Personalisation of the Dutch combined lifestyle intervention SLIMMER improves participant retention and weight-loss in people at risk for cardiometabolic disease

Personalisation of the Dutch combined lifestyle intervention SLIMMER improves participant retention and weight-loss in people at risk for cardiometabolic disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Personalisation of the Dutch combined lifestyle intervention SLIMMER improves participant retention and weight-loss in people at risk for cardiometabolic disease Johanneke E. Oosterman, Dagmar J. Smid, Regina J.M. Kamstra, Iris M. de Hoogh, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6530079/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The Dutch combined lifestyle intervention (CLI) program SLIMMER is effective in changing lifestyle behaviour. However, achieving and maintaining a healthier lifestyle is difficult. Here, a personalised version of the SLIMMER CLI was evaluated for effects on weight loss, metabolism and lifestyle advice adherence. In this cluster-allocated controlled open-label parallel intervention study, 61 participants were included in the personalised and 60 participants in the regular SLIMMER CLI (control). Adults at risk for cardiometabolic disease were followed for 6 months. Body composition and blood samples were measured at baseline and end of study. Adherence to lifestyle advice, self-efficacy and maintenance was assessed by questionnaires. At 6 months, body weight (-4.5 kg, p < 0.001), BMI (-1.5 kg/m 2 , p < 0.001) and body fat percentage (-3.5%, p < 0.001) improved more in the intervention than control group. No consistent difference in adherence to lifestyle advice or cardiometabolic outcomes was found between groups. The dropout rate was lower in the intervention group (11%) than in the control group (23%, p < 0.001). Personalisation was effective in participants retention and body weight loss, unexplained by differences in adherence to lifestyle advice. This could potentially lead to favourable long-term health outcomes. Biological sciences/Psychology Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Precision Medicine Primary Health Care Weight Loss Life Style Obesity Risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Overweight and obesity are risk factors for development of insulin resistance and type 2 diabetes (T2D) and are associated with a number of metabolic and cardiovascular risk factors ( 1 ). Evidence is mounting that lifestyle changes can prevent or delay progression of metabolic disease. Previous studies showed that weight loss, changes in dietary intake, and increased physical activity resulted in long-term reduction in the incidence of T2D in persons with impaired glucose tolerance ( 2 , 3 ) and a reduction of low-grade inflammation ( 4 ). Based on these results, combined lifestyle intervention (CLI) programs have been implemented within primary care in the Netherlands and are reimbursed for people with increased cardiometabolic risk or living with obesity. SLIMMER is one of those CLI programs. In the two-year SLIMMER program, adults with overweight (BMI 25–30 kg/m 2 ) in combination with cardiometabolic risk factors and/or comorbidities, or living with obesity (BMI ≥ 30 kg/m 2 ) are supported to improve their lifestyle with respect to dietary intake, physical activity, sleep, stress and social habits. The SLIMMER program resulted in on average 2.7 kg and 2.5 kg weight loss after 12 and 18 months, respectively ( 5 ). Despite promising results of CLI programs, adherence is difficult, as represented by high dropout rates in Dutch CLI programs ( 6 ). For instance, 26% of participants that started a CLI in the Netherlands did not complete the full program in the first year and another 35% of the participants stopped in the second year ( 7 ). The primary reason for dropout was lack of motivation ( 7 ). Personalisation, i.e. adapting lifestyle advice to an individual’s needs and preferences, may lead to better lifestyle advice adherence and contribute to achieving sustainable healthy lifestyle habits ( 8 ). For acceptance of lifestyle advice, using behavioural change techniques is important for effective communication of the personalised advice ( 9 ). Key behavioural determinants to establish behavioural maintenance include intrinsic motivation, self-efficacy, and creating a supporting environment ( 10 ). Effective behaviour change techniques that could impact these determinants include goal setting, self-monitoring, self-evaluation, and provision of feedback and advice based on personal goals or specific behaviours ( 8 , 9 ). Multiple systematic reviews indicated that personalised nutritional behaviour-change interventions based on information of a person’s diet, biology and lifestyle improved both nutritional intake as well as health outcomes as compared to generalized nutritional advice ( 11 – 14 ). Furthermore, blended care, defined as a combination of face-to-face care and dynamically tailored eHealth support adapted to behavioural, psychosocial and contextual factors of a person, has been shown to be more effective for lifestyle changes than face-to-face care only ( 15 ). In the current study, the effect of a personalised SLIMMER program (intervention group) was assessed and compared to the regular SLIMMER program (control group) in a real-life primary care setting. On top of the regular SLIMMER CLI, personalisation included 1) personalised lifestyle advice based on (pre)diabetes subtyping 2) an extensive 360 degrees diagnosis, 3) personalised goal setting and monitoring, and 4) blended-care based personalised behaviour change support between the regular SLIMMER coaching sessions. For an extensive 360 degrees diagnosis, insight into contextual lifestyle factors is important. Here, the 360 degrees diagnosis was used as basis to personalise lifestyle advice and to set personal lifestyle goals in shared decision between participant and health care professional. Furthermore, personalised dietary and physical activity recommendations were made based on the participant’s insulin resistance (IR) subtype that arise from variations in genetics, phenotype, lifestyle and environment ( 16 , 17 ). Research in primary healthcare practice showed that people with different IR subtypes benefit from differential dietary and physical activity recommendations ( 18 ), which resulted in remission and reversal rates of 33–75% of type 2 diabetes depending on the severity and subtype of T2D ( 19 , 20 ). In addition, individual self-monitoring, personalised goal setting, behavioural monitoring, feedback and problem-solving were incorporated to achieve behaviour change. The personalised, sustainable behaviour change was supported by a blended care format, including digital and physical coaching. The primary objective for this study was to assess effects of a personalised SLIMMER program on adherence to lifestyle advice, evaluated by qualitative and quantitative changes in lifestyle behaviour. The secondary objective was to assess effects of a personalised SLIMMER on health status, measured by well-being, anthropometrics, clinical metabolic parameters and biomarkers related to low-grade inflammation. It was hypothesized that the personalised SLIMMER program would result in better adherence to lifestyle advice and result in greater improvements in body-weight and cardiometabolic health outcomes as compared to the regular SLIMMER program. Results Participant flow A total of 172 people were assessed for eligibility from which 27 people were excluded from the intervention group and 9 from the control group, because they declined to participate or did not meet inclusion criteria (Figure 2). A total of 66 and 70 participants were allocated to the intervention or control group, respectively. In both groups, some participants did not start, because they were not interested anymore or due to COVID-19. Eventually, 61 and 60 participants received the allocated treatment, in the intervention and control group, respectively. In the control group, 8 participants were lost to follow-up within the 6 months period. In the intervention group, no participants were lost to follow-up. However, in both the control and intervention group, some participants discontinued the SLIMMER program, and therefore were assigned as dropouts for our study. In the control group, 14 participants discontinued the SLIMMER program (reasons: time constraints, not interested anymore), whereas in the intervention group, 7 participants discontinued the intervention (reasons: SLIMMER program did not meet expectations, COVID). This dropout rate was significantly lower in the intervention group (11%) than in the control group (23%, p 80% of baseline data was available. This resulted in 61 and 38 participants for analysis in the intervention and control group, respectively (Figure 2). Baseline characteristics Table 1 shows baseline characteristics of the intervention group (n = 61) and control group (n = 38). Average age of the participants was 49.6 ± 11.4 and 48.8 ± 12.6 years in the intervention and control group, respectively. For both the intervention (33.0 ± 4.8 kg/m 2 ) and control group (34.6 ± 5.3 kg/m 2 ), mean BMI indicates that most participants had obesity. At baseline, BMI did not differ significantly between both groups ( p = 0.144). Body weight showed a significant difference ( p < 0.038) between the intervention group (95.5 ± 15.0 kg) compared to the control group (103.0 ± 16.9 kg). Fasting glucose and insulin did not differ between groups at baseline. However, there was a significant difference in HbA1c between intervention (36.4 ± 10.5 mmol/mol) and control group (40.9 ± 12.4 mmol/l, p < 0.001). Qualitative and quantitative assessment of lifestyle advice adherence Qualitative adherence to physical activity and diet advice Participants in the control and intervention group improved significantly on self-reported, qualitative adherence to the physical activity guideline at 3 and 6 months compared to baseline ( p < 0.001 for both time points). However, the change from baseline to 6 months was significantly different in self-reported adherence to the physical activity guideline between control and intervention group, in favour of the control group ( p < 0.05). With respect to adherence to the dietary guideline, participants in the control and intervention group improved significantly at 3 and 6 months compared to baseline ( p < 0.001 for both time points). There was no interaction effect between the two intervention groups (Figure 3A). Quantitative adherence to Dutch Healthy Diet Index At baseline, there was no difference in diet quality as assessed by the DHDI between the intervention and control group (100.9 ± 17.8 vs 94.0 ± 18.5), corresponding to an adherence of 60% to the Dutch Dietary Guidelines in both groups. There was a significant improved change at 3 months and 6 months in the control group and in the intervention group (p < 0.05 for both time points). There was no interaction effect nor statistical difference between control and intervention group (Figure 3B). At 6 months after the start of the intervention, scores were 107.3 ± 18.0 and 100.2 ± 15.0 for intervention and control groups, respectively. There were significant changes over time in following food groups: whole-grain products (significantly higher score at 6 months, but not different between control and intervention group), dairy products (significant changes over time, higher score change at 6 months in favour of control group), red meat (increase in score at 3 months, with significant higher score change for control vs intervention group), and salt (increase in score at 3 months for both groups). Self-efficacy There was a significant difference ( p < 0.05) in user-rated self-efficacy scores between intervention and control group, both on physical activity ( p = 0.02) and diet ( p = 0.02). Self-efficacy in the intervention group was higher at baseline and persisted higher over time as compared to the control group. No interaction effect was observed between the two groups over time, indicating that self-efficacy over time was not differentially affected by the intervention (Figure 4). Maintenance of lifestyle behaviour change Maintenance of lifestyle behaviour change was determined by rating difficulty of maintaining the dietary and physical activity advice and the participant’s own goals. Maintenance of lifestyle behaviour change remained similar between 3 and 6 months of the intervention. There was no significant maintenance difference between the groups, nor was an interaction or time effect observed (Figure 5). Quality of life Quality of life (QoL) improved in both groups at 3 and 6 months compared to baseline (Figure 6, p<0.001). At baseline, QoL score was 72.9 ± 5.4 in the control group and 75.3 ±.3.4 in the intervention group. The intervention group did not score significantly different at baseline compared to control. At 3 months QoL score improved on average with 7.1 ±.2.7 points. At 6 months, the control group showed a significant increase to 81.9 , while the QoL score in the intervention group was 75.7. The change from baseline to 6 months was significantly different between the two groups in favour of the control group ( p < 0.05). Appreciation of the lifestyle program Overall, appreciation of intervention and control programs were rated 6.9 ± 1.6 and 7.0 ± 1.1 (scale 1-10), respectively. There was no significant difference in appreciation items between the intervention and control group (Table 2). Comparison of clinical and metabolic measurements between intervention and control group Beneficial changes in clinical and metabolic measurements over 6 months were seen in both the intervention and control group (Table 3). In both groups, body weight decreased significantly, from 95.5 ± 15.0 kg to 90.8 ± 13.5 kg in the intervention group ( p < 0.001) and from 103.0 ± 16.9 kg to 101.0 ± 14.8 kg in the control group ( p = 0.007). Body weight loss was significantly higher in the intervention group compared to the control group (4.7 kg vs 2.0 kg; p = 0.006). Body fat percentage and waist-hip ratio significantly changed in the intervention group only (-3.5 %; p < 0.001 and p = 0.025, respectively) and these changes over time were significantly different from the control group ( p = 0.002 and p = 0.013, respectively). In both groups systolic blood pressure decreased, however the control group also showed a significant reduction in diastolic blood pressure ( p < 0.001), which was significantly different from the intervention group ( p = 0.011). Fasting glucose significantly decreased in the intervention group ( p = 0.003), but this change over time was not significantly different from the control group ( p = 0.210). Insulin significantly decreased in the control group ( p = 0.002), but not in the intervention group ( p = 0.968), yet all within normal ranges. This change was significantly different between the groups ( p = 0.012). HbA1c showed a significant interaction effect between the two groups ( p = 0.009), with maintained concentrations in the intervention group, whereas concentrations in the control group non-significantly increased. No changes in total, HDL and LDL cholesterol were found. Cholesterol ratio, TG and eGFR showed significant decreases in the intervention group over time ( p = 0.038, p = 0.015 and p = 0.003, respectively), which were not observed for the control group ( p = 0.133, p = 0.205 and p = 0.247, respectively). However, these changes did not differ significantly from the control group ( p = 0.915, p = 0.584 and p = 0.310, respectively). Inflammation Changes in inflammatory biomarkers were seen for both groups (Table 4 & Figure 7). Leptin decreased significantly in the intervention group ( p < 0.001), while leptin increased markedly, but not significant, in the control group. Adiponectin significantly increased in both groups ( p < 0.001 for intervention group and p = 0.012 for control group). Resistin increased in both groups at 6 months, but to a differential extent ( p = 0.006), with significantly increased concentrations in the control group and no significant increase in the intervention group. Furthermore, in the intervention group, but not in the control group, hsCRP ( p = 0.016) and AAT ( p = 0.002) significantly decreased from baseline to 6 months. E-selectin significantly decreased in both groups ( p < 0.001 for intervention group, p = 0.012 for control group). Discussion The effects of personalisation of a proven effective CLI program (SLIMMER) was assessed on lifestyle advice adherence and clinical outcomes. The most important observation was the difference in dropouts between intervention and control group (p < 0.001), being 11% in the intervention group versus 23% in the control group. Main reasons for premature dropout included time constraints and dissatisfaction with the program or group. The dropout percentage of 11% in the intervention is not only lower than the control group, but also lower than average dropout rates (26%) as reported for CLIs in the first year ( 7 ). This suggests that personalisation prevents premature dropout of the lifestyle program and increases retention. A clear difference between the control and intervention group was the amount of contact and attention the participants received, which may contribute to these results. Interestingly, a lower dropout rate was not related to differences in adherence to lifestyle advice assessed by questionnaires on self-reported adherence, self-efficacy and maintenance to diet and physical activity advices. Self-reported adherence to the combined lifestyle program as well as the DHDI score improved in both groups at 3 and 6 months, whereas self-efficacy and maintenance to diet and physical activity advices did not change as a result of the intervention. For self-reported adherence to physical activity guidelines a greater change at 6 months was observed in favour of the control group. These results were surprising as it has repeatedly been shown that behavioural treatment strategies, with emphasis on self-efficacy, motivation and feedback - as was implemented in the personalised SLIMMER - improve adherence to lifestyle advice ( 9 , 28 ). QoL improved in both groups with approximately 8 points (11%), but this was mainly maintained at 6 months in the control group. A change in QoL of 8 points is in accordance to the average score for QoL of Dutch CLIs ( 7 ) and clinically relevant. Another important observation is that the personalised SLIMMER program improved body weight, BMI and body fat percentage to a larger extent than the regular SLIMMER program. Body weight decreased with 4.7 kg (4.9%) in the intervention group versus 2 kg (1.9%) in the control group. Body weight loss in the intervention group is higher than average body weight loss after 6 months of any CLI in the Netherlands (3.4%), suggesting positive effect of personalisation. As overweight is associated with development of cardiometabolic diseases, this is an important and clinically relevant outcome. Several biomarkers related to cardiometabolic health showed improved values in both groups after 6 months of the program, but did not show differences between both groups, except the differences in weight loss. A possible explanation for absence of clear improvements in cardiometabolic outcome parameters in the intervention group as compared to control despite weight loss differences can be related to metabolic heterogeneity within the groups. Presumably both groups included persons with a healthy metabolism as well as persons with disturbed metabolism. A person who is metabolically healthy at baseline may show fewer improvements in contrast to a person that has a higher risk for developing comorbidities ( 29 ). This difference may be further exaggerated by the higher dropout rate in the control group. Possibly, people who did not experience improvements were less motivated to continue with the program, leaving a relatively high degree of participants with beneficial changes in the control group. The lower standard deviation (i.e. lower variation) within the control participants may reflect this. Importantly, it should be taken into account that the intervention group is compared to a control group that followed an already proven effective program. The SLIMMER program, and in particular the personalised SLIMMER program, led to minor, but favourable alterations in low-grade inflammatory markers. The acute-phase protein SAA is a clinically useful marker of inflammation ( 30 ). Plasma concentrations of SAA in healthy participants are usually very low, but increase in response to inflammation ( 30 ). In the present study, SAA concentrations at baseline are above the threshold for healthy people and remained elevated over the course of the intervention in both groups, which indicates ongoing (low-grade) inflammation. In the intervention group, serpin A1/AAT ( 31 ), leptin ( 32 ), E-selectin ( 33 ), hsCRP ( 31 ) and adiponectin ( 34 ) levels improved, whereas in the control group E-selectin and adiponectin improved, while resistin ( 35 ) deteriorated. This suggests that specially the personalised SLIMMER program resulted in a slightly improved inflammatory state. This may be explained by the difference in body weight loss. However, multiple inflammatory markers including TNF-α, haptoglobin, MPO, IP-10, SAA and IFN-γ did not change during the 6 months intervention in neither group. Therefore, the inflammatory results should be carefully interpreted and need further follow-up, e.g. in a longer study period or using more inflammatory parameters. The strength of this study is that it was a real-life study that took place in a primary care setting and included all study participants that were referred by their GP to follow the SLIMMER program either with or without personalisation. This therefore reflects the real population participating in this CLI program. There was a close collaboration with the healthcare professionals from the different SLIMMER practices, who were trained by researchers in motivational interviewing, using the 360 degrees diagnosis and providing motivational exercises. Using this method, interindividual differences between treatment of participants was kept to a minimum. Study limitations include the inability to assess what part of personalisation contributed to the observed results, e.g., whether it was the enhanced diagnosis at baseline or the personalised behaviour support that contributed most to the difference in weight loss and program retention outcomes. Another limitation is, that incomplete information was collected by the health care professionals on compliance to the SLIMMER program, e.g. how well participants participated in the different appointments. This together with the large number of dropouts or persons that not meet ITT criteria in the control hindered per protocol evaluation of the data. Lastly, as it was not feasible to use the SQUASH algorithm to compare the amount of quantitative physical activity between intervention and control group, these results were not presented and not taken into account in the conclusion. Activity tracker data within the intervention group showed increased physically active behaviour especially in the month before start of the SLIMMER intervention, which was not maintained in the six months of the intervention itself ( 36 ). This data, however, was not available for control. It is a limitation that we did not have reliable information on quantitative amount of physical activity. The current study was performed during the COVID pandemic, which may have influenced study outcomes and hamper interpretation of the results. To evaluate this effect, a questionnaire was filled in by the intervention participants at their 6-months visit. Most participants indicated, that COVID did not influence their changes in diet or exercise. Some participants reported a negative influence of the COVID pandemic on participating in the SLIMMER program, due to closing of sport facilities or increased stress. The control group participated in the same study period and it is, therefore assumed they were similarly affected by COVID-regulations. So, it can be concluded that found effects can be extrapolated to a period without COVID-regulations. In conclusion, in this controlled intervention study, the personalized SLIMMER program improved body weight, BMI and body fat percentage, more than in the regular SLIMMER program. Major finding was the remarkable lower dropout rate in the intervention group, suggesting that the personalized approach resulted in increased program retention. The personalized intervention included increased individual attention, blended behavior change support and self-monitoring which may have contributed to the improved retention. The higher extent in body weight loss and retention was however not explained by improved qualitative and quantitative measurements related to self-reported adherence, self-efficacy and maintenance for lifestyle advice. In contrast, self-rated adherence to physical activity guidelines and Quality of Life improve to a higher extent in control at 6 months. Albeit differences in weight loss this did not translate into additional beneficial changes in cardiometabolic outcomes for the personalized SLIMMER within this six months’ time period. Taken into account that the personalized intervention group was compared to a control group that followed an already proven effective lifestyle program, observed effects may stress the importance of personalization of a CLI since the effects related to weight loss and body composition are clinically relevant. Methods Recruitment, study population and treatment allocation In November 2020 the study was approved by the Dutch Medical Ethics Committee (METC Brabant, NL75482.028.20) and registered at www.onderzoekmetmensen.nl (Dutch trial database) on the 11th of December 2020 with ID NL9145 ( https://onderzoekmetmensen.nl/nl/trial/22186 ). Participants in this study were referred by their general practitioner (GP) to participate in the SLIMMER program. Inclusion criteria for the SLIMMER program were men and women aged 18–70 years with a BMI > 25 kg/m 2 or an increased waist circumference (women > 80 cm, men > 90 cm) and at risk or having cardiometabolic disease (CVD and/or T2D) as determined by the GP, and people living with obesity (BMI > 30 kg/m 2 ). Exclusion criteria for the SLIMMER program were psychosocial problems interfering with complying to the program. For the intervention group, additional exclusion criteria included a planned surgery during the study period, regular use of anti-inflammatory drugs, corticosteroids, TNF-α blockers and salicylates and having a chronic inflammatory disease or bowel disease. For the complete overview of in- and exclusion criteria, see https://onderzoekmetmensen.nl/nl/trial/22186 . Written informed consent was obtained from all participants. Participants were recruited from the area of Utrecht and Zutphen, the Netherlands, between March 2021 and November 2022. Participants were allocated to intervention or control group based on their primary care centre. Per study group four different healthcare professionals provided participants for the study. The study period took place during the COVID-19 pandemic, where the Dutch government took different measures such as lock downs and restrictions for sport facilities or group based activities, thereby also affecting the SLIMMER program. Trial design and intervention groups This was an open label cluster allocated controlled parallel study with an intervention and a control arm (Fig. 1 ). In the current study, participants were followed during the first six months of the two-year SLIMMER program ( 21 ), which consisted of group sessions about nutrition and physical activity, and three to five individual appointments with a dietician. Participants in the control group followed the regular SLIMMER program, where for study purposes blood was drawn in overnight fasted state at a local clinical lab (Saltro) and anthropometrics and blood pressure (Medisana MTX) were measured by the involved healthcare professionals at baseline (t = 0) and at six months. All participants were referred to the SLIMMER program by their GP started during the study period (between March 2021 and November 2022) at different time-points. Participants filled in multiple online questionnaires at baseline, at t = 3 and t = 6 months. These included the EQ-5D to determine quality of life ( 22 ), the Eetscore to assess diet quality based on the Dutch Healthy Diet Index (DHDI) ( 23 ), a questionnaire on subjective compliance and effort and an appreciation questionnaire (see Supplementary Materials for full details). The intervention group followed the SLIMMER program to which validated personalisation tools were added (see personalised SLIMMER intervention). Participants came to the research facility one month before the start of the SLIMMER program after an overnight fast. The day before the visit, participants had to consume a low fat evening meal and had to refrain from alcohol and exercise. At the test day, anthropometrics were measured, followed by the PhenFlex test (PFT). Participants could self-monitor body weight, physical activity and food intake during the entire study. At t = 3 months, participants were asked to have blood drawn in a fasted state at a local clinical lab (Saltro). At t = 6 months, the test day at the research facility was repeated. Personalised SLIMMER intervention The following validated personalization tools were used for the personalised SLIMMER intervention: 1) Subtyping based on a PFT; a mixed-meal challenge test (PFT, FoodPilot, Melle, Belgium), validated against an oral glucose tolerance test, was included to determine the IR subtype ( 17 ). First, a venous canula was placed, from which fasted blood samples were taken. Next, participants consumed the PFT, after which blood was drawn at 30, 60, 120, and 240 min after start of consumption ( 17 ). Based on plasma glucose and insulin measurements, it was calculated whether participants had (pre)diabetes, and if so, which organs were affected ( 19 ). Based on the IR subtype, a personalised dietary and/or physical activity advice was given at the start of the SLIMMER program ( 19 ). 