The role of smartphones in encouraging physical activity in adults

Figures & data

Figure 1 PRISMA flow diagram.

Table 1 Overview of included studies

Study	Country	Participants	Population	Duration	Behavior theory	Physical activity measurement	Physical activity outcomes
Choi et al^Citation8	USA	n=30, aged 33.7±2.6 years, 0% male	Inactive pregnant women	12 weeks	Social Cognitive Theory	Step count (FitBit)	No change
Cowdery et al^Citation9	USA	n=40, aged 18–69 years, 15% male	Healthy adults	12 weeks	Self-Determination Theory	IPAQ	No change
Gilson et al^Citation10	Australia	n=44, aged 47.5±9.8 years, 100% male	Truck drivers	20 weeks	None	Step count (Jawbone UP activity tracker)	No change
Glynn et al^Citation11	Ireland	n=90, aged >16 years, 36% male	Primary care, rural	8 weeks	None	Step count (app)	Increased 1631±3842 steps/day
Hales et al^Citation12	USA	n=9, aged 39±14.5 years, 11% male	Overweight or obese adults	2 months	Social Cognitive Theory	Paffenbarger Physical Activity Questionnaire	No change
Hebden et al^Citation13	Australia	n=51, aged 18–35 years, 20% male	University students and staff	12 weeks	Transtheoretical Model	IPAQ	Increased light intensity activity 34.2±35.1 min/day
Khalil and Abdallah^Citation14	United Arab Emirates	n=8, aged 23±2.6 years, 0% male	Young adults	2 weeks	Theory of Reasoned Action	Step count (motion classifier)	No change
King et al^Citation15	USA	n=63, aged >45 years, 26.5% male	Adults	8 weeks	3 apps: Social Cognitive Theory, Social Influence Theory, Operant Conditioning	CHAMPS Physical Activity Questionnaire	Increased brisk walking by 100.8±167.0 min/day
Knight et al^Citation16	Canada	n=45, aged 55–75 years, 44% male	Primary care clinic	12 weeks	Fogg Behavior Model (for counseling. None for app)	Step count (pedometer)	No change
Laing et al^Citation17	USA	n=180, aged >18 years, 27% male	Primary care clinic	6 months	Self-Regulation Theory, Social Cognitive Theory	Self-report	No change
Macias et al^Citation18	USA	n=10, aged 22–61 years, 50% male	Psychiatric disorder	4 weeks	Stage of change	Accelerometer (Smartphone)	No change
Martin et al^Citation19	USA	n=48, aged 18–69 years, 54% male	Ambulatory cardiology center	5 weeks	None	Step count (accelerometer)	Increased by 2334±1714 steps/day (with text message only)
Oh et al^Citation20	Korea	n=422, aged >20 years, 51% male	Obese, metabolic syndrome	24 weeks	None	Step count (pedometer)	No change
Rabbi et al^Citation21	USA	n=17, aged 18–49 years, 53% male	Healthy adults	3 weeks	Learning Theory, Social Cognitive Theory, Fogg Behavior Model	Self-report	78% of INT with positive trends in physical activity, vs 75% with negative trends in CTL
Stuckey et al^Citation22	Canada	n=24, aged 30–71 years, 25% male	Metabolic risk factors	8 weeks	Counseling based on Transtheoretical Model	Step count (Pedometer)	Increased 1085±1613 steps/day
Tabak et al^Citation23	the Netherlands	n=15, aged 66±9.2 years, 60% male	COPD	4 weeks	None	Accelerometer	No change
Turner-McGrievy et al^Citation24	USA	n=85, aged 18–60 years, 25% male	Overweight	6 months	None	Self-report	Increased intentional PA/day 196.4±45.9 kcal/day
Verwey et al^Citation25	the Netherlands	n=20, aged 41–84 years, 55% male	COPD or Type 2 Diabetes	8–12 weeks	Self-management with Five As Model for Primary Care	Accelerometer	Increased from 28.7±21.1 to 39.3±24.2 min/day

Abbreviations: apps, applications; CHAMPS, Community Healthy Activities Model Program for Seniors; COPD, chronic obstructive pulmonary disease; CTL, control group; INT, intervention group; IPAQ, International Physical Activity Questionnaire.

Table 2 Summary of behavioral change theories included in this review

Theory	Description
Five A’s Model	A model to guide patient–provider interaction for behavioral change to support self-management of chronic disease.
Fogg Behavior Model	Three elements (motivation, ability, and trigger) must be present for a behavior to occur.
Learning Theory/Operant Conditioning	Behavioral change results from an individual’s response to environmental stimuli or consequences of actions. Rewards are used to reinforce positive behaviors.
Self-Determination Theory	Based on intrinsic motivation. Interventions support an individual’s natural tendency to exhibit effective and healthy behaviors.
Social Cognitive Theory	Behaviors are influenced by observing others in the context of social interactions and experiences.
Social Influence Theory	Behavioral change occurs based on how an individual perceives oneself in relation to others.
Theory of Reasoned Action	Behaviors are a result of one’s attitudes and one’s subjective norms.
Transtheoretical Model (Stages of Change)	Provides strategies for behavioral change based on an individual’s readiness for action.

