Predictors of Patient Engagement With an Interprofessional Lifestyle Medicine Program

Abstract

Background

Changes in lifestyle habits can reduce morbidity and mortality, but not everyone who can benefit from lifestyle intervention is ready to do so.

Purpose

To describe characteristics of patients who did and did not engage with a lifestyle medicine program, and to identify predictors of engagement.

Methods

This was a single-center, retrospective cohort study of 276 adult patients who presented for consultation to a goal-directed, individualized, interprofessional lifestyle medicine program. The primary outcome was patients’ extent of engagement. Candidate predictors considered in multivariable multinomial logistic regression models included baseline sociodemographic, psychological, and health-related variables.

Results

A predictor of full engagement over no engagement was having private or Medicare insurance (rather than Medicaid, other, or no insurance) (OR 4.2 [95% CI 1.3-14.2], P = .021). A predictor of partial engagement over no engagement was having a primary goal to lose weight (OR 3.1 [1.1-8.4], P = .026).

Conclusions

System-level efforts to support coverage of lifestyle medicine services by all insurers may improve equitable engagement with lifestyle medicine programs. Furthermore, when assessing patients’ readiness to engage with a lifestyle medicine program, clinicians should consider and address their goals of participation.

Keywords

lifestyle medicine engagement insurance goals resource utilization

“Having private or Medicare insurance (rather than Medicaid, other, or no insurance) was independently and most strongly associated with fully engaging (rather than not engaging) with the interprofessional lifestyle medicine program.”

Introduction

Lifestyle medicine is a board-recognized field of medicine that focuses on the “prevention and treatment of chronic disorders attributed to lifestyle factors and preventable causes of death.”^1,2 The six pillars of lifestyle medicine are: a whole-food, plant-predominant eating pattern, regular physical activity, restorative sleep, stress management, avoidance of risky substances, and positive social connection.³ It is well-established that these lifestyle habits have a dramatic influence on numerous health conditions including hypertension, cardiovascular disease, type 2 diabetes, obesity, the development of cancer, anxiety, depression, musculoskeletal pain, and the development of osteoarthritis, among others.^4-13 A growing body of evidence is also demonstrating that changes in lifestyle habits can reduce morbidity and sometimes even lead to disease remission for some of these chronic diseases.^6,14-26

Nevertheless, not everyone who could benefit from lifestyle changes is ready to engage with a lifestyle medicine based approach to care.²⁷ Treating disease with lifestyle medicine requires the resources and commitment to initiate and sustain major lifestyle changes, and less than one in five people is typically ready to make and sustain major behavior change at any given time point.^28-30 If clinicians and policy makers can identify which patients are likely to meaningfully engage in a lifestyle medicine based approach to care, they can: (1) focus referral and intensive lifestyle medicine resources on patients who are most likely prepared to benefit at this time, and (2) address modifiable factors that could increase a patient’s likelihood of meaningfully engaging in the future.

The goals of this study were to: (1) describe biopsychosocial characteristics of patients who fully engage, partially engage, and do not engage with an interprofessional lifestyle medicine program, and (2) identify predictors of engagement. We hypothesized that among patients who present for consultation, increased self-reported importance of change would be associated with a greater likelihood of engaging with the program. Furthermore, increased self-reported readiness to change, confidence in ability to change, and self-efficacy would be associated with a greater likelihood of fully engaging rather than partially engaging.

