Identifying Evidence-Informed Physical Activity Apps: Content Analysis
Notice bibliographique
Résumé
BACKGROUND: Regular moderate to vigorous physical activity is essential for maintaining health and preventing the onset of chronic diseases. Both global rates of smartphone ownership and the market for physical activity and fitness apps have grown rapidly in recent years. The use of physical activity and fitness apps may assist the general population in reaching evidence-based physical activity recommendations. However, it remains unclear whether there are evidence-informed physical activity apps and whether behavior change techniques (BCTs) previously identified as effective for physical activity promotion are used in these apps. OBJECTIVE: This study aimed to identify English and German evidence-informed physical activity apps and BCT employment in those apps. METHODS: We identified apps in a systematic search using 25 predefined search terms in the Google Play Store. Two reviewers independently screened the descriptions of apps and screenshots applying predefined inclusion and exclusion criteria. Apps were included if (1) their description contained information about physical activity promotion; (2) they were in English or German; (3) physical activity recommendations of the World Health Organization or the American College of Sports Medicine were mentioned; and (4) any kind of objective physical activity measurement was included. Two researchers downloaded and tested apps matching the inclusion criteria for 2 weeks and coded their content using the Behavioral Change Technique Taxonomy v1 (BCTTv1). RESULTS: The initial screening in the Google Play Store yielded 6018 apps, 4108 of which were not focused on physical activity and were not in German or English. The descriptions of 1216 apps were further screened for eligibility. Duplicate apps and light versions (n=694) and those with no objective measurement of physical activity, requiring additional equipment, or not outlining any physical activity guideline in their description (n=1184) were excluded. Of the remaining 32 apps, 4 were no longer available at the time of the download. Hence, 28 apps were downloaded and tested; of these apps, 14 did not contain any physical activity guideline as an app feature, despite mentioning it in the description, 5 had technical problems, and 3 did not provide objective physical activity measurement. Thus, 6 were included in the final analyses. Of 93 individual BCTs of the BCTTv1, on average, 9 (SD 5) were identified in these apps. Of 16 hierarchical clusters, on average, 5 (SD 3) were addressed. Only BCTs of the 2 hierarchical clusters "goals and planning" and "feedback and monitoring" were identified in all apps. CONCLUSIONS: Despite the availability of several thousand physical activity and fitness apps for Android platforms, very few addressed evidence-based physical activity guidelines and provided objective physical activity measurement. Furthermore, available descriptions did not accurately reflect the app content and only a few evidence-informed physical activity apps incorporated several BCTs. Future apps should address evidence-based physical activity guidelines and a greater scope of BCTs to further increase their potential impact for physical activity promotion.
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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 | 0,054 | 0,223 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,043 | 0,029 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».