case study of depression patient

Mental Health Case Study: Understanding Depression through a Real-life Example

Through the lens of a gripping real-life case study, we delve into the depths of depression, unraveling its complexities and shedding light on the power of understanding mental health through individual experiences. Mental health case studies serve as invaluable tools in our quest to comprehend the intricate workings of the human mind and the various conditions that can affect it. By examining real-life examples, we gain profound insights into the lived experiences of individuals grappling with mental health challenges, allowing us to develop more effective strategies for diagnosis, treatment, and support.

The Importance of Case Studies in Understanding Mental Health

Case studies play a crucial role in the field of mental health research and practice. They provide a unique window into the personal narratives of individuals facing mental health challenges, offering a level of detail and context that is often missing from broader statistical analyses. By focusing on specific cases, researchers and clinicians can gain a deeper understanding of the complex interplay between biological, psychological, and social factors that contribute to mental health conditions.

One of the primary benefits of using real-life examples in mental health case studies is the ability to humanize the experience of mental illness. These narratives help to break down stigma and misconceptions surrounding mental health conditions, fostering empathy and understanding among both professionals and the general public. By sharing the stories of individuals who have faced and overcome mental health challenges, case studies can also provide hope and inspiration to those currently struggling with similar issues.

Depression, in particular, is a common mental health condition that affects millions of people worldwide. Disability Function Report Example Answers for Depression and Bipolar: A Comprehensive Guide offers valuable insights into how depression can impact daily functioning and the importance of accurate reporting in disability assessments. By examining depression through the lens of a case study, we can gain a more nuanced understanding of its manifestations, challenges, and potential treatment approaches.

Understanding Depression

Before delving into our case study, it’s essential to establish a clear understanding of depression and its impact on individuals and society. Depression is a complex mental health disorder characterized by persistent feelings of sadness, hopelessness, and loss of interest in activities. It can affect a person’s thoughts, emotions, behaviors, and overall well-being.

Some common symptoms of depression include:

– Persistent sad, anxious, or “empty” mood – Feelings of hopelessness or pessimism – Irritability – Loss of interest or pleasure in hobbies and activities – Decreased energy or fatigue – Difficulty concentrating, remembering, or making decisions – Sleep disturbances (insomnia or oversleeping) – Appetite and weight changes – Physical aches or pains without clear physical causes – Thoughts of death or suicide

The prevalence of depression worldwide is staggering. According to the World Health Organization, more than 264 million people of all ages suffer from depression globally. It is a leading cause of disability and contributes significantly to the overall global burden of disease. The impact of depression extends far beyond the individual, affecting families, communities, and economies.

Depression can have profound consequences on an individual’s quality of life, relationships, and ability to function in daily activities. It can lead to decreased productivity at work or school, strained personal relationships, and increased risk of other health problems. The economic burden of depression is also substantial, with costs associated with healthcare, lost productivity, and disability.

The Significance of Case Studies in Mental Health Research

Case studies serve as powerful tools in mental health research, offering unique insights that complement broader statistical analyses and controlled experiments. They allow researchers and clinicians to explore the nuances of individual experiences, providing a rich tapestry of information that can inform our understanding of mental health conditions and guide the development of more effective treatment strategies.

One of the key advantages of case studies is their ability to capture the complexity of mental health conditions. Unlike standardized questionnaires or diagnostic criteria, case studies can reveal the intricate interplay between biological, psychological, and social factors that contribute to an individual’s mental health. This holistic approach is particularly valuable in understanding conditions like depression, which often have multifaceted causes and manifestations.

Case studies also play a crucial role in the development of treatment strategies. By examining the detailed accounts of individuals who have undergone various interventions, researchers and clinicians can identify patterns of effectiveness and potential barriers to treatment. This information can then be used to refine existing approaches or develop new, more targeted interventions.

Moreover, case studies contribute to the advancement of mental health research by generating hypotheses and identifying areas for further investigation. They can highlight unique aspects of a condition or treatment that may not be apparent in larger-scale studies, prompting researchers to explore new avenues of inquiry.

Examining a Real-life Case Study of Depression

To illustrate the power of case studies in understanding depression, let’s examine the story of Sarah, a 32-year-old marketing executive who sought help for persistent feelings of sadness and loss of interest in her once-beloved activities. Sarah’s case provides a compelling example of how depression can manifest in high-functioning individuals and the challenges they face in seeking and receiving appropriate treatment.

Background: Sarah had always been an ambitious and driven individual, excelling in her career and maintaining an active social life. However, over the past year, she began to experience a gradual decline in her mood and energy levels. Initially, she attributed these changes to work stress and the demands of her busy lifestyle. As time went on, Sarah found herself increasingly isolated, withdrawing from friends and family, and struggling to find joy in activities she once loved.

Presentation of Symptoms: When Sarah finally sought help from a mental health professional, she presented with the following symptoms:

– Persistent feelings of sadness and emptiness – Loss of interest in hobbies and social activities – Difficulty concentrating at work – Insomnia and daytime fatigue – Unexplained physical aches and pains – Feelings of worthlessness and guilt – Occasional thoughts of death, though no active suicidal ideation

Initial Diagnosis: Based on Sarah’s symptoms and their duration, her therapist diagnosed her with Major Depressive Disorder (MDD). This diagnosis was supported by the presence of multiple core symptoms of depression that had persisted for more than two weeks and significantly impacted her daily functioning.

The Treatment Journey

Sarah’s case study provides an opportunity to explore the various treatment options available for depression and examine their effectiveness in a real-world context. Supporting a Caseworker’s Client Who Struggles with Depression offers valuable insights into the role of support systems in managing depression, which can complement professional treatment approaches.

Overview of Treatment Options: There are several evidence-based treatments available for depression, including:

1. Psychotherapy: Various forms of talk therapy, such as Cognitive Behavioral Therapy (CBT) and Interpersonal Therapy (IPT), can help individuals identify and change negative thought patterns and behaviors associated with depression.

2. Medication: Antidepressants, such as Selective Serotonin Reuptake Inhibitors (SSRIs), can help regulate brain chemistry and alleviate symptoms of depression.

3. Combination Therapy: Many individuals benefit from a combination of psychotherapy and medication.

4. Lifestyle Changes: Exercise, improved sleep habits, and stress reduction techniques can complement other treatments.

5. Alternative Therapies: Some individuals find relief through approaches like mindfulness meditation, acupuncture, or light therapy.

Treatment Plan for Sarah: After careful consideration of Sarah’s symptoms, preferences, and lifestyle, her treatment team developed a comprehensive plan that included:

1. Weekly Cognitive Behavioral Therapy sessions to address negative thought patterns and develop coping strategies.

2. Prescription of an SSRI antidepressant to help alleviate her symptoms.

3. Recommendations for lifestyle changes, including regular exercise and improved sleep hygiene.

4. Gradual reintroduction of social activities and hobbies to combat isolation.

Effectiveness of the Treatment Approach: Sarah’s response to treatment was monitored closely over the following months. Initially, she experienced some side effects from the medication, including mild nausea and headaches, which subsided after a few weeks. As she continued with therapy and medication, Sarah began to notice gradual improvements in her mood and energy levels.

The CBT sessions proved particularly helpful in challenging Sarah’s negative self-perceptions and developing more balanced thinking patterns. She learned to recognize and reframe her automatic negative thoughts, which had been contributing to her feelings of worthlessness and guilt.

The combination of medication and therapy allowed Sarah to regain the motivation to engage in physical exercise and social activities. As she reintegrated these positive habits into her life, she experienced further improvements in her mood and overall well-being.

The Outcome and Lessons Learned

Sarah’s journey through depression and treatment offers valuable insights into the complexities of mental health and the effectiveness of various interventions. Understanding the Link Between Sapolsky and Depression provides additional context on the biological underpinnings of depression, which can complement the insights gained from individual case studies.

Progress and Challenges: Over the course of six months, Sarah made significant progress in managing her depression. Her mood stabilized, and she regained interest in her work and social life. She reported feeling more energetic and optimistic about the future. However, her journey was not without challenges. Sarah experienced setbacks during particularly stressful periods at work and struggled with the stigma associated with taking medication for mental health.

One of the most significant challenges Sarah faced was learning to prioritize her mental health in a high-pressure work environment. She had to develop new boundaries and communication strategies to manage her workload effectively without compromising her well-being.

Key Lessons Learned: Sarah’s case study highlights several important lessons about depression and its treatment:

1. Early intervention is crucial: Sarah’s initial reluctance to seek help led to a prolongation of her symptoms. Recognizing and addressing mental health concerns early can prevent the condition from worsening.

2. Treatment is often multifaceted: The combination of medication, therapy, and lifestyle changes proved most effective for Sarah, underscoring the importance of a comprehensive treatment approach.

3. Recovery is a process: Sarah’s improvement was gradual and non-linear, with setbacks along the way. This emphasizes the need for patience and persistence in mental health treatment.

4. Social support is vital: Reintegrating social activities and maintaining connections with friends and family played a crucial role in Sarah’s recovery.

5. Workplace mental health awareness is essential: Sarah’s experience highlights the need for greater understanding and support for mental health issues in professional settings.

6. Stigma remains a significant barrier: Despite her progress, Sarah struggled with feelings of shame and fear of judgment related to her depression diagnosis and treatment.

Sarah’s case study provides a vivid illustration of the complexities of depression and the power of comprehensive, individualized treatment approaches. By examining her journey, we gain valuable insights into the lived experience of depression, the challenges of seeking and maintaining treatment, and the potential for recovery.

The significance of case studies in understanding and treating mental health conditions cannot be overstated. They offer a level of detail and nuance that complements broader research methodologies, providing clinicians and researchers with invaluable insights into the diverse manifestations of mental health disorders and the effectiveness of various interventions.

As we continue to explore mental health through case studies, it’s important to recognize the diversity of experiences within conditions like depression. Personal Bipolar Psychosis Stories: Understanding Bipolar Disorder Through Real Experiences offers insights into another complex mental health condition, illustrating the range of experiences individuals may face.

Furthermore, it’s crucial to consider how mental health issues are portrayed in popular culture, as these representations can shape public perceptions. Understanding Mental Disorders in Winnie the Pooh: Exploring the Depiction of Depression provides an interesting perspective on how mental health themes can be embedded in seemingly lighthearted stories.