Table 3 Features included in smartphone applications of interventions that did and did not change physical activity behaviors

Feature	Increased physical activity (n=8)	No change in physical activity (n=10)
Feedback	5 (63%)	2 (20%)
Motivational cuing	3 (38%)	2 (20%)
Goal setting	2 (25%)	2 (20%)
Information and education	1 (13%)	2 (20%)
Reminders	1 (13%)	3 (30%)
Rewards or reinforcement	1 (13%)	1 (10%)
Social support	1 (13%)	3 (30%)
Gamification	0 (0%)	1 (10%)

Note: Data are presented as n (%), where n is the number of studies.

ChoiJLeeJHVittinghoffEFukuokaYmHealth physical activity intervention: a randomized pilot study in physically inactive pregnant womenMatern Child Health J20162051091110126649879

 PubMed Web of Science ®Google Scholar

CowderyJMajeskePFrankRBrownDExergame apps and physical activity: the results of the ZOMBIE trialAm J Health Educ2015464216222

 Web of Science ®Google Scholar

GilsonNDPaveyTGVandelanotteCChronic disease risks and use of a smartphone application during a physical activity and dietary intervention in Australian truck driversAust N Z J Public Health2016401919326713400

 PubMed Web of Science ®Google Scholar

GlynnLGHayesPSCaseyMEffectiveness of a smartphone application to promote physical activity in primary care: the SMART MOVE randomised controlled trialBr J Gen Pract201464624e384e39124982490

 PubMed Web of Science ®Google Scholar

HalesSTurner-McGrievyGFahimAA mixed-methods approach to the development, refinement, and pilot testing of social networks for improving healthy behaviorsJMIR Hum Factors201631e827026535

 PubMedGoogle Scholar

HebdenLCookAvan der PloegHPKingLBaumanAAllman-FarinelliMA mobile health intervention for weight management among young adults: a pilot randomised controlled trialJ Hum Nutr Diet201427432233223992038

 PubMed Web of Science ®Google Scholar

KhalilAAbdallahSHarnessing social dynamics through persuasive technology to promote healthier lifestyleComput Human Behav201329626742681

 Web of Science ®Google Scholar

KingACHeklerEBGriecoLAHarnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adultsPLoS One201384e6261323638127

 PubMed Web of Science ®Google Scholar

KnightEStuckeyMIPetrellaRJHealth promotion through primary care: enhancing self-management with activity prescription and mHealthPhys Sportsmed2014423909925295771

 PubMed Web of Science ®Google Scholar

LaingBYMangioneCMTsengCHEffectiveness of a smart-phone application for weight loss compared with usual care in overweight primary care patients: a randomized, controlled trialAnn Intern Med201416110 SupplS5S1225402403

 PubMed Web of Science ®Google Scholar

MaciasCPanchTHicksYMUsing smartphone apps to promote psychiatric and physical well-beingPsychiatr Q201586450551925636496

 PubMed Web of Science ®Google Scholar

MartinSSFeldmanDIBlumenthalRSmActive: a randomized clinical trial of an automated mHealth intervention for physical activity promotionJ Am Heart Assoc2015411 pii:e002239

 Web of Science ®Google Scholar

OhBChoBHanMKThe effectiveness of mobile phone-based care for weight control in metabolic syndrome patients: randomized controlled trialJMIR Mhealth Uhealth201533e8326293568

 PubMed Web of Science ®Google Scholar

RabbiMPfammatterAZhangMSpringBChoudhuryTAutomated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adultsJMIR Mhealth Uhealth201532e4225977197

 PubMed Web of Science ®Google Scholar

StuckeyMRussell-MindaEReadEDiabetes and Technology for Increased Activity (DaTA) study: results of a remote monitoring intervention for prevention of metabolic syndromeJ Diabetes Sci Technol20115492893521880236

 PubMedGoogle Scholar

TabakMop den AkkerHHermensHMotivational cues as real-time feedback for changing daily activity behavior of patients with COPDPatient Educ Couns201494337237824332934

 PubMed Web of Science ®Google Scholar

Turner-McGrievyGMBeetsMWMooreJBKaczynskiATBarr-AndersonDJTateDFComparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss programJ Am Med Inform Assoc201320351351823429637

 PubMed Web of Science ®Google Scholar

VerweyRvan der WeegenSSpreeuwenbergMTangeHvan der WeijdenTde WitteLA pilot study of a tool to stimulate physical activity in patients with COPD or type 2 diabetes in primary careJ Telemed Telecare2014201293424414397

 PubMed Web of Science ®Google Scholar