Risk Factors for Community-Dwelling Older Adults Dropping Out of Self-Guided, Remote, and Web-Based Longitudinal Research: Predictive Modeling of Data from the Web-LABrainS Platform
Notice bibliographique
Résumé
Background: Little is currently known regarding the feasibility of using a self-guided, remote, web-based platform as the basis for a longitudinal study of aging in community-dwelling older adults (OAs). This study describes the feasibility and risk factors for participant dropout found when using this approach as part of the web-based Louisiana Aging Brain Study (web-LABrainS). Objective: This study used data from 402 participants in the web-LABrainS effort to determine the feasibility of using a self-guided, remote, and web-based platform as the basis for conducting longitudinal research in community-dwelling older adults. Additionally, we sought to determine the risk factors associated with participant dropout over a 12-month period in web-LABrainS and determine whether the same risk factors associated with dropout from in-clinic longitudinal studies were observed in web-LABrainS dropouts. Methods: Participants were enrolled in web-LABrainS on a rolling basis using word-of-mouth promotional efforts. Participants used the web-LABrainS platform to provide electronic consent, demographic and health information, answer questionnaires, and complete assessments as part of a self-guided and web-based effort off-site of the clinic (remote). Following completion of the baseline battery, participants were contacted by email every 6 months to complete another round of the web-LABrainS battery. The data in this study were collected from 402 participants, 217 (54.0%) of whom completed baseline, 6-month, and 12-month assessments (adherent participants) and 185 (46%) of whom participated in only the baseline and no subsequent web-LABrainS batteries (dropout participants). Results: Our study indicates that even with limited participant outreach and retention efforts, it is feasible to conduct longitudinal clinical research studies in community-dwelling OAs using a self-guided, remote, and web-based approach. In contrast to traditional in-clinic longitudinal studies, dropouts from web-LABrainS were not observed to be significantly different with respect to age, education, gender, marital status, or living alone (P=.67, .16, .29, .051, .31). Similar to traditional longitudinal studies, dropouts from web-LABrainS had significantly higher use of depression medication, decreased self-reported mobility, and decreased delayed recall performance (P=.007, .007, .004). Interestingly, no differences in technology use, comfort with technology, time of assessment, or consent to be contacted about future research were observed between adherents and dropouts (P=.17, .36, .47, .40). Predictive binary logistic regression yielded a moderately accurate model and further supported a negative association between cognitive ability and dropout (OR 0.77, 95% CI 0.61-0.96). Conclusions: Our study provides some of the first clinical evidence for the feasibility of conducting longitudinal human research using a self-guided, remote, and web-based approach. Additionally, these data highlight the similarities and differences in key factors associated with participant dropout using this type of approach compared to traditional longitudinal study formats. The findings from this study may help guide the design and deployment of future longitudinal studies of older adults focused on self-guided, remote, or web-based approaches.
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 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 | 0,004 | 0,002 |
| 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 | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 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 ».