ABSTRACT
Introduction
Health‐related culinary intervention, termed culinary medicine (CM), is an innovative evidence‐based strategy in the field of nutrition to improve dietary quality and prevent/manage chronic diseases. Long‐term effects on dietary intake and obesity using well‐designed studies are limited.
Methods
A randomized controlled trial evaluated the impact of a CM intervention on body weight. Participants were age 25–70 with BMI 27.5–35 kg/m2. Intervention: Dietary counseling and twelve 30‐min CM sessions. Control: Dietary counseling and CM resources. Body weight, nutrition, and health outcomes were measured at 0, 3, 6, and 12 months.
Results
Fifty participants are included in a modified intention to treat analysis. Average intervention group weight loss was: at 3 months, −3.23% (3.52%), net difference −2.52% (CI: −0.48% to −4.56%; p = 0.016); at 6 months, −4.2% (5.24%), net difference −2.98% (CI: −0.36% to −5.60%; p = 0.027); and at 12 months, −4.02% (6.24%), net difference −4.30% (CI: −0.69% to −7.92%; p = 0.021). Intervention group had: fat mass loss at 6 months, 1.86% (1.54%), net difference 1.96% (CI: −3.82% to 0.11%, p = 0.039); Mediterranean diet score improvement at 3 months, 2 points, net difference 1.62 (CI: 0.263 to 2.968; p = 0.020); and calorie consumption decrease at 6 months, 452 cal, net difference −390 (CI: −701.1 to −78.13; p = 0.015).
Conclusions
CM can be an effective strategy to promote weight and body fat loss.
Trial Registration
ClinicalTrials.gov identifier NCT03823469; preregistered on June 30, 2019
1. Introduction
The importance of nutrition in disease reduction and optimal health is well established; a healthy diet is linked to reduced risk of obesity, cardiovascular disease, and all‐cause mortality [1, 2]. Balanced nutrition becomes even more relevant with the availability of potent weight loss medication including glucagon‐like peptide‐1 (GLP1) receptor agonists that profoundly decrease food intake [3, 4]. The Mediterranean diet, one of the most well‐studied dietary patterns, has been shown consistently to have beneficial health impacts [5, 6]. A commonality among healthy diets is high intake of minimally processed foods (MPF) and low intake of ultraprocessed foods (UPF) [7]—industrial foods and drinks made of food‐derived substances and additives, usually containing little to no whole foods [8]. UPF intake is linked to numerous adverse health outcomes including overweight/obesity [9, 10], cardiometabolic disease [11], and overall mortality [12]. The need to reduce UPF consumption was identified in several high impact publications [13, 14].
Health‐related culinary intervention, termed culinary medicine (CM), is emerging as an innovative strategy to improve dietary quality, reduce UPF consumption, and promote health [15]. Systematic reviews assessing CM outcomes indicate short‐term improvements in cooking skills and nutrition intake [16, 17]. Randomized clinical trials (RCT) evaluating the impact of CM interventions on adults with cardiometabolic risk factors found short‐term improvement in vegetable intake [18], Mediterranean diet score [19], and weight [20, 21]. A recent systematic review evaluating remote CM programs found that the precise effect of these interventions is limited by the quality and duration of the interventions [22]. Well‐designed studies are needed that rigorously evaluate the long‐term impact of CM interventions on obesity and health [17, 22].
The need for evidence‐based behavioral change components has been identified to support sustainable home cooking habits [23, 24]. Health coaching is a patient‐centered approach wherein a health coach empowers patients to determine their goals through self‐discovery or active learning process and to self‐monitor behaviors for increased accountability [25]. The culinary coaching telemedicine program combines culinary training with health coaching principles as a two‐prong approach to improve nutrition and health [26]. Our previous publications showed short‐term improvement in culinary confidence and nutrition [27] and long‐term improvement in culinary skills [28]. Our goal is to address the gap in long‐term rigorous studies that evaluate health outcomes of CM programs. This study presents the 1‐year impact of a CM program integrated with health coaching on weight using data from a bi‐center RCT.
2. Methods
2.1. Study Design and Participants
This bi‐center RCT was designed to evaluate the impact of a CM telemedicine intervention on body weight of participants with overweight/obesity. The trial was conducted at Spaulding Rehabilitation Hospital, Boston, Massachusetts; and Sheba Medical Center, Ramat Gan, Israel, between May 2019 and September 2022. Participants with overweight and stage 1 obesity were chosen for this study as they were less likely to use weight loss medications. In order to also collect data on adiposity‐related health outcomes, the upper half of overweight was chosen to increase the likelihood of glucose or lipids abnormalities at baseline. Major inclusion criteria included age 25–70, body mass index (BMI) ≥ 27.5 kg/m2 and ≤ 35 kg/m2, and fewer than five home cooked meals per week. Participants were recruited from the Tel Aviv and Boston area primarily by mass mailing, social media, and physician referrals. The full study protocol is available in the online Supporting Information. Participants were allocated to a group by randomized sequence at each site using permuted blocks of size 2 and 4, generated before the study started by staff not otherwise involved in the study. After ascertaining eligibility, staff obtained randomization assignment through email. This study was approved by both sites' institutional review boards (IRB) (Spaulding #2018P002115, Sheba #SMC541918) and follows the Consolidated Standards of Reporting Trials (CONSORT) guideline [29].
