Impact of a Cloud-based Clinical Decision Support System for Addressing Physical Activity and/or Healthy Eating during Smoking Cessation Treatment: Hybrid Type I Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND People who smoke also have other risk factors for chronic disease such as sedentary behaviours and poor diet. Usual practice is to address smoking with the exclusion of these other behaviours. Clinical decision support systems (CDSS) are a promising resource to effectively support health care practitioners integrate interventions for diet and physical activity as part of their smoking cessation programming. OBJECTIVE The aims of this study are to: (1) assess whether adding a CDSS for physical activity and diet to a smoking cessation program affects smoking cessation outcomes, and (2) assess the implementation of the study. METHODS We conducted a pragmatic, hybrid type I effectiveness/implementation trial with 232 team-based primary care practices in Ontario Canada from November 2019 to May 2021. We measured the effectiveness of the CDSS using a two-arm randomized control trial comparing a CDSS for addressing physical activity and diet as part of a smoking cessation program with treatment as usual, and used the RE-AIM Framework to measure implementation outcomes. RESULTS In total, 5331 smokers did not meet the recommended Canadian guidelines for physical activity and/or fruit/vegetable consumption were enrolled in this study). We randomized 2599 people to the control group and 2732 to the intervention group. At the six month follow-up, 552 of 2020 respondents (27.3%) in the control arm and 634 of 2137 respondents (29.7%) in the intervention arm reported abstinence from tobacco. After multiple imputation, these proportions were 25.9% (95% CI = 24.2%, 27.6%) and 28.0% (95% CI = 26.1%, 29.8%), respectively, corresponding to an absolute group difference of 2.1% (95% CI = -0.5%, 4.6%). This difference did not meet our threshold for significance (F(1, 1000.42)=2.43, p=0.12). From baseline to six month follow-up, mean exercise minutes changed from 32 to 113 in the control arm and from 32 to 110 in the intervention arm (group effect: B = -3.7 minutes, 95% CI = -17.8, 10.4, p=0.61). For servings of fruit and vegetables, group means changed from 2.52 at baseline to 2.45 at six month in the control group and from 2.64 to 2.42 in the intervention group (incidence rate ratio for intervention group = 0.98, 95% CI = 0.93, 1.02, p=0.35). CONCLUSIONS Our study findings illustrate that the introduction of a CDSS that guides health care practitioners to address multiple health behaviours among their patients did not negatively affect smoking cessation outcomes and did not have a significant impact on participants’ physical activity nor fruit/vegetable consumption. Although there are still challenges that need to be addressed, thoughtful implementation of CDSS could transform the smoking cessation program into a more holistic program in primary care. CLINICALTRIAL ClinicalTrials.gov (NCT04223336). https://clinicaltrials.gov/ct2/show/NCT04223336 INTERNATIONAL REGISTERED REPORT RR2-10.2196/19157
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".