Methods

This was a retrospective cohort study of prospectively collected data from a single tertiary care academic medical center in the US Midwest. Patient encounters occurred between November 6, 2018 and February 14, 2023, and data analysis was performed in 2023. All procedures were performed in accordance with the ethical standards of the Washington University Human Research Protection Office and with the 1964 Helsinki declaration and its later amendments. This study was approved under IRB number 202203103 via a waiver of informed consent. Reporting follows STROBE guidelines.³¹

Lifestyle Medicine Program

The intervention investigated was the Washington University Living Well Center’s goal-directed, individualized, interprofessional lifestyle medicine clinical program that has previously been described.²⁷ Notably, the program is housed within an orthopedic department, so many (although not all) patients present to address lifestyle habits that are contributing to chronic musculoskeletal pain, related impairment, and metabolic and psychological comorbidities. The program offers goal-setting and nutrition counseling, behavioral health counseling, smoking cessation counseling, sleep hygiene counseling, physical activity counseling, mindfulness and stress reduction counseling, physical therapy, medical management, and acupuncture and medical massage therapy (to facilitate behavior change by addressing pain). Patients’ combination of services and total duration of participation is determined by shared decision making, patients’ preferences, and progress toward their goal. Therapeutic content is delivered via a combination of in-person and virtual appointments, as preferred by the patient. In addition to one-on-one sessions, group sessions in the format of Shared Medical Appointments have also been offered since January 2022.^32-34 Clinical team members during the study period included board-certified physical medicine and rehabilitation physicians (physiatrists), a PA (physician assistant) with subspecialty expertise and board certification in lifestyle medicine, a registered dietitian, a licensed counselor, a nurse practitioner certified in smoking cessation counseling, physical therapists, a physician acupuncturist, certified massage therapists, and a clinical nurse coordinator. The nurse coordinator assists in coordinating patients’ schedules, and the interprofessional clinical team holds monthly meetings to discuss patients and troubleshoot barriers to progress.³⁵

Patients

Patients who present for consultation to the interprofessional lifestyle medicine program complete a comprehensive set of self-reported measures and are evaluated by a physiatrist or the lifestyle medicine trained PA. The purpose of the consultation is to determine whether the patient has: (1) lifestyle-related habits and medical conditions which can be addressed with the program’s services, and (2) a concrete functional or medical goal for which lifestyle change would be beneficial. Patients who meet these criteria and are expected to benefit from engaging with two or more of the program’s services are considered to be appropriate for the interprofessional lifestyle medicine program.

The study cohort included adult (18 years or older) patients who presented for consultation to the interprofessional lifestyle medicine program. Patients were excluded if they: (1) were assessed at consultation to not be appropriate for the program’s lifestyle medicine services; (2) received treatment from a single discipline within the program (e.g., acupuncture only), rather than for consultation for the full interprofessional program; or (3) were still enrolled in the program at the time of data analysis. Eligible patients for the current analysis were identified from the program’s clinical data repository which houses the data patients provide at initial consultation.

Measures

The primary study outcome was patients’ extent of engagement with the interprofessional lifestyle medicine program. Patients were categorically defined as: (1) fully engaging (i.e., enrolled in and completed the interprofessional program in accordance with the clinical team’s recommendations, regardless whether their pre-specified goals were met), (2) partially engaging (i.e., enrolled in the program but did not complete follow-up in accordance with the team’s recommendations and did not meet their pre-specified goals prior to disengaging), and (3) not engaging (i.e., never returned to the program after the initial consultation). For descriptive purposes, patients’ engagement was also captured as the total number of program visits and as the duration from date of consultation to date of disengagement with the program (regardless of the reason).

Several domains of patient characteristics were recorded at the time of patients’ initial consultation. Sociodemographic characteristics included self-reported age, sex, race, ethnicity, insurance coverage, and social deprivation (measured by patients’ nine-digit zip codes using the national Area Deprivation Index (ADI) percentile).^36,37 Medical history characteristics included clinician-measured body mass index (BMI), waist-to-height ratio, and documented medical history of hypertension, hyperlipidemia, cardiovascular/heart disease, obstructive sleep apnea, lung disease, diabetes, anxiety, depression, and/or chronic pain (documented in the medical record).^38,39 Self-reported symptoms of physical function, pain interference, anxiety, and depression were also collected using Adult Patient-Reported Outcomes Measurement Information System (PROMIS) Computer Adaptive Test (CAT) measures.⁴⁰ Psychological profile characteristics included participants’ self-reported primary goal of participation in the lifestyle medicine program, resiliency (measured by the Brief Resilience Scale),⁴¹ self-efficacy (measured using PROMIS),⁴⁰ transtheoretical stage of change,⁴² perceived importance of change,⁴³ readiness to change,^43,44 confidence in ability to change,⁴³ motivators for change,⁴⁵ and barriers to change.⁴⁵ Lifestyle-related characteristics included self-reported nicotine use, average nightly sleep duration, nutrition habits (i.e., frequency of processed food consumption, quantity of fruit/vegetable consumption), average weekly minutes of moderate/strenuous exercise, frequency of sense of purpose/meaning, and frequency of feeling connected with a support network.^45,46

