This paper is in the following e-collection/theme issue:

Behavior Change (861) Registered Report (790) Web-based and Mobile Health Interventions (4012) mHealth for Wellness, Behavior Change and Prevention (3310) Obesity and Nutrition as Public Health Problem (396) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2109) Peer-to-Peer Support and Online Communities (797)

Published on in Vol 12 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66518, first published .
Effect of Minimal Individual or Group Enhancement in an eHealth Program on Mental Health, Health Behavior, and Work Ability in Employees With Obesity: Randomized Controlled Trial

Effect of Minimal Individual or Group Enhancement in an eHealth Program on Mental Health, Health Behavior, and Work Ability in Employees With Obesity: Randomized Controlled Trial

Effect of Minimal Individual or Group Enhancement in an eHealth Program on Mental Health, Health Behavior, and Work Ability in Employees With Obesity: Randomized Controlled Trial

Authors of this article:

Siniriikka A Männistö1, 2 Author Orcid Image ;   Joona Muotka3 Author Orcid Image ;   Laura-Unnukka Suojanen1 Author Orcid Image ;   Raimo Lappalainen3 Author Orcid Image ;   Kirsi H Pietiläinen1, 2 Author Orcid Image ;   Riitta Korpela4 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1HealthyWeightHub, Endocrinology, Abdominal Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland

    2Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, Helsinki, Finland

    3Department of Psychology, University of Jyväskylä, Jyväskylä, Finland

    4Medical Nutrition Physiology, Department of Pharmacology and Research Programme for Human Microbiome, Faculty of Medicine, University of Helsinki, Helsinki, Finland

    Corresponding Author:

    Siniriikka A Männistö, PsyM, MHS


    Background: Mental health problems and adverse health behaviors are enriched in individuals with obesity and need to be considered in weight loss interventions. Regarding weight loss, hybrid interventions combining digital and in-person elements have proven superior to eHealth-only interventions. However, it remains unclear whether minimal group or individual enhancement could bring additional benefits to the mental health and health behavior domains in individuals with obesity.

    Objective: This study aimed to explore whether minimal group or individual enhancements could offer additional benefits to an eHealth intervention in relation to mental health, perceived work ability, and health behavior in a sample of occupational health patients with obesity. In addition, the study sought to examine the overall effects of the health behavior–focused intervention across these domains.

    Methods: This study was a randomized controlled trial with a 12-month intervention (March 2021-2022), with selected variables followed for another 12 months without additional support. Recruited from occupational health care, 111 working-age adults with BMI 30‐40 kg/m2 were randomized to one of the 3 treatment arms, such as eHealth, eHealth+Group, or eHealth+Individual. All treatment arms received a web-administered, coach-assisted eHealth program based on acceptance and commitment therapy, and, in addition, the eHealth+Group and eHealth+Individual arms received 3 remotely facilitated group or individual meetings. The overall intervention effects were evaluated quasi-experimentally by combining the 3 treatment arms into a single eHealth arm. Participants were assessed for depression (the Beck Depression Index 21 [BDI-21]), burnout (Bergen Burnout Inventory 18 [BBI-15]), perceived work ability, eating behavior (ecSatter 2.0, Three Factor Eating Questionnaire [TFEQ], and Binge Eating Scale [BES]), and physical activity (Baecke Habitual Physical Activity Questionnaire, BHPAQ).

    Results: We observed inconsistent fluctuations between the treatment arms in depression and burnout scores, indicating a lack of meaningful intervention effects despite statistical significance. Therefore, none of the treatment arms showed superiority over another. Across all participants, depression showed an estimated mean decrease of 2.5 BDI-21 points, with older participants experiencing a greater reduction in depressive symptoms. Furthermore, binge eating tendency decreased by 4.9 BES points during the 12-month intervention. We also observed increases in eating competence, controlled restraint, and physical activity. However, the 24-month measurements showed an adverse effect on eating competence, especially on attitudes toward eating and food, during the follow-up period without further support.

    Conclusions: Minimal enhancement through either group or individual video-conference meetings did not provide additional benefits in the mental health or eating habit domains compared with the eHealth intervention alone. Nevertheless, our results indicate that eHealth interventions for weight loss have the potential to reduce depression symptoms and binge-eating tendencies, while also improving eating competence and physical activity across the study population. Continued support may be necessary to sustain positive changes.

    Trial Registration: ClinicalTrials.gov NCT04785586; https://clinicaltrials.gov/study/NCT04785586

    International Registered Report Identifier (IRRID): RR2-10.1016/j.conctc.2020.100638

    JMIR Ment Health 2025;12:e66518

    doi:10.2196/66518

    Keywords

    eHealth; mental health; health behavior; obesity; obese; weight; acceptance and commitment therapy; healthy weight coaching; coach; coaching; ACT; psychotherapy; RCT; randomized; controlled trial; occupational health; burnout; eating habits; weight management; health intervention; virtual support; randomized controlled trial 


    Obesity and mental health problems are intricately connected, with evidence suggesting a bidirectional relationship between the two [1,2]. Depression, one of the most extensively studied mental health issues, increases the risk of obesity by 58%, while obesity elevates the risk of depression by 55% [3]. Furthermore, both obesity and depression are significant risk factors for reduced work ability [4,5], and occupational burnout has been linked to these conditions [6-8]. Several mechanisms, including weight-related stigma [9], hypothalamic-pituitary-adrenal axis dysregulation [10], and adverse health behaviors [11], have been proposed to explain these associations, with these factors likely interacting in complex ways [12].

    Originally developed for mental health treatment, transdiagnostic treatment approaches target common underlying factors across multiple disorders [13] and are also applied in obesity treatment to support weight management [14]. Among these, cognitive behavior therapy is often considered the first-line treatment option. However, research suggests that cognitive behavior therapy may not consistently produce desired weight loss outcomes, and its emphasis on thought suppression could even contribute to binge eating and weight gain [15,16]. As a result, mindfulness-based methods have gained popularity as alternatives in psychological obesity treatment, with acceptance and commitment therapy (ACT) showing the most consistent results in reducing BMI in adults with overweight and obesity [17].

    ACT aims to enhance psychological flexibility, a trait linked to improved psychological well-being [18,19], and has demonstrated efficacy not only in weight management but also in the treatment of various mental and somatic health conditions [20]. This is particularly relevant in obesity treatment, as individuals with obesity face an elevated risk of mental health issues and eating disorders, which can be exacerbated by weight-normative interventions that emphasize weight loss [21,22]. In their review, Iturbe et al [21] found that while ACT supports psychological well-being even in weight loss contexts, more favorable outcomes were observed in interventions that prioritized health promotion over weight reduction. Consequently, ACT-based interventions that focus on sustainable health behaviors rather than weight loss alone may be more effective in promoting long-term well-being.

    Furthermore, ACT is applicable in a variety of formats [23], including digital and hybrid interventions [24,25]. These modes of delivery are underscored by the widespread prevalence of obesity and mental health issues, as they have shown great potential for cost-effectiveness, scalability, and accessibility [26,27]. Hybrid interventions, which integrate digital and in-person elements, have shown superior outcomes in somatic metrics compared with fully digital approaches [28,29]. Nevertheless, the introduction of in-person elements reduces the scalability of the intervention, emphasizing the need for minimal face-to-face input.

    To date, it remains unclear whether group or individual contact could bring additional benefits to mental health and health behavior domains following a weight-management intervention, and if these benefits are notable following minimal face-to-face enhancement. To take a step forward in evaluating the optimal mode and frequency of contact for digital support [30], we aimed to explore whether a 3-time group or individual enhancement could offer additional benefits to a coach-assisted eHealth intervention in relation to mental health, perceived work ability, and health behavior. In addition, we examined the overall effects of the health behavior–focused intervention that combined ACT and behavioral components across these domains. We hypothesized that the hybrid treatment arms would be more effective than the eHealth-only arm. Given the mixed findings of previous research, we could not predict whether the group or individual format would differ in overall effectiveness.


