MétaCan
Menu
Back to cohort
Record W2978737164 · doi:10.2196/13776

Usability, Acceptability, Feasibility, and Effectiveness of a Gamified Mobile Health Intervention (Triumf) for Pediatric Patients: Qualitative Study

2019· article· en· W2978737164 on OpenAlexvenueno aff
Riin Tark, Mait Metelitsa, Kirsti Akkermann, Kadri Saks, Sirje Mikkel, Kadri Haljas

Bibliographic record

VenueJMIR Serious Games · 2019
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityPsychological interventionIntervention (counseling)Mental healthDigital healthPsychologyQualitative researchMedicineApplied psychologyClinical psychologyMedical educationHealth carePsychiatryComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Mental disorders are notably prevalent in children with chronic illnesses, whereas a lack of access to psychological support might lead to potential mental health problems or disruptions in treatment. Digitally delivered psychological interventions have shown promising results as a supportive treatment measure for improving health outcomes during chronic illness. OBJECTIVE: This study aimed to evaluate the usability, acceptability, and feasibility of providing psychological and treatment support in a clinical setting via a mobile game environment. In addition, the study aimed to evaluate the preliminary effectiveness of the mobile health game. METHODS: Patients aged 7 to 14 years with less than a year from their diagnosis were eligible to participate in the study. In total, 15 patients were invited to participate by their doctor. A total of 9 patients (age range: 7-12 years; mean age 9.1 years) completed the 60-day-long study in which the Triumf mobile health game was delivered as a digital intervention. In an engaging game environment, patients were offered psychological and treatment support, cognitive challenges, and disease-specific information. The fully digital intervention was followed by a qualitative interview conducted by a trained psychologist. The results of the interview were analyzed in conjunction with patient specific in-game qualitative data. Ethical approval was obtained to conduct the study. RESULTS: Patients positively perceived the game, resulting in high usability and acceptability evaluations. Participants unanimously described the game as easy to use and engaging in terms of gamified activities, while also providing beneficial and trustworthy information. Furthermore, the overall positive evaluation was emphasized by an observed tendency to carry on gaming post study culmination (67%, 10/15). Psychological support and mini games were the most often used components of the game, simultaneously the participants also highlighted the education module as one of the most preferred. On average, the patients sought and received psychological support or education on 66.6 occasions during the 60-day intervention. Participants spent the most time collecting items from the city environment (on average 15.6 days, SD 8.1), indicative of exploratory behavior, based on the quantitative in-game collected data. During the intervention period, we observed a statistically significant decrease in general health problems (P=.003) and saw a trend toward a decrease in depression and anxiety symptoms. CONCLUSIONS: This study demonstrated that a game environment could be a promising medium for delivering comprehensive supportive care to pediatric patients with cancer alongside standard treatment, with potential application across a variety of chronic conditions. Importantly, the results indicate that the study protocol was feasible with modifications to randomized controlled trials, and the game could be considered applicable in a clinical context. By giving an empirical evaluation of delivering psychological support via the game environment, our work stands to inform future mobile health interventions.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.455
Teacher spread0.420 · 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.

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

Citations49
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Serious GamesSame topicDigital Mental Health InterventionsFrench-language works237,207