The field of mental health research and treatment continues to evolve, driven by the insights gained from individual experiences and comprehensive studies. By combining the rich, detailed narratives provided by case studies with broader research methodologies, we can develop more effective, personalized approaches to mental health care. As we move forward, it is essential to continue exploring and sharing these stories, fostering greater understanding, empathy, and support for those facing mental health challenges.

References:

1. World Health Organization. (2021). Depression. Retrieved from https://www.who.int/news-room/fact-sheets/detail/depression

2. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.

3. Beck, A. T., & Alford, B. A. (2009). Depression: Causes and treatment. University of Pennsylvania Press.

4. Cuijpers, P., Quero, S., Dowrick, C., & Arroll, B. (2019). Psychological treatment of depression in primary care: Recent developments. Current Psychiatry Reports, 21(12), 129.

5. Malhi, G. S., & Mann, J. J. (2018). Depression. The Lancet, 392(10161), 2299-2312.

6. Otte, C., Gold, S. M., Penninx, B. W., Pariante, C. M., Etkin, A., Fava, M., … & Schatzberg, A. F. (2016). Major depressive disorder. Nature Reviews Disease Primers, 2(1), 1-20.

7. Sapolsky, R. M. (2004). Why zebras don’t get ulcers: The acclaimed guide to stress, stress-related diseases, and coping. Holt paperbacks.

8. Yin, R. K. (2017). Case study research and applications: Design and methods. Sage publications.

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Patient case navigator: major depressive disorder.

Introduction

Learning Objectives

• How to perform a structured psychiatric interview
• Standardized psychiatric rating scales appropriate for patients with depressive symptoms
• Common barriers to adequate treatment response
• How to assess and monitor patients for treatment side effects and adequate treatment response

Watch the video:

History and Examination

Medical History

Examination

History of Present Illness

Eric is a 60-year-old man who presents to his primary care nurse practitioner, Tina, with irritability, excessive sleeping, and a lack of interest in his usual hobbies, such as attending baseball games and going to the movies with his wife. He also has been spending much time at home alone, watching television, rather than spending time with his friends or wife, as he usually does. Eric recently retired from his job as a general contractor remodeling people’s kitchens and bathrooms. He enjoyed his job very much and felt a sense of pride in helping people make their homes more functional and attractive. However, his job was very physical, and at times stressful, so Eric felt it was time to retire and find something new with which to occupy his time.

Eric was diagnosed with hypothyroidism 5 years ago and has been on medication ever since. Annual lab tests indicate his thyroid levels have remained within the normal range for the past few years. He also has mild hypertension, which is well-controlled at an adequate dose.

Psychosocial History

Eric reports that he has several close friends and that he got along well with people at work. He denies a history of substance misuse and reports that he occasionally drinks a glass of wine with dinner. He does not smoke. Eric describes his marriage as “very good.” He is also close with his adult daughter and enjoys spending time with his 2 grandchildren.

At age 33, Eric experienced a period of depressed mood after losing his job. During that time, he had problems getting out of bed in the morning because he felt hopeless and sad, stopped socializing with friends, and lost about 4 lbs of body weight in 4 weeks without intentionally dieting. He sought treatment from his primary care physician, who referred him to a psychiatrist for medication and a psychologist for outpatient cognitive-behavioral therapy (CBT). Eric worked with his psychiatrist and tried 4 different selective serotonin reuptake inhibitors (SSRIs) before he ultimately found one that seemed to work for him. He and his psychiatrist decided together that he could stop taking the medication after 1 year because his mood had improved and stabilized. He saw his therapist once weekly for approximately 2.5 years and reports that CBT also helped improve his mood and functioning.

Family History

Eric reports that, throughout his life, his mother had “very low periods” when she seemed extremely sad and had trouble functioning. However, she never sought treatment for these episodes.

Eric’s physical examination indicates he is generally healthy for his age. His vital signs are all within the normal range, and the mental status examination indicates he is fully oriented and alert. Eric’s appearance is that of an older man. His affect is flat, and he has trouble making eye contact, often staring at the floor instead.

Patient Interview

Quiz #1: initial presentation and diagnosis, dsm-5 diagnostic criteria for mdd.

MDE Diagnostic Criteria

Safety Plan

Major Depressive Episode (MDE)

A. Five (or more) of the following symptoms have been present during the same 2-week period and represent a change from previous function; at least one of the symptoms is either (1) depressed mood or (2) loss of interest or pleasure.

• Depressed mood most of the day, nearly every day, as indicated by either subjective report or observation made by others
• Markedly diminished interest or pleasure in all, or almost all, activities most of the day, nearly every day
• Significant weight loss when not dieting or weight gain, or decrease or increase in appetite nearly every day
• Insomnia or hypersomnia nearly every day
• Psychomotor agitation or retardation nearly every day
• Fatigue or loss of energy nearly every day
• Feelings of worthlessness or excessive or inappropriate guilt nearly every day
• Diminished ability to think or concentrate, or indecisiveness, nearly every day
• Recurrent thoughts of death, recurrent suicidal ideation without a specific plan, or a suicide attempt or a specific plan for committing suicide

B. The symptoms cause clinically significant distress or impairment in social, occupational, or other important areas of function

C. The episode is not attributable to the physiological effects of a substance or another medical condition

Diagnostic and Statistical Manual of Mental Disorders. 5th ed. American Psychiatric Association; 2013.

• It is important to thoroughly review each of these 9 symptoms with your patients when assessing them for MDD.
• Clinical rating scales can help identify which patients require more in-depth screening for depression.

Quiz #2: DSM-5 Diagnostic Criteria for MDD

Scales for mdd.

PHQ-9 Scale Scoring

QIDS Scale Scoring

Patient Health Questionnaire-9 (PHQ-9)

This scale was developed by Drs Robert L. Spitzer, Janet B.W. Williams, Kurt Kroenke, and colleagues with an educational grant from Pfizer inc. No permission required.

Scoring Criteria

Kroenke K, Spitzer RL. Psychiatric Annals. 2002;32:509-521.

The Quick Inventory of Depressive Symptomatology (QIDS)

• The QIDS is a 16-item, multiple-choice questionnaire in which depressive symptoms are rated on a 0-3 scale according to severity
• Items are derived from the 9 diagnostic criteria for major depressive disorder used in the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV), including sadness, loss of interest or pleasure, poor concentration or decision-making, self-outlook, suicidal ideation, lack of energy, sleep disturbance, appetite change, and psychomotor agitation
• Although the QIDS was initially developed based on DSM-IV criteria, the scale is also compatible with the DSM-5. The core criteria for MDD are consistent across these editions

Rush AJ, et al. Biol Psychiatry. 2003;54(5):573-583.

Bernstein IH, et al. Int J Methods Psychiatr Res. 2009;18(2):138-146.

Quiz #3: Scales for MDD

Treatment initiation and monitoring.

APA Guidelines

Eric's PHQ-9 Score

Treatment Options

American Psychiatric Association (APA) Guidelines for Treatment of MDD

1-2 weeks: Improvement from pharmacologic therapy can be seen as early as 1-2 weeks after starting treatment

2-4 weeks: Some patients may achieve improvement in 2-4 weeks

4-6 weeks: Short-term efficacy trials show antidepressant therapy appears to require 4-6 weeks to achieve maximum therapeutic effects

4-8 weeks: The APA recommends 4-8 weeks of adequate* treatment is needed before concluding that a patient is partially responsive or unresponsive to treatment *Adequate dose and duration Practice Guideline for the Treatment of Patients With Major Depressive Disorder. 3rd ed. American Psychiatric Association; 2010.

*Adequate dose and duration

Practice Guideline for the Treatment of Patients with Major Depressive Disorder. 3rd ed. American Psychiatric Association; 2010.

Quiz #4: Treatment Initiation and Monitoring

Assessing for treatment challenges.

Treatment Challenges

Eric's Updated PHQ-9 Score

Possible Challenges to Antidepressant Therapy

• Suboptimal efficacy due to the wrong dose, inadequate length of time on the medication, or the person's individual biology not being responsive to the medication
• Unpleasant side effects of antidepressants can occur, such as weight gain, insomnia, and sexual dysfunction
• Nonadherence to the antidepressant
• As a reminder, the American Psychiatric Association (APA) recommends 4-8 weeks of adequate* treatment is needed before concluding that a patient is partially responsive or unresponsive to treatment

Practice Guideline for the Treatment of Patients With Major Depressive Disorder. 3rd ed. American Psychiatric Association; 2010.

MDD Diagnosis

Clinical Probes

Treatment Assessment

Monitoring Considerations

Factors to Consider When Making a MDD Diagnosis

• Take a thorough patient history
• Previous or current depressive episodes
• Previous or current manic or hypomanic episodes
• Family history of MDD, bipolar disorder
• Medical comorbidities
• Consider a broad differential diagnosis

Clinical Queries That Aid in Diagnosing Major Depressive Episodes

1. Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. American Psychiatric Association; 2013. 2. Kroenke K, et al. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606-613.

APA Practice Guidelines on Treatment Assessment

• Wait 4 to 8 weeks to assess treatment response to antidepressants
• In patients without adequate response, clinicians can consider changing or augmenting with a second medication
• Changes to treatment plans, such as augmenting with a second-generation antipsychotic medication, are reasonable if a patient does not have adequate improvement in 6 weeks
• Consistently follow-up with patients to assess treatment effects, adverse medication effects, and risk of self-harm

APA Practice Guidelines note that the frequency of monitoring should be based on:

• Symptom severity (including suicidal ideation)
• Co-occurring disorders (including general medical conditions)
• Treatment adherence
• Availability of social supports
• Frequency and severity of side effects with medication

Tina Matthews-Hayes is a paid consultant for Abbvie Medical Affairs and was compensated for her time.