    Study Design

    This study was a randomized controlled trial (RCT) aiming to improve participants’ health behavior and thus, weight loss [31]. Participants were assigned to one of the 3 treatment arms: eHealth, eHealth+Group, or eHealth+Individual. Each treatment arm used an eHealth program, Healthy Weight Coaching (HWC), extensively detailed by Suojanen et al [32]. In addition, the hybrid treatment arms received 3 remotely facilitated group or individual sessions. In all 3 treatment arms, the intervention lasted 12 months (March 2021-2022), followed by a 12-month unsupported follow-up period.

    The HWC integrates behavioral weight management themes (eg, eating habits, physical activity, sleep, and stress management) with the core processes of ACT (values, committed action, contact with the present moment, acceptance, defusion, and self as context) with information and exercises covering these themes (refer to Table S1 in Multimedia Appendix 1 for a more detailed description of the intervention). The aim of the HWC is to teach weight-management skills and improve psychological flexibility, resulting in long-term weight loss. Participants were asked to complete 1 “session” per week, covering information and exercises. They received tailored one-on-one written feedback every 2 weeks for the initial 3 months and every 3 weeks for the remaining year, totaling 19 interactions. The same coach was responsible for the HWC program support and any supplementary contacts. All 3 coaches had master’s degrees in nutrition and further education in psychology and were randomly allocated to one-third of the participants in each treatment arm.

    Helsinki city employees with a BMI of 30-40 kg/m2 were recruited for the study from February to March 2021 via advertisements on city employees’ platforms and the nursing staff of Occupational Health Care Helsinki. Besides BMI, inclusion criteria included the capacity to engage in potential group or individual sessions and access to a computer with internet connectivity. Exclusion criteria included participation in other weight loss programs, recent pregnancy or lactation, weight fluctuations of more than 5 kg in the past 3 months, major medical conditions impacting safety or readiness for weight loss, and the use of weight loss medication.

    After passing the initial screening by a research assistant, eligible candidates underwent phone-based health screening conducted by a medical doctor to assess their health status and medications. Suitable participants then proceeded with laboratory assessments and body composition measurements. When written consent was provided, the statistician (JM) used stratified random allocation for SPSS (IBMCorp) to randomize the participants into 3 treatment arms stratified by sex, age, and BMI. The sample size was determined with a priori power analysis by a statistician (JM) and was based on the expected weight change.

    Treatment Arms

    eHealth

    Participants received the HWC eHealth program, which included a 20-minute kickoff call 3 weeks into the program to monitor their progress. They also received written feedback every 2 weeks for the first 3 months and then every 3 weeks for the rest of the year, totaling 19 contacts.

    eHealth+Group

    Participants received the HWC eHealth program (as detailed in item 1) and participated in 3 remotely facilitated group meetings via video (each lasting 2 h). Due to the COVID-19 pandemic, these meetings were held remotely and included brief introductions to topics, such as goal setting, stress management, and self-compassion, followed by small-group discussions (Table S2 in Multimedia Appendix 1). Group meetings were held at 1, 6, and 10 months during the 12-month treatment period.

    eHealth+Individual

    Participants received the HWC eHealth program (as described in item 1) and engaged in 3 remotely facilitated individual meetings via video (each lasting 45 min). These sessions were tailored to each participant’s current needs, recognizing their achievements and addressing challenges. Individual meetings were held at 1, 6, and 10 months within the 12-month treatment period.

    Outcome Measures

    Depression Symptoms

    Depression symptoms were measured using the Beck Depression Index 21 (BDI-21) [33]. BDI-21 is one of the most used depression indicators measuring both cognitive and somatic symptoms of depression [34,35]. Question 19 was modified to ask if the participant had lost or gained weight. Since all participants were involved in a weight-management intervention, weight loss was considered intentional and was scored as zero. Each question is scored on a scale of 0‐3. The total score of the questionnaire ranges from 0 to 63, with higher scores indicating more severe symptoms. A score of 0‐12 indicates a normal condition, 13‐18 indicates mild depression, 19‐29 indicates moderate depression, and 30 or above indicates severe depression.

    Burnout Symptoms

    Burnout symptoms were measured using the Finnish version of the Bergen Burnout Inventory (BBI-15) [36]. The BBI-15 assesses exhaustion, cynicism, and reduced professional efficacy, which are the 3 key symptoms of burnout according to the most widely accepted definition [37]. Each question is scored on a scale of 1‐6, with a total score range of 15‐90. Higher scores indicate more severe symptoms. The total score of the questionnaire is calculated as the sum of these subcategories.

    Perceived Work Ability

    Perceived work ability was measured using a subjective single-question assessment (“What score would you give your current work ability on a scale of 0‐10?”), with 8‐10 indicating good, 6‐7 reduced, and 0‐5 poor work ability. This question is part of the work ability index and has a good predictive value for work ability risk [38,39].

    Eating Behavior

    Eating behavior was measured with 3 different questionnaires covering different aspects of eating behaviors and attitudes. Eating competence was measured with the Finnish version of the ecSatter Inventory 2.0 (ecSI2.0) questionnaire [40,41], including 4 subcategories: eating attitudes (has positive attitude toward food), food acceptance (is comfortable with familiar foods and skilled at trying unfamiliar foods), internal regulation skills (recognizing and trusting hunger and satiety signals in choosing food amounts), and contextual skills (prioritizing meals and having the skills and resources for food handling). All questions are scored on a scale of 0‐3, with a total score range of 0‐48. Higher scores indicate better eating competence, with a cutoff score of 32 used to indicate good eating competence. Cognitive restraint, uncontrolled eating, and emotional eating were measured with an 18-item Finnish version of the Three Factor Eating Questionnaire (TFEQ-R18) [42,43], reporting the relative proportion (0%‐100%) of the maximum possible raw score for each subscale (the higher the value, the greater the level). Binge eating was measured by the Binge Eating Scale (BES) [44,45]. The BES has 16 items scored 0‐2 or 0‐3, with the total score range of 0‐46. A score below 20 is considered normal, whereas scoring 20‐29 indicates moderate binge eating tendency, and 30 or above indicates a severe binge eating tendency.

    Physical Activity

    Physical activity was measured with the Baecke Habitual Physical Activity Questionnaire (BHPAQ), covering work, sports, and leisure [46]. The questionnaire has 16 items, each scored 1‐5. For each subcategory (work, sports, and leisure), a mean score is calculated, and the overall score is the total sum of the subcategory scores. A higher score indicates greater physical activity.

    Participant assessments were conducted at baseline, midintervention (6 mo), postintervention (12 mo), and follow-up (24 mo). Baseline, midintervention, and postintervention questionnaires were answered as a part of the eHealth program, and follow-up questionnaires were mailed to all participants regardless of their activity in the program. Hence, most questionnaires cover data up to 12 months, while a selection of questionnaires also cover 24-month follow-up data.

    Of the questionnaires, BDI-21, BBI-15, perceived work ability, TFEQ-R18, and BHPAQ were added to the HWC program at a specific time point (0, 6, and 12 mo), while the ecSI2.0 and the BES were integrated into the program structure. Since participants filled the ecSI2.0 and the BES while progressing through the program, not everyone reaching the recommended pace of 1 session per week, we pooled the answers to the 0-, 6-, and 12-month time points from the nearest actual answering time (baseline questionnaire answered during intervention months ranged 0‐3, 6-month questionnaire during months ranged 4‐8, and 12-month questionnaire during months ranged 9‐12). We report the subscales only for variables where the total score showed significance, except for the TFEQ, for which only subscale results are provided. Age, sex, and profession were retrieved from the electronic health record system, and BMI was calculated from height and weight measured by trained research assistants [31].

    Statistical Methods

    Data analysis was conducted using IBM SPSS Statistics (version 28.0.0.0). Baseline differences were analyzed by Fisher exact test for categorical variables, ANOVA for the parametric continuous variables, and Kruskal-Wallis nonparametric continuous variables.

