Search

Background: Metabolic syndrome (MetS) is a common public health challenge. Health-promoting behaviors such as diet and physical activity are central to preventing and controlling MetS. However, the adoption of diet and physical activity behaviors has always been challenging. An individualized mobile health (mHealth)-based intervention using the Behavior Change Wheel is promising in promoting health behavior change and reducing atherosclerotic cardiovascular disease (ASCVD) risk. However, the effects of this intervention are not well understood among people with MetS in mainland China., Objective: We aimed to evaluate the effects of the individualized mHealth-based intervention using the Behavior Change Wheel on behavior change and ASCVD risk in people with MetS., Methods: We conducted a quasi-experimental, nonrandomized study. Individuals with MetS were recruited from the health promotion center of a tertiary hospital in Zhejiang province, China. The study involved 138 adults with MetS, comprising a control group of 69 participants and an intervention group of 69 participants. All participants received health education regarding diet and physical activity. The intervention group additionally received a 12-week individualized intervention through a WeChat mini program and a telephone follow-up in the sixth week of the intervention. Primary outcomes included diet, physical activity behaviors, and ASCVD risk. Secondary outcomes included diet self-efficacy, physical activity self-efficacy, knowledge of MetS, quality of life, and the quality and efficiency of health management services. The Mann-Whitney U test and Wilcoxon signed rank test were primarily used for data analysis. Data analysis was conducted based on the intention-to-treat principle using SPSS (version 25.0; IBM Corp)., Results: Baseline characteristics did not differ between the 2 groups. Compared with the control group, participants in the intervention group showed statistically significant improvements in diet behavior, physical activity behavior, diet self-efficacy, physical activity self-efficacy, knowledge of MetS, physical health, and mental health after a 12-week intervention (P=.04, P=.001, P=.04, P=.04, P=.001, P=.04, P=.04, and P<.05). The intervention group demonstrated a statistically significant improvement in outcomes from pre- to postintervention evaluations (P<.001, P=.03, P<.001, P=.04, P<.001, P<.001, and P<.001). The intervention also led to enhanced health management services and quality., Conclusions: The individualized mHealth-based intervention using the Behavior Change Wheel was effective in promoting diet and physical activity behaviors in patients with MetS. Nurses and other health care professionals may incorporate the intervention into their health promotion programs., (©Dandan Chen, Hui Zhang, Jingjie Wu, Erxu Xue, Pingping Guo, Leiwen Tang, Jing Shao, Nianqi Cui, Xiyi Wang, Liying Chen, Zhihong Ye. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.11.2023.)

