The Effectiveness of Mobile Phone-Based Care for Weight Control in Metabolic Syndrome Patients: Randomized Controlled Trial

Background Overweight and obesity, due to a Westernized diet and lack of exercise, are serious global problems that negatively affect not only personal health, but national economies as well. To solve these problems, preventative-based approaches should be taken rather than medical treatments after the occurrence of disease. The improvement of individual life habits, through continuous care, is thus a paramount, long-term treatment goal. This study describes the effects of ubiquitous health care (uHealth care) or SmartCare services in the treatment of weight loss and obesity. Objective The aim of this study is to evaluate the effect of SmartCare services on weight loss compared to the effects of existing outpatient treatments in obese patients with metabolic syndrome. Methods Metabolic syndrome patients who met the inclusion/exclusion criteria were enrolled in the study and randomized into an intervention or control group. The intervention group was provided with remote monitoring and health care services in addition to the existing treatment. The control group was provided with only the existing treatment. Pedometers were given to all of the patients. Additionally, mobile phones and body composition monitors were provided to the intervention group while body weight scales were provided to the control group. The patients visited the hospitals at 12 and 24 weeks following the baseline examination to receive efficacy and safety evaluations. Results Mean weight reduction from baseline to week 24 was measured as a primary efficacy evaluation parameter and was found to be 2.21 kg (SD 3.60) and 0.77 kg (SD 2.77) in the intervention and control group, respectively. The intervention group had a larger decrement compared to the control group (P<.001). Among the secondary efficacy evaluation parameters, body mass index (BMI) (P<.001), body fat rate (P=.001), decrement of waist measurement (P<.001), and diet habit (P=.012) improvement ratings from baseline to week 24 were found to be superior in the intervention group compared with the control group. The proportion of patients whose body weight decreased by ≥10%, lipid profiles, blood pressure, prevalence of metabolic syndrome, change in the number of metabolic syndrome elements, smoking rate, drinking rate, and physical activity were not statistically significant between the groups. Conclusions The efficacy of SmartCare services was confirmed as the intervention group that received both SmartCare services and the existing treatment had superior results compared with the control group that only received the existing treatment. Importantly, no specific problems with respect to safety concerns were observed. SmartCare service is thus an effective way to control the weight of obese patients with metabolic syndrome. Trial Registration Clinicaltrials.gov NCT01344811; https://clinicaltrials.gov/ct2/show/NCT01344811 (Archived by Webcite at http://www.webcitation.org/6alT2MmIB)

[1]  Karin Proper,et al.  Evaluation of the results of a randomized controlled trial: how to define changes between baseline and follow-up. , 2004, Journal of clinical epidemiology.

[2]  M. Fernández The metabolic syndrome. , 2007, Nutrition reviews.

[3]  M. Sevick,et al.  Using mHealth technology to enhance self-monitoring for weight loss: a randomized trial. , 2012, American journal of preventive medicine.

[4]  Howard Cabral,et al.  Weight, Blood Pressure, and Dietary Benefits After 12 Months of a Web-based Nutrition Education Program (DASH for Health): Longitudinal Observational Study , 2008, Journal of medical Internet research.

[5]  J. Schwartz,et al.  Interactive computer-based interventions for weight loss or weight maintenance in overweight or obese people. , 2012, The Cochrane database of systematic reviews.

[6]  D. Altman,et al.  Analysing controlled trials with baseline and follow up measurements , 2001, BMJ : British Medical Journal.

[7]  A. Hevener,et al.  Metabolic syndrome and insulin resistance: underlying causes and modification by exercise training. , 2013, Comprehensive Physiology.

[8]  S. Nickols-Richardson,et al.  Determinants of Weight Gain Prevention in Young Adult and Midlife Women: Study Design and Protocol of a Randomized Controlled Trial , 2015, JMIR research protocols.

[9]  Neil J Stone,et al.  Approach to treatment of the patient with metabolic syndrome: lifestyle therapy. , 2005, The American journal of cardiology.

[10]  Margaret Allman-Farinelli,et al.  Development of Smartphone Applications for Nutrition and Physical Activity Behavior Change , 2012, JMIR research protocols.

[11]  M. Carter,et al.  Adherence to a Smartphone Application for Weight Loss Compared to Website and Paper Diary: Pilot Randomized Controlled Trial , 2013, Journal of medical Internet research.

[12]  Doo-Kwon Baik,et al.  A Context-Aware Fitness Guide System for Exercise Optimization in U-Health , 2009, IEEE Transactions on Information Technology in Biomedicine.

[13]  P. Cudd,et al.  Interventions employing mobile technology for overweight and obesity: an early systematic review of randomized controlled trials , 2012, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[14]  T. Lakka,et al.  Physical activity in prevention and treatment of the metabolic syndrome. , 2007, Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme.

[15]  Thomas Jg,et al.  Health-e-call, a smartphone-assisted behavioral obesity treatment: pilot study. , 2013 .