    EcSI2.0 was normally distributed. Perceived work ability, ecSI’s subcategories Eating attitude and Internal regulation skills, BHPAQ, and its subcategory Leisure had negatively skewed distribution, and thus, inverted variables were used in the analysis with gamma distribution and log link function. All other variables had positively skewed distribution, and thus, gamma distribution with log link function was used in the analysis.

    We used the Generalized Estimating Equations method [47] to analyze the differences between the treatment arms and overall change over time. The Generalized Estimating Equations method includes all available data points and uses estimated means in the analysis, applying the intention-to-treat principle to decrease attrition bias. Following the unadjusted primary analysis, we added sex, age, and weight change (0-6 mo and 0-12 mo) to the model as covariates to explore their impact on the results. For mental health variables, we also included psychotropic medication use or changes in the medication (0-12 mo and 12-24 mo) as covariates (Table S3 in Multimedia Appendix 1). Considering the model size, each covariate was initially tested individually in a preliminary covariate model, reported in Multimedia Appendix 1. Statistically significant covariates were then combined into a single model, and the results of the full covariate model were reported in the text. To avoid multicollinearity, the 2 time points of weight change and medication change variables were tested separately in different models.

    Little’s missing completely at random (MCAR) analysis was used to determine whether the data were MCAR. With variables whose data were not MCAR, we used Full Information Maximum Likelihood estimation with Mplus computer software version 8.11 to correct for the potential bias. Crosstabs with Fisher exact test were used to analyze change in depression and burnout incidence between the measurement points. Incidence was separately calculated for completers and for the entire baseline sample with last observation carried forward. The Spearman correlation coefficient was used for correlation analysis, and the Dancey and Reidy definition was applied to determine correlation strength [48].

    Cohen d effect size was calculated for the differences between different arms, using the square root of the average variance of measures and correcting for baseline differences. In the analyses for the entire group, the pooled SD between measurement times was used to calculate the effect size [49]. Effect size of 0.2 was considered small, 0.5 medium, and 0.8 large [50]. Figures were drawn with GraphPad Prism.

    Ethical Considerations

    The study protocol was approved by the Ethics Committee of the Helsinki and Uusimaa Hospital District (29.4.2020, HUS/922/2020). Informed consent was obtained from all individuals participating in the study. To protect privacy and confidentiality, the data were pseudonymized and securely processed to prevent identification of individual participants. Participants received access to the intervention and their personal health data but did not receive any additional compensation. Detailed reporting of the study is provided in the CONSORT-EHEALTH checklist (Checklist 1) .


    Baseline Characteristics

    After the screening, 111 participants fulfilling the inclusion criteria were randomized into the 3 treatment arms with a 1:1:1 ratio (Figure 1). The mean age was 50 (SD 8.9) years and mean BMI was 34.2 (SD 2.9) kg/m2 (Table S4 in Multimedia Appendix 1). Of the 111 participants, 90 (83%) were female and 97 (87%) were “white collar” workers (manager, specialized expert, expert, or office and customer service worker).

    ‎
    Figure 1. Flowchart of participant progress through the three study groups (eHealth, eHealth+Group, and eHealth+Individual) in the randomized controlled trial, with a total sample size of N=111.

    Figure 1 illustrates group allocation and participant retention over the 24-month follow-up, showing variation in the number of respondents across questionnaire time points. As 1 participant did not reply to the baseline questionnaires, the total data pool analyzed for the mental health and health variables included 110 participants (Table S4 in Multimedia Appendix 1). Baseline characteristics did not differ between the treatment arms except for 1 subscale (leisure) of the physical activity questionnaire, where the eHealth+Group treatment showed lower baseline values than the other 2 arms. Regarding mental health and work ability, 36/110 (33%) participants indicated at least mild depression, 32/100 (29%) participants at least mild burnout, 36/100 (33%) participants reduced or poor perceived work ability, and 15/100 (14%) participants reported using psychotropic medication (Table S3 in Multimedia Appendix 1). Considering eating behavior, 16/110 (15%) participants indicated at least moderate binge eating tendency, and 91 (88%) participants had “poor” eating competence. No between-groups differences were observed in these categories.

    The correlation between depression and burnout scores was moderate at baseline (r=0.563, P<.001), indicating that depression and burnout symptoms occurred largely in the same participants. Furthermore, the participants whose BDI score indicated clinical depression at baseline had a higher baseline burnout score (P<.001), lower perceived work ability (P<.001), higher emotional eating score (P=.02), and higher binge eating tendency score (P<.001) than nondepressed participants (Table S5 in Multimedia Appendix 1). The participants whose BDI score indicated clinical burnout at baseline had a higher depression score (P<.001), lower perceived work ability (P<.001), and lower eating competence (P=.03) at baseline compared with those without burnout. The participants using psychotropic medication had higher baseline depression (P<.01), lower perceived work ability (P<.001), and higher binge eating tendency (P<.001) than nonmedication users.

    Comparison of the Treatment Arms

    To compare the impact of the 3 treatment arms concerning mental health, perceived work ability, and health behavior variables, we analyzed the interaction of the intervention arms over time (Table 1). Below we report results using the full dataset with estimated mean values to account for missing data, while completer results are presented in Table S6 in Multimedia Appendix 1.

