A Novel Diabetes Prevention Intervention Using a Mobile App

A Randomized Controlled Trial With Overweight Adults at Risk


      Mobile phone technology may be a cost-effective and convenient way to deliver proven weight-loss interventions and thereby prevent or delay onset of type 2 diabetes. The purpose of this study was to examine the feasibility and efficacy of a diabetes prevention intervention combined with a mobile app and pedometer in English-speaking overweight adults at risk for type 2 diabetes.




      Participants included 61 overweight adults with a mean age (SD) of 55.2 (9.0) years. Seventy-seven percent were women, 48% were racial/ethnic minorities, and baseline BMI was 33.3 (6.0).


      The curriculum was adapted from the Diabetes Prevention Program, with the frequency of in-person sessions reduced from 16 to six sessions and group exercise sessions replaced by a home-based exercise program. A study-developed mobile phone app and pedometer augmented the intervention and provided self-monitoring tools.

      Main outcome measure

      Weight loss.


      Data were collected in 2012 and 2013 and were analyzed in 2014. In intention-to-treat analyses, the intervention group (n=30) lost an average of 6.2 (5.9) kg (–6.8% [5.7%]) between baseline and 5-month follow-up compared to the control group’s (n=31) gain of 0.3 (3.0) kg (0.3% [5.7%]) (p<0.001). The intervention group’s steps per day increased by 2,551 (4,712) compared to the control group’s decrease of 734 (3,308) steps per day (p<0.001). In comparison, the intervention group had greater reductions in hip circumference (p<0.001); blood pressure (p<0.05); and intake of saturated fat (p=0.007) and sugar-sweetened beverages (p=0.02). The intervention had no significant effect on fasting lipid or glucose levels.


      The significant weight loss resulting from this modified combined mobile app and pedometer intervention for overweight adults warrants further investigation in a larger trial.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to American Journal of Preventive Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Knowler W.C.
        • Barrett-Connor E.
        • Fowler S.E.
        • et al.
        • Diabetes Prevention Program Research Group
        Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.
        N Engl J Med. 2002; 346: 393-403
        • Pan X.R.
        • Li G.W.
        • Hu Y.H.
        • et al.
        Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study.
        Diabetes Care. 1997; 20: 537-544
        • Tuomilehto J.
        • Lindstrom J.
        • Eriksson J.G.
        • et al.
        Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.
        N Engl J Med. 2001; 344: 1343-1350
        • Hernan W.H.
        • Brandle M.
        • Zhang P.
        • et al.
        Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program.
        Diabetes Care. 2003; 26: 36-47
      1. Pew Research Institute. Mobile Technology Fact Sheet. 2014.