Background: Little is known about the potential mechanisms of healthy eating and exercise change, and design interventions which aim to promote healthy eating and exercise change among individuals at risk of metabolic syndrome. This study aimed to identify key determinants of healthy eating, exercise behaviors, and health among individuals at risk of metabolic syndrome using the integrated common-sense model of illness self-regulation., Method: A cross-sectional study with a multi-wave data collection strategy. A total of 275 participants at risk of metabolic syndrome based on the clinical prediction model were included in the final analysis. Path analysis was employed to explore the pattern of relationships between key variables using AMOS., Results: The mediation analysis suggested that personal and treatment control, and coherence can positively affect self-reported health via intentions and health behaviors (exercise and healthy eating). Additionally, relationships between self-efficacy (exercise and healthy eating) and health outcomes can be mediated by health behaviors, and both intentions and health behaviors., Conclusions: This current research used the integrated common-sense model of illness self-regulation to predict healthy eating, exercise behaviors, and self-reported health among individuals at risk of metabolic syndrome. The results suggested that self-efficacy, intention, consequences, personal control, treatment control, and coherence were the key determinants of behavior and health, which can help design interventions to encourage healthy eating and exercise changes among individuals with a high risk of MetS., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Aims: Adherence to diet and exercise recommendations is crucial among metabolic syndrome (MetS) individuals. However, no studies have focused on comprehensive behavioural changes of diet and exercise among individuals with MetS. The present study aimed to explore determinants of adherence to diet and exercise behaviours among people with MetS based on the Capability, Opportunity, Motivation, and Behaviour (COM-B) model., Methods and Results: A cross-sectional study was conducted in a health promotion centre of a large and general university hospital in Zhejiang Province, China, in 2021. A total of 241 individuals with MetS completed all scales. The mediation model was tested using structural equation modelling with bootstrapped samples. In the regression-based path analysis, MetS knowledge (β = 0.140), socioeconomic status (β = 0.162), and social support (β = 0.143) directly positively influenced diet behaviour. In addition, social support indirectly positively influenced exercise behaviour through coping and adaptation (β = 0.090). The final theoretical model showed a good fit (root mean square error of approximation = 0.057, comparative fit index = 0.946)., Conclusion: Factors associated with diet behaviour were knowledge of MetS, socioeconomic status, and social support. Adaptation may be a mediator between social support and exercise behaviour. Intervention programmes targeting increased adherence to diet and exercise could include these factors for individuals with MetS., Competing Interests: Conflict of interest: none declared., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Metabolic Syndrome (MetS) is a serious public health issue. Dietary changes form the core of MetS treatment. The adherence to dietary recommendations is critical for reducing the severity of MetS components and preventing complications. However, the adherence to dietary recommendations was not adequate among adults with MetS. This study utilizes the Behaviour Change Wheel (BCW) to develop an individualized WeChat mini program-based behavioural change intervention aimed at strengthening adherence to dietary recommendations in people with MetS., Methods: The BCW theory was used to design an individualized WeChat mini program-based behavioural change intervention. A descriptive qualitative study was conducted to identify the determinants of adherence to dietary recommendations in individuals with MetS. The study was conducted at the health promotion centre of a prominent general university hospital in Zhejiang, China. Subsequently, the intervention functions (IFs) and policy categories were selected following the identified determinants. Afterwards, behaviour change techniques (BCTs) were chosen to translate into potential intervention strategies, and the delivery mode was determined., Results: Our study identified fifteen barriers to improve the adherence to dietary recommendations in this population. These were linked with six IFs: education, training, persuasion, enablement, modelling, and environmental restructuring. Then, twelve BCTs were linked with the IFs and fifteen barriers. The delivery mode was a WeChat mini program. After these actions, an individualized WeChat mini program-based behavioural change intervention was developed to enhance adherence to dietary recommendations for individuals with MetS., Conclusions: The BCW theory helped scientifically and systematically develop an individualized WeChat mini program-based behavioural change intervention for individuals with MetS. In the future, our research team will refine and upgrade the WeChat mini program and then test the usability and effectiveness of the individualized WeChat mini program-based behavioural change intervention program.

Objective To explore the heterogenous subtypes and the associated factors of health literacy among patients with metabolic syndrome. Methods A cross-sectional study was conducted, and 337 patients with metabolic syndrome were recruited from Sir Run Run Shaw Hospital in Zhejiang Province from December 2021 to February 2022. The Social Support Questionnaire, Short version of the Health Literacy Scale European Questionnaire (HLS-EU-Q16), and MacArthur Scale of Subjective Social Status were used for investigation. Latent class analysis (LCA) was performed to explore the heterogenous subtypes of health literacy among Metabolic syndrome patients. Univariate analysis and logistic regression were used to identify the predictors of the latent classes. Results The findings of LCA suggested that three heterogeneous subtypes of health literacy among individuals with metabolic syndrome were identified: high levels of health literacy, moderate levels of health literacy, and low levels of health literacy. The multinomial logistic regression results indicated that compared with low levels of health literacy class, the high levels of health literacy class were predicted by age (OR 0.932, 95%CI[0.900-0.966]), socio-economic status (OR 1.185, 95%CI[1.058-1.328]), and social support (OR 1.065, 95%CI[1.012-1.120]). Compared with low levels of health literacy class, the moderate levels of health literacy class were predicted by age (OR 0.964, 95%CI[0.934-0.995]), socio-economic status (OR 1.118, 95%CI[1.006-1.242]), male (OR 0.229, 95%CI[0.092-0.576]). Conclusion The levels of health literacy among patients with metabolic syndrome can be divided into three heterogenous subtypes. The results can inform policy-makers and care professionals to design targeted interventions for different subgroups among patients with metabolic syndrome who are male, at older age, have less social support, and with disadvantaged socio-economic status to improve health literacy.