2) 360 degrees diagnosis; the 360 degree diagnosis tool is described extensively elsewhere ( 24 ). In short, the 360 degrees diagnosis is a web-based tool for shared-decision making between patient and health care professional based on a holistic diagnosis covering the 4 domains body, thinking and feeling, behaviour and environment. The domain body focuses on physiological health and included biomedical data such as HbA1c, blood lipids and blood pressure. Outcomes of the domains focusing on psychological health, lifestyle behaviour and socio-economic factors were the output of validated online questionnaires filled in by the participant. The 360 degree diagnosis tool is composed of a total of 20 factors divided over the 4 domains. For each domain factor evidence-based cut-offs were developed based on decision rules allowing for categorization as unhealthy (red), healthy (green) or in-between (orange). The 360 degree diagnosis was included at t = 0 and t = 6 for shared decision making between participants and healthcare professionals, to set personal lifestyle goals (t = 0) and to evaluate outcomes related to physiological health, thinking and feeling, lifestyle behaviour and environment. 3) Self-monitoring; participants were equipped with a Fitbit Charge 4 activity tracker (Fitbit Charge 4; Fitbit Inc., San Fransisco, CA, USA) and a smart-scale (Fitbit Aria Air; Fitbit Inc., San Francisco, CA, USA), and were stimulated to use a food-diary app for food-logging (FatSecret made available via the HowAmI-application, TNO, Leiden, the Netherlands). In this way, participants could monitor their physical activity (step count), food intake and body weight for their own motivation. 4) Personalised behavioural support (blended care format); participants had access to personalised behavioural change support via the HowAmI-app (TNO, Leiden, The Netherlands), where participants could set personal lifestyle goals. Participants received notifications for data-entering. Goal achievement could be tracked daily, which included feedback and reinforcement. Data from the activity tracker, food-logging app and smart scale was automatically synchronized to a personal internet portal. This portal was used during coaching sessions to evaluate personal lifestyle goals and to assess possible barriers in goal adherence and to adapt goals if desired. Furthermore, it served as input for dynamic tailoring during coaching sessions between participant and healthcare professional which took place at t = 6 weeks, t = 12 weeks, t = 18 weeks and t = 24 weeks. Healthcare professionals were trained in interpreting the results from the 360 degrees diagnosis and in motivational interviewing, and were supported with behavioural change exercises to support participants with enhancing motivation, self-efficacy, planning, problem solving, relieving stress, arranging social support, or improving mood. Measurement of study outcomes Adherence to lifestyle advice The primary outcome of the current study was adherence to lifestyle advice. Qualitative adherence to the dietary and physical activity guidelines was assessed at months 0, 3 and 6 by questions ”are you currently exercising regularly” and “do you currently follow a healthy diet” from the subjective compliance and effort questionnaire (7 points Likert scale; see Supplementary Materials). Quantitative adherence to Dutch Healthy Eating Index was assessed using the DHDI 2015 at months 0, 3 and 6 ( 23 ). Self-efficacy for aspects of the program was assessed based on questions 1–9 of the subjective compliance and effort questionnaire evaluated on months 0, 3 and 6 (7 points Likert scale; see Supplementary Materials). Finally, maintenance of lifestyle behaviour change was evaluated based on question 16 related to diet, question 17 related to physical activity and question 22 related to maintaining your own goal from the subjective compliance and effort questionnaire (7 points Likert scale; see Supplementary Materials). Quantitative assessment of the amount of physical activity was done at baseline, t = 3 and t = 6 months, based on the SQUASH questionnaire ( 25 ). During analysis of the results, it appeared that the SQUASH questionnaires for the intervention and control groups differed on one crucial item. This made it impossible to use the SQUASH algorithm to reliably compare amount and intensity of physical activity between the groups. Therefore, these results are not presented here. Quality of Life and appreciation of combined lifestyle intervention program Quality of life was assessed using the EQ-5D questionnaire at months 0, 3 and 6. Overall appreciation, as well as appreciation for diverse specific aspects of the program was evaluated at 6 months using a scale from 1–10 (question 1) and a 7-points Likert scale (questions 2–13 of the appreciation questionnaire), respectively (see Supplementary Materials). Anthropometry and blood pressure measurements At the test day, anthropometrics were executed, including blood pressure (Medisana MTX) measurements and body weight and body composition measurements using the InBody770 (InBody Co., Ltd., Korea). Length, waist and hip circumference were measured using a measuring tape according to a standard operating procedure. Measurement of biomarkers To assess changes in metabolism, multiple clinical chemistry biomarkers including concentrations of glucose, insulin, glycated hemoglobin (HbA1c), total cholesterol, high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), triglycerides (TG), creatinine, albumin, estimated glomerular filtration rate (eGFR), alkaline phosphatase (AF), gamma-glutamyl transferase (GGT), alanine transaminase (ALAT) and aspartate transaminase (ASAT) were quantified in plasma following standard operating procedures. Furthermore, plasma biomarkers related to inflammation were measured and included the cytokines interleukin 6 (IL-6), tumor necrosis factor α (TNF-α) and interferon γ (IFN-γ) (MesoScale Discovery ‘human proinflammatory panel I’, Rockville, Maryland, USA) as well as resistin (DY1359), leptin (DY398), adiponectin (DY1065), high-sensitive C-reactive protein (hsCRP; DY1707), IFN-γ-induced protein 10 (IP-10; DIP100), myeloperoxidase (MPO; DY3174), E-selectin (DY724), serpin A1/alpha-1-antitrypsin (AAT; DY1268), haptoglobin (DY8465-05) and serum amyloid A1 (SAA; DY3019) as measured by ELISAs (with antibody sets from R&D Systems, Abingdon, UK). Sample size Sample size was calculated using a significance level of 0.05, tested 2-sided giving Zα = 1.96; a power of 80%; 1-β = 0.80 giving Zβ = 0.842; the expected deviation (as a measure of variance or distribution) is σ = 1. The compliance scale is a Likert-scale of 1–7); the expected effect, being the difference in compliance between intervention and control, δ = 0.6 (based upon scores as found in the study of (Doets, et al., 2019); Table 3 ): $$\:\frac{{(Z\alpha\:+Z\beta\:)}^{2}\bullet\:\:{\sigma\:}^{2}}{{\delta\:}^{2}}\bullet\:2=\:\frac{{\left(1.96+0.842\right)}^{2}\bullet\:{1}^{2}}{{0.6}^{2}}\bullet\:2=44\:subjects\:per\:condition$$ Taking into account possible dropouts, n = 60 subjects were recruited for each arm. Statistical analysis Statistical analyses were done according to the intention-to-treat principle, in which participants were included from whom > 80% of baseline data was available, with p -values < 0.05 as statistically significant. Exploratory factor analysis was used to determine self-efficacy for the followed programs (based on questions 1–9 from subjective compliance and effort questionnaire, see Supplementary Materials). Scree plots, eigenvalues and parallel analysis all suggested the use of two factors. Exploratory factor analysis revealed that outcomes from the self-efficacy questions related to diet and related to physical activity were highly related. Chronbach’s alpha for items related to diet was 0.91, and Chronbach’s alpha for items on physical activity was 0.92. Self-efficacy factors for diet and for physical activity respectively were calculated by taking the mean of items with factor scores larger than 0.3. These two self-efficacy factors were used for statistical analysis with linear mixed-effects models. Linear mixed-effects models were fitted using the lmer function from the lmerTest package ( 26 ). Two models were constructed for each variable: The default model comprised fixed effects of timepoint and group, and a random intercept for participants ( 1 ). The covariate model included fixed effects of timepoint, group, as well as HbA1c and fasting glucose, along with a random intercept for participants ( 2 ). The response variable in all models was the natural logarithm of the value, and models were fitted using the Restricted Maximum Likelihood (REML) estimation. Analysis of Variance (ANOVA) was utilized to compare the default model against the full model, with p -values determining statistical significance. In most cases, the default model was the preferred model. For analyses of body weight, self-efficacy for physical activity, self-efficacy for diet, DHDI, EQ-5D, BMI, body fat percentage, TG, eGFR and E-selectin the covariate model was used, because there was a better fit. Samples with absolute standardized residuals exceeding 3 were excluded from the final model. Any results reported for HbA1c and fasting glucose were obtained using the default model. Contrasts were generated from estimated marginal means to study the interaction of timepoint and group and pairwise comparisons between both timepoints and groups. Wilcoxon tests were performed for inflammatory biomarkers that did not meet the criteria for using linear mixed-effects models to assess differences between groups and timepoints, using the rstatix package ( 27 ). Three different comparisons were made: ( 1 ) changes in inflammatory biomarkers were calculated for each participant by subtracting baseline values from follow-up values. Cases with missing values were excluded from further analysis. Wilcoxon tests were then applied to change in values to compare different groups. ( 2 ) Paired Wilcoxon tests were used to compare values between the two timepoints within each group. ( 3 ) Unpaired Wilcoxon tests were utilized to compare values between different groups at each timepoint. Finally, appreciation question outcomes were analysed using two-sided Student’s T-test, since data were only available at the 6 months timepoint. Abbreviations AAT alpha-1-antitrypsin AF alkaline phosphatase ALAT alanine transaminase ANOVA Analysis of variance ASAT aspartate transaminase BMI body mass index BP blood pressure CLI Combined lifestyle intervention CVD cardiovascular diseases DHDI Dutch Healthy Diet Index eGFR estimated glomerular filtration rate GGT gamma-glutamyl transferase GP general practitioner HbA1c glycated hemoglobin HDL high density lipoprotein hsCRP high-sensitive C-reactive protein IFN-γ interferon γ IL interleukin IP-10 IFN-γ-induced protein 10 IR insulin resistance LDL low density lipoprotein MPO myeloperoxidase PFT PhenFlex test QoL Quality of life REML Restricted Maximum Likelihood SAA serum amyloid A SLIMMER SLIM iMplementation Experience Region Noord- en Oost-Gelderland T2D Type 2 diabetes TG Triglycerides TNF-α tumor necrosis factor α Declarations Funding The study was sponsored by internal funding. Author Contribution Conceptualization: J.E.O., I.M.d.H., P.v.E., W.J.P., and S.W.; Methodology: J.E.O., I.M.d.H., R.K., P.v.E., W.J.P., and S.W.; Investigation: J.E.O, D.S., R.J.M.K., I.M.d.H., J.t.B., and W.J.P.; Data analysis: A.H.J.H. and T.J.v.d.B.; Data Curation: M.P.M.C. and T.J.v.d.B.; Interpretation of data: J.E.O., D.J.S, R.J.M.K., I.M.d.H., R.K., P.v.E., J.t.B., W.J.P. and S.W.; Writing—Original Draft Preparation: J.E.O., D.J.S and S.W., Writing — review and editing: all authors; Visualization, T.J.v.d.B. All authors have read and agreed to the published version of the manuscript. Acknowledgement We greatly acknowledge the healthcare professionals of the SLIMMER practices for their effort in the study execution: Wytse Brongers, Lilly Alefs, Mirte Breumelhof, Bea Pinkert, Wilma Meijer and Ingrid van der Linden (intervention group practices) and Thomas Kalkman, Annelies Dormans, Marjolein Coers and Donna Lischer (control group practices). Furthermore, Hilde van Keulen, Kim Kranenborg, Jessica Snabel, Remon Dulos, Ferry Jagers, Eugene van Someren, Gino Kalkman, Marlies Otto, Bowien van Leijden, Floris Dekker, Nicole Plomp, Angelique Speulman, Kerstin Schorr and Ilse van Dijk are acknowledged for their contribution in the preparations and execution of the study. Lastly, we thank all study participants for their efforts. Data Availability The datasets presented in this publication are available upon reasonable request. Requests to access the datasets should be directed to the corresponding author. The data are being stored in the phenotype database (https://dashin.eu/interventionstudies/), which is a data repository for clinical studies that makes use of ontologies and the principles of F.A.I.R (findable, accessible, interoperable and reusable) to allow for reuse of data. References DeFronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care. 1991;14(3):173-94. Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343-50. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. 2002. Herder C, Peltonen M, Koenig W, Sütfels K, Lindström J, Martin S, et al. Anti-inflammatory effect of lifestyle changes in the Finnish Diabetes Prevention Study. Diabetologia. 2009;52(3):433-42. Duijzer G, Haveman-Nies A, Jansen SC, Beek JT, van Bruggen R, Willink MGJ, et al. Effect and maintenance of the SLIMMER diabetes prevention lifestyle intervention in Dutch primary healthcare: a randomised controlled trial. Nutr Diabetes. 2017;7(5):e268. Leung AWY, Chan RSM, Sea MMM, Woo J. An Overview of Factors Associated with Adherence to Lifestyle Modification Programs for Weight Management in Adults. Int J Environ Res Public Health. 2017;14(8). RIVM. Monitor Gecombineerde Leefstijlinterventie 2023. [Available from: www.rivm.nl/documenten/monitor-gecombineerde-leefstijlinterventie-2023] van Ommen B, Wopereis S, van Empelen P, van Keulen HM, Otten W, Kasteleyn M, et al. From Diabetes Care to Diabetes Cure-The Integration of Systems Biology, eHealth, and Behavioral Change. Front Endocrinol (Lausanne). 2017;8:381. Reinders MJ, Starke AD, Fischer ARH, Verain MCD, Doets EL, Van Loo EJ. Determinants of consumer acceptance and use of personalized dietary advice: A systematic review. Trends in Food Science & Technology. 2023;131:277-94. Dominika Kwasnicka SUDMW, Falko S. Theoretical explanations for maintenance of behaviour change: a systematic review of behaviour theories. Health Psychology Review. 2016;10(3):277--96. Jinnette R, Narita A, Manning B, McNaughton SA, Mathers JC, Livingstone KM. Does Personalized Nutrition Advice Improve Dietary Intake in Healthy Adults? A Systematic Review of Randomized Controlled Trials. Advances in Nutrition. 2021;12(3):657-69. Celis-Morales C, Livingstone KM, Marsaux CF, Macready AL, Fallaize R, O'Donovan CB, et al. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int J Epidemiol. 2017;46(2):578-88. Lau Y, Wong SH, Chee DGH, Ng BSP, Ang WW, Han CY, Cheng LJ. Technology‐delivered personalized nutrition intervention on dietary outcomes among adults with overweight and obesity: A systematic review, meta‐analysis, and meta‐regression. Obesity Reviews. 2024;25(5):e13699. Robertson S, Clarke ED, Gómez-Martín M, Cross V, Collins CE, Stanford J. Do Precision and Personalised Nutrition Interventions Improve Risk Factors in Adults with Prediabetes or Metabolic Syndrome? A Systematic Review of Randomised Controlled Trials. Nutrients. 2024;16(10):1479. Wang L, Miller LC. Just-in-the-Moment Adaptive Interventions (JITAI): A Meta-Analytical Review. Health Commun. 