Change in Patient Comfort Using Mobile Phones Following the Use of an App to Monitor Tuberculosis Treatment Adherence: Longitudinal Study
Notice bibliographique
Résumé
BACKGROUND: As mHealth apps proliferate, it is necessary for patients to feel capable and comfortable using devices that run them. However, limited research is available on changes in comfort level before and after the use of an mHealth app. OBJECTIVE: The objective of this study was to determine whether patients with tuberculosis who used an mHealth app called Video Directly Observed Therapy (VDOT) to monitor their antituberculosis treatment became more comfortable using mobile phones after the intervention and to identify factors associated with change in comfort. METHODS: We analyzed data from a longitudinal study assessing the feasibility and acceptability of the VDOT app among patients receiving antituberculosis treatment from public health departments in San Diego, San Francisco, and New York City. Comfort levels on six domains of mobile phone use (making phone calls, taking pictures, recording videos, text messaging, internet and email use on the phone) were measured on a 10-point scale (1=very uncomfortable; 10=very comfortable) at the start and end of treatment using VDOT via telephone interviews. The main outcomes were change in comfort level on each domain (recoded as binary measures) and an overall change score (sum of individual measures). Linear and logistic regression analyses were performed to assess whether sociodemographics, risk factors, and VDOT perceptions were associated with change of comfort measures. RESULTS: Among 120 participants with complete data, mean age was 39.8 years (SD 14.8, range 18-87 years), 46.7% (56/120) were female, and 76.7% (92/120) were foreign born. The combined comfort level at baseline was high overall (mean 48.8, SD 14.2, interquartile range 43.0-60.0) and the mean comfort score increased by 1.92 points at follow-up (P=.07). Statistically significant increases in comfort on individual domains included taking pictures (P=.02) and recording videos (P=.002). Females were more likely to have increased comfort in using the internet on the phone compared to males (odds ratio [OR] 3.03, 95% CI 1.08-8.52, P=.04). Participants who worked less hours per week were more likely to have increased comfort recording videos although this did not meet statistical significance (OR 1.03, 95% CI 1.00-1.05, P=.06). CONCLUSIONS: Findings suggest that, despite a high level of comfort using mobile phones at baseline, experience using the VDOT app was associated with increased comfort using mobile phone features. Additional research involving participants with lower baseline mobile phone experience is needed. An implication of these findings is that as patients begin to use mHealth apps for one health condition, they could acquire skills and confidence to more quickly adapt to using mHealth apps for other conditions.
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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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».