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Abstract

Non-communicable diseases (NCDs) are the leading cause of mortality and morbidity worldwide. Underlying NCDs are modifiable risk factors, which may be targeted through Lifestyle Medicine (LM). LM is an evidence-based and clinical discipline that supports healthy lifestyle habits. Much of LM integration in practice is rooted in the education afforded within health professional’s curricula. The study aimed to determine the effectiveness of LM educational interventions within health professional’s curricula on knowledge, competence, self-efficacy/confidence and skills. A systematic review and meta-analysis were conducted with data analyzed using descriptive statistics and a Random Effect Meta-analysis. A total of 14 studies were included. Interventions centered around substance use, nutrition and physical activity with no studies obtained on the sleep health, stress management and social connectedness pillars. Interventions showed a positive impact on improving knowledge standardized mean difference (SMD): 0.71 (95% CI: 0.25-1.18), self-esteem/self-confidence SMD: 1.34 (95% CI: 0.61-2.07), and outcome practice SMD: 0.78 (95% CI 0.29-1.26). There was insufficient power to provide reliable estimates for the attitude outcome. Integrating LM educational interventions within health professional’s curricula is promising and recommended to better equip trainees and future health care providers to support patients with the adoption of a healthy lifestyle.

Keywords

lifestyle medicine health professionals curricula educational interventions systematic review and meta-analysis

“LM is uniquely positioned to empower patients and communities to make sustainable lifestyle habits, prevent NCDs, and improve patient’s overall health and outcomes.”

Background

Non-communicable diseases (NCDs) represent a global epidemic of the 21^st century. The 4 major types—cardiovascular diseases (e.g., stroke, heart disease), cancers, chronic respiratory diseases and diabetes—are the leading cause of mortality and morbidity worldwide.^1,2 Globally, 1 in 3 adults are living with more than one NCD,³ which has resulted in a significant increase in demand of health care services, treatment, and expenditure. Societally, this is coupled with productivity loss due to premature exit from the labor force, increased absenteeism, reduced work capacity, and early mortality.⁴ Economically, NCDs are responsible for 90% of annual health care expenditure with projections of an accumulated global economic loss of $47 trillion (US) by 2030.⁵ Importantly, the majority of NCDs are attributed to lifestyle choices and behaviors such as unhealthy diets (e.g., high sugar and ultra-processed food),⁶ physical inactivity, as well as tobacco and alcohol use⁷; thereby making NCDs, in part, preventable. Non-communicable diseases also account for 74% of deaths worldwide (∼41 million deaths per year) and 15 million premature deaths yearly among persons between 30 and 69 years of age.⁸ Similar to global trends, an estimated 88% of all deaths are attributed to NCDs in the United States and Canada.^9,10

Lifestyle Medicine

Lifestyle medicine (LM) is an evidence-based and clinical discipline that applies medical, environmental, motivational, and behavioral principles to comprehensively support healthy lifestyle habits to prevent, treat and potentially reverse NCDs.^11–14 LM comprises of 6 pillars: Nutrition, Physical activity, Sleep health, Stress reduction, Social connectedness, and Substance use¹² (Figure 1). This rapidly expanding discipline is an adjunct to conventional medical practice¹⁵ that introduces progressive, life-long lifestyle habits to effectively reduce many NCDs and the additional health complications.^16,17 The investment in primary prevention through LM may result in a 4-fold improvement in health outcomes compared to secondary prevention that is often based on pharmacological treatment.¹⁸ Over the past 30 years, there has been a cumulative body of empirical and epidemiological evidence reporting on the significant and positive impact of LM on health.^14,17 Seminal publications include the Lifestyle Heart Trial,¹⁹ Diabetes Prevention Program,²⁰ the Lyon Heart Study,²¹ the PREDIMED Study²² and the Framingham Heart Study.²³ The strengths of the scientific evidence is further underscored by numerous evidence-based guidelines^24–26 (p20) and the World Health Organization recommendations that highlight the necessity of healthy lifestyle habits for the prevention, treatment and management of NCDs at the personal and societal level with the ultimate goal of improving population health.²⁷

Figure 1.

LSM pillar.

Gaps in Curricula

Lifestyle Medicine holds great promise and potential to address NCDs, however, the integration of LM-related content in health professional’s curricula is minimal to non-existent²⁸; resulting in inadequate LM-related competencies among health professional trainees and health care providers (HCPs).²⁹ This was reflected in a systematic review on the current practices of LM instruction in medical curricula (N = 32), in which 81% of the studies were conducted in the United States.³⁰ Of the 32 studies, most reported delivery of education on nutrition (78%) and exercise (59%)³⁰ with limited education on smoking cessation (34%), stress management (25%), sleep health (16%) and no mention of education on social connectedness.³⁰ Similar findings were observed in a systematic review (N = 24) that aimed to critically synthesize the literature on nutrition education provided to medical students.³¹ The review yielded insufficiency with the incorporation of nutrition in medical curricula irrespective of setting, year of education and country.³¹ The lack of foundational LM integration in health professionals curricula results in health professional trainees and in turn, HCPs being ill equipped to counsel patients on LM therapeutic modalities due to their own limited LM-related knowledge, competence, training, and skills.^31,32

Over the years, there has been increased momentum and efforts to integrate LM in medical curricula and programs particularly in the United States (e.g., Harvard School of Medicine, University of Texas Rio Grande Valley, New York School of Medicine, University of Florida, University of North Carolina). However, there continues to be glaring gaps and a dearth of training to allied HCPs, specifically registered practical nurse (RPN)/licensees practical nurse (LPN), registered nurse (RN), nurse practitioner (NP) and physician assistant (PA).^33,34 Such allied HCPs are uniquely positioned to provide patient education and counseling in their clinical practice and collectively comprise a large group of HCPs.³³ Appreciating the need and importance for LM integration in health professionals curricula³⁵ and to align with some of the recent medical LM curricula transformation,³⁶ it is imperative to first understand and map the current LM curricula integration practices. The aim of the study was twofold: (i) to critically synthesize the existing literature on the current state of LM integration in health professional trainees’ education; and (ii) to quantify the effectiveness of such educational interventions on improving health professional trainee’s LM-related knowledge, skills, and competence.

Methods

Design

A systematic review and meta-analysis were conducted. The protocol was not registered, however the study was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.³⁷

Search Strategy and Selection Criteria

The search was conducted in May 2024. The databases used to systematically retrieve relevant publications were: Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, PubMed, and Embase. The following keywords were used in conjunction with Boolean operators to refine the literature search: “lifestyle medicine” OR “nutrition” OR “physical activity” OR “exercise” OR “substance use” OR “alcohol” OR “smoking” OR “stress” AND “sleep” OR “sleep health” OR “social connections” OR “social connectedness” AND “education” OR “information” OR “educational interventions” AND OR “nursing curricula*” OR “registered nurse curricula*” OR “physician assistant curricula*” OR “nurse practitioner curricula*” OR “registered practical nurse*,” OR “licensed practical nurse*” (Supplemental File 1).