2020;35(12):1531-44. van den Broek TJ, Bakker GCM, Rubingh CM, Bijlsma S, Stroeve JHM, van Ommen B, et al. Ranges of phenotypic flexibility in healthy subjects. Genes Nutr. 2017;12:32. Wopereis S, Stroeve JHM, Stafleu A, Bakker GCM, Burggraaf J, van Erk MJ, et al. Multi-parameter comparison of a standardized mixed meal tolerance test in healthy and type 2 diabetic subjects: the PhenFlex challenge. Genes Nutr. 2017;12:21. Trouwborst I, Gijbels A, Jardon KM, Siebelink E, Hul GB, Wanders L, et al. Cardiometabolic health improvements upon dietary intervention are driven by tissue-specific insulin resistance phenotype: A precision nutrition trial. Cell Metab. 2023;35(1):71-83 e5. de Hoogh IM, Oosterman JE, Otten W, Krijger AM, Berbee-Zadelaar S, Pasman WJ, et al. The Effect of a Lifestyle Intervention on Type 2 Diabetes Pathophysiology and Remission: The Stevenshof Pilot Study. Nutrients. 2021;13(7). de Hoogh IM, Pasman WJ, Boorsma A, van Ommen B, Wopereis S. Effects of a 13-Week Personalized Lifestyle Intervention Based on the Diabetes Subtype for People with Newly Diagnosed Type 2 Diabetes. Biomedicines. 2022;10(3):643. GGD-NOG. [Available from: www.nogslimmer.nl]. Rabin R, Charro Fd. EQ-5D: a measure of health status from the EuroQol Group. Annals of medicine. 2001;33(5):337-43. van Lee L, Feskens EJ, Meijboom S, van Huysduynen EJH, van’t Veer P, de Vries JH, Geelen A. Evaluation of a screener to assess diet quality in the Netherlands. British Journal of Nutrition. 2016;115(3):517-26. Harakeh Z, de Hoogh IM, van Keulen H, Kalkman G, van Someren E, van Empelen P, Otten W. 360° Diagnostic Tool to Personalize Lifestyle Advice in Primary Care for People With Type 2 Diabetes: Development and Usability Study. JMIR Form Res. 2023;7:e37305. Wendel-Vos GC, Schuit AJ, Saris WH, Kromhout D. Reproducibility and relative validity of the short questionnaire to assess health-enhancing physical activity. J Clin Epidemiol. 2003;56(12):1163-9. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software. 2017;82(13):1 - 26. Kassambara A. Pipe-Friendly Framework for Basic Statistical Tests [R package rstatix version 0.7.2]. 2020. Burgess E, Hassmén P, Welvaert M, Pumpa KL. Behavioural treatment strategies improve adherence to lifestyle intervention programmes in adults with obesity: a systematic review and meta-analysis. Clin Obes. 2017;7(2):105-14. Fiamoncini J, Rundle M, Gibbons H, Thomas EL, Geillinger-Kästle K, Bunzel D, et al. Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements. Faseb j. 2018;32(10):5447-58. Carbone T, Pafundi V, Schievano C, Assunta D, Padula MC, Giordano M, et al. Serum amyloid A in healthy subjects: assessment of reference value using ELISA method. J Immunoassay Immunochem. 2021;42(2):129-37. van Bilsen JHM, van den Brink W, van den Hoek AM, Dulos R, Caspers MPM, Kleemann R, et al. Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue. Front Physiol. 2021;12:703370. van Dielen FMH, van ‘t Veer C, Buurman WA, Greve JWM. Leptin and Soluble Leptin Receptor Levels in Obese and Weight-Losing Individuals. The Journal of Clinical Endocrinology & Metabolism. 2002;87(4):1708-16. Simons N, Bijnen M, Wouters KAM, Rensen SS, Beulens JWJ, van Greevenbroek MMJ, et al. The endothelial function biomarker soluble E-selectin is associated with nonalcoholic fatty liver disease. Liver Int. 2020;40(5):1079-88. Ukkola O, Santaniemi M. Adiponectin: a link between excess adiposity and associated comorbidities? J Mol Med (Berl). 2002;80(11):696-702. Recinella L, Orlando G, Ferrante C, Chiavaroli A, Brunetti L, Leone S. Adipokines: New Potential Therapeutic Target for Obesity and Metabolic, Rheumatic, and Cardiovascular Diseases. Front Physiol. 2020;11:578966. Braem CIR, Pasman WJ, van den Broek TJ, Caspers MPM, Jagers FLPW, Yavuz US et al. The association of physical activity, heart rate and sleep from an activity tracker with weight loss during a 6-month personalized combined lifestyle intervention: a retrospective analysis. BMC Digit Health. 2025; 3:8. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable.docx Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Jun, 2025 Reviews received at journal 13 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviews received at journal 28 May, 2025 Reviews received at journal 20 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 08 May, 2025 Editor assigned by journal 29 Apr, 2025 Editor invited by journal 28 Apr, 2025 Submission checks completed at journal 28 Apr, 2025 First submitted to journal 25 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6530079","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":453840252,"identity":"c2f84a41-8686-4e58-9e39-fdcfe6ac4c5b","order_by":0,"name":"Johanneke E. Oosterman","email":"","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":false,"prefix":"","firstName":"Johanneke","middleName":"E.","lastName":"Oosterman","suffix":""},{"id":453840253,"identity":"026cbaaf-977e-4d2b-af64-3dd7350a266b","order_by":1,"name":"Dagmar J. Smid","email":"","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":false,"prefix":"","firstName":"Dagmar","middleName":"J.","lastName":"Smid","suffix":""},{"id":453840254,"identity":"1acc9048-b100-4e10-a785-fe8565f83728","order_by":2,"name":"Regina J.M. Kamstra","email":"","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":false,"prefix":"","firstName":"Regina","middleName":"J.M.","lastName":"Kamstra","suffix":""},{"id":453840255,"identity":"83600dca-ab51-4a51-bc13-d301af7bc1e3","order_by":3,"name":"Iris M. de Hoogh","email":"","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":false,"prefix":"","firstName":"Iris","middleName":"M.","lastName":"de Hoogh","suffix":""},{"id":453840256,"identity":"0dca4af1-16a8-4637-88ed-1e8403439089","order_by":4,"name":"Arjan H.J. Huizing","email":"","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":false,"prefix":"","firstName":"Arjan","middleName":"H.J.","lastName":"Huizing","suffix":""},{"id":453840257,"identity":"9d2fbcc4-b709-4021-850c-f3ec1ed8ba40","order_by":5,"name":"Martien P.M. Caspers","email":"","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":false,"prefix":"","firstName":"Martien","middleName":"P.M.","lastName":"Caspers","suffix":""},{"id":453840258,"identity":"9fdc7e4f-b195-4b28-bbd8-1fff70c73ab6","order_by":6,"name":"Tim J. van den Broek","email":"","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":false,"prefix":"","firstName":"Tim","middleName":"J. van den","lastName":"Broek","suffix":""},{"id":453840259,"identity":"4e1b9355-0c70-4d54-8995-a5a6cec179a6","order_by":7,"name":"Robert Kleemann","email":"","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Kleemann","suffix":""},{"id":453840260,"identity":"e5822906-ab87-4a1a-ad25-9e80f41f428a","order_by":8,"name":"Pepijn van Empelen","email":"","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":false,"prefix":"","firstName":"Pepijn","middleName":"van","lastName":"Empelen","suffix":""},{"id":453840261,"identity":"b1267a5e-d4b3-4c59-83f1-69a25c0105f8","order_by":9,"name":"Josien ter Beek","email":"","orcid":"","institution":"GGD Noord- and Oost-Gelderland (Community Health Service)","correspondingAuthor":false,"prefix":"","firstName":"Josien","middleName":"ter","lastName":"Beek","suffix":""},{"id":453840262,"identity":"e4c65ced-6f0b-4268-b8a4-5323cdeb86fa","order_by":10,"name":"Wilrike J. Pasman","email":"","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":false,"prefix":"","firstName":"Wilrike","middleName":"J.","lastName":"Pasman","suffix":""},{"id":453840263,"identity":"ca316183-75c7-4e58-8511-02434e3c6c72","order_by":11,"name":"Suzan Wopereis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYNCDBAYbCOMBCVrSJKAM4sFhwlrkZ6Q//FzAcFjOnL334YMHf87XmbM3sD3Ap8XgRkKy9AyGw8aWPceNDRLbbktY9hxgN8CrRSLhgDQPw+3EDTfS2CQSG25LAA1hk8DvsMTm32At95+x/0j4c46wFoYbyWxQW9jYGBLYDhDWYnDmGZs1j8F/oF/SmCUS25IlN5w52IbfYe3pj2/zVKQBQ+wY48cff+z4DY43H5P4gM9hELvACAYYGwhqgOkaBaNgFIyCUYAdAAAXf0qKeDNx/AAAAABJRU5ErkJggg==","orcid":"","institution":"Netherlands Organization for Applied Scientific Research (TNO)","correspondingAuthor":true,"prefix":"","firstName":"Suzan","middleName":"","lastName":"Wopereis","suffix":""}],"badges":[],"createdAt":"2025-04-25 15:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6530079/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6530079/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-27899-6","type":"published","date":"2025-12-23T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82620975,"identity":"682ac36f-d632-4c76-8e8e-48ca82060f0a","added_by":"auto","created_at":"2025-05-13 12:18:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design including an overview of study procedures of the intervention and control group.\u003c/strong\u003e T=0 is referred to as baseline, t=6 is end of study.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6530079/v1/d7c0dd33daccc1c718eee784.jpg"},{"id":82622187,"identity":"2a63126b-3344-45ea-8a42-19a378d59b2d","added_by":"auto","created_at":"2025-05-13 12:26:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":342286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow diagram.\u003c/strong\u003e Dropouts are defined as participants discontinuing the study or were lost to follow-up. Number of participants analysed is based on the intention-to-treat (ITT) principle in which participants are included in the analysis of whom \u0026gt;80% of baseline data was available. CLI; combined lifestyle intervention.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6530079/v1/7b438b798a04350636298e7d.jpg"},{"id":82620976,"identity":"6f2c3327-47a6-4f1b-b7ff-354a972f46af","added_by":"auto","created_at":"2025-05-13 12:18:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51780,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelf-reported adherence to combined lifestyle program. \u003c/strong\u003eSelf-reported adherence to physical activity guidelines (A, left) and dietary guidelines (A, right) of intervention (blue) and control (red) participants over the course of the program. Grey circles represent the fraction size expressed as percentage. Total Dutch Healthy Diet Index (DHDI) score (B) for intervention (blue) and control (red) participants. X-axis: months after start intervention. Y-axis: mean DHDI score (0-160, higher score is better diet quality) ± SE. *p\u0026lt;=0.05; **p\u0026lt;=0.01; ***p\u0026lt;=0.001; a interaction effect p\u0026lt;=0.05.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6530079/v1/41211b8280ffe775e17553e7.jpg"},{"id":82620985,"identity":"9f26be2e-250e-4ce3-bd43-7dbc28183319","added_by":"auto","created_at":"2025-05-13 12:18:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":276745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelf-efficacy for combined lifestyle program.\u003c/strong\u003e Self-efficacy for physical activity (left) and self-efficacy for diet (right) of intervention (blue) and control (red) over the course of the program. X-axis: months after start intervention. Y-axis: mean score on Likert scale (1-7) ± SE.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6530079/v1/017cf49583774d56f06de674.jpg"},{"id":82622636,"identity":"931b016c-c5a1-4adf-a69e-23c39ee612aa","added_by":"auto","created_at":"2025-05-13 12:34:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":278667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaintenance of lifestyle behaviour change.\u003c/strong\u003e Level of difficulty of conforming to advice about physical activity (left); about diet (middle); maintaining your own goal for a longer period of time (right) between intervention (blue) and control group (red). Grey circles represent the fraction size expressed as percentage, X-axis: months after start intervention. Y-as: mean score on Likert scale (1-7).\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6530079/v1/fe77262d6877a7aa936bb141.jpg"},{"id":82620983,"identity":"b53a00b9-87e0-4f5b-89cc-cf829fde5af2","added_by":"auto","created_at":"2025-05-13 12:18:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":53530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuality of life during study.\u003c/strong\u003e Quality of life scored using the EQ-5D questionnaire for the intervention (blue) and control group (red). X-axis: months after start of intervention. Y-axis: mean score (scale 0-100) ± SE. *p\u0026lt;=0.05; **p\u0026lt;=0.01; ***p\u0026lt;=0.001; a interaction effect p\u0026lt;=0.05; a interaction effect p\u0026lt;=0.05.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6530079/v1/ef97532199cde0b9a078c43c.jpg"},{"id":82620989,"identity":"70651a1a-1c7e-4e76-a99c-43b55491d794","added_by":"auto","created_at":"2025-05-13 12:18:10","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":822832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlasma inflammatory biomarkers.\u003c/strong\u003e Comparison of inflammatory biomarkers between intervention (blue) and control group (red) over 6 months intervention. CRP; C-reactive Protein, IFNg; Interferon- γ, Il; Interleukin, IP10; IFN-γ-induced protein 10, MPO; myeloperoxidase, SAA; serum amyloid A1, TNFa; tumor necrosis factor α, *p\u0026lt;=0.05; **p\u0026lt;=0.01; ***p\u0026lt;=0.001.