Patients’ Perceptions of mHealth Apps: Meta-Ethnographic Review of Qualitative Studies
Bibliographic record
Abstract
BACKGROUND: Mobile phones and tablets are being increasingly integrated into the daily lives of many people worldwide. Mobile health (mHealth) apps have promising possibilities for optimizing health systems, improving care and health, and reducing health disparities. However, health care apps often seem to be underused after being downloaded. OBJECTIVE: The aim of this paper is to reach a better understanding of people's perceptions, beliefs, and experience of mHealth apps as well as to determine how highly they appreciate these tools. METHODS: A systematic review was carried out on qualitative studies published in English, on patients' perception of mHealth apps between January 2013 and June 2018. Data extracted from these articles were synthesized using a meta-ethnographic approach and an interpretative method. RESULTS: A total of 356 articles were selected for screening, and 43 of them met the inclusion criteria. Most of the articles included populations inhabiting developed countries and were published during the last 2 years, and most of the apps on which they focused were designed to help patients with chronic diseases. In this review, we present the strengths and weaknesses of using mHealth apps from the patients' point of view. The strengths can be categorized into two main aspects: engaging patients in their own health care and increasing patient empowerment. The weaknesses pointed out by the participants focus on four main topics: trustworthiness, appropriateness, personalization, and accessibility of these tools. CONCLUSIONS: Although many of the patients included in the studies reviewed considered mHealth apps as a useful complementary tool, some major problems arise in their optimal use, including the need for more closely tailored designs, the cost of these apps, the validity of the information delivered, and security and privacy issues. Many of these issues could be resolved with more support from health providers. In addition, it would be worth developing standards to ensure that these apps provide patients accurate evidence-based information.
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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 | 0.055 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".