TEXT2PLAN: TESTING THE EFFECTIVENESS OF TAILORED TEXT MESSAGES FOR PROMOTING PLANNING FOR PHYSICAL ACTIVITY
Bibliographic record
Abstract
Text messages can promote physical activity plan execution (Prestwich, Perugini & Hurling, 2010), but it is unknown if they can promote plan formation. Our study investigated whether text messages could be used to promote the formation of physical activity plans. We determined if 1) text messages about planning increased planning more than text messages about physical activity, 2) if tailored text messages about planning increased planning more than generic text messages about planning, and 3) if planning was maintained over time. Participants were inactive adults (n=239, Mage=30.7±4.8yrs) with access to email and text messaging. Participants received generic messages about physical activity, generic messages about planning or tailored messages about planning. Each week for two months, participants were emailed a tool to plan their physical activity. Whether participants used this tool was assessed at baseline (T0), after one month of receiving text messages (T1) and after an additional month without text messages (T2). There were no differences in planning between groups that received messages about planning or physical activity at T1 or T2, ps>.05. More participants who received tailored text messages about planning made at least one plan by T1 than participants who received generic messages about planning, χ2(1)=3.889, p .05. For all groups, planning was maintained from T0 to T1, ps>.05, but decreased from T1 to T2, McNemars χ2(1)>17.455, ps<.001. Generic text messages about physical activity or tailored messages about planning can increase planning, but planning may not be sustained over time.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".