Contribution of mobile health applications to self-management by consumers: review of published evidence
Bibliographic record
Abstract
Objective The aim of the present study was to review the contribution of mobile health applications ('apps') to consumers' self-management of chronic health conditions, and the potential for this practice to inform health policy, procedures and guidelines. Methods A search was performed on the MEDLINE, Cochrane Library, ProQuest and Global Health (Ovid) databases using the search terms 'mobile app*', 'self-care', 'self-monitoring', 'trial', 'intervention*' and various medical conditions. The search was supplemented with manual location of emerging literature and government reports. Mapping review methods identified relevant titles and abstracts, followed by review of content to determine extant research, reports addressing the key questions, and gaps suggesting areas for future research. Available studies were organised by disease state, and presented in a narrative analysis. Results Four studies describing the results of clinical trials were identified from Canada, England, Taiwan and Australia; all but the Australian study used custom-made apps. The available studies examined the effect of apps in health monitoring, reporting positive but not robust findings. Australian public policy and government reports acknowledge and support self-management, but do not address the potential contribution of mobile interventions. Conclusions There are limited controlled trials testing the contribution of health apps to consumers' self-management. Further evidence in this field is required to inform health policy and practice relating to self-management. What is known about the topic? Australian health policy encourages self-care by health consumers to reduce expenditure in health services. A fundamental component of self-care in chronic health conditions is self-monitoring, which can be used to assess progress towards treatment goals, as well as signs and symptoms of disease exacerbation. An abundance of mobile health apps is available for self-monitoring. What does this study add? A limited number of randomised control trials have assessed the clinical impact of health apps for self-monitoring. The body of evidence relating to current and long-term clinical impact is developing. Despite endorsing self-care, Australian health policy does not address the use and potential contribution of mobile health apps to health care. What are the implications? Widespread and sustained use of validated mobile health apps for chronic health conditions should have potential to improve consumer independence, confidence and burden on health services in the longer term. However, a significant body of scientific evidence has not yet been established; this is mirrored in the lack of acknowledgement of health apps in Australian health policy referring to consumers' self-management.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".