Impact of Providing a Personalized Data Dashboard on Ecological Momentary Assessment Compliance Among College Students Who Use Substances: Pilot Microrandomized Trial
Bibliographic record
Abstract
BACKGROUND: The landscape of substance use behavior among young adults has observed rapid changes over time. Intensive longitudinal designs are ideal for examining and intervening in substance use behavior in real time but rely on high participant compliance in the study protocol, representing a significant challenge for researchers. OBJECTIVE: This study aimed to evaluate the effect of including a personalized data dashboard (DD) in a text-based survey prompt on study compliance outcomes among college students participating in a 21-day ecological momentary assessment (EMA) study. METHODS: Participants (N=91; 61/91, 67% female and 84/91, 92% White) were college students who engaged in recent alcohol and cannabis use. Participants were randomized to either complete a 21-day EMA protocol with 4 prompts/d (EMA Group) or complete the same EMA protocol with 1 personalized message and a DD indicating multiple metrics of progress in the study, delivered at 1 randomly selected prompt/d (EMA+DD Group) via a microrandomized design. Study compliance, completion time, self-reported protocol experiences, and qualitative responses were assessed for both groups. RESULTS: Levels of compliance were similar across groups. Participants in the EMA+DD Group had overall faster completion times, with significant week-level differences in weeks 2 and 3 of the study (P=.047 and P=.03, respectively). Although nonsignificant, small-to-medium effect sizes were observed when comparing the groups in terms of compensation level (P=.08; Cohen w=0.19) and perceived burden (P=.09; Cohen d=-0.36). Qualitative findings revealed that EMA+DD participants perceived that seeing their progress facilitated engagement. Within the EMA+DD Group, providing a DD at the moment level did not significantly impact participants' likelihood of completing the EMA or completion time at that particular prompt (all P>.05), with the exception of the first prompt of the day (P=.01 and P<.001). CONCLUSIONS: Providing a DD may be useful to increase engagement, particularly for researchers aiming to assess health behaviors shortly after a survey prompt is deployed to participants' mobile devices. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/57664.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".