Predicting Patients’ Intention to Use a Personal Health Record Using an Adapted Unified Theory of Acceptance and Use of Technology Model: Secondary Data Analysis
Notice bibliographique
Résumé
BACKGROUND: With the rise in the use of information and communication technologies in health care, patients have been encouraged to use eHealth tools such as personal health records (PHRs) for better health and well-being services. PHRs support patient-centered care and patient engagement. To support the achievement of the Kingdom of Saudi Arabia's Vision 2030 ambitions, the National Transformation program provides a framework to use PHRs in meeting the 3-fold aim for health care-increased access, reduced cost, and improved quality of care-and to provide patient- and person-centered care. However, there has been limited research on PHR uptake within the country. OBJECTIVE: Using the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical framework, this study aims at identifying predictors of patient intention to utilize the Ministry of National Guard-Health Affairs PHR (MNGHA Care) app. METHODS: Using secondary data from a cross-sectional survey, data measuring the intention to use the MNGHA Care app, along with its predictors, were collected from among adults (n=324) visiting Ministry of National Guard-Health Affairs facilities in Riyadh, Jeddah, Dammam, Madinah, Al Ahsa, and Qassim. The relationship of predictors (main theory constructs) and moderators (age, gender, and experience with health apps) with the dependent variable (intention to use MNGHA Care) was tested using hierarchical multiple regression. RESULTS: Of the eligible population, a total of 261 adult patients were included in the analysis. They had a mean age of 35.07 (SD 9.61) years, 50.6 % were male (n=132), 45.2% had university-level education (n=118), and 53.3% had at least 1 chronic medical condition (n=139). The model explained 48.9% of the variance in behavioral intention to use the PHR (P=.38). Performance expectancy, effort expectancy, and positive attitude were significantly associated with behavioral intention to use the PHR (P<.05). Prior experience with health apps moderated the relationship between social influence and behavioral intention to use the PHR (P=.04). CONCLUSIONS: This study contributes to the existing literature on PHR adoption broadly as well as in the context of the Kingdom of Saudi Arabia. Understanding which factors are associated with patient adoption of PHRs can guide future development and support the country's aim of transforming the health care system. Similar to previous studies on PHR adoption, performance expectancy, effort expectancy, and positive attitude are important factors, and practical consideration should be given to support these areas.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».