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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.
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.
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.
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.
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.
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).
Worldwide, 80.3% (95% CI 80.1-80.5) of adolescents aged 13 to 15 years do not achieve current physical activity (PA) recommendations [
Physical activity declines with age throughout adolescence [
Many existing interventions, including school-based interventions, are limited because they are resource intensive. Young people are considered a hard-to-reach group and typically have limited adherence or exposure to PA interventions [
Young people are early adopters of new consumer technology, such as smartphones and apps. Smartphone ownership among young people is on the rise, having increased from 23% in 2011 to 37% in 2012 in the United States, with similar distribution across ethnicity and family income [
The effectiveness of mHealth-delivered interventions to promote PA that use texting or short message service (SMS) [
Therefore, the primary aim of the Apps for IMproving FITness (AIMFIT) trial was to evaluate the effectiveness of two popular commercially available smartphone apps [
A 3-arm parallel RCT was conducted in Auckland, New Zealand. Details on the rationale, design, and methods have been previously described [
Participants were recruited through advertisements in electronic mailing lists, local newspapers, schools, and flyers posted in community locations. Consenting schools and churches allowed the researcher to present a brief outline of the study. Those interested provided contact details and their eligibility was assessed via telephone. If eligible, participant information and informed consent documentation were either mailed or emailed and participants were scheduled to attend a face-to-face baseline assessment at the university.
Eligible participants were aged 14 to 17 years, lived in Auckland, owned an iPod touch or smartphone running at least Android 2.2 or iOS 6.0, and were able to perform physical activities but were not achieving [
Participants were enrolled by author AD and were randomly assigned at a 1:1:1 ratio to 1 of 3 conditions. Stratified block randomization in variable blocks was used to maintain balance across gender, an important prognostic factor [
Commercially available apps targeting fitness were identified during previous work evaluating the most popular (ie, top-20 free and top-20 paid) downloaded apps in the Health and Fitness Category of the iTunes New Zealand store [
Participants randomized to the immersive app group received the Zombies, Run! 5K Training app developed by Six to Start with Naomi Alderman for iOS and the Android platform. It was released worldwide for iOS on October 2012. Even though data on number of downloads is not publicly available, the Google Play Store reports 100,000 to 500,000 installs of this app, and the Zombies, Run! community has more than 800,000 players worldwide [
Following randomization, the respective app was paid for and installed by AD on each participant’s mobile device and a short instruction on the features and settings of the app was given. Participants were encouraged to use their app 3 times per week and work their way through each of the workouts, but because this was a pragmatic study [
The control group was asked to continue with their usual physical activities for the duration of the study and was not offered any information about increasing PA. Both apps were provided (free of charge) to participants after trial completion.
Assessments were conducted at baseline and 8 weeks at the university by AD. Participants were assessed individually. At both time points, participants completed a field test of CRF (1 mile run/walk test), had their height and weight measured, self-reported their physical activity and related psychological variables, were given an Actigraph accelerometer to wear for the following 7 days (to provide an objective assessment of their free-living PA), and completed a booklet detailing their accelerometer use. AD collected the accelerometers and booklets from the participants’ homes (during the randomization visit that took place after the baseline assessment and at the last visit after the follow-up assessment). Follow-up assessments were not blinded. Participants received a NZ $10 gift card to a local shopping center for each visit to complete study measures (ie, maximum NZ $30 for 3 visits). The vouchers were not conditional on usage of the app; they were offered to compensate for participants’ time and encourage completion of study measures.
