Text2Plan: Exploring changes in the quantity and quality of action plans and physical activity in a text messaging intervention
Bibliographic record
Abstract
BACKGROUND: The primary purpose of our study was to determine if the content and tailoring of text messages affected action planning and physical activity. Second, we determined if the quantity and the quality of action plans changed between a month of receiving text messages (T1-T2) and a month without text messages (T2-T3). We further explored if the quantity and quality of action plans predicted changes in physical activity at T2 and T3. METHODS: Adults (n = 337, M(age) = 30.72 ± 4.80) with intentions to be active were recruited on the internet. Participants were assigned to receive tailored text messages about action planning for physical activity, generic text messages about action planning for physical activity or generic text messages about physical activity. All participants received weekly planning tools. At T2 and T3, number of action plans created each month was tallied to generate a plan quantity score. For each plan created, three components (what, where and when) were assessed by independent coders to determine plan quality. Physical activity was assessed at each time point using the Godin Leisure Time Exercise Questionnaire. Mixed model ANOVA, paired sample t-tests and multiple regression were applied to test our hypotheses. RESULTS: There were no differences in action planning or physical activity based on the content or tailoring of text messages. The absence of text messages corresponded with declines in the quantity, but not the quality, of action plans between T2 and T3. The quantity of action plans predicted changes in physical activity. CONCLUSIONS: Although there were no differences in action planning or physical activity based on the content or tailoring of messages, the absence of text messages corresponded with declines in the quantity, but not the quality, of action plans. Furthermore, the quantity of action plans predicted changes in physical activity. Future research is needed to determine ways to facilitate sustained formation of multiple, specific action plans over the duration of action planning interventions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".