The Shape of Mobile Health: A Systematic Review of Health Visualization on Mobile Devices
Bibliographic record
Abstract
mHealth apps and tracking technology are becoming popular because they help people adopt a healthier lifestyle and form healthy habits. One way mHealth apps can help users is by presenting visuals to help them better understand their health data. Commendable efforts have been carried out to personalize health-promoting interventions to help users become more aware of their own health, and health practitioners for providing healthcare services. For example, digital self-tracking apps nowadays can be used to monitor physical activity, nutrition and sleep patterns. This systematic review aims to investigate the current trends, challenges, gaps, and opportunities in health visualizations on mobile devices. Peer-reviewed papers in English collected using online databases (ACM Digital Library, PubMed, and Web of Science) from 2012 to 2022 were considered, and 56 studies were selected out of 1,168 studies. Results showed that among 11 different health domains, general health and physical health were the most heavily studied. Relatedly, results also showed that physical fitness data is the most frequently collected data type automatically from sensors/trackers. Furthermore, bar and line charts are the most popular type of visualizations used for presenting a variety of health data, and while most apps present static visualizations, interactive visualizations, as well as a combination of both static and interactive visualizations, are becoming more common. Based on our results, we offer recommendations for future research as designers and researchers continue to improve the presentation of data visualizations in mHealth apps.
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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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".