Statistical Analysis

Descriptive statistics were reported for each patient characteristic available at the time of consultation, subgrouped by patients’ engagement category. Select patient characteristics were treated as candidate predictors of engagement, such that candidate predictors largely focused on sociodemographic and psychological profile characteristics in order to capture social determinants of health and our a priori hypotheses. Additional candidate predictors included body mass index (which is associated with the other metabolic health measures collected), mental health measures (i.e., history of anxiety and depression, PROMIS Anxiety and Depression scores), and being connected with a support network (which is both a pillar of lifestyle medicine and an established contributor to successful behavior change).⁴⁷ Univariable logistic regression with a multinomial link function was used to test the null hypothesis that each predictor was independently associated with the probability of the patient fully engaging compared to not engaging, and partially engaging compared to not engaging. An additional model was used to determine the probability of the patient fully compared to partially engaging. Univariable (unadjusted) odds ratios (ORs) with 95% confidence intervals (CIs) are reported (Appendix 1). Multivariable multinomial logistic regression models were built using forward selection such that all univariable predictors with P < .1 were included as candidate predictors in the multivariable models. P < .05 was considered statistically significant for the final multivariable models. Adjusted odds ratios with 95% CIs are reported for each variable included in the final multivariable model, adjusted for all variables in the model. For unordered categorical predictors, odds ratios are expressed compared to the referent category. For ordered predictors, ORs are expressed for a one-unit increase in the predictor, except where noted. Due to skewed distributions and/or poor model fit, some inherently continuous variables were categorized for analyses. We a priori defined quartile categories of national ADI percentiles. Diagnostics of collinearity was performed using linear regression, and lack of collinearity was verified when the variance inflation factor was two or less.⁴⁸ In assessing collinearity between PROMIS Anxiety and Depression scores, both had variance inflation factors greater than two. Because evidence suggests that anxiety is more readily modifiable than depression by addressing musculoskeletal dysfunction, PROMIS Anxiety was included rather than PROMIS Depression.⁴⁹ The sample size included all eligible patients at the time of analysis. As applicable, patients with missing data for candidate predictors in the multivariate multinomial logistic regression model were excluded. Analyses were performed with SAS v9.4 (SAS Institute Inc., Cary, NC, USA).

Results

Of 401 patients who presented for consultation to the interprofessional lifestyle medicine program, 276 were appropriate for participation and eligible for analysis (Figure 1). This cohort had a median (IQR) age 58 (44-66) years, 222 (80%) were female, and 129 (47%) fully engaged, 76 (28%) partially engaged, and 71 (26%) did not engage (Table 1).

Figure 1.

Inclusion flowsheet.

Table 1.

Patients’ Sociodemographic, Medical, and Self-Reported Health Characteristics Subgrouped by Engagement With an Interprofessional Lifestyle Medicine Program.

