Acceptance, Drivers, and Barriers to Use of mHealth Apps to Improve Quality of Life in Female Patients Affected by Hypothyroidism: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Hypothyroidism is a common chronic disease that can substantially impair physical and mental well-being and is associated with lower quality of life, a trend that interventions delivered by mobile health (mHealth) apps could ameliorate. Objective: The objective of this study was to evaluate the acceptance and its influencing predictors of mHealth interventions in female patients affected by hypothyroidism to improve their quality of life. The focus on female patients reflects the significantly higher prevalence of hypothyroidism in women and their underrepresentation in many prior studies on technology acceptance and mHealth use. Methods: A survey-based, cross-sectional study, which included 318 female patients affected by hypothyroidism (assessed via self-reported diagnosis according to International Classification of Diseases-10 criteria, aged 18 y or older), was conducted online between April 2023 and April 2024 in Germany. Participants were recruited via local and online self-help groups, social media platforms, and medical practices using flyers. Sociodemographic, health, and eHealth-related data were assessed. To determine acceptance and its drivers and barriers, an extended version of the unified theory of acceptance and use of technology (UTAUT) model was applied. Group comparisons (t tests, ANOVAs) and multiple hierarchical regression analyses were conducted. Only complete datasets were included in the analysis. Results: Acceptance of mHealth apps was high (mean 4.10, SD 0.91), with 76.1% (n=242) of the participants reporting high acceptance, 18.6% (n=59) reporting moderate acceptance, and only 5.3% (n=17) reporting low acceptance. Significant predictors of acceptance were place of residence: medium-sized city (β=0.34; P=.02) and small town or rural area (β=0.28; P=.003), fatigue (β=0.54; P<.001), internet anxiety (β=-0.20; P=.002), and the UTAUT predictors effort expectancy (β=0.37; P<.001), performance expectancy (β=0.32; P<.001), and social influence (β=0.20; P<.001). The extended model explained 56.1% of the variance in acceptance. Conclusions: The high level of acceptance of mHealth apps observed among female patients affected by hypothyroidism indicates that mHealth interventions can provide such patients with valuable support to manage the disease and improve their quality of life. Addressing drivers and barriers of acceptance will be crucial for the successful implementation of mHealth interventions in hypothyroidism management, for example, by mHealth developers, clinicians, or policy makers. These include intuitive and accessible design (effort expectancy), clear communication of app benefits (performance expectancy), and fostering health care professional support (social influence), while addressing barriers such as internet anxiety. The study also contributes to advancing gender-sensitive mHealth research by applying the UTAUT model to this patient group.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».