    Table 1. Estimated mean values (standard error) of mental health, perceived work ability, eating behavior, and physical activity variables at baseline, midintervention, postintervention, and follow-up, across treatment arms and all participants (N=110), analyzed using generalized estimating equations.
    VariablesBaseline (0 mo), mean (SE)Midintervention (6 mo), mean (SE)Postintervention (12 mo), mean (SE)Follow-up (24 mo), mean (SE)Overall change (0-6-12[-24] mo)
    Wald chi-square (df)P value
    Depression10.13 (0.69)9.08 (0.67)7.62 (0.75)—a12.41 (1).002
    eHealth10.11 (1.08)9.92 (1.24)6.32 (1.07)—17.99 (4).001
    eHealth+ Group8.65 (1.07)9.20 (1.05)7.20 (1.20)—17.99 (4).001
    eHealth+ Individual11.47 (1.34)8.18 (1.20)8.76 (1.41)—17.99 (4).001
    Burnout39.68 (1.34)43.75 (1.50)40.53 (1.53)42.75 (2.05)10.47 (3).01
    eHealth40.84 (2.12)46.24 (2.71)42.09 (2.24)41.89 (3.30)12.91 (6).04
    eHealth+ Group37.50 (2.37)44.84 (2.64)36.48 (3.21)43.12 (3.69)12.91 (6).04
    eHealth+ Individual40.47 (2.43)40.38 (2.40)42.25 (2.36)42.77 (3.34)12.91 (6).04
    Exhaustion14.02 (0.48)15.29 (0.63)13.92 (0.57)14.71 (0.71)10.03 (3).02
    eHealth14.50 (0.70)15.50 (0.85)13.70 (0.73)14.09 (1.23)7.88 (6).25
    eHealth+ Group13.78 (0.96)16.42 (1.39)13.91 (1.22)15.31 (1.22)7.88 (6).25
    eHealth+ Individual13.71 (0.80)14.01 (0.93)13.92 (0.92)14.66 (1.13)7.88 (6).25
    Cynicism12.09 (0.49)13.77 (0.57)12.67 (0.60)13.19 (0.74)12.70 (3).005
    eHealth12.18 (0.79)14.83 (1.12)13.00 (1.00)12.67 (1.14)17.01 (6)009
    eHealth+ Group11.44 (0.83)14.28 (0.99)11.63 (1.16)13.74 (1.45)17.01 (6).009
    eHealth+ Individual12.58 (0.90)12.34 (0.80)13.15 (0.96)13.07 (1.17)17.01 (6).009
    Efficacy13.93 (0.56)15.15 (0.58)14.17 (0.63)14.98 (0.87)5.87 (3).12
    eHealth14.16 (0.95)15.93 (1.03)15.35 (1.07)15.01 (1.44)10.83 (6).09
    eHealth+ Group13.38 (0.99)15.59 (1.03)11.74 (1.28)14.68 (1.81)10.83 (6).09
    eHealth+ Individual14.18 (0.99)14.05 (0.97)15.20 (0.88)15.01 (1.34)10.83 (6).09
    Perceived work ability7.86 (0.15)7.50 (0.18)7.80 (0.19)7.67 (0.23)5.19 (3).16
    eHealth8.00 (0.23)7.56 (0.31)8.29 (0.16)7.69 (0.46)9.02 (6).17
    eHealth+ Group7.76 (0.24)7.62 (0.27)7.95 (0.28)7.59 (0.55)9.02 (6).17
    eHealth+ Individual7.82 (0.31)7.33 (0.34)7.27 (0.41)7.70 (0.27)9.02 (6).17
    Eating comp.23.04 (0.76)24.80 (0.87)26.99 (0.91)20.51 (0.95)72.79 (3)<.001
    eHealth23.97 (1.31)22.55 (1.44)27.34 (1.65)23.06 (1.57)6.46 (6).37
    eHealth+ Group22.20 (1.18)23.24 (1.29)26.90 (1.55)19.79 (1.56)6.46 (6).37
    eHealth+ Individual22.77 (1.40)25.51 (1.73)26.75 (1.50)19.10 (1.53)6.46 (6).37
    Eating attitudes8.82 (0.32)8.40 (0.37)9.38 (0.35)6.79 (0.38)44.81 (3)<.001
    Food acceptance3.72 (0.22)3.93 (0.23)5.72 (0.26)3.82 (0.31)47.53 (3)<.001
    Regulation of food intake4.87 (0.23)5.26 (0.24)4.30 (0.30)4.92 (0.34)9.55 (3).02
    Eating context5.66 (0.31)7.25 (0.34)7.70 (0.34)5.41 (0.37)51.27 (3)<.001
    Controlled restraint38.91 (1.67)51.59 (1.93)46.70 (2.10)—45.65 (1)<.001
    eHealth43.78 (3.04)53.80 (4.05)51.43 (3.92)—6.43 (4).17
    eHealth+Group37.8 (2.87)47.39 (2.92)42.48 (3.09)—6.43 (4).17
    eHealth+Indiv42.48 (3.09)53.51 (3.02)46.31 (3.49)—6.43 (4).17
    Uncontrolled eating44.44 (1.8234.36 (1.99)32.50 (2.23)—50.34 (1)<.001
    eHealth45.07 (3.01)36.09 (3.52)32.37 (3.26)—2.79 (4).59
    eHealth+Group44.99 (3.11)33.33 (3.44)28.95 (3.34)2.79 (4).59
    eHealth+Indiv.43.28 (3.32)33.69 (3.31)35.14 (4.31)—2.79 (4).59
    Emotional eating50.73 (2.39)46.98 (2.35)46.75 (2.90)—2.73 (1).26
    eHealth52.94 (4.36)47.38 (4.80)40.87 (3.81)—3.77 (4).44
    eHealth+Group49.06 (4.75)44.57 (3.69)48.10 (4.82)—3.77 (4).44
    eHealth+Indiv.49.73 (3.23)48.90 (3.71)50.36 (5.01)—3.77 (4).44
    Binge eating12.84 (0.71)8.75 (0.74)7.92 (0.86)—43.22 (1)<.001
    eHealth13.18 (1.28)8.76 (1.32)8.09 (1.53) —0.41 (4).98
    eHealth+ Group12.72 (1.06)8.39 (1.18)7.48 (1.60)—0.41 (4).98
    eHealth+ Individual12.63 (1.29)9.07 (1.32)8.19 (1.21) —0.41 (4).98
    Physical activity10.86 (0.15)11.09 (0.14)11.39 (0.16)—13.50 (1)<.001
    eHealth11.07 (0.24)11.06 (0.23)11.57 (0.27)—5.41 (4).25
    eHealth+ Group10.59 (0.21)10.88 (0.22)11.00 (0.23)—5.41 (4).25
    eHealth+ Individual10.90 (0.32)11.30 (0.25)11.53 (0.29)—5.41 (4).25
    Work2.12 (0.39)2.18 (0.40)2.15 (0.04)—7.62 (1).02
    Sports2.55 (0.10)2.84 (0.09)2.95 (0.10) —18.54 (1)<.001
    Leisure6.19 (0.10)6.07 (0.10)6.29 (0.11) —7.31 (1).03

    aNot applicable

    In Table 1, the results for all participants are presented in the title row, while between-group results are reported in the “eHealth” rows. Concerning depression, we observed an overall time–group interaction (χ24=17.99; P<.01), indicating that the treatment arms responded differently to the intervention (Table 1 and Figure 2). The significant differences between the groups were pinpointed between 0 and 6 months (χ21=11.17; P<.01), and 6 and 12-month time points (χ21=9.12; P=.01). Post hoc analysis comparing the 3 groups revealed that the eHealth+Individual yielded greater declines in the depression score compared with the eHealth (χ21=6.73; P<.01; d=0.42) between 0- and 6-month time points. Between 6- and 12-month time points, the eHealth condition showed significantly greater decline in the depression score than the eHealth+ Individual condition (χ21=8.87; P=<.01, d=0.14). The statistically significant time–group interaction was not observed between 0- and 12-month time points (χ21=3.27; P=.20), suggesting an absence of a relevant intervention effect. The covariate model showed that the between-group interaction was attributed to 12-month weight change (χ26=20.43; P<.01), baseline use of psychotropic medication (χ26=70.04; P<.001) and 0‐12 month changes in psychotropic medication use (χ2=73.93; P<.001). In the full covariate model, the time-group interaction did not remain significant (χ24=7.37; P=.12), suggesting that the observed changes were not attributable to differences between the intervention arms.

    ‎
    Figure 2. Changes in depression symptoms between the eHealth, eHealth + Group and eHealth + Individual treatment arms during the 12-month intervention.

    We also observed a statistically significant time–group interaction in burnout (χ22=12.910; P=.04), pinpointed between the 0- and 6-month (χ22=8.065; P=.02), and 6- and 12-month time points (χ22=8.299; P=.02) (Table 1 and Figure 3). Between the 0- and 6-month time points, we observed no difference between the eHealth and the intervention arms. Between the 6- and 12-month time points, however, the eHealth+ Group treatment showed a greater decrease in burnout compared with the eHealth treatment (χ21=4.611; P=.03; d=0.29). These differences were not statistically significant between 0- and 12-month (χ22=0.73; P=.70) or 0- and 24-month time points (χ22=3.60; P=.17), indicating a lack of a meaningful intervention effect. Furthermore, the covariate model showed that the between-group interaction was driven by sex (χ28=30.05; P<.001), baseline use of psychotropic medication (χ28=80.35; P<.001) and 0‐12 month changes in psychotropic medication use (χ26=70.38; P<.001). The time-group interaction did not remain significant (χ26=9.75; P=.14) when using the full covariate model, indicating that none of the observed changes were attributable to differences between the intervention arms.

    ‎
    Figure 3. Changes in burnout symptoms between the eHealth, eHealth+ Group and eHealth+ Individual treatment arms during the 12-month intervention and further 12-month follow-up without additional support.

    We did not observe significant time–group interactions in perceived work ability, eating behavior, or physical activity, indicating that the changes between the treatment arms did not differ over time (Table 1).

    Overall Effects

    To study the overall impact of the intervention, we combined the 3 treatment arms into a single eHealth arm for further analysis. Across all participants and time points, we observed an estimated mean decrease of 2.5 BDI points in depression (χ21=12.41; P<.01; d=0.38) (Table 1 and Figure 2). Covariate analysis revealed that older participants experienced a greater reduction in depressive symptoms (χ22=8.74; P=.03). Notably, the main effect of time remained statistically significant in the full covariate model, controlling for age, and this significance was maintained when applying Full Information Maximum Likelihood estimation to correct for potential bias, both in the full model (P=.007) and age-controlled model (P=.02). Furthermore, we observed a statistically significant decrease in clinical depression incidence in the completer (P=.02) but not in the intention-to-treat analysis (P=.23; Tables S8a and S8b in Multimedia Appendix 1).

