Effects of the Active Choices Program on Self-Managed Physical Activity and Social Connectedness in Australian Defence Force Veterans: Protocol for a Cluster-Randomized Trial
Bibliographic record
Abstract
BACKGROUND: A stepped-down program is one in which clients transition from the care of a health professional to self-managed care. Very little is known about the effectiveness of stepped-down physical activity (PA) programs for military service veterans. OBJECTIVE: This study will test Active Choices, a stepped-down behavioral support program designed to help Australian Defence Force veterans and their dependents who are clients of the Department of Veterans' Affairs, transition from treatment by an exercise physiologist or physiotherapist to self-managed PA. METHODS: The study is a parallel-group, randomized trial, with city-based exercise physiology or physiotherapy practices that recruit eligible Department of Veterans' Affairs clients assigned to Active Choices or a comparison program. The study aims to recruit 52 participants (26 in each group). The Active Choices program will consist of 2 face-to-face (Weeks 1, 12) and 2 telephone (Weeks 4 and 8) consultations. During these sessions, the participant and Active Choices consultant will utilize an evidence-based resource booklet to review the key benefits of an active lifestyle, build an action plan for PA preferences, set and review goals, self-monitor progress relative to set goals, and discuss strategies to overcome PA barriers. Linking participants to local PA communities to overcome social isolation will be a program priority. The comparison program will consist of 2 consultations (Weeks 1 and 12) and use fewer behavioral support strategies (education, self-monitoring, and action planning only) than Active Choices. Outcome measures will be administered at baseline, end-intervention (12 weeks), and follow-up (24 weeks) to assess changes in moderate intensity self-managed PA, psychological well-being, and social connectedness. We will also measure health service utilization and costs as well as PA choices across the intervention period. End-intervention interviews will capture participant experiences. RESULTS: Due to the impacts of the COVID-19 pandemic on human research activities in Australia, participant recruitment will commence when it is safe and feasible to do so. CONCLUSIONS: Findings will provide valuable pilot data to support up-scaling of the program and larger effectiveness trials with regional and rural as well as city-based Australian Defence Force veterans and their dependents. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry (ANZCTR): ACTRN12620000559910; https://www.anzctr.org.au/ACTRN12620000559910.aspx. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/21911.
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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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 0.012 |
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".