Median (IQR) or N (%)	All	Fully Engaged	Partially Engaged	Did Not Engage
Median (IQR) or N (%)	(n = 276)	(n = 129)	(n = 76)	(n = 71)
Duration of participation (days)	86 (2-183)	180 (112-267)	63 (17-113)	0 (0-0)
Number of total visits	7 (2-16)	16 (9-25)	5 (3-10)	1 (1-1)
Sociodemographics
Age (years)	58 (44-66)	59 (47-67)	55 (43-65)	53 (44-65)
Sex
Female	222 (80%)	104 (81%)	60 (79%)	58 (82%)
Male	54 (20%)	25 (19%)	16 (21%)	13 (18%)
Race
White	224 (81%)	109 (85%)	56 (74%)	59 (83%)
Black	43 (16%)	17 (13%)	16 (21%)	10 (14%)
Asian	3 (1%)	1 (1%)	1 (1%)	1 (1%)
Other	5 (2%)	1 (1%)	3 (4%)	1 (1%)
More than one race	1 (0%)	1 (1%)	0 (0%)	0 (0%)
Ethnicity
Hispanic	6 (2%)	3 (2%)	0 (0%)	3 (4%)
Not Hispanic	269 (98%)	126 (98%)	76 (100%)	67 (94%)
Other	1 (0%)	0 (0%)	0 (0%)	1 (1%)
Insurance coverage
Private	167 (61%)	78 (61%)	44 (58%)	45 (63%)
Medicare	83 (30%)	45 (36%)	24 (32%)	14 (20%)
Medicaid	12 (5%)	3 (2%)	7 (9%)	2 (3%)
Other	4 (2%)	1 (1%)	0 (0%)	3 (1%)
None	10 (4%)	2 (2%)	1 (1%)	7 (10%)
Area deprivation index (ADI)^a
Quartile 1 (ADI <25, least deprived)	53 (19%)	28 (22%)	15 (20%)	10 (14%)
Quartile 2 (25 < ADI <50)	113 (41%)	54 (42%)	27 (36%)	32 (45%)
Quartile 3 (50< ADI <75)	73 (27%)	32 (25%)	17 (22%)	24 (34%)
Quartile 4 (ADI >75, most deprived)	37 (13%)	15 (12%)	17 (22%)	5 (7%)
Medical history
Body mass index (BMI), kg/m² (n = 250)^b	34 (27-42)	34 (26-42)	37 (29-44)	33 (27-43)
Waist-to-height ratio^c (n = 240)	.6 (.5-.7)	.6 (.5-.7)	.7 (.5-.8)	.6 (.5-.7)
Hypertension	141 (51%)	63 (49%)	45 (59%)	33 (47%)
Hyperlipidemia	128 (46%)	67 (52%)	37 (49%)	24 (34%)
Cardiovascular/heart disease	63 (23%)	31 (24%)	17 (22%)	15 (21%)
Obstructive sleep apnea (OSA)	88 (31%)	42 (33%)	29 (38%)	17 (24%)
Lung disease	44 (16%)	20 (16%)	14 (18%)	10 (14%)
Diabetes	42 (17%)	21 (16%)	13 (17%)	8 (11%)
Anxiety	117 (42%)	55 (43%)	30 (40%)	32 (45%)
Depression	114 (41%)	50 (39%)	34 (45%)	30 (42%)
Chronic pain
Back	152 (55%)	67 (52%)	41 (54%)	44 (62%)
Hip/pelvis	65 (24%)	30 (23%)	18 (24%)	17 (26%)
Lower extremity	88 (32%)	39 (30%)	29 (39%)	20 (28%)
Upper extremity	66 (24%)	31 (24%)	19 (25%)	16 (23%)
Self-reported health
Adult PROMIS CAT physical function v2.0^d (n = 266)	38 (33-42)	38 (32-43)	38 (34-41)	38 (32-42⁾
Adult PROMIS CAT pain interference v1.1^d (n = 266)	62 (56-67)	62 (56-67)	62 (56-67)	61 (54-67)
Adult PROMIS CAT anxiety v1.0^d (n = 266)	56 (47-63)	57 (50-63)	54 (46-61)	56 (45-65)
Adult PROMIS CAT depression v1.0^d (n = 266)	51 (43-58)	51 (46-53)	49 (42-58)	53 (34-61)

Abbreviation: PROMIS (Patient-Reported Outcomes Measurement Information System), CAT (Computer Adaptive Test).

