptoms of anxiety and depression in children and adolescents: a systematic review. Frontiers in Pediatrics. 2020;8:78.
• Khunti K, Boniface S, Norris E, et al. The effects of yoga on mental health in school-aged children: a systematic review and narrative synthesis of randomised control trials . Clinical Child Psychology and Psychiatry. 2023;28(3):1217-1238.
• Kolasinski SL, Neogi T, Hochberg MC, et al.  2019 American College of Rheumatology/Arthritis Foundation guideline for the management of osteoarthritis of the hand, hip, and knee. Arthritis and Rheumatology . 2020;72(2):220-233.
• Lauche R, Hunter DJ, Adams J, et al. Yoga for osteoarthritis: a systematic review and meta-analysis . Current Rheumatology Reports. 2019;21(9):47.
• Li Y, Li S, Jiang J, et al.  Effects of yoga on patients with chronic nonspecific neck pain: a PRISMA systematic review and meta-analysis. Medicine (Baltimore). 2019;98(8):e14649.
• Long C, Ye J, Chen M, et al. Effectiveness of yoga therapy for migraine treatment: a meta-analysis of randomized controlled studies . American Journal of Emergency Medicine. 2022;58:95-99.
• Qaseem A, Wilt TJ, McLean RM, et al.  Noninvasive treatments for acute, subacute, and chronic low back pain: a clinical practice guideline from the American College of Physicians. Annals of Internal Medicine. 2017;166(7):514-530.
• Rioux JG, Ritenbaugh C.  Narrative review of yoga intervention clinical trials including weight-related outcomes. Alternative Therapies in Health and Medicine. 2013;19(3):32-46.
• Selvan P, Hriso C, Mitchell J, et al. Systematic review of yoga for symptom management during conventional treatment of breast cancer patients . Complementary Therapies in Clinical Practice.  2022;48:101581.
• Seshadri A, Adaji A, Orth SS, et al. Exercise, yoga, and tai chi for treatment of major depressive disorder in outpatient settings: a systematic review and meta-analysis . Primary Care Companion for CNS Disorders. 2020;23(1):20r2722.
• Skelly AC, Chou R, Dettori JR, et al.  Noninvasive Nonpharmacological Treatment for Chronic Pain: A Systematic Review Update. Comparative Effectiveness Review no. 227. Rockville, MD: Agency for Healthcare Research and Quality; 2020. AHRQ publication no. 20-EHC009.
• Swain TA, McGwin G.  Yoga-related injuries in the United States from 2001 to 2014. Orthopaedic Journal of Sports Medicine. 2016;4(11):2325967116671703.
• Wang F, Szabo A.  Effects of yoga on stress among healthy adults: a systematic review. Alternative Therapies in Health and Medicine. 2020;26(4):AT6214.
• Wieland LS, Skoetz N, Pilkington K, et al. Yoga for chronic non-specific low back pain . Cochrane Database of Systematic Reviews. 2022;11(11):CD010671. Accessed at cochranelibrary.com on January 9, 2023.

