Motivational Interviewing to Promote Healthy Lifestyle Behaviors: Evidence, Implementation, and Digital Applications
Bibliographic record
Abstract
Ahmed A Bahri Department of Family and Community Medicine, Jazan University, Jazan, Saudi ArabiaCorrespondence: Ahmed A Bahri, Email dr.bahri2010@gmail.comBackground: Chronic lifestyle-related diseases, including poor diet, physical inactivity, and smoking, pose a global health challenge. Motivational Interviewing (MI), a client-centered approach, effectively promotes behavior change. This review examines MI’s impact on healthy lifestyle behaviors and its implications for healthcare practice and research.Methodology: Relevant studies published between 2015 and 2025 were sourced from PubMed, Google Scholar, and ScienceDirect. Inclusion criteria focused on research evaluating the role of MI in promoting healthy behaviors, while studies unrelated to MI were excluded. The selected literature included diverse study designs assessing both the effectiveness and implementation of MI across various health domains.Results: The evidence shows that MI promotes short-term improvements in diet, physical activity, smoking cessation, and treatment adherence. It encourages healthier eating patterns, greater exercise participation, and higher abstinence rates, while also enhancing engagement in psychological therapies. However, effectiveness is highly dependent on practitioner fidelity and demographic tailoring, with outcomes varying by gender, age, and population needs. Despite promising initial results, long-term benefits often decline due to dropout, logistical challenges, and socioeconomic barriers. Digital and hybrid delivery models offer a potential solution, improving accessibility, scalability, and sustained adherence, and represent an important direction for future implementation.Conclusion: MI is a versatile, evidence-based tool for fostering healthy lifestyle behaviors. Its non-confrontational, autonomy-supportive approach aligns well with diverse clinical and public health contexts. Future research should prioritize scalable digital/hybrid models, strategies for sustained outcomes, and implementation science frameworks to address real-world barriers.Keywords: motivational interviewing, MI, smoking cessation, substance use disorder, telehealth care, chronic diseases
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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 | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".