\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6530079/v1/ebfeaed04dc0d5c0fc6f1131.jpg"},{"id":99172471,"identity":"e1e028a1-47cd-44fc-ad3d-167456c0ae16","added_by":"auto","created_at":"2025-12-29 16:10:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2837318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6530079/v1/0bbb7227-e220-42ad-8300-221cfcc008b9.pdf"},{"id":82620981,"identity":"f36be01a-fa8b-4bde-8d3a-dd75795ea091","added_by":"auto","created_at":"2025-05-13 12:18:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":157516,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-6530079/v1/ca7a5810ec205f3621fb220c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Personalisation of the Dutch combined lifestyle intervention SLIMMER improves participant retention and weight-loss in people at risk for cardiometabolic disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOverweight and obesity are risk factors for development of insulin resistance and type 2 diabetes (T2D) and are associated with a number of metabolic and cardiovascular risk factors (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Evidence is mounting that lifestyle changes can prevent or delay progression of metabolic disease. Previous studies showed that weight loss, changes in dietary intake, and increased physical activity resulted in long-term reduction in the incidence of T2D in persons with impaired glucose tolerance (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and a reduction of low-grade inflammation (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on these results, combined lifestyle intervention (CLI) programs have been implemented within primary care in the Netherlands and are reimbursed for people with increased cardiometabolic risk or living with obesity. SLIMMER is one of those CLI programs. In the two-year SLIMMER program, adults with overweight (BMI 25\u0026ndash;30 kg/m\u003csup\u003e2\u003c/sup\u003e) in combination with cardiometabolic risk factors and/or comorbidities, or living with obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) are supported to improve their lifestyle with respect to dietary intake, physical activity, sleep, stress and social habits. The SLIMMER program resulted in on average 2.7 kg and 2.5 kg weight loss after 12 and 18 months, respectively (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite promising results of CLI programs, adherence is difficult, as represented by high dropout rates in Dutch CLI programs (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). For instance, 26% of participants that started a CLI in the Netherlands did not complete the full program in the first year and another 35% of the participants stopped in the second year (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The primary reason for dropout was lack of motivation (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Personalisation, i.e. adapting lifestyle advice to an individual\u0026rsquo;s needs and preferences, may lead to better lifestyle advice adherence and contribute to achieving sustainable healthy lifestyle habits (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). For acceptance of lifestyle advice, using behavioural change techniques is important for effective communication of the personalised advice (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Key behavioural determinants to establish behavioural maintenance include intrinsic motivation, self-efficacy, and creating a supporting environment (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Effective behaviour change techniques that could impact these determinants include goal setting, self-monitoring, self-evaluation, and provision of feedback and advice based on personal goals or specific behaviours (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Multiple systematic reviews indicated that personalised nutritional behaviour-change interventions based on information of a person\u0026rsquo;s diet, biology and lifestyle improved both nutritional intake as well as health outcomes as compared to generalized nutritional advice (\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Furthermore, blended care, defined as a combination of face-to-face care and dynamically tailored eHealth support adapted to behavioural, psychosocial and contextual factors of a person, has been shown to be more effective for lifestyle changes than face-to-face care only (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the current study, the effect of a personalised SLIMMER program (intervention group) was assessed and compared to the regular SLIMMER program (control group) in a real-life primary care setting. On top of the regular SLIMMER CLI, personalisation included 1) personalised lifestyle advice based on (pre)diabetes subtyping 2) an extensive 360 degrees diagnosis, 3) personalised goal setting and monitoring, and 4) blended-care based personalised behaviour change support between the regular SLIMMER coaching sessions.\u003c/p\u003e \u003cp\u003eFor an extensive 360 degrees diagnosis, insight into contextual lifestyle factors is important. Here, the 360 degrees diagnosis was used as basis to personalise lifestyle advice and to set personal lifestyle goals in shared decision between participant and health care professional. Furthermore, personalised dietary and physical activity recommendations were made based on the participant\u0026rsquo;s insulin resistance (IR) subtype that arise from variations in genetics, phenotype, lifestyle and environment (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Research in primary healthcare practice showed that people with different IR subtypes benefit from differential dietary and physical activity recommendations (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), which resulted in remission and reversal rates of 33\u0026ndash;75% of type 2 diabetes depending on the severity and subtype of T2D (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In addition, individual self-monitoring, personalised goal setting, behavioural monitoring, feedback and problem-solving were incorporated to achieve behaviour change. The personalised, sustainable behaviour change was supported by a blended care format, including digital and physical coaching.\u003c/p\u003e \u003cp\u003eThe primary objective for this study was to assess effects of a personalised SLIMMER program on adherence to lifestyle advice, evaluated by qualitative and quantitative changes in lifestyle behaviour. The secondary objective was to assess effects of a personalised SLIMMER on health status, measured by well-being, anthropometrics, clinical metabolic parameters and biomarkers related to low-grade inflammation. It was hypothesized that the personalised SLIMMER program would result in better adherence to lifestyle advice and result in greater improvements in body-weight and cardiometabolic health outcomes as compared to the regular SLIMMER program.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eParticipant flow\u003c/h2\u003e\n\u003cp\u003eA total of 172 people were assessed for eligibility from which 27 people were excluded from the intervention group and 9 from the control group, because they declined to participate or did not meet inclusion criteria (Figure 2). A total of 66 and 70 participants were allocated to the intervention or control group, respectively. In both groups, some participants did not start, because they were not interested anymore or due to COVID-19. Eventually, 61 and 60 participants received the allocated treatment, in the intervention and control group, respectively. In the control group, 8 participants were lost to follow-up within the 6 months period. In the intervention group, no participants were lost to follow-up. However, in both the control and intervention group, some participants discontinued the SLIMMER program, and therefore were assigned as dropouts for our study. In the control group, 14 participants discontinued the SLIMMER program (reasons: time constraints, not interested anymore), whereas in the intervention group, 7 participants discontinued the intervention (reasons: SLIMMER program did not meet expectations, COVID). This dropout rate was significantly lower in the intervention group (11%) than in the control group (23%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Therefore, an intention-to-treat analysis was performed in which participants were included from whom \u0026gt;80% of baseline data was available. This resulted in 61 and 38 participants for analysis in the intervention and control group, respectively (Figure 2).\u003c/p\u003e\n\u003ch2\u003eBaseline characteristics\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTable 1 shows baseline characteristics of the intervention group (n = 61) and control group (n = 38). Average age of the participants was 49.6\u0026nbsp;\u0026plusmn;\u0026nbsp;11.4 and 48.8\u0026nbsp;\u0026plusmn;\u0026nbsp;12.6 years in the intervention and control group, respectively. For both the intervention (33.0\u0026nbsp;\u0026plusmn; 4.8 kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;and control group (34.6\u0026nbsp;\u0026plusmn; 5.3 kg/m\u003csup\u003e2\u003c/sup\u003e), mean BMI indicates that most participants had obesity. At baseline, BMI did not differ significantly between both groups (\u003cem\u003ep\u003c/em\u003e = 0.144). Body weight showed a significant difference (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.038) between the intervention group (95.5 \u0026plusmn; 15.0 kg) compared to the control group (103.0 \u0026plusmn; 16.9 kg). Fasting glucose and insulin did not differ between groups at baseline. However, there was a significant difference in HbA1c between intervention (36.4\u0026nbsp;\u0026plusmn; 10.5\u0026nbsp;mmol/mol) and control group (40.9\u0026nbsp;\u0026plusmn; 12.4\u0026nbsp;mmol/l, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eQualitative and quantitative assessment of lifestyle advice adherence\u0026nbsp;\u003c/h2\u003e\n\u003ch3\u003eQualitative adherence to physical activity and diet advice\u003c/h3\u003e\n\u003cp\u003eParticipants in the control and intervention group improved significantly on self-reported, qualitative adherence to the physical activity guideline at 3 and 6 months compared to baseline (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001 for both time points). However, the change from baseline to 6 months was significantly different in self-reported adherence to the physical activity guideline between control and intervention group, in favour of the control group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). With respect to adherence to the dietary guideline, participants in the control and intervention group improved significantly at 3 and 6 months compared to baseline (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001 for both time points). There was no interaction effect between the two intervention groups (Figure 3A).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eQuantitative adherence to Dutch Healthy Diet Index\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eAt baseline, there was no difference in diet quality as assessed by the DHDI between the intervention and control group (100.9 \u0026plusmn; 17.8 vs 94.0 \u0026plusmn; 18.5), corresponding to an adherence of 60% to the Dutch Dietary Guidelines in both groups. There was a significant improved change at 3 months and 6 months in the control group and in the intervention group (p \u0026lt; 0.05 for both time points). There was no interaction effect nor statistical difference between control and intervention group (Figure 3B). At 6 months after the start of the intervention, scores were 107.3 \u0026plusmn; 18.0 and 100.2 \u0026plusmn; 15.0 for intervention and control groups, respectively. There were significant changes over time in following food groups: whole-grain products (significantly higher score at 6 months, but not different between control and intervention group), dairy products (significant changes over time, higher score change at 6 months in favour of control group), red meat (increase in score at 3 months, with significant higher score change for control vs intervention group), and salt (increase in score at 3 months for both groups).\u003c/p\u003e\n\u003ch3\u003eSelf-efficacy\u003c/h3\u003e\n\u003cp\u003eThere was a significant difference (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) in user-rated self-efficacy scores between intervention and control group, both on physical activity (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.02) and diet (\u003cem\u003ep\u003c/em\u003e = 0.02). Self-efficacy in the intervention group was higher at baseline and persisted higher over time as compared to the control group. No interaction effect was observed between the two groups over time, indicating that self-efficacy over time was not differentially affected by the intervention (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eMaintenance of lifestyle behaviour change\u003c/h3\u003e\n\u003cp\u003eMaintenance of lifestyle behaviour change was determined by rating difficulty of maintaining the dietary and physical activity advice and the participant\u0026rsquo;s own goals. Maintenance of lifestyle behaviour change remained similar between 3 and 6 months of the intervention. There was no significant maintenance difference between the groups, nor was an interaction or time effect observed (Figure 5).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eQuality of life\u003c/h3\u003e\n\u003cp\u003eQuality of life (QoL) improved in both groups at 3 and 6 months compared to baseline (Figure 6, p\u0026lt;0.001). At baseline, QoL score was 72.9 \u0026plusmn; 5.4 in the control group and 75.3 \u0026plusmn;.3.4 in the intervention group. The intervention group did not score significantly different at baseline compared to control. At 3 months QoL score improved on average with 7.1 \u0026plusmn;.2.7 points. At 6 months, the control group showed a significant increase to 81.9 , while the QoL score in the intervention group was 75.7. The change from baseline to 6 months was significantly different between the two groups in favour of the control group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003ch3\u003eAppreciation of the lifestyle program\u003c/h3\u003e\n\u003cp\u003eOverall, appreciation of intervention and control programs were rated 6.9\u0026nbsp;\u0026plusmn;\u0026nbsp;1.6 and 7.0\u0026nbsp;\u0026plusmn;\u0026nbsp;1.1 (scale 1-10), respectively. There was no significant difference in appreciation items between the intervention and control group (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eComparison of clinical and metabolic measurements between intervention and control group\u003c/h2\u003e\n\u003cp\u003eBeneficial changes in clinical and metabolic measurements over 6 months were seen in both the intervention and control group (Table 3). In both groups, body weight decreased significantly, from 95.5 \u0026plusmn; 15.0 kg to 90.8 \u0026plusmn; 13.5 kg in the intervention group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and from 103.0 \u0026plusmn; 16.9 kg to 101.0 \u0026plusmn; 14.8 kg in the control group (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.007). Body weight loss was significantly higher in the intervention group compared to the control group (4.7 kg vs 2.0 kg; \u003cem\u003ep\u003c/em\u003e = 0.006). Body fat percentage and waist-hip ratio significantly changed in the intervention group only (-3.5 %; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 and \u003cem\u003ep\u003c/em\u003e = 0.025, respectively) and these changes over time were significantly different from the control group (\u003cem\u003ep\u003c/em\u003e = 0.002 and \u003cem\u003ep\u003c/em\u003e = 0.013, respectively). In both groups systolic blood pressure decreased, however the control group also showed a significant reduction in diastolic blood pressure (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), which was significantly different from the intervention group (\u003cem\u003ep\u003c/em\u003e = 0.011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFasting glucose significantly decreased in the intervention group (\u003cem\u003ep\u003c/em\u003e = 0.003), but this change over time was not significantly different from the control group (\u003cem\u003ep\u003c/em\u003e = 0.210). Insulin significantly decreased in the control group (\u003cem\u003ep\u003c/em\u003e = 0.002), but not in the intervention group (\u003cem\u003ep\u003c/em\u003e = 0.968), yet all within normal ranges. This change was significantly different between the groups (\u003cem\u003ep\u003c/em\u003e = 0.012). HbA1c showed a significant interaction effect between the two groups (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.009), with maintained concentrations in the intervention group, whereas concentrations in the control group non-significantly increased.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo changes in total, HDL and LDL cholesterol were found. Cholesterol ratio, TG and eGFR showed significant decreases in the intervention group over time (\u003cem\u003ep\u003c/em\u003e = 0.038, \u003cem\u003ep\u003c/em\u003e = 0.015 and \u003cem\u003ep\u003c/em\u003e =\u0026nbsp;0.003, respectively), which were not observed for the control group (\u003cem\u003ep\u003c/em\u003e = 0.133, \u003cem\u003ep\u003c/em\u003e = 0.205 and \u003cem\u003ep\u003c/em\u003e = 0.247, respectively). However, these changes did not differ significantly from the control group (\u003cem\u003ep\u003c/em\u003e = 0.915, \u003cem\u003ep\u003c/em\u003e = 0.584 and \u003cem\u003ep\u003c/em\u003e = 0.310, respectively).