Acceptance Factors of Mobile Apps for Diabetes by Patients Aged 50 or Older: A Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Mobile apps for people with diabetes offer great potential to support therapy management, increase therapy adherence, and reduce the probability of the occurrence of accompanying and secondary diseases. However, they are rarely used by elderly patients due to a lack of acceptance. OBJECTIVE: We investigated the question "Which factors influence the acceptance of diabetes apps among patients aged 50 or older?" Particular emphasis was placed on the current use of mobile devices/apps, acceptance-promoting/-inhibiting factors, features of a helpful diabetes app, and contact persons for technical questions. This qualitative study was the third of three substudies investigating factors influencing acceptance of diabetes apps among patients aged 50 or older. METHODS: Guided interviews were chosen in order to get a comprehensive insight into the subjective perspective of elderly diabetes patients. At the end of each interview, the patients tested two existing diabetes apps to reveal obstacles in (first) use. RESULTS: Altogether, 32 patients with diabetes were interviewed. The mean age was 68.8 years (SD 8.2). Of 32 participants, 15 (47%) knew apps, however only 2 (6%) had already used a diabetes app within their therapy. The reasons reported for being against the use of apps were a lack of additional benefits (4/8, 50%) compared to current therapy management, a lack of interoperability with other devices/apps (1/8, 12%), and no joy of use (1/8, 12%). The app test revealed the following main difficulties in use: nonintuitive understanding of the functionality of the apps (26/29, 90%), nonintuitive understanding of the menu navigation/labeling (19/29, 66%), font sizes and representations that were too small (14/29, 48%), and difficulties in recognizing and pressing touch-sensitive areas (14/29, 48%). Furthermore, the patients felt the apps lacked individually important functions (11/29, 38%), or felt the functions that were offered were unnecessary for their own therapy needs (10/29, 34%). The most important contents of a helpful diabetes app were reported as the ability to add remarks to measured values (9/28, 32%), the definition of thresholds for blood glucose values and highlighting deviating values (7/28, 25%), and a reminder feature for measurement/medication (7/28, 25%). The most important contact persons for technical questions were family members (19/31, 61%). CONCLUSIONS: A lack of additional benefits and ease of use emerged as the key factors for the acceptance of diabetes apps among patients aged 50 or older. Furthermore, it has been shown that the needs of the investigated target group are highly heterogeneous due to varying previous knowledge, age, type of diabetes, and therapy. Therefore, a helpful diabetes app should be individually adaptable. Personal contact persons, especially during the initial phase of use, are of utmost importance to reduce the fear of data loss or erroneous data input, and to raise acceptance among this target group.
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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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 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.
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