^aThe national Area Deprivation Index is a community-level measure of social disadvantage based on a person’s nine-digit zip code.^36,37

^bTable rows with sample sizes listed reflect the presence of missing data for those specific variables.

^cNormal waist-to-height ratio is approximately <.5.⁵⁶

^dPROMIS measures are normalized to the general US population, with a mean of 50 and standard deviation of 10. Higher scores represent “more” of the domain such that high scores are favorable for PROMIS Physical Function, and low scores are favorable for PROMIS Pain Interference, Depression, and Anxiety.⁴⁰

Baseline variables that were related (P < .1) to patients’ level of engagement, unadjusted for the variable’s possible correlation with other baseline variables, included patient age, race, type of insurance coverage, body mass index, PROMIS Anxiety and Depression scores, primary goal of participation, and transtheoretical stage of change (Appendix 1). Specifically, patients who fully engaged in the program were generally older; more likely to identify as White (especially compared to patients who partially engaged); more likely to have private or Medicare insurance (rather than Medicaid, other, or no insurance); had a lower body mass index (especially compared to patients who partially engaged); reported more anxiety symptoms (especially compared to patients who partially engaged); reported more depressive symptoms than patients who partially engaged but fewer depressive symptoms than patients who did not engage at all; were more likely to have a primary goal to improve function (rather than to reduce pain or lose weight); and were more likely to be in the contemplation or action (rather than preparation or maintenance) stage of change (Table 2).

Table 2.

Patients’ Psychological Profiles and Lifestyle Habits Subgrouped by Engagement With an Interprofessional Lifestyle Medicine Program.