Articles were included in the review if the following selection criteria were met: (i) the sample represents health professionals, specifically RN, RPN/LPN, NP, and PA; (ii) educational interventions delivered or integrated within undergraduate and/or graduate health professionals education, postgraduate health professions education or as a continuing education opportunity; (iii) focus on any of the 6 pillars of LM; (iv) description (e.g., duration, frequency, objectives, method of delivery) of the LM-related content; (v) clearly defined outcome(s) specifically LM-related knowledge, skills, competence, confidence and/or attitudes; (vi) peer-reviewed publication; (vii) primary data sources; (viii) papers written in English; and (ix) papers published between 2000 and 2024, which aligned with the publication of the landmark LM textbook.³⁸ The PICO elements guiding the study are presented in Table 1. The retrieved articles from the databases were exported into Covidence.

Table 1.

PICO Research Question.

PICO- elements	Research question
Population	Nursing, nurse practitioner, physician assistant, registered practical nursing/Licensed practical nursing students that received LSM-related education during health professions school
Intervention	Any form of LSM education (curriculum or elective) received during health professions school
Comparison	Baseline measures (reporting a difference over time) or a control group (difference between groups)
Outcome	Health professions trainees’ LSM-related knowledge, skills, competence, confidence, or attitudes

Screening and Data Extraction

Two investigators (JS and SI) screened the titles and abstracts of the studies using the inclusion criteria. Full-text articles were retrieved for all studies that met the selection criteria and/or required more information that was not included in the respective abstract to inform a decision. One investigator (JS) independently assessed the full-text articles using the selection criteria. A second investigator (SI) reviewed the full-text articles and met to discuss any disagreements in order to reach a consensus. The following data were extracted from the articles: (i) general article information—author’s last name(s), publication date, study setting, country, and continent of publication; (ii) study purpose, research design, and target population (where available: age, sex/gender, ethnicity, race, socioeconomic, and sample size); (iii) educational program (e.g., nursing, nurse practitioner); and (iv) sample size. The following data were extracted on the LM educational intervention: (i) type of course (mandatory or elective); (ii) who delivered the information; (iii) mode of delivery; (iv) focus of LM Pillar; (v) topic objectives; and (vi) length/duration; and (vii) year of training.

Risk of Bias Assessment

The quality appraisal was performed using: (i) the Joanna Briggs Institute tool for randomized controlled trials³⁹ and quasi-experimental studies⁴⁰; and (ii) Mixed Methods Appraisal Tool (MMAT),⁴¹ depending on the study design (Tables 2 -4).

Table 2.

Risk of Bias Table. Quasi-Experimental Design.

Quasi-experimental using JBI checklist for Quasi-experimental studies (N = 11)
	Arthur, 2001³¹	Butler, 2009³²	Hamadeh, 2018³³	Martinez, 2003³⁵	Rassool, 2008⁴¹	Schwindt, 2019⁴²	Whitehea d, 2014³⁷	Kelley, 2006³⁴	Chan, 2008³⁸	Keyes, 2020²⁵	Fruh, 2019⁴³
1. Is it clear in the study what is the “cause” and what is the “effect” (i.e., there is no confusion about which variable comes first)?	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
2. Was there a control group?	YES	YES	NO	YES	YES	NO	NO	NO	NO	NO	NO
3. Were participants included in any comparisons similar?	N/A	N/A	N/A	N/A	NA	YES	YES	YES	YES	YES	YES
4. Were the participants included in any comparisons receiving similar treatment/care, other than the exposure or intervention of interest?	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
5. Were there multiple measurements of the outcome, both pre and post the intervention/exposure?	NO	NO	YES	NO	NO	NO	NO	NO	NO	NO	YES
6. Were the outcomes of participants included in any comparisons measured in the same way?	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	NO
7. Were outcomes measured in a reliable way?	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
8. Was follow-up complete and if not, were differences between groups in terms of their follow-up adequately	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
9. Was appropriate statistical analysis used?	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES

Table 3.

Risk of Bias Table. Randomized Controlled Trials.

RCTs using JBI checklist for RCTs (N = 2)
	Sejr, 2002³⁶	Schwindt, 2016⁴⁰
1. Was true randomization used for assignment of participants to treatment groups?	NO	N/A
2. Was allocation to groups concealed?	NO	NO
3. Were treatment groups similar at the baseline?	N/A	YES
4. Were participants blind to treatment assignment?	N/A	NO
5. Were those delivering the treatment blind to treatment assignment?	NO	NO
6. Were treatment groups treated identically other than the intervention of interest?	YES	YES
7. Were outcome assessors blind to treatment assignment?	N/A	NO
8. Were outcome assessors blind to treatment assignment?	YES	YES
9. Were outcomes measured in a reliable way?	YES	YES
10. Was follow up complete and if not, were differences between groups in terms of their follow up adequately described and analyzed?	YES	YES
11. Were participants analyzed in the groups to which they were randomized?	YES	YES
12. Was appropriate statistical analysis used?	YES	YES
13. Was the trial design appropriate and any deviations from the standard RCT design (individual randomization, parallel groups) accounted for in the conduct and analysis of the trial?	YES	YES

Table 4.

Risk of Bias Table. Mixed Methods Design.

Mixed methods studies - mixed methods appraisal tool (MMAT) (N = 1)
	Lavilla-Gracia, 2023³⁹
Screening questions
S1. Are there clear research questions?	YES
S2. Do the collected data allow to address the research questions?	YES
Qualitative
1.1. Is the qualitative approach appropriate to answer the research question?	YES
1.2. Are the qualitative data collection methods adequate to address the research question?	YES
1.3. Are the findings adequately derived from the data?	YES
1.4. Is the interpretation of results sufficiently substantiated by data?	YES
1.5. Is there coherence between qualitative data sources, collection, analysis and interpretation?	YES
Quantitative descriptive
4.1. Is the sampling strategy relevant to address the research question?	YES
4.2. Is the sample representative of the target population?	YES
4.3. Are the measurements appropriate?	YES
4.4. Is the risk of nonresponse bias low?	NO
4.5. Is the statistical analysis appropriate to answer the research question?	YES
Mixed method as a whole
5.1. Is there an adequate rationale for using a mixed methods design to address the research question?	YES
5.2. Are the different components of the study effectively integrated to answer the research question?	YES
5.3. Are the outputs of the integration of qualitative and quantitative components adequately interpreted?	YES
5.4. Are divergences and inconsistencies between quantitative and qualitative results adequately addressed?	YES
5.5. Do the different components of the study adhere to the quality criteria of each tradition of the methods involved?	YES

Statistical Analysis

The studies that met the selection criteria were categorized according to one of the 6 LM pillars. Further, the outcome domains (Knowledge, Attitudes, Satisfaction, Confidence, and Skills) were also categorized by 2 authors (JRS and MV). Descriptive statistics were used to present the study characteristics, which were expressed as numbers and proportions, means and standard deviations (SD), or medians and interquartile ranges, as appropriate. For studies reporting educational interventions with pre-post data (e.g., a 6-h interactive substance use intervention or 7 online modules on obesity management), effect sizes were estimated using a two‐step procedure. When paired t-test statistics were available (Table 5), the standardized mean change was derived directly from the reported t-value and sample size. For studies without a reported t-value, the effect size was calculated from the pre- and post-intervention means and standard deviations,^42,43 while accounting for the dependency between measurements by assuming a pre–post correlation of 0.5. In both cases, a small-sample correction was applied to obtain Hedges’ g. Variance estimates were calculated accordingly.^44–46 Given the variability in study designs, populations, and measurement tools, a random-effects (RE) meta-analysis was conducted separately for each pillar. Heterogeneity among studies was assessed using the I-squared (I²) statistic. An I² of 0 to 25% was considered a low heterogeneity, between 25 and 75% moderate and 75% or higher was considered a high level of heterogeneity.⁴⁷ Furthermore, prediction intervals were computed to capture the expected range of effect sizes in future studies. Forest plots were generated for each domain to visually present the individual study effect sizes and the overall pooled estimates. These plots included 95% confidence intervals (CIs) for each study’s SMD, as well as study-specific weights. Retrospective power calculations were conducted using the overall pooled effect size and the estimated between‐study variance from the random‐effects meta‐analysis to compute the cumulative information available, which was then used to estimate the probability of detecting the observed effect at a two‐tailed 5% significance level. A power level of 80% or higher was considered indicative of sufficient power to detect the observed effect.⁴⁸

Table 5.

