Temporal Dynamics of Treatment Receipt in a Text Message Intervention for Physical Activity: Single-Group, Within-Person Trial
Bibliographic record
Abstract
BACKGROUND: Mobile technology has increased the reach of health behavior interventions but raised new challenges in assessing the fidelity of treatment receipt. Fidelity can be compromised if participant fatigue or burden reduces engagement, leading to missed or delayed treatments for just-in-time interventions. OBJECTIVE: This study aimed to investigate the temporal dynamics of text message receipt confirmations. METHODS: Community-dwelling adults (N=10) were sent five text messages daily for 4 months (5598 messages sent in total), with a financial incentive to confirm receipt of 75% or more messages. RESULTS: Overall, the message receipt confirmation rate was very high (5504/5598, 98.32%) and timely (eg, two-thirds of confirmations within 2 min). Confirmation times were slightly slower on weekends (vs weekdays) and as a function of the cumulative time in the study. Neither time of message delivery nor message content was associated with message confirmation latencies. CONCLUSIONS: Participants receiving financial incentives to confirm text message receipt exhibit extremely high and fast confirmation rates, although receipt confirmations were somewhat less timely on weekends (vs weekdays) and later in the intervention. The social calendar and treatment fatigue should be considered when planning text message-based interventions, especially if treatments are intended for a just-in-time delivery that requires extended engagement and precise timing.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".