Median (IQR) or N (%)	All	Fully Engaged	Partially Engaged	Did Not Engage
Median (IQR) or N (%)	(n = 276)	(n = 129)	(n = 76)	(n = 71)
Psychological profile
Primary goal of participation
Reduce pain	78 (29%)	36 (28%)	14 (19%)	28 (40%)
Lose weight	57 (21%)	23 (18%)	24 (32%)	10 (14%)
Improve function	41 (15%)	26 (20%)	6 (8%)	9 (13%)
Reduce perioperative risk for elective surgery	39 (14%)	20 (16%)	13 (18%)	6 (9%)
Avoid orthopedic surgery	32 (12%)	16 (13%)	7 (10%)	9 (13%)
Brief resilience scale (BRS)^a (n = 263)^b	3.5 (3.0-4.0)	3.5 (3.0-4.0)	3.5 (2.8-4.0)	3.5 (3.0-4.0)
Adult PROMIS CAT self-efficacy v1.0 (General)^c (n = 237)	49 (41-53)	49 (40-53)	49 (41-53)	46 (41-52)
Transtheoretical stage of change (n = 261)
Precontemplation	10 (4%)	5 (4%)	0 (0%)	5 (7%)
Contemplation	37 (14%)	22 (18%)	11 (16%)	4 (6%)
Preparation	128 (49%)	49 (41%)	40 (57%)	39 (55%)
Action	77 (30%)	39 (33%)	16 (23%)	22 (31%)
Maintenance	9 (3.4%)	5 (4%)	3 (4%)	1 (1%)
Importance-of-change ruler^d (n = 205)	88 (73-95)	85 (71-100)	89 (77-100)	87 (70-97)
Readiness-for-change ruler^d (n = 261)	70 (53-81)	67 (50-80)	72 (57-83)	70 (57-83)
Confidence-to-change ruler^d (n = 205)	72 (56-85)	73 (55-86)	70 (59-80)	72 (50-91)
Motivators for change
Increase energy	77 (28%)	35 (27%)	20 (26%)	22 (26%)
Weight reduction	63 (23%)	31 (24%)	19 (25%)	13 (18%)
Body image	39 (14%)	20 (16%)	11 (15%)	8 (11%)
Family or partner	39 (14%)	15 (12%)	12 (16%)	12 (17%)
Decrease stress	34 (12%)	20 (16%)	7 (10%)	7 (10%)
Improve sleep	30 (11%)	15 (12%)	6 (8%)	9 (13%)
Improve mood	27 (10%)	13 (10%)	6 (8%)	8 (11%)
Increase self-esteem	17 (6%)	10 (8%)	4 (24%)	3 (4%)
Prevent heart disease	12 (4%)	7 (5%)	1 (1%)	4 (6%)
Reduce blood pressure	9 (3%)	2 (2%)	3 (4%)	4 (6%)
Prevent bone loss	7 (3%)	4 (3%)	2 (3%)	1 (1%)
Control blood sugar	3 (1%)	3 (2%)	0 (0%)	0 (0%)
Barriers to change
Energy	59 (21%)	29 (23%)	19 (25%)	11 (16%)
Time	40 (15%)	18 (14%)	12 (16%)	10 (14%)
Work or family responsibility	38 (14%)	19 (15%)	12 (16%)	7 (10%)
Cost	33 (12%)	16 (12%)	11 (15%)	6 (9%)
Depression	24 (9%)	10 (8%)	10 (13%)	4 (6%)
Life transition period	17 (6%)	13 (10%)	4 (5%)	0 (0%)
Fear	15 (5%)	6 (5%)	3 (4%)	6 (8%)
Other barriers	30 (11%)	15 (12%)	6 (8%)	9 (13%)
No barriers	26 (9%)	14 (11%)	4 (5%)	8 (11%)
Lifestyle habits^e
Nicotine use	17 (6%)	5 (4%)	4 (5%)	8 (11%)
Average nightly sleep (n = 133)
Less than 4 hours	4 (3%)	2 (3%)	1 (3%)	1 (3%)
4-5 hours	23 (17%)	13 (20%)	4 (12%)	6 (18%)
6 hours	38 (29%)	17 (26%)	14 (41%)	7 (21%)
7-8 hours	58 (44%)	29 (44%)	12 (35%)	17 (52%)
9 or more hours	10 (8%)	5 (8%)	3 (9%)	2 (6%)
Processed food consumption (n = 133)
Not at all	21 (16%)	10 (15%)	4 (12%)	7 (21%)
Several days	68 (51%)	35 (53%)	17 (50%)	16 (19%)
More than half the days	21 (16%)	11 (17%)	7 (21%)	3 (9%)
Nearly every day	23 (17%)	10 (15%)	6 (18%)	7 (21%)
Daily servings of fruits/vegetables (n = 133)
Less than 2 servings	37 (28%)	19 (29%)	7 (21%)	11 (33%)
2-3 servings	72 (54%)	30 (45%)	21 (62%)	21 (64%)
4-5 servings	20 (15%)	13 (20%)	6 (18%)	1 (3%)
More than 5 servings	4 (3%)	4 (6%)	0 (0%)	0 (0%)
Weekly minutes of moderate/strenuous exercise (n = 263)	20 (0-120)	20 (0-150)	20 (0-80)	20 (0-120)
Feeling of sense of purpose/meaning (n = 143)
Nearly every day	84 (63%)	42 (64%)	21 (62%)	21 (64%)
More than half the days	22 (17%)	12 (18%)	5 (15%)	5 (15%)
Several days	18 (14%)	8 (12%)	5 (15%)	5 (15%)
Not at all	9 (7%)	4 (6%)	3 (9%)	2 (6%)
Connected with support network (n = 143)
Nearly every day	59 (44%)	28 (42%)	15 (44%)	16 (49%)
More than half the days	19 (14%)	12 (18%)	4 (12%)	3 (9%)
Several days	29 (22%)	15 (23%)	5 (15%)	9 (27%)
Not at all	26 (20%)	11 (17%)	10 (29%)	5 (15%)

Abbreviation: PROMIS (Patient-Reported Outcomes Measurement Information System), CAT (Computer Adaptive Test).