2.2. The Culinary Coaching Intervention
Participants from both groups received two nutrition sessions focusing on the Mediterranean diet. Intervention group participants were also referred to a culinary coaching program that includes 12 weekly, one‐on‐one, 30‐min tele‐sessions combining culinary training with health coaching principles. At the first session, participants identified their home cooking vision and 3‐month goals. During each subsequent meeting, participants reviewed their progress toward reaching the prior week's culinary goals and identified goals for the coming week, using a self‐discovery process facilitated by a CM trained health coach [30]. When participants detected a new culinary skill that was necessary for their progress, they were either taught by the health coach through discussions or referred to active learning from open access web resources (a detailed description has been published [26, 27]). The control group received CM resources (i.e., recommended open access resources), also available to intervention group participants.
2.3. Outcomes
Outcomes were measured at baseline, 3 months, 6 months, and 1 year in the hospital clinical research center (CRC) with staff who were blinded to patient groupings and measurements. Body weight (kg) and height (cm) were measured by a registered dietitian (RD). Body composition (baseline and 6 months) was measured using whole body dual‐energy X‐ray absorptiometry (DXA) (Hologic, Quantitative Digital Radiography (QDR)‐Horizon A software version 5.6.0.5:3).
Dietary intake was calculated based on 4‐day food records, which were reviewed by an RD for clarity and completeness. Intake was analyzed using site‐specific software (Nutrient Data System for Research software version 2018, developed by the Nutrition Coordinating Center (NCC), University of Minnesota, Minneapolis, MN, Spaulding; Tzameret dietary analysis program, developed by the Israel Ministry of Health) for caloric content, nutrients, and food groups and then using the alternate Mediterranean Index (aMED) and the NOVA group classification [8]. A 14‐item Mediterranean diet assessment tool [31] was also completed at each visit.
Culinary attitudes and self‐efficacy were evaluated by the validated Cooking With Chef instrument [32]. Stress level was measured by the validated Perceived Stress Scale [33]. Physical activity status was measured using the validated International Physical Activity Questionnaire [34]. Demographics, medications, smoking status, and use of other nutritional education and resources were also obtained. Qualitative outcomes, participant stages of change, and coping strategies were also collected and reported [28, 35, 36].
Data were also collected on obesity‐related health outcomes including blood pressure, which was measured in a seated position by a nurse using a sphygmomanometer; and lipid profile and HbA1c, analyzed in accredited laboratories at both sites. During the COVID‐19 pandemic the study protocol was adjusted to meet the IRB risk regulations; thus, 48 visits were conducted online. Weight measurement was self‐reported (including a scale image for quality assurance); and body composition, lipid profile, blood pressure, and HbA1c were not performed. More information is provided in Figure S1.
2.4. Sample Size
Based on preliminary data and the effect of coaching programs [37], 60 total subjects would provide power > 0.85 to detect significance with α = 0.05 a clinically relevant change of 5% body weight [38] between groups with an assumed SD of 133% of the effect (6.5% body weight). Assuming 85% retention rate for 12 months, we expected to recruit 36 participants (18 intervention, 18 control) at each site, a total of 72 participants.
2.5. Statistical Analysis
Data analysis was performed from May 2023 to April 2025 using Stata versions 17, 18 (StataCorp LLC). The primary outcome was changes in body weight at 6 months. Secondary outcomes included changes in dietary intake and clinical outcomes, as well as changes in all outcomes at 3 and 12 months. NOVA% was calculated based on the NOVA group percentage of total calories, and aMED was calculated using an overall median.
Data were analyzed using a modified intention to treat (mITT) analysis [39]. Primary and secondary outcomes were measured using basic linear regression models through the visits. We made no adjustment for multiple comparisons for secondary outcomes. The number of participants who lost > 5% of body weight was measured using chi‐square test. Outcomes with missing data due to modified COVID‐19 visits were also analyzed using complete case analysis. Due to COVID‐19‐related out of window visits, two additional sensitivity analyses were performed: (1) per visit analysis using outcomes measured within 2 months from the study visit date and (2) per date analysis, using days since baseline.
