The Impact of Health Coaching on Weight and Physical Activity in Obese Adults: A Randomized Control Trial

Abstract

Health Coaching (HC) is an evidence-based, patient-centered approach to assisting individuals in achieving their health-related goals. Studies have generally shown positive effects of HC on weight loss in obese adults. However, limitations do exist, that if addressed would further clarify HC’s viability as a clinical, obesity treatment approach. To examine the effects of HC on weight loss, moderate-to-vigorous physical activity (MVPA), and psychosocial constructs in obese adults. A randomized control trial with 44 [Mean body mass index (BMI) 36.5] middle-aged, White adults. Participants were randomly assigned to HC (n = 22) or control (n = 22) groups. A certified health coach provided bi-weekly, in-person and telehealth HC for 12 weeks. Percent excess weight loss was 15.7% in HC vs. 2.5% in controls (p< .001). The change in MVPA was significantly greater in HC (+50.3 min/wk) vs controls (+7.1 min/wk). Psychosocial constructs also changed more favorably in HC than controls. Health coaching is an effective approach for weight loss in obese adults. The results of this study support the consideration of HC as a treatment option for obese adults looking to lose weight.

Keywords

Obesity treatment counseling intervention lifestyle behaviors

Introduction

Obesity is a pandemic affecting 600 million people worldwide and nearly 40% (93 million) of adults and 18.5% (13.7 million) of children and adolescents in the U.S.^1-3 Between 1960 and 2010, a 600% increase (.9% to 6.6%) occurred in the proportion of U.S. adults considered extremely obese [Body Mass Index (BMI) > 40].⁴ Adult obesity in the U.S. has been associated with annual medical costs between $172 and $260.^5,6 At the individual level, each one-unit increase in BMI has been estimated to contribute to an additional annual medical cost of $253.⁶

Primary care practitioners (PCP; e.g., physicians) play a critical role in helping obese patients lose and sustain weight.⁷ Unfortunately, managing obese patients in primary care has proven challenging for PCPs, and considerable variability in weight loss has been reported (1–7 kg).^8,9 Increasing PCP involvement (e.g., via health behavior counseling) in their patients’ weight loss programs is highly unrealistic given the demands on a PCP’s time and the time commitment needed when working with obese patients.^8,10 As an alternative, researchers have proposed the use auxiliary professions to address health issues such as obesity, that are related to poor lifestyle behaviors (e.g., physical inactivity).^10,11 A systematic review of primary care-based interventions for obesity showed that both in-person and remote interventions delivered by non-PCP professionals (e.g., nurse practitioners) fared as well or better than PCP-only care.¹² Further evidence suggests that primary-care office staff (e.g., medical assistants) trained on behavioral change strategies may enhance PCP-led efforts to help obese patients lose weight.^13-15

Including professionals outside of primary care who are trained on behavioral change concepts associated with weight loss is appealing. However, the effectiveness of such an approach may be further enhanced by utilizing professionals trained in health coaching (HC).¹⁶ Health coaching is an evidence-based, patient-centered process that facilitates and empowers individuals to develop and achieve self-determined health and wellness goals.^17,18 According to the National Society of Health Coaches (NSHC), educated, trained, and certified health coaches are preferred when clinical outcomes (e.g., weight loss) are sought.¹⁸ The majority of studies have found HC can contribute to significant reductions in weight and/or body mass index (BMI) and positive changes in lifestyle behaviors.^16,19-22 Nevertheless, limitations do exist, that if addressed would further substantiate HC as a viable clinical, obesity treatment approach. First, in most studies, the coaches were students or members of the health care team (e.g., nurse practitioners) who received brief HC training (e.g., a 2-week course, a handbook). Second, although most of the studies mentioned a theoretical framework upon which the intervention and assessments were based (e.g., two reported changes in self-efficacy and one in self-management), only a few explored how the different components of the construct influenced weight loss or health behaviors.²⁰ Third and finally, most studies on HC interventions lacked sufficient detail about the HC components making it difficult to explain the mechanism by which the HC component contributed to the outcomes in obese adults. Therefore, the current study systematically evaluated the effects of a well-designed, theoretically-grounded, HC intervention delivered by certified health coaches on weight loss in obese adults.