\u003c/p\u003e\n\u003ch3\u003eInflammation\u003c/h3\u003e\n\u003cp\u003eChanges in inflammatory biomarkers were seen for both groups (Table 4 \u0026amp; Figure 7). Leptin decreased significantly in the intervention group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), while leptin increased markedly, but not significant, in the control group. Adiponectin significantly increased in both groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for intervention group and \u003cem\u003ep\u003c/em\u003e = 0.012 for control group). Resistin increased in both groups at 6 months, but to a differential extent (\u003cem\u003ep\u003c/em\u003e = 0.006), with significantly increased concentrations in the control group and no significant increase in the intervention group. Furthermore, in the intervention group, but not in the control group, hsCRP (\u003cem\u003ep\u003c/em\u003e = 0.016) and AAT (\u003cem\u003ep\u003c/em\u003e = 0.002) significantly decreased from baseline to 6 months. E-selectin significantly decreased in both groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for intervention group, \u003cem\u003ep\u003c/em\u003e = 0.012 for control group).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe effects of personalisation of a proven effective CLI program (SLIMMER) was assessed on lifestyle advice adherence and clinical outcomes. The most important observation was the difference in dropouts between intervention and control group (p \u0026lt; 0.001), being 11% in the intervention group \u003cem\u003eversus\u003c/em\u003e 23% in the control group. Main reasons for premature dropout included time constraints and dissatisfaction with the program or group. The dropout percentage of 11% in the intervention is not only lower than the control group, but also lower than average dropout rates (26%) as reported for CLIs in the first year (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This suggests that personalisation prevents premature dropout of the lifestyle program and increases retention. A clear difference between the control and intervention group was the amount of contact and attention the participants received, which may contribute to these results. Interestingly, a lower dropout rate was not related to differences in adherence to lifestyle advice assessed by questionnaires on self-reported adherence, self-efficacy and maintenance to diet and physical activity advices. Self-reported adherence to the combined lifestyle program as well as the DHDI score improved in both groups at 3 and 6 months, whereas self-efficacy and maintenance to diet and physical activity advices did not change as a result of the intervention. For self-reported adherence to physical activity guidelines a greater change at 6 months was observed in favour of the control group. These results were surprising as it has repeatedly been shown that behavioural treatment strategies, with emphasis on self-efficacy, motivation and feedback - as was implemented in the personalised SLIMMER - improve adherence to lifestyle advice (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). QoL improved in both groups with approximately 8 points (11%), but this was mainly maintained at 6 months in the control group. A change in QoL of 8 points is in accordance to the average score for QoL of Dutch CLIs (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and clinically relevant.\u003c/p\u003e \u003cp\u003eAnother important observation is that the personalised SLIMMER program improved body weight, BMI and body fat percentage to a larger extent than the regular SLIMMER program. Body weight decreased with 4.7 kg (4.9%) in the intervention group versus 2 kg (1.9%) in the control group. Body weight loss in the intervention group is higher than average body weight loss after 6 months of any CLI in the Netherlands (3.4%), suggesting positive effect of personalisation. As overweight is associated with development of cardiometabolic diseases, this is an important and clinically relevant outcome.\u003c/p\u003e \u003cp\u003eSeveral biomarkers related to cardiometabolic health showed improved values in both groups after 6 months of the program, but did not show differences between both groups, except the differences in weight loss. A possible explanation for absence of clear improvements in cardiometabolic outcome parameters in the intervention group as compared to control despite weight loss differences can be related to metabolic heterogeneity within the groups. Presumably both groups included persons with a healthy metabolism as well as persons with disturbed metabolism. A person who is metabolically healthy at baseline may show fewer improvements in contrast to a person that has a higher risk for developing comorbidities (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This difference may be further exaggerated by the higher dropout rate in the control group. Possibly, people who did not experience improvements were less motivated to continue with the program, leaving a relatively high degree of participants with beneficial changes in the control group. The lower standard deviation (i.e. lower variation) within the control participants may reflect this. Importantly, it should be taken into account that the intervention group is compared to a control group that followed an already proven effective program.\u003c/p\u003e \u003cp\u003eThe SLIMMER program, and in particular the personalised SLIMMER program, led to minor, but favourable alterations in low-grade inflammatory markers. The acute-phase protein SAA is a clinically useful marker of inflammation (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Plasma concentrations of SAA in healthy participants are usually very low, but increase in response to inflammation (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In the present study, SAA concentrations at baseline are above the threshold for healthy people and remained elevated over the course of the intervention in both groups, which indicates ongoing (low-grade) inflammation. In the intervention group, serpin A1/AAT (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), leptin (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), E-selectin (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), hsCRP (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and adiponectin (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) levels improved, whereas in the control group E-selectin and adiponectin improved, while resistin (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) deteriorated. This suggests that specially the personalised SLIMMER program resulted in a slightly improved inflammatory state. This may be explained by the difference in body weight loss. However, multiple inflammatory markers including TNF-α, haptoglobin, MPO, IP-10, SAA and IFN-γ did not change during the 6 months intervention in neither group. Therefore, the inflammatory results should be carefully interpreted and need further follow-up, e.g. in a longer study period or using more inflammatory parameters.\u003c/p\u003e \u003cp\u003eThe strength of this study is that it was a real-life study that took place in a primary care setting and included all study participants that were referred by their GP to follow the SLIMMER program either with or without personalisation. This therefore reflects the real population participating in this CLI program. There was a close collaboration with the healthcare professionals from the different SLIMMER practices, who were trained by researchers in motivational interviewing, using the 360 degrees diagnosis and providing motivational exercises. Using this method, interindividual differences between treatment of participants was kept to a minimum. Study limitations include the inability to assess what part of personalisation contributed to the observed results, e.g., whether it was the enhanced diagnosis at baseline or the personalised behaviour support that contributed most to the difference in weight loss and program retention outcomes. Another limitation is, that incomplete information was collected by the health care professionals on compliance to the SLIMMER program, e.g. how well participants participated in the different appointments. This together with the large number of dropouts or persons that not meet ITT criteria in the control hindered per protocol evaluation of the data. Lastly, as it was not feasible to use the SQUASH algorithm to compare the amount of quantitative physical activity between intervention and control group, these results were not presented and not taken into account in the conclusion. Activity tracker data within the intervention group showed increased physically active behaviour especially in the month before start of the SLIMMER intervention, which was not maintained in the six months of the intervention itself (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This data, however, was not available for control. It is a limitation that we did not have reliable information on quantitative amount of physical activity.\u003c/p\u003e \u003cp\u003eThe current study was performed during the COVID pandemic, which may have influenced study outcomes and hamper interpretation of the results. To evaluate this effect, a questionnaire was filled in by the intervention participants at their 6-months visit. Most participants indicated, that COVID did not influence their changes in diet or exercise. Some participants reported a negative influence of the COVID pandemic on participating in the SLIMMER program, due to closing of sport facilities or increased stress. The control group participated in the same study period and it is, therefore assumed they were similarly affected by COVID-regulations. So, it can be concluded that found effects can be extrapolated to a period without COVID-regulations.\u003c/p\u003e \u003cp\u003eIn conclusion, in this controlled intervention study, the personalized SLIMMER program improved body weight, BMI and body fat percentage, more than in the regular SLIMMER program. Major finding was the remarkable lower dropout rate in the intervention group, suggesting that the personalized approach resulted in increased program retention. The personalized intervention included increased individual attention, blended behavior change support and self-monitoring which may have contributed to the improved retention. The higher extent in body weight loss and retention was however not explained by improved qualitative and quantitative measurements related to self-reported adherence, self-efficacy and maintenance for lifestyle advice. In contrast, self-rated adherence to physical activity guidelines and Quality of Life improve to a higher extent in control at 6 months. Albeit differences in weight loss this did not translate into additional beneficial changes in cardiometabolic outcomes for the personalized SLIMMER within this six months’ time period. Taken into account that the personalized intervention group was compared to a control group that followed an already proven effective lifestyle program, observed effects may stress the importance of personalization of a CLI since the effects related to weight loss and body composition are clinically relevant.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eRecruitment, study population and treatment allocation\u003c/p\u003e\u003cp\u003eIn November 2020 the study was approved by the Dutch Medical Ethics Committee (METC Brabant, NL75482.028.20) and registered at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.onderzoekmetmensen.nl\u003c/span\u003e\u003cspan address=\"http://www.onderzoekmetmensen.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Dutch trial database) on the 11th of December 2020 with ID NL9145 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onderzoekmetmensen.nl/nl/trial/22186\u003c/span\u003e\u003cspan address=\"https://onderzoekmetmensen.nl/nl/trial/22186\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eParticipants in this study were referred by their general practitioner (GP) to participate in the SLIMMER program. Inclusion criteria for the SLIMMER program were men and women aged 18–70 years with a BMI \u0026gt; 25 kg/m\u003csup\u003e2\u003c/sup\u003e or an increased waist circumference (women \u0026gt; 80 cm, men \u0026gt; 90 cm) and at risk or having cardiometabolic disease (CVD and/or T2D) as determined by the GP, and people living with obesity (BMI \u0026gt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e). Exclusion criteria for the SLIMMER program were psychosocial problems interfering with complying to the program. For the intervention group, additional exclusion criteria included a planned surgery during the study period, regular use of anti-inflammatory drugs, corticosteroids, TNF-α blockers and salicylates and having a chronic inflammatory disease or bowel disease. For the complete overview of in- and exclusion criteria, see \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onderzoekmetmensen.nl/nl/trial/22186\u003c/span\u003e\u003cspan address=\"https://onderzoekmetmensen.nl/nl/trial/22186\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Written informed consent was obtained from all participants. Participants were recruited from the area of Utrecht and Zutphen, the Netherlands, between March 2021 and November 2022. Participants were allocated to intervention or control group based on their primary care centre. Per study group four different healthcare professionals provided participants for the study. The study period took place during the COVID-19 pandemic, where the Dutch government took different measures such as lock downs and restrictions for sport facilities or group based activities, thereby also affecting the SLIMMER program.\u003c/p\u003e\u003cp\u003eTrial design and intervention groups\u003c/p\u003e\u003cp\u003eThis was an open label cluster allocated controlled parallel study with an intervention and a control arm (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the current study, participants were followed during the first six months of the two-year SLIMMER program (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), which consisted of group sessions about nutrition and physical activity, and three to five individual appointments with a dietician. Participants in the control group followed the regular SLIMMER program, where for study purposes blood was drawn in overnight fasted state at a local clinical lab (Saltro) and anthropometrics and blood pressure (Medisana MTX) were measured by the involved healthcare professionals at baseline (t = 0) and at six months. All participants were referred to the SLIMMER program by their GP started during the study period (between March 2021 and November 2022) at different time-points. Participants filled in multiple online questionnaires at baseline, at t = 3 and t = 6 months. These included the EQ-5D to determine quality of life (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), the Eetscore to assess diet quality based on the Dutch Healthy Diet Index (DHDI) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), a questionnaire on subjective compliance and effort and an appreciation questionnaire (see Supplementary Materials for full details). The intervention group followed the SLIMMER program to which validated personalisation tools were added (see personalised SLIMMER intervention). Participants came to the research facility one month before the start of the SLIMMER program after an overnight fast. The day before the visit, participants had to consume a low fat evening meal and had to refrain from alcohol and exercise. At the test day, anthropometrics were measured, followed by the PhenFlex test (PFT). Participants could self-monitor body weight, physical activity and food intake during the entire study. At t = 3 months, participants were asked to have blood drawn in a fasted state at a local clinical lab (Saltro). At t = 6 months, the test day at the research facility was repeated.