Acceptability and Usability of the Mobile Digital Health App NoObesity for Families and Health Care Professionals: Protocol for a Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Almost a quarter or more than a fifth of children in the United Kingdom are overweight or obese by the time they start school. The UK Department of Health and Social Care's national policy for combating childhood obesity has critical outcomes centered on sugar and caloric consumption reduction. Health Education England has developed two digital apps for families with children up to 15 years and for their associated health care professionals to provide a digital learning resource and tool aimed at encouraging healthy lifestyles to prevent obesity. OBJECTIVE: This feasibility study assesses the usability and acceptability of Health Education England's NoObesity app for undertaking activities to improve families' diet and physical activity. The purpose of the study is to evaluate the app's influence on self-efficacy and goal setting and to determine what can be learnt to improve its design for future studies, if there is evidence of adoption and sustainability. METHODS: The study population will include 20 to 40 families and their linked health care professionals. Considering issues related to digital access associated with socioeconomic status and the impact on information technology use, study recruitment will be regionally focused in a low socioeconomic status area. The study will last for 9 months (3-month intervention period and 6-month follow-up). The evaluations of feasibility, acceptability, and usability will be conducted using the following scales and theoretical frameworks: (1) system usability scale; (2) Reach Effectiveness Adoption Implementation Maintenance framework; (3) Bandura model of health promotion; and (4) Nonadoption, Abandonment, and Challenges to the Scale-up, Spread, and Suitability framework. App use will be captured and quantitatively analyzed for net use patterns (eg, number of screens viewed, number of logins, cumulative minutes using the app, number of plans made, and number of times goals met) and to triangulate qualitative feedback from study participants. RESULTS: This study was funded in March 2019 by Health Education England and received University of Oxford Medical Sciences Interdivisional Research Ethics Committee approval on January 31, 2020 (R62092/RE001). At manuscript submission, study recruitment is pending, and expected results will be published in 2021. CONCLUSIONS: This study will provide evidence on the NoObesity app's influence on self-efficacy and goal-setting and determine what can be learnt to improve its design for future studies, if there is evidence of adoption and sustainability. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/18068.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".