The primary outcome was CRF, assessed with the 1-mile run/walk test. Following the procedures outlined in the Fitnessgram test administration manual, participants were instructed to run and/or walk at their own pace until completing the distance in the shortest possible time [
Secondary outcomes included anthropometrics, self-reported PA and associated psychological variables, objectively measured PA, and self-reported acceptability and usability of the apps assessed via an exit survey conducted with intervention participants. A series of closed and open-ended questions were asked to determine features perceived as more and less acceptable as well as which features participants found more useful to support their fitness. Body weight (in kg, without shoes) was measured with a Salter scale to 1 decimal place. Height was measured to the nearest 0.1 cm with a Seca stadiometer. Two measurements were taken for each and the means were used for analysis. Body mass index (BMI) was calculated by using the standard equation (weight in kilograms/height in meters squared). BMI-for-age was calculated using the World Health Organization (WHO) growth standards macro [
Using instruments validated in this population, participants self-reported (1) physical activity using the Physical Activity Questionnaire for Adolescents (PAQ-A) [
Participants were instructed to wear the accelerometer (Actigraph GT1M) on their right hip during waking hours for 7 days after each assessment, removing it when engaging in activities involving water and/or contact sports. A 10-second epoch was used and data were aggregated into minute intervals for subsequent processing. To determine valid wear time, periods of more than 60 minutes of consecutive zeroes and days with less than 600 minutes of valid records were removed before data analysis [
Adverse events were collected at each study visit or voluntarily reported by contacting the researcher. An adverse event was considered serious if it required hospitalization.
A total of 51 participants (17 per group) was estimated to provide 80% power and α=.05 overall to detect a difference of 17 seconds in CRF, assuming a 15-second SD in time to complete the 1-mile run/walk test between each of the conditions compared to the control [
Treatment evaluations were performed on the principle of intent-to-treat, including all randomized participants as allocated. Statistical analyses were performed with SAS version 9.4 software (SAS Institute, Cary, NC, USA). All statistical tests were 2-sided at a 5% significance level, with adjustment for multiple comparisons on the primary outcome. Analysis of covariance (ANCOVA) regression model was used to evaluate the main treatment effects on the primary outcome, adjusting for baseline measure and gender. Model-adjusted means, 95% confidence intervals, and
Recruitment began October 2013 and finished in June 2014. The final follow-up visit was in September 2014.
Participants had a mean age of 15.7 years (SD 1.2, range 14-17 years) and a BMI of 22.9 (SD 4.3) kg/m2. The majority were NZ European (61%, 31/51), whereas 22% (11/51) were Pacific Islanders, and 57% (29/51) were female. Follow-up assessments at 8 weeks were completed for 17 (100%, 17/17) immersive app group participants, 15 (94%, 15/16) nonimmersive app group participants, and 17 (94%, 17/18) control group participants, which represents an overall retention rate of 96% (49/51) from baseline.
Baseline demographic and clinical characteristics.
Characteristic | Zombies, Run |
Get Running |
Control |
Total |
|
Age (years), mean (SD) | 15.78 (1.11) | 15.69 (1.04) | 15.55 (1.32) | 15.67 (1.15) | |
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Male | 8 (47) | 6 (38) | 8 (44) | 22 (43) |
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Female | 9 (53) | 10 (63) | 10 (56) | 29 (57) |
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Maori | 3 (18) | 0 (0) | 0 (0) | 3 (6) |
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NZ European | 9 (53) | 9 (56) | 13 (72) | 31 (61) |
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Pacific | 4 (24) | 3 (19) | 4 (22) | 11 (22) |
|
Asian | 0 (0) | 3 (19) | 1 (6) | 4 (8) |
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Other | 1 (6) | 1 (6) | 0 (0) | 2 (4) |
|
23.17 (3.60) | 21.85 (3.14) | 23.43 (5.56) | 22.85 (4.25) | |
|
BMI-for-agea (z-score), mean (SD) | 0.77 (0.86) | 0.36 (0.93) | 0.64 (1.46) | 0.60 (1.12) |
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iPhone | 8 (47) | 6 (38) | 11 (61) | 25 (49) |
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Android | 5 (29) | 7 (44) | 5 (28) | 17 (33) |
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iPod Touch | 4 (24) | 3 (19) | 2 (11) | 9 (18) |
Estimated VO2peak b (mL/kg/min), mean (SD) | 43.51 (6.11) | 43.58 (5.47) | 44.20 (6.95) | 43.78 (6.12) |
a WHO growth reference.
b Prediction equation from 1-mile run/walk test.
Flow diagram of the Apps for IMproving FITness (AIMFIT) trial. Those who were unable to complete the postintervention fitness assessment due to injury or sickness still completed self-reported outcomes and were included in all analyses.
Treatment effects at 8 weeks.