3. Results
3.1. Participants
Of 863 prescreened participants, 78 underwent screening. A total of 75 participants were enrolled to the CM study, and 50 (67%) were included in mITT analysis (Figure S1). Participants' baseline characteristics are shown in Table 1. Most participants (70%) were female; mean (SD) age was 47.5 (13.81) years, weight 86.14 (12.47) kg, and BMI 30.7 (1.97) kg/m2 with 40.37% (6.47%) fat mass. There were no differences in weight and dietary intake between groups; the intervention group had a higher level of LDL cholesterol.
TABLE 1.
Baseline characteristics of study participants.
| Characteristic | Control (n = 26) | Intervention (n = 24) | Total (n = 50) | p value |
|---|---|---|---|---|
| Gender, n (%) | 0.9 | |||
| Male | 8 (31%) | 7 (29%) | 15 (30%) | |
| Female | 18 (69%) | 17 (71%) | 35 (70%) | |
| Age, mean (SD), years | 50.58 (14.14) | 44.17 (12.93) | 47.5 (13.81) | 0.1 |
| Ethnicity, n, (%) | 0.54 | |||
| American Indian | 1 (4%) | 1 (4%) | 2 (4%) | |
| Asian | 1 (4%) | 2 (8%) | 3 (6%) | |
| Black American | 1 (4%) | 0 | 1 (2%) | |
| White | 15 (57%) | 9 (38%) | 24 (48%) | |
| Israeli Jewish | 8 (31%) | 12 (50%) | 20 (40%) | |
| Marital status, n (%) | 0.34 | |||
| Live together | 12 (46%) | 10 (42%) | 22 (44%) | |
| Never married | 2 (8%) | 4 (17%) | 6 (12%) | |
| Separated | 7 (27%) | 8 (33%) | 15 (30%) | |
| Divorced | 5 (19%) | 1 (4%) | 6 (12%) | |
| Widowed | 0 | 1 (4%) | 1 (2%) | |
| Employment, n (%) | 0.58 | |||
| Unemployed | 22 (85%) | 21 (88%) | 43 (86%) | |
| Student | 3 (12%) | 1 (4%) | 4 (8%) | |
| Other | 0 (0%) | 1 (4%) | 1 (2%) | |
| Missing | 1 (4%) | 1 (4%) | 2 (4%) | |
| Education, n (%) | 0.13 | |||
| High school | 4 (15%) | 0 (0%) | 4 (8%) | |
| Academic degree | 21 (81%) | 23 (96%) | 44 (88%) | |
| Other | 1 (4%) | 1 (4%) | 2 (4%) | |
| Type 2 diabetes, n (%) | 1 (4%) | 0 (0%) | 1 (2%) | 0.33 |
| Hyperlipidemia, n (%) | 10 (50%) | 15 (79%) | 25 (64%) | 0.06 |
| Hypertension, n (%) | 15 (75%) | 10 (53%) | 25 (64%) | 0.15 |
| Cardiovascular disease, n (%) | 9 (45%) | 6 (32%) | 15 (38%) | 0.39 |
| Smoking, n (%) | 0.5 | |||
| Never | 17 (65%) | 19 (79%) | 36 (72%) | |
| Former | 8 (31%) | 4 (17%) | 12 (24%) | |
| Current | 1 (4%) | 1 (4%) | 2 (4%) | |
| Weight, mean (SD), kg | 86.3 (14.33) | 86 (10.39) | 86.1 (12.46) | 0.93 |
| BMI, mean (SD), kg/m2 | 30.8 (2.21) | 30.6 (1.71) | 30.7 (1.97) | 0.72 |
| Systolic blood pressure, mean (SD), mm Hg | 123.2 (12.93) | 121.87 (14.39) | 122.5 (13.54) | 0.75 |
| Diastolic blood pressure, mean (SD), mm Hg | 71.96 (9.75) | 69.00 (12.94) | 70.48 (11.43) | 0.39 |
| LDL cholesterol, mean (SD), mg/dL | 115.7 (38.22) | 141.9 (35.58) | 128.8 (38.83) | 0.02 |
| HDL cholesterol, mean (SD), mg/dL | 54.17 (14.19) | 52.57 (14.83) | 53.37 (14.38) | 0.71 |
| Triglycerides, mean (SD), mg/dL | 121.8 (70.11) | 145.0 (75.59) | 133.4 (73.04) | 0.29 |
| HbA1c, mean (SD) | 5.42 (0.51) | 5.35 (0.40) | 5.38 (0.45) | 0.61 |
| Lean body mass, mean (SD), kg | 49.99 (11.90) | 48.27 (9.63) | 49.13 (10.73) | 0.59 |
| Total fat mass, mean (SD), kg | 34.78 (5.75) | 33.77 (6.54) | 34.28 (6.11) | 0.58 |
| Total fat mass, mean (SD), % | 40.71 (5.72) | 40.03 (7.26) | 40.37 (6.47) | 0.71 |
| Minimally processed foods, mean (SD), % | 68.4 (15.5) | 68.6 (19.0) | 68.5 (17.1) | 0.97 |
| Ultra‐processed food, mean (SD), % | 19.1 (13.6) | 17.9 (12.1) | 18.5 (12.8) | 0.75 |
| Calorie intake, mean (SD) | 2023 (697) | 2012 (608) | 2018 (649) | 0.95 |
