A distance-based, randomized controlled trial for reducing sedentary behavior among prostate cancer survivors: a study protocol
Bibliographic record
Abstract
BACKGROUND: Prostate cancer survivors (PCS) experience long-term side effects beyond treatment such as fatigue, depression and anxiety. Quality and engaging supportive care programs are needed to reduce these chronic and debilitating effects. Independent of physical activity (PA), high volumes of sedentary behavior (SB) are associated with chronic disease-related risk factors and poorer cancer-specific quality of life (QoL). Simultaneously increasing PA and decreasing SB may be an effective health promotion strategy. Given that PCS may face several barriers to engaging in supervised programs, there is a need to develop and assess the efficacy of interventions that employ distance-based approaches for behavior change. The primary aim of this study is to determine the effects of a 12-week intervention (Fitbit + behavioral counselling) vs. Fitbit-only control group in reducing SB among PCS. Secondary outcomes include light-intensity PA, QoL, motivational outcomes, and patient satisfaction. METHODS: This two-armed, randomized controlled trial will recruit inactive PCS (stage I-IV) across Canada who self-report engaging in >8 hours/day of SB. Participants will be randomized to the intervention (n=60; Fitbit and behavioral support) or active control group (n=60; Fitbit-only). The intervention consists of the use of a Fitbit and a series of six behavioral support sessions (two group, four individual) to aid PCS in gradually replacing SB with light-intensity PA by increasing their daily step counts to 3,000 steps above their baseline values. The Fitbit-only control condition will receive a Fitbit and public health PA resources. The primary outcome is change in SB measured objectively using activPAL inclinometers. All secondary outcomes will be measured via self-report, except for PA which will be measuring using Fitbits. Data will be collected at baseline, post-intervention, and at 6-month post-intervention. DISCUSSION: Reducing SB and increasing light-intensity PA plays an important, yet often undervalued role in the health and well-being of PCS. This study will create a unique distance-based platform that can be used by clinical and community-based organizations as a low-cost, supportive care tool to improve health outcomes for PCS. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT05214937 . Registered January 28, 2022 Protocol version: v.1.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".