Outcome | Zombies, Run (1), |
Get Running (2), |
Control (3), |
Adjusted differencea (95% CI) at 8 weeks | |||||||
|
Baseline | 8 week | Baseline | 8 week | Baseline | 8 week | 1 vs 3 |
|
2 vs 3 |
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Time to complete 1-mile walk/run (sec) | 574.06 (145.68) | 560.06 (139.27) | 586.56 (129.74) | 576.75 (147.91) | 585.89 (600.17) | 600.17 (191.38) | –28.36 (–66.54, 9.82) | .20 | –24.67 (–63.51, 14.18) | .32 | |
PAQ-A | 2.20 (0.66) | 2.27 (0.53) | 2.09 (0.73) | 2.31 (0.74) | 2.30 (0.67) | 2.21 (0.62) | 0.14 (–0.26, 0.54 | .78 | 0.23 (–0.18, 0.64) | .42 | |
PACES | 4.08 (0.47) | 4.00 (0.46) | 3.99 (0.46) | 3.85 (0.46) | 3.96 (0.58) | 4.00 (0.57) | –0.10 (–0.33, 0.13) | .62 | –0.17 (–0.40, 0.06) | .19 | |
|
4.52 (0.69) | 4.49 (0.78) | 4.48 (0.89) | 4.56 (0.56) | 4.67 (0.85) | 4.68 (0.76) | –0.08 (–0.46, 0.31) | .95 | 0.01 (–0.38, 0.40) | >.99 | |
|
Competence | 4.27 (0.83) | 4.24 (0.94) | 4.25 (1.09) | 4.32 (0.94) | 4.54 (1.17) | 4.52 (1.22) | –0.08 (–0.67, 0.51) | .98 | 0.03 (–0.57, 0.63) | .99 |
|
Autonomy | 4.87 (0.98) | 4.92 (0.94) | 4.94 (0.78) | 5.21 (0.56) | 4.94 (0.99) | 4.84 (0.91) | 0.12 (–0.44, 0.69) | .93 | 0.36 (–0.22, 0.94) | .34 |
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Relatedness | 4.40 (0.79) | 4.31 (0.87) | 4.26 (1.15) | 4.14 (0.91) | 4.53 (1.16) | 4.67 (1.27) | –0.26 (–0.88, 0.35) | .65 | –0.34 (–0.97, 0.29) | .45 |
PASES | 2.50 (0.32) | 2.44 (0.40) | 2.38 (0.47) | 2.43 (0.31) | 2.38 (0.41) | 2.39 (0.34) | –0.02 (–0.24, 0.19) | .99 | 0.04 (–0.18, 0.26) | .96 | |
Average daily valid use (min) | 801.16 (87.85) | 784.43 (93.69) | 819.39 (85.45) | 831.08 (78.74) | 815.59 (57.14) | 814.12 (73.61) | –21.72 (–84.00, 40.56) | .77 | 13.71 (–49.56, 76.99) | .93 | |
Average daily activity counts (counts/min) | 354.27 (123.75) | 341.21 (146.22) | 269.97 (82.72) | 270.20 (84.77) | 364.83 (153.81) | 327.53 (140.02) | 17.74 (–63.07, 98.55) | .93 | 0.90 (–85.41, 87.22) | >.99 | |
Average daily time spent in sedentary activities (min) | 526.63 (106.37) | 535.22 (113.90) | 553.64 (96.80) | 570.63 (95.58) | 529.43 (94.64) | 548.18 (94.11) | –10.94 (–69.83, 48.00) | .96 | 3.95 (–56.26, 64.16) | .99 | |
Average daily time spent in light PA (min) | 237.41 (73.32) | 216.17 (67.46) | 244.46 (61.97) | 237.11 (58.45) | 250.39 (64.05) | 235.40 (63.89) | –10.54 (–53.96, 32.88) | .91 | 4.12 (–39.94, 48.17) | .99 | |
Average daily time spent in moderate PA (min) | 30.28 (14.28) | 25.07 (12.83) | 17.03 (7.81) | 18.03 (10.20) | 25.52 (13.75) | 22.33 (11.69) | 1.42 (–7.96, 10.81) | .98 | –1.71 (–11.51, 8.10) | .96 | |
Average daily time spent in vigorous PA (min) | 6.84 (6.03) | 7.97 (9.16) | 4.26 (4.21) | 5.30 (4.34) | 10.26 (11.72) | 8.22 (9.22) | 1.26 (–3.82, 6.33) | .90 | 0.52 (–4.79, 5.83) | .99 | |
Average daily time spent in MVPA (min) | 37.12 (16.84) | 33.04 (20.61) | 21.29 (11.25) | 23.34 (14.04) | 35.78 (22.54) | 30.54 (17.99) | 1.74 (–11.45, 14.93) | .98 | –1.82 (–16.00, 12.36) | .99 |
a Adjusted for baseline, gender, and multiple comparisons.