| aMED score, mean (SD) | 3.42 (1.65) | 3.08 (1.61) | 3.26 (1.62) | 0.47 |
| Mediterranean diet assessment tool, mean (SD) | 7.27 (2.13) | 6.29 (2.37) | 6.80 (2.28) | 0.13 |
| Negative cooking attitudes, mean (SD) | 2.82 (1.07) | 2.93 (0.79) | 2.87 (0.94) | 0.68 |
| Self‐efficacy for cooking techniques and meal preparation, mean (SD) | 3.74 (0.80) | 3.67 (0.75) | 3.70 (0.77) | 0.76 |
| Self‐efficacy for eating/cooking fruit and vegetables, mean (SD) | 3.45 (0.92) | 3.06 (0.89) | 3.27 (0.92) | 0.13 |
| MET, mean (SD), minutes/week | 1047 (954) | 1509 (875) | 1268 (9370) | 0.10 |
| PSS, mean (SD) | 12.38 (6.5) | 12.5 (6.8) | 12.4 (6.5) | 0.9 |
Note: Data are presented as mean (SD) for continuous measures and n (%) for categorical measures. p values come from t‐test if quantitative variables and chi‐square if categorical variables. SI conversion factors: To convert glucose to mmol/L, multiply by 0.0555; total cholesterol to mmol/L, multiply by 0.0259; HDL cholesterol to mmol/L, multiply by 0.0259; LDL cholesterol to mmol/L, multiply by 0.0259; triglycerides to mmol/L, multiply by 0.0113.
Abbreviations: aMED, alternate Mediterranean Index; MET, metabolic equivalent of task; PSS, Perceived Stress Scale.
3.2. Weight Loss and Body Composition
Body weight loss was identified in both groups at 3 months. The average intervention group weight loss was −3.23% (3.52%), compared to −0.71% (3.6%) in the control, with a significant weight change difference of −2.52% (95% confidence interval [CI]: −0.48% to −4.56%, p = 0.016). Both groups' maximum weight loss occurred at 6 months. The intervention group average weight loss was −4.2% (5.24%), and −1.22% (3.47%) in the control, with a significant weight change difference of −2.98% (CI: −0.36% to −5.60%, p = 0.027). At 1 year, the intervention group maintained weight loss with an average of −4.02% (6.24%), compared to control with weight gain of +0.28% (5.88%), with a maximum weight change difference of −4.30% (CI: −0.69% to −7.92%, p = 0.021). There were more participants in the intervention group who lost ≥ 5% of their body weight compared to control: 25% versus 7.7% (odds ratio [OR]: 2.78, p = 0.095), 39% versus 16% (OR: 3.25, p = 0.072), and 43% versus 17% (OR: 3.7, p = 0.055) after 3, 6, and 12 months, respectively.
Table S1 includes participant body composition. There were no significant changes at 6 months in all body composition parameters using mITT analysis. However, complete case analysis demonstrated an average fat mass loss of −1.86% (−1.54%) in the intervention group compared to +0.11% (1.03%) in the control, resulting in a significant change difference between the groups of 1.96% (CI: −3.82% to 0.11%, p = 0.039) without significant changes in lean body mass.
3.3. Nutritional Outcomes
Table 2 presents changes in dietary outcomes from baseline. Increases in the Mediterranean diet assessment tool score through all visits were identified with a significant change of 1.62 between groups at 3 months (CI: 0.263–2.968, p = 0.020). A decrease in the intervention group calorie consumption was identified through all visits, with a significant decrease between groups of −390 (−701.1 to −78.13, p = 0.015) at 3 months. Both groups reported an increase in MPF intake and a decrease in UPF intake through all visits, with no significant changes between groups. No changes between intervention and the control were observed in the aMED index through all visits.
TABLE 2.
