Tackling Mental Health in Youth Sporting Programs: A Pilot Study of a Holistic Program

Dowell, Tiah L.; Waters, Allison M.; Usher, Wayne; Farrell, Lara J.; Donovan, Caroline L.; Modecki, Kathryn L.; Zimmer‐Gembeck, Melanie J.; Castle, Mike; Hinchey, James

doi:10.1007/s10578-020-00984-9

Cited by 26 publications

(10 citation statements)

References 61 publications

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“…The machine learning (ML) technique is widely used in various fields and applications for prediction, detection, recognition, and identification. The ML technique improves the overall accuracy rate in the detection and prediction process and improves the system efficiency [ 11 ]. The ML technique is also used in the infant health prediction process by using sports data.…”

Section: Introductionmentioning

confidence: 99%

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Zhang

2022

BioMed Research International

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The sensed data from infant sports and training programs are useful in analyzing their health conditions and forecasting any disorders or abnormalities. The sensed information is processed for providing errorless predictions for infant diseases/disorders, coupled with artificial intelligence and sophisticated healthcare technologies. The problem of noncongruent sensed data impacting the forecast occurs due to errors between consecutive training iterations. This problem is addressed using the deep learning (PEST-DL) proposed perceptible error segregation technique. The training process is halted between two consecutive iterations generating errors until a similarity verification based on infant history is performed. The similarity output determines the errors due to mismatching data observations, and therefore, the data augmentation is performed. The first perceptible error is mitigated by training the learning paradigm with all possible infant history data in the learning process. This prevents prediction lag and data omissions due to discrete availability. The learning is trained from the identified error with the precise detected disorder/abnormality data previously detected. Therefore, the first and consecutive training data segregate error instances from the actual training iterations. This improves the prediction accuracy and precision with controlled error and time complexity.

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Section: Introductionmentioning

confidence: 99%

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Zhang

2022

BioMed Research International

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“…The study identified significant enablers of, and barriers to, successful implementation of the system via a multi-technology format. The observation that 5 participants did not complete the pre-RISE assessment and 18 participants did not complete the post-RISE assessment was a significant barrier to successful implementation and reach of the Assess step and has been observed in our prior studies that included greater in-person delivery (Dowell et al, 2021;Waters et al, 2021). An identified enabler of more successful Assess step completions in Locations B and C than Location A was the greater engagement of local coaches/personnel in reinforcing the importance of completing the Assess step.…”

Section: Discussionmentioning

confidence: 69%

“…The players also received a detailed Life-Fit-Learning workbook. Workshop content included: (1) Healthy Habits: emphasising the healthy consumption of vegetables, fruit and water, adequate sleep, and social media usage, (2) Strong Minds: emphasising grit and optimism, (3) Keep Cool: emphasising breathing, muscle relaxation, and mindfulness to manage emotions, and (4) Stay Connected: emphasising acts of kindness and gratitude (see Dowell et al, 2021;Waters et al, 2021 for further details). Within the week after completing the program, text messages were sent to the players, or their parents/carers mobile phone, with the satisfaction rating questions to which the players replied with their responses.…”

Section: Methodsmentioning

confidence: 99%

“…The present study represents one successful example and is a synthesis of youth sports program strategies and mental health service adaptations to ensure the continued provision of an integrated youth sports development program for 12 to 15-year-old, Australian rugby league players during the COVID-19 pandemic when travel restrictions, border closures, and quarantine procedures prevented its original in-person delivery. In prior studies, we successfully co-designed and co-delivered an integrated mental health and wellbeing system of care (i.e., Life-Fit-Learning) within the context of the rugby league youth development program and school-based programs to enhance reach, and accessibility to, mental health support for young people (Dowell et al, 2021;Waters et al, 2021). The system involves a three-step approach aimed at assessing young peoples' mental health and wellbeing via self-report (Assess step), providing immediate feedback (Reflect step), and connecting them to mental health and wellbeing programs and resources proportionate to their needs and the services available in their communities (Connect step).…”

Section: Introductionmentioning

confidence: 99%

“…In our prior study with junior rugby league players (e.g., Dowell et al, 2021), the Assess and Reflect steps were conducted online and via telephone, while the Connect step involved a combination of telephone support, access to digital psychoeducation resources, and 4× in-person workshops with players and their coaches. In this previous study with 74 Australian junior rugby league players, the integrated system was found to be feasible to implement, highly acceptable to players, and efficacious in reducing anxiety and depression symptoms and improving personal strengths.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Implementation of a Multi-Technology Delivery of a Mental Health and Wellbeing System of Care Within a Youth Sports Development Program During the COVID-19 Pandemic

et al. 2022

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The COVID-19 pandemic presents challenges to the provision of community programs and access to mental health services for young people. We examined the feasibility, reach, and acceptability of multi-technology delivery of an integrated system that assesses and provides feedback on youth mental health and wellbeing and connects them to care within the context of a youth sports development program. The system was delivered via computer, telephone, and teleconference with 66 adolescent boys participating in a rugby league development program in three communities in Australia. Young people completed online wellbeing and mental health measures (Assess step), parents were provided with telephone feedback on results, support, and referral options (Reflect step), and youth received teleconferenced workshops and online resources (Connect step). The multi-technology delivery was feasible to implement, and reach was high, with barriers experienced at the Assess step but minimally experienced at the Reflect and Connect steps. Delivering the system via multiple forms of technology was rated as highly beneficial and enjoyable by young people. Players improved in self-reported prosocial behaviour, gratitude, and anxiety symptoms from pre- to post-program. Strong collaboration between researchers, organisational personnel, and community members is important for achieving these outcomes.

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Mental health needs assessment for youth in out of school programs: A scoping review

Brodkin,

Sibalis,

Bedard

et al. 2024

Community & Applied Soc Psy

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Out‐of‐school programs are an accessible option to bolster the mental well‐being of youth, who may be at risk of developing further emotional and behavioural concerns. Developing a more nuanced understanding of the tools and methods available to understand youth mental health needs, in the context of out‐of‐school programs is needed to provide relevant services. However, many out‐of‐school programs do not include such an assessment. One barrier to doing so may be a lack of knowledge regarding the tools that can be used in this specialised context. The present scoping review was conducted to identify the tools that have been used to determine the emotional and behavioural needs of youth attending out‐of‐school programs and to synthesise information regarding the context in which these tools have been used. Fifty‐seven articles met the criteria for the review, and within these articles, 69 unique measures of emotional and behavioural needs were identified. The measures were sorted into six thematic categories (self‐concept, emotion and behaviour regulation, mood, general mental health, social skills and resilience) and relevant characteristics were described. The findings of the present review may be helpful to out‐of‐school programs as a step to best meet the needs of participating youth. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

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Tackling Mental Health in Youth Sporting Programs: A Pilot Study of a Holistic Program

Cited by 26 publications

References 61 publications

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Health Information Prediction System of Infant Sports Based on Deep Learning Network

Evaluating the Implementation of a Multi-Technology Delivery of a Mental Health and Wellbeing System of Care Within a Youth Sports Development Program During the COVID-19 Pandemic

Mental health needs assessment for youth in out of school programs: A scoping review

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