An Intervention for Changing Sedentary Behavior Among African Americans With Multiple Sclerosis: Protocol
Bibliographic record
Abstract
BACKGROUND: Sedentary behavior is a major concern among patients with multiple sclerosis (MS), as it may accelerate disease progression and exacerbate physical disability. This is especially concerning among African Americans, a segment of the MS population who present with greater neurological disability and higher odds of physical comorbidities than their Caucasian counterparts. OBJECTIVE: To date, researchers have not proposed interventions that focus on changing sedentary behavior in African Americans with MS. METHODS: This paper describes a pilot study that examines the feasibility and efficacy of using text messaging along with theory-driven newsletters and behavioral coaching for changing sedentary behavior in African Americans with MS. We herein present the methods, procedures, and outcomes for our ongoing study. RESULTS: Enrollment began in February 2018 and is expected to conclude in April 2019. Study results will be reported in the fall of 2019. CONCLUSIONS: After completion of this pilot intervention, we will summarize our study results in manuscripts for publication in peer-reviewed journals that will provide critical information on the feasibility and efficacy of our strategy. These results will inform future studies and, potentially, larger interventions for remotely reducing sedentary behavior in African Americans with MS. TRIAL REGISTRATION: ClinicalTrials.gov NCT03671499; https://clinicaltrials.gov/ct2/show/NCT03671499 (Archived by WebCite at http://www.webcitation.org/77MZnxyNy). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12973.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.104 | 0.016 |
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".