No intervention effects were found for self-reported secondary outcomes of physical activity (PAQ-A; immersive app group: adjusted mean difference 0.14, 95% CI –0.26 to 0.54,
For accelerometry, 48 of 51 (94%) participants provided valid data for analysis at baseline, whereas compliance with wearing the device slightly decreased at postintervention (46/51, 90%). Group assignment did not have a significant effect on overall activity (ie, mean counts per minute) or mean daily time spent in MVPA. Compared to the control group, mean baseline daily time spent in MVPA-, gender-, and multiple comparisons-adjusted time in MVPA difference was 1.74 min (95% CI –11.45 to 14.93,
A total of 6 adverse events (1 serious) were reported in 6 participants, 4 of which were in the control group (ankle injury-2 events, lower back pain, and hospitalization because of tonsils removal) and 1 in each of the intervention groups (ankle injury-2 events). None of the adverse events were deemed related to the study intervention.
Approximately two-thirds of participants in the intervention groups reported using the app either 2 (10/32, 31%) or 3 times per week (10/32, 31%), whereas 8 of 32 (25%) only used it 1 time per week (see
For the app Zombies, Run!, the features mostly used by participants were the “workout mission tasks” (n=14) and “story and run log” of completed workouts (n=10), whereas social networking features (“share my runs”: n=0; “ZombieLink account”: n=3) were seldom or never used. Results were similar when participants reported the features they liked (“workout mission tasks”: n=14; “story and run log”: n=8) and disliked (“share my runs”: n=5; “ZombieLink account”: n=5).
For the app Get Running, the feature mostly used by participants was the description of the “week-runs” (n=13), whereas only 1 participant reported using the social networking feature “status updates.” The description of the “week-runs” was also the feature participants predominantly liked (n=11), whereas the main feature disliked was the “status updates” (n=3).
Regardless of the app used, similar themes emerged when participants reported their willingness and motives to continue using their app after study participation. Those willing to continue stated personal benefits (eg, “It will help me to build my fitness”, “Because I can improve how far I run”) and app-related motives (eg, “A fun way to get fit”, “Because it is an enjoyable alternative to exercise”). For those unwilling to continue, “not enough time” was the most common barrier, followed by lack of interest (eg, “I didn’t find the app engaging enough”). The nonimmersive app received less positive feedback around motivational aspects (eg, “Using the app became too tedious”).
Overall, participants perceived the layout of the apps and menus as well structured and “straightforward” to use. Being able to receive clear instructions (eg, “Tells me what to do and when”), listen to their own music during the training sessions, task difficulty increasing gradually, and encouragement provided were features highlighted as useful to support participants’ fitness. Some also considered it helpful if the app allowed choosing between different goals and activities (eg, “I prefer to run to my own goals”). For the immersive app, the storyline (“The back story made it interesting”) and the ability to track progress (ie, app used the device’s Global Positioning System [GPS] and/or accelerometer to log distance) (eg, “It records the distance you ran and your time so you are able to view it for next time and compare”) were also reported as important features.
The majority of participants (21/32, 66%) had no prior experience of using their smartphone for PA purposes. Examples of prior experience included listening to music while engaging in PA or previous use of free apps (eg, MyFitnessPal). Overall, 81% (26/32) were interested in trying different PA-promoting apps in the future.
