Exercise as it relates to Disease/Smartphone social networks and weight loss
Over 70 per cent of adults in the US are overweight or obese with a Body Mass Index (BMI) exceeding 25 kg/m2. Studies have shown that behavioral interventions targeting improvements in diet and physical activity are an effective method to help individuals to lose weight, maintain it and decrease overall chronic disease and health risks that come with being overweight.
With the majority (64%) of adults in the US owning a smartphone, mobile health apps have many advantages over other weight loss interventions including accessibility, low cost, ease of implementation, and overall reduction of participant burden. This study tests the efficacy of a weight loss/weight tracking mobile app used by US adults.
- 1 Where is the research from?
- 2 What kind of research was this?
- 3 Does the level of evidence from these, and other types of studies, differ?
- 4 What did the research involve?
- 5 What were the basic results?
- 6 How did the researchers interpret the results?
- 7 What conclusions can we take from this research?
- 8 What real-world implications does this research have?
- 9 References
- 10 Further reading
Where is the research from?Edit
The study was conducted by the USC’s Department of Heath Promotion and Behavior and its Department of Computer Science and Engineering. The research was approved and monitored by the USC’s Institutional review board.
What kind of research was this?Edit
This is a randomised clinical trial (RCT) a type of scientific (often medical) experiment which aims to reduce bias when testing a new treatment. The people participating in the trial are randomly allocated to either the group receiving the treatment under investigation or to a control group who receive standard or placebo treatment.
Does the level of evidence from these, and other types of studies, differ?Edit
Findings from this research are consistent with similar studies suggesting that activities based on Social Cognitive Theory (SCT) like social support, self efficacy, self monitoring, and outcome expectations lead to positive behaviours and significant reductions in participant’s BMI.
What did the research involve?Edit
The 12 week study involved 42 overweight or obese adults (BMI 25-49.9 kg m2). Half were allocated to the treatment group using the Social POD app developed by the researchers. The remainder formed the control group and used a commercially available weight tracking app (Calorie Counter by Fat Secret).
Participants first completed a series of baseline tests and dietary recalls and were trained on how to operate their assigned mobile app. Weight and height measurements were also obtained. Over the 12 week study, all participants were sent 2 emails each week containing Theory Based Podcasts (TBPs) with nutrition and exercise information, goal setting and an audio diary of weight loss progress and challenges experienced by people who successfully lost weight. Participants also completed a weekly survey on their progress.
In addition, participants in the treatment group were sent notifications around meal times and could send messages of encouragement and star rewards to each other.
Table 1. Results
|Progression Measures||Baseline||Post 12 weeks|
|Average Weight (kg)|
|Social POD app (n=21)||102.1||96.8|
|Fat Secret app (n=21)||92.0||89.7|
|Average BMI (kg/m2)|
|Social POD app (n=21)||36.4||34.5|
|Fat Secret app (n=21)||33.3||32.4|
What were the basic results?Edit
At the end of the 12 week study the people using the Social POD app had on average lost 5.3 kgs, more than double the weight loss of those who used the commercially available weight tracking app (control group) and experimental groups in previous studies (2.2 kgs).
How did the researchers interpret the results?Edit
The researchers found that there were statistically significant benefits (p <0.05) with using the Social POD mobile app compared to the comparison app in terms of engagement with the app, level of positive expectations, weight loss and BMI reduction.
Interestingly, there were no significant differences in the reported calorific intake or energy expenditure between the two groups. This points to possible issues of bias in participant recall.
What conclusions can we take from this research?Edit
Carefully designed, theory based techniques to increase physical activity and lower calorific intake can lead to greater decreases in BMI and weight for users compared to commercially available weight loss mobile apps. The approach used by the Social POD app that incentivized self monitoring through a digital point system and prompted participants to self monitor throughout the day, demonstrates that these may be key features that could be added to standard mobile health interventions to achieve more effective weight loss in adults.