Change in nutrition outcomes from baseline within and between groups over the course of the study.
| Variable | Change from baseline (95% CI) | Change from baseline, intervention vs. control group (95% CI) | p | |
|---|---|---|---|---|
| Intervention (n = 24) | Control (n = 26) | |||
| Calorie intake | ||||
| 3 months | −322 (−553 to −91.4) | −212 (−382 to −42.3) | −110 (−396.9 to 176.9) | 0.445 |
| 6 months | −452 (−688 to −216) | −62.4 (−266 to 141) | −390 (−701.1 to −78.13) | 0.015 |
| 12 months | −360 (−616 to −106) | −106 (−300 to 88.2) | −255 (−575 to 66.01) | 0.117 |
| NOVA1% (i.e., minimally processed food) | ||||
| 3 months | 3.99 (−1.21 to 9.19) | 6.64 (0.64 to 12.64) | −2.65 (5.294 to −10.59) | 0.506 |
| 6 months | 4.89 (0.73 to 10.52) | 4.66 (−0.09 to 9.41) | 0.238 (7.600 to −7.123) | 0.948 |
| 12 months | 4.19 (−0.34 to 8.73) | 2.58 (−2.91 to 8.08) | 1.610 (8.735 to −5.515) | 0.652 |
| NOVA4% (i.e., ultraprocessed food) | ||||
| 3 months | −5.99 (−9.18 to −2.80) | −5.72 (−10.46 to −0.98) | −0.271 (5.442 to −5.986) | 0.924 |
| 6 months | −6.59 (−10.52 to −2.65) | −4.43 (−8.47 to 0.39) | −2.154 (3.486 to −7.794) | 0.446 |
| 12 months | −6.15 (−8.88 to −3.40) | −4.01 (−8.62 to 0.60) | −2.138 (3.221 to −7.497) | 0.427 |
| aMED score | ||||
| 3 months | 0.125 (−0.609 to 0.859) | 0.308 (−0.316 to 0.932) | −0.183 (−1.146 to 0.781) | 0.705 |
| 6 months | 0.699 (−0.278 to 1.677) | 0.577 (0.003 to 1.151) | 0.122 (−1.011 to 1.256) | 0.829 |
| 12 months | −0.174 (−1.017 to 0.669) | 0.403 (−0.349 to 1.154) | −0.577 (−1.707 to 0.552) | 0.309 |
| Mediterranean diet assessment tool | ||||
| 3 months | 2 (1.01 to 2.99) | 0.38 (−0.54 to 1.31) | 1.62 (0.263 to 2.968) | 0.020 |
| 6 months | 2.05 (0.83 to 3.29) | 1.09 (0.26 to 1.92) | 0.97 (−0.521 to 2.451) | 0.198 |
| 12 months | 1.39 (0.28 to 2.50) | 0.91 (−0.21 to 2.03) | 0.48 (−1.100 to 2.056) | 0.546 |
Abbreviation: aMED, alternate Mediterranean Index.
Full analysis of the Mediterranean diet assessment tool factors is presented in Table S2. Intervention changes were significant compared to control in decreased commercial sweets at 3 months (p = 0.05) and sweet beverage consumption at 12 months (p = 0.036). In addition, the intervention group score of most factors improved through all visits. Of them, increased legumes (p = 0.001) and decreased commercial sweets consumption (p = 0.001) at 3 months; decreased commercial sweets (0.003) and increased nuts (p = 0.018) consumption at 6 months; and decreased commercial sweets consumption (0.016) at 12 months were significant within the intervention group. The control group score for fruit significantly improved compared to intervention at 12 months (p = 0.032). Table S3 includes full analysis of the aMED factors. The intervention group score of most factors improved through all visits. Significant changes within the intervention group include vegetables at 3 months (p = 0.005); monounsaturated/saturated fat ratio (p = 0.003) and nuts (p = 0.05) at 6 months; and monounsaturated/saturated fat ratio (p = 0.05) and nuts (p = 0.05) at 12 months. None of these changes was statistically significant compared to control.
3.4. Clinical Outcomes
Table 3 presents changes in the lipid profile from baseline. Trends were identified in the decrease of triglycerides between groups at 6 months, −32.8 mg/dL (−66.4 to 0.84, p = 0.056), and 12 months, −38.65 mg/dL (−82.7 to −5.38, p = 0.084). No differences between groups were identified in the changes of HDL and LDL cholesterol, blood pressure, and HbA1c.
TABLE 3.
Change in lipids profile from baseline within and between groups over the course of the study.
