Apps for IMproving FITness and Increasing Physical Activity Among Young People: The AIMFIT Pragmatic Randomized Controlled Trial

Background Given the global prevalence of insufficient physical activity (PA), effective interventions that attenuate age-related decline in PA levels are needed. Mobile phone interventions that positively affect health (mHealth) show promise; however, their impact on PA levels and fitness in young people is unclear and little is known about what makes a good mHealth app. Objective The aim was to determine the effects of two commercially available smartphone apps (Zombies, Run and Get Running) on cardiorespiratory fitness and PA levels in insufficiently active healthy young people. A second aim was to identify the features of the app design that may contribute to improved fitness and PA levels. Methods Apps for IMproving FITness (AIMFIT) was a 3-arm, parallel, randomized controlled trial conducted in Auckland, New Zealand. Participants were recruited through advertisements in electronic mailing lists, local newspapers, flyers posted in community locations, and presentations at schools. Eligible young people aged 14-17 years were allocated at random to 1 of 3 conditions: (1) use of an immersive app (Zombies, Run), (2) use of a nonimmersive app (Get Running), or (3) usual behavior (control). Both smartphone apps consisted of a fully automated 8-week training program designed to improve fitness and ability to run 5 km; however, the immersive app featured a game-themed design and narrative. Intention-to-treat analysis was performed using data collected face-to-face at baseline and 8 weeks, and all regression models were adjusted for baseline outcome value and gender. The primary outcome was cardiorespiratory fitness, objectively assessed as time to complete the 1-mile run/walk test at 8 weeks. Secondary outcomes were PA levels (accelerometry and self-reported), enjoyment, psychological need satisfaction, self-efficacy, and acceptability and usability of the apps. Results A total of 51 participants were randomized to the immersive app intervention (n=17), nonimmersive app intervention (n=16), or the control group (n=18). The mean age of participants was 15.7 (SD 1.2) years; participants were mostly NZ Europeans (61%, 31/51) and 57% (29/51) were female. Overall retention rate was 96% (49/51). There was no significant intervention effect on the primary outcome using either of the apps. Compared to the control, time to complete the fitness test was –28.4 seconds shorter (95% CI –66.5 to 9.82, P=.20) for the immersive app group and –24.7 seconds (95% CI –63.5 to 14.2, P=.32) for the nonimmersive app group. No significant intervention effects were found for secondary outcomes. Conclusions Although apps have the ability to increase reach at a low cost, our pragmatic approach using readily available commercial apps as a stand-alone instrument did not have a significant effect on fitness. However, interest in future use of PA apps is promising and highlights a potentially important role of these tools in a multifaceted approach to increase fitness, promote PA, and consequently reduce the adverse health outcomes associated with insufficient activity. Trial Registration Australian New Zealand Clinical Trials Registry: ACTRN12613001030763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12613001030763 (Archived by WebCite at http://www.webcitation.org/6aasfJVTJ).

[1]  Artur Direito,et al.  Smartphone apps to improve fitness and increase physical activity among young people: protocol of the Apps for IMproving FITness (AIMFIT) randomized controlled trial , 2015, BMC Public Health.

[2]  Christopher C. Cushing,et al.  A systematic review: is there an app for that? Translational science of pediatric behavior change for physical activity and dietary interventions. , 2015, Journal of pediatric psychology.

[3]  B. Kuehn Is there an app to solve app overload? , 2015, JAMA.

[4]  U. Ekelund,et al.  Association between physical activity, sedentary time, and healthy fitness in youth. , 2015, Medicine and science in sports and exercise.

[5]  John Carson Allen,et al.  Short-Term Trajectories of Use of a Caloric-Monitoring Mobile Phone App Among Patients With Type 2 Diabetes Mellitus in a Primary Care Setting , 2015, Journal of medical Internet research.

[6]  K. Lee,et al.  Korean translation of the CONSORT 2010 Statement: updated guidelines for reporting parallel group randomized trials , 2014, Epidemiology and health.