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• Agarwal RP, Maroko-Afek A. Yoga into cancer care: a review of the evidence-based research.  International Journal of Yoga.  2018;11(1):3-29.
• Alphonsus KB, Su Y, D’Arcy C. The effect of exercise, yoga and physiotherapy on the quality of life in people with multiple sclerosis: systematic review and meta-analysis.  Complementary Therapies in Medicine.  2019;43:188-195.
• Anheyer D, Koch AK, Thoms MS, et al. Yoga in women with abdominal obesity—do lifestyle factors mediate the effect? Secondary analysis of a RCT.  Complementary Therapies in Medicine.  2021;60:102741.
• Bandealy SS, Sheth NC, Matuella SK, et al. Mind-body interventions for anxiety disorders: a review of the evidence base for mental health practitioners . Focus (American Psychiatric Publishing).  2021;19(2):173-183.
• Bolgla LA, Amodio L, Archer K, et al. Trunk and hip muscle activation during yoga poses: do sex-differences exist?  Complementary Therapies in Clinical Practice.  2018;31:256-261.
• Bridges L, Sharma M. The efficacy of yoga as a form of treatment for depression.  Journal of Evidence-Based Complementary and Alternative Medicine.  2017;22(4):1017-1028.
• Cheshire A, Richards R, Cartwright T. ‘Joining a group was inspiring’: a qualitative study of service users’ experiences of yoga on social prescription.  BMC Complementary Medicine and Therapies.  2022;22(1):67.
• Cocchiara RA, Peruzzo M, Mannocci A, et al. The use of yoga to manage stress and burnout in healthcare workers: a systematic review.  Journal of Clinical Medicine.  2019;8(3):284.
• Cramer H, Anheyer D, Saha FJ, et al. Yoga for posttraumatic stress disorder—a systematic review and meta-analysis.  BMC Psychiatry.  2018;18(1):72.
• Cramer H, Haller H, Klose P, et al. The risks and benefits of yoga for patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.  Clinical Rehabilitation.  2019;33(12):1847-1862.
• Cramer H, Peng W, Lauche R. Yoga for menopausal symptoms—a systematic review and meta-analysis.  Maturitas.  2018;109:13-25.
• Cramer H, Quinker D, Schumann D, et al. Adverse effects of yoga: a national cross-sectional survey.  BMC Complementary and Alternative Medicine.  2019;19(1):190.
• Cramer H, Ward L, Steel A, et al. Prevalence, patterns, and predictors of yoga use: results of a U.S. nationally representative survey.  American Journal of Preventive Medicine.  2016;50(2):230-235.
• Dai C-L, Sharma M, Chen C-C, et al. Yoga as an alternative therapy for weight management in child and adolescent obesity: a systematic review and implications for research.  Alternative Therapies in Health and Medicine.  2021;27(1):48-55.
• Dunne EM, Balletto BL, Donahue ML, et al. The benefits of yoga for people living with HIV/AIDS: a systematic review and meta-analysis.  Complementary Therapies in Clinical Practice.  2019;34:157-164.
• Forseth B, Hunter SD. Range of yoga intensities from savasana to sweating: a systematic review.  Journal of Physical Activity and Health.  2020;17(2):242-249.
• Gothe NP, McAuley E. Yoga is as good as stretching-strengthening exercises in improving functional fitness outcomes: results from a randomized controlled trial.  Journals of Gerontology. Series A, Biological Sciences and Medical Sciences.  2016;71(3):406-411.
• Green E, Huynh A, Broussard L, et al. Systematic review of yoga and balance: effect on adults with neuromuscular impairment.  American Journal of Occupational Therapy.  2019;73(1):7301205150p1-7301205150p11.
• Groessl EJ, Weingart KR, Johnson N, et al. The benefits of yoga for women veterans with chronic low back pain.  Journal of Alternative and Complementary Medicine.  2012;18(9):832-838.
• Hart N, Fawkner S, Niven A, et al. Scoping review of yoga in schools: mental health and cognitive outcomes in both neurotypical and neurodiverse youth populations . Children (Basel).  2022;9(6):849.
• Jeffries ER, Zvolensky MJ, Buckner JD. The acute impact of hatha yoga on craving among smokers attempting to reduce or quit.  Nicotine & Tobacco Research.  2020;22(3):446-451.
• Jeter PE, Nkodo A-F, Moonaz SH, et al. A systematic review of yoga for balance in a healthy population.  Journal of Alternative and Complementary Medicine.  2014;20(4):221-232.
• Keosaian JE, Lemaster CM, Dresner D, et al. “We’re all in this together”: a qualitative study of predominantly low income minority participants in a yoga trial for chronic low back pain.  Complementary Therapies in Medicine.  2016;24:34-39.
• Klifto CS, Bookman JS, Kaplan DJ, et al. Musculoskeletal injuries in yoga.  Bulletin of the Hospital for Joint Diseases.  2018;76(3):192-197.
• Lack S, Brown R, Kinser PA. An integrative review of yoga and mindfulness-based approaches for children and adolescents with asthma.  Journal of Pediatric Nursing.  2020;52:76-81.
• Lin P-J, Peppone LJ, Janelsins MC, et al. Yoga for the management of cancer treatment-related toxicities.  Current Oncology Reports.  2018;20(1):5.
• Lipton L. Using yoga to treat disease: an evidence-based review.  Journal of the American Academy of Physician Assistants.  2008;21(2):34-36, 38, 41.
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Acknowledgments

NCCIH thanks Inna Belfer, M.D., Ph.D., and David Shurtleff, Ph.D., NCCIH, for their review of the 2023 update of this publication.

This publication is not copyrighted and is in the public domain. Duplication is encouraged.

NCCIH has provided this material for your information. It is not intended to substitute for the medical expertise and advice of your health care provider(s). We encourage you to discuss any decisions about treatment or care with your health care provider. The mention of any product, service, or therapy is not an endorsement by NCCIH.

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