[16]  Andrew T. Kaczynski,et al.  Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program , 2013, J. Am. Medical Informatics Assoc..

[17]  D. Lackland,et al.  Addressing the Global Cardiovascular Risk of Hypertension, Dyslipidemia, Diabetes Mellitus, and the Metabolic Syndrome in the Southeastern United States, Part II: Treatment Recommendations for Management of the Global Cardiovascular Risk of Hypertension, Dyslipidemia, Diabetes Mellitus, and the Meta , 2005, The American journal of the medical sciences.

[18]  E. Eakin,et al.  Relationship between Intervention Dose and Outcomes in Living Well with Diabetes—A Randomized Trial of a Telephone-Delivered Lifestyle-Based Weight Loss Intervention , 2015, American journal of health promotion : AJHP.

[19]  A G Dulloo,et al.  Body composition, inflammation and thermogenesis in pathways to obesity and the metabolic syndrome: an overview , 2012, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[20]  Janet Wittes,et al.  Sample size calculations for randomized controlled trials. , 2002, Epidemiologic reviews.

[21]  Victoria J. Burley,et al.  Development of ‘My Meal Mate’ – A smartphone intervention for weight loss , 2013 .

[22]  T. L. Lewis,et al.  mHealth and Mobile Medical Apps: A Framework to Assess Risk and Promote Safer Use , 2014, Journal of medical Internet research.

[23]  Arlen C. Moller,et al.  A smartphone-supported weight loss program: design of the ENGAGED randomized controlled trial , 2012, BMC Public Health.

[24]  Paul Zimmet,et al.  The metabolic syndrome—a new worldwide definition , 2005, The Lancet.

[25]  M. Carter,et al.  'My Meal Mate' (MMM): validation of the diet measures captured on a smartphone application to facilitate weight loss. , 2013, The British journal of nutrition.

[26]  Bryant T Karras,et al.  Mobile eHealth Interventions for Obesity: A Timely Opportunity to Leverage Convergence Trends , 2005, Journal of medical Internet research.

[27]  H. Kwon,et al.  Prevalence, awareness, and management of obesity in Korea: data from the Korea national health and nutrition examination survey (1998-2011). , 2014, Diabetes & metabolism journal.

[28]  Alan D. Lopez,et al.  Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013 , 2014, The Lancet.

[29]  Farid Touati,et al.  U-Healthcare System: State-of-the-Art Review and Challenges , 2013, Journal of Medical Systems.

[30]  N. Day,et al.  Do different dimensions of the metabolic syndrome change together over time? Evidence supporting obesity as the central feature. , 2001, Diabetes care.

[31]  Christine M Hunter,et al.  Weight management using the internet a randomized controlled trial. , 2008, American journal of preventive medicine.

[32]  Amanda Burls,et al.  Exploring the Usability of a Mobile App for Adolescent Obesity Management , 2014, JMIR mHealth and uHealth.

[33]  Jerilyn K Allen,et al.  Mobile phone interventions to increase physical activity and reduce weight: a systematic review. , 2013, The Journal of cardiovascular nursing.

[34]  T. Lakka,et al.  Weight Gain and the Risk of Developing Insulin Resistance Syndrome , 1998, Diabetes Care.

[35]  H. Paik,et al.  Reanalysis of 2007 Korean National Health and Nutrition Examination Survey (2007 KNHANES) Results by CAN-Pro 3.0 Nutrient Database , 2009 .

[36]  L. Englberger,et al.  The tonga healthy weight loss program 1995-97. , 1999, Asia Pacific journal of clinical nutrition.

[37]  C. Gore-felton,et al.  A review of efficacious technology-based weight-loss interventions: five key components. , 2010, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[38]  R. Winett,et al.  Using Internet technology to deliver a behavioral weight loss program. , 2001, JAMA.

[39]  S. Oh,et al.  Cut-off point of BMI and obesity-related comorbidities and mortality in middle-aged Koreans. , 2004, Obesity research.

[40]  Seewon Ryu,et al.  U-Health Service for Managing Chronic Disease: A Case Study on Managing Metabolic Syndrome in a Health Center in South Korea , 2011, Healthcare informatics research.

[41]  D R Jacobs,et al.  Weight gain continues in the 1990s: 10-year trends in weight and overweight from the CARDIA study. Coronary Artery Risk Development in Young Adults. , 2000, American journal of epidemiology.

[42]  George F Borm,et al.  A simple sample size formula for analysis of covariance in randomized clinical trials. , 2007, Journal of clinical epidemiology.

[43]  B. Spring,et al.  PDA+: A Personal Digital Assistant for Obesity Treatment - An RCT testing the use of technology to enhance weight loss treatment for veterans , 2011, BMC public health.

[44]  R. Callister,et al.  The SHED‐IT Randomized Controlled Trial: Evaluation of an Internet‐based Weight‐loss Program for Men , 2009, Obesity.