American Psychiatric Association. Practice Guideline for the Treatment of Patients with Major Depressive Disorder. 3rd ed. American Psychiatric Association; 2010.​

• American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders . 5th ed. American Psychiatric Association; 2013. ​
• Kapfhammer HP. Somatic symptoms in depression. Dialogues Clin Neurosci . 2006;8(2):227-239.​
• Bobo WV. The diagnosis and management of bipolar I and II disorders: clinical practice update. Mayo Clin Proc . 2017;92(10):1532-1551.​
• Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med . 2001;16:606-613.​
• Smarr KL, Keefer AL. Measures of depression and depressive symptoms. Arthritis Care Res . 2011;63(S11):S454-S466. doi:10.1002/acr.20556​
• Rush AJ, Trivedi MH, Ibrahim HM, et al. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), Clinician Rating (QIDS-C), and Self-Report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003;54:573-583.​
• Brown ES, Murray M, Carmody TJ, et al. The Quick Inventory of Depressive Symptomatology–Self-report: a psychometric evaluation in patients with asthma and major depressive disorder. Ann Allergy Asthma Immunol. 2008;100(5):433-438. doi:10.1016/S1081-1206(10)60467-X​
• Liu R, Wang F, Liu S, et al. Reliability and validity of the Quick Inventory of Depressive Symptomatology-Self-Report Scale in older adults with depressive symptoms. Front Psychiatry . 2021;12:686711. doi:10.3389/fpsyt.2021.686711 ​
• Bernstein IH, Rush AJ, Suppes T, et al. A psychometric evaluation of the clinician-rated Quick Inventory of Depressive Symptomatology (QIDS-C16) in patients with bipolar disorder. Int J Methods Psychiatr Res . 2009;18(2):138-146. doi:10.1002/mpr.2855​
• Bernstein IH, Rush AJ, Trivedi MH, et al. Psychometric properties of the Quick Inventory of Depressive Symptomatology in adolescents. Int J Methods Psychiatr Res. 2010;19(4):185-194. doi:10.1002/mpr.321 ​
• Kroenke K. Enhancing the clinical utility of depression screening. CMAJ . 2012;184(3):281-282.doi:10.1503/cmaj.112004 ​
• Levinstein MR, Samuels BA. Mechanisms underlying the antidepressant response and treatment resistance. Front Behav Neurosci . 2014;8:208. doi:10.3389/fnbeh.2014.00208​
• Haddad PM, Talbot PS, Anderson IM, McAllister-Williams RH. Managing inadequate antidepressant response in depressive illness. Br Med Bull. 2015;115(1):183-201. doi:10.1093/bmb/ldv03​

This resource is intended for educational purposes only and is intended for US healthcare professionals. Healthcare professionals should use independent medical judgment. All decisions regarding patient care must be handled by a healthcare professional and be made based on the unique needs of each patient.

This is not a diagnostic tool and is not intended to replace a clinical evaluation by a healthcare provider.

Reach out to your family or friends for help if you have thoughts of harming yourself or others, or call the National Suicide Prevention Helpline for information at 800-273-8255.

ABBV-US-00976-MC, V1.0 Approved 12/2023 AbbVie Medical Affairs

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Recurrent Major Depressive Disorder of a Young Woman

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This exercise is designed to test your comprehension of material relevant to this issue of Focus as well as your ability to evaluate, diagnose, and manage clinical problems. Answer the questions below to the best of your ability with the information provided, making your decisions as if the individual were one of your patients. Questions are presented at “consideration points” that follow a section that gives information about the case. One or more choices may be correct for each question; make your choices on the basis of your clinical knowledge and the history provided. Read all of the options for each question before making any selections. You are given points on a graded scale for the best possible answer(s), and points are deducted for answers that would result in a poor outcome or delay your arriving at the right answer. Answers that have little or no impact receive zero points. At the end of the exercise, you will add up your points to obtain a total score.

Case Vignette

Consideration point a., case vignette continues, consideration point b., consideration point c., case vignette concludes, answers: scoring, relative weights, and comments, consideration point a, consideration point b, consideration point c.

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Patient Case Presentation

Ms. A. M. is a 23 year old biracial female who arrives to her primary care clinic for her annual physical. Ms A.M. has had a 15lb weight gain since her last visit. She complains of feelings of guilt and sadness with her parents’ recent divorce. Patient states she is not sleeping well and feels that she cannot concentrate during her nursing orientation classes. When asked about her interpersonal relationships, she states that she just ended her two-year relationship with her boyfriend and she states that she hasn’t been able to meet new friends or keep up with current friendships.

Past Medical/Surgical History

• Asthma diagnosed at age 8
• Hypothyroidism diagnosed at age 16

Pertinent Family History

• Older sister with suicide attempt 3 years prior.
• Father diagnosed with Bipolar II in his thirties, currently taking medication.
• Mother alive and healthy at age 54
• Uncle with history of substance abuse

Pertinent Social History

• recently graduated college and is starting her career as a nurse at a large teaching hospital on the intensive care unit
• parents recently divorced
• moving out on her own for the first time

Patient Case #1: 27-Year-Old Woman With Bipolar Disorder

• Theresa Cerulli, MD
• Tina Matthews-Hayes, DNP, FNP, PMHNP

Custom Around the Practice Video Series

Experts in psychiatry review the case of a 27-year-old woman who presents for evaluation of a complex depressive disorder.

EP: 1 . Patient Case #1: 27-Year-Old Woman With Bipolar Disorder

Ep: 2 . clinical significance of bipolar disorder, ep: 3 . clinical impressions from patient case #1, ep: 4 . diagnosis of bipolar disorder, ep: 5 . treatment options for bipolar disorder, ep: 6 . patient case #2: 47-year-old man with treatment resistant depression (trd), ep: 7 . patient case #2 continued: novel second-generation antipsychotics, ep: 8 . role of telemedicine in bipolar disorder.

Michael E. Thase, MD : Hello and welcome to this Psychiatric Times™ Around the Practice , “Identification and Management of Bipolar Disorder. ”I’m Michael Thase, professor of psychiatry at the Perelman School of Medicine at the University of Pennsylvania in Philadelphia, Pennsylvania.

Joining me today are: Dr Gustavo Alva, the medical director of ATP Clinical Research in Costa Mesa, California; Dr Theresa Cerulli, the medical director of Cerulli and Associates in North Andover, Massachusetts; and Dr Tina Matthew-Hayes, a dual-certified nurse practitioner at Western PA Behavioral Health Resources in West Mifflin, Pennsylvania.

Today we are going to highlight challenges with identifying bipolar disorder, discuss strategies for optimizing treatment, comment on telehealth utilization, and walk through 2 interesting patient cases. We’ll also involve our audience by using several polling questions, and these results will be shared after the program.

Without further ado, welcome and let’s begin. Here’s our first polling question. What percentage of your patients with bipolar disorder have 1 or more co-occurring psychiatric condition? a. 10%, b. 10%-30%, c. 30%-50%, d. 50%-70%, or e. more than 70%.

Now, here’s our second polling question. What percentage of your referred patients with bipolar disorder were initially misdiagnosed? Would you say a. less than 10%, b. 10%-30%, c. 30%-50%, d. more than 50%, up to 70%, or e. greater than 70%.

We’re going to go ahead to patient case No. 1. This is a 27-year-old woman who’s presented for evaluation of a complex depressive syndrome. She has not benefitted from 2 recent trials of antidepressants—sertraline and escitalopram. This is her third lifetime depressive episode. It began back in the fall, and she described the episode as occurring right “out of the blue.” Further discussion revealed, however, that she had talked with several confidantes about her problems and that she realized she had been disappointed and frustrated for being passed over unfairly for a promotion at work. She had also been saddened by the unusually early death of her favorite aunt.

Now, our patient has a past history of ADHD [attention-deficit/hyperactivity disorder], which was recognized when she was in middle school and for which she took methylphenidate for adolescence and much of her young adult life. As she was wrapping up with college, she decided that this medication sometimes disrupted her sleep and gave her an irritable edge, and decided that she might be better off not taking it. Her medical history was unremarkable. She is taking escitalopram at the time of our initial evaluation, and the dose was just reduced by her PCP [primary care physician]from 20 mg to 10 mg because she subjectively thought the medicine might actually be making her worse.

On the day of her first visit, we get a PHQ-9 [9-item Patient Health Questionnaire]. The score is 16, which is in the moderate depression range. She filled out the MDQ [Mood Disorder Questionnaire] and scored a whopping 10, which is not the highest possible score but it is higher than 95% of people who take this inventory.

At the time of our interview, our patient tells us that her No. 1 symptom is her low mood and her ease to tears. In fact, she was tearful during the interview. She also reports that her normal trouble concentrating, attributable to the ADHD, is actually substantially worse. Additionally, in contrast to her usual diet, she has a tendency to overeat and may have gained as much as 5 kg over the last 4 months. She reports an irregular sleep cycle and tends to have periods of hypersomnolence, especially on the weekends, and then days on end where she might sleep only 4 hours a night despite feeling tired.

Upon examination, her mood is positively reactive, and by that I mean she can lift her spirits in conversation, show some preserved sense of humor, and does not appear as severely depressed as she subjectively describes. Furthermore, she would say that in contrast to other times in her life when she’s been depressed, that she’s actually had no loss of libido, and in fact her libido might even be somewhat increased. Over the last month or so, she’s had several uncharacteristic casual hook-ups.

So the differential diagnosis for this patient included major depressive disorder, recurrent unipolar with mixed features, versus bipolar II disorder, with an antecedent history of ADHD. I think the high MDQ score and recurrent threshold level of mixed symptoms within a diagnosable depressive episode certainly increase the chances that this patient’s illness should be thought of on the bipolar spectrum. Of course, this formulation is strengthened by the fact that she has an early age of onset of recurrent depression, that her current episode, despite having mixed features, has reverse vegetative features as well. We also have the observation that antidepressant therapy has seemed to make her condition worse, not better.

Transcript Edited for Clarity

Dr. Thase is a professor of psychiatry at the Perelman School of Medicine at the University of Pennsylvania in Philadelphia, Pennsylvania.

Dr. Alva is the medical director of ATP Clinical Research in Costa Mesa, California.

Dr. Cerulli is the medical director of Cerulli and Associates in Andover, Massachusetts.

Dr. Tina Matthew-Hayes is a dual certified nurse practitioner at Western PA Behavioral Health Resources in West Mifflin, Pennsylvania.

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COVID-19 tied to higher risk of depression, anxiety, PTSD and other conditions, with the unvaccinated most affected, study shows

Editor's note: If you or anyone you know is struggling with mental health, there are a number of ways to get help, including by calling or texting the Suicide Crisis Helpline at 9-8-8. A list of local crisis centres is also available here.

Having a severe case of COVID-19 appears to be linked with an increased risk of subsequent mental illness, including depression and anxiety disorders, and a new study finds that the association is strongest among people who were not vaccinated against the disease.