The dose-response association between sedentary time and the risk of metabolic syndrome is unclear, which indicates an important knowledge gap in public health. The objective of this study was to determine the categorical and continuous dose-response associations between sedentary time and the risk of metabolic syndrome. A systematic literature search of English articles published in PubMed, CINHAL, Embase, and Web of Science Core Collection prior to June 2022 was conducted. All cohort and cross-sectional studies that examined the association between sedentary time and the risk of metabolic syndrome were considered, and duplicate and non-related studies were excluded. Data extraction using a standardized chart and quality assessment using two appraisal tools were also performed. Two independent reviewers were involved in these processes. In categorical meta-analyses, the pooled effect sizes for metabolic syndrome associated with different categories of sedentary time were calculated by comparing the highest and intermediate with the lowest categories. In continuous meta-analyses, the linear and nonlinear dose-response associations were estimated using generalized least squares and restricted cubic spline models, respectively. Data were collected and analyzed from March to June 2022. Four prospective cohort studies and 22 cross-sectional studies with 105,239 participants and 16,185 MetS cases were included in this study. In categorical analyses, both intermediate (median duration: 4.11 h/day; pooled OR: 1.17, 95% CI: 1.08-1.26, P &lt; 0.001) and high levels (median duration: 7.26 h/day; pooled OR: 1.71, 95% CI: 1.43-2.04, P &lt; 0.001) of total sedentary time were significantly associated with an increased risk of metabolic syndrome. Similarly, a significant association between screen time and the risk of metabolic syndrome was also found in intermediate (median duration: 2.22 h/day; pooled OR: 1.20, 95% CI: 1.10-1.32, P &lt; 0.001) and high levels (median duration: 3.40 h/day; pooled OR: 1.63, 95% CI: 1.44-1.86, P &lt; 0.001) of exposure. Of note, these associations were significantly stronger in women. Different patterns of the behavior-disease association were not observed in children, adolescents, and adults. The findings of continuous meta-analyses could not provide solid evidence for the linearity and nonlinearity of the behavior-disease association. This study demonstrated that long-time sedentary behavior was associated with a higher risk of MetS independent of physical activity and the patterns of association varied by gender instead of age. These findings have implications for future guideline recommendations on physical activity, sedentary behavior, and prevention of metabolic syndrome., (© 2022 World Obesity Federation.)

Background: Adherence to physical activity is inadequate in adults with metabolic syndrome. Adherence to physical activity recommendations is crucial and can result in improved health outcomes and reduced medical burdens. A comprehensive behavior change intervention, including identifying determinants of adherence to physical activity recommendations, intervention options, intervention content and implementation options, was imperative for enhancing physical activity adherence. The aim of the study is to develop an intervention to increase physical activity adherence among individuals with metabolic syndrome., Methods: The study followed the eight steps of the Behavior Change Wheel guide, including defining the problem in behavioral terms (Step 1), selecting target behavior (Step 2), specifying target behavior (Step 3), identifying what needs to change (Step 4), identifying intervention functions (Step 5), identifying policy categories (Step 6), identifying behavior change techniques (Step 7), and determining model of delivery (Step 8). The semi-structured, in-depth interviews were employed to identify the determinants of adherence to physical activity among twenty-eight individuals with metabolic syndrome based on capability, opportunity, motivation and behavior model. Next, the intervention functions and policy categories were chosen to address these determinants. Finally, behavior change techniques were selected to assist in the delivery of the intervention functions and be translated into intervention content., Results: Our study identified eighteen facilitators and fifteen barriers to physical activity adherence. It resulted in the selection of seven intervention functions and nineteen behavior change techniques for the intervention program. Then, the current study identified an app as the delivery mode. Finally, a behavioral change intervention was generated for individuals with metabolic syndrome to increase physical activity recommendation adherence., Conclusions: The Behavior Change Wheel provided a systematic approach to designing a behavior change intervention, which helped improve the health outcomes and reduce medical burdens and economic burdens among individuals with metabolic syndrome. The findings suggested that potential intervention should pay special attention to increasing knowledge in metabolic syndrome, imparting skills of physical activity, offering a supportive environment, and providing suggestions on regular physical activity using the appropriate behavior change techniques. A feasibility study will be undertaken to assess the acceptability and effectiveness of the intervention program in the future., (© 2022. The Author(s).)

: To explore the potential associations between perceived stress and health-promoting behaviors based on the theoretical schema of the middle-range theory of adaptation to chronic illness. From January to May 2021, a convenience sampling method was used to recruit 230 young and middle-aged patients with metabolic syndrome who underwent physical examination in the inpatient center of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine. The Health-Promoting Health Profile-Ⅱ, Chinese Perceived Stress Scale, Coping and Adaptation Processing Scale-Short Form, and Multi-dimensional Scale of Perceived Social Support were used in the cross-sectional study. The chain mediation effect procedure and bootstrap sampling test were used to examine the mediating role of adaptability and social support between perceived stress and health-promoting behaviors. The mean score of health-promoting behaviors was 100.0±14.6, the mean score of perceived stress was 22.0± 6.9, the mean score of adaptability was 47.0±6.1, and the mean score of social support was 63.8±10.8. Perceived stress had a negative impact on patients' health-promoting behaviors (=-0.309, <0.05). The adaptability (effect size= -0.112, 95%:-0.199~-0.038) and social support (effect size= -0.032, 95%:-0.083~played a mediating role and a chain mediating role in the process of perceived stress influencing patients' health-promoting behaviors (effect size= -0.045, 95%:-0.093~-0.020). Adaptability and social support play an intermediary role between perceived stress and health-promoting behaviors in young and middle-aged patients with metabolic syndrome. Healthcare professionals can motivate patients to develop healthy behaviors by developing intervention strategies on adaptability and social support.

