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British Journal of Sports Medicine Sep 2023To synthesise the evidence on the effects of physical activity on symptoms of depression, anxiety and psychological distress in adult populations. (Review)
Review
OBJECTIVE
To synthesise the evidence on the effects of physical activity on symptoms of depression, anxiety and psychological distress in adult populations.
DESIGN
Umbrella review.
DATA SOURCES
Twelve electronic databases were searched for eligible studies published from inception to 1 January 2022.
ELIGIBILITY CRITERIA FOR SELECTING STUDIES
Systematic reviews with meta-analyses of randomised controlled trials designed to increase physical activity in an adult population and that assessed depression, anxiety or psychological distress were eligible. Study selection was undertaken in duplicate by two independent reviewers.
RESULTS
Ninety-seven reviews (1039 trials and 128 119 participants) were included. Populations included healthy adults, people with mental health disorders and people with various chronic diseases. Most reviews (n=77) had a critically low A MeaSurement Tool to Assess systematic Reviews score. Physical activity had medium effects on depression (median effect size=-0.43, IQR=-0.66 to -0.27), anxiety (median effect size=-0.42, IQR=-0.66 to -0.26) and psychological distress (effect size=-0.60, 95% CI -0.78 to -0.42), compared with usual care across all populations. The largest benefits were seen in people with depression, HIV and kidney disease, in pregnant and postpartum women, and in healthy individuals. Higher intensity physical activity was associated with greater improvements in symptoms. Effectiveness of physical activity interventions diminished with longer duration interventions.
CONCLUSION AND RELEVANCE
Physical activity is highly beneficial for improving symptoms of depression, anxiety and distress across a wide range of adult populations, including the general population, people with diagnosed mental health disorders and people with chronic disease. Physical activity should be a mainstay approach in the management of depression, anxiety and psychological distress.
PROSPERO REGISTRATION NUMBER
CRD42021292710.
Topics: Adult; Female; Humans; Pregnancy; Anxiety; Chronic Disease; Depression; Health Status; Mental Disorders; Quality of Life; Systematic Reviews as Topic
PubMed: 36796860
DOI: 10.1136/bjsports-2022-106195 -
International Journal of Nursing Studies Oct 2023Depression, anxiety, and apathy are highly prevalent in older people with preclinical dementia and mild cognitive impairment. These symptoms have also proven valuable in... (Observational Study)
Observational Study
Developing a machine learning model for detecting depression, anxiety, and apathy in older adults with mild cognitive impairment using speech and facial expressions: A cross-sectional observational study.
BACKGROUND
Depression, anxiety, and apathy are highly prevalent in older people with preclinical dementia and mild cognitive impairment. These symptoms have also proven valuable in predicting the progression from mild cognitive impairment to dementia, enabling a timely diagnosis and treatment. However, objective and reliable indicators to detect and distinguish depression, anxiety, and apathy are relatively scarce.
OBJECTIVE
This study aimed to develop a machine learning model to detect and distinguish depression, anxiety, and apathy based on speech and facial expressions.
DESIGN
An observational, cross-sectional study design.
SETTING(S)
The memory outpatient department of a tertiary hospital.
PARTICIPANTS
319 older adults diagnosed with mild cognitive impairment.
METHODS
Depression, anxiety, and apathy were evaluated by the Public Health Questionnaire, General Anxiety Disorder, and Apathy Evaluation Scale, respectively. Speech and facial expressions of older adults with mild cognitive impairment were digitally captured using audio and video recording software. Open-source data analysis toolkits were utilized to extract speech, facial, and text features. The multiclass classification was used to develop classification models, and shapely additive explanations were used to explain the contribution of each feature within the model.
RESULTS
The random forest method was used to develop a multiclass emotion classification model, which performed well in classifying emotions with a weighted-average F1 score of 96.6 %. The model also demonstrated high accuracy, precision, and recall, with 87.4 %, 86.6 %, and 87.6 %, respectively.
