Considerations for Improved Mobile Health Evaluation: Retrospective Qualitative Investigation
Bibliographic record
Abstract
BACKGROUND: Mobile phone use and, consequently, mobile health (mHealth) interventions have seen an exponential increase in the last decade. There is an excess of 318,000 health-related apps available free of cost for consumers to download. However, many of these interventions are not evaluated and are lacking appropriate regulations. Randomized controlled trials are often considered the gold standard study design in determining the effectiveness of interventions, but recent literature has identified limitations in the methodology when used to evaluate mHealth. OBJECTIVE: The objective of this study was to investigate the system developers' experiences of evaluating mHealth interventions in the context of a developing country. METHODS: We employed a qualitative exploratory approach, conducting semistructured interviews with multidisciplinary members of an mHealth project consortium. A conventional content analysis approach was used to allow codes and themes to be identified directly from the data. RESULTS: The findings from this study identified the system developers' perceptions of mHealth evaluation, providing an insight into the requirements of an effective mHealth evaluation. This study identified social and technical factors which should be taken into account when evaluating an mHealth intervention. CONCLUSIONS: Contextual issues represented one of the most recurrent challenges of mHealth evaluation in the context of a developing country, highlighting the importance of a mixed method evaluation. There is a myriad of social, technical, and regulatory variables, which may impact the effectiveness of an mHealth intervention. Failure to account for these variables in an evaluation may limit the ability of the intervention to achieve long-term implementation and scale.
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 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 | 0.101 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 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".