In the study, published Wednesday in the journal JAMA Psychiatry, the incidence of mental illness was higher in the weeks after a COVID-19 diagnosis , but the increased incidence was much lower in people who had been vaccinated against the coronavirus compared with those who were unvaccinated. Among people who were unvaccinated, the elevated incidence of mental illnesses was higher for up to a year after severe COVID-19.

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The study also found that the elevated incidence of mental illnesses was higher and lasted longer if a person was hospitalized for Covid-19, compared with not being hospitalized for COVID-19.

“The main surprise was that the association of COVID-19 with subsequent mental ill-health appeared restricted to severe COVID-19 that led to hospitalisation. There was little association of COVID-19 that did not lead to hospitalisation with subsequent mental ill-health,” Dr. Jonathan Sterne, an author of the study and professor of medical statistics and epidemiology at the University of Bristol Medical School, said in an email.

The researchers, from the University of Bristol Medical School and other institutions in the United Kingdom, also found stronger associations among older adults and men, compared with younger age groups and women.

“The most likely explanation for the stronger associations in older adults is that they are more likely to develop severe COVID-19 leading to hospitalisation,” Sterne said. “This may also explain the somewhat stronger associations in men, but we do not have a definite explanation.”

The new study included electronic health record data for three groups of adults, ages 18 to 110, in England. One group included about 18.6 million people who were diagnosed with COVID-19 between January 2020 and June 2021, before vaccinations were available. People in the two other groups – including about 14 million people who were vaccinated and about 3.2 million people who were unvaccinated – were diagnosed with COVID-19 between June 2021 and December 2021.

The researchers took a close look at how many people in each group were diagnosed with mental illnesses in the weeks after their COVID-19 diagnoses. Those conditions included depression, general anxiety disorders, post-traumatic stress disorder, eating disorders, addiction, self-harm, suicide and other severe mental illnesses such as schizophrenia, bipolar disorder and psychotic depression.

Overall, depression was the most common mental illness included in the study.

The incidence of depression in the four weeks after a COVID-19 diagnosis was 1.93 times higher in people who had COVID-19 before vaccinations were available, 1.79 times higher among the unvaccinated group and 1.16 times higher among the vaccinated group, the researchers found.

The overall incidence of depression remained elevated through 28 weeks – and up to 102 weeks specifically in the group that had COVID-19 before vaccinations were available, the data showed.

People who were hospitalized with severe COVID-19 had the strongest association with depression. Among those who had COVID-19 before vaccines were available, the incidence of depression was 16.3 times higher after a COVID-19 diagnosis if the infection required hospitalization, compared with being 1.22 times higher without hospitalization.

“Our findings have important implications for public health and mental health service provision, as serious mental illnesses are associated with more intensive healthcare needs and longer-term health and other adverse effects,” Dr. Venexia Walker, senior research fellow in epidemiology at the University of Bristol and one of the study’s lead authors, said in a news release

The new study comes amid a significant COVID-19 wave in the United States. Viral activity levels in wastewater have reached the highest that has been recorded for a summer surge since July 2022. And the US Food and Drug Administration is expected to soon greenlight updated COVID-19 vaccines for the fall and winter season.

But the new data may not reflect the current climate, said Dr. Dan Barouch, director of the Center for Virology and Vaccine Research at Beth Israel Deaconess Medical Center, who was not involved in the research.

“It’s a study that only looks at individuals in 2020 and 2021, in the early pre-Omicron days of the pandemic. So the applicability of these data to the current epidemic is not clear, because in 2024, we have a much higher level of population immunity; most people have been infected or vaccinated multiple times,” Barouch said.

“It’s a very different population now than it was in 2020 and 2021. So while this paper is interesting and important, it really reflects a population at a different time in the pandemic, when people’s baseline immunity was very different,” he said. “It really is not clear the extent to which these data are applicable to the current epidemic we have in 2024.”

The new research is not the first to show that COVID-19 is associated with an increased risk of mental illness, said Dr. Ziyad Al-Aly, a clinical epidemiologist at the Washington University School of Medicine in St. Louis who was not involved with the paper but has studied mental health outcomes in people with COVID-19.

“I think the picture here is clear from this paper, and it’s aligned with what we have learned over the past several years on COVID’s effect on the brain – and that is, it leaves its mark on the brain, and here, that’s in the form of several mental health disorders,” Al-Aly said.

The increased incidence of mental illness that appears to be associated with severe COVID-19 may be a result of the infection itself or could be due simply to hospitalization. Separate research suggests that hospitalizations for any severe sickness can be associated with a higher long-term risk of new mental health diagnoses.

Although the new study does not answer the question of whether the association is driven by COVID-19 specifically or being severely sick in general, Al-Aly said that he suspects both factors are playing a role.

“When people get hospitalized, they don’t eat well, they don’t sleep well, it’s an unfamiliar environment to them, it’s enormously stressful. Does it put some people at risk of depression or stress disorders and all of that? Absolutely yes,” he said.

But in a paper published last year in The Lancet Infectious Diseases, Al-Aly and his colleagues found that among more than 92,000 people, those hospitalized for COVID-19 faced an increased risk of several mental health outcomes while those hospitalized for the flu had no increased risk.

“We found that the people who were hospitalized for COVID had a much higher risk of serius neurologic problems, including neuropsychiatric disorders, including mental health problems,” Al-Aly said. “When you do a head-to-head evaluation, people who were hospitalized for Covid versus people who were hospitalized for the flu, it’s very clear that it’s something sort of unique or peculiar about COVID that generates a higher risk of neuropsychiatric problems.”

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Case Report

When a patient with depression is feeling sleepy, be aware of sleep apnoea, carlijn wiersema.

University Center for Psychiatry, University Medical Center Groningen, Groningen, The Netherlands

Willeke Van Zelst

Richard oude voshaar.

A 67-year-old man was referred to an outpatient clinic of geriatric psychiatry because of persistent symptoms of depression and anxiety, accompanied by sleepiness. The latter had been evaluated multiple times in the general practice over several years; each time it was considered to be a symptom of depression. After referral, the patient was diagnosed with severe obstructive sleep apnoea (OSA), comorbid to a depressive and anxiety disorder. Retrospectively, we conclude that affective symptoms accompanying OSA and sleepiness were wrongfully interpreted as depression, but after having led to problems at work, they have triggered psychiatric comorbidity. Treatment of OSA in addition to the psychiatric disorders resulted in a full recovery over time. The delayed diagnosis of OSA has certainly diminished the patient’s quality of life and might have precipitated the depressive disorder. Moreover, OSA poses patients at an increased risk of cardiovascular disease, hypertension, stroke and traffic accidents.

Obstructive sleep apnoea (OSA) is the most common sleep disorder worldwide, affecting 2%–6% of the adult population, increasing up to 15%–26% in those aged 70–100 years old. 1 OSA is characterised by repetitive complete (apnoea) and partial (hypopnoea) obstruction of the upper airway during sleep, which decreases arterial oxygen saturation. 2 Daytime functioning may be impaired due to loss of energy, depressed mood, irritability and concentration difficulties, which are core symptoms of affective disorders. Therefore, the diagnosis of OSA may easily be missed in patients with depression. Moreover, sleepiness itself is one of the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) criteria of depressive disorder and also overlaps with the criterion of fatigue or loss of energy. 1 These diagnostic complexities may delay proper diagnosis of OSA in patients with depression. The aim of this case report is to increase awareness of the possibility of OSA in patients with depression.

Case presentation

A 67-year-old man was referred to our outpatient clinic of geriatric psychiatry because of persistent affective symptoms for years. The onset of symptoms coincided with an unpleasant work situation that, after a year of sick leave, had led to demotion at 49 years of age. From that moment on, he suffered from depressed mood, anhedonia, loss of initiative, increased appetite, worrying, catastrophising, feelings of anxiety with excessive sweating, difficulties staying asleep, nightmares, increased need of sleep, loss of energy and tiredness. Symptoms fluctuated over the years but increased during autumn/winter, after life events and during vacation periods. Over the years, he had gradually withdrawn from social activities.

His general practitioner (GP) had prescribed benzodiazepine hypnotics, but without any improvement of symptoms. Six weeks prior to his referral, he was started on citalopram 20 mg one time a day, a first-line serotonergic antidepressant. He had lost 7 kg bodyweight due to nausea. This weight loss increased his physical fitness, which made him feel slightly better.

His medical history did not reveal other mental or physical symptoms.

The patient was monitored, but not treated, for mild hypertension by his GP. He had no other previous medical or psychiatric history. His family history revealed no psychiatric problems or neurodegenerative diseases like M. Parkinson, M. Alzheimer or M. Huntington. The patient did not smoke or drink alcohol. Besides citalopram, the patient used simvastatin 40 mg one time a day.

Heteroanamnestic information of his wife and son revealed a change in character since the onset of his symptoms; he became more emotional and insecure about himself. Since his retirement, he tended to lose his temper over seemingly insignificant events. His wife explained that they slept apart for years because of his snoring. She nevertheless confirmed that his breathing paused regularly during sleep. Although they consulted their GP several times for his conspicuous sleepiness, they accepted the explanation that it was an inevitable part of his depression.

At psychiatric examination, we saw a tired man with bags under his eyes and reduced facial expression. Higher cognitive functions were intact. He had a depressed mood and flat affect, but denied suicidal ideation. Evaluation of his sleep pattern revealed a rapid sleep onset, but strikingly, he never woke up well rested, regularly dozed off during the day and he was often at risk of falling asleep while driving his car.

A full physical and neurological examination was remarkable only for an overweight body mass index (28.9 kg/m 2 ) and hypertension (130/100 mm Hg). Routine blood chemistry did not reveal any abnormalities.

Investigations

In his next visits to our clinic, the patient repeatedly brought forward his work situation as being a traumatic event. This made him anxious in situations where he did not feel in control, like during his children’s holiday. He did not meet DSM-5 criteria of post-traumatic stress disorder, but he met the criteria for a depressive disorder, partly seasonal, with a superimposed generalised anxiety disorder.

