Smartphone App–Based Noncontact Ecological Momentary Assessment With Experienced and Naïve Older Participants: Feasibility Study
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
BACKGROUND: Smartphone app-based ecological momentary assessment (EMA) without face-to-face contact between researcher and participant (app-based noncontact EMA) potentially provides a valuable data collection tool when geographic, time, and situational factors (eg, COVID-19 restrictions) place constraints on in-person research. Nevertheless, little is known about the feasibility of this method, particularly in older and naïve EMA participants. OBJECTIVE: This study aims to assess the feasibility of app-based noncontact EMA as a function of previous EMA experience, by recruiting and comparing a group of participants who had never participated in EMA before against a group of participants who had been part of an earlier in-person EMA study, and age, by recruiting middle-aged to older adults. METHODS: Overall, 151 potential participants were invited via email; 46.4% (70/151) enrolled in the study by completing the baseline questionnaire set and were emailed instructions for the EMA phase. Of these participants, 67% (47/70) downloaded an EMA app and ran the survey sequence for 1 week. In total, 5 daytime surveys and 1 evening survey, each day, assessed participants' listening environment, social activity, and conversational engagement. A semistructured exit telephone interview probed the acceptability of the method. As markers of feasibility, we assessed the enrollment rate, study completion rate, reason for noncompletion, EMA survey response rate, and likelihood of reporting an issue with survey alerts and requested assistance from researchers, family, or friends. RESULTS: Enrollment rates among invitees (63.3% vs 38.2%; P=.004) and completion rates among enrollees (83.9% vs 53.8%; P<.001) were higher in the experienced than in the naïve EMA group. On average, experienced participants responded to 64.1% (SD 30.2%) of the daytime EMA surveys, and naïve participants responded to 54.3% (SD 29.5%) of the daytime EMA surveys (P=.27). Among participants who retrospectively reported issues with survey alerts, only 19% (3/16) requested researcher assistance during data collection. Older participants were more likely to report not being alerted to EMA surveys (P=.008), but age was unrelated to all other markers of feasibility. Post hoc analyses of the effect of the phone operating system on markers of feasibility revealed that response rates were higher among iOS users (mean 74.8%, SD 20.25%) than among Android users (mean 48.5%, SD 31.35%; P=.002). CONCLUSIONS: Smartphone app-based noncontact EMA appears to be feasible, although participants with previous EMA experience, younger participants, and iOS users performed better on certain markers of feasibility. Measures to increase feasibility may include extensive testing of the app with different phone types, encouraging participants to seek timely assistance for any issues experienced, and recruiting participants who have some previous EMA experience where possible. The limitations of this study include participants' varying levels of existing relationship with the researcher and the implications of collecting data during the COVID-19 social restrictions.
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.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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) | 0,004 | 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 découle