Dataset Information

Evaluating social network-based weight loss interventions in Chinese population: An agent-based simulation.

ABSTRACT: OBJECTIVE:The aim of this study is to assess network-based weight loss interventions in the Chinese setting using agent-based simulation. METHODS:An agent-based model incorporating social, environmental and personal influence is developed to simulate the obesity epidemic through an interconnected social network among a population of 2197 individuals from the nationally representative survey. Model parameters are collected from literature and existing database. To ensure the robustness of our findings, the model is validated against empirical observations and sensitivity analyses are performed on calibrated parameters. RESULTS:When compared with the baseline model, significant weight difference is detected using paired samples t tests for network-based intervention strategies (p<0.05) but no difference is observed for the two conventional intervention strategies including choosing random or high-risk individuals (p>0.05). Targeting the most connected individuals minimizes the average population weight, average BMI, and generates a reduction of 2.70% and 1.38% in overweight and obesity prevalence. CONCLUSIONS:The simulations shows that targeting individuals on the basis of their social network attributes outperforms conventional targeting strategies. Future work needs to focus on how to further leverage social networks to curb obesity prevalence and enhance interventions for other chronic conditions using agent-based simulation.

SUBMITTER: Shi L

PROVIDER: S-EPMC7398540 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Evaluating social network-based weight loss interventions in Chinese population: An agent-based simulation.

Shi Liuyan L Zhang Liang L Lu Yun Y

PloS one 20200803 8

<h4>Objective</h4>The aim of this study is to assess network-based weight loss interventions in the Chinese setting using agent-based simulation.<h4>Methods</h4>An agent-based model incorporating social, environmental and personal influence is developed to simulate the obesity epidemic through an interconnected social network among a population of 2197 individuals from the nationally representative survey. Model parameters are collected from literature and existing database. To ensure the robust ...[more]

PMID: 32745125

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Project description:BACKGROUND:Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviors, including physical activity. However, designing successful social network interventions is a considerable research challenge. In this study, we rely on social network analysis and agent-based simulations to better understand and capitalize on the complex interplay of social networks and health behaviors. More specifically, we investigate criteria for selecting influence agents that can be expected to produce the most successful social network health interventions. OBJECTIVE:The aim of this study was to test which selection criterion to determine influence agents in a social network intervention resulted in the biggest increase in physical activity in the social network. To test the differences among the selection criteria, a computational model was used to simulate different social network interventions and observe the intervention's effect on the physical activity of primary and secondary school children within their school classes. As a next step, this study relied on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes than others based on network characteristics. METHODS:We used a previously validated agent-based model to understand how physical activity spreads in social networks and who was influencing the spread of behavior. From the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions with different selection criteria for the influence agents (ie, in-degree centrality, betweenness centrality, closeness centrality, and random influence agents) and a control condition (ie, no intervention). Subsequently, we investigated whether the detected variation of an intervention's success within school classes could be explained by structural characteristics of the social networks (ie, network density and network centralization). RESULTS:The 1-year simulations showed that social network interventions were more effective compared with the control condition (beta=.30; t100=3.23; P=.001). In addition, the social network interventions that used a measure of centrality to select influence agents outperformed the random influence agent intervention (beta=.46; t100=3.86; P<.001). Also, the closeness centrality condition outperformed the betweenness centrality condition (beta=.59; t100=2.02; P=.046). The anticipated interaction effects of the network characteristics were not observed. CONCLUSIONS:Social network intervention can be considered as a viable and promising intervention method to promote physical activity. We demonstrated the usefulness of applying social network analysis and agent-based modeling as part of the social network interventions' design process. We emphasize the importance of selecting the most successful influence agents and provide a better understanding of the role of network characteristics on the effectiveness of social network interventions.