Methods

Study Design and Hypotheses

An RCT was selected because it limits some biases associated with systematic differences between two comparison groups in known and latent factors that may affect outcomes.²³ The CONSORT flow diagram for this RCT (18A01018) is provided in Appendix 1. We estimated that 20 participants per group would be needed to detect a 10% difference in percent excess weight loss (%EWL) at a follow-up between groups while maintaining adequate power (85% power, 2-tailed test, α = .05). This sample size also enabled the detection of a 15% difference in minutes per week of MVPA between groups at a follow-up. An additional four individuals were recruited per group to account for an expected 15% attrition related to participant dropout. The following hypotheses were tested:

Hypothesis 1Obese adults receiving HC experience significantly greater increases in percent excess weight loss (%EWL) than obese adults not receiving HC.

Hypothesis 2Obese adults receiving HC experience significantly greater increases in minutes per week of MVPA than obese adults not receiving HC.

Hypothesis 3Obese adults receiving HC experience significantly more favorable changes in SCT constructs than obese adults not receiving HC.

Hypothesis 4Obese adults receiving HC become significantly more activated (involved with their healthcare) than obese adults not receiving HC.

Participants

A referral process and direct recruitment procedures (e.g., flyers, ads) were used to solicit potential participants from primary care centers and the general population in Newark, Delaware, between August 2020 and January 2021. Individuals interested in participating in the study were screened over the phone by a trained and CITI-certified researcher to determine if they (a) met the inclusion criteria as per self-report (planning to begin PCP-led weight loss care, not currently enrolled in a weight loss program, age ≥18 years, capable of reading and filling in the survey, not pregnant, BMI ≥30), willing to refrain from using HC services for the duration of the study if assigned to the control group) and (b) need medical clearance from their doctor before becoming physically active using the Physical Activity Readiness Questionnaire.²⁴ A total of 44 participants met all inclusion criteria and signed the consent form. Next, a random number table was used to devise a group assignment order for participants to enter the study. For example, the first recruited and successfully screened participant was assigned to the control group, the second to the control group, the third to the HC group, and so on until 24 participants were in each group. Researchers were blinded to group assignment during the intervention and when administering measures. Within one week of group assignment, all participants independently completed an electronic Qualtrics survey to obtain demographic, behavior, and psychosocial construct data. Participants in the HC group were contacted by a health coach to schedule HC sessions. All research procedures were approved by the University of Delaware’s Institutional Review Board for the protection of human subjects in research.

Measures

Demographics and Health History

A series of questions were used to obtain demographic information on participants including their age, sex, ethnicity/race, and education level. In addition, participants self-reported whether they were current smokers, taking prescribed medications, and/or had diabetes, hypertension, and/or sleep apnea.

Primary Outcomes

Percent Excess Weight Loss: Weight was measured by a study investigator using a standard, portable scale that was calibrated after each use and height using a portable stadiometer with a right-angle headpiece. Percent excess weight loss was calculated as: [(pre wgt − post wgt)/(pre wgt − ideal wgt)] where ideal wgt = 50 (men) or 45.5 (women) + (.91 × (height in centimeters − 152.4)). This is a standard metric for reporting weight loss after bariatric surgery, and it has been proposed for evaluating outcomes from obesity interventions.²⁵

MVPA: The International PA Questionnaire Short Form (IPAQ-SF), a seven-item self-report questionnaire, was used to assess participants’ minutes of MVPA per week. The instrument has acceptable reliability (Spearman’s ρ = .8) and validity (Spearman’s ρ = .33).²⁶

Psychosocial Measures

Self-Regulation: The Treatment Self-Regulation Questionnaire (TSRQ) was used to examine the degree of autonomous self-regulation (ASR) and externally controlled motivation (ECM) regarding why people engage in weight control behaviors. Participants indicated the extent to which each of the 12 items was true using a seven-point scale ranging from 1 (not at all true) to 7 (very true). Higher scores on the ASR subscale are better as they represent diet and exercise behaviors undertaken because they are perceived as interesting or personally important. The ECM subscale reflects diet and exercise behaviors affected by a desire for external rewards or fear of punishment. Thus, lower scores on this subscale would be desirable. It has demonstrated adequate reliability and validity (Cronbach’s α = .83).^27-29

