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

Bumjo Oh, Belong Cho, Min Kyu Han, Hochun Choi, Mi Na Lee, Hee-Cheol Kang, Chang Hee Lee, Heeseong Yun, Youngho Kim, Bumjo Oh, Belong Cho, Min Kyu Han, Hochun Choi, Mi Na Lee, Hee-Cheol Kang, Chang Hee Lee, Heeseong Yun, Youngho Kim

Abstract

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://ichgcp.net/clinical-trials-registry/NCT01344811 (Archived by Webcite at http://www.webcitation.org/6alT2MmIB).

Keywords: diet; exercise; health information management; metabolic syndrome X; mobile health; obesity.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Examples of the SmartCare app.
Figure 2
Figure 2
Study flowchart and design.
Figure 3
Figure 3
Selection of the study participants.

References

    1. Maison P, Byrne CD, Hales CN, Day NE, Wareham NJ. Do different dimensions of the metabolic syndrome change together over time? Evidence supporting obesity as the central feature. Diabetes Care. 2001 Oct;24(10):1758–63.
    1. Everson SA, Goldberg DE, Helmrich SP, Lakka TA, Lynch JW, Kaplan GA, Salonen JT. Weight gain and the risk of developing insulin resistance syndrome. Diabetes Care. 1998 Oct;21(10):1637–43.
    1. Bestermann W, Houston MC, Basile J, Egan B, Ferrario CM, Lackland D, Hawkins RG, Reed J, Rogers P, Wise D, Moore MA. 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 metabolic syndrome. Am J Med Sci. 2005 Jun;329(6):292–305.
    1. Dulloo A, Montani J. Body composition, inflammation and thermogenesis in pathways to obesity and the metabolic syndrome: an overview. Obes Rev. 2012 Oct 29;13:1–5. doi: 10.1111/j.1467-789X.2012.01032.x.
    1. Lakka TA, Laaksonen DE. Physical activity in prevention and treatment of the metabolic syndrome. Appl Physiol Nutr Metab. 2007 Feb;32(1):76–88. doi: 10.1139/h06-113.
    1. Lewis C, Jacobs DJ, McCreath H, Kiefe C, Schreiner P, Smith D, Williams OD. Weight gain continues in the 1990s: 10-year trends in weight and overweight from the CARDIA study. Coronary Artery Risk Development in Young Adults. Am J Epidemiol. 2000 Jun 15;151(12):1172–81.
    1. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, Mullany EC, Biryukov S, Abbafati C, Abera SF, Abraham JP, Abu-Rmeileh NM, Achoki T, AlBuhairan FS, Alemu ZA, Alfonso R, Ali MK, Ali R, Guzman NA, Ammar W, Anwari P, Banerjee A, Barquera S, Basu S, Bennett DA, Bhutta Z, Blore J, Cabral N, Nonato IC, Chang J, Chowdhury R, Courville KJ, Criqui MH, Cundiff DK, Dabhadkar KC, Dandona L, Davis A, Dayama A, Dharmaratne SD, Ding EL, Durrani AM, Esteghamati A, Farzadfar F, Fay DF, Feigin VL, Flaxman A, Forouzanfar MH, Goto A, Green MA, Gupta R, Hafezi-Nejad N, Hankey GJ, Harewood HC, Havmoeller R, Hay S, Hernandez L, Husseini A, Idrisov BT, Ikeda N, Islami F, Jahangir E, Jassal SK, Jee SH, Jeffreys M, Jonas JB, Kabagambe EK, Khalifa SE, Kengne AP, Khader YS, Khang Y, Kim D, Kimokoti RW, Kinge JM, Kokubo Y, Kosen S, Kwan G, Lai T, Leinsalu M, Li Y, Liang X, Liu S, Logroscino G, Lotufo PA, Lu Y, Ma J, Mainoo NK, Mensah GA, Merriman TR, Mokdad AH, Moschandreas J, Naghavi M, Naheed A, Nand D, Narayan KM, Nelson EL, Neuhouser ML, Nisar MI, Ohkubo T, Oti SO, Pedroza A, Prabhakaran D, Roy N, Sampson U, Seo H, Sepanlou SG, Shibuya K, Shiri R, Shiue I, Singh GM, Singh JA, Skirbekk V, Stapelberg NJ, Sturua L, Sykes BL, Tobias M, Tran BX, Trasande L, Toyoshima H, van de Vijver S, Vasankari TJ, Veerman JL, Velasquez-Melendez G, Vlassov VV, Vollset SE, Vos T, Wang C, Wang X, Weiderpass E, Werdecker A, Wright JL, Yang YC, Yatsuya H, Yoon J, Yoon S, Zhao Y, Zhou M, Zhu S, Lopez AD, Murray CJ, Gakidou E. 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. Lancet. 2014 Aug 30;384(9945):766–81. doi: 10.1016/S0140-6736(14)60460-8.
    1. Kim CS, Ko S, Kwon H, Kim NH, Kim JH, Lim S, Choi SH, Song K, Won JC, Kim DJ, Cha B, Taskforce Team of Diabetes Fact Sheet of the Korean Diabetes Association Prevalence, awareness, and management of obesity in Korea: data from the Korea national health and nutrition examination survey (1998-2011) Diabetes Metab J. 2014 Feb;38(1):35–43. doi: 10.4093/dmj.2014.38.1.35.