    We found an increase in burnout symptoms between 0- and 6-month time points (χ21=9.56; P<.01; d=−0.25), and a decrease between 6- and 12-month time points (χ21=5.65; P=.02; Table 1). The results were attributed to changes in psychotropic medication at 0‐12 months (χ23=20.08; P<.001), as the main effect did not remain significant (χ23=5.55; P=.14) in the covariate model. The increase in the burnout score was not reflected on the clinical burnout incidence between the 0- and 6-month time points (Tables S9a and S9b in Multimedia Appendix 1). Perceived work ability did not change over time (Table 1).

    Furthermore, we observed an increase in eating competence (χ23=72.788; P<.001), controlled restraint (χ21=45.65; P<.001), and physical activity (χ21=13.501; P=.001, and a decrease in uncontrolled eating (χ21=50.34; P=.001) and binge eating tendency (χ21=43.22; P<.001; Table 1) during the 12-month intervention. However, the 24-month follow-up measurements showed an adverse effect in eating competence, especially in attitudes toward eating and food, in the absence of continued support.

    Specifically, eating competence increased between 0- and 6-month (χ21=5.34; P=.02; d=0.23) and 0- and 12-month time points (χ21=22.86; P<.001; d=0.53), and decreased between 0- and 24-month time points (χ21=7.27; P<.01; d=−0.36; Figure 4). The subscale analysis revealed increases in Contextual skills at 0‐6 months (χ211=25.35; P<.001; d=0.52) and 0‐12 months (χ21=37.09; P<.001; d=0.69) and Food acceptance in 0‐12 months (χ21=37.61; P<.001; d=0.92), and a decrease in the Eating attitudes subscale at 0‐24 months (χ21= ; P<.001; d=−0.68; Figure 5). The change in eating competence was not explained by any of the controlled variables when using the full covariate model.

    ‎
    Figure 4. Changes in eating competence in all participants during the 12-month intervention and further 12-month follow-up without additional support.
    ‎
    Figure 5. Changes in controlled restraint, uncontrolled eating, and emotional eating in all participants during the 12-month intervention.

    Uncontrolled eating decreased between the 0‐6-month (χ21=43.29; P<.001; d=0.54) and 0‐12–month time points (χ21=25.90; P<.001; d=0.67). The decrease in uncontrolled eating was explained by age (χ22=7.97; P=.02) and 6-month weight change (χ22=8.44; P=.02), with older participants and those who lost more weight showing greater reduction in uncontrolled eating (Table S7 in Multimedia Appendix 1). The main effect of time on uncontrolled eating did not remain significant (χ22=2.69; P=.26) after controlling for age and weight change, indicating that the observed changes were attributed to these confounders.

    Conversely, controlled restraint increased from baseline to 6 months (χ21=44.87; P<.001; d=0.74) and to 12-months (χ21=14.20; P<.001; d=0.40; Figure 5). Covariate analysis (Table S7 in Multimedia Appendix 1) revealed that the increase in controlled restraint was greater in the participants who lost more weight from baseline to 6 months (χ22=15.92; P<.001) and to 12 months (χ22=27.90; P<.001). The main effect of time on controlled restraint remained significant (χ22=30.46; P<.001 and χ22=30.64; P<.001, respectively) after controlling for changes in weight.

    Binge eating tendency showed a steady decline, decreasing 4.9 BES-points during the intervention (0‐6 month χ21=31.51; P<.001; d=0.58 and 0‐12 month χ21=22.97; P<.001; d=0.67 Figure 6). The covariate analysis revealed that the participants who lost more weight from baseline to 6 months exhibited greater improvement in the binge eating tendency (χ22=4.88; P=.03). The main effect of time remained significant in the full covariate model (χ22=42.14; P<.001).

    ‎
    Figure 6. Changes in binge eating tendency during the 12-month intervention in all participants.

    Physical activity (χ21=13.51; P<.001; d=0.38), and the sports subscale (χ21=18.54; P<.001; d=0.44) increased between 0- and 12-month time points. The change in physical activity was not explained by any of the controlled variables.


    Principal Findings

    This study examined whether minimal group or individual enhancement would offer additional benefits to a 12-month eHealth weight maintenance intervention on mental health, perceived work ability, and health behavior. We observed the 3 intervention arms to change differently over time in terms of the depression and burnout scores. Nevertheless, post hoc analysis revealed that these changes were not consistent over the 12- and 24-month follow-up periods, indicating fluctuations rather than meaningful intervention effects. Furthermore, the different trajectories of the treatment arms were explained by baseline use and changes in psychotropic medication for both depression and burnout. In addition, 12-month weight change and sex contributed to the differences in depression and burnout, respectively. These results further indicated that the observed group-level differences were explained by factors other than the intervention.

    In addition, we sought to explore the overall effects of an ACT-based weight management intervention on these domains. Across the entire study population, we observed statistically significant reductions in depressive symptoms and binge eating tendencies, along with improvements in eating competence and physical activity. Although the overall findings were quasi-experimental and must be interpreted cautiously, they collectively suggest that the standard HWC protocol may provide mental health and health behavioral benefits.

    Comparison With Previous Work

    While hybrid weight management interventions have shown superiority over digital interventions in somatic health, no relevant studies have reported their differences in mental health domains. In mental health interventions, personal guidance has been shown to enhance efficacy compared with unguided approaches [51]. Regarding health behavior, early findings suggest that personalized coaching leads to greater reductions in emotional eating than fully digital weight maintenance programs [52]. To our knowledge, no previous research has directly compared coach-assisted eHealth interventions with those supplemented by face-to-face contact. Given the low engagement levels of stand-alone digital applications, theoretical considerations suggest incorporating face-to-face elements to improve the efficacy and acceptability of interventions [30,53,54].

    In our study design, each participant was supported by the same coach through written communication in the eHealth program, and during all face-to-face sessions in the enhanced arm. Therefore, we hypothesized that group or individual meetings would strengthen the therapeutic alliance [55] with the coach, leading to greater engagement with the program, increased exposure to therapeutic content, and, ultimately, improved outcomes.

    Several factors may explain the absence of meaningful intergroup differences. One possibility is that the 3 sessions in the hybrid conditions were insufficient to create a meaningful distinction from the standard HWC intervention in relation to mental health variables, mirroring the findings of our previous paper on somatic health [31]. Increasing the number of sessions in the hybrid intervention might have led to more noticeable differences between the treatment arms. Alternatively, written coach assistance alone may provide sufficient support to participants, rendering face-to-face sessions unnecessary.

    To our knowledge, no previous studies have examined the effects of an ACT-based eHealth weight loss intervention on depression. While our results with the overall sample are preliminary due to the quasi-experimental design, the small-to-medium effect size reduction in depressive symptoms across the entire sample is consistent with previous research demonstrating the effectiveness of ACT interventions for depression [56,57], including in the context of eHealth [58] and weight loss interventions [21]. Although our study included a general population that on average did not score within the range of clinical depression, the result might be meaningful from a population health perspective. Notably, among the completers, a reduction in the number of participants with clinical depression was observed. This effect was not evident in the intention-to-treat analysis, which may indicate the importance of retention in meaningful mental health improvements. Furthermore, while eHealth interventions targeting weight loss have been shown to effectively reduce depression symptoms in participants with obesity [59], our results show that the reduction in depressive symptoms was not explained by the amount of weight loss. This is supported by a meta-analysis by Fabricatore et al [60], which showed that nondiet interventions can significantly reduce depression symptoms in the absence of weight loss.