\u003c/p\u003e\u003cp\u003ePersonalised SLIMMER intervention\u003c/p\u003e\u003cp\u003eThe following validated personalization tools were used for the personalised SLIMMER intervention: \u003cb\u003e1) Subtyping based on a PFT;\u003c/b\u003e a mixed-meal challenge test (PFT, FoodPilot, Melle, Belgium), validated against an oral glucose tolerance test, was included to determine the IR subtype (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). First, a venous canula was placed, from which fasted blood samples were taken. Next, participants consumed the PFT, after which blood was drawn at 30, 60, 120, and 240 min after start of consumption (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Based on plasma glucose and insulin measurements, it was calculated whether participants had (pre)diabetes, and if so, which organs were affected (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Based on the IR subtype, a personalised dietary and/or physical activity advice was given at the start of the SLIMMER program (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). \u003cb\u003e2) 360 degrees diagnosis;\u003c/b\u003e the 360 degree diagnosis tool is described extensively elsewhere (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In short, the 360 degrees diagnosis is a web-based tool for shared-decision making between patient and health care professional based on a holistic diagnosis covering the 4 domains body, thinking and feeling, behaviour and environment. The domain body focuses on physiological health and included biomedical data such as HbA1c, blood lipids and blood pressure. Outcomes of the domains focusing on psychological health, lifestyle behaviour and socio-economic factors were the output of validated online questionnaires filled in by the participant. The 360 degree diagnosis tool is composed of a total of 20 factors divided over the 4 domains. For each domain factor evidence-based cut-offs were developed based on decision rules allowing for categorization as unhealthy (red), healthy (green) or in-between (orange). The 360 degree diagnosis was included at t = 0 and t = 6 for shared decision making between participants and healthcare professionals, to set personal lifestyle goals (t = 0) and to evaluate outcomes related to physiological health, thinking and feeling, lifestyle behaviour and environment. \u003cb\u003e3) Self-monitoring;\u003c/b\u003e participants were equipped with a Fitbit Charge 4 activity tracker (Fitbit Charge 4; Fitbit Inc., San Fransisco, CA, USA) and a smart-scale (Fitbit Aria Air; Fitbit Inc., San Francisco, CA, USA), and were stimulated to use a food-diary app for food-logging (FatSecret made available via the HowAmI-application, TNO, Leiden, the Netherlands). In this way, participants could monitor their physical activity (step count), food intake and body weight for their own motivation. \u003cb\u003e4) Personalised behavioural support (blended care format);\u003c/b\u003e participants had access to personalised behavioural change support via the HowAmI-app (TNO, Leiden, The Netherlands), where participants could set personal lifestyle goals. Participants received notifications for data-entering. Goal achievement could be tracked daily, which included feedback and reinforcement. Data from the activity tracker, food-logging app and smart scale was automatically synchronized to a personal internet portal. This portal was used during coaching sessions to evaluate personal lifestyle goals and to assess possible barriers in goal adherence and to adapt goals if desired. Furthermore, it served as input for dynamic tailoring during coaching sessions between participant and healthcare professional which took place at t = 6 weeks, t = 12 weeks, t = 18 weeks and t = 24 weeks. Healthcare professionals were trained in interpreting the results from the 360 degrees diagnosis and in motivational interviewing, and were supported with behavioural change exercises to support participants with enhancing motivation, self-efficacy, planning, problem solving, relieving stress, arranging social support, or improving mood.\u003c/p\u003e\u003cp\u003eMeasurement of study outcomes\u003c/p\u003e\u003cp\u003eAdherence to lifestyle advice\u003c/p\u003e\u003cp\u003eThe primary outcome of the current study was adherence to lifestyle advice. Qualitative adherence to the dietary and physical activity guidelines was assessed at months 0, 3 and 6 by questions ”are you currently exercising regularly” and “do you currently follow a healthy diet” from the subjective compliance and effort questionnaire (7 points Likert scale; see Supplementary Materials). Quantitative adherence to Dutch Healthy Eating Index was assessed using the DHDI 2015 at months 0, 3 and 6 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Self-efficacy for aspects of the program was assessed based on questions 1–9 of the subjective compliance and effort questionnaire evaluated on months 0, 3 and 6 (7 points Likert scale; see Supplementary Materials). Finally, maintenance of lifestyle behaviour change was evaluated based on question 16 related to diet, question 17 related to physical activity and question 22 related to maintaining your own goal from the subjective compliance and effort questionnaire (7 points Likert scale; see Supplementary Materials). Quantitative assessment of the amount of physical activity was done at baseline, t = 3 and t = 6 months, based on the SQUASH questionnaire (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). During analysis of the results, it appeared that the SQUASH questionnaires for the intervention and control groups differed on one crucial item. This made it impossible to use the SQUASH algorithm to reliably compare amount and intensity of physical activity between the groups. Therefore, these results are not presented here.\u003c/p\u003e\u003cp\u003eQuality of Life and appreciation of combined lifestyle intervention program\u003c/p\u003e\u003cp\u003eQuality of life was assessed using the EQ-5D questionnaire at months 0, 3 and 6. Overall appreciation, as well as appreciation for diverse specific aspects of the program was evaluated at 6 months using a scale from 1–10 (question 1) and a 7-points Likert scale (questions 2–13 of the appreciation questionnaire), respectively (see Supplementary Materials).\u003c/p\u003e\u003cp\u003eAnthropometry and blood pressure measurements\u003c/p\u003e\u003cp\u003eAt the test day, anthropometrics were executed, including blood pressure (Medisana MTX) measurements and body weight and body composition measurements using the InBody770 (InBody Co., Ltd., Korea). Length, waist and hip circumference were measured using a measuring tape according to a standard operating procedure.\u003c/p\u003e\u003cp\u003eMeasurement of biomarkers\u003c/p\u003e\u003cp\u003eTo assess changes in metabolism, multiple clinical chemistry biomarkers including concentrations of glucose, insulin, glycated hemoglobin (HbA1c), total cholesterol, high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), triglycerides (TG), creatinine, albumin, estimated glomerular filtration rate (eGFR), alkaline phosphatase (AF), gamma-glutamyl transferase (GGT), alanine transaminase (ALAT) and aspartate transaminase (ASAT) were quantified in plasma following standard operating procedures. Furthermore, plasma biomarkers related to inflammation were measured and included the cytokines interleukin 6 (IL-6), tumor necrosis factor α (TNF-α) and interferon γ (IFN-γ) (MesoScale Discovery ‘human proinflammatory panel I’, Rockville, Maryland, USA) as well as resistin (DY1359), leptin (DY398), adiponectin (DY1065), high-sensitive C-reactive protein (hsCRP; DY1707), IFN-γ-induced protein 10 (IP-10; DIP100), myeloperoxidase (MPO; DY3174), E-selectin (DY724), serpin A1/alpha-1-antitrypsin (AAT; DY1268), haptoglobin (DY8465-05) and serum amyloid A1 (SAA; DY3019) as measured by ELISAs (with antibody sets from R\u0026amp;D Systems, Abingdon, UK).\u003c/p\u003e\u003cp\u003eSample size\u003c/p\u003e\u003cp\u003eSample size was calculated using a significance level of 0.05, tested 2-sided giving Zα = 1.96; a power of 80%; 1-β = 0.80 giving Zβ = 0.842; the expected deviation (as a measure of variance or distribution) is σ = 1. The compliance scale is a Likert-scale of 1–7); the expected effect, being the difference in compliance between intervention and control, δ = 0.6 (based upon scores as found in the study of (Doets, et al., 2019); Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\frac{{(Z\\alpha\\:+Z\\beta\\:)}^{2}\\bullet\\:\\:{\\sigma\\:}^{2}}{{\\delta\\:}^{2}}\\bullet\\:2=\\:\\frac{{\\left(1.96+0.842\\right)}^{2}\\bullet\\:{1}^{2}}{{0.6}^{2}}\\bullet\\:2=44\\:subjects\\:per\\:condition$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eTaking into account possible dropouts, n = 60 subjects were recruited for each arm.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were done according to the intention-to-treat principle, in which participants were included from whom \u0026gt; 80% of baseline data was available, with \u003cem\u003ep\u003c/em\u003e-values \u0026lt; 0.05 as statistically significant.\u003c/p\u003e\u003cp\u003eExploratory factor analysis was used to determine self-efficacy for the followed programs (based on questions 1–9 from subjective compliance and effort questionnaire, see Supplementary Materials). Scree plots, eigenvalues and parallel analysis all suggested the use of two factors. Exploratory factor analysis revealed that outcomes from the self-efficacy questions related to diet and related to physical activity were highly related. Chronbach’s alpha for items related to diet was 0.91, and Chronbach’s alpha for items on physical activity was 0.92. Self-efficacy factors for diet and for physical activity respectively were calculated by taking the mean of items with factor scores larger than 0.3. These two self-efficacy factors were used for statistical analysis with linear mixed-effects models.\u003c/p\u003e\u003cp\u003eLinear mixed-effects models were fitted using the \u003cem\u003elmer\u003c/em\u003e function from the \u003cem\u003elmerTest\u003c/em\u003e package (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Two models were constructed for each variable: The default model comprised fixed effects of timepoint and group, and a random intercept for participants (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The covariate model included fixed effects of timepoint, group, as well as HbA1c and fasting glucose, along with a random intercept for participants (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The response variable in all models was the natural logarithm of the value, and models were fitted using the Restricted Maximum Likelihood (REML) estimation. Analysis of Variance (ANOVA) was utilized to compare the default model against the full model, with \u003cem\u003ep\u003c/em\u003e-values determining statistical significance. In most cases, the default model was the preferred model. For analyses of body weight, self-efficacy for physical activity, self-efficacy for diet, DHDI, EQ-5D, BMI, body fat percentage, TG, eGFR and E-selectin the covariate model was used, because there was a better fit. Samples with absolute standardized residuals exceeding 3 were excluded from the final model. Any results reported for HbA1c and fasting glucose were obtained using the default model. Contrasts were generated from estimated marginal means to study the interaction of timepoint and group and pairwise comparisons between both timepoints and groups.\u003c/p\u003e\u003cp\u003eWilcoxon tests were performed for inflammatory biomarkers that did not meet the criteria for using linear mixed-effects models to assess differences between groups and timepoints, using the \u003cem\u003erstatix\u003c/em\u003e package (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Three different comparisons were made: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) changes in inflammatory biomarkers were calculated for each participant by subtracting baseline values from follow-up values. Cases with missing values were excluded from further analysis. Wilcoxon tests were then applied to change in values to compare different groups. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Paired Wilcoxon tests were used to compare values between the two timepoints within each group. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Unpaired Wilcoxon tests were utilized to compare values between different groups at each timepoint. Finally, appreciation question outcomes were analysed using two-sided Student’s T-test, since data were only available at the 6 months timepoint.