    1. Thomas JG, Wing RR. Health-e-call, a smartphone-assisted behavioral obesity treatment: pilot study. JMIR Mhealth Uhealth. 2013;1(1):e3. doi: 10.2196/mhealth.2164.
    1. Kumanyika SK, Obarzanek E, Stettler N, Bell R, Field AE, Fortmann SP, Franklin BA, Gillman MW, Lewis CE, Poston WC, Stevens J, Hong Y, American Heart Association Council on EpidemiologyPrevention‚ Interdisciplinary Committee for Prevention Population-based prevention of obesity: the need for comprehensive promotion of healthful eating, physical activity, and energy balance: a scientific statement from American Heart Association Council on Epidemiology and Prevention, Interdisciplinary Committee for Prevention (formerly the expert panel on population and prevention science) Circulation. 2008 Jul 22;118(4):428–64. doi: 10.1161/CIRCULATIONAHA.108.189702.
    1. Stone N, Saxon D. Approach to treatment of the patient with metabolic syndrome: lifestyle therapy. Am J Cardiol. 2005 Aug 22;96(4A):15E–21E. doi: 10.1016/j.amjcard.2005.05.010.
    1. O'Malley G, Dowdall G, Burls A, Perry I, Curran N. Exploring the usability of a mobile app for adolescent obesity management. JMIR Mhealth Uhealth. 2014;2(2):e29. doi: 10.2196/mhealth.3262.
    1. Metzgar CJ, Nickols-Richardson SM. Determinants of weight gain prevention in young adult and midlife women: study design and protocol of a randomized controlled trial. JMIR Res Protoc. 2015;4(1):e36. doi: 10.2196/resprot.4008.
    1. Goode A, Winkler EA, Reeves M, Eakin E. Relationship between intervention dose and outcomes in living well with diabetes-a randomized trial of a telephone-delivered lifestyle-based weight loss intervention. Am J Health Promot. 2014 Nov 5; doi: 10.4278/ajhp.140206-QUAN-62.
    1. Hunter CM, Peterson AL, Alvarez LM, Poston WC, Brundige AR, Haddock CK, Van Brunt DL, Foreyt JP. Weight management using the internet a randomized controlled trial. Am J Prev Med. 2008 Feb;34(2):119–26. doi: 10.1016/j.amepre.2007.09.026.
    1. Tate DF, Wing RR, Winett RA. Using Internet technology to deliver a behavioral weight loss program. JAMA. 2001 Mar 7;285(9):1172–7.
    1. Hebden L, Cook A, van der Ploeg HP, Allman-Farinelli M. Development of smartphone applications for nutrition and physical activity behavior change. JMIR Res Protoc. 2012;1(2):e9. doi: 10.2196/resprot.2205.
    1. Khaylis A, Yiaslas T, Bergstrom J, Gore-Felton C. A review of efficacious technology-based weight-loss interventions: five key components. Telemed J E Health. 2010 Nov;16(9):931–8. doi: 10.1089/tmj.2010.0065.
    1. Lim J, Choi O, Na H, Baik D. A context-aware fitness guide system for exercise optimization in U-health. IEEE Trans Inf Technol Biomed. 2009 May;13(3):370–9. doi: 10.1109/TITB.2009.2013941.
    1. Carter MC, Burley VJ, Cade JE. Development of ‘My Meal Mate’ - a smartphone intervention for weight loss. Nutrition Bulletin. 2013 Feb 07;38(1):80–84. doi: 10.1111/nbu.12016.
    1. Alberti KG, Zimmet P, Shaw J, IDF Epidemiology Task Force Consensus Group The metabolic syndrome--a new worldwide definition. Lancet. 2005;366(9491):1059–62. doi: 10.1016/S0140-6736(05)67402-8.
    1. Oh SW, Shin S, Yun YH, Yoo T, Huh B. Cut-off point of BMI and obesity-related comorbidities and mortality in middle-aged Koreans. Obes Res. 2004 Dec;12(12):2031–40. doi: 10.1038/oby.2004.254.
    1. Shim Y, Paik H. Reanalysis of 2007 Korean National Health and Nutrition Examination Survey (2007 KNHANES) results by CAN-Pro 3.0 Nutrient Database. Korean J Nutr. 2009;42(6):577–95. doi: 10.4163/kjn.2009.42.6.577.
    1. Twisk J, Proper K. Evaluation of the results of a randomized controlled trial: how to define changes between baseline and follow-up. J Clin Epidemiol. 2004 Mar;57(3):223–8. doi: 10.1016/j.jclinepi.2003.07.009.
    1. Vickers AJ, Altman DG. Statistics notes: analysing controlled trials with baseline and follow up measurements. BMJ. 2001 Nov 10;323(7321):1123–4.
    1. Wittes J. Sample size calculations for randomized controlled trials. Epidemiol Rev. 2002;24(1):39–53.
