Examining the influence of a text message-based sleep and physical activity intervention among young adult smokers in the United States
Bibliographic record
Abstract
BACKGROUND: Sleep and physical activity are two health behaviors associated with improved smoking cessation outcomes. Text message-based interventions have previously been used to promote physical activity and smoking cessation; however, this type of intervention has not targeted sleep habits. This study examined the effectiveness of a text message-based active control intervention in improving sleep and physical activity habits among a U.S. national sample of young adult smokers participating in a smoking cessation intervention. METHODS: This study was a secondary analysis of data from the Stop My Smoking USA randomized controlled trial. Baseline and 3-month follow-up data were collected from 116 young adult smokers (mean age 21.8 years, SD = 2.1) who were randomized at a 2:1 ratio to receive a 6-week text messaging program focused on either smoking cessation (n = 72), or improving sleep and physical activity (n = 44). Three main outcomes were assessed: 1) sleep quantity (on work/school nights, and non-work/non-school nights), 2) sleep quality, and 3) physical activity at follow-up. Multivariable linear regression analysis was used to quantify the differences in these outcomes between the groups. To identify possible effect modification by baseline sleep and physical activity, the sample was stratified by indicators defined for both of these variables. RESULTS: At follow-up, sleep quantity and quality were similar for participants in the smoking cessation and sleep/activity groups when assessed among the total sample and those sleeping ≥6 hours/night at baseline. Among short sleepers (<6 hours/night at baseline), sleep quantity on work/school nights improved for those receiving sleep/activity messages compared to those receiving smoking cessation messages, after adjusting for covariates (^β = 1.373, 95% CI [0.262, 2.484]; p = 0.02). Physical activity at follow-up was similar for the two groups, when examined among the total sample and when stratified by baseline activity level. CONCLUSIONS: This study provides preliminary evidence that a text message-based intervention may be a promising approach for improving sleep quantity among young adult smokers who are short sleepers and interested in quitting smoking. Similar programs should be further explored as a novel approach for improving sleep habits among individuals with insufficient sleep. TRIAL REGISTRATION: ClinicalTrials.gov NCT01516632.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".