Baseline Characteristics of Studies.

Author	Country	Study design	Target population	Year of training	Sample size	Participant characteristics	Objectives
Substance Use
Arthur, 2001³⁵	Hong Kong	Quasi-experimental	NS	2	212	87% Female	Increase knowledge, attitude and confidence toward alcohol consumption
						40% less than 21 years;
						20% greater than 30 years
Butler, 2009³⁶	US	Quasi experimental	NS	2 and/or 4	104	Cohort 1: 92.4% Female; 91.1% White, mean age: 21.4 (SD = 3.5, range = 18 to 39)	Increase knowledge, skills and confidence on tobacco use and addiction
Butler, 2009³⁶	US	Quasi experimental	NS	2 and/or 4	104	Cohort 2: 87.8% Female, 96% White; mean age: 24.7 (SD = 7.8, range = 18 to 55)
Chan, 2008⁴²	Hong Kong	Quasi experimental	NS	1	78	88.5% Female	Increase knowledge, attitude and perception on tobacco use
Chan, 2008⁴²	Hong Kong	Quasi experimental	NS	1	78	Age range 20-33	Increase knowledge, attitude and perception on tobacco use
Hamadeh, 2018³⁷	Bahrain	Quasi experimental	NS	All	94	68.5% Female	Increase knowledge on tobacco use
Hamadeh, 2018³⁷	Bahrain	Quasi experimental	NS	All	94	Mean age: 21.1 (SD = 1.6)	Increase knowledge on tobacco use
Kelley, 2006³⁸	US	Quasi experimental	NS	NA	38	97% Female, 86% White	Increase skills, self-efficacy, and knowledge in tobacco cessation
Kelley, 2006³⁸	US	Quasi experimental	NS	NA	38	Mean age 29 (range = 23-45)
Lavilla-Gracia, 2023⁴³	Spain	Mixed methods	NS	4	21	66.7% Female	Train motivational interviewing, and increase knowledge and feedback skills on alcohol use
Lavilla-Gracia, 2023⁴³	Spain	Mixed methods	NS	4	21	Mean age: 21.4 (SD = 1.34)
Martinez, 2003³⁹	US	Quasi experimental	NS	NA	75	Cohort 1: 90% Female, mean age 30.3 (range = 23 to 51)	Increase knowledge on alcohol addiction
Martinez, 2003³⁹	US	Quasi experimental	NS	NA	75	Cohort 2: 82.2% Female, mean age 27.4 (range = 23 to 51)	Increase knowledge on alcohol addiction
Rassool, 2008⁴⁴	UK	Quasi experimental	NS	NA	110	57% Female. Mean age: 32.91( SD: 7.98, range = 20 to 55)	Increase knowledge, attitude and skills on alcohol and substance use
Schwindt, 2016⁴⁵	US	RCT	NS	NA	134	NA	Increase knowledge on tobacco use
Schwindt, 2019⁴⁶	US	Quasi experimental	NS	3	13	72.2% Female, White: 88.9%	Increase self-efficacy on tobacco use interventions
Sejr, 2002⁴⁰	Denmark	RCT	NS	1	220	91% Female, mean Age: 23	Increase knowledge and attitudes on tobacco use
Whitehead, 2014⁴¹	US	Quasi experimental	NPS	NA	36	NA	Increase knowledge on tobacco use and addiction
Nutrition and physical activity
Fruh, 2019⁴⁷	US	Longitudinal cohort	NPS	NA	45	53% White/Caucasian American, 40% Black/African American, and 2% Asian American; mean age: 34 (SD:7.9)	Obesity management
Keyes, 2020²⁶	US	Quasi experimental	PAS	1	40	66.7% Female.	Knowledge on nutrition
Keyes, 2020²⁶	US	Quasi experimental	PAS	1	40	Mean: Age 21.40 (SD:1.3)	Knowledge on nutrition

Abbreviations: NA = not available N = number of participants, NPS = Nurse Practitioner Students, NS = Nursing Students, hr = hour, hrs = hours, min = minutes, PAS = Physician Assistant Students, RCT = Randomized Controlled Trial, UK = United Kingdom, US = United States, wk = week, wks = weeks.

For all analysis with sufficient power, we performed sensitivity analysis with different pre–post correlation values (0.3, 0.5 and 0.7) to assess the robustness of the provided estimates. To explore potential sources of heterogeneity among meta-analysis with high level of heterogeneity and sufficient power, we conducted analysis by stratifying studies based on intervention characteristics. First, studies were stratified by Delivery Method into “course” and “lecture” intervention. Second, studies were categorized by duration into interventions delivered in “multiple sessions” vs “single sessions.” Sensitivity analyses were performed to assess the robustness of the meta-analytic results, including the exclusion of studies identified as potential outliers, and a stratification based on country. We refrained from performing a meta-regression given the limited number of observations and prone to overfitting. While heterogeneity remained high after the aforementioned sensitivity analyses, we further assessed the robustness of our meta-analytic estimates through influence diagnostics and stepwise sensitivity analyses. Influence analysis was conducted using Cook’s Distance, DFFITS, and tau² deletion analysis to evaluate the impact of study removal on between-study variance.⁴⁹ A stepwise sensitivity analysis was performed by iteratively removing high-leverage studies and recalculating heterogeneity statistics. Further, we conducted a Bayesian sensitivity analysis using an informative normal prior on the pre–post correlation coefficient r (mean = 0.5, SD = 0.1),^50–52 recalculating Hedges’ g and its variance across a plausible range of r values (0.3 to 0.7). Random-effects meta-analyses were then performed at each level of r, and posterior-weighted effect sizes were computed. In addition, for all outcomes where multiple effect sizes per study were available, we fitted robust variance estimation (RVE) models using the “robumeta” package with hierarchical weights, following current recommendations for small meta-analyses.^53,54 All formulas used to perform the statistical analysis are described in Table S1.

Role of the Funding Source

The study was supported by internal institutional funds.

Results

Search Outcome and Study Characteristics

Of the 6, 253 studies originally screened, 206 full-text articles were assessed for eligibility and 14 were included. The PRISMA flow diagram is presented in Figure 2. The reasons for excluding articles were related to (i) no description of the LM intervention; (ii) incorrect target population (e.g., being faculty, HCPs and patients); and (iii) wrong/unrelated outcomes (e.g., oral health). The studies encompassed randomized controlled trials (n = 2, 14%),^55,56 quasi-experimental (n = 10, 71%),^57–66 one mixed methods (7%),⁶⁷ and 1 longitudinal cohort (7%).⁶⁸ All studies were published between 2001 and 2023. Participant numbers for the studies widely ranged from 13 to 220.