In prespecified per-protocol analyses (ie, the app was used 3 times/week), there were statistically significant differences observed on the primary outcome between the nonimmersive app group and the control (adjusted mean difference –79.39 sec, 95% CI –133.01 to –25.77,
This is the first randomized trial comparing the effects of a stand-alone immersive mobile app and a nonimmersive app on CRF, PA levels, and its predictors in young people. Key findings were that fitness improved in both app groups, but these did not significantly differ from the control. Despite the availability of readily available commercial apps to improve health behavior, these findings suggest that, compared to usual care, no major improvements were found for these 2 top downloaded apps.
The small increases in fitness in the present trial (0.6 to 1.0 mL/kg/min) were lower than those observed in a Cochrane review of school-based PA interventions, which found increases of 1.6 to 3.7 mL/kg/min in VO2peak [
Although apps have the potential to increase the reach of health behavior change interventions, our results mirror recent research highlighting that only some participants will consistently use an offered app (approximately 20%) [
An important consideration of app content is whether or not they incorporate behavior change techniques (BCTs). Further, modeling, providing consequences for behavior, providing information on others’ approval, prompting intention formation, self-monitoring, and a behavioral contract were identified as effective BCTs for increasing PA in young people in a recent meta-regression [
Consistent with the primary outcome findings, we found no changes in any of the measured psychological variables. Fulfillment of the 3 basic psychological needs (ie, autonomy, competence, and relatedness)—key elements in the development of intrinsic motivation required to drive behavioral change [
A major strength of AIMFIT was the use of a RCT design to determine the effectiveness of 2 off-the-shelf commercially available interventions. We chose a pragmatic approach in which participants used their own device and apps were used ad libitum. Contact with participants was minimal, which reflects app use in a real-world context and therefore increases the generalizability of the findings. Moreover, the primary outcome was assessed objectively with a valid and reliable measurement, as well as PA via accelerometry, which adds to the study’s internal validity. Unlike the commonly observed high attrition in eHealth and mHealth interventions, follow-up assessments were completed for 96% of participants, which represents a high retention rate. Further, using paid apps instead of free ones has likely reduced the chances of contamination and/or cointerventions. Although the accessibility of these apps on the app stores could have led participants to download them, our study sample was a young population and the apps under investigation required purchase. Although inexpensive, purchasing an app on an app store requires a credit card, which young people do not typically own.
The major limitation of this study was its low statistical power and small sample size. We based our sample size calculation on a smaller standard deviation of the primary outcome than the actual standard deviation observed, which meant the power of the trial was smaller than 80%. We used readily available apps and consequently were limited to the decisions made by the app developers on content, duration of the program, and design features. This also meant that we were unable to access data on app utilization (eg, menus accessed in the app). Further, the relatively short duration of the programs precluded investigation of long-term effects or sustainability. This study also highlights that the peer-reviewed literature will always lag behind consumer technology life cycles because during the lifetime of this study innovative apps were developed at a rate that far outpaced our capacity to test them.
Among app users, fitness apps are the most popular (78% users in 2014 compared to 39% in 2013) [
Readily available commercial apps as a stand-alone intervention to improve fitness and increase PA in young people did not increase CRF compared to usual care. Given that smartphone technology appears to resonate with young people and that this type of self-guided intervention has the potential to increase reach at a low cost, this may be best suited as part of a multicomponent intervention, providing additional support and encouragement to the participants (eg, maintenance phases).
CONSORT-EHEALTH checklist V1.6.2 [
Supplementary word file containing graphics with exit survey questions for intervention participants.
behavior change technique
body mass index
cardiorespiratory fitness
moderate-to-vigorous physical activity
physical activity
Physical Activity Enjoyment Scale
Physical Activity Questionnaire for Adolescents
Physical Activity Self-Efficacy Scale
Psychological Need Satisfaction in Exercise Scale
randomized controlled trial
short message service
We would like to acknowledge the trial participants, guardians, and schools who gave time to be in the study. This is an investigator-initiated study supported by internal funding from the University of Auckland Postgraduate Research Student Support. AD is supported by a Foundation for Science and Technology scholarship (FCT-Portugal SFRH/BD/95762/2013). FCT had no role in experimental design, data collection, or manuscript preparation.
AD and RM conceived the study, participated in its design and coordination, and helped draft the manuscript. YJ and RW participated in the design of the study and helped draft the manuscript. All authors read and approved the final manuscript.
None declared.