Self-Efficacy: The Adapted Weight Efficacy Life-Style Questionnaire short form (WEL) is an 8-item questionnaire that uses a 10-point response scale ranging from 0 (not confident) to 9 (very confident) to assess an individual’s confidence in losing weight. Participants were asked to respond to statements addressing diet, exercise, and genetic factors contributing to weight control. It has demonstrated good external and convergent validity (Cronbach’s α = .92).^30,31

Social Support: The Social Support from Family and Friends Questionnaire was used to measure social support from family and friends for diet and exercise. Using a five-point scale, participants indicate how often family and friends said or did any of 13-items related to exercise or 10-items related to diet. It has demonstrated good test-retest reliability, internal consistency, and concurrent criterion-related validity.³² The frequency of support offered by health care team members was assessed with the Social Support from the Healthcare Team survey, a five-item, seven-point scale, which has excellent reliability (Cronbach’s α = .94).³³

Patient Activation: The Patient Activation Measure includes 13 items used to assess participants’ empowerment (knowledge, skills, beliefs, and confidence) to partner with their provider to manage their health. It has demonstrated high reliability and construct and criterion validity.³⁴

Interventions

Control Group

Participants in the control group we allowed to receive standard health care (e.g., primary care) and/or engage in organized or individualized weight loss programs. They were instructed not to utilize any HC services during the study. After completing the follow-up survey, all control group participants were notified that they could receive free HC from our certified health coaches.

Health Coaching Group

The HC component of the intervention was delivered individually in-person or tele-medically (phone or Zoom) to each participant by a certified health coach who had at least 500 h of training through the University of Delaware’s Health Coaching program. Part of the training involved the completion of 15 course credits on behavior changes principles and a 120-h clinical practicum. Health coaching followed a session structure outlined by the National Board Certified Health and Wellness Coach (NBHWC), which typically includes one HC session every two weeks for a total of six HC sessions.¹⁷ The health coaches worked independent from the PCP-led health care team, but were able to communicate with any member of the health care team when needed. For example, the health coach could provide feedback on a participant to the PCP upon request and they were instructed to refer back to the PCP if the client needed support outside of the coach’s scope of practice.

The initial session was 60–90 min in length and involved rapport building, setting the agenda, and reviewing the coaching protocol and participant intake forms. Supplemental coaching activities were utilized to expand a coach-facilitated conversation, which explored a participant’s areas of health concern, readiness to change areas of concern, and self-reported strengths that can be used to support behavioral change. Support by the coach allowed the participant to create a self-identified action plan in health-concern areas. The participant action plan followed the SMART goal format and included creating a participant-identified health vision, three-month goals, and weekly goals. Follow-up sessions were 30–40 min in length and focused on continued participant-coach rapport building, goal reviewing, brainstorming new ideas, and summarizing successes through discussion and various written and verbal coaching tools. Participants were encouraged to explore support systems, personal self-reported strengths, goal achievement challenges, and goals modifications to support their action plan in all follow-up sessions. The final session was a debriefing session where the coaching experience was discussed and summarized by the coach and the participant. The aim of the last session was to affirm the participant’s progress, growth, and learning throughout the coaching process. A strategic plan for the next steps was discussed and created. An outline of the model and an example of a typical coaching session can be found in Appendix 2.

Analysis

Shapiro–Wilk tests indicated non-Gaussian distributions for %EWL, baseline and follow-up MVPA, ASR, friend support, healthcare team support, and the change scores for MVPA, family support, and ASR. The results from Levene’s test and the Box’s M test across the two groups indicated that homogeneity of variance was met for all dependent measures (p> .05). Paired t-tests or related-samples Wilcoxon signed-rank tests were used to test for significant within-group differences from pre- to post-measurements. Baseline values for all outcome variables and change scores (follow-up – baseline values) were compared between experimental groups with independent t-tests (Gaussian distributions) or Mann–Whitney U tests (non-Gaussian distributions). Effect sizes for Mann–Whitney U tests were calculated by dividing the Z score by the square root of the total sample (n = 44) and Cohen’s d was derived for t-tests by dividing the t value by the square root of the total sample. The magnitude of effect sizes was interpreted according to Cohen³⁵ [67] as small (.2), medium (.5), and large (.8). Pearson Product Moment (Gaussian distributions) or Spearman rank (non-Gaussian distributions) correlation coefficients were calculated to examine univariate relationships among variables within each group. Two participants from each group did not complete the follow-up survey. Thus, a total of four participants were lost to follow-up. An intention-to-treat analysis was conducted with 24 participants with variable means specific to the PCP+HC group used to replace <5% of missing follow-up values. All tests were two-sided. A p-value of .05 was considered statistically significant. Analyses were conducted using the SPSS statistical software package (IBM: SPSS for Windows, Rel. 26.0. 2021).