Figure 2.

PRISMA flow diagram.

Of the 14 studies, more than half (n = 8, 57%) were conducted in the United States,^{55,58,61,62,64,68,69} 3 in Europe; Denmark (n = 1, 7%),⁵⁶ Spain (n = 1, 7%),⁶⁷ the United Kingdom (N = 1, 7%)⁶³] and 3 in Asia; Hong Kong (n = 2, 14%),^57,59 and Bahrain (n = 1, 7%]⁶⁰. Further, of the 14 studies, the majority involved trainees from nursing (n = 11, 79%),^55–64,67 nurse practitioner (n = 2, 14%),^65,68 and 1 physician assistant (7%) programs.⁶⁶ Of the 14 studies, 43% (n = 6) did not specify the year of training,^{55,61–63,65,68} and among the others (n = 8, 57%) the years ranged from first year to all years of the respective health professionals program.^{56–59,64,66,67}

Educational Intervention Description

The LM educational interventions were integrated in mostly public institutions (64%), embedded within mandatory classes (64%) and delivered by faculty (100%). The LM educational interventions were predominately delivered through modules (57%), followed by lectures (29%), and courses (11%), with problem-based learning accounting for 4%. The common mode of delivery for the LM educational interventions was in-person (75%) for an average duration ranging from 1 to 24 h (Table 5).

LM Characteristics

Specific to the LM pillars, 12 studies (86%) focused on substance use, and 2 (14%) addressed nutrition and physical activity. No studies were retrieved for the other LM pillars—sleep health, social connectedness and stress. A summary of the baseline characteristics of the 14 studies are presented in Table 5.

Substance Use Pillar

Among the 12 studies that focused on the substance use pillar of LM, the majority (n = 8, 67%) aimed to improve knowledge, attitudes, work satisfaction, confidence, or skills related to tobacco misuse or cessation. The remaining studies (n = 4, 33%) targeted alcohol or other substance misuse. The educational interventions were primarily delivered in person (n = 9, 75%), with some utilizing a hybrid approach of online and in-person methods (n = 2, 17%) or solely conducted online (n = 1, 8%). Across these studies, 11 interventions were aimed at increasing knowledge, 9 at improving attitudes, 2 at enhancing work satisfaction, 8 at boosting self-esteem/confidence, and 9 at advancing practice/skills.

Outcome: Knowledge

Of the 9 studies describing 11 educational interventions aimed at increasing knowledge, only 2 used the same instrument to measure the outcome, though with different scaling methods (Table 6). Data sufficient for calculating a standardized mean difference (SMD) was available for 9 educational interventions involving a total of 709 participants.^{56–58,60–62,65} The RE meta-analysis showed an overall SMD: 0.71 (95% CI: 0.25 - 1.18, PI: −0.70 – 2.13) for these educational interventions, with a high level of heterogeneity (I²: 93.65%) (Figure 3). Power analysis indicated an analytical power of 85.1%. Two studies did not provide enough data to calculate a SMD, however, one showed a significant decrease in misconceptions regarding smoking health hazards,⁵⁹ while the other one showed that 80% passed a 37-item multiple-choice test after the educational intervention.⁶⁷

Table 6.

Results of Outcomes for Substance Use Pillar.