        • Khalaf S.
        Apps solidify leadership six years into the mobile revolution. 2014;
        • Bacigalupo R.
        • Cudd P.
        • Littlewood C.
        • Bissell P.
        • Hawley M.S.
        • Buckley Woods H.
        Interventions employing mobile technology for overweight and obesity: an early systematic review of randomized controlled trials.
        Obes Rev. 2013; 14: 279-291
        • WHO.
        Physical Status: The Use and Interpretation of Anthropometry..
        WHO, Geneva1995
        • American Diabetes Association
        Are you at risk for type 2 diabetes?.
        Diabetes Risk Test. 2014;
        • Taylor-Piliae R.E.
        • Norton L.C.
        • Haskell W.L.
        • et al.
        Validation of a new brief physical activity survey among men and women aged 60-69 years.
        Am J Epidemiol. 2006; 164: 598-606
        • Borson S.
        • Scanlan J.M.
        • Chen P.
        • Ganguli M.
        The Mini-Cog as a screen for dementia: validation in a population-based sample.
        J Am Geriatr Soc. 2003; 51: 1451-1454
        • Borson S.
        • Scanlan J.
        • Brush M.
        • Vitaliano P.
        • Dokmak A.
        The Mini-Cog: a cognitive “vital signs” measure for dementia screening in multi-lingual elderly..
        Int J Geriatr Psychiatry. 2000; 15: 1021-1027<1021::AID-GPS234>3.0.CO;2-6
        • USDHHS.
        Prediabetes: What You Need to Know.
        National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD2007
        • Tudor-Locke C.
        • Bassett Jr, D.R.
        • Rutherford W.J.
        • et al.
        BMI-referenced cut points for pedometer-determined steps per day in adults.
        J Phys Act Health. 2008; 5: S126-S139
        • Block G.
        • Woods M.
        • Potosky A.
        • Clifford C.
        Validation of a self-administered diet history questionnaire using multiple diet records.
        J Clin Epidemiol. 1990; 43: 1327-1335
        • Blair S.N.
        • Haskell W.L.
        • Ho P.
        • et al.
        Assessment of habitual physical activity by a seven-day recall in a community survey and controlled experiments.
        Am J Epidemiol. 1985; 122: 794-804
        • Marcus B.H.
        • Selby V.C.
        • Niaura R.S.
        • Rossi J.S.
        Self-efficacy and the stages of exercise behavior change.
        Res Q Exerc Sport. 1992; 63: 60-66
        • Sallis J.F.
        • Grossman R.M.
        • Pinski R.B.
        • Patterson T.L.
        • Nader P.R.
        The development of scales to measure social support for diet and exercise behaviors.
        Prev Med. 1987; 16: 825-836
        • CDC.
        Barriers to Being Active Quiz.
        • Weissman M.M.
        • Sholomskas D.
        • Pottenger M.
        • Prusoff B.A.
        • Locke B.Z.
        Assessing depressive symptoms in five psychiatric populations: a validation study.
        Am J Epidemiol. 1977; 106: 203-214
        • Imai K.
        • Keele L.
        • Yamamoto T.
        Identification, inference and sensitivity analysis for causal mediation effects.
        Stat Sci. 2010; 25: 51-71
        • National Cholesterol Education Program
        Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults.
        National Heart, Lung, and Blood Institute, Bethesda, MD2002
        • Turner-McGrievy G.M.
        • Beets M.W.
        • Moore J.B.
        • Kaczynski A.T.
        • Barr-Anderson D.J.
        • Tate D.F.
        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; 20: 513-518
        • Carter M.C.
        • Burley V.J.
        • Nykjaer C.
        • Cade J.E.
        Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial.
        J Med Internet Res. 2013; 15: e32
        • Shaw R.
        • Bosworth H.
        Short message service (SMS) text messaging as an intervention medium for weight loss: a literature review.
        Health Inform J. 2012; 18: 235-250
        • Aguilar-Martinez A.
        • Sole-Sedeno J.M.
        • Mancebo-Moreno G.
        • Medina F.X.
        • Carreras-Collado R.
        • Saigi-Rubio F.
        Use of mobile phones as a tool for weight loss: a systematic review.
        J Telemed Telecare. 2014; 20: 339-349
        • Free C.
        • Phillips G.
        • Galli L.
        • et al.
        The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review.
        PLoS Med. 2013; 10: e1001362
        • Lyzwinski L.N.
        A systematic review and meta-analysis of mobile devices and weight loss with an intervention content analysis.
        J Pers Med. 2014; 4: 311-385
        • Pagoto S.
        • Schneider K.
        • Jojic M.
        • DeBiasse M.
        • Mann D.
        Evidence-based strategies in weight-loss mobile apps.
        Am J Prev Med. 2013; 45: 576-582
        • Wadden T.A.
        • Webb V.L.
        • Moran C.H.
        • Bailer B.A.
        Lifestyle modification for obesity: new developments in diet, physical activity, and behavior therapy.
        Circulation. 2012; 125: 1157-1170
        • Ramachandran A.
        • Snehalatha C.
        • Ram J.
        • et al.
        Effectiveness of mobile phone messaging in prevention of type 2 diabetes by lifestyle modification in men in India: a prospective, parallel-group, randomised controlled trial.
        Lancet Diabetes Endocrinol. 2013; 1: 191-198
        • Lin P.H.
        • Wang Y.
        • Levine E.
        • et al.
        A text messaging-assisted randomized lifestyle weight loss clinical trial among overweight adults in Beijing.
        Obesity. 2014; 22: E29-E37
        • Patrick K.
        • Norman G.J.
        • Davila E.P.
        • et al.
        Outcomes of a 12-month technology-based intervention to promote weight loss in adolescents at risk for type 2 diabetes.
        J Diabetes Sci Technol. 2013; 7: 759-770