Results

Based on the session structure outline provided by the NBHWC, a typical HC program would include about six sessions over a 12-week period. In the current study, participants attended 79.2% (95 sessions) of a possible 120 sessions (20 participants with complete data × 6 sessions/participant = 120 possible sessions). The sessions were either in-person (31% of sessions) or telephonic (69% of sessions). No participants attended less than four sessions.

The HC and control groups were similar at baseline with regards to age, sex/gender, education, and racial/ethnic minority background (Table 1). Both groups were obese, middle-aged, non-Hispanic, White adults with advanced education degrees. Provided in Table 2 are disease-related characteristics for both groups at baseline and follow-up. The participants were non-smokers with a high prevalence of obesity-related conditions. A considerable proportion of the participants in the HC (45% baseline and 40% follow-up) and control (33% baseline and 39% follow-up) groups were currently dealing with either hypertension, diabetes, or sleep apnea. In addition, nearly half of the control group and ∼33% of the HC group were taking prescription drugs for various health issues. Although the control group was on average 12.7 kg lighter than the HC group at baseline, this difference was not statistically significant (t = −1.91; p = .06) (Table 3)

Table 1.

Personal Demographics at Baseline.

	HC (n = 22)	Control (n = 22)
Age y M (SD)	48.1 (13.9)	50.2 (9.5)
Female (%)	80	77.8
Non-Hispanic (%)	100	95
White (%)	80	94.4
≥ Four-year college (%)	55	50

Table 2.

Disease-Related Outcomes at Baseline and Follow-up.

	HC (n = 22)		Control (n = 22)
	Baseline	Follow-up	Baseline	Follow-up
Non-smoker (%)	95	95	94	100
Hypertensive (%)	35	35	22	28
Diabetic (%)	10	10	11	11
With sleep apnea (%)	20	10	11	17
Hypertensive, diabetic, or apneustic (%)	45	40	33	39
On medication (%)	35	25	44	50

Comorbidities were self-reported by participants.

Table 3.

Weight-Related and Physical Activity Outcomes by Experimental Group (Means ± standard deviations).

	HC (n = 22)			Control (N = 22)
	Baseline	Follow-up	Change	Baseline	Follow-up	Change
MVPA min/wk	93.5 (157.8)	143.8 (210.6)	50.3^a	101.0 (108.1)	108.1 (131.5)	7.1
%Excess weight loss	NA	15.7 (16.7)^b	—	NA	2.5 (15.1)	NA
Weight (kg)	108.5 (21.5)	103.1 (22.7)	−5.4^a	95.7 (20.7)	95.4 (21.7)	−.3
BMI (kg/m²)	38.6 (7.9)	36.7 (8.6)	−1.9^b	34.8 (4.5)	34.9 (5.1)	.1

Abbreviations: HC, health coaching; MVPA, moderate-to-vigorous physical activity; BMI, body mass index; NA, not applicable.

^aP < .005.

^bP <.05.

Hypothesis 1

The HC group had an average %EWL of 15.6 ± 15.9 (Mdn = 13.7) compared to 2.5 ± 13.6 (Mdn = 3.1) in the control group (Table 3). These between-group differences were statistically significant (U = 139.0, p = .015; r = .37, medium ES). Change in friend support for diet and exercise was significantly correlated with %EWL (r_s = .54, p = .009).

Hypothesis 2

No difference in MVPA was noted between groups at baseline (U = 192.5.0, p = .24). From baseline to the 12-wk follow-up, MVPA increased significantly in the HC group (Z = −3.08, p = .002) but not in the control group (Z = −.66, p = .51). There were statistically significant between-group differences in MVPA change scores (U = 119.0, p = .004; r = .48, medium ES). Increases in MVPA per week were correlated with increases in friend support for diet and exercise (r_s = .59, p = .004) and patient activation (r_s = .44, p = .04).

