Evaluation of Digital Interventions for Physical Activity Promotion: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Digital interventions are interventions supported by digital tools or technologies, such as mobile apps, wearables, or web-based software. Digital interventions in the context of public health are specifically designed to promote and improve health. Recent reviews have shown that many digital interventions target physical activity promotion; however, it is unclear how such digital interventions are evaluated. OBJECTIVE: We aimed to investigate evaluation strategies in the context of digital interventions for physical activity promotion using a scoping review of published reviews. We focused on the target (ie, user outcomes or tool performance), methods (ie, tool data or self-reported data), and theoretical frameworks of the evaluation strategies. METHODS: A protocol for this study was preregistered and published. From among 300 reviews published up to March 19, 2021 in Medline, PsycINFO, and CINAHL databases, 40 reviews (1 rapid, 9 scoping, and 30 systematic) were included in this scoping review. Two authors independently performed study selection and data coding. Consensus was reached by discussion. If applicable, data were coded quantitatively into predefined categories or qualitatively using definitions or author statements from the included reviews. Data were analyzed using either descriptive statistics, for quantitative data (relative frequencies out of all studies), or narrative synthesis focusing on common themes, for qualitative data. RESULTS: Most reviews that were included in our scoping review were published in the period from 2019 to 2021 and originated from Europe or Australia. Most primary studies cited in the reviews included adult populations in clinical or nonclinical settings, and focused on mobile apps or wearables for physical activity promotion. The evaluation target was a user outcome (efficacy, acceptability, usability, feasibility, or engagement) in 38 of the 40 reviews or tool performance in 24 of the 40 reviews. Evaluation methods relied upon objective tool data (in 35/40 reviews) or other data from self-reports or assessments (in 28/40 reviews). Evaluation frameworks based on behavior change theory, including goal setting, self-monitoring, feedback on behavior, and educational or motivational content, were mentioned in 22 out of 40 reviews. Behavior change theory was included in the development phases of digital interventions according to the findings of 20 out of 22 reviews. CONCLUSIONS: The evaluation of digital interventions is a high priority according to the reviews included in this scoping review. Evaluations of digital interventions, including mobile apps or wearables for physical activity promotion, typically target user outcomes and rely upon objective tool data. Behavior change theory may provide useful guidance not only for development of digital interventions but also for the evaluation of user outcomes in the context of physical activity promotion. Future research should investigate factors that could improve the efficacy of digital interventions and the standardization of terminology and reporting in this field. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/35332.
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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 | 0.090 | 0.254 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.031 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".