^aThe Brief Resilience Scale is scored from 1 to 5, with higher scores representing greater resilience.⁴¹

^bTable rows with sample sizes listed reflect the presence of missing data for those specific variables.

^cPROMIS measures are normalized to the general US population, with a mean of 50 and standard deviation of 10. Higher scores represent “more” of the domain such that high scores are favorable for PROMIS Self-Efficacy.⁴⁰

^dPotential for change measures are scored from 0 to 100, and higher scores are favorable.⁴³

^eAside from nicotine use and exercise, the lifestyle measures were collected using the Lifestyle Assessment Short Form.⁴⁵

In the final multivariable models which adjusted for relationships between baseline variables, a predictor of full engagement over no engagement was having private or Medicare insurance (rather than Medicaid, other, or no insurance) (OR 4.2 [95% CI 1.3 to 14.2], P = .021), and having a primary goal to improve function (as opposed to a different primary goal such as to reduce pain or lose weight) just missed the a priori threshold for statistical significance (OR 2.6 [1.0 to 6.7], P = .0500) (Figure 2A). A predictor of partial engagement over no engagement was having a primary goal to lose weight (OR 3.1 [1.1 to 8.4], P = .026) (Figure 2B). No baseline variables met statistical significance to predict full engagement over partial engagement (Figure 2C).

Figure 2.

Candidate predictors of (A) Full vs no engagement, (B) Partial vs no engagement, and (C) Full vs partial engagement with an interprofessional lifestyle medicine program (N = 276). Multivariable odds ratios are presented, and error bars represent 95% confidence intervals.

Discussion

In this single-center retrospective cohort study, approximately half (47%) of patients who presented for consultation to an interprofessional lifestyle medicine program fully engaged with the program, one quarter (28%) partially engaged and then disengaged prematurely, and one quarter (26%) did not engage after the initial consultation. Several sociodemographic, health-related, and psychological profile differences between engagement groups were identified, including differences in the distributions of age, race, type of insurance coverage, body mass index, self-reported anxiety and depression symptoms, primary goal of participation, and transtheoretical stage of change. When accounting for all these variables together, having private or Medicare insurance (rather than Medicaid, other, or no insurance) was independently and most strongly associated with fully engaging (rather than not engaging) with the interprofessional lifestyle medicine program. Furthermore, having a primary goal to lose weight was associated with a greater likelihood of partially engaging with the program, rather than not engaging at all.

The predictors of engagement that we identified are notably inconsistent with our a priori hypotheses. Although a person’s self-efficacy and perceived importance, readiness, and confidence in ability to change have previously been associated with successful behavior change efforts,^43,50,51 patients’ self-report of these constructs did not readily predict their level of engagement with the lifestyle medicine program. Additional investigation to explain this finding is warranted, but it may indicate that patients’ self-report of these constructs is more useful in reference to specific lifestyle habits such as smoking cessation or increased aerobic exercise,^43,50,51 rather than in reference to engagement in a multidimensional program that addresses several lifestyle habits together. For instance, a patient may report high importance and readiness to “get healthier,” but this does not necessarily indicate that the patient envisions incorporation of a plant-predominant diet, reduction of alcohol use, and/or increased aerobic activity as preferred methods to accomplish this overarching goal.

Furthermore, our results suggest that patients’ means, and potentially reasons for wanting to work toward behavior change, are more reliable predictors of whether they will meaningfully engage with a lifestyle medicine program than their degree of self-efficacy. Although these factors are not easily modifiable, they are also not immutable. Specifically, system-level interventions can be implemented to improve access to lifestyle medicine programs, and our findings underscore the need for continued prioritization of intentional action to address factors that exacerbate health disparities. Third party payers such as state Medicaid programs, commercial insurance companies, and self-insured employers are in a unique position to improve equitable, affordable access to lifestyle medicine services. To address patients’ reasons for pursuing lifestyle change, counseling and goal-setting sessions can be refined in an effort to help patients identify intrinsic motivators and potentially shift their mindset toward focusing on function, rather than focusing on changing numbers on a scale or ruminating on the sensation of pain. Because successful behavior change often requires numerous attempts (and may be accomplished via various means),^{27,29,30,52-54} an alternative intervention strategy that warrants further investigation is to provide brief education and/or motivational interviewing with patients who are not currently interested in engaging in an interprofessional program, with a clear follow-up plan that the patient can pursue when ready. In this way, the hope is that the “seeds for behavior change” are planted and cultivated, even if clinical change is not imminent.