Hypothesis 3

There was no significant difference between groups in ASR at baseline (U = 194.0; p = .26) (Table 4). The average ASR scores decreased from 12.86 to 12.13 in the control group (Z = −2.72, p = .01) and increased from 11.90 to 12.27 in the HC group (Z = −1.92, p = .05). The change in ASR from baseline to follow-up was significantly greater in the HC than the control group (t = −3.64, p = <.001; d = .55, medium ES). Positive changes in ASR were correlated with increases in family support for diet and exercise (r_s = .45, p = .002).

Table 4.

Socio-Ecological Construct Changes in the HC and Control groups (M ± SD).

	Control Change	HC Change	Difference	P-value
Autonomous regulation diet/exercise	−.73 (1.29)	+.37 (.88)	1.10	.005
Externally controlled motivation diet/exercise	.66 (1.95)	−.35 (.95)	1.01	.048
Self-efficacy for weight loss	2.51 (9.33)	8.55 (13.67)	6.04	.18
Family support diet/exercise	−2.94 (10.46)	2.25 (8.38)	5.19	.10
Friend support diet/exercise	1.11 (8.37)	2.75 (6.61)	1.64	.51
Health care team support	−1.00 (8.80)	2.55 (8.94)	3.55	.23
Patient activation	.06 (6.22)	7.50 (9.42)	7.44	.007

No difference in ECM was found between groups at baseline (U = 238.0, p = .93). The average ECM scores decreased from 6.05 to 5.70 (Z = −1.86, p = .06) in the HC group and increased from 6.02 to 6.68 in the control group (Z = −1.90, p = .06). The ECM change score differed significantly between groups (t = 2.48, p = .02; d = .37, small to medium ES). Changes in ECM were correlated with changes in self-efficacy for weight control (r = −.44, p = .04).

Average self-efficacy for weight control scores increased from baseline to follow-up in the HC group (from 35.10 to 43.65) and control group (from 42.10 to 44.61). The increase was statistically significant in the HC group (t = −3.05, p = .003), but not in the control group (t = −1.39, p = .09). There were no statistically significant between-group differences in the self-efficacy for weight control change scores (t = −1.78, p = .08). Change in self-efficacy for weight control was significantly correlated with change in ECM (r = −.44, p = .04) and change in support from the healthcare team (r = .49, p = .02).

Family support did not differ between groups at baseline (t = 1.46, p = .15). Family support for diet and exercise scores at baseline and follow-up were not significantly different in the HC (t = −1.38, p = .18) or control (t = 1.47, p = .16) groups. The family support for diet and exercise change scores did not differ significantly between groups (t = −2.01, p = .05). Changes in family support were related to changes in friend support for diet and exercise (r_s = .50, p = .02) and changes in ASR (r_s = .45, p = .002).

Baseline social support from friends for diet and exercise was similar (U = 237.0, p = .91) between the HC (21.8 ± 7.6) and control (21.3 ± 9.5) groups. Baseline and follow-up scores did not differ significantly in the HC (Z = −1.9, p = .05) or control (Z = −.35, p = .73) groups. The change scores in friends’ support for diet and exercise were not significantly different between groups (U = 192.0, p = .24). In addition to being significantly correlated with %EWL, MVPA, and family support for diet and exercise, changes in friend support were also related to changes in patient activation (r = .45, p = .04).

Baseline support from the healthcare team was similar at baseline (U = 171.0, p = .10) between the HC and control groups. The change scores between the baseline and follow-up were not significant in the HC (Z = −1.22, p = .22) or control (Z = −.96, p = .34) groups. Although there was a trend for participants in the HC group to report more support from the healthcare team after the three-month HC intervention compared to those in the control group, the change score was not significantly different between groups (U = 152.0, p = .05). Change in healthcare team support was significantly correlated with self-efficacy for weight control (r = .49, p = .02).

Hypothesis 4

Baseline patient activation was similar between groups (t = .73, p = .47). Follow-up patient activation was significantly higher than baseline in the HC group (t = −3.9, p< .001), but not in the control group (t = .07, p = .95). The change scores for patient activation differed significantly between groups (t = −3.34, p = .002; d = .50, medium ES). Patient activation increased from 72.30 to 79.80 in the HC group, but remained relatively stable in the control group (from 74.80 to 74.86). Changes in patient activation were significantly correlated with changes in MVPA (r_s = .44, p = .04) and friend support for diet and exercise (r = .45, p = .04).

