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• W3109898602 endingPage "e22755" @default.
• W3109898602 startingPage "e22755" @default.
• W3109898602 abstract "Background There is a persistent need for mental ill-health prevention and intervention among at-risk and vulnerable subpopulations. Major disruptions to life, such as the COVID-19 pandemic, present an opportunity for a better understanding of the experience of stressors and vulnerability. Faster and better ways of psychological screening and tracking are more generally required in response to the increased demand upon mental health care services. The argument that mental and physical health should be considered together as part of a biopsychosocial approach is garnering acceptance in elite athlete literature. However, the sporting population are unique in that there is an existing stigma of mental health, an underrecognition of mental ill-health, and engagement difficulties that have hindered research, prevention, and intervention efforts. Objective The aims of this paper are to summarize and evaluate the literature on athletes’ increased vulnerability to mental ill-health and digital mental health solutions as a complement to prevention and intervention, and to show relationships between athlete mental health problems and resilience as well as digital mental health screening and tracking, and faster and better treatment algorithms. Methods This mini review shapes literature in the fields of athlete mental health and digital mental health by summarizing and evaluating journal and review articles drawn from PubMed Central and the Directory of Open Access Journals. Results Consensus statements and systematic reviews indicated that elite athletes have comparable rates of mental ill-health prevalence to the general population. However, peculiar subgroups require disentangling. Innovative expansion of data collection and analytics is required to respond to engagement issues and advance research and treatment programs in the process. Digital platforms, machine learning, deep learning, and artificial intelligence are useful for mental health screening and tracking in various subpopulations. It is necessary to determine appropriate conditions for algorithms for use in recommendations. Partnered with real-time automation and machine learning models, valid and reliable behavior sensing, digital mental health screening, and tracking tools have the potential to drive a consolidated, measurable, and balanced risk assessment and management strategy for the prevention and intervention of the sequelae of mental ill-health. Conclusions Athletes are an at-risk subpopulation for mental health problems. However, a subgroup of high-level athletes displayed a resilience that helped them to positively adjust after a period of overwhelming stress. Further consideration of stress and adjustments in brief screening tools is recommended to validate this finding. There is an unrealized potential for broadening the scope of mental health, especially symptom and disorder interpretation. Digital platforms for psychological screening and tracking have been widely used among general populations, but there is yet to be an eminent athlete version. Sports in combination with mental health education should address the barriers to help-seeking by increasing awareness, from mental ill-health to positive functioning. A hybrid model of care is recommended, combining traditional face-to-face approaches along with innovative and evaluated digital technologies, that may be used in prevention and early intervention strategies." @default.
• W3109898602 created "2020-12-07" @default.
• W3109898602 creator A5052658317 @default.
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• W3109898602 date "2020-12-14" @default.
• W3109898602 modified "2023-10-18" @default.
• W3109898602 title "Psychological Screening and Tracking of Athletes and Digital Mental Health Solutions in a Hybrid Model of Care: Mini Review" @default.
• W3109898602 cites W1603808064 @default.
• W3109898602 cites W1606273975 @default.
• W3109898602 cites W1965036934 @default.
• W3109898602 cites W2007738054 @default.
• W3109898602 cites W2030052562 @default.
• W3109898602 cites W2035009704 @default.
• W3109898602 cites W2058395130 @default.
• W3109898602 cites W2061596809 @default.
• W3109898602 cites W2107198093 @default.
• W3109898602 cites W2108787152 @default.
• W3109898602 cites W2159808148 @default.
• W3109898602 cites W2278084024 @default.
• W3109898602 cites W2345230743 @default.
• W3109898602 cites W2466504507 @default.
• W3109898602 cites W2594658054 @default.
• W3109898602 cites W2605914054 @default.
• W3109898602 cites W2751258775 @default.
• W3109898602 cites W2797763839 @default.
• W3109898602 cites W2800574848 @default.
• W3109898602 cites W2803196025 @default.
• W3109898602 cites W2803295712 @default.
• W3109898602 cites W2808102992 @default.
• W3109898602 cites W2884985620 @default.
• W3109898602 cites W2894495999 @default.
• W3109898602 cites W2898661895 @default.
• W3109898602 cites W2912809994 @default.
• W3109898602 cites W2913231970 @default.
• W3109898602 cites W2914097626 @default.
• W3109898602 cites W2916945592 @default.
• W3109898602 cites W2918051339 @default.
• W3109898602 cites W2934360636 @default.
• W3109898602 cites W2942420449 @default.
• W3109898602 cites W2942914075 @default.
• W3109898602 cites W2943406779 @default.
• W3109898602 cites W2943613834 @default.
• W3109898602 cites W2944177380 @default.
• W3109898602 cites W2945081425 @default.
• W3109898602 cites W2946503716 @default.
• W3109898602 cites W2950176603 @default.
• W3109898602 cites W2973657444 @default.
• W3109898602 cites W2985355520 @default.
• W3109898602 cites W2991538927 @default.
• W3109898602 cites W2993379060 @default.
• W3109898602 cites W2995065074 @default.
• W3109898602 cites W3003824610 @default.
• W3109898602 cites W3005474290 @default.
• W3109898602 cites W3009036522 @default.
• W3109898602 cites W3012307067 @default.
• W3109898602 cites W3014854501 @default.
• W3109898602 cites W3020732051 @default.
• W3109898602 cites W3021985088 @default.
• W3109898602 cites W3022557181 @default.
• W3109898602 cites W3022682862 @default.
• W3109898602 cites W3024715320 @default.
• W3109898602 cites W3025210173 @default.
• W3109898602 cites W3025668095 @default.
• W3109898602 cites W3027105729 @default.
• W3109898602 cites W3028184988 @default.
• W3109898602 cites W3032376952 @default.
• W3109898602 cites W3032905501 @default.
• W3109898602 cites W3033293557 @default.
• W3109898602 cites W3033626851 @default.
• W3109898602 cites W3033785558 @default.
• W3109898602 cites W3034382968 @default.
• W3109898602 cites W3034674374 @default.
• W3109898602 cites W3035248755 @default.
• W3109898602 cites W3037803689 @default.
• W3109898602 cites W3042947149 @default.
• W3109898602 cites W3044541442 @default.
• W3109898602 cites W3046113880 @default.
• W3109898602 cites W3046413668 @default.
• W3109898602 cites W3047132612 @default.
• W3109898602 cites W3047382484 @default.
• W3109898602 cites W3047739654 @default.
• W3109898602 doi "https://doi.org/10.2196/22755" @default.
• W3109898602 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7746225" @default.
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