Current Knowledge and Adoption of Mobile Health Apps Among Australian General Practitioners: Survey Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».