    Contrary to expectations, we did not observe changes in burnout symptoms or perceived work ability between the baseline and postintervention assessments. Interestingly, burnout symptoms increased between the baseline and the 6-month time point, which contradicts previous research showing that ACT-based interventions can alleviate severe burnout [57,61]. Given the strong influence of workplace psychosocial factors on work ability [5], these findings suggest that interventions focused solely on individual change may be insufficient to impact occupational well-being. Future efforts might benefit from incorporating workplace-focused interventions along with individual treatments to enhance work ability more effectively.

    Our results show positive changes in eating behavior, with medium-to-large effect sizes, and physical activity, with small to medium effect sizes. These results align with previous studies reporting positive effects of ACT-based interventions on eating behavior [62-65] and physical activity [65,66].

    Overall eating competence improved during the intervention, particularly in the food acceptance and eating context subdomains, suggesting a more versatile, mindful, and planned approach to eating. Despite these improvements, eating competence was not sustained at the follow-up, as indicated by a significant decline in the eating attitude subscore, which reflects positive emotions related to eating. This suggests that, following the end of the intervention, the participants became less relaxed and comfortable about eating. The weight-neutral approach may have initially promoted a more balanced attitude toward eating, but once the intervention ended, participants may have struggled to maintain this mindset independently. Considering that longer treatment durations are associated with more substantial improvements in dysregulated eating behaviors [64], providing continued support during the follow-up phase could help preserve any positive changes achieved during the intervention.

    The binge eating tendency decreased with medium-to-large effect sizes, consistent with previous ACT research [64,65,67]. Unlike behavioral interventions that emphasize daily self-weighing, an approach that has not been shown to impact binge eating [68], our intervention integrated ACT and behavioral strategies without imposing external constraints, such as calorie restrictions or mandatory physical activity plans. This suggests that a more intuitive treatment approach might be more effective in reducing binge eating than focusing on weight loss. Furthermore, we observed an increase in controlled restraint, indicating that the participants’ conscious efforts to restrict their food intake increased during the intervention. This finding contrasts with previous ACT-based weight loss interventions, which did not report significant increases in controlled restraint [65,67]. This result appears to contradict the “restraint theory,” which suggests that higher controlled restraint may lead to higher levels of overeating and binge eating [69]. Nevertheless, this effect may differ in populations with obesity, where increased controlled restraint has been linked to a decreased binge eating tendency [70]. This discrepancy could also be attributed to the method used in this study. The 18-item version of the TFEQ used here does not distinguish between rigid and flexible cognitive restraint, with the latter being associated with successful weight management [71].

    Strengths and Limitations

    The strengths of this study include the use of an RCT setting to explore the differences between treatment arms. Furthermore, we used validated questionnaires with robust psychometric properties and a lengthy follow-up period of up to 24 months for some measures, providing a comprehensive view of the long-term effects.

    Despite these strengths, there are several limitations to consider. First, the requirement for internet access and the predominantly female, middle-aged, and “white-collar” sample limits generalizability to other populations. Although we controlled for sex and age using them in stratification and including them as covariates in our analyses, these characteristics are still likely to influence the outcomes. Second, the analysis could have been underpowered, as we used prospective weight change rather than mental health outcomes to calculate the statistical power, which may have led to biases in the observed effect sizes. Third, the substantial amount of missing data due to attrition and inactivity may have affected the reliability of the questionnaires used in this study. To mitigate potential bias from missing data, we used a statistical method with an intention-to-treat approach and applied the necessary statistical corrections. Fourth, although the between-group analysis was conducted in an RCT setting, the overall analysis relied on a quasi-experimental design. We were therefore unable to fully evaluate the individual intervention effect without a control group for comparison, making the overall results sensitive to bias.

    Finally, the timing of the intervention during the COVID-19 pandemic may have substantially affected the results. The pandemic’s social restrictions, along with the increase in remote work, could have exacerbated the symptoms of burnout and disordered eating, as well as reduced physical activity levels. These factors may have confounded the observed effects of the intervention. In addition, the need to adapt the intervention to a remote format, which had originally been planned for in-person support groups, may have further affected the effectiveness of the support provided.

    Conclusions

    Minimal enhancement through either group or individual video-conference meetings did not provide additional benefits in the mental health or eating habit domains compared with the eHealth intervention alone. Across the entire study population, we observed positive changes in depressive symptoms, binge eating tendencies, eating competence, and physical activity during the intervention. Future research should continue to explore the differences between hybrid and digital interventions on mental health domains, distinguishing for delivery mode and contact frequency, as well as the impact of continued support for the maintenance of positive outcomes.

    Acknowledgments

    The authors sincerely thank all involved parties at HealthyWeightHub and Occupational Health Helsinki for their significant contributions to the intervention and related research. We are especially grateful to the parties funding the study: Finnish Work Environment Fund and Helsinki City (grant 200379); Juho Vainio Foundation (grant 202400066); Academy of Finland (grants 335443, 314383, and 266286); the Academy of Finland, Centre of Excellence in Research on Mitochondria, Metabolism and Disease (FinMIT; grant 272376); Finnish Medical Foundation; Gyllenberg Foundation; Novo Nordisk Foundation (grants NNF20OC0060547, NNF17OC0027232, and NNF10OC1013354); Finnish Diabetes Research Foundation; Paulo Foundation; Occupational Health Helsinki, City of Helsinki, University of Helsinki and Helsinki University Hospital; and government research funds. Open access to this paper was funded by the University of Helsinki Library. The funders had no role in study design, data collection and analysis, decision to publish, or paper preparation.

    Data Availability

    The datasets generated or analyzed during this study are available from the corresponding author on reasonable request.

    Authors' Contributions

    SAM contributed to the conceptualization, data curation, funding acquisition, investigation, project administration, formal analysis, writing of the original draft, and reviewed and edited the writing. JM handled the statistical methodology, formal analysis, review, and editing. LS was involved in the conceptualization, project administration, funding acquisition, resources, and review and editing of the writing, RL worked on the conceptualization, supervision, review and editing. KP assisted with the conceptualization, project administration, funding acquisition, resources, visualization, supervision, and review and editing of the writing. RK aided in the conceptualization, funding acquisition, supervision, and review and editing of the writing.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Supplementary tables.

    DOCX File, 219 KB
    Checklist 1

    CONSORT-eHEALTH checklist (V 1.6.1).