The patient scored 38 points on the 30-item Inventory of Depressive Symptomatology (IDS), which is indicative of a severe depression. The scores on the Mini-Mental State Exam (MMSE) and the Cambridge Examination for Mental Disorders of the Elderly/Cambridge Cognition test (CAMDEX/CAMCOG) were not indicative of cognitive problems (MMSE: 30/30, CAMDEX/CAMCOG 90/120). Based on his wife’s information, he was additionally referred to a sleep clinic at our hospital. The score on the Epworth Sleepiness Scale was 16/24, which is indicative of excessive daytime sleepiness. Polysomnography showed 356 apnoeas in 6 hours of sleep and an Apnoea–Hypopnoea Index (AHI) of 57. The arterial oxygen saturation varied between 76% and 95%. The sleep architecture was distorted, as no slow wave sleep was present. No significant periodic leg movements or other unusual movements were observed. The patient was diagnosed with severe OSA.

Differential diagnosis

Depressive disorder.

Depressed mood, anhedonia, increased appetite, constant worrying, loss of initiative, withdrawal of social activities, sleeping problems, loss of energy, increasing symptoms during autumn/wintertime and lesser facial expression.

Anxiety disorder

Feelings of anxiety with excessive sweating, catastrophising, nightmares, withdrawal of social situations and constant worrying.

Post-traumatic stress disorder

As a result of the unpleasant work situation, social withdrawal, nightmares and feelings of anxiety with excessive sweating.

Neurodegenerative disease

Change in character, mood disruptions and temper issues.

Obstructive sleep apnoea

Severe loss of energy/tiredness, falling asleep while driving, dozing off during daytime, wake up not fully rested, snoring, breathing pauses during sleep, irritability, change of character, obesity and hypertension.

Citalopram, indicated for depressive as well as anxiety disorders, slightly improved his depressed mood and reduced anxiety symptoms over the course of 8 weeks. The patient accepted cognitive–behavioural therapy (CBT), to improve coping with his work situation and to increase his self-esteem. Four weeks after starting CBT, the patient started with continuous positive airway pressure (CPAP) at night to treat his OSA.

Outcome and follow-up

Since starting citalopram, the patient experienced a small improvement of his affective symptoms. With CBT, his self-image became more positive. Over the course of 3 months, prior to the start of CPAP, his affective symptoms further improved, without any effect on his sleep (related) problems and fatigue. After four nights of CPAP treatment, the patient felt more energetic and started rebuilding his life. Full recovery was achieved after 4 months of treatment. His IDS score decreased from 38 to 8 (no depression present).

Although (hetero)anamnestic information may point to OSA, assessing the AHI by polysomnography is the gold standard for proper diagnosis. The AHI is the number of apnoeas or hypopnoeas recorded per hour of sleep. An AHI of 5–14 per hour indicates a mild, 15–30 a moderate and ≥30 a severe OSA. Hypopnoeas and apnoeas interrupt deep sleep and Rapid Eye Movement (REM) sleep and cause a fragmented sleeping pattern. 2  Patients wake up feeling unrefreshed and they stay tired during the day. Night-time symptoms include snoring, breathing pauses, a feeling of choking, excessive salivation, excessive sweating, gastro-oesophageal reflux, nocturia, waking up with a dry mouth and/or a headache. 1 3 During the day, the patient experiences excessive sleepiness, loss of energy, irritability, 4 withdrawal of social activities, difficulties with concentrating, 5 cognitive dysfunction, 6 7 loss of interest in daily activities, 8 temper issues, 5 psychomotor changes 4–8 and anxiousness and depressed mood. 9 These symptoms bear a striking resemblance to symptoms of depression. 3 10 11 Indeed, up to 63% of patients with OSA report depressive symptoms. 9 11 12 Most sleep clinics therefore regularly evaluate depressive symptoms of their patients with screening questionnaires. 3 The similarity in phenotypic expression of OSA and depressive disorders might be enough to misdiagnose sleep apnoea for a depressive disorder and wrongfully start antidepressant treatment. 13 14 In our case, the affective symptoms accompanying OSA have been wrongfully attributed to a depression by his GP and even temporarily treated with benzodiazepines. When his work problems due to OSA triggered a full-blown depressive disorder, he was referred for specialised mental healthcare.

Of particular interest for this case is that depressive disorders and sleep disorders co-occur more often than can be expected by chance. 15 16 This association is considered to be bidirectional as depression can evoke sleep disturbances and vice versa. Overall, 90% of patients with depression experience sleep disturbances. Also, sleep apnoea is found more often in patients primarily diagnosed with depressive disorders compared with the general population. 3 4 17–22 Because sleep disturbances are common in mood disorders, further examination is rarely employed. 3 17 Several authors have advocated screening for OSA in psychiatric patients, as this will hasten appropriate diagnosis. 5 17 19 22–26 Treatment of OSA will alleviate hypopnoeas and apnoeas and may improve affective symptoms. 18 23 27–32 Especially in patients with therapy-resistant depression and/or patients with risk factors for OSA like male sex, obesity and older age, OSA must be considered. 17 19 23 Our patient noticed some improvement after starting on citalopram, losing 7 kg of bodyweight and starting CBT, but the most significant improvement was following start of CPAP therapy.

Aside from improving psychiatric symptoms, the treatment of OSA offers other health benefits by reducing the risk of cardiovascular disease 33–35 and neurological damage. 36 37 The hypertension our patient presented with might be related to his OSA. Untreated OSA is associated with a twofold to threefold increased risk of traffic accidents because of daytime sleepiness. 38 39 Our patient also described almost falling asleep while driving his car. Ascribing symptoms of OSA to a psychiatric disorder may result in inappropriate prescriptions of benzodiazepines, as was the case in our patient. Whereas benzodiazepines are regularly used for treatment of anxiety and sleep(related) problems, benzodiazepines worsen hypopnoeas and apnoeas, 24 which can be life threatening for a patient with OSA. Apart from depression, our patient also suffered from an anxiety disorder. For this patient, it is quite possible that the anxiety was exacerbated by the OSA, as anxiety disorders are also associated with OSA. 30 40 Moreover, daytime tiredness and anxiety are positively correlated in patients with OSA. 41 CPAP treatment also has a positive effect on anxiety, 32 as our patient experienced.

With this case report, we underline the importance of diagnosing and treating OSA in patients with a depressive disorder, even when the depression is preceded by psychosocial stressors. As illustrated by our patient, proper diagnostics may improve quality of life and prevent misdiagnosing affective symptoms as a treatment-resistant depression.

Patient’s perspective

“The whole thing has actually been a positive experience for me, the discovery of the sleep apnoea has changed my life for the better. In hindsight I already had sleep problems and loss of energy for years, before the start of my depressive symptoms. I did not want to complain so I tried to live with it. I am very happy the doctors discovered my sleep apnoea, although it wasn’t what I expected when my general practitioner referred me to the psychiatric clinic! The medication and CBT really helped me, but the CPAP completed it. I am well now. I have my moments of feeling a little bit low, but that’s only around the fall/wintertime and is always of short duration. I think that’s just part of who I am. It has been never as bad as at that time. I realise how important it is to be healthy; the impact of the sleep apnoea, my depression and anxiety has been immense. But now I have my life back. I hope that physicians will learn from my case and will think of sleep apnoea as a cause of depression at an earlier stage.”

Learning points

• The similarity between daytime symptoms of obstructive sleep apnoea and depressive disorders presents a diagnostic challenge.
• It is important to be vigilant regarding symptoms of excessive daytime sleepiness and risk factors of obstructive sleep apnoea in patients with depressive disorder.
• Good history taking of snoring, breathing pauses during sleep, waking up unrefreshed (sometimes with dry mouth and headaches) as well as a thorough physical examination (obesity, hypertension and crowded oropharynx) can point in the direction of obstructive sleep apnoea.
• Treatment of obstructive sleep apnoea should complement psychiatric or psychological treatment of depression to optimise quality of life, as well as physical health.

Contributors: The manuscript was written by CW and supervised by WVZ and ROV.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: None declared.

Patient consent: Obtained.

Provenance and peer review: Not commissioned; externally peer reviewed.

• Open access
• Published: 21 August 2024

The multiple mediating effects of vision-specific factors and depression on the association between visual impairment severity and fatigue: a path analysis study

• Wouter Schakel   ORCID: orcid.org/0000-0001-7189-1451 1 , 2 ,
• Christina Bode   ORCID: orcid.org/0000-0002-1641-8028 3 ,
• Peter M. van de Ven 4 ,
• Hilde P. A. van der Aa   ORCID: orcid.org/0000-0002-1853-5674 1 , 2 , 5 , 6 ,
• Carel T. J. Hulshof   ORCID: orcid.org/0000-0002-2720-456X 7 ,
• Gerardus H. M. B. van Rens 1 , 2 &
• Ruth M. A. van Nispen   ORCID: orcid.org/0000-0003-1227-1177 1 , 2  

BMC Psychiatry volume  24 , Article number:  572 ( 2024 ) Cite this article

Metrics details

Severe fatigue is a common symptom for people with visual impairment, with a detrimental effect on emotional functioning, cognition, work capacity and activities of daily living. A previous study found that depression was one of the most important determinants of fatigue, but less is known about disease-specific factors in this patient population. This study aimed to explore the association between visual impairment severity and fatigue in adults with low vision, both directly and indirectly, with vision-specific factors and depression as potential mediators.

Cross-sectional data were collected from 220 Dutch low vision service patients by telephone interviews. Fatigue was defined as a latent variable by severity and impact on daily life. Potential mediators included vision-related symptoms, adaptation to vision loss and depression. Hypothesized structural equation models were constructed in Mplus to test (in)direct effects of visual impairment severity (mild/moderate, severe, blindness) on fatigue through above mentioned variables.

The final model explained 60% of fatigue variance and revealed a significant total effect of visual impairment severity on fatigue. Patients with severe visual impairment (reference group) had significantly higher fatigue symptoms compared to those with mild/moderate visual impairment (β = -0.50, 95% bias-corrected confidence interval [BC CI] [-0.86, -0.16]) and those with blindness (β = -0.44, 95% BC CI [-0.80, -0.07]). Eye strain & light disturbance, depression and vision-related mobility mediated the fatigue difference between the severe and mild/moderate visual impairment categories. The fatigue difference between the severe visual impairment and blindness categories was solely explained by eye strain & light disturbance. Moreover, depressive symptoms (β = 0.65, p  < 0.001) and eye strain & light disturbance (β = 0.19, p  = 0.023) were directly associated with fatigue independent of visual impairment severity.

Conclusions

Our findings indicate an inverted-U shaped relationship between visual impairment severity and fatigue in patients with low vision. The complexity of this relationship is likely explained by the consequences of visual impairment, in particular by strained eyes and depressive mood, rather than by severity of the disability itself.