Discussion

The purpose of the current study was to systematically evaluate the effects of HC, delivered by certified health coaches, on weight loss in obese adults. Compared to participants in a control group who did not receive HC, participants receiving HC experienced greater %EWL and a MVPA changes. Additionally, HC led to relatively larger, positive changes in the participant’s self-regulation, social support, and patient activation.

Encouraging results in terms of the effects of HC on weight loss have emerged from several studies albeit with only a limited number using a RCT design.^20,25,36,37 For instance, Bennet et al.²¹ found that a web-based intervention based on a motivational interviewing approach produced significantly greater weight loss (−2.28 kg) than controls (+.28). In a RCT by Conroy et al.,²² the use of an electronic health record-based weight maintenance plus HC intervention resulted in lower average weight regain (+2.1 kg) in overweight adults at 24 months than the electronic health record-based weight maintenance intervention alone (+4.9 kg). In the current study, obese adults in the HC group lost on average 5.4 kg of weight at 12 weeks, which is substantially greater than the average weight loss noted with other forms of weight loss interventions without HC.¹¹ Additionally, HC versus controls achieved a higher %EWL (15.6% vs. 2.5%). Although the %EWL parameter is commonly used as the primary outcome with bariatric patients, it is seldom used in weight-loss intervention studies with obese, non-bariatric adults. This is somewhat puzzling given that %EWL has been proposed for evaluating obesity intervention outcomes including metabolic syndrome conditions, and it accounts for changes relative to ideal weight while factoring in sex and height variations.^25,38,39 Clinically, it is a better estimate of the amount of weight a person lost relative to a defined goal level, which may increase the motivation of obese adults.³⁹ Only one other study was found that used a similar metric to evaluate a HC, weight-loss intervention in overweight/obese adults.⁴⁰ In this observational study, HC was associated with an average loss of 7.24% of initial weight after 12 months. Based on the available evidence, it can be concluded that HC is a promising weight-loss strategy for obese adults. It also appears that the positive impact of HC on weight loss does not depend on integrating HC with other weight loss activities such as those provided by a PCP.^21,22,40,41 Given the significant results achieved in only three months, the current study provides evidence that a HC intervention delivered by certified health coaches may accelerate weight loss.

In addition to achieving a better physical outcome, as evidenced by %EWL, the HC group also became significantly more physically active than the control group. Other studies have shown HC to facilitate PA in mostly sedentary adults, although the amounts of change have been variable.^42,43 Gains in PA in the HC intervention group corresponded with more support from friends for diet and exercise and greater levels of patient activation. Relatedly, increased friend support correlated with greater %EWL. This is consistent with previous work showing that theory is critical to help investigators explain intervention effects on weight loss and PA.^44,45 A HC intervention, such as the one implemented in this study, attempts to enhance aspects of behavior related to the social and the cognitive domain. The findings of Jimison et al.⁴⁶ indicate that HC improves social interactions and ratings of social satisfaction while decreasing loneliness. Furthermore, previous research had shown a strong coupling between HC and patient activation, with significantly better clinical outcomes achieved when HC was matched to a patient’s activation level.⁴⁷ This study found significant increases in patient activation in the HC group with no change in the control group. In addition, the positive changes in patient activation correlated with greater support for diet and exercise from friends. These findings support conclusions from other studies suggesting that friend support for behavior change and patient activation are modifiable and predictive of future health outcomes.^48,49 Unfortunately, the design of this study does not allow us to sequence the changes in psychosocial factors; for example, HC caused the formation of more social ties with friends leading participants to become more involved in their healthcare. Nevertheless, this is an important line of inquiry that warrants further investigation.

Other theoretical constructs assessed in this study also changed significantly in directions favoring more positive health behaviors. Two indicators of engaging in weight control behaviors, ASR (autonomous self-regulation) and ECM (externally controlled motivation), increased and decreased, respectively. These changes suggest the HC participants became more likely to engage in diet and exercise behaviors because they perceived them as interesting or personally important rather than because of the external rewards/punishments associated with the behaviors. Other HC intervention studies have not specifically examined ASR and ECM, but they have found that HC promotes gains in self-efficacy and, as stated above, social support.^49,50 Additionally, the current study observed a strong relationship between ECM and self-efficacy. This finding, along with evidence from previous studies, implies that HC can help individuals become more positively motivated to engage in lifestyle activities, allowing them to develop a more concrete self-efficacy framework supporting healthy lifestyles.⁵¹ Family support for diet and exercise may play a role, along with self-efficacy, by enabling the emergence of positive self-regulation skills.⁵²