    PDF File, 1184 KB
    1. de Wit L, Luppino F, van Straten A, Penninx B, Zitman F, Cuijpers P. Depression and obesity: a meta-analysis of community-based studies. Psychiatry Res. Jul 30, 2010;178(2):230-235. [CrossRef] [Medline]
    2. Avila C, Holloway AC, Hahn MK, et al. An overview of links between obesity and mental health. Curr Obes Rep. Sep 2015;4(3):303-310. [CrossRef] [Medline]
    3. Luppino FS, de Wit LM, Bouvy PF, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry. Mar 2010;67(3):220-229. [CrossRef] [Medline]
    4. Andersen LL, Izquierdo M, Sundstrup E. Overweight and obesity are progressively associated with lower work ability in the general working population: cross-sectional study among 10,000 adults. Int Arch Occup Environ Health. Nov 2017;90(8):779-787. [CrossRef] [Medline]
    5. van den Berg TIJ, Alavinia SM, Bredt FJ, Lindeboom D, Elders LAM, Burdorf A. The influence of psychosocial factors at work and life style on health and work ability among professional workers. Int Arch Occup Environ Health. Aug 2008;81(8):1029-1036. [CrossRef] [Medline]
    6. de Souza e Silva D, das Merces MC, Lua I, et al. Association between burnout syndrome and obesity: a cross-sectional population-based study. WOR. 2023;74(3):991-1000. [CrossRef]
    7. Koutsimani P, Montgomery A, Georganta K. The relationship between burnout, depression, and anxiety: a systematic review and meta-analysis. Front Psychol. 2019;10:284. [CrossRef] [Medline]
    8. Seibt R, Spitzer S, Blank M, Scheuch K. Predictors of work ability in occupations with psychological stress. J Public Health. Feb 2009;17(1):9-18. [CrossRef]
    9. Puhl RM, Heuer CA. Obesity stigma: important considerations for public health. Am J Public Health. Jun 2010;100(6):1019-1028. [CrossRef] [Medline]
    10. Plotsky PM, Owens MJ, Nemeroff CB. Psychoneuroendocrinology of depression. Hypothalamic-pituitary-adrenal axis. Psychiatr Clin North Am. Jun 1998;21(2):293-307. [CrossRef] [Medline]
    11. Jacka FN, Cherbuin N, Anstey KJ, Butterworth P. Dietary patterns and depressive symptoms over time: examining the relationships with socioeconomic position, health behaviours and cardiovascular risk. PLOS ONE. 2014;9(1):e87657. [CrossRef] [Medline]
    12. Rutters F, La Fleur S, Lemmens S, Born J, Martens M, Adam T. The hypothalamic-pituitary-adrenal axis, obesity, and chronic stress exposure: foods and HPA axis. Curr Obes Rep. Dec 2012;1(4):199-207. [CrossRef]
    13. Dalgleish T, Black M, Johnston D, Bevan A. Transdiagnostic approaches to mental health problems: current status and future directions. J Consult Clin Psychol. Mar 2020;88(3):179-195. [CrossRef] [Medline]
    14. Castelnuovo G, Pietrabissa G, Manzoni GM, et al. Cognitive behavioral therapy to aid weight loss in obese patients: current perspectives. Psychol Res Behav Manag. 2017;10:165-173. [CrossRef] [Medline]
    15. Grilo CM, Masheb RM, Wilson GT, Gueorguieva R, White MA. Cognitive-behavioral therapy, behavioral weight loss, and sequential treatment for obese patients with binge-eating disorder: a randomized controlled trial. J Consult Clin Psychol. Oct 2011;79(5):675-685. [CrossRef] [Medline]
    16. Barnes RD, Tantleff-Dunn S. Food for thought: examining the relationship between food thought suppression and weight-related outcomes. Eat Behav. Aug 2010;11(3):175-179. [CrossRef] [Medline]
    17. Rogers JM, Ferrari M, Mosely K, Lang CP, Brennan L. Mindfulness-based interventions for adults who are overweight or obese: a meta-analysis of physical and psychological health outcomes. Obes Rev. Jan 2017;18(1):51-67. [CrossRef] [Medline]
    18. Hayes SC, Luoma JB, Bond FW, Masuda A, Lillis J. Acceptance and commitment therapy: model, processes and outcomes. Behav Res Ther. Jan 2006;44(1):1-25. [CrossRef] [Medline]
    19. Guerrini Usubini A, Varallo G, Granese V, et al. The impact of psychological flexibility on psychological well-being in adults with obesity. Front Psychol. 2021;12:636933. [CrossRef] [Medline]
    20. A-Tjak JGL, Davis ML, Morina N, Powers MB, Smits JAJ, Emmelkamp PMG. A meta-analysis of the efficacy of acceptance and commitment therapy for clinically relevant mental and physical health problems. Psychother Psychosom. 2015;84(1):30-36. [CrossRef] [Medline]
    21. Iturbe I, Echeburúa E, Maiz E. The effectiveness of acceptance and commitment therapy upon weight management and psychological well-being of adults with overweight or obesity: a systematic review. Clin Psychol Psychother. May 2022;29(3):837-856. [CrossRef] [Medline]
    22. da Luz FQ, Hay P, Touyz S, Sainsbury A. Obesity with comorbid eating disorders: associated health risks and treatment approaches. Nutrients. Jun 27, 2018;10(7):829. [CrossRef] [Medline]
    23. Dindo L, Van Liew JR, Arch JJ. Acceptance and commitment therapy: a transdiagnostic behavioral intervention for mental health and medical conditions. Neurotherapeutics. Jul 2017;14(3):546-553. [CrossRef] [Medline]
    24. Han A, Kim TH. Efficacy of internet-based acceptance and commitment therapy for depressive symptoms, anxiety, stress, psychological distress, and quality of life: systematic review and meta-analysis. J Med Internet Res. Dec 9, 2022;24(12):e39727. [CrossRef] [Medline]
    25. Puolakanaho A, Tolvanen A, Kinnunen SM, Lappalainen R. A psychological flexibility -based intervention for burnout: a randomized controlled trial. J Contextual Behav Sci. Jan 2020;15:52-67. [CrossRef]
    26. Gentili A, Failla G, Melnyk A, et al. The cost-effectiveness of digital health interventions: a systematic review of the literature. Front Public Health. 2022;10:787135. [CrossRef] [Medline]
    27. Kupila SKE, Joki A, Suojanen LU, Pietiläinen KH. The effectiveness of eHealth interventions for weight loss and weight loss maintenance in adults with overweight or obesity: a systematic review of systematic reviews. Curr Obes Rep. Sep 2023;12(3):371-394. [CrossRef] [Medline]
    28. Hutchesson MJ, Rollo ME, Krukowski R, et al. eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis. Obes Rev. May 2015;16(5):376-392. [CrossRef] [Medline]
    29. Besson M, Gurviez P, Carins J. Using digital devices to help people lose weight: a systematic review. JSOCM. Jul 31, 2020;10(3):289-319. [CrossRef]
    30. Chen K, Huang JJ, Torous J. Hybrid care in mental health: a framework for understanding care, research, and future opportunities. NPP—Digit Psychiatry Neurosci. 2024;2(1):16. [CrossRef]
    31. Männistö SA, Pietiläinen KH, Muotka J, Suojanen LU, Lappalainen R, Korpela R. Coach-assisted eHealth with group or individual support for employees with obesity: randomized controlled trial on weight, body composition, and health metrics. J Med Internet Res. Mar 12, 2025;27:e60436. [CrossRef] [Medline]
    32. Suojanen LU, Ahola AJ, Kupila S, Korpela R, Pietiläinen KH. Effectiveness of a web-based real-life weight management program: Study design, methods, and participants’ baseline characteristics. Contemp Clin Trials Commun. Sep 2020;19:100638. [CrossRef] [Medline]
    33. Beck AT, Steer RA, Ball R, Ranieri W. Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J Pers Assess. Dec 1996;67(3):588-597. [CrossRef] [Medline]
    34. BECK AT, WARD CH, MENDELSON M, MOCK J, ERBAUGH J. An inventory for measuring depression. Arch Gen Psychiatry. Jun 1961;4:561-571. [CrossRef] [Medline]
    35. Shafer AB. Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. J Clin Psychol. Jan 2006;62(1):123-146. [CrossRef] [Medline]
    36. Näätänen P, Aro A, Matthiesen S, Salmela-Aro K. Bergen Burnout Indicator-15. Edita; 2003. ISBN: 951-37-3943-0
    37. Cooper CL, Dewe PJ, O’Driscoll MP. Organizational Stress: A Review and Critique of Theory, Research, and Applications. Sage Publications, Inc; 2001. ISBN: 9781452231235