Peer Review reports

Fatigue in visual impairment is described by patients as a daily, uncontrollable sensation with feelings of mental and physical exhaustion [ 1 ]. Evidence from recent meta-analyses with 14 studies indicates that fatigue severity levels and the odds of fatigue are higher in adults with visual impairment compared to normally sighted controls, with small to medium effect sizes reported [ 2 ]. Other results showed that symptoms of severe fatigue were present in 57% of adults with visual impairment which is at least twice as high compared to the general Dutch population [ 3 ]. We found that consequences of severe fatigue not only affect patients’ lives, but also pose an economic burden for society at large through loss of work participation [ 3 ]. To our knowledge, in-depth analyses investigating underlying mechanisms that may explain the association between visual impairment and fatigue are lacking.

Qualitative insights indicate that multiple factors may play a role in the onset and course of fatigue in patients with visual impairment. High cognitive load due to necessary adjustments for functioning in daily life, intensity of light, negative cognitions with regard to vision loss and the effort necessary for visual perception were among the most important causes of fatigue mentioned by visually impaired adults with severe fatigue symptoms [ 1 ]. In more recent work, we developed multidimensional path models using structural equation modelling (SEM) to explore psychological and health-related factors as determinants of fatigue in adults with visual impairment and adults with normal sight. The results indicate that fatigue in visual impairment is directly associated with depressive symptoms, and to a lesser extent with perceived health, poor somatic comorbidity and flexible goal adjustment coping tendencies [ 4 ]. Depressive symptoms and perceived health were identified as mediators in the relationship between sleep disorders, self-efficacy and fatigue. Due to the nature of the comparison with healthy adults, these path models did not include vision-specific factors that have been mentioned as important causes of fatigue by persons with visual impairment [ 1 ]. Knowledge of disease-specific fatigue factors may aid healthcare professionals to develop and tailor interventions to the needs of the target population. Therefore, the first objective of the present study was to explore vision-specific factors (e.g. eye strain, light disturbance) as determinants of fatigue severity and the impact of fatigue on daily life within a sample of adults with visual impairment. The influence of depression was also examined in the present study because it is a common symptom in patients with visual impairment [ 5 ] that also shares a strong relationship with fatigue [ 6 , 7 ] and vision-specific factors [ 8 , 9 , 10 ].

Furthermore, there are some indications that poor visual acuity in adults with visual impairment may be linked to higher fatigue symptomatology. Two studies with objective measures of visual acuity found that older adults with moderate to severe visual impairment had higher fatigue levels than older adults with mild visual impairment [ 11 , 12 ]. Some other population-based studies with self-reported measures have also reported higher levels of fatigue symptomatology for older adults who rated their vision to be poorer [ 13 , 14 ]. However, this evidence is based on a small number of studies and conflicting results have been reported by Williams et al. [ 15 ], who found that persons with legal blindness in both eyes experienced less fatigue relative to persons with moderate visual impairment in the better eye. These findings suggest that vision loss may not directly influence fatigue, but may play a role through an interplay with other related factors. In the present study we therefore explored the association between visual impairment severity and fatigue and examined whether this link may be mediated by other vision-specific factors.

Design and participants

Data for the present study were collected as part of a larger cross-sectional survey on fatigue among patients with visual impairment. Two related studies on this project have been previously published, including an economic evaluation of the burden of visual impairment and comorbid fatigue [ 3 ], and a path analysis of generic factors that determine fatigue in adults with and without visual impairment [ 3 ]. A random sample of 1281 patients with visual impairment who were registered and received care at two low vision services in the Netherlands (Royal Dutch Visio and Bartiméus) at the time of the study (2015–2016) were invited by letter to participate. Patients with at least mild visual impairment according to the World Health Organization (WHO) criteria (defined as presenting visual acuity (VA) worse than 20/40 (6/12, 0.50) and/or concentric visual field impairment worse than < 45° in the better-seeing eye), sufficient mastery of the Dutch language and who were 18 years of age or older were eligible. Exclusion criteria were severe cognitive impairment, as defined by 3 or more errors on the 6-item version of the Mini Mental State Examination (MMSE) [ 16 ], and a diagnosis or receiving treatment in the last year for the following chronic conditions of which fatigue is a common symptom: cancer, multiple sclerosis, chronic fatigue syndrome and psychiatric disorders. Patients who agreed to participate completed a battery of validated questionnaires and gave information about socio-demographic and clinical characteristics through a structured telephone interview (performed by two experienced researchers with MSc and BSc in psychology).

Measures and data preparation

Socio-demographic characteristics collected include age, gender, living situation (living alone vs. living together with a partner or family), education and employment status. Somatic comorbidity was defined as having no comorbidity or being treated for one or more of seven comorbid chronic conditions: asthma or chronic obstructive pulmonary disease; osteoarthritis and rheumatoid arthritis; peripheral arterial disease; diabetes mellitus; cardiac disease; cerebrovascular accident or stroke; cancer; and other chronic somatic or psychiatric conditions.

Data on visual acuity, visual fields, ophthalmic diagnoses and other descriptions of vision loss and/or visual field impairments were obtained from patient medical records at low vision services, and were used to supplement missing values. In accordance with the WHO criteria, four categories of visual impairment were defined based on the better-seeing eye. Mild visual impairment referred to presenting VA worse than 20/40 (6/12, 0.50) and equal or better than 20/60 (6/18, 0.33), or concentric visual field impairment < 45° and ≥ 0.30. Moderate visual impairment referred to presenting VA worse than 20/60 (6/18, 0.33) and equal or better than 20/200 (6/60, 0.10), or concentric visual field impairment < 30° and ≥ 20°, or loss of upper visual field/hemianopia. Severe visual impairment referred to presenting VA worse than 20/200 (6/60, 0.10) and equal or better than 20/400 (6/120, 0.05), or concentric visual field impairment < 20°. Blindness (societal and total blindness) referred to presenting VA worse than 20/400 (6/120, 0.05) or concentric visual field impairment < 10°. Because mild visual impairment was only present in 9 patients, moderate and mild visual impairment were combined into one category for SEM analyses.

The Fatigue Assessment Scale (FAS) [ 17 ], Modified Fatigue Impact Scale (MFIS) [ 18 ], Patient Health Questionnaire (PHQ-9) [ 19 ] and Adaptation to Vision Loss questionnaire (AVL-9) [ 20 , 21 ] were analyzed with item response theory (IRT) models (i.e. the graded response model) to ensure these measures had satisfactory psychometric properties using R studio version 1.1.456, and R version 3.5.1. Questionnaires were adjusted based on their performance in these statistical models, followed by calculation of respondents’ thetas, which reflect an interval score of the underlying trait. This procedure has already been described in great detail in our previous path analysis study [ 4 ]. IRT outcomes with fit indices, questionnaire adjustments and theta parameters are available in Supplement 1. All other questionnaires used for independent, potential mediating and latent variables in SEM analyses are shown in Table 1 .

In accordance with the model of our previous path analysis study [ 4 ], a latent fatigue variable was defined by two indicators: fatigue severity (FAS) and fatigue impact (MFIS). As a measure for eye strain & light disturbance, a latent variable was created from three Low Vision Quality of Life questionnaire (LVQOL) [ 22 , 23 ] items and one item of the Dutch ICF Activity Inventory (D-AI) feeling fit subscale [ 24 ], because, to the best of our knowledge, no specific questionnaires regarding these concepts were available. LVQOL item “how much of a problem do you have: getting the right amount of light to be able to see” was removed due to poor factor loadings and strong collinearity, resulting in a three factor latent variable that was used for SEM analysis (Table 1 ).

Statistical analyses

Statistical analyses were performed using SPSS version 22.0.0.0 and Mplus version 7.4 [ 25 ]. First, descriptive statistics, Pearson and Spearman’s rho correlations were performed to examine the distribution of the data and explore statistical significance of univariate relationships between variables. Variables that were not significantly correlated with fatigue severity and fatigue impact were excluded from SEM analysis.

Second, multivariate analysis of variance (MANOVA) was used to test whether there were differences between visual impairment severity levels and vision-specific, depression and fatigue outcomes. Significant associations were followed up by univariate analyses and pairwise comparisons using the Bonferroni post-hoc test.

Third, a step-wise path model was developed within a SEM framework to investigate whether the differences among visual impairment severity on fatigue could be explained by eye strain and light disturbance, adaptation to vision loss, vision-related mobility and depressive symptoms. In contrast to our previous study [ 4 ], most psychosocial and health-related factors were omitted from path analyses because we were primarily interested in vision-related fatigue determinants that could be specific for people with visual impairment. Depression was maintained in the model, as it was associated (medium to high correlations) with all variables of interest. Furthermore, we hypothesized depressive symptoms would mediate the effect of adaptation to vision loss on fatigue [ 21 , 26 , 27 , 28 ]. Mediation was evaluated based upon the statistical significance of the estimated relative direct, indirect and total effects within each path model. The maximum likelihood estimation based on the delta method was used to calculate direct and indirect effects. This estimation method is robust to non-normality and appropriate for models with continuous and categorical variables [ 29 ]. Finally, the significance of relative indirect effects of visual impairment severity were tested with the bias-corrected bootstrapping method proposed by Preacher et al. [ 30 ]. A total of 5000 iterations was set to impute 95% bias-corrected confidence interval (BC CI) limits and standard errors for the evaluation of relative indirect effects. In this approach, the indirect effects are deemed significant if the upper and lower bound of the 95% CI does not include 0. Given that visual impairment severity was a multicategorical variable, it was represented in the model by a set of dummy variables created by indicator coding in accordance with the principles of Hayes and Preacher [ 31 ]. Severe visual impairment served as the reference category, to which all estimated direct and indirect effects for mild/moderate visual impairment and blindness were compared. These results were therefore described in terms of relative effects. In SEM analyses, a hypothesized model with assumed relationships between fatigue and potential mediating variables was initially tested and further optimized in a step-wise procedure. Model fit was improved by removal of non-significant pathways and by inclusion of additional theoretical pathways based on the modification indices. Each model was assessed using several fit criteria as advised by Wang and Wang [ 32 ]: χ 2 -goodness-of-fit, Root Mean Square Error of Approximation (RMSEA < 0.06 represents good fit), the Standardized Root Mean Residual (SRMR < 0.08 represents good fit), the Tucker-Lewis index (TLI > 0.95 represents good fit) and the Comparative Fit Index (CFI > 0.95 represents good fit).