| Variable | Change from baseline (95% CI) | Change from baseline, intervention vs. control group (95% CI) | p | |
|---|---|---|---|---|
| Intervention (n = 23) | Control (n = 23) | |||
| LDL cholesterol (mg/dL) | ||||
| 3 months | −9.86 (−21.2 to 1.45) | −5.35 (−17.7 to 6.95) | −4.51 (−21.22 to 12.2) | 0.827 |
| 6 months | −12.66 (−27.76 to 2.44) | −5.23 (−17.9 to 7.4) | −7.42 (−27.12 to 12.3) | 0.255 |
| 12 months | −4.73 (−19.19 to 9.74) | 2.49 (−10.8 to 15.8) | −7.22 (−26.9 to 12.44) | 0.268 |
| HDL cholesterol (mg/dL) | ||||
| 3 months | −3.83 (−9.24 to 1.58) | −0.1 (−4.82 to 4.82) | −3.82 (−11.1 to 3.43) | 0.294 |
| 6 months | −1.69 (−8.28 to 4.90) | −2.24 (−8.14 to 3.67) | 0.55 (−8.30 to 9.39) | 0.902 |
| 12 months | 0.37 (−5.62 to 6.37) | −3.8 (−10.0 to 2.45) | 4.16 (−4.49 to 12.81) | 0.338 |
| Triglycerides (mg/dL) | ||||
| 3 months | −35.3 (−68.4 to −2.13) | −17.7 (−42.8 to 7.47) | −17.6 (−59.2 to −24.0) | 0.399 |
| 6 months | −29.0 (−52.3 to −5.79) | 3.74 (−20.5 to 28.0) | −32.8 (−66.4 to 0.84) | 0.056 |
| 12 months | −13.9 (−36.9 to 9.18) | 24.8 (−12.7 to 62.3) | −38.65 (−82.7 to −5.38) | 0.084 |
Note: SI conversion factors: To convert HDL cholesterol to mmol/L, multiply by 0.0259; LDL cholesterol to mmol/L, multiply by 0.0259; triglycerides to mmol/L, multiply by 0.0113.
Abbreviations: HDL, high density lipoprotein; LDL, low density lipoprotein.
3.5. Home Cooking
Table 4 presents the total scores of the Cooking With Chef questionnaire domains: negative cooking attitudes, self‐efficacy for cooking techniques and meal preparation, and self‐efficacy for eating/cooking fruit and vegetables. All three scores gradually improved over the study length in the intervention group but remained essentially stable in the control. Self‐efficacy for cooking techniques and meal preparation (p = 0.040 at 12 months) improved significantly in intervention group participants; trends were observed in negative cooking attitudes (0.061 at 12 months) and self‐efficacy for eating/cooking fruit and vegetables (0.078 at 3 months).
TABLE 4.
Change in participant cooking attitude and self‐efficacies over the course of the study.
| Variable | Change from baseline (95% CI) | Change from baseline, intervention vs. control group (95% CI) | p | |
|---|---|---|---|---|
| Intervention (n = 24) | Control (n = 26) | |||
| Negative cooking attitudes | ||||
| 3 months | −0.72 (−1.09 to −0.35) | −0.28 (−0.66 to 0.10) | 0.44 (−0.09 to 0.97) | 0.102 |
| 6 months | −0.60 (−1.04 to −0.17) | −0.22 (−0.67 to 0.24) | 0.38 (−0.67 to 0.24) | 0.228 |
| 12 months | −0.69 (−1.10 to −0.28) | −0.09 (−0.57 to 0.39) | 0.60 (−0.03 to 1.23) | 0.061 |
| Self‐efficacy for cooking techniques and meal preparation | ||||
| 3 months | 0.40 (0.08 to 0.72) | 0.17 (−0.10 to 0.43) | −0.23 (−0.65 to 0.19) | 0.272 |
| 6 months | 0.42 (0.15 to 0.70) | 0.23 (0.00 to 0.47) | −0.19 (−0.55 to 0.17) | 0.305 |
| 12 months | 0.61 (0.30 to 0.91) | 0.20 (−0.04 to 0.44) | −0.41 (−0.80 to −0.02) | 0.040 |
| Self‐efficacy for eating/cooking fruit and vegetables | ||||
| 3 months | 0.58 (0.20 to 0.97) | 0.16 (−0.10 to 0.43) | −0.42 (−0.89 to 0.05) | 0.078 |
| 6 months | 0.47 (0.08 to 0.86) | 0.19 (−0.12 to 0.50) | −0.28 (−0.78 to 0.21) | 0.259 |
| 12 months | 0.45 (0.07 to 0.82) | 0.19 (−0.12 to 0.50) | −0.26 (−0.74 to 0.23) | 0.288 |
Table S4 includes full analysis of all items. Eleven self‐efficacy items address general cooking techniques. Of them, several changes between groups were observed including using knife skills (p = 0.1 at 12 months), using basic cooking techniques (0.09 at 3 months, 0.004 at 12 months), steaming (p = 0.03 at 12 months), sautéing (p = 0.05 at 3 months, p = 0.003 at 3 months), stewing (p = 0.03 at 12 months), and cooking from basic ingredients (p = 0.016 at 3‐month, p = 0.059 at 6 month). Seven self‐efficacy items address cooking specific food groups. No changes were identified between the groups in these items.
