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Record W4205347980 · doi:10.2196/29832

A Smartphone Serious Game for Adolescents (Grow It! App): Development, Feasibility, and Acceptance Study

2021· article· en· W4205347980 on OpenAlexvenueno aff
Evelien Dietvorst, Michelle A Aukes, Jeroen S. Legerstee, Annabel Vreeker, Micah Hrehovcsik, Loes Keijsers, Manon H. J. Hillegers

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersStichting Vrienden van het SophiaNederlandse Organisatie voor Wetenschappelijk OnderzoekZonMw
KeywordsmHealthMoodBoredomAnxietyPsychologyCoping (psychology)Video gameClinical psychologyMindfulnessAgile software developmentApplied psychologyMultimediaComputer sciencePsychological interventionPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Anxiety and mood problems in adolescents often go unnoticed and may therefore remain untreated. Identifying and preventing the development of emotional problems requires monitoring and effective tools to strengthen adolescents' resilience, for example, by enhancing coping skills. OBJECTIVE: This study describes the developmental process, feasibility, and acceptance of Grow It!, a multiplayer serious game app for adolescents aged 12-25 years. The app consists of the experience sampling method (ESM) to monitor thoughts, behaviors, and emotions in daily life to enhance self-insight and daily cognitive behavioral therapy-based challenges to promote adaptive coping. METHODS: Our approach entails an iterative game design process combined with an agile method to develop the smartphone app. The incorporated game features (ie, challenges, chat functionality, and visual representation) in the Grow It! app were co-designed with adolescent end users to increase participant engagement and adherence. RESULTS: The Grow It! app was delivered for Android and iOS in May 2020. Grow It! was offered to adolescents during the COVID-19 crisis between May and December 2020. Participants of the Grow It! COVID-19 study (sample 1: N=685; mean age 16.19, SD 3.11 years; 193/685, 28.2% boys; sample 2: N=1035; mean age 18.78, SD 3.51 years; 193/1035, 18.64% boys) completed 31.5% (13.2/42) to 49.5% (10.4/21) of challenges. Compliance of ESM was suboptimal (35.1/210, 16.7% to 32.5/105, 30.9%). Follow-up questionnaires indicated an overall score of the app of 7.1 out of 10. Moreover, 72.6% (278/383) to 75.6% (487/644) would recommend the app to friends. CONCLUSIONS: To our knowledge, Grow It! is the first gamified ESM app that both measures individual differences in emotional dynamics and offers an integrated cognitive behavioral therapy-based intervention. Our findings support the feasibility and acceptance, and therefore applicability, of the Grow It! app in adolescents. Further iterations of this serious game app will focus on the increase of compliance and on providing participants feedback through their personal mood profiles.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.526
Teacher spread0.352 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations32
Published2021
Admission routes1
Has abstractyes

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