CONCLUSIONS
The machine learning model developed in this study demonstrated strong classification performance in detecting and differentiating depression, anxiety, and apathy. This innovative approach combines text, audio, and video to provide objective methods for precise classification and remote monitoring of these symptoms in nursing practice.
REGISTRATION
This study was registered at the Chinese Clinical Trial Registry (registration number: ChiCTR1900023892; registration date: June 19th, 2019).
Topics: Humans; Aged; Apathy; Depression; Cross-Sectional Studies; Facial Expression; Speech; Cognitive Dysfunction; Anxiety; Dementia; Machine Learning
PubMed: 37531702
DOI: 10.1016/j.ijnurstu.2023.104562 -
Psychiatry Research Jul 2023Short-video applications like TikTok are increasingly popular. This study examines the association between short-video application use (SVU) and psychosocial factors in...
Short-video applications like TikTok are increasingly popular. This study examines the association between short-video application use (SVU) and psychosocial factors in 1,346 adolescents (M = 14.97, 51.8% female). 199 non-users and 1147 users (686 moderate users, 461 addictive users) were identified. Results revealed a high prevalence of addictive SVU in the sample. Addictive users exhibited worse mental health conditions than non-users and moderate users, including higher levels of depression, anxiety, stress, loneliness, social anxiety, attention problems, and lower life satisfaction and sleep quality. Addictive users also faced higher academic stress, poorer academic performance, more bullying victimization, worse parental relationships, more negative parenting styles, and lower parental education levels. Moderate users had different family environments than non-users, but no differences in mental health or school performance. Together, these findings suggest that addictive users experience a more disadvantageous situation across mental health, family, and school conditions, while non-users have advantageous family environments. Moderate SVU may not be associated with negative mental health condition or poor school performance. Moderate and addictive SVU should be considered distinct phenomena. Given the psychiatric symptoms present in addictive users of TikTok and similar apps, targeted interventions and treatments are urgently needed.
Topics: Humans; Female; Adolescent; Male; Social Media; Anxiety; Anxiety Disorders; Schools; Educational Status
PubMed: 37167877
DOI: 10.1016/j.psychres.2023.115247 -
Clinics in Sports Medicine Jan 2024Athletes and non-athletes experience many anxiety-related symptoms and disorders at comparable rates. Contributory factors may include pressure to perform, public... (Review)
Review
Athletes and non-athletes experience many anxiety-related symptoms and disorders at comparable rates. Contributory factors may include pressure to perform, public scrutiny, sporting career dissatisfaction, injury, and harassment and abuse in sport. Anxiety may negatively impact sport performance. Specific types of anxiety may have unique presentations in athletes. It is important to rule out general medical and substance-related causes of anxiety symptoms. Psychotherapy and pharmacology treatment options should be considered, bearing in mind athletes' environmental circumstances and physiologies.
Topics: Humans; Athletes; Anxiety Disorders; Sports; Anxiety; Psychotherapy
PubMed: 37949513
DOI: 10.1016/j.csm.2023.06.002 -
Frontiers in Psychology 2023
PubMed: 38094704
DOI: 10.3389/fpsyg.2023.1328762 -
Journal of the American College of... Nov 2023Performance anxiety is fear, anxiety, or avoidance of performative tasks, due to possible evaluation or criticism by others. Performance anxiety is well described in...
Performance anxiety is fear, anxiety, or avoidance of performative tasks, due to possible evaluation or criticism by others. Performance anxiety is well described in public speakers, musicians, and even surgeons. Its impact on radiologists and especially radiology trainees has not been explored. This article details performance anxiety, framing radiologists as performers, and highlights its potential impact on trainees and practicing radiologists. We offer strategies to manage and enhance the effects of performance anxiety that can be implemented in a training environment.
Topics: Humans; Radiology; Radiologists; Internship and Residency; Performance Anxiety; Anxiety
PubMed: 37634799
DOI: 10.1016/j.jacr.2023.06.038