3.6. Other Lifestyle Factors and Adverse Events
No differences between the groups were observed in the changes in physical activity, stress, and smoking. In addition, no differences between groups were observed in utilizing weight loss resources outside of the study including weight loss programs, exercise programs, individual nutrition sessions, and lifestyle apps. No serious adverse events were reported. Two participants were diagnosed with COVID‐19, one in each group (more information in the online Supporting Information).
3.7. Sensitivity Analysis
Figure 1 presents the percentage of the average weight change per date from baseline. At 220 days the average weight loss of the intervention was −4.79%, compared to −1.20% in the control, with a change difference of −3.56% (CI: −0.80% to −6.32%, p = 0.012). This weight difference was maintained for up to 415 days with an average weight loss of −4.14% within the intervention group, compared to −0.48% in the control, and a weight loss difference between groups of 3.66% (95% CI: −7.10% to −0.21%, p = 0.038). Sensitivity analyses for all outcomes showed no qualitative difference; thus, nothing has changed in the conclusion.
FIGURE 1.
Change in weight from baseline by date. Error bars present 95% CI. [Color figure can be viewed at wileyonlinelibrary.com]
4. Discussion
This bi‐center RCT assessed 1‐year weight change following a remote CM program integrated with health coaching. We hypothesized significant weight loss at 6 months in the intervention compared to control and found significant weight loss up to 1 year. This study demonstrated weight loss of −4.2%, supported by fat mass loss of 1.86% without significant change in lean body mass. This weight loss is comparable with the weight loss effect of naltrexone/bupropione [40]. Despite the affordable remote delivery, this effect is greater compared to short‐term weight loss of up to −2.2% in other CM programs that required a teaching kitchen [20, 41]. Further studies are needed to determine whether this beneficial effect is due to the integrative health coaching principles or other factors. Over 5% weight loss is considered clinically meaningful [38], and this study demonstrated a trend with high odds ratio for losing > 5% body weight up to 1 year. Larger studies are needed to demonstrate a 5% weight loss and whether adding prominent nutrition counseling and physical activity modules can further enhance the impact. Despite the new era of GLP1 receptor agonists that can lower weight between 5% and 18% with risk of side effects, lifestyle modifications are the first line in weight loss guidelines [42]. This study is an important step in considering CM interventions as an effective patient‐centered nutrition strategy for weight loss.
Despite consistent weight loss, dietary changes varied through visits and are aligned with participants' different goals. Significant changes that may have led to weight loss included a decrease in calorie and commercial sweets consumption [43] at 3 months, an increase in the Mediterranean diet score [19] at 6 months, and a decrease in sweet beverages [44] at 12 months. In addition, numerous potentially beneficial but nonsignificant changes in nutrients and food group intake may also have supported the weight change. The differences between the aMED that did not demonstrate significant changes and the Mediterranean diet assessment tool that found few significant changes can be explained by (1) limitations in the aMED food groups definition (e.g., including some UPF such as energy bars and sweetened cereals as whole grains); and (2) the strength of the Mediterranean diet assessment tool [31] that addresses UPF factors such as commercial sweets. This study adds to the literature knowledge about the long‐term nutritional impacts of CM interventions. Future studies are needed to determine specific nutritional outcomes of CM interventions that may also promote weight change.
Previous studies have shown that UPF consumption is correlated with overweight and obesity [10], and increased intake of MPF leads to weight loss [9]. This intervention results in a nonsignificant increase in MPF and decreased UPF consumption. While the NOVA classification already showed significant improvements in UPF and MPF consumption following a CM intervention, it was in a cafeteria setting [45], and the NOVA might be too broad to detect personal changes [46]. Recent evidence about types of UPF that may have impact on type 2 diabetes includes refined breads, sauces, and condiments [47]. More research is needed to understand the impact of CM interventions on types of UPF intake and weight.
Consistent with other CM studies, this program demonstrated improved self‐efficacy in various home cooking skills [16, 17] and added a 12‐month follow‐up of maintaining these positive changes. This is consistent with participant qualitative experience of acquiring new cooking skills and maintaining them up to 1 year as a facilitator to home cooking habits and weight loss [28]. This report also demonstrated the improvement in the self‐efficacy of general culinary skills compared to cooking specific healthier food options that remained unchanged. A recent publication called for the establishment of a consensus for competencies for CM programs [48]. Future studies are needed to determine home cooking skills that may be improved following CM programs that can also promote health benefits.
An important part of this CM program is the health coaching component [26, 27]. While health coaching is a fundamental part of lifestyle medicine interventions [49], its use in CM interventions to date has been limited. Participant perspectives described that in addition to the culinary skills they acquired, several coaching elements such as goals setting, organizing and planning, and improved confidence were significant factors in adopting and maintaining home cooking habits [28]. Further research is needed to determine how to further integrate health coaching into CM programs.