    1. Borm GF, Fransen J, Lemmens WA. A simple sample size formula for analysis of covariance in randomized clinical trials. J Clin Epidemiol. 2007 Dec;60(12):1234–8. doi: 10.1016/j.jclinepi.2007.02.006.
    1. Burke LE, Styn MA, Sereika SM, Conroy MB, Ye L, Glanz K, Sevick MA, Ewing LJ. Using mHealth technology to enhance self-monitoring for weight loss: a randomized trial. Am J Prev Med. 2012 Jul;43(1):20–6. doi: 10.1016/j.amepre.2012.03.016.
    1. Lewis TL, Wyatt JC. mHealth and mobile medical apps: a framework to assess risk and promote safer use. J Med Internet Res. 2014;16(9):e210. doi: 10.2196/jmir.3133.
    1. Song T, Ryu S, Lee SH. U-health service for managing chronic disease: a case study on managing metabolic syndrome in a health center in South Korea. Healthc Inform Res. 2011 Dec;17(4):260–6. doi: 10.4258/hir.2011.17.4.260.
    1. Bacigalupo R, Cudd P, Littlewood C, Bissell P, Hawley MS, Buckley WH. Interventions employing mobile technology for overweight and obesity: an early systematic review of randomized controlled trials. Obes Rev. 2013 Apr;14(4):279–91. doi: 10.1111/obr.12006.
    1. Fernandez ML. The metabolic syndrome. Nutr Rev. 2007 Jun;65(6 Pt 2):S30–4.
    1. Roberts CK, Hevener AL, Barnard RJ. Metabolic syndrome and insulin resistance: underlying causes and modification by exercise training. Compr Physiol. 2013 Jan;3(1):1–58. doi: 10.1002/cphy.c110062.
    1. Englberger L, Halavatau V, Yasuda Y, Yamazaki R. The tonga healthy weight loss program 1995-97. Asia Pac J Clin Nutr. 1999 Jun;8(2):142–8.
    1. Moore TJ, Alsabeeh N, Apovian CM, Murphy MC, Coffman GA, Cullum-Dugan D, Jenkins M, Cabral H. Weight, blood pressure, and dietary benefits after 12 months of a web-based nutrition education program (DASH for health): longitudinal observational study. J Med Internet Res. 2008;10(4):e52. doi: 10.2196/jmir.1114.
    1. Wieland L, Falzon L, Sciamanna C, Trudeau K, Brodney S, Schwartz J, Davidson KW. Interactive computer-based interventions for weight loss or weight maintenance in overweight or obese people. Cochrane Database Syst Rev. 2012;8:CD007675. doi: 10.1002/14651858.CD007675.pub2.
    1. Turner-McGrievy GM, Beets MW, Moore JB, Kaczynski AT, Barr-Anderson DJ, Tate DF. Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inform Assoc. 2013 May 1;20(3):513–8. doi: 10.1136/amiajnl-2012-001510.
    1. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs. 2013;28(4):320–9. doi: 10.1097/JCN.0b013e318250a3e7.
    1. Pellegrini CA, Duncan JM, Moller AC, Buscemi J, Sularz A, DeMott A, Pictor A, Pagoto S, Siddique J, Spring B. A smartphone-supported weight loss program: design of the ENGAGED randomized controlled trial. BMC Public Health. 2012;12:1041. doi: 10.1186/1471-2458-12-1041.
    1. Morgan PJ, Lubans DR, Collins CE, Warren JM, Callister R. The SHED-IT randomized controlled trial: evaluation of an Internet-based weight-loss program for men. Obesity (Silver Spring) 2009 Nov;17(11):2025–32. doi: 10.1038/oby.2009.85.
    1. Carter MC, Burley VJ, Nykjaer C, Cade JE. Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. J Med Internet Res. 2013;15(4):e32. doi: 10.2196/jmir.2283.
    1. Carter MC, Burley VJ, Nykjaer C, Cade JE. 'My Meal Mate' (MMM): validation of the diet measures captured on a smartphone application to facilitate weight loss. Br J Nutr. 2013 Feb 14;109(3):539–46. doi: 10.1017/S0007114512001353.
    1. Duncan JM, Janke EA, Kozak AT, Roehrig M, Russell SW, McFadden HG, Demott A, Pictor A, Hedeker D, Spring B. PDA+: a personal digital assistant for obesity treatment - an RCT testing the use of technology to enhance weight loss treatment for veterans. BMC Public Health. 2011;11:223. doi: 10.1186/1471-2458-11-223.
    1. Tufano JT, Karras BT. Mobile eHealth interventions for obesity: a timely opportunity to leverage convergence trends. J Med Internet Res. 2005;7(5):e58. doi: 10.2196/jmir.7.5.e58.
    1. Touati F, Tabish R. u-Healthcare system: state-of-the-art review and challenges. J Med Syst. 2013 Jun;37(3):9949. doi: 10.1007/s10916-013-9949-0.

Source: PubMed

3
Abonnere