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Record W4365443720 · doi:10.2196/39913

Adaptation of ACTivate Your Wellbeing, a Digital Health and Well-being Program for Young Persons: Co-design Approach

2023· article· en· W4365443720 on OpenAlexvenueno aff
Menna Brown, Emily Lord, Ann John

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthFocus groupFormative assessmentPsychologyAdaptation (eye)Resource (disambiguation)Applied psychologyMedical educationIntervention (counseling)MedicineNursingComputer sciencePedagogySociology

Abstract

fetched live from OpenAlex

BACKGROUND: ACTivate your wellbeing is a digital health and well-being program designed to support and encourage positive lifestyle behavior change. The website includes 5 lifestyle behavior change modules and a 12-week well-being intervention based on acceptance and commitment therapy. It was timely to adapt the resource for a new audience in the wake of the COVID-19 pandemic. Young persons' mental health needs have increased substantially, and lifestyle behaviors play a critical role in both mental and physical health statuses. OBJECTIVE: This study aimed to adapt an existing health and well-being website for use by young persons aged 16 to 24 years. METHODS: A 3-staged participatory, co-design approach was adopted. The participants reviewed the existing program and provided feedback (stage 1) before cocreating new content (stage 2). Finally, the updated program underwent formative evaluation (stage 3). Two groups were created: one had access for 3 weeks and the other could self-select their study duration. The options were 3 weeks, 60 days, or 90 days. Outcome measures were the Warwick and Edinburgh Mental Well-being Scale, 4-item Patient Health Questionnaire, and Acceptance and Action Questionnaire version 2. RESULTS: Stage 1 identified that the website was appealing to the new audience (19/24, 79%), and the 3 web-based focus group discussions explored data from the written review in more depth to identify and clarify the main areas for update and adaptation. Overall, 3 themes were developed, and the data informed the creation of 6 tasks for use in 5 web-based co-design workshops. Stage 2 led to the cocreation of 36 outputs, including a new name, new content, scenarios, images, and a new user dashboard, which included streaks and an updated color scheme. After the website update program was completed, 40 participants registered to use the website for formative evaluation (stage 3). Data analysis revealed differences in engagement, completion, and mean well-being after intervention between the 2 groups. The completion rate was 68% in the 3-week duration group, and well-being scores improved after intervention. CONCLUSIONS: Young persons engaged actively with the participatory design process. The participants discussed the updates they desired during the web-based discussions, which worked well via Zoom (Zoom Video Communications Inc) when small groups were used. The participants easily cocreated new content during the web-based co-design workshops. The web-based format enabled a range of participants to take part, share their ideas, search for images, and design digital content creatively together. The Zoom software enabled screen sharing and collaborative whiteboard use, which helped the cocreation process. The formative evaluation suggested that younger users who engage more with the website for a shorter duration may benefit more.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.175
GPT teacher head0.508
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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