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAAT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;alpha-1-antitrypsin\u003c/p\u003e\n\u003cp\u003eAF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;alkaline phosphatase\u003c/p\u003e\n\u003cp\u003eALAT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;alanine transaminase\u003c/p\u003e\n\u003cp\u003eANOVA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Analysis of variance\u003c/p\u003e\n\u003cp\u003eASAT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;aspartate transaminase\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;body mass index\u003c/p\u003e\n\u003cp\u003eBP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;blood pressure\u003c/p\u003e\n\u003cp\u003eCLI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Combined lifestyle intervention\u003c/p\u003e\n\u003cp\u003eCVD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;cardiovascular diseases\u003c/p\u003e\n\u003cp\u003eDHDI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Dutch Healthy Diet Index\u003c/p\u003e\n\u003cp\u003eeGFR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eGGT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;gamma-glutamyl transferase\u003c/p\u003e\n\u003cp\u003eGP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;general practitioner\u003c/p\u003e\n\u003cp\u003eHbA1c\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;glycated hemoglobin\u003c/p\u003e\n\u003cp\u003eHDL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;high density lipoprotein\u003c/p\u003e\n\u003cp\u003ehsCRP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;high-sensitive C-reactive protein\u003c/p\u003e\n\u003cp\u003eIFN-\u0026gamma;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;interferon \u0026gamma;\u003c/p\u003e\n\u003cp\u003eIL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;interleukin\u003c/p\u003e\n\u003cp\u003eIP-10\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;IFN-\u0026gamma;-induced protein 10\u003c/p\u003e\n\u003cp\u003eIR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;insulin resistance\u003c/p\u003e\n\u003cp\u003eLDL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;low density lipoprotein\u003c/p\u003e\n\u003cp\u003eMPO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;myeloperoxidase\u003c/p\u003e\n\u003cp\u003ePFT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;PhenFlex test\u003c/p\u003e\n\u003cp\u003eQoL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Quality of life\u003c/p\u003e\n\u003cp\u003eREML\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Restricted Maximum Likelihood\u003c/p\u003e\n\u003cp\u003eSAA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;serum amyloid A\u003c/p\u003e\n\u003cp\u003eSLIMMER\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;SLIM iMplementation Experience Region Noord- en Oost-Gelderland\u003c/p\u003e\n\u003cp\u003eT2D\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Type 2 diabetes\u003c/p\u003e\n\u003cp\u003eTG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Triglycerides\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; tumor necrosis factor \u0026alpha;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was sponsored by internal funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: J.E.O., I.M.d.H., P.v.E., W.J.P., and S.W.; Methodology: J.E.O., I.M.d.H., R.K., P.v.E., W.J.P., and S.W.; Investigation: J.E.O, D.S., R.J.M.K., I.M.d.H., J.t.B., and W.J.P.; Data analysis: A.H.J.H. and T.J.v.d.B.; Data Curation: M.P.M.C. and T.J.v.d.B.; Interpretation of data: J.E.O., D.J.S, R.J.M.K., I.M.d.H., R.K., P.v.E., J.t.B., W.J.P. and S.W.; Writing\u0026mdash;Original Draft Preparation: J.E.O., D.J.S and S.W., Writing \u0026mdash; review and editing: all authors; Visualization, T.J.v.d.B. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe greatly acknowledge the healthcare professionals of the SLIMMER practices for their effort in the study execution: Wytse Brongers, Lilly Alefs, Mirte Breumelhof, Bea Pinkert, Wilma Meijer and Ingrid van der Linden (intervention group practices) and Thomas Kalkman, Annelies Dormans, Marjolein Coers and Donna Lischer (control group practices). Furthermore, Hilde van Keulen, Kim Kranenborg, Jessica Snabel, Remon Dulos, Ferry Jagers, Eugene van Someren, Gino Kalkman, Marlies Otto, Bowien van Leijden, Floris Dekker, Nicole Plomp, Angelique Speulman, Kerstin Schorr and Ilse van Dijk are acknowledged for their contribution in the preparations and execution of the study. Lastly, we thank all study participants for their efforts.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets presented in this publication are available upon reasonable request. Requests to access the datasets should be directed to the corresponding author. The data are being stored in the phenotype database (https://dashin.eu/interventionstudies/), which is a data repository for clinical studies that makes use of ontologies and the principles of F.A.I.R (findable, accessible, interoperable and reusable) to allow for reuse of data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDeFronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care. 1991;14(3):173-94.\u003c/li\u003e\n\u003cli\u003eTuomilehto J, Lindstr\u0026ouml;m J, Eriksson JG, Valle TT, H\u0026auml;m\u0026auml;l\u0026auml;inen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343-50.\u003c/li\u003e\n\u003cli\u003eKnowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. 2002.\u003c/li\u003e\n\u003cli\u003eHerder C, Peltonen M, Koenig W, S\u0026uuml;tfels K, Lindstr\u0026ouml;m J, Martin S, et al. Anti-inflammatory effect of lifestyle changes in the Finnish Diabetes Prevention Study. Diabetologia. 2009;52(3):433-42.\u003c/li\u003e\n\u003cli\u003eDuijzer G, Haveman-Nies A, Jansen SC, Beek JT, van Bruggen R, Willink MGJ, et al. Effect and maintenance of the SLIMMER diabetes prevention lifestyle intervention in Dutch primary healthcare: a randomised controlled trial. Nutr Diabetes. 2017;7(5):e268.\u003c/li\u003e\n\u003cli\u003eLeung AWY, Chan RSM, Sea MMM, Woo J. An Overview of Factors Associated with Adherence to Lifestyle Modification Programs for Weight Management in Adults. Int J Environ Res Public Health. 2017;14(8).\u003c/li\u003e\n\u003cli\u003eRIVM. Monitor Gecombineerde Leefstijlinterventie 2023. [Available from: www.rivm.nl/documenten/monitor-gecombineerde-leefstijlinterventie-2023]\u003c/li\u003e\n\u003cli\u003evan Ommen B, Wopereis S, van Empelen P, van Keulen HM, Otten W, Kasteleyn M, et al. From Diabetes Care to Diabetes Cure-The Integration of Systems Biology, eHealth, and Behavioral Change. Front Endocrinol (Lausanne). 2017;8:381.\u003c/li\u003e\n\u003cli\u003eReinders MJ, Starke AD, Fischer ARH, Verain MCD, Doets EL, Van Loo EJ. Determinants of consumer acceptance and use of personalized dietary advice: A systematic review. Trends in Food Science \u0026amp; Technology. 2023;131:277-94.\u003c/li\u003e\n\u003cli\u003eDominika Kwasnicka SUDMW, Falko S. Theoretical explanations for maintenance of behaviour change: a systematic review of behaviour theories. Health Psychology Review. 2016;10(3):277--96.\u003c/li\u003e\n\u003cli\u003eJinnette R, Narita A, Manning B, McNaughton SA, Mathers JC, Livingstone KM. Does Personalized Nutrition Advice Improve Dietary Intake in Healthy Adults? A Systematic Review of Randomized Controlled Trials. Advances in Nutrition. 2021;12(3):657-69.\u003c/li\u003e\n\u003cli\u003eCelis-Morales C, Livingstone KM, Marsaux CF, Macready AL, Fallaize R, O\u0026apos;Donovan CB, et al. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int J Epidemiol. 2017;46(2):578-88.\u003c/li\u003e\n\u003cli\u003eLau Y, Wong SH, Chee DGH, Ng BSP, Ang WW, Han CY, Cheng LJ. Technology‐delivered personalized nutrition intervention on dietary outcomes among adults with overweight and obesity: A systematic review, meta‐analysis, and meta‐regression. Obesity Reviews. 2024;25(5):e13699.\u003c/li\u003e\n\u003cli\u003eRobertson S, Clarke ED, G\u0026oacute;mez-Mart\u0026iacute;n M, Cross V, Collins CE, Stanford J. Do Precision and Personalised Nutrition Interventions Improve Risk Factors in Adults with Prediabetes or Metabolic Syndrome? A Systematic Review of Randomised Controlled Trials. Nutrients. 2024;16(10):1479.\u003c/li\u003e\n\u003cli\u003eWang L, Miller LC. Just-in-the-Moment Adaptive Interventions (JITAI): A Meta-Analytical Review. Health Commun. 2020;35(12):1531-44.\u003c/li\u003e\n\u003cli\u003evan den Broek TJ, Bakker GCM, Rubingh CM, Bijlsma S, Stroeve JHM, van Ommen B, et al. Ranges of phenotypic flexibility in healthy subjects. Genes Nutr. 2017;12:32.\u003c/li\u003e\n\u003cli\u003eWopereis S, Stroeve JHM, Stafleu A, Bakker GCM, Burggraaf J, van Erk MJ, et al. Multi-parameter comparison of a standardized mixed meal tolerance test in healthy and type 2 diabetic subjects: the PhenFlex challenge. Genes Nutr. 2017;12:21.\u003c/li\u003e\n\u003cli\u003eTrouwborst I, Gijbels A, Jardon KM, Siebelink E, Hul GB, Wanders L, et al. Cardiometabolic health improvements upon dietary intervention are driven by tissue-specific insulin resistance phenotype: A precision nutrition trial. Cell Metab. 2023;35(1):71-83 e5.\u003c/li\u003e\n\u003cli\u003ede Hoogh IM, Oosterman JE, Otten W, Krijger AM, Berbee-Zadelaar S, Pasman WJ, et al. The Effect of a Lifestyle Intervention on Type 2 Diabetes Pathophysiology and Remission: The Stevenshof Pilot Study. Nutrients. 2021;13(7).\u003c/li\u003e\n\u003cli\u003ede Hoogh IM, Pasman WJ, Boorsma A, van Ommen B, Wopereis S. Effects of a 13-Week Personalized Lifestyle Intervention Based on the Diabetes Subtype for People with Newly Diagnosed Type 2 Diabetes. Biomedicines. 2022;10(3):643.\u003c/li\u003e\n\u003cli\u003eGGD-NOG. [Available from: www.nogslimmer.nl].\u003c/li\u003e\n\u003cli\u003eRabin R, Charro Fd. EQ-5D: a measure of health status from the EuroQol Group. Annals of medicine. 2001;33(5):337-43.\u003c/li\u003e\n\u003cli\u003evan Lee L, Feskens EJ, Meijboom S, van Huysduynen EJH, van\u0026rsquo;t Veer P, de Vries JH, Geelen A. Evaluation of a screener to assess diet quality in the Netherlands. British Journal of Nutrition. 2016;115(3):517-26.\u003c/li\u003e\n\u003cli\u003eHarakeh Z, de Hoogh IM, van Keulen H, Kalkman G, van Someren E, van Empelen P, Otten W. 360\u0026deg; Diagnostic Tool to Personalize Lifestyle Advice in Primary Care for People With Type 2 Diabetes: Development and Usability Study. JMIR Form Res. 2023;7:e37305.\u003c/li\u003e\n\u003cli\u003eWendel-Vos GC, Schuit AJ, Saris WH, Kromhout D. Reproducibility and relative validity of the short questionnaire to assess health-enhancing physical activity. J Clin Epidemiol. 2003;56(12):1163-9.\u003c/li\u003e\n\u003cli\u003eKuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software. 2017;82(13):1 - 26.\u003c/li\u003e\n\u003cli\u003eKassambara A. Pipe-Friendly Framework for Basic Statistical Tests [R package rstatix version 0.7.2]. 2020.\u003c/li\u003e\n\u003cli\u003eBurgess E, Hassm\u0026eacute;n P, Welvaert M, Pumpa KL. Behavioural treatment strategies improve adherence to lifestyle intervention programmes in adults with obesity: a systematic review and meta-analysis. Clin Obes. 2017;7(2):105-14.\u003c/li\u003e\n\u003cli\u003eFiamoncini J, Rundle M, Gibbons H, Thomas EL, Geillinger-K\u0026auml;stle K, Bunzel D, et al. Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements. Faseb j. 2018;32(10):5447-58.\u003c/li\u003e\n\u003cli\u003eCarbone T, Pafundi V, Schievano C, Assunta D, Padula MC, Giordano M, et al. Serum amyloid A in healthy subjects: assessment of reference value using ELISA method. J Immunoassay Immunochem. 2021;42(2):129-37.\u003c/li\u003e\n\u003cli\u003evan Bilsen JHM, van den Brink W, van den Hoek AM, Dulos R, Caspers MPM, Kleemann R, et al. Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue. Front Physiol. 2021;12:703370.\u003c/li\u003e\n\u003cli\u003evan Dielen FMH, van \u0026lsquo;t Veer C, Buurman WA, Greve JWM. Leptin and Soluble Leptin Receptor Levels in Obese and Weight-Losing Individuals. The Journal of Clinical Endocrinology \u0026amp; Metabolism. 2002;87(4):1708-16.\u003c/li\u003e\n\u003cli\u003eSimons N, Bijnen M, Wouters KAM, Rensen SS, Beulens JWJ, van Greevenbroek MMJ, et al. The endothelial function biomarker soluble E-selectin is associated with nonalcoholic fatty liver disease. Liver Int. 2020;40(5):1079-88.\u003c/li\u003e\n\u003cli\u003eUkkola O, Santaniemi M. Adiponectin: a link between excess adiposity and associated comorbidities? J Mol Med (Berl). 2002;80(11):696-702.\u003c/li\u003e\n\u003cli\u003eRecinella L, Orlando G, Ferrante C, Chiavaroli A, Brunetti L, Leone S. Adipokines: New Potential Therapeutic Target for Obesity and Metabolic, Rheumatic, and Cardiovascular Diseases. Front Physiol. 2020;11:578966.\u003c/li\u003e\n\u003cli\u003eBraem CIR, Pasman WJ, van den Broek TJ, Caspers MPM, Jagers FLPW, Yavuz US et al. The association of physical activity, heart rate and sleep from an activity tracker with weight loss during a 6-month personalized combined lifestyle intervention: a retrospective analysis. BMC Digit Health. 2025; 3:8. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Precision Medicine, Primary Health Care, Weight Loss, Life Style, Obesity, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-6530079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6530079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Dutch combined lifestyle intervention (CLI) program SLIMMER is effective in changing lifestyle behaviour. However, achieving and maintaining a healthier lifestyle is difficult. Here, a personalised version of the SLIMMER CLI was evaluated for effects on weight loss, metabolism and lifestyle advice adherence. In this cluster-allocated controlled open-label parallel intervention study, 61 participants were included in the personalised and 60 participants in the regular SLIMMER CLI (control). Adults at risk for cardiometabolic disease were followed for 6 months. Body composition and blood samples were measured at baseline and end of study. Adherence to lifestyle advice, self-efficacy and maintenance was assessed by questionnaires. At 6 months, body weight (-4.5 kg, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BMI (-1.5 kg/m\u003csup\u003e2\u003c/sup\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and body fat percentage (-3.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) improved more in the intervention than control group. No consistent difference in adherence to lifestyle advice or cardiometabolic outcomes was found between groups. The dropout rate was lower in the intervention group (11%) than in the control group (23%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Personalisation was effective in participants retention and body weight loss, unexplained by differences in adherence to lifestyle advice. This could potentially lead to favourable long-term health outcomes.\u003c/p\u003e","manuscriptTitle":"Personalisation of the Dutch combined lifestyle intervention SLIMMER improves participant retention and weight-loss in people at risk for cardiometabolic disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 12:18:05","doi":"10.21203/rs.3.rs-6530079/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-20T13:30:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-13T07:01:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231946294850785812570186078390276751289","date":"2025-06-02T06:12:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-28T15:59:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T23:16:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37717753496408046842469102300380500192","date":"2025-05-08T13:49:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267921678479170865598251030397519312837","date":"2025-05-08T12:19:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T06:08:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-30T03:54:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-29T02:42:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-29T02:39:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-25T14:57:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8e06ed7-cad0-4fcd-90e2-92387127a03e","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48261744,"name":"Biological sciences/Psychology"},{"id":48261745,"name":"Health sciences/Biomarkers"},{"id":48261746,"name":"Health sciences/Diseases"},{"id":48261747,"name":"Health sciences/Endocrinology"},{"id":48261748,"name":"Health sciences/Health care"},{"id":48261749,"name":"Health sciences/Medical research"},{"id":48261750,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-12-29T16:05:16+00:00","versionOfRecord":{"articleIdentity":"rs-6530079","link":"https://doi.org/10.1038/s41598-025-27899-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-23 15:57:58","publishedOnDateReadable":"December 23rd, 2025"},"versionCreatedAt":"2025-05-13 12:18:05","video":"","vorDoi":"10.1038/s41598-025-27899-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27899-6","workflowStages":[]},"version":"v1","identity":"rs-6530079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6530079","identity":"rs-6530079","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}