[7]  K. Shuval,et al.  Sedentary behavior, cardiorespiratory fitness, physical activity, and cardiometabolic risk in men: the cooper center longitudinal study. , 2014, Mayo Clinic proceedings.

[8]  Wen-Ying Sylvia Chou,et al.  Predictors of eHealth Usage: Insights on The Digital Divide From the Health Information National Trends Survey 2012 , 2014, Journal of medical Internet research.

[9]  R. Whittaker,et al.  Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques? , 2014, BMC Public Health.

[10]  Nicole L. Nollen,et al.  Mobile technology for obesity prevention: a randomized pilot study in racial- and ethnic-minority girls. , 2014, American journal of preventive medicine.

[11]  B. Wansink,et al.  Factors Related to Sustained Use of a Free Mobile App for Dietary Self-Monitoring With Photography and Peer Feedback: Retrospective Cohort Study , 2014, Journal of medical Internet research.

[12]  A. Bell,et al.  Internet trends in New Zealand, 2007-2013 , 2014 .

[13]  G. Tomkinson,et al.  Abstract 13498: Global Changes in Cardiovascular Endurance of Children and Youth Since 1964: Systematic Analysis of 25 Million Fitness Test Results from 28 Countries , 2013 .

[14]  U. Ekelund,et al.  Change in objectively measured physical activity during the transition to adolescence , 2012, British Journal of Sports Medicine.

[15]  Paul A Estabrooks,et al.  Assessing the Internal and External Validity of Mobile Health Physical Activity Promotion Interventions: A Systematic Literature Review Using the RE-AIM Framework , 2013, Journal of medical Internet research.

[16]  D. Spruijt-Metz,et al.  mHealth approaches to child obesity prevention: successes, unique challenges, and next directions , 2013, Translational behavioral medicine.

[17]  P. Schulz,et al.  Mapping mHealth Research: A Decade of Evolution , 2013, Journal of medical Internet research.

[18]  Maureen Dobbins,et al.  School-based physical activity programs for promoting physical activity and fitness in children and adolescents aged 6 to 18. , 2013, The Cochrane database of systematic reviews.

[19]  Theresa M. Beckie,et al.  The Importance of Cardiorespiratory Fitness in the United States: The Need for a National Registry A Policy Statement From the American Heart Association , 2013, Circulation.

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

[21]  W. Henley,et al.  Effectiveness of intervention on physical activity of children: systematic review and meta-analysis of controlled trials with objectively measured outcomes (EarlyBird 54) , 2012, BMJ : British Medical Journal.

[22]  I-Min Lee,et al.  Physical activity: more of the same is not enough , 2012, The Lancet.

[23]  S. Blair,et al.  Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy , 2012, BDJ.

[24]  U. Ekelund,et al.  Global physical activity levels: surveillance progress, pitfalls, and prospects , 2012, The Lancet.

[25]  Morwenna Kirwan,et al.  Using Smartphone Technology to Monitor Physical Activity in the 10,000 Steps Program: A Matched Case–Control Trial , 2012, Journal of medical Internet research.

[26]  Ulf Ekelund,et al.  Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents. , 2012, JAMA.

[27]  Allan Bell,et al.  The Internet in New Zealand 2013 , 2013 .

[28]  G. Eysenbach CONSORT-EHEALTH: Improving and Standardizing Evaluation Reports of Web-based and Mobile Health Interventions , 2011, Journal of medical Internet research.

[29]  Jing Wang,et al.  Physical activity self-monitoring and weight loss: 6-month results of the SMART trial. , 2011, Medicine and science in sports and exercise.

[30]  Stewart G Trost,et al.  Comparison of accelerometer cut points for predicting activity intensity in youth. , 2011, Medicine and science in sports and exercise.

[31]  M. Domingues,et al.  Physical activity change during adolescence: a systematic review and a pooled analysis. , 2011, International journal of epidemiology.

[32]  M. Pearce,et al.  Stability of habitual physical activity and sedentary behavior monitoring by accelerometry in 6- to 8-year-olds. , 2011, Journal of physical activity & health.