This study addresses the need for well‐designed studies that rigorously evaluate the long‐term impact of CM programs on dietary intake and body weight. The majority of CM literature uses pre/post self‐developed questionnaires, and this study uses validated instruments, lab data, DXA scans, and dietary records, which were analyzed through site‐specific software to allow each site to more accurately code culturally specific foods. Extensive discussions during data collection/entry ensured that food group and NOVA coding was as similar as possible between the two sites. Additional strengths include the RCT method, bi‐center approach, long‐term follow‐up, qualitative evaluation [28], acceptable retention rate (67%), robust randomization, and experienced CRC with staff who were blinded to the intervention. Additional strengths are related to the CM intervention. Incorporating health coaching in a CM intervention and using remote delivery without a teaching kitchen are invaluable strengths that permitted an individualized culinary intervention with a reduced cost in a health coaching program (e.g., health coach compensation with administrative support). While significant changes in obesity‐related health outcomes were not expected, significant change in triglyceride level was found. Other studies with different inclusion criteria will be needed to determine the impact of CM programs on blood pressure, lipid profile, and HbA1c levels. Most CM studies have presented the impact of programs that combine culinary training with nutrition [16, 17]; this study isolates the impact of the culinary training. Lastly, the findings were robust in sensitivity analyses including a per date analysis.
This study has limitations, which are mostly related to the COVID‐19 pandemic. First, there was high dropout after the first visit (attrition bias), with a high adherence of those who continued after visit 1, which was addressed using mITT analysis. Second, there were modified online visits that included self‐measurement of weight and did not include body composition and health outcomes. Further studies with onsite measurements are needed to determine whether the fat mass loss will be duplicated using full ITT analysis. The risk for weight misreport (social desirability bias) was addressed by requiring a supplementary scale image, and the risk for discrepancies between the home and CRC scales was addressed by collecting home together with onsite measurements at the following meeting. Third, out of window visits were addressed by adding per visit analysis using only outcomes measured within 2 months from the study visit date and per date analysis. Future research is required outside of the COVID‐19 restrictions that may impact participants' availability to implement home cooking habits. Lastly, future studies are also required to evaluate how this model could address various social determinants of health, thus improving health equity.
5. Conclusion
In this study, a CM intervention integrated with health coaching led to decreased weight and body fat mass of participants with overweight and obesity. Nutritional changes were generally positive and consistent through all measures. More research is needed with larger cohorts and with specific populations. CM might be an effective lifestyle strategy for weight loss through behavior change.
Author Contributions
Rani Polak had full access to all data in the study and takes responsibility for the integrity of data and the accuracy of the data analysis. Concept and design: Rani Polak, Adi Finkelstein, Maggi A. Budd, Amir Tirosh. Analysis and interpretation of data: Rani Polak, Rebecca Goldsmith, Richard Goldstein, Brianna E. Gray, Rom Keshet. Drafting of the manuscript: Rani Polak. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Richard Goldstein. Obtained funding: Rani Polak, Amir Tirosh.
Funding
This research was funded by US Israel Binational Science Foundation (2017035) and by the NIH Clinical Center Grant number 1UL1TR002541‐01.
Disclosure
The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Conflicts of Interest
Rani Polak discloses royalties from a home cooking book and honorarium from Wellcoaches. The other authors declare no conflicts of interest.
Supporting information
Data S1: oby70193‐sup‐0001‐Supinfo.docx.
Figure S1: Study flow diagram. *COVID‐related stressors such as change in work, unwillingness to contribute to control, personal matter, disease in the family.
Table S1: Change in body composition at 6 months within and between groups.
Table S2: Changes in Mediterranean diet assesment tool from basline.
Table S3: Change in aMED factors from baseline within and between groups.
Table S4: Changes in Cooking with Chef questionnaire.
Data S2: CONSORT 2025 checklist item.
Acknowledgments
We thank all study participants and research study staff for contributing to the study. We would like to thank the Clinical Research Center teams at Massachusetts General Hospital and Sheba Medical Center for their professionalism and dedicated work that they did supporting the research. We also thank Julie Silver, Jacob Mirsky, Sabrina Paganoni, and Gary Sforzo for their contribution. These individuals were not compensated beyond their normal salaries.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1: oby70193‐sup‐0001‐Supinfo.docx.
Figure S1: Study flow diagram. *COVID‐related stressors such as change in work, unwillingness to contribute to control, personal matter, disease in the family.
Table S1: Change in body composition at 6 months within and between groups.
Table S2: Changes in Mediterranean diet assesment tool from basline.
Table S3: Change in aMED factors from baseline within and between groups.
Table S4: Changes in Cooking with Chef questionnaire.
Data S2: CONSORT 2025 checklist item.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.