    38. Työntekijän koettu työkyky ennustaa sairauspoissaoloja ja työterveyshuollon kustannuksia [Artcle in Finnish]. Lääkärilehti. 2020;2020(41):2138.
    39. Pohjonen T, Meyer L, Teerimäki R, Alho M. Asiakasprofiloinnista vaikuttavuutta työterveyshuoltoon [Artcle in Finnish]. Työterveyslääkäri. 2017(2):42-45.
    40. ecSI 2.0 clinical and research tool. Ellyn Satter Institute URL: https://www.ellynsatterinstitute.org/ecsi-2-0/ [Accessed 2023-10-24]
    41. Lohse B, Satter E, Horacek T, Gebreselassie T, Oakland MJ. Measuring eating competence: psychometric properties and validity of the ecSatter Inventory. J Nutr Educ Behav. 2007;39(5 Suppl):S154-S166. [CrossRef] [Medline]
    42. Karlsson J, Persson LO, Sjöström L, Sullivan M. Psychometric properties and factor structure of the three-factor eating questionnaire (TFEQ) in obese men and women. Results from the Swedish obese subjects (SOS) study. Int J Obes. Dec 2000;24(12):1715-1725. [CrossRef]
    43. Malkki-Keinänen K, Lankinen M, Karhunen L, Schwab U. Psychometric evaluation of three-factor eating questionnaire -R18 in aging Finnish men with increased risk for type 2 diabetes. Nutr Health. Jun 2024;30(2):279-290. [CrossRef] [Medline]
    44. Gormally J, Black S, Daston S, Rardin D. The assessment of binge eating severity among obese persons. Addict Behav. 1982;7(1):47-55. [CrossRef] [Medline]
    45. Duarte C, Pinto-Gouveia J, Ferreira C. Expanding binge eating assessment: validity and screening value of the Binge Eating Scale in women from the general population. Eat Behav. Aug 2015;18:41-47. [CrossRef] [Medline]
    46. Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr. Nov 1982;36(5):936-942. [CrossRef] [Medline]
    47. Ziegler A, Vens M. Generalized estimating equations. Methods Inf Med. 2010;49(5):421-425. [CrossRef]
    48. Akoglu H. User’s guide to correlation coefficients. Turk J Emerg Med. Sep 2018;18(3):91-93. [CrossRef] [Medline]
    49. Effect size calculators. University of Colorado Colorado Springs. URL: https://lbecker.uccs.edu/ [Accessed 2024-02-01]
    50. Sullivan GM, Feinn R. Using effect size-or why the P value is not enough. J Grad Med Educ. Sep 2012;4(3):279-282. [CrossRef] [Medline]
    51. Leung C, Pei J, Hudec K, Shams F, Munthali R, Vigo D. The effects of nonclinician guidance on effectiveness and process outcomes in digital mental health interventions: systematic review and meta-analysis. J Med Internet Res. Jun 15, 2022;24(6):e36004. [CrossRef] [Medline]
    52. Kim M, Kim Y, Go Y, et al. Multidimensional cognitive behavioral therapy for obesity applied by psychologists using a digital platform: open-label randomized controlled trial. JMIR Mhealth Uhealth. Apr 30, 2020;8(4):e14817. [CrossRef] [Medline]
    53. Balcombe L, De Leo D. Digital mental health challenges and the horizon ahead for solutions. JMIR Ment Health. Mar 29, 2021;8(3):e26811. [CrossRef] [Medline]
    54. Villalobos N, Vela FS, Hernandez LM. Digital healthcare intervention to improve self-management for patients with type 2 diabetes: a scoping review. J Sci Innov Med. Aug 11, 2020;3(3). [CrossRef]
    55. Tremain H, McEnery C, Fletcher K, Murray G. The therapeutic alliance in digital mental health interventions for serious mental illnesses: narrative review. JMIR Ment Health. Aug 7, 2020;7(8):e17204. [CrossRef] [Medline]
    56. Gloster AT, Walder N, Levin ME, Twohig MP, Karekla M. The empirical status of acceptance and commitment therapy: a review of meta-analyses. J Contextual Behav Sci. Oct 2020;18:181-192. [CrossRef]
    57. Towey-Swift KD, Lauvrud C, Whittington R. Acceptance and commitment therapy (ACT) for professional staff burnout: a systematic review and narrative synthesis of controlled trials. J Ment Health. Apr 2023;32(2):452-464. [CrossRef] [Medline]
    58. Brown M, Glendenning AC, Hoon AE, John A. Effectiveness of web-delivered acceptance and commitment therapy in relation to mental health and well-being: a systematic review and meta-analysis. J Med Internet Res. Aug 24, 2016;18(8):e221. [CrossRef] [Medline]
    59. Kocol D, Bäuerle A, Schadendorf T, et al. Efficacy of eHealth interventions to reduce depression symptoms in individuals with obesity: a systematic review of randomized controlled trials. Front Psychiatry. 2024;15:1296433. [CrossRef] [Medline]
    60. Fabricatore AN, Wadden TA, Higginbotham AJ, et al. Intentional weight loss and changes in symptoms of depression: a systematic review and meta-analysis. Int J Obes. Nov 2011;35(11):1363-1376. [CrossRef]
    61. Kinnunen SM, Puolakanaho A, Mäkikangas A, Tolvanen A, Lappalainen R. Does a mindfulness-, acceptance-, and value-based intervention for burnout have long-term effects on different levels of subjective well-being? Int J Stress Manag. 2020;27(1):82-87. [CrossRef]
    62. Sairanen E, Tolvanen A, Karhunen L, et al. Psychological flexibility and mindfulness explain intuitive eating in overweight adults. Behav Modif. Jul 2015;39(4):557-579. [CrossRef] [Medline]
    63. Järvelä-Reijonen E, Karhunen L, Sairanen E, et al. The effects of acceptance and commitment therapy on eating behavior and diet delivered through face-to-face contact and a mobile app: a randomized controlled trial. Int J Behav Nutr Phys Act. Feb 27, 2018;15(1):22. [CrossRef] [Medline]
    64. Di Sante J, Akeson B, Gossack A, Knäuper B. Efficacy of ACT-based treatments for dysregulated eating behaviours: a systematic review and meta-analysis. Appetite. Apr 1, 2022;171:105929. [CrossRef] [Medline]
    65. Mueller J, Richards R, Jones RA, et al. Supporting weight management during COVID-19 (SWiM-C): twelve-month follow-up of a randomised controlled trial of a web-based, ACT-based, guided self-help intervention. Int J Obes (Lond). Jan 2023;47(1):51-59. [CrossRef] [Medline]
    66. Pears S, Sutton S. Effectiveness of Acceptance and Commitment Therapy (ACT) interventions for promoting physical activity: a systematic review and meta-analysis. Health Psychol Rev. Mar 2021;15(1):159-184. [CrossRef] [Medline]
    67. Levin ME, Petersen JM, Durward C, et al. A randomized controlled trial of online acceptance and commitment therapy to improve diet and physical activity among adults who are overweight/obese. Transl Behav Med. Jun 17, 2021;11(6):1216-1225. [CrossRef] [Medline]
    68. Steinberg DM, Tate DF, Bennett GG, Ennett S, Samuel-Hodge C, Ward DS. Daily self-weighing and adverse psychological outcomes. Am J Prev Med. Jan 2014;46(1):24-29. [CrossRef]
    69. Williamson DA, Martin CK, York-Crowe E, et al. Measurement of dietary restraint: validity tests of four questionnaires. Appetite. Mar 2007;48(2):183-192. [CrossRef] [Medline]
    70. Johnson F, Pratt M, Wardle J. Dietary restraint and self-regulation in eating behavior. Int J Obes. May 2012;36(5):665-674. [CrossRef]
    71. Sairanen E, Lappalainen R, Lapveteläinen A, Tolvanen A, Karhunen L. Flexibility in weight management. Eat Behav. Apr 2014;15(2):218-224. [CrossRef] [Medline]

    ‎
    ACT: acceptance and commitment therapy
    BDI-21: Beck Depression Index 21
    BES: Binge Eating Scale
    BHPAQ: Baecke Habitual Physical Activity Questionnaire
    HWC: health weight coaching
    MCAR: missing completely at random
    RCT: randomized controlled trial
    RCT: randomized controlled trial
    TFEQ: Three Factor Eating Questionnaire

    Edited by John Torous; submitted 15.09.24; peer-reviewed by Christian Huber, Christian Pankhurst, Joseph Abua; final revised version received 17.04.25; accepted 04.05.25; published 07.07.25.

    Copyright

    © Siniriikka A Männistö, Joona Muotka, Laura-Unnukka Suojanen, Raimo Lappalainen, Kirsi H Pietiläinen, Riitta Korpela. Originally published in JMIR Mental Health (https://mental.jmir.org), 7.7.2025.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.