Author	Intervention	Instrument	Outcome
Knowledge
Arthur, et al 2001³¹	Problem-based learning (5 wks, 1 hr/wk tutorial, in person)	AEIPQ: 2 questions on a 7-point Likert scale regarding adequacy	Pre-intervention: mean = 3.66, SD = 1.50, N = 212
Arthur, et al 2001³¹	Problem-based learning (5 wks, 1 hr/wk tutorial, in person)		Post-intervention: mean = 4.97, SD = 1.29, N = 212 t = 1.046, P < 0.001
Butler, et al 2009³²	Training module (2 or 6 hrs, in person)^a	Rx for change survey: 10 questions regarding knowledge of cessation (score ranges 0-100)	Pre-intervention: mean = 59.0, SD = 14.3, N = 104
Butler, et al 2009³²	Training module (2 or 6 hrs, in person)^a		Post-intervention: mean = 73.0, SD = 15.2, N = 99 t = 7.5, P < 0.0001
Chan, et al 2008³⁸	Lecture (2 hrs, in person)	Modified Questionnaire by Chung, et al⁴⁵: 6 questions to assess knowledge of health hazard related to smoking.	Pre-intervention: Median = 5 (misconception), N = 78
			Post-intervention: Median = 3 (misconception), N = 78
			Sign test: P < 0.001
Hamadeh, et al 2018³³	Lecture and 5 min. video (in person)	Center for Disease and Prevention: 20 knowledge items on the health effect of Smoking⁴⁶	Pre-intervention: mean = 77.45, SD = 14.63, N = 92
Hamadeh, et al 2018³³	Lecture and 5 min. video (in person)		Post-intervention: mean = 85.77, SD = 15.67, N = 52
Kelley, et al 2006³⁴	Course (6 hrs, in person)	Rx for change survey: 10 questions regarding knowledge of cessation	Pre-intervention: mean = 6.25, SD = 1.70, N = 35
Kelley, et al 2006³⁴	Course (6 hrs, in person)		Post-intervention: mean = 7.41, SD = 1.74, N = 35 t = 3.0 P = 0.006
Lavilla-Gracia, et al 2023³⁹	Course (2 hrs for 12 wks, in person)	A 37-item multiple-choice test to evaluate knowledge of motivational interviewing and alcohol (score ranges 0-10)	Post-intervention: mean 8.39, SD = 0.65, N = 21
Lavilla-Gracia, et al 2023³⁹	Course (2 hrs for 12 wks, in person)		80% passed the test
Martinez, et al 2003³⁵	Lecture	Certified Addictions Registered Nurse (CARN) review guide. 30 multiple-choice, matching, and true/false questions.	Pre-intervention: mean = 22.00 SD = 2.52, N = 30
Martinez, et al 2003³⁵	Lecture		Post-intervention: mean = 23.60, SD = 2.92, N = 30 P < 0.05
Martinez, et al 2003³⁵	Lecture and patient lecture	Certified Addictions Registered Nurse (CARN) review guide. 30 multiple-choice, matching, and true/false questions.	Pre-intervention: mean = 22.76, SD = 2.85, N = 45
Martinez, et al 2003³⁵	Lecture and patient lecture		Post-intervention: mean = 25.16, SD = 2.66, N = 45 P < 0.05
Sejr, et al 2002³⁶	Control: General nursing training^b	A 5-item questionnaire on a 4-point Likert scale was used to assess knowledge related to smoking as a risk factor for various diseases	Pre-intervention: mean = 2.98, SD = 1.28 = , N = 81
Sejr, et al 2002³⁶	Control: General nursing training^b		Post-intervention: mean = 3.29, SD = 1.37, N = 81
Sejr, et al 2002³⁶	Intervention: Specialized lecture (8 lectures)^b	A 5-item questionnaire on a 4-point Likert scale was used to assess knowledge related to smoking as a risk factor for various diseases	Pre-intervention: mean = 2.91, SD = 1.59, N = 74
Sejr, et al 2002³⁶	Intervention: Specialized lecture (8 lectures)^b		post-intervention: mean = 3.67, SD = 1.38, N = 74
Whitehead, et al 2014³⁷	Education program and 5 PowerPoint modules (online)	A 20-item questionnaire using multiple-choice questions to assess level of knowledge on tobacco use and cessation	Pre-intervention: mean = 9.77, SD = 2.02, N = 36
			Post-intervention: mean = 17.47, SD = 2.26 N = 36
			T = 17.427, P = 0.001
Attitude
Chan, et al 2008³⁸	Lecture (2 hrs, in person)	Modified Questionnaire by Chung, et al⁴⁵: 5 questions to attitudes and beliefs toward tobacco control.	Pre-intervention: Median = 5 (misconception), N = 78
			Post-intervention: Median = 5 (misconception), N = 78
			Sign test: P < 0.01
Schwindt, et al 2016⁴⁰	Control: Lecture (1 hr, in person)	SRQ-L: 6 questions on a 7-point Likert scale on intrinsic motivation toward tobacco cessation	Pre-intervention: mean = 5.97, SD = 0.82, N = 67
Schwindt, et al 2016⁴⁰	Control: Lecture (1 hr, in person)		Post-intervention: mean = 6.02, SD = 0.80, N = 67
Schwindt, et al 2016⁴⁰	Intervention: Lecture (1 hr, in person) and module (4 hrs, online)	SRQ-L: 6 questions on a 7-point Likert scale on intrinsic motivation toward tobacco cessation	Pre-intervention: mean = 6.09, SD = 0.72, N = 67
Schwindt, et al 2016⁴⁰			Post-intervention: mean = 6.16, SD = 0.71, N = 67
Schwindt, et al 2016⁴⁰	Control: Lecture (1 hr, in person)	SRQ-L: 6 questions on a 7-point Likert scale on extrinsic motivation toward tobacco cessation	Pre-intervention: mean = 3.56, SD = 0.85, N = 67
Schwindt, et al 2016⁴⁰	Control: Lecture (1 hr, in person)		Post-intervention: mean = 3.79, SD = 1.07, N = 67
Schwindt, et al 2016⁴⁰	Intervention: Lecture (1 hr, in person) and module (4 hrs, online)	SRQ-L: 6 questions on a 7-point Likert scale on extrinsic motivation toward tobacco cessation	Pre-intervention: mean = 3.55, SD = 1.05, N = 67
Schwindt, et al 2016⁴⁰			Post-intervention: mean = 4.01, SD = 1.09, N = 67
Schwindt, et al 2019⁴²	Module (2 hrs, online) and classroom training (3 hrs, in person)	A 1 question on a 5-point Likert scale on Intent to ask about tobacco use	Pre-intervention: mean = 4.3, N = 36
Schwindt, et al 2019⁴²			Post-intervention: mean = 4.7, N = 36 t = −3.16, P = 0.003
Schwindt, et al 2019⁴²	Module (2 hrs, online) and classroom training (3 hrs, in person)	A 1 question on a 5-point Likert scale on Intent to provide counseling for tobacco cessation	Pre-intervention: mean = 3.8, N = 36
Schwindt, et al 2019⁴²			Post-intervention: mean = 4.6, N = 36 t = −4.25, P < 0.001
Sejr, et al 2002³⁶	Control: General nursing training^b	A 6-item questionnaire on a 4-point Likert scale was used to assess attitudes toward smoking prevention	Pre-intervention: mean = 13.80, SD = 4.71, N = 81
Sejr, et al 2002³⁶	Control: General nursing training^b		Post-intervention: mean = 14.52, SD = 5.02, N = 81
Sejr, et al 2002³⁶	Intervention: Specialized lecture (8 lectures)^b	A 6-item questionnaire on a 4-point Likert scale was used to assess attitudes toward smoking prevention	Pre-intervention: mean = 13.14, SD = 4.20, N = 74
Sejr, et al 2002³⁶	Intervention: Specialized lecture (8 lectures)^b		Post-intervention: mean = 12.41 SD = 4.27, N = 74
Work satisfaction
Arthur, et al 2001³¹	Problem-based learning (5 wks, 1 hr/wk tutorial, in person)	AEIPQ: 3 questions on a 7-point Likert scale regarding work satisfaction	Pre-intervention: mean = 3.43, SD = 1.14, N = 212
Arthur, et al 2001³¹	Problem-based learning (5 wks, 1 hr/wk tutorial, in person)		Post-intervention: mean = 4.63, SD = 0.84, N = 212 t = 6.23, P < 0.001
Lavilla-Gracia, et al 2023³⁹	Course (2 hrs for 12 wks, in person)	Survey with 11 questions on a 5-point Likert scale regarding satisfaction with the motivational interviewing training course	Post-intervention: mean = 4.5, SD = 0.19, N = 19
Self-esteem/Confidence
Arthur, et al 2001³¹	Problem-based learning (5 wks, 1 hr/wk tutorial, in person)	AEIPQ: 2 questions on a 7-point Likert scale regarding self-esteem	Pre-intervention: mean = 4.99, SD = 1.30, N = 212
Arthur, et al 2001³¹	Problem-based learning (5 wks, 1 hr/wk tutorial, in person)		Post-intervention: mean = 5.22, SD = 1.32, N = 212 t = 2.22, P < 0.050
Butler, et al 2009³²	Training module (2 or 6 hrs, in person)^a	Rx for change survey: 12 questions on a 5-point Likert scale regarding confidence in counseling (score ranges 12-60)	Pre-intervention: mean = 27.9, SD = 7.5, N = 104
Butler, et al 2009³²	Training module (2 or 6 hrs, in person)^a		Post-intervention: mean = 42.5, SD = 5.8, N = 98 t = 17.6, P < 0.0001
Kelley, et al 2006³⁴	Course (6 hrs, in person)	Rx for change Survey: 10 items on self-efficacy 5-point Likert scale	Pre-intervention: mean = 2.2, SD = 0.62, N = 35
Kelley, et al 2006³⁴	Course (6 hrs, in person)		Post-intervention: mean = 3.4, SD = 0.57, N = 35 t = 9.31, P < 0.001
Rassool, et al 2008⁴¹	Course (10 hrs for 8 wks, in person)	AICSQ-16: 16 questions on confidence level in conducting substance use interventions⁴⁴	Post-intervention: mean difference = −17.31, t = 9.8, P < 0.001
Schwindt, et al 2016⁴⁰	Control: Lecture (1 hr, in person)	PCS: 4 items on a 7-point Likert scale to evaluate the perceived competence to engage in tobacco cessation interventions⁴⁷	Pre-intervention: mean = 3.68, SD = 1.29, N = 67
Schwindt, et al 2016⁴⁰	Control: Lecture (1 hr, in person)		Post-intervention: mean = 4.30, SD = 1.44, N = 67
Schwindt, et al 2016⁴⁰	Intervention: Lecture (1 hr, in person) and module (4 hrs, online)	PCS: 4 items on a 7-point Likert scale to evaluate the perceived competence to engage in Tobacco cessation interventions⁴⁷	Pre-intervention: mean: 3.68, SD = 1.12, N = 67
Schwindt, et al 2016⁴⁰			Post-intervention: mean = 5.54, SD = 0.89, N = 67
Schwindt, et al 2019⁴²	Module (2 hrs, online) and classroom training (3 hrs, in person)	A 10-item questionnaire with 10-point Likert scale to rate the confidence in various aspects of tobacco cessation counseling⁴⁸	Pre-intervention: mean = 5.4, N = 36
Schwindt, et al 2019⁴²			Post-intervention: mean = 2.6, N = 36 t = −9.92, P < 0.001
Whitehead, et al 2014³⁷	Education program and 5 PowerPoint modules (online)	A-15 item questionnaire using a 5-point Likert scale to measure the confidence in providing smoking cessation education⁴⁹	Pre-intervention: mean = 16.41, SD = 6.46, N = 36
Whitehead, et al 2014³⁷	Education program and 5 PowerPoint modules (online)		Post-intervention: mean = 39.36, SD = 5.01, N = 36 t = −16.090, P = 0.001
Practice
Arthur, et al 2001³¹	Problem-based learning (5 wks, 1 hr/wk tutorial, in person)	AEIPQ: 2 questions on a 7-point Likert scale regarding legitimacy	Pre-intervention: mean = 4.47, SD = 1.46, N = 212
Arthur, et al 2001³¹	Problem-based learning (5 wks, 1 hr/wk tutorial, in person)		Post-intervention: mean = 4.75, SD = 1.33, N = 212 t = 6.23, P < 0.001
Butler, et al 2009³²	Training module (2 or 6 hrs, in person)^a	Rx for change survey: 2 questions on 3-point Likert scale assessing how active nurses should promote quitting (score ranging 2 to 6)	Pre-intervention: mean = 5.9, SD = 0.3, N = 104
Butler, et al 2009³²	Training module (2 or 6 hrs, in person)^a		Post-intervention: mean = 5.9, SD = 0.3, N = 98 t = 2.2, P = 0.03
Butler, et al 2009³²	Training module (2 or 6 hrs, in person)^a	Rx for change survey: 6 questions on 5-point Likert scale assessing self-rated skill related to cessation (score ranging 6 to 30)	Pre-intervention: mean = 16.5 SD = 4.2, N = 104
Butler, et al 2009³²	Training module (2 or 6 hrs, in person)^a		Post-intervention: mean = 22.4, SD = 2.3, N = 98 t = 13.8, P < 0.0001
Kelley, et al 2006³⁴	Course (6 hrs, in person)	Rx for change survey: 5 items: Related to 5 A’s (ask, advise, assess, assist, arrange). 5-point scale: poor performance to excellent performance	Pre-intervention: mean = 2.06, SD = 0.81, N = 35
Kelley, et al 2006³⁴	Course (6 hrs, in person)		Post-intervention: mean = 3.38, SD = 0.78, N = 35 P < 0.001
Lavilla-Gracia, et al 2023³⁹	Course (2 hrs for 12 wks, in person)	Checklist of 27 specific topics during a videotaped motivational intervention session	Pre-intervention: mean = 15.70, SD = 4.37, N = 40
Lavilla-Gracia, et al 2023³⁹	Course (2 hrs for 12 wks, in person)		Post-intervention: mean: 19.90, SD = 3.75, N = 40
Schwindt, et al 2019⁴²	Module (2 hrs, online) and classroom training (3 hrs, in person)	A 6-item questionnaire with 5-point Likert scale to assess self-reported counseling abilities in tobacco cessation⁵⁰	Pre-intervention: mean = 2.6, N = 36
Schwindt, et al 2019⁴²			Post-intervention, mean = 3.7, N = 36 t = −7.36, P < 0.001
Sejr, et al 2002³⁶	Control: General nursing training^c	A 4-item questionnaire on a 4-point Likert scale was used to assess preventive behavior related to advising patients against smoking in various scenarios.	Pre-intervention: mean = 6.22, SD = 1.79, N = 81
Sejr, et al 2002³⁶	Control: General nursing training^c		Post-intervention: mean = 6.34, SD = 1.78, N = 81
Sejr, et al 2002³⁶	Intervention: Specialized lecture (8 lectures)^c	A 4-item questionnaire on a 4-point Likert scale was used to assess preventive behavior related to advising patients against smoking in various scenarios.	Pre-intervention: mean = 6.11, SD = 1.54, N = 74
Sejr, et al 2002³⁶	Intervention: Specialized lecture (8 lectures)^c		Post-intervention: mean = 6.11, SD = 1.98, N = 74
Whitehead, et al 2014³⁷	Education program and 5 PowerPoint modules (online)	A 6-item questionnaire using a 5-point Likert scale to assess skills in smoking cessation⁴⁹	Pre-intervention: mean = 14.61, SD = 4.06, N = 36
Whitehead, et al 2014³⁷	Education program and 5 PowerPoint modules (online)		Post-intervention: mean = 25.74, SD = 3.99, N = 36 t = −10.990, P = 0.001

