The Effects of Social Support and Self-Efficacy on Weight Loss Maintenance

2015 ◽  
Vol 47 ◽  
pp. 136
Author(s):  
Kathryn Bus ◽  
Karissa L. Peyer ◽  
Laura D. Ellingson ◽  
Gregory J. Welk
Keyword(s):  
Social Support ◽  
Weight Loss ◽  
Self Efficacy ◽  
Weight Loss Maintenance

scholarly journals Social Network Analysis of the Effects of a Social Media–Based Weight Loss Intervention Targeting Adults of Low Socioeconomic Status: Single-Arm Intervention Trial

10.2196/24690 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e24690
Author(s):  
Ran Xu ◽  
David Cavallo
Keyword(s):  
Social Support ◽  
Social Media ◽  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Self Efficacy ◽  
Web Based ◽  
Network Characteristics ◽  
Using Data

Background Obesity is a known risk factor for cardiovascular disease risk factors, including hypertension and type II diabetes. Although numerous weight loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from web-based platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. Objective The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social media–based weight loss intervention. Methods We performed secondary analysis by using data from a pilot study that delivered a dietary and physical activity intervention to a group of participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period and linked participants’ network characteristics (eg, in-degree, out-degree, network constraint) to participants’ changes in theoretical mediators (ie, dietary knowledge, perceived social support, self-efficacy) and weight loss by using regression analysis. We also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. Results In this analysis, 47 participants from 2 waves completed the study and were included. We found that increases in the number of posts, comments, and reactions significantly predicted weight loss (β=–.94, P=.04); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009), and the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight loss (β=–.89, P=.02). Conclusions Our analyses using data from this pilot study linked participants’ network characteristics with changes in several important study outcomes of interest such as self-efficacy, social support, and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which web-based behavioral interventions affect participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and to further explore the relationship between network dynamics and study outcomes in similar and larger trials.

scholarly journals Using Social Network Analysis to Understand the Effects of a Social Media-based Weight Loss Intervention Targeting Low-SES Adults (Preprint)

2020 ◽  
Author(s):  
Ran Xu ◽  
David Cavallo
Keyword(s):  
Social Support ◽  
Social Media ◽  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Self Efficacy ◽  
Network Characteristics ◽  
Using Data ◽  
The Relationship

BACKGROUND Obesity is a known risk factor for cardiovascular disease (CVD) risk factors including hypertension and type II diabetes. Although numerous weight-loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from online platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. OBJECTIVE The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social-media based weight loss intervention. METHODS This study performed secondary analysis using data from a pilot study that delivered a dietary and physical activity intervention to a group of low-SES participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period, and linked participants’ network characteristics (e.g. in-degree, out-degree and network constraint) to participants’ changes in theoretical mediators (i.e. dietary knowledge, perceived social support, self-efficacy) and weight loss using regression analysis. This study also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. RESULTS 47 participants from two waves completed the study and were included in the analysis. We found that participants creating posts, comments and reactions predicted weight-loss (β=-.94, P=.042); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009); the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight-loss (Indirect effect=-.89, P=.017). CONCLUSIONS Our analyses using data from this pilot study have linked participants’ network characteristics with changes in several important study outcomes of interest, such as self-efficacy, social support and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which online behavioral interventions affects participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and further explore the relationship between network dynamics and study outcomes in similar and larger trials.

The role of social support in weight loss maintenance: results from the MedWeight study

2016 ◽  
Vol 39 (3) ◽  
pp. 511-518 ◽  
Author(s):  
Eleni Karfopoulou ◽  
Costas A. Anastasiou ◽  
Evangelia Avgeraki ◽  
Mary H. Kosmidis ◽  
Mary Yannakoulia
Keyword(s):  
Social Support ◽  
Weight Loss ◽  
Weight Loss Maintenance

scholarly journals A Test of Social Cognitive Theory to Explain Men’s Physical Activity During a Gender-Tailored Weight Loss Program

2016 ◽  
Vol 10 (6) ◽  
pp. NP176-NP187 ◽  
Author(s):  
Myles D. Young ◽  
Ronald C. Plotnikoff ◽  
Clare E. Collins ◽  
Robin Callister ◽  
Philip J. Morgan
Keyword(s):  
Physical Activity ◽  
Social Support ◽  
Weight Loss ◽  
Social Cognitive Theory ◽  
Self Efficacy ◽  
Social Cognitive ◽  
Cognitive Theory ◽  
Outcome Expectations ◽  
Weight Loss Program ◽  
Medium Effect

Physical inactivity is a leading contributor to the burden of disease in men. Social–cognitive theories may improve physical activity (PA) interventions by identifying which variables to target to maximize intervention impact. This study tested the utility of Bandura’s social cognitive theory (SCT) to explain men’s PA during a 3-month weight loss program. Participants were 204 overweight/obese men ( M [ SD] age = 46.6 [11.3] years; body mass index = 33.1 [3.5] kg/m2). A longitudinal, latent variable structural equation model tested the associations between SCT constructs (i.e., self-efficacy, outcome expectations, intention, and social support) and self-reported moderate-to-vigorous PA (MVPA) and examined the total PA variance explained by SCT. After controlling for Time 1 cognitions and behavior, the model fit the data well (χ2 = 73.9, degrees of freedom = 39, p < .001; normed χ2 = 1.9; comparative fit index = 0.96; standardized root mean residual = 0.059) and explained 65% of the variance in MVPA at Time 2. At Time 2, self-efficacy demonstrated the largest direct and total effects on MVPA (βdirect = .45, p < .001; βtotal = .67, p = .002). A small-to-medium effect was observed from intention to MVPA, but not from outcome expectations or social support. This study provides some evidence supporting the tenets of SCT when examining PA behavior in overweight and obese men. Future PA and weight loss interventions for men may benefit by targeting self-efficacy and intention, but the utility of targeting social support and outcome expectations requires further examination.

scholarly journals The SELF trial: A self-efficacy-based behavioral intervention trial for weight loss maintenance

Obesity ◽  
2015 ◽  
Vol 23 (11) ◽  
pp. 2175-2182 ◽  
Author(s):  
Lora E. Burke ◽  
Linda J. Ewing ◽  
Lei Ye ◽  
Mindi Styn ◽  
Yaguang Zheng ◽  
...  
Keyword(s):  
Weight Loss ◽  
Behavioral Intervention ◽  
Self Efficacy ◽  
Weight Loss Maintenance ◽  
The Self ◽  
Intervention Trial

Behavioral Mediators of Long-Term Weight Loss Maintenance

2013 ◽  
Author(s):  
J. W. Coughlin ◽  
C. M. Gullion ◽  
P. J. Brantley ◽  
V. J. Stevens ◽  
A. Bauck ◽  
...  
Keyword(s):  
Weight Loss ◽  
Weight Loss Maintenance
Sign in / Sign up

Export Citation Format

Share Document