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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".