Influence of Patient Characteristics and Psychological Needs on Diabetes Mobile App Usability in Adults With Type 1 or Type 2 Diabetes: Crossover Randomized Trial
Notice bibliographique
Résumé
BACKGROUND: More than 1100 diabetes mobile apps are available, but app usage by patients is low. App usability may be influenced by patient factors such as age, sex, and psychological needs. OBJECTIVE: Guided by Self-Determination Theory, the purposes of this study were to (1) assess the effect of patient characteristics on app usability, and (2) determine whether patient characteristics and psychological needs (competence, autonomy, and connectivity)-important for motivation in diabetes care-are associated with app usability. METHODS: Using a crossover randomized design, 92 adults with type 1 or 2 diabetes tested two Android apps (mySugr and OnTrack) for seven tasks including data entry, blood glucose (BG) reporting, and data sharing. We used multivariable linear regression models to examine associations between patient characteristics, psychological needs, user satisfaction, and user performance (task time, success, and accuracy). RESULTS: Participants had a mean age of 54 (range 19-74) years, and were predominantly white (62%, 57/92), female (59%, 54/92), with type 2 diabetes (70%, 64/92), and had education beyond high school (67%, 61/92). Participants rated an overall user satisfaction score of 62 (SD 18), which is considered marginally acceptable. The satisfaction mean score for each app was 55 (SD 18) for mySugr and 68 (SD 15) for OnTrack. The mean task completion time for all seven tasks was 7 minutes, with a mean task success of 82% and an accuracy rate of 68%. Higher user satisfaction was observed for patients with less education (P=.04) and those reporting more competence (P=.02), autonomy (P=.006), or connectivity with a health care provider (P=.03). User performance was associated with age, sex, education, diabetes duration, and autonomy. Older patients required more time (95% CI 1.1-3.2) and had less successful task completion (95% CI 3.5-14.3%). Men needed more time (P=.01) and more technical support than women (P=.04). High school education or less was associated with lower task success (P=.003). Diabetes duration of ≥10 years was associated with lower task accuracy (P=.02). Patients who desired greater autonomy and were interested in learning their patterns of BG and carbohydrates had greater task success (P=.049). CONCLUSIONS: Diabetes app usability was associated with psychological needs that are important for motivation. To enhance patient motivation to use diabetes apps for self-management, clinicians should address competence, autonomy, and connectivity by teaching BG pattern recognition and lifestyle planning, customizing BG targets, and reviewing home-monitored data via email. App usability could be improved for older male users and those with less education and greater diabetes duration by tailoring app training and providing ongoing technical support.
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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 | 0,005 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».