Determining the Right Levels of Health Coaching and Heart Rate Variability Biofeedback in a Workplace Behavior Change Intervention: Multiphase Optimization Strategy Preparation Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Work-related stress is associated with poor job performance and negative health outcomes. Changing health behaviors through corporate wellness programs can improve physical and mental health and help employees manage stress. This project sought to pilot the potential addition of brief coaching and biofeedback to an 8-week web-based self-help program to improve employee stress using the multiphase optimization strategy. OBJECTIVE This study aims to determine which candidate components will be tested in a later optimization phase and at what dose they will be tested, examine the feasibility and acceptability of delivering the different components, investigate whether the outcomes can be feasibly measured, and review evidence to build a conceptual model before the optimization phase. METHODS The study was positioned within the preparation phase of the multiphase optimization strategy. It is a 2×2×2×2 design with 4 components: 2 types of health coaching and 2 types of biofeedback. All components were tested by turning them on or off. A total of 16 adult office workers (mean age 40, SD 14.3 years; n=15 women) completed an 8-week self-paced web-based stress management and health behavior change program and were randomly assigned to 1 of the 16 conditions, created from a combination of the 4 candidate components. Assessments included web analytics, surveys, and interviews regarding program recommendations, likes, and dislikes. RESULTS Findings from the interviews provided suggestions to improve the intervention (eg, separating wellness from stress content) and trial conduct (eg, streamlining the onboarding process). On average, participants logged into the wellness program 83 times (range 36-291), with 75% (12/16) participant retention and 67% (8/12) survey completion. There were no reported problems with coaching or obtaining data from interviews or apps. The interview findings suggested potential mediators to include and assess in a future conceptual model. CONCLUSIONS The results provided areas to improve the intervention content and trial methods. Instead of progressing to the next scheduled large-scale optimization phase, our plan to iterate through a second preparation phase after making changes to the protocol, apps, and corporate coaching partner. CLINICALTRIAL
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".