Current Knowledge and Adoption of Mobile Health Apps Among Australian General Practitioners: Survey Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) apps can be prescribed as an effective self-management tool for patients. However, it is challenging for doctors to navigate 350,000 mHealth apps to find the right ones to recommend. Although medical professionals from many countries are using mHealth apps to varying degrees, current mHealth app use by Australian general practitioners (GPs) and the barriers and facilitators they encounter when integrating mHealth apps in their clinical practice have not been reported comprehensively. OBJECTIVE: The objectives of this study were to (1) evaluate current knowledge and use of mHealth apps by GPs in Australia, (2) determine the barriers and facilitators to their use of mHealth apps in consultations, and (3) explore potential solutions to the barriers. METHODS: We helped the Royal Australian College of General Practitioners (RACGP) to expand the mHealth section of their annual technology survey for 2017 based on the findings of our semistructured interviews with GPs to further explore barriers to using mHealth apps in clinical practice. The survey was distributed to the RACGP members nationwide between October 26 and December 3, 2017 using Qualtrics Web-based survey tool. RESULTS: A total of 1014 RACGP members responded (response rate 4.6% [1014/21,884], completion rate 61.2% [621/1014]). The median years practiced was 20.7 years. Two-thirds of the GPs used apps professionally in the forms of medical calculators and point-of-care references. A little over half of the GPs recommended apps for patients either daily (12.9%, 80/621), weekly (25.9%, 161/621), or monthly (13.4%, 83/621). Mindfulness and mental health apps were recommended most often (32.5%, 337/1036), followed by diet and nutrition (13.9%, 144/1036), exercise and fitness (12.7%, 132/1036), and women's health (10%, 104/1036) related apps. Knowledge and usage of evidence-based apps from the Handbook of Non-Drug Interventions were low. The prevailing barriers to app prescription were the lack of knowledge of effective apps (59.9%, 372/621) and the lack of trustworthy source to access them (15.5%, 96/621). GPs expressed their need for a list of safe and effective apps from a trustworthy source, such as the RACGP, to overcome these barriers. They reported a preference for online video training material or webinar to learn more about mHealth apps. CONCLUSIONS: Most GPs are using apps professionally but recommending apps to patients sparingly. The main barriers to app prescription were the lack of knowledge of effective apps and the lack of trustworthy source to access them. A curated compilation of effective mHealth apps or an app library specifically aimed at GPs and health professionals would help solve both barriers.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".