: To design and develop a Wechat applet for intelligent health management of metabolic syndrome. Based on the needs and requirements of individuals undergoing health check-up, patients with metabolic syndrome and medical workers, a Wechat applet for metabolic syndrome management was designed and developed, which involving health data collection, health risk prediction, health management knowledge base fusion and intelligent recommendation, data privacy and security. The platform consists of three user ports: individuals undergoing health check and patients with metabolic syndrome, the medical workers and the system administrators. The main functions of the platform included metabolic syndrome risk prediction, intelligent recommendation of health management strategies, health behavior record and supervision, experts' consultation and health knowledge guide. The Wechat applet developed in this study can be used for metabolic syndrome risk prediction for general population, and health management for patients with metabolic syndrome, which helps them to enhance health management awareness and health behavior adherence.

To evaluate and summarize the evidence of diet and physical activity management in patients with metabolic syndrome (MS). BMJ Best Practice, UpToDate, Joanna Briggs Institute (JBI) database, Agency for Healthcare Research and Quality (AHRQ) network, National Institute for Health and Clinical Excellence (NICE) network, Scottish Intercollegiate Guidelines Network (SIGN), Guidelines International Network (GIN), Medlive, Registered Nurses' Association of Ontario (RNAO) network, American Diabetes Association (ADA) network, New Zealand Guideline Group (NZGG) network, Canadian medical association clinical practice guidelines network, PubMed, EmBase, Web of Science, CINAHL, Cochrane Library, CNKI, China Science and Technology Journal Database, Wanfang Knowledge Data Service Platform and Chinese biomedical database were searched systematically to obtain guidelines, evidence summary, expert consensus, best practice information book, clinical decision-making, recommended practice, and systematic review on diet and physical activity management in patients with MS. The retrieval period is from the establishment of database to November 2021. Two researchers with evidence-based medicine background evaluated the quality and evidence level of the included literature. A total of 36 articles met the criteria, including 3 guidelines, 5 expert consensus, 1 clinical decision and 27 systematic reviews. We summarized 49 pieces of evidence related to diet and physical activity in patients with MS, involving 15 aspects, namely diet goals, diet patterns, diet time, carbohydrate intake, fat intake, fiber intake, salt intake, fruits, vegetables and grains intake, coffee intake, effects of diet, principle of physical activity, intensity, form, time of physical activity, effects of physical activity, physical activity prescription of patients with MS and cardiovascular disease, and the joint effects of diet and physical activity. Diet and physical activity management can effectively improve the health outcomes of patients with MS. Health professionals should choose and apply the best evidence with consideration of the clinical situation and patient preference.

Objective: We aimed to examine whether eHealth interventions can effectively improve anthropometric and biochemical indicators of patients with metabolic syndrome (MetS)., Design: Systematic review and meta-analysis., Methods: PubMed, the Web of Science, Embase, Medline, CINAHL, PsycINFO, the Cochrane Library, the Chinese National Knowledge Infrastructure, the Wanfang and Weipu databases were comprehensively searched for papers that were published from database inception to May 2019. Articles were included if the participants were metabolic syndrome (MetS) patients, the participants received eHealth interventions, the participants in the control group received usual care or were wait listed, the outcomes included anthropometric and biochemical indicators of MetS, and the study was a randomised controlled trial (RCT) or a controlled clinical trial (CCT). The Quality Assessment Tool for Quantitative Studies was used to assess the methodological quality of the included articles. The meta-analysis was conducted using Review Manager V.5.3 software., Results: In our review, seven RCTs and two CCTs comprising 935 MetS participants met the inclusion criteria. The results of the meta-analysis revealed that eHealth interventions resulted in significant improvements in body mass index (standardised mean difference (SMD)=-0.36, 95% CI (-0.61 to -0.10), p<0.01), waist circumference (SMD=-0.47, 95% CI (-0.84 to -0.09), p=0.01) and systolic blood pressure(SMD=-0.35, 95% CI (-0.66 to -0.04), p=0.03) compared with the respective outcomes associated with the usual care or wait-listed groups. Based on the included studies, we found significant effects of the eHealth interventions on body weight. However, we did not find significant positive effects of the eHealth interventions on other metabolic parameters., Conclusions: The results indicated that eHealth interventions were beneficial for improving specific anthropometric outcomes, but did not affect biochemical indicators of MetS. Therefore, whether researchers adopt eHealth interventions should be based on the purpose of the study. More rigorous studies are needed to confirm these findings., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: Machine learning (ML) techniques have emerged as a promising tool to predict risk and make decisions in different medical domains. We aimed to compare the predictive performance of machine learning-based methods for 4-year risk of metabolic syndrome in adults with the previous model using logistic regression. Patients and Methods: This was a retrospective cohort study that employed a temporal validation strategy. Three popular ML techniques were selected to build the prognostic models. These techniques were artificial neural networks, classification and regression tree, and support vector machine. The logistic regression algorithm and ML techniques used the same five predictors. Discrimination, calibration, Brier score, and decision curve analysis were compared for model performance. Results: Discrimination was above 0.7 for all models except classification and regression tree model in internal validation, while the logistic regression model showed the highest discrimination in external validation (0.782) and the smallest discrimination differences. The logistic regression model had the best calibration performance, and ANN also showed satisfactory calibration in internal validation and external validation. For overall performance, logistic regression had the smallest Brier score differences in internal validation and external validation, and it also had the largest net benefit in external validation. Conclusion: Overall, this study indicated that the logistic regression model performed as well as the flexible ML-based prediction models at internal validation, while the logistic regression model had the best performance at external validation. For clinical use, when the performance of the logistic regression model is similar to ML-based prediction models, the simplest and more interpretable model should be chosen. [ABSTRACT FROM AUTHOR]