Sample size calculation

The sample size for this study was determined using commonly accepted rule-of-thumb practices for SEM. Although there is no consensus on exact sample size requirements, previous guidelines have recommended a minimum of 200 participants [ 33 , 34 ] and a range of 5–20 observations per estimated parameter [ 35 , 36 ]. Given that our model includes up to 30 estimated parameters, a minimum sample size range of 150 to 600 participants was considered appropriate to ensure robust and reliable results.

Participants

Out of 1281 invited patients, 321 agreed to participate and gave written informed consent (response rate 25.1%). Of those, 73 were not eligible (56 were diagnosed/treated for chronic conditions and/or psychiatric disorders, 14 had no visual impairment, 3 were not fluent in Dutch), 10 could not be contacted after multiple attempts and 5 withdrew from participation. In addition, 13 had missing values on essential items for analysis, resulting in data of 220 patients that were included in the present study. The most common reasons reported by non-responders for declining participation were: too much of a burden to participate, not interested and already participating in another study. Specific information on the eye examination dates was missing for nearly half of the study sample, limiting our ability to precisely calculate the time interval between these assessments and study participation. Out of 110 participants with complete data, more than half (71%) had their eye examinations conducted within approximately one year before or after participation in our study.

Table 2 shows the sociodemographic characteristics of the study sample. Retinitis pigmentosa (26.8%) and age-related macular degeneration (24.5%) were the most common causes of visual impairment, followed by glaucoma (13.2%) and homonymous hemianopia (8.2%). The majority of participants (72.7%) reported a progressive disease course with declining visual acuity and/or increasing visual field problems.

Preliminary analysis

The descriptive statistics confirmed the assumptions of normality and multicollinearity for all study data. As shown in Table  3 , there were significant correlations between all potential mediating variables and the dependent fatigue variables. In contrast, none of the independent variables (age, education, gender, years since diagnosis and disease course) were significantly correlated with both fatigue variables, and were therefore excluded from SEM analysis. Sample characteristics and MANOVA results with visual impairment severity as the single factor and all continuous study variables as the dependent variables are presented in Table  2 . There was a statistically significant difference in potential mediating variables and dependent fatigue variables based on a patient’s severity of visual impairment, F (18, 416) = 3.02, p  < 0.001; Wilk's Λ = 0.782, partial η 2  = 0.12. Further analysis with the Bonferroni procedure (statistical significance was accepted at p  < 0.006) revealed that visual impairment severity had a statistically significant effect on fatigue severity, depressive symptoms, vision-related mobility difficulties and eye strain & light disturbance. Follow-up post-hoc tests indicated that mean fatigue and depressive symptoms were significantly higher for patients with severe visual impairment relative to those with mild/moderate visual impairment, mean levels of eye strain & light disturbance were significantly higher for patients with severe visual impairment compared to those with blindness, and mean vision-related mobility problems were significantly higher for blind patients compared to patients with mild/moderate visual impairment (Fig.  1 ).

Box plot showing thetas for fatigue, depressive symptoms and vision-specific factors by visual impairment severity category. Boxes display the median and the 25th and 75th percentiles. The plus sign within each box represents the mean. Whiskers and extreme values (dots) were plotted using the Tukey method. Asterisks indicate significant differences between visual impairment severity categories in Bonferroni corrected post-hoc tests. * Statistically significant at p  < 0.05. ** Statistically significant at p  < 0.01. VI  visual impairment

Path analysis

In the initial hypothesized model, three vision-specific potential mediators (eye strain & light disturbance, adaptation to vision loss, vision-specific mobility), one psychosocial potential mediator (depressive symptoms) and two independent dummy variables representing visual impairment severity were included in the model to evaluate their (in)direct relationships with the latent fatigue variable.

As can be seen in Table  4 , fit criteria for the initial model were acceptable in terms of CFI and TLI but RSMEA and SRMR exceeded their threshold values of 0.06 and 0.08, respectively (model 1). Because adaptation to vision loss was not significantly related to fatigue and the two dummy variables representing visual impairment severity it was excluded from further analysis. In addition, pathways from D 1 and D 2 to depressive symptoms were added to the second model, resulting in good fit across all criteria (Table  4 : model 2). In a final effort, removal of insignificant pathways failed to improve fit statistics over the previous model (Table  4 : model 3). Hence, model 2 was chosen as our final model, which explained 60% of the latent fatigue variable.

A visualization of the final model together with standardized path coefficients of all direct and indirect effects are shown in Fig.  2 and Table  5 . Eye strain & light disturbance (pathway b 1 : β = 0.19, p  = 0.023) and depressive symptoms (pathway b 2 : β = 0.65, p  < 0.001) were directly associated with fatigue. Holding visual impairment severity constant, those who experienced increased symptoms of eye strain & light disturbance and higher levels of depressive symptoms had higher levels of fatigue. Furthermore, eye strain & light disturbance (β = 0.20, 95% BC CI [0.11, 0.30]) and vision-related mobility (β = -0.16, 95% BC CI [-0.25, -0.09]) were significantly associated with the latent fatigue variable through mediation of depressive symptoms. Specifically, higher levels of eye strain and light disturbance, and more problems with vision-related mobility, were associated with higher levels of depressive symptoms (D 1 and D 2 pathways), which in turn was associated with greater fatigue.

Path analysis output for the final multicategorical SEM model ( n  = 220). Arrows represent direct effects with standardized regression coefficients (StdYX for continuous variables, StdY for categorical variables). Constructs of latent variables (diamond shapes) are shown in dotted boxes. * Statistically significant at p  < 0.05. ** Statistically significant at p  < 0.01

Path analysis revealed significant relative total effects of D 1 and D 2 on fatigue, indicating that patients with severe visual impairment had significantly higher levels of fatigue compared to those with mild/moderate visual impairment (pathway c 1 : β = -0.50, 95% BC CI [-0.86, -0.16]) and blindness (pathway c 2 : β = 0.44, 95% BC CI [0.07, 0.80]). In contrast, the direct effects of D 1 and D 2 on fatigue were non-significant when controlling for all other variables in the model. This finding indicates that the visual impairment severity-fatigue association is completely mediated by the other variables included in our model. Bias-corrected bootstrap analysis identified a significant relative total indirect effect of D 1 (β = -0.54, 95% BC CI [-0.81, -0.26]) and D 2 (β = -0.37, 95% BC CI [-0.69, -0.05]) on fatigue, accounting for 93% and 83% of their relative total effects, respectively (Table  5 ).

Eye strain & light disturbance, depressive symptoms and vision-related mobility were all identified as mediators in the link between fatigue and visual impairment severity for the D1 contrast. Relative to those with mild/moderate visual impairment, patients with severe visual impairment had significantly higher levels of depressive symptoms and symptoms of eye strain & light disturbance, and more problems with vision-related mobility, which in turn was associated with increased fatigue symptoms (see Table  5 ). In addition, the indirect effects of eye strain & light disturbance and vision-related mobility were also sequentially mediated by depression (see Table  5 ). As for the D2 contrast, the bias-corrected bootstrap analysis showed that the indirect association with fatigue was mediated by eye strain & light disturbance, and by serial mediation of eye strain & light disturbance via depressive symptoms (see Table  5 ). In other words, compared to patients with blindness, patients with severe visual impairment experienced elevated fatigue levels via higher symptoms of eye strain and light disturbances and related depression.

The present study served two purposes: to test visual impairment severity, eye strain & light disturbances, adaptation to vision loss, vision-related mobility problems as determinants of fatigue, and to examine whether the association between visual impairment severity and fatigue would be mediated by these vision-specific factors and depressive symptoms. To this end, a well-fitting SEM was developed that explained 60% of the variance in fatigue severity and impact on daily life.

With regard to our primary aim, one of the most important findings was the direct association between eye strain & light disturbance and fatigue. Since optimal lighting conditions are essential for improving visual acuity and contrast sensitivity for persons with low vision [ 37 ], light disturbances may lead to fatigue as compensatory efforts might be needed to establish visual perception. Besides, it is possible that persons with visual impairment need to invest additional mental resources to counteract focusing problems and accommodative dysfunctions of the eye, potentially leading to excessive strain and fatigue. Similar hypotheses have been formulated to explain the increased levels of fatigue frequently observed in persons with hearing impairments [ 38 ]. In these studies, fatigue is often linked to an increased cognitive load, resulting from the extra effort necessary to process degraded speech and auditory signals [ 39 , 40 , 41 ]. However, the extent to which mental effort influences fatigue remains disputed and the various mechanisms involved are not fully understood.

Another important finding from our study was the strong influence of depressive symptoms on fatigue. Depression was not only a direct determinant of fatigue with the largest effect size of all variables, it also mediated the indirect associations of vision-related mobility and eye strain & light disturbance with fatigue. These findings are consistent with previous modelling studies in multiple sclerosis [ 42 ] and rheumatoid arthritis [ 43 ], in which depression has been considered one of the most prominent determinants and mediating factors of fatigue. Psychological interventions that focus on depression, such as cognitive behavioural therapy [ 44 ], may therefore also be beneficial in fatigue management of people with visual impairment.

Contrary to expectations, adaptation to vision loss was unrelated to fatigue and was not found to be a significant mediator. However, it did have an indirect effect through depressive symptoms in the first hypothesized model, with better adaptation predicting less depression which in turn decreased fatigue. One possible explanation is that the direct effects of depression and eye strain & light disturbance on fatigue were much greater than that of adaptation to vision loss.

The second aim of the present study involved the association between visual impairment severity, fatigue and potential mediation by vision-specific factors and depression. In general, patients with severe visual impairment reported the highest levels of fatigue severity and fatigue impact on daily life, whereas fatigue levels of patients with blindness were comparable to those with mild/moderate visual impairment. Similar results have been reported by Cypel et al. [ 12 ], in which fatigue symptoms were the lowest for older adults with blindness, but seemed to increase with a greater degree of vision loss. Taken together, these findings indicate that the association between fatigue and visual impairment severity follows an inverted U-shape. Several explanations for this complex relationship seem to arise from our modelling as well.

