An exploration of health action process approach post-intentional factors and the use of physical activity devices and apps
Bibliographic record
Abstract
Physical activity requires long-term participation to reap health benefits. After the formation of behavioural intentions, initiation and maintenance are required. Many individuals who initiate physical activity do not maintain the behaviour (see Marcus 2005). The Health Action Process Approach (HAPA; Schwarzer, 2008) identifies post-intentional processes related to behaviour maintenance. Advances in technology have resulted in numerous devices (e.g., Nike Fuel Band) and APPS (e.g., Endomondo) that could possibly serve to boost post-intentional processes by providing exercisers convenient access to goal-setting, planning and monitoring tools. This study included a cross-sectional comparison of HAPA post-intentional processes among exercisers who do (N=29) and do not (N=76) use physical activity devices or APPS. Adult exercisers completed an online questionnaire assessing use of devices and APPS, HAPA post-intentional processes and physical activity behaviour. Box’s M indicated no significant group difference in covariance matrices (p = .57). A significant multivariate effect was found (Wilks’ Lambda =.84; F [6, 91] = 2.84, p =.01). Follow-up univariate analyses were calculated with adjusted alpha (p<.008).In the presence of significant Levene’s tests (p<.05), Brown-Forsythe’s F was calculated as a robust measure of group differences. Recovery self-efficacy was greater among exercisers who used tracking devices and APPS compared to those who did not (F [1,96]= 11.41, p = 0.007). There were trends toward differences in maintenance self-efficacy (F [1, 96] = 5.44, p =.02), action control (Brown-Forsythe = 6.08, p =.02) and planning (F [1,96] = 13.84, p =.02). The use of tracking devices and APPS may be related to greater recovery self-efficacy. Tools that boost recovery self-efficacy may be valuable given the likelihood of lapses (see Marcus et al., 2000). Future research should further explore the use of physical activity tracker devices and APPS in relation to post-intentional processes related to long-term physical activity.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".