A strength of this study was the utilization of an RCT, which reduces certain biases and produces the most consistent and valid scientific evidence in clinical trials.²³ Another strength was the use of reliable instruments for assessing MVPA and a broad range of constructs mainly targeting the individual, but with some relevance to the proximal environment (e.g., healthcare team). The current study describes the HC component with details related to each session and the expected outcomes. In addition, extensively trained and certified health coaches delivered the HC component, and details about their training are provided. Lastly, evidence is presented linking HC with beneficial changes in theoretical constructs. Although most weight-loss or HC intervention studies mention theory, only a few provide evidence on the effects of the intervention on relevant theoretical constructs.¹⁹ This limits their capacity to explain the mechanism of behavior change, hampers replicative efforts by other scientists, and constrains the translational boundaries within which HC can be implemented.

There are limitations worth considering when interpreting this study’s findings. First, the sample size was relatively small and drawn from a population residing in one city on the east coast. Participant characteristics were homogeneous, and the representation from minorities and low-socioeconomic status (SES) groups was inadequate. However, the participants were typical (in terms of race, education, and income) of individuals who use HC services in the study area. Therefore, replication studies are needed to determine whether the same effects are present in dissimilar (minority, low-income) groups. Second, although the PA measure has acceptable reliability and validity for the target population, the use of objective assessment methods (e.g., accelerometry) might reveal subtle differences in MVPA and its relationships with other outcomes such as %EWL. Third, this study lacks formative and process evaluations to elucidate perceptions of participants and health coaches about the HC intervention. It might also be helpful to conduct qualitative observations or interviews of the health coaches to increase the fidelity of the interventions and enhance interpretations of the study findings. Fourth, although our HC was effective, the results were obtained after a short time period when participant motivation is highest. It will need to be determined if HC promotes weight loss maintenance, which is critical for realizing more impactful health benefits (e.g., reduced low density lipoprotein, lower blood pressure). Fifth, participation in this study occurred prior to and during the COVID-19 pandemic before COVID-19 vaccinations were readily available in the U.S. and at a time when pandemic-related restrictions were having a severe impact on normal behavior. For example, most physical activity opportunities (e.g., community centers, gyms, parks) either ceased operations or imposed regulations limiting use during the pandemic. How such dramatic changes affected study participants, especially those in the control group without the support and direction of health coaches, is not known. However, it is likely pandemic effects occurred, which is important to consider when comparing this study’s outcomes with others not influenced by a life-altering event like the COVID-19 pandemic. Finally, to be included in the study, participants had to self-report that they were planning to begin a weight loss program with their PCP and that they were not currently enrolled in a weight loss program. Beyond this, no other information was obtained on participant interactions with other health care personnel such as their PCP.

In conclusion, participants receiving HC compared to participants not receiving HC achieved better results in terms of weight loss. Future studies in this area should explore the long-term effects of HC while also expanding data collection to include objective measures of PA and qualitative information on the HC intervention and the health coaches. In addition, evidence is needed from large-scale effectiveness trials that compare the impact of HC as a stand-alone service versus a service integrated within a patient’s health care team (e.g., PCP-led weight loss). Of particular interest would be the scalability of our HC model when considering implementation within, for example, a large primary care practice. Regardless of the mechanism used to provide HC services, the current study and others in this area, make a strong case for considering HC as an insurable expense, at least for obese adults.

Supplemental Material

Supplemental Material - The Impact of Health Coaching on Weight and Physical Activity in Obese Adults: A Randomized Control Trial

Supplemental Material for The Impact of Health Coaching on Weight and Physical Activity in Obese Adults: A Randomized Control Trial by Richard R. Suminski, MPH, PhD, Tara Leonard, MS, Iva Obrusnikova, PhD, and Kristin Kelly, MS in American Journal of Lifestyle Medicine

Footnotes

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 author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Institute of Coaching International, Harnisch Grant.

Ethical Approval

The study was approved by the institutional review board of the University of Delaware.

Informed Consent

All study participants gave informed consent.

ORCID iD

Richard R. Suminski

Supplemental Material

Supplemental material for this article is available online.

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