[33]  Kimberly A. Patton Teens and Technology , 2011 .

[34]  D. Moher,et al.  CONSORT 2010 Statement: updated guidelines for reporting parallel group randomized trials , 2010, Obstetrics and gynecology.

[35]  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.

[36]  S. Michie,et al.  Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis, Use of Behavior Change Techniques, and Mode of Delivery on Efficacy , 2010, Journal of medical Internet research.

[37]  Yoram Eshet,et al.  Changes Over Time in Digital Literacy , 2009, Cyberpsychology Behav. Soc. Netw..

[38]  C. Abraham,et al.  Effective techniques in healthy eating and physical activity interventions: a meta-regression. , 2009, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[39]  R. Telama Tracking of Physical Activity from Childhood to Adulthood: A Review , 2009, Obesity Facts.

[40]  Yasuo Ohashi,et al.  Cardiorespiratory fitness as a quantitative predictor of all-cause mortality and cardiovascular events in healthy men and women: a meta-analysis. , 2009, JAMA.

[41]  J. Jobe,et al.  The effect of a physical activity intervention on bias in self-reported activity. , 2009, Annals of epidemiology.

[42]  G. Dunton,et al.  Factorial Validity and Gender Invariance of the Physical Activity Enjoyment Scale (PACES) in Older Adolescents , 2009, Research quarterly for exercise and sport.

[43]  M. Dobbins,et al.  School-based physical activity programs for promoting physical activity and fitness in children and adolescents aged 6-18. , 2009, The Cochrane database of systematic reviews.

[44]  R. Mcmurray,et al.  Calibration of two objective measures of physical activity for children , 2008, Journal of sports sciences.

[45]  R. Houts,et al.  Moderate-to-vigorous physical activity from ages 9 to 15 years. , 2008, JAMA.

[46]  Kathleen F Janz,et al.  Measuring activity in children and adolescents using self-report: PAQ-C and PAQ-A. , 2008, Medicine and science in sports and exercise.

[47]  M. Sjöström,et al.  Physical fitness in childhood and adolescence: a powerful marker of health , 2008, International Journal of Obesity.

[48]  S. Griffin,et al.  Effectiveness of interventions to promote physical activity in children and adolescents: systematic review of controlled trials , 2009 .

[49]  C. Nishida,et al.  Development of a WHO growth reference for school-aged children and adolescents. , 2007, Bulletin of the World Health Organization.

[50]  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.

[51]  Philip M. Wilson,et al.  The Psychological Need Satisfaction in Exercise Scale , 2006 .

[52]  J. Bartholomew,et al.  Validation of the Physical Activity Self-Efficacy Scale: Testing Measurement Invariance Between Hispanic and Caucasian Children , 2006 .

[53]  Bernard F Fuemmeler,et al.  Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. , 2005, Medicine and science in sports and exercise.

[54]  H. MacPherson Pragmatic clinical trials. , 2004, Complementary therapies in medicine.

[55]  Georges Baquet,et al.  Endurance Training and Aerobic Fitness in Young People , 2003, Sports medicine.

[56]  R. Motl,et al.  Measuring enjoyment of physical activity in adolescent girls. , 2001, American journal of preventive medicine.

[57]  J F Sallis,et al.  A physical activity screening measure for use with adolescents in primary care. , 2001, Archives of pediatrics & adolescent medicine.

[58]  S G Trost,et al.  Factorial validity and invariance of questionnaires measuring social-cognitive determinants of physical activity among adolescent girls. , 2000, Preventive medicine.

[59]  P. Crocker,et al.  Convergent Validity of the Physical Activity Questionnaire for Adolescents , 1997 .

[60]  Kennon M. Sheldon,et al.  Intrinsic motivation and exercise adherence. , 1997 .

[61]  N. Black CONSORT , 1996, The Lancet.

[62]  K. Cureton,et al.  A generalized equation for prediction of VO2peak from 1-mile run/walk performance. , 1995, Medicine and science in sports and exercise.