Purpose: A prediction model for 4-year risk of metabolic syndrome in adults was previously developed and internally validated. However, external validity or generalizability for this model was not assessed so it is not appropriate for clinical application. We aimed to externally validate this model based on a retrospective cohort. Patients and Methods: A retrospective cohort design and a temporal validation strategy were used in this study based on a dataset from 1 January 2015 to 31 December 2018. Multiple imputation was used for missing values. Model performance was evaluated by using discrimination, calibration (calibration plot, calibration slope, and calibration intercept), overall performance (Brier score), and decision curve analysis. Results: In external validation, the C-statistic was 0.782 (95% CI, 0.771– 0.793). The calibration plot shows good calibration, calibration slope was 1.006 (95% CI, − 0.011– 1.063), and calibration intercept was − 0.045 (95% CI, − 0.113– 0.022). Brier score was 0.164.The discrimination and calibration of the prediction model were good in temporal external validation. Conclusion: The discrimination and calibration of the prediction model were satisfactory in the temporal external validation. However, clinicians should be aware that this prediction model was developed and validated in a tertiary setting. It is strongly recommended that further studies validate this model in international cohorts and large, prospective cohorts in different institutions. [ABSTRACT FROM AUTHOR]

Purpose: A prognostic prediction model for metabolic syndrome can help nurses or physicians evaluate the future individual absolute risk of MetS in order to develop personalized care strategies. We aimed to derive and internally validate a prognostic prediction model for 4-year risk of metabolic syndrome in adults. Patients and Methods: This was a retrospective cohort study conducted in a tertiary care setting, and the dataset was obtained from the Healthcare Information and Management Systems of a tertiary hospital. The cohort included Chinese adults attending health examination from 1 January 2011 to 31 December 2014. A total of 6793 participants without metabolic syndrome were included in the cohort and were followed up for 4 years. Available candidate predictors in the dataset were weight, MCV, MCH, AST, ALT, BMI, NGC, TC, serum uric acid, gender, smoking, WBC, LC, Hb, HCT, and age. A logistic regression model was adopted to build the risk equation, and bootstrapping was used when considering internal validation. Calibration, discrimination, and the clinical utility were calculated for the model's performance. Results: Of the 6793 participants, 1750 participants were diagnosed with metabolic syndrome within 4 years. The developed prediction model contained 5 predictors (body mass index, age, total cholesterol, alanine transaminase, and serum uric acid). After internal validation, the C-statistic was 0.783 (95% CI, 0.772– 0.795). Additionally, the current model had good calibration. Calibration slope was 0.995 (95% CI, 0.934– 1.058), and calibration intercept was − 0.008 (95% CI, − 0.088– 0.073). The Brier score was 0.156. The decision-curve analysis indicated that the prediction model provided greater net benefit than the default strategies of providing treatment or not providing treatment for all patients. Conclusion: A prognostic risk prediction model for determining 4-year risk of metabolic syndrome onset in adults was developed and internally validated. This model was based on routine clinical measurements that quantified individual future risk of metabolic syndrome. [ABSTRACT FROM AUTHOR]