What real-world implications does this research have?Edit
With the fast rise in both smartphone use and rates of obesity in developed countries, this mobile health intervention has the potential to be widely used to help adults to lose weight and reduce their risk of developing or further developing chronic diseases. This intervention was successful with little participant contact from those running the study, increasing the potential of successfully scaling up this type of intervention in future.
- Flegal, Katherine M; Carroll, Margaret D; Kit, Brian K; Ogden, Cynthia L (2012). "Prevalence of Obesity and Trends in the Distribution of Body Mass Index Among US Adults, 1999-2010". JAMA 307 (5): 491–7. doi:10.1001/jama.2012.39. PMID 22253363.
- Lyzwinski, Lynnette (2014). "A Systematic Review and Meta-Analysis of Mobile Devices and Weight Loss with an Intervention Content Analysis". Journal of Personalized Medicine 4 (3): 311–85. doi:10.3390/jpm4030311. PMID 25563356.
- Chang, Tammy; Chopra, Vineet; Zhang, Catherine; Woolford, Susan J (2013). "The Role of Social Media in Online Weight Management: Systematic Review". Journal of Medical Internet Research 15 (11): e262. doi:10.2196/jmir.2852. PMID 24287455.
- Turner-McGrievy, Brie (June 23, 2015). "The Social Pounds Off Digitally (Social POD) Study (SocialPOD)". ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT02344836.
- Ko, Linda K; Turner-Mcgrievy, Gabrielle M; Campbell, Marci K (2013). "Information Processing Versus Social Cognitive Mediators of Weight Loss in a Podcast-Delivered Health Intervention". Health Education & Behavior 41 (2): 197–206. doi:10.1177/1090198113504413. PMID 24082027.
- Bahr, David B; Browning, Raymond C; Wyatt, Holly R; Hill, James O (2009). "Exploiting Social Networks to Mitigate the Obesity Epidemic". Obesity 17 (4): 723–8. doi:10.1038/oby.2008.615. PMID 19148124.
- Vanderweele, Tyler J (2011). "Sensitivity Analysis for Contagion Effects in Social Networks". Sociological Methods & Research 40 (2): 240–255. doi:10.1177/0049124111404821. PMID 25580037.
- Hales, Sarah; Turner-Mcgrievy, Gabrielle M; Wilcox, Sara; Fahim, Arjang; Davis, Rachel E; Huhns, Michael; Valafar, Homayoun (2016). "Social networks for improving healthy weight loss behaviors for overweight and obese adults: A randomized clinical trial of the social pounds off digitally (Social POD) mobile app". International Journal of Medical Informatics 94: 81–90. doi:10.1016/j.ijmedinf.2016.07.003. PMID 27573315.
- Liu, Chang; Zhu, Qing; Holroyd, Kenneth A; Seng, Elizabeth K (2011). "Status and trends of mobile-health applications for iOS devices: A developer's perspective". Journal of Systems and Software 84 (11): 2022. doi:10.1016/j.jss.2011.06.049.
- Martínez-Pérez, Borja; de la Torre-Díez, Isabel; López-Coronado, Miguel (2013). "Mobile Health Applications for the Most Prevalent Conditions by the World Health Organization: Review and Analysis". Journal of Medical Internet Research 15 (6): e120. doi:10.2196/jmir.2600. PMID 23770578.
- Kahn, J. G; Yang, J. S; Kahn, J. S (2010). "'Mobile' Health Needs and Opportunities in Developing Countries". Health Affairs 29 (2): 252–8. doi:10.1377/hlthaff.2009.0965. PMID 20348069.
- Weinstein, Ronald S; Lopez, Ana Maria; Joseph, Bellal A; Erps, Kristine A; Holcomb, Michael; Barker, Gail P; Krupinski, Elizabeth A (2014). "Telemedicine, Telehealth, and Mobile Health Applications That Work: Opportunities and Barriers". The American Journal of Medicine 127 (3): 183–7. doi:10.1016/j.amjmed.2013.09.032. PMID 24384059.