Dataset Information

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model.

ABSTRACT: High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecological concepts that often guide the design of in-person and electronically-mediated interventions. Therefore, this paper: (1) proposes a conceptual model that integrates principles from network science and ecology across in-person and electronically-mediated intervention modes; and (2) illustrates the application of this model to the design and evaluation of a social network intervention for PA.A conceptual model for engineering social networks was developed based on a scoping literature review of modifiable social influences on PA. The model guided the design of a cluster randomized controlled trial in which 308 sedentary adults were randomly assigned to three groups: WalkLink+: prompted and provided feedback on participants' online and in-person social-network interactions to expand networks for PA, plus provided evidence-based online walking program and weekly walking tips; WalkLink: evidence-based online walking program and weekly tips only; Minimal Treatment Control: weekly tips only. The effects of these treatment conditions were assessed at baseline, post-program, and 6-month follow-up. The primary outcome was accelerometer-measured PA. Secondary outcomes included objectively-measured aerobic fitness, body mass index, waist circumference, blood pressure, and neighborhood walkability; and self-reported measures of the physical environment, social network environment, and social network interactions. The differential effects of the three treatment conditions on primary and secondary outcomes will be analyzed using general linear modeling (GLM), or generalized linear modeling if the assumptions for GLM cannot be met.Results will contribute to greater understanding of how to conceptualize and implement social networks to support long-term PA. Establishing social networks for PA across multiple life settings could contribute to cultural norms that sustain active living.ClinicalTrials.gov NCT01142804.

SUBMITTER: Rovniak LS

PROVIDER: S-EPMC3844372 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model.

Rovniak Liza S LS Sallis James F JF Kraschnewski Jennifer L JL Sciamanna Christopher N CN Kiser Elizabeth J EJ Ray Chester A CA Chinchilli Vernon M VM Ding Ding D Matthews Stephen A SA Bopp Melissa M George Daniel R DR Hovell Melbourne F MF

BMC public health 20130814

<h4>Background</h4>High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecologic ...[more]

PMID: 23945138

Similar Datasets

Project description:BACKGROUND:While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. OBJECTIVE:The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. METHODS:We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. RESULTS:The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. CONCLUSIONS:We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.

Project description:IntroductionThe World Health Organisation endorses community-based programmes as a cost-effective, feasible and a 'best buy' in the prevention and management of non-communicable diseases (NCDs). These programmes are particularly successful when the community actively participates in its design, implementation and evaluation. However, they may be only useful insofar as they can be scaled up and sustained in some meaningful way. Social network research may serve as an important tool for determining the underlying mechanisms that contribute to this process. The aim of this planned scoping review is to map and collate literature on the role of social networks in scaling-up and sustaining community-based physical activity and diet programmes in low-income and middle-income countries.Methods and analysisThis scoping review protocol has been planned around the Arksey and O'Malley framework and its enhancement. Inclusion criteria are peer-reviewed articles and grey literature exploring the role of social networks in the scale-up and/or sustainability of NCD prevention community-based programmes in adult populations. Studies must have been published since 2000, in English, and be based in a low-income or middle-income country. The following databases will be used for this review: PubMed, Cochrane, Scopus, Web of Science, CINAHL, SocIndex, the International Bibliography of the Social Sciences, Google and Google Scholar. Books, conference abstracts and research focused only on children will be excluded. Two reviewers will independently select and extract eligible studies. Included publications will be thematically analysed using the Framework Approach.Ethics and disseminationEthical approval will not be sought for this review as no individual-level data or human participants will be involved. This protocol is registered on the Open Science Framework (https://doi.org/10.17605/OSF.IO/KG7TX). The findings from the review will be published in an accredited journal. The Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews checklist will be used to support transparency and guide translation of the review.

Dataset Information

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model.

Publications

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets