Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

Background Growing evidence shows that fixed, nonpersonalized daily step goals can discourage individuals, resulting in unchanged or even reduced physical activity. Objective The aim of this randomized controlled trial (RCT) was to evaluate the efficacy of an automated mobile phone–based personalized and adaptive goal-setting intervention using machine learning as compared with an active control with steady daily step goals of 10,000. Methods In this 10-week RCT, 64 participants were recruited via email announcements and were required to attend an initial in-person session. The participants were randomized into either the intervention or active control group with a one-to-one ratio after a run-in period for data collection. A study-developed mobile phone app (which delivers daily step goals using push notifications and allows real-time physical activity monitoring) was installed on each participant’s mobile phone, and participants were asked to keep their phone in a pocket throughout the entire day. Through the app, the intervention group received fully automated adaptively personalized daily step goals, and the control group received constant step goals of 10,000 steps per day. Daily step count was objectively measured by the study-developed mobile phone app. Results The mean (SD) age of participants was 41.1 (11.3) years, and 83% (53/64) of participants were female. The baseline demographics between the 2 groups were similar (P>.05). Participants in the intervention group (n=34) had a decrease in mean (SD) daily step count of 390 (490) steps between run-in and 10 weeks, compared with a decrease of 1350 (420) steps among control participants (n=30; P=.03). The net difference in daily steps between the groups was 960 steps (95% CI 90-1830 steps). Both groups had a decrease in daily step count between run-in and 10 weeks because interventions were also provided during run-in and no natural baseline was collected. Conclusions The results showed the short-term efficacy of this intervention, which should be formally evaluated in a full-scale RCT with a longer follow-up period. Trial Registration ClinicalTrials.gov: NCT02886871; https://clinicaltrials.gov/ct2/show/NCT02886871 (Archived by WebCite at http://www.webcitation.org/6wM1Be1Ng).

[1]  A. Bandura.  Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychology Review.

[2]  A. Bandura,et al.  Self-evaluative and self-efficacy mechanisms governing the motivational effects of goal systems. , 1983 .

[3]  Geert Molenberghs,et al.  Linear Mixed Models in Practice , 1997 .

[4]  R. Glasgow,et al.  Review of primary care-based physical activity intervention studies: effectiveness and implications for practice and future research. , 2000, The Journal of family practice.

[5]  R Lindberg,et al.  Active living: on the road with the 10,000 Steps program. , 2000, Journal of the American Dietetic Association.

[6]  C Cimino,et al.  Computerized weight loss intervention optimizes staff time: the clinical and cost results of a controlled clinical trial conducted in a managed care setting. , 2001, Journal of the American Dietetic Association.

[7]  N M Laird,et al.  Tutorial in Biostatistics: Evaluating the impact of 'critical periods' in longitudinal studies of growth using piecewise mixed effects models. , 2001, International journal of epidemiology.

[8]  G. Molenberghs,et al.  Linear Mixed Models for Longitudinal Data , 2001 .

[9]  C. Tudor-Locke.  Taking Steps toward Increased Physical Activity: Using Pedometers To Measure and Motivate. , 2002 .

[10]  J. Annesi,et al.  Goal-Setting Protocol in Adherence to Exercise by Italian Adults , 2002, Perceptual and motor skills.

[11]  B Stetd,et al.  TAKING STEPS TOWARD INCREASED PHYSICAL ACTIVITY: USING PEDOMETERS TO MEASURE AND MOTIVATE , 2002 .

[12]  E. A. Locke,et al.  Building a practically useful theory of goal setting and task motivation. A 35-year odyssey. , 2002, The American psychologist.

[13]  B. Ainsworth,et al.  International physical activity questionnaire: 12-country reliability and validity. , 2003, Medicine and science in sports and exercise.

[14]  Fernando Costa,et al.  Exercise and physical activity in the prevention and treatment of atherosclerotic cardiovascular disease: a statement from the Council on Clinical Cardiology (Subcommittee on Exercise, Rehabilitation, and Prevention) and the Council on Nutrition, Physical Activity, and Metabolism (Subcommittee on Ph , 2003, Circulation.

[15]  P. Thompson,et al.  Exercise and physical activity in the prevention and treatment of atherosclerotic cardiovascular disease. , 2003, Arteriosclerosis, thrombosis, and vascular biology.

[16]  L. Bandini,et al.  Energy-dense snack food intake in adolescence: longitudinal relationship to weight and fatness. , 2004, Obesity research.

[17]  Catrine Tudor-Locke,et al.  Health benefits of a pedometer-based physical activity intervention in sedentary workers. , 2004, Preventive medicine.

[18]  Charles B. Corbin,et al.  Promoting Physical Activity among Sedentary Women Using Pedometers , 2004, Research quarterly for exercise and sport.

[19]  Andrea L. Paiva,et al.  Assessing stage of change and informed decision making for Internet participation in health promotion and disease management. , 2004, Managed care interface.

[20]  K. Croteau,et al.  A Preliminary Study on the Impact of a Pedometer-Based Intervention on Daily Steps , 2004, American journal of health promotion : AJHP.

[21]  Mical K. Shilts,et al.  Goal Setting as a Strategy for Dietary and Physical Activity Behavior Change: A Review of the Literature , 2004, American journal of health promotion : AJHP.

[22]  L. Ransdell,et al.  Generations Exercising Together to Improve Fitness (GET FIT): A Pilot Study Designed to Increase Physical Activity and Improve Health-Related Fitness in Three Generations of Women , 2005, Women & health.

[23]  S. Payne,et al.  A randomised controlled trial of three pragmatic approaches to initiate increased physical activity in sedentary patients with risk factors for cardiovascular disease. , 2004, The British journal of general practice : the journal of the Royal College of General Practitioners.

[24]  Lili Tian,et al.  A Comparison of the General Linear Mixed Model and Repeated Measures ANOVA Using a Dataset with Multiple Missing Data Points , 2004, Biological research for nursing.

[25]  W. Kroeze,et al.  The internet and nutrition education: challenges and opportunities , 2005, European Journal of Clinical Nutrition.

[26]  C. Albright,et al.  Comparison of walking recommendations in previously inactive women. , 2005, Medicine and science in sports and exercise.

[27]  B. Center,et al.  Pedometers as a means to increase ambulatory activity for patients seen at a family medicine clinic. , 2005, The Journal of the American Board of Family Practice.

[28]  R. Leavitt,et al.  The Effect of a Behavioral Contract on Adherence to a Walking Program in Postmenopausal African American Women , 2005 .

[29]  David R. Bassett,et al.  Effects of a 10,000 Steps per Day Goal in Overweight Adults , 2006, American journal of health promotion : AJHP.

[30]  Michael E. Miller,et al.  Effects of a physical activity intervention on measures of physical performance: Results of the lifestyle interventions and independence for Elders Pilot (LIFE-P) study. , 2006, The journals of gerontology. Series A, Biological sciences and medical sciences.

[31]  David M Williams,et al.  Social-cognitive determinants of physical activity: the influence of social support, self-efficacy, outcome expectations, and self-regulation among participants in a church-based health promotion study. , 2006, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[32]  Glen P. Kenny,et al.  Physical Activity/Exercise and Type 2 Diabetes , 2006, Diabetes Care.

[33]  C. Vella,et al.  Efficacy of a pedometer-based physical activity program on parameters of diabetes control in type 2 diabetes mellitus. , 2006, Metabolism: clinical and experimental.

[34]  M. H. van den Berg,et al.  Internet-Based Physical Activity Interventions: A Systematic Review of the Literature , 2007, Journal of medical Internet research.

[35]  I. Olkin,et al.  Using pedometers to increase physical activity and improve health: a systematic review. , 2007, JAMA.

[36]  S. Laffrey,et al.  Promotion of physical activity in low-income mothers using pedometers. , 2007, Journal of the American Dietetic Association.

[37]  J. S. Sodhi,et al.  Using Internet and Mobile Phone Technology to Deliver an Automated Physical Activity Program: Randomized Controlled Trial , 2007, Journal of medical Internet research.

[38]  Chris Rissel,et al.  Promoting walking with pedometers in the community: the step-by-step trial. , 2007, American journal of preventive medicine.

[39]  David M Buchner,et al.  A randomized study of financial incentives to increase physical activity among sedentary older adults. , 2008, Preventive medicine.

[40]  A. Avenell,et al.  Systematic review of the use of financial incentives in treatments for obesity and overweight , 2008, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[41]  A. Ströhle.  Physical activity, exercise, depression and anxiety disorders , 2009, Journal of Neural Transmission.

[42]  Audie A Atienza,et al.  Promoting physical activity through hand-held computer technology. , 2008, American journal of preventive medicine.

[43]  Christopher D Gardner,et al.  Using hand-held computer technologies to improve dietary intake. , 2008, American journal of preventive medicine.

[44]  Mika Kivimäki,et al.  Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study , 2009, The Lancet.

[45]  Catrine Tudor-Locke,et al.  Accelerometer-determined steps per day in US adults. , 2009, Medicine and science in sports and exercise.

[46]  Aino Ahtinen,et al.  User experiences of mobile wellness applications in health promotion: User study of Wellness Diary, Mobile Coach and SelfRelax , 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare.

[47]  E. Vittinghoff,et al.  Innovation to motivation--pilot study of a mobile phone intervention to increase physical activity among sedentary women. , 2010, Preventive medicine.

[48]  Brianna S Fjeldsoe,et al.  MobileMums: A Randomized Controlled Trial of an SMS-Based Physical Activity Intervention , 2010, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[49]  J. Russo,et al.  Barriers and Facilitators to Walking and Physical Activity Among American Indian Elders , 2011, Preventing chronic disease.

[50]  Minsoo Kang,et al.  Convergent validity of the International Physical Activity Questionnaire (IPAQ): meta-analysis , 2012, Public Health Nutrition.

[51]  J. Knight,et al.  Physical inactivity: associated diseases and disorders. , 2012, Annals of clinical and laboratory science.

[52]  Sean P Mullen,et al.  Increasing Physical Activity With Mobile Devices: A Meta-Analysis , 2012, Journal of medical Internet research.

[53]  Donna Spruijt-Metz,et al.  Current mHealth technologies for physical activity assessment and promotion. , 2013, American journal of preventive medicine.

[54]  L. John,et al.  Financial incentives for exercise adherence in adults: systematic review and meta-analysis. , 2013, American journal of preventive medicine.

[55]  J. McLester,et al.  A Comparison of Motivational Factors and Barriers to Physical Activity Among Traditional Versus Nontraditional College Students , 2013, Journal of American college health : J of ACH.

[56]  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.

[57]  D. French,et al.  What are the most effective techniques in changing obese individuals’ physical activity self-efficacy and behaviour: a systematic review and meta-analysis , 2013, International Journal of Behavioral Nutrition and Physical Activity.

[58]  M. Adams,et al.  An Adaptive Physical Activity Intervention for Overweight Adults: A Randomized Controlled Trial , 2013, PloS one.

[59]  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..

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

[61]  R. Klaren,et al.  Randomized controlled trial of physical activity, cognition, and walking in multiple sclerosis , 2014, Journal of Neurology.

[62]  Gregory J Welk,et al.  Validity of consumer-based physical activity monitors. , 2014, Medicine and science in sports and exercise.

[63]  Thomas Zimmermann,et al.  Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness , 2014, CHI.

[64]  J. Wylie-Rosett,et al.  Short and long-term lifestyle coaching approaches used to address diverse participant barriers to weight loss and physical activity adherence , 2014, International Journal of Behavioral Nutrition and Physical Activity.

[65]  David P French,et al.  Which Behaviour Change Techniques Are Most Effective at Increasing Older Adults’ Self-Efficacy and Physical Activity Behaviour? A Systematic Review , 2014, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[66]  K. Volpp,et al.  Accuracy of smartphone applications and wearable devices for tracking physical activity data. , 2015, JAMA.

[67]  Julie B. Wang,et al.  Wearable Sensor/Device (Fitbit One) and SMS Text-Messaging Prompts to Increase Physical Activity in Overweight and Obese Adults: A Randomized Controlled Trial. , 2015, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[68]  Mohamed Ismail Nounou,et al.  Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial? , 2015, Healthcare informatics research.

[69]  B. Ainsworth,et al.  Print versus a culturally-relevant Facebook and text message delivered intervention to promote physical activity in African American women: a randomized pilot trial , 2015, BMC Women's Health.

[70]  Kevin L. Joiner,et al.  A Novel Diabetes Prevention Intervention Using a Mobile App: A Randomized Controlled Trial With Overweight Adults at Risk. , 2015, American journal of preventive medicine.

[71]  Gregory J Welk,et al.  Comparison of Consumer and Research Monitors under Semistructured Settings. , 2016, Medicine and science in sports and exercise.

[72]  Nathan K. Cobb,et al.  Effectiveness of an Activity Tracker- and Internet-Based Adaptive Walking Program for Adults: A Randomized Controlled Trial , 2016, Journal of medical Internet research.

[73]  M. Marcus,et al.  Effect of Wearable Technology Combined With a Lifestyle Intervention on Long-term Weight Loss: The IDEA Randomized Clinical Trial. , 2016, JAMA.

[74]  R. Schwarzer,et al.  Self-efficacy, planning, and preparatory behaviours as joint predictors of physical activity: A conditional process analysis , 2016, Psychology & health.

[75]  Self-conscious emotions in response to physical activity success and failure: Findings from a global 112-day pedometer intervention , 2016 .

[76]  Carol Maher,et al.  Engagement, compliance and retention with a gamified online social networking physical activity intervention , 2017, Translational behavioral medicine.

[77]  Catherine L. Jarrett,et al.  Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults’ physical activity , 2017, BMC Public Health.

[78]  S. Honda,et al.  Exercise Self-Efficacy as a Mediator between Goal-Setting and Physical Activity: Developing the Workplace as a Setting for Promoting Physical Activity , 2016, Safety and health at work.

[79]  Anil Aswani,et al.  Behavioral analytics for myopic agents , 2017, Eur. J. Oper. Res..

[80]  J. Leskovec,et al.  Large-scale physical activity data reveal worldwide activity inequality , 2017, Nature.

[81]  S. Vannoy,et al.  The Relations of Cognitive, Behavioral, and Physical Activity Variables to Depression Severity in Traumatic Brain Injury: Reanalysis of Data From a Randomized Controlled Trial , 2017, The Journal of head trauma rehabilitation.

[82]  Dong Wen,et al.  Evaluating the Consistency of Current Mainstream Wearable Devices in Health Monitoring: A Comparison Under Free-Living Conditions , 2017, Journal of medical Internet research.

[83]  Anil Aswani,et al.  Behavioral modeling in weight loss interventions , 2019, Eur. J. Oper. Res..