How much will we pay to increase steps per day? Examining the cost-effectiveness of a pedometer-based lifestyle program in primary care
Bibliographic record
Abstract
We previously demonstrated the Healthy Eating and Active Living for Diabetes (HEALD) intervention was effective for increasing daily steps. Here, we consider the cost-effectiveness of the HEALD intervention implemented in primary care. HEALD was a pedometer-based program for adults with type-2 diabetes in Alberta, Canada completed between January 2010 and September 2012. The main outcome was the change in pedometer-determined steps/day compared to usual care. We estimated total costs per participant for HEALD, and total costs of health care utilization through linkage with administrative health databases. An incremental cost-effectiveness ratio (ICER) was estimated with regression models for differences in costs and effects between study groups. The HEALD intervention cost $340 per participant over the 6-month follow-up. The difference in total costs (intervention plus health care utilization) was $102 greater per HEALD participant compared to usual care. The intervention group increased their physical activity by 918 steps/day [95% CI 116, 1666] compared to usual care. The resulting ICER was $111 per 1000 steps/day, less than an estimated cost-effectiveness threshold. Increasing daily steps through an Exercise Specialist-led group program in primary care may be a cost-effective approach towards improving daily physical activity among adults with type-2 diabetes. Alternative delivery strategies may be considered to improve the affordability of this model for primary care.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".