Our results showed that the association between visual impairment severity and fatigue was fully mediated by an interplay of vision-related factors and depression. Eye strain & light disturbance was found to be an important mediator in the relationship between visual impairment severity and fatigue for both contrasts. A possible explanation for the specific impact of eye strain & light disturbance on fatigue in severe visual impairment might be that these patients rely heavily on their residual vision and therefore likely use it as much as possible. Vision-related mobility problems and depressive symptoms on the other hand, only explained variations in fatigue between patients with mild/moderate and severe visual impairment. The observation that mobility problems and depression no longer contribute to fatigue in patients with blindness suggests that some form of adjustment or coping may occur once visual decline stabilizes or cannot deteriorate any further. However, our SEM did not provide evidence for such a mediating role of adaptation to vision loss in the relationship between visual impairment severity and fatigue. All things considered, our findings indicate that the elevated levels of fatigue patients with severe visual impairment are not a direct result of decreased visual acuity and/or increased visual field problems, but could rather be explained through the consequences of these limitations. Supportive evidence for this possibility comes from previous path analysis studies in which the effect of visual impairment on mental health outcomes was largely explained by physical and psychosocial factors [ 45 , 46 , 47 ].

The findings of the present study are subject to several limitations. First of all, the cross-sectional design prevents us from inferring a causal order of the associations in our model. Although the assumptions of our model were based on a theoretical framework from previous studies among populations with visual impairment, it is important for future research to test the suggested causal pathways within a longitudinal design. A second limitation of the present study pertains the use of self-report measures only. For future studies, performance based measure such as accelerometers [ 48 ] or mobility courses [ 49 ] for vision-related mobility and the use of stray light meters as a proxy for disability glare and light disturbances, might provide some more objective insight into actual performance on vision-related measures and fatigue [ 50 ]. Fourth, the participation rate of our study was relatively low (25.1%), which may have introduced selection bias. Finally, a notable limitation is the large proportion of incomplete data regarding the time intervals between visual acuity assessments and the administration of questionnaires. This lack of precise timing data may affect the interpretation of the association between visual impairment severity and the study outcomes. However, for the 110 participants with complete data, the majority (71%) underwent their eye examination within a year before or after participating in our study. This timeframe provides some reassurance about the validity of the visual acuity measures in relation to the study outcomes. A strength of the present study is the use of visual field data in addition to visual acuity outcomes which added to the accuracy of visual impairment categorization. Another strength is the application of IRT models to optimize the psychometric properties for the majority of our outcome measures. Furthermore, the statistical advantages of SEM analysis enabled us to construct a proxy for eye strain & light disturbance in the absence of a reliable outcome measure in the scientific literature. Although this latent variable was defined by relatively few numbers of single-item indicators, it had high factor loadings and proved to be an important determinant of fatigue in patients with low vision. Another methodological strength of SEM analysis was the ability to conceptualize fatigue in terms of both severity and impact on daily functioning.

Findings from our SEM model indicate that eye strain & lighting disturbances is a vision-specific determinant of fatigue in patients with low vision regardless of the degree of visual impairment severity. Depression was also a strong direct determinant of fatigue and fully mediated the indirect effect of vision-related mobility and adaptation to vision loss on fatigue. Furthermore, our study suggests that patients with severe visual impairment may experience increased levels of fatigue compared to patients with mild/moderate visual impairment and blindness due to higher levels of eye strain & light disturbance. In contrast, the influence of vision-related mobility and depression on fatigue seems to vary by level of visual impairment.

The factors identified by our model provide key elements that can be targeted by future studies when developing treatment options for vision-related fatigue. Our findings suggest that multifaceted interventions aimed at improving underlying symptoms, such as depression and light disturbances, may support adults with visual impairment in coping with fatigue. Findings from a recent usability study showed that a newly developed vision-specific eHealth intervention based on behavioural therapy and self-management has the potential to reduce fatigue severity and fatigue impact in patients with low vision [ 51 ]. Moreover, including screening instruments of depression, (eye) fatigue and lighting disturbances during the intake and early stages of rehabilitation may identify vulnerable patients at risk of developing severe fatigue.

Availability of data and materials

The data sets used and analyzed in the current study are available from the corresponding author on reasonable request.

Abbreviations

Bias-corrected confidence interval

Structural equation modelling

World Health Organization

Visual acuity

Mini Mental State Examination

Fatigue Assessment Scale

Modified Fatigue Impact Scale

Patient Health Questionnaire

Adaptation to Vision Loss questionnaire

Item response theory

Low Vision Quality of Life questionnaire

Dutch ICF Activity Inventory

Multivariate analysis of variance

Root Mean Square Error of Approximation

Standardized Root Mean Residual

Tucker-Lewis Index

Comparative Fit Index

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Acknowledgements

We would like to express our gratitude to all participants who agreed to be interviewed and shared their experiences in this study.

Financial support was provided by ‘ZonMw Inzicht’, the Netherlands Organizations for Health Research and Development – InSight Society [grant number 60–0063598146], Katholieke Stichting voor Blinden en Slechtzienden, Stichting tot Verbetering van het Lot der Blinden and Stichting Blindenhulp. The funders had no role in the design and conduct of the present study or in the writing of the manuscript.

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RvN, GvR, CB and WS contributed to the study conception and design. Statistical analysis was performed by WS and PvdV. RvN, CB and WS interpreted the data. The first draft of the manuscript was written by WS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Schakel, W., Bode, C., van de Ven, P.M. et al. The multiple mediating effects of vision-specific factors and depression on the association between visual impairment severity and fatigue: a path analysis study. BMC Psychiatry 24 , 572 (2024). https://doi.org/10.1186/s12888-024-06014-5

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Utilization of Artificial Intelligence to Improve Door-In Door-Out Times for Mechanical Thrombectomy-Eligible Patients at a Hub-and-Spoke Community-Based Comprehensive Stroke Center: A Single Case Study Presentation AI Improving DIDO Times

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Background:

Delays in the transfer of patients with hyperacute stroke may lead to treatment ineligibility due to the degree of cerebral hypoxic injury suffered. Cloud-based artificial intelligence applications may improve transfer times and expand access to advanced therapies. One case between a community-based comprehensive stroke center (CSC) and a primary stroke center (PSC) using a third-party telemedicine service and shared cloud-based artificial intelligence application may provide insight in opportunities to improve stroke systems of care. 

  Case Presentation:

A 62-year-old female with a past medical history of hypertension, current everyday tobacco smoker, and marijuana user presented to an outlying emergency department (ED) with dense left-sided hemiplegia affecting the arm and leg, right-sided gaze preference, and severe dysarthria. Her last known well (LKW) time was 0900 hours. CNS imaging revealed a right middle cerebral artery occlusion, visible to members of the CSC stroke team through the use of a cloud-based artificial intelligence cell phone application. The patient was treated with intravenous thrombolytics at the PSC, and she was transferred to the CSC, where she underwent a diagnostic cerebral arteriogram with carotid artery stenting. Later, Magnetic Resonance Imaging (MRI) of the brain revealed a 3.5 cm x 2.5 cm hemorrhagic lesion in the right frontal lobe and diffusion restriction in the right frontal and right posterior temporal lobes. The patient’s hospital stay was three days and, at the time of discharged, her modified Rankin score and NIHSS were zero. She was discharged on dual antiplatelet therapy, statin therapy, and nicotine replacement. 

Utilization of Artificial Intelligence:

            Transfer delays are complicated by organizing care at PSC and CSC and can be lengthy when communication across different facilities and subspecialties. Implementing cloud-based AI image sharing in stroke systems of care has reduced DIDO times by providing rapid imaging interpretation, streamlining communication, and enhancing coordination between PSCs and CSCs. 

Conclusions:

Our case presentation showed how a hub-and-spoke model combined with cloud-based AI utilization can improve DIDO times and enhance stroke systems of care.

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Clinical characteristics, prognostic factors, and outcomes of covid-19 in autoimmune rheumatic disease patients: a retrospective case–control study from astana, kazakhstan.

1. Introduction

2. materials and methods, 2.1. study design, 2.2. settings, 2.3. participant selection.

• Patients 18 years of age and older.
• Patients tested with a positive reverse transcription RNA PCR test against SARS-CoV-2.
• Patients with confirmed rheumatic diseases such as SLE, RA, AS, and SSC before SARS-CoV-2 infection. The recruited diagnoses were explained by these four most prevalent AIRDs in Kazakhstan [ 13 ].
• Patients who have filled out the information consent for the processing of personal data and participation in the survey.
• Patients with an unclear diagnosis purely on the basis of symptoms and other rheumatic diseases.
• Patients under the age of 18.
• Patients residing outside of Astana city. The place of residence is indicated on the title page of the case note. The study did not include patients with registered residence in other cities or regions of Kazakhstan.
• Patients who died from SARS-CoV-2.

2.4. Registration of COVID-19 Patients and Results

2.5. the sample size calculation, 2.6. statistical analysis, 2.7. ethical statement, 3.1. demographic and baseline clinical characteristics, 3.1.1. airds baseline characteristics, 3.1.2. sars-cov-2 infection characteristics of the study groups and results, 3.2. factors related to severe sars-cov-2 infection in patients with airds, 4. discussion, limitations and strengths of the study, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Rutskaya-Moroshan, K.; Abisheva, S.; Abisheva, A.; Amangeldiyeva, Z.; Vinnik, T.; Batyrkhan, T. Clinical Characteristics, Prognostic Factors, and Outcomes of COVID-19 in Autoimmune Rheumatic Disease Patients: A Retrospective Case–Control Study from Astana, Kazakhstan. Medicina 2024 , 60 , 1377. https://doi.org/10.3390/medicina60091377

Rutskaya-Moroshan K, Abisheva S, Abisheva A, Amangeldiyeva Z, Vinnik T, Batyrkhan T. Clinical Characteristics, Prognostic Factors, and Outcomes of COVID-19 in Autoimmune Rheumatic Disease Patients: A Retrospective Case–Control Study from Astana, Kazakhstan. Medicina . 2024; 60(9):1377. https://doi.org/10.3390/medicina60091377

Rutskaya-Moroshan, Kristina, Saule Abisheva, Anilim Abisheva, Zhadra Amangeldiyeva, Tatyana Vinnik, and Tansholpan Batyrkhan. 2024. "Clinical Characteristics, Prognostic Factors, and Outcomes of COVID-19 in Autoimmune Rheumatic Disease Patients: A Retrospective Case–Control Study from Astana, Kazakhstan" Medicina 60, no. 9: 1377. https://doi.org/10.3390/medicina60091377

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