Strengths and Limitations

This study contributes novel information regarding how to identify patients who are likely to meaningfully engage with an interprofessional lifestyle medicine program. Nevertheless, some limitations affect the generalizability of the study findings. First, this was a single-center retrospective cohort study, so our findings could be influenced by the specific referral patterns and treatment protocol of our program. Worth particular mention, referral patterns to our program have changed over time such that participation in our previously described pilot program was a focused recruitment effort by the program clinicians,²⁷ whereas patients can now be referred from any of their clinicians or can refer themselves for consultation. As a result, the proportion of patients who fully engaged with the program in this study reflects an “all-comers” population. Our patient cohort also had suboptimal sociodemographic diversity, which we suspect reflects broader inequity in access and exposure to lifestyle medicine intervention and is a topic that warrants dedicated investigation.⁵⁵ A second limitation is that the regression analyses considered a large number of candidate predictors relative to the available sample size, and as such, other relevant predictors of engagement may exist which were not detected due to an insufficient sample size. Finally, patients’ reasons for premature disengagement and non-engagement were not systematically captured in this study.

Conclusion

In this study, patients who had more comprehensive health insurance coverage (i.e., private or Medicare insurance) and who had a primary goal other than weight loss were more likely to meaningfully engage with an interprofessional lifestyle medicine program than patients who were underinsured (i.e., Medicaid, other, or no insurance) and/or were primarily motivated by a different goal. Patients’ self-reported self-efficacy and perceived importance, readiness, and confidence in ability to change were not associated with their level of engagement with the program. System-level efforts, especially by third party payers, to ensure all patients have affordable access to lifestyle medicine services may improve equitable engagement with these programs. Furthermore, when assessing whether a patient will engage with an interprofessional lifestyle medicine approach to care, clinicians should consider and address patients’ goals of participation. For patients who are not yet ready to fully engage, brief, low-intensity lifestyle education is a noninvasive, minimally burdensome intervention which may facilitate their readiness for change in the future. Future investigation should consider qualitative work to better understand patients’ perceptions regarding drivers of incomplete engagement, and intervention studies should consider focusing on identifying strategies that increase patients’ likelihood of engagement based on the relevant variables identified in this analysis.

Supplemental Material

Supplemental Material - Predictors of Patient Engagement With an Interprofessional Lifestyle Medicine Program

Supplemental Material for Predictors of Patient Engagement With an Interprofessional Lifestyle Medicine Program by Abby L Cheng, Mollie E Dwivedi, Adriana Martin, Christina G Leslie, Madeline M Pashos, Viola B Donahue, Julia B Huecker, Elizabeth A Salerno, Karen Steger-May, and Devyani M Hunt in American Journal of Lifestyle Medicine.

Footnotes

Acknowledgments

The authors would like to thank the Washington University Living Well Center clinical team members for their contributions to data collection as part of standard clinical care.

Declaration of conflicting interests

The author(s) 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: This was supported by the National Institutes of Health (K23AR074520, TL1TR002344, UL1TR002345, P30CA091842); Doris Duke Charitable Foundation; and Jacqueline N. Baker Washington University Living Well Center Fund.

ORCID iDs

Abby L Cheng

Mollie E Dwivedi

Madeline M Pashos

Data Availability Statement

As instructed by the Washington University IRB, de-identified data from this study are not available because data were obtained from the retrospective review of clinical medical records such that patients did not have an opportunity to consent for data sharing.

Supplemental Material

Supplemental material for this article is available online.

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