Project description:PurposeThis study aims to examine whether enhancing standard behavioral weight loss interventions (standard behavioral weight loss program (SBWP)) with additional strategies at the initiation of intervention (ADOPT) or providing the additional strategies at predetermined times over the intervention period (MAINTAIN) enhances 18-month weight loss.MethodsThis was a clinical trial of participants (N = 195; mean ± SEM: age, 43.2 ± 8.6 yr; body mass index, 33.0 ± 3.4 kg·m) randomized to SBWP, ADOPT, or MAINTAIN. All participants were prescribed an energy-restricted diet and physical activity, with group intervention sessions delivered over 18 months. ADOPT participants received additional phone contact (months 1-3), supervised exercise (months 1-6), and behavior campaigns (months 4-9). MAINTAIN participants received additional phone contact (months 4-6), supervised exercise (months 7-12), and behavior campaigns (months 13-18).ResultsThere was a significant group-time interaction for weight loss (P = 0.0032). SBWP participants lost 9.3 ± 0.9, 7.8 ± 1.1, and 5.9 ± 1.2 kg at 6, 12, and 18 months, respectively. ADOPT participants lost 8.9 ± 0.9, 7.6 ± 1.2, and 5.8 ± 1.2 kg, whereas MAINTAIN participants lost 9.7 ± 0.9, 11.0 ± 1.2, and 9.0 ± 1.2 kg at 6, 12, and 18 months, respectively. The group-time interactions for SBWP versus MAINTAIN (P = 0.0033) and for ADOPT versus MAINTAIN (P = 0.0075) were significant. There was a significant group-time interaction for change in fitness (P = 0.0060). The group-time interaction for MAINTAIN versus ADOPT (P = 0.0018) was significant, with a trend for MAINTAIN versus SBWP (P = 0.0525).ConclusionsMAINTAIN improves 18-month weight loss compared with SBWP and ADOPT, with statistical trends indicating that MAINTAIN results in greater improvements in fitness. These results suggest that time-based strategies emphasizing physical activity confer greater benefits when delivered later and over the full course of intervention. This provides valuable information for the implementation of time-based strategies for improving long-term weight loss and fitness in overweight and obese adults.

Project description:BackgroundThe World Health Organization (WHO) estimates that the number of obese or overweight individuals worldwide will increase to 1.5 billion by 2015. Chronic diseases associated with overweight or obesity include diabetes, heart disease, hypertension and stroke.ObjectivesTo assess the effects of interactive computer-based interventions for weight loss or weight maintenance in overweight or obese people.Search methodsWe searched several electronic databases, including CENTRAL, MEDLINE, EMBASE, CINAHL, LILACS and PsycINFO, through 25 May 2011. We also searched clinical trials registries to identify studies. We scanned reference lists of included studies and relevant systematic reviews.Selection criteriaStudies were included if they were randomized controlled trials or quasi-randomized controlled trials that evaluated interactive computer-based weight loss or weight maintenance programs in adults with overweight or obesity. We excluded trials if the duration of the intervention was less than four weeks or the loss to follow-up was greater than 20% overall.Data collection and analysisTwo authors independently extracted study data and assessed risk of bias. Where interventions, control conditions, outcomes and time frames were similar between studies, we combined study data using meta-analysis.Main resultsWe included 14 weight loss studies with a total of 2537 participants, and four weight maintenance studies with a total of 1603 participants. Treatment duration was between four weeks and 30 months. At six months, computer-based interventions led to greater weight loss than minimal interventions (mean difference (MD) -1.5 kg; 95% confidence interval (CI) -2.1 to -0.9; two trials) but less weight loss than in-person treatment (MD 2.1 kg; 95% CI 0.8 to 3.4; one trial). At six months, computer-based interventions were superior to a minimal control intervention in limiting weight regain (MD -0.7 kg; 95% CI -1.2 to -0.2; two trials), but not superior to infrequent in-person treatment (MD 0.5 kg; 95% -0.5 to 1.6; two trials). We did not observe consistent differences in dietary or physical activity behaviors between intervention and control groups in either weight loss or weight maintenance trials. Three weight loss studies estimated the costs of computer-based interventions compared to usual care, however two of the studies were 11 and 28 years old, and recent advances in technology render these estimates unlikely to be applicable to current or future interventions, while the third study was conducted in active duty military personnel, and it is unclear whether the costs are relevant to other settings. One weight loss study reported the cost-effectiveness ratio for a weekly in-person weight loss intervention relative to a computer-based intervention as USD 7177 (EUR 5678) per life year gained (80% CI USD 3055 to USD 60,291 (EUR 2417 to EUR 47,702)). It is unclear whether this could be extrapolated to other studies. No data were identified on adverse events, morbidity, complications or health-related quality of life.Authors' conclusionsCompared to no intervention or minimal interventions (pamphlets, usual care), interactive computer-based interventions are an effective intervention for weight loss and weight maintenance. Compared to in-person interventions, interactive computer-based interventions result in smaller weight losses and lower levels of weight maintenance. The amount of additional weight loss, however, is relatively small and of brief duration, making the clinical significance of these differences unclear.

Dataset Information

Evaluating social network-based weight loss interventions in Chinese population: An agent-based simulation.

Publications

Evaluating social network-based weight loss interventions in Chinese population: An agent-based simulation.

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