^aNo differences between the 2 groups (6-h- and 2-h intervention) in pretraining skill, knowledge, or activity scores. Significant difference between the 2 groups on mean baseline confidence score, with the 6-h group (seniors) being more confident at the initial assessment (mean = 30.5, SD = 7.2) than the 2-hour group (mean = 25.9, SD = 7.1) (t = 3.2, P = 0.002).

^bsignificant difference between control and intervention group.

^cno significant difference between 2 groups.

Figure 3.

Meta-analysis.

Outcome: Attitudes

Four studies described 9 educational interventions that aimed to increase attitude; however, none used the same instrument (Table 5). Data to calculate the SMD was provided by 2 studies describing 6 educational interventions; 1 on general attitude,⁵⁶ and 1 on intrinsic motivation and extrinsic motivation.⁵⁵ The RE meta-analysis showed an insignificant effect of these educational interventions on attitudes SMD: 0.13 (95%CI: −0.03 – 0.28, PI: −0.12-0.38) with a moderate level of heterogeneity (I²: 25.49%) (Figure 3). Analytical power for this meta-analysis was 35.8%.

Two studies did not provide sufficient data to calculate a SMD: one showed that the educational intervention of a 2-h online module and 3-h in person training significantly increased the intent to ask about tobacco use and provide counseling for tobacco cessation,⁶⁴ the other one found no absolute difference in number of misconceptions in attitudes and beliefs after a 2-h in person lecture.⁵⁹

Outcome: Work Satisfaction

Two studies described work satisfaction, both using different instruments to assess (Table 6). One showed a significant improvement regarding work satisfaction after a 5-week problem-based learning intervention.⁵⁷ The other study only showed results after the educational intervention,⁶⁷ showing that work satisfaction was high (mean = 4.5 out of 5). There was insufficient data to perform a meta-analysis.

Outcome: Self-esteem/confidence

Seven studies described 8 educational interventions, of which 2 used the same instrument (Table 6). Five studies describing 6 educational interventions provided data to calculate a SMD.^{55,57,58,61,65} Meta-analysis, including 521 participants, showed an overall SMD of 1.34 (95%CI: 0.61 – 2.07, PI: −0.53 - 3.21) with a high level of heterogeneity (I² = 95.7%) (Figure 3). Retrospective power analysis showed an analytical power of 95.0%. Two studies did not provide enough data to calculate an SMD, both show that their intervention significantly improved confidence in counseling.^63,64

Outcome: Practice

Seven studies described 9 educational interventions, of which 2 used the same instrument to measure the outcome^58,61 (Table 6). Six studies provided data to perform a meta-analysis.^{56–58,61,65,67}The overall effect size was significant with a SMD: 0.78 (95% CI: 0.29 – 1.26, PI: −0.64 – 2.19) and a high level of heterogeneity (I²: 94.2%). Retrospective power analysis indicated an analytical power of 87.5%. One study did not provide sufficient data to calculate a SMD, but showed a significant increase in counseling abilities in tobacco cessation.⁶⁴

Nutrition and Physical Activity Pillars

Two studies focused on nutrition and physical activity (Table 7). One study showed that 7 online modules significantly increased knowledge and confidence on obesity management.⁶⁸ The other study showed that 4 in person modules had a significant effect in self-reported competency in LM including nutrition and physical activity.³³

Table 7.

