Purposes of Smartphone Health Care Apps and the Practicality of Their Functions in Disaster Situations: Qualitative Function Assessment Study
Bibliographic record
Abstract
Background: Japan has experienced various natural disasters, including the Great East Japan Earthquake in 2011. It becomes crucial to focus on strengthening self-help measures through health care apps that are used in normal times to help people during disasters. However, little is known about what health care apps would be useful in times of disaster. Objective: This study aimed to investigate the prevalent functionalities and purposes of using health care apps during normalcy, explore their potential utility, and propose strategies for disaster response through their utilization. Methods: We focus on highly ranked health care apps (within the top 100 in the health care category for iPhones by Apple, Inc for a certain period) and reclassify their purpose of use, such as sleep, relaxation, and exercise, in detail. We also investigate the functions within each health care app (measurement, recording, advice, content provision, and guidance to actual services), based on which we determine their potential utilization during disasters and anticipate potential solutions to address disaster-related challenges. We also consider the ideal framework of health care apps in disaster response, exploring possibilities such as the necessity of new disaster-specific apps or the adaptation of existing health care apps for disaster scenarios. Results: Among the 70 free apps, the predominant functions included "recording" (n=60 cases, 86%) and "measurement" (n=47 cases, 67%), primarily encompassing the mechanical functions of wearable devices and smartphones. A similar trend was seen in the 77 paid apps, but "content provision" (n=54 cases, 70%) was the most prevalent. Furthermore, the "content provision" function was particularly common in the "purposes of use" categories "sleep" (χ211=29; P<.001), "relaxation" (χ211=14.6; P<.001), and "exercise" (χ211=9.3; P=.002). This suggested the possibility of using the content provision function in existing health care apps to support mental and physical health even during a disaster. Conclusions: The widespread use of apps during normal times could minimize hesitation in adopting them during disasters. The findings emphasize the potential for augmenting disaster-specific content within existing apps rather than developing new ones. This approach aligns with the likelihood of preinstalled app use during emergencies, indicating a pragmatic strategy for enhancing disaster response content within prevalent 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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".