Results of Outcomes for Nutrition and Physical Activity Pillars.

Author	Intervention	Instrument	Outcome
Knowledge
Fruh, 2019⁴⁷	7 modules (online)	An 18-item questionnaire with true/false and multiple-choice questions about obesity management	Pre-intervention: mean = 52%, N = 45
Fruh, 2019⁴⁷	7 modules (online)		Post-intervention: mean = 67%, N = 43 t = −5.97, P < 0.001
Confidence
Fruh, 2019⁴⁷	7 online modules (online)	An 8-item questionnaire on a 5-point Likert scale to assess level of comfort in managing obesity	T = 3.47, P = 0.01
Practice
Keyes, 2020²⁶	4 modules (50 min per module, in person)	A 9-item questionnaire on a 9-point Likert scale to assess level of self-reported competency in lifestyle medicine (healthy behavior in general, nutrition and physical activity)	Pre-intervention: mean = 3.22 post-intervention: mean = 3.97 P < 0.001

Stratification on Delivery Methods and Duration

Knowledge

Stratification by delivery method showed that course-based interventions had non-significant effects with high heterogeneity, whereas lecture-based interventions were significantly effective with low heterogeneity. Stratifying by duration revealed that multiple-session interventions were non-significant with high heterogeneity, whereas single-session interventions were significantly effective with moderate heterogeneity (Figure S1).

Confidence and Practice

For both confidence (Figure S2) and practice (Figure S3) the single study for lecture-based interventions was not considered. Effect sizes remained significant across stratification. No difference in heterogeneity was observed.

Sensitivity Analysis

Different Pre–Post Correlation Values

Obtained estimates remained significant and consistent when different pre–post correlation values were used (Table S2).

Excluding Outliers

In the sensitivity analysis, we excluded Whitehead, et al (2014)⁶⁵ as a potential outlier in the meta-analyses performed for knowledge, confidence and practice. All effect sizes remained significant, while heterogeneity went from high to moderate for the knowledge outcome (Figures S4-6).

Stratification Based on Country

In the second sensitivity analysis, we performed meta-analyses stratified studies based on country. This was only possible for the US, as there was insufficient data for the other countries to perform a meta-analysis. All effect sizes remained significant, while heterogeneity went from moderate to low for the attitude outcome (Figures S7-9).

Influence Diagnostics and Stepwise Sensitivity Analysis to Reduce Heterogeneity

After excluding high-leverage studies identified through influence diagnostics and stepwise sensitivity analysis, heterogeneity was reduced while effect sizes remained significant and directionally consistent (Table S3). For knowledge, removing Arthur (2001), Whitehead (2014), and Butler (2009) reduced heterogeneity from high to low (I² = 18.90). For confidence, excluding Arthur (2001) and Schwindt (2016) eliminated heterogeneity (I² = 0). For practice, removing Arthur (2001) and both arms of Sejr (2002) lowered heterogeneity to a moderate level (I² = 35.17%).

The posterior-weighted SMDs were 0.956 (SE = 0.317) for Knowledge, 0.129 (SE = 0.083) for Attitude, 1.714 (SE = 0.561) for Confidence, and 0.887 (SE = 0.364) for Practice. RVE models yielded pooled estimates consistent with the Bayesian analyses: knowledge (SMD = 0.68, SE = 0.21, P = 0.016), attitude (SMD = 0.13, SE = 0.10, P = 0.36). confidence (SMD = 1.34, SE = 0.33, P = 0.02), and practice (SMD = 0.76, SE = 0.22, P = 0.016).

Discussion

Health care providers are trusted sources of health information and are in a unique position to have a profound impact on addressing the escalating burden of NCDs. In the ever-changing landscape of healthcare, LM is uniquely positioned to empower patients and communities to make sustainable lifestyle habits, prevent NCDs, and improve patient’s overall health and outcomes^14,70,71 (p. 959). This study is the first, to our knowledge, to critically synthesize the literature on the current state of LM integration in health professionals’ curricula and quantify the effectiveness of such educational interventions on the knowledge, skills and competence among health professional trainees. The results showed a relatively smaller quantity of published studies regarding LM educational interventions in health professionals curricula compared to medical trainees—for example, an estimated 3 quarters of the published LM educational literature pertains to medical programs.³⁰

Overall, the LM educational interventions embedded within the health professionals’ curricula had a statistically significant and positive impact on increasing LM-related knowledge, confidence and practice among health professional trainees, particularly related to the substance use, nutrition, and physical activity pillars. There was insufficient power to provide reliable estimates for the attitude outcome. Both RE meta-analysis and Bayesian analyses showed inconclusive results. Additional studies with larger sample sizes are needed to obtain reliable effect estimation in this domain. Furthermore, there remains a significant gap in the LM educational literature on the sleep health, social connectedness, and stress management pillars. The findings from this review are consistent with the limited but current LM and educational literature.^30,31,72 As an illustration, across the international medical schools sampled (N = 106), education on sleep health was found to be under 2.5 h and 27% of the responding schools did not provide any sleep health education.⁷³ Similar findings were reported with the social connectedness and stress pillars of LM—with insufficient data available to report on the hours of related education in the UGME curricula.³⁶ These findings are in parallel with a systematic review on nutritional education provided to medical trainees (N = 24). Specifically, nutritional education was found to be insufficiently incorporated in medical curricula,³¹ which has implications on medical trainees being able to develop their knowledge, skills and confidence in providing nutrition-related care to patients in clinical practice.³¹ Additionally, in a systematic review on RNs’ perceptions of their roles with promoting physical activity to patients , they had (i) poor knowledge of related guidelines; (ii) limited training, skills and techniques to educate and advise patients on physical activity; and (iii) lack of skills to develop care plans to promote physical activity for patients.⁷²

Further to the integration of only half of the LM pillars in the health professionals’ curricula found in this study, most were incorporated in U.S. based academic institutions. This finding is in parallel with a scoping review on culinary medicine for nutritional education in medical schools.⁷⁴ Potential explanations to this geographic footprint are fourfold: (i) the increasing interest in LM in general and as a paradigm shift in the prevention and/or management of modifiable risk factors associated with NCDs in North America; (ii) recognized LM-related guidelines such as the American Heart Association⁷⁵ and the American Diabetes Association⁷⁶; (iii) the American College of Lifestyle Medicine (ACLM) which has established undergraduate medical education courses, certifications, residencies and fellowships³⁶; and (iv) the Resolution 959 (I-17) supporting policies that incentivize and provide funding for the inclusion of LM and social determinants of health in medical education in the United States.⁷¹

The LM educational interventions within the respective programs were also predominately delivered in-person and through lecture format. This finding is aligned with reviews on culinary medicine and LM in medical curricula.^30,74 While the delivery of content through lectures is a commonly used approach in health professional’s curricula, alternative and more innovative pedagogical approaches can be easily integrated in existing courses. For instance, simulation provides health professional trainees with an authentic clinical environment that supports enhancement in knowledge, refinement of skills (e.g., communication, counseling, teaching) as well as the ability to bridge the gap from knowledge acquisition (theory that is commonly attained in the classroom, didactic learning) to knowledge application (e.g., interactive workshops).^77,78 Such innovation was illustrated in a LM educational intervention that integrated clinical simulation for nursing students to address social isolation and loneliness among older adults.⁷⁷ In the quasi-experimental study that encompassed a simulation program (asynchronous theoretical training and synchronous practicing training using an online platform) among nursing students (N = 25, 27.44 years, 76% women), statistically significant improvements (P < 0.05) with knowledge and attitudes as well as their ability to address loneliness and social isolation towards older adults was reported.⁷⁷ Likewise, simulation is being used for educating nursing, psychology, social work, medical trainees and dietitians on the substance use⁷⁹ and nutrition⁷⁸ pillars.

The pace of advancement and discoveries in science to improve patient health outcomes and clinical practice requires continued integration of new material in existing health professionals curricula.¹⁸ Such programs often have long-standing education models and processes,¹⁸ causing curricula tensions and challenges. However, a solution to such challenges is a tailored approach to the respective health professional’s programs through several ways (Figure 4). First, is through curriculum mapping, which is instrumental to identify patterns, overlaps, and gaps in a strategic way to enhance curricula⁸⁰ afforded to health professional trainees as future HCPs. As an illustrative example, the authors conducted a content analysis of LM curriculum in Canadian UGME to identify the current state and gaps with the intent of informing future educational interventions to enhance medical trainee’s LM-related knowledge, skills and competencies in Canada.⁸¹ Second, is the use of existing and established modules, materials, courses and professional development opportunities that can be integrated rather than added to minimize curriculum overload. For example, the ACLM has developed an introductory LM course with the curriculum delineated along with the slide decks and teacher’s manual.⁸² Further, the Institute of Lifestyle Medicine through Harvard Medical School’s Department of Continuing Education and the American College of Preventive Medicine deliver a 2-day workshop with online materials⁸³ as well as a 30-h comprehensive LM course through webinars,⁸⁴ respectively. Finally, is the use of available LM-related frameworks and competencies that are currently geared towards UGME that can be easily adapted to guide health professional school deans and program directors on systematic and strategic ways to integrate learning objectives and content. Examples of core LM-related frameworks and competencies include the American Heart Association general framework for US medical schools,¹⁸ LM competencies published in the American Journal of Lifestyle Medicine,⁸⁵ and nutrition competencies published in the Journal of the Medical Association.⁸⁶ Appreciating the intersection of lifestyle and health care, LM is integral in health professionals curricula to equip trainees and the future generation of HCPs with the knowledge, skills and competencies to meaningfully and effectively integrate in practice as an antidote³⁶ for the prevention, management and treatment of NCDs, and consequential complications such as stroke, heart disease, cancer or dementia.

Figure 4.

Study implications.

Limitations

This systematic review and meta-analysis have several limitations. First, the findings from meta-analyses should be interpreted with caution due to the high level of heterogeneity and wide prediction intervals. Differences in study populations, interventions, and outcome measures contributed to this variability. This may make the results more suitable for providing an overall overview rather than definitive conclusions as well as identifying gaps in the literature to be addressed in future research studies. Due to the limited number of studies, meta-regression was not deemed feasible. Influence diagnostics identified Arthur (2001) as a key driver of heterogeneity across all outcomes, despite similar study characteristics. The remaining heterogeneity, particularly from Whitehead (2014), Butler (2009), Schwindt (2016), and Sejr (2002), remains unexplained, suggesting unmeasured methodological differences. However, removing these studies reduced heterogeneity while maintaining effect size directionality, statistical significance, and yielding narrower prediction intervals. These sensitivity analyses strengthened the robustness of the study findings. Second, sensitivity analysis showed lower levels of heterogeneity among participants receiving lecture-based or single-session interventions compared to those in multi-session or course-based formats for the knowledge outcomes. This might suggest that the more streamlined and less complex interventions yield more consistent outcomes. However, we did not observe a similar trend for confidence and practice. Third, while we followed the terminology used in the original articles to categorize outcomes (e.g., labeling a questionnaire as measuring “practice”), there may be arbitrariness in these classifications. Specific definitions for the outcomes were not uniformly applied, which may affect the consistency and interpretation of the results. Fourth, while we tested a limited number of hypotheses, with additional analyses conducted solely to assess robustness, we acknowledge that multiple comparisons may introduce a potential risk of Type I errors. Finally, there was insufficient power to obtain reliable estimates for the attitude outcome. Future studies should aim to include larger, more homogeneous samples and utilize standardized outcome measures to enhance the power and precision of meta-analytic estimates in this domain.

Conclusion

A small (N = 14) but notable body of literature describing LM educational interventions embedded within health professional’s curricula were identified and evaluated based on the effectiveness on improving LM-related knowledge, skills, competence, and practice. Findings from the systematic review and meta-analysis showed that LM educational interventions when delivered have a statistically significant and positive impact on health care trainee’s LM-related knowledge, confidence and practice particularly related to the nutrition, physical activity and substance use pillars, while there was insufficient power for the attitude outcome. Significant gaps continue to exist on the sleep health, social connectedness and stress management pillars. Study findings highlight several notable implications: (i) curriculum mapping to support the systematic identification of patterns, overlaps and gaps for LM-related content within health professionals’ programs; (ii) use of existing and established modules, courses and materials to integrate within existing health professional’s programs; and (iii) adapting existing frameworks and competencies that are geared towards UGME towards health professionals. Recognizing the rising global prevalence of NCDs and the associated increase in concordant health care expenditure, LM is strategically poised in a unique point in time to rethink and redesign health professionals’ curricula to better prepare future generations of HCPs to support the health and well-being of persons, communities and populations.

Supplemental Material

Supplemental Material - Lifestyle Medicine Education in Health Professionals Curricula: A Systematic Review and Meta-Analysis

Supplemental Material for Lifestyle Medicine Education in Health Professionals Curricula: A Systematic Review and Meta-Analysis by Sarah Ibrahim, RN, MN, PhD, CHSE, Jasper R. Senff, MD, Janani Sivakumar, BSc(Hons), Matthew Ventresca, MSc, MBA, CHE, CCRP, Judith Coulson, MSN, NP, Sanjula Singh, MD, PhD, and Aleksandra Pikula, MD, DipABPN, DipABLM in American Journal of Lifestyle Medicine

Footnotes

Author Contributions

SI supervised the study conception, design, data collection and led the manuscript preparation. JRS assisted with the study conception, led the data analysis of the study and advised on all components of the study methods, interpretation of study findings, reviewed and approved the manuscript. JS and MV assisted with the data screening and extraction as well as reviewed the manuscript. SS assisted with the study conception and advised on the data collection and analysis as well as reviewed and approved the manuscript. AP provided funding through the center and advised on the study conception and design, data collection, manuscript preparation, and reviewed and approved the manuscript. All authors approved the submitted version of the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work is supported by the Jay and Sari Sonshine Centre for Stroke Prevention & Cerebrovascular Brain Health.

Ethical Considerations

This article does not contain any studies with human or animal participants. There are no human participants in this article and informed consent is not required.

ORCID iDs

Sarah Ibrahim

Jasper R. Senff

Supplemental Material

Supplemental material for this article is available online.

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