This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
Use of mobile health (mHealth) technology is on an exponential rise. mHealth apps have the capability to reach a large number of individuals, but until now have lacked the integration of evidence-based theoretical constructs to increase exercise behavior in users.
The purpose of this study was to assess the effectiveness of a theory-based, self-monitoring app on exercise and self-monitoring behavior over 8 weeks.
A total of 56 adults (mean age 40 years, SD 13) were randomly assigned to either receive the mHealth app (experimental; n=28) or not to receive the app (control; n=28). All participants engaged in an exercise goal-setting session at baseline. Experimental condition participants received weekly short message service (SMS) text messages grounded in social cognitive theory and were encouraged to self-monitor exercise bouts on the app on a daily basis. Exercise behavior, frequency of self-monitoring exercise behavior, self-efficacy to self-monitor, and self-management of exercise behavior were collected at baseline and at postintervention.
Engagement in exercise bouts was greater in the experimental condition (mean 7.24, SD 3.40) as compared to the control condition (mean 4.74, SD 3.70,
The successful integration of social cognitive theory into an mHealth exercise self-monitoring app provides support for future research to feasibly integrate theoretical constructs into existing exercise apps. In addition, findings provide preliminary support for theory-based apps to increase self-monitoring and exercise behavior in comparison to a control, no-app condition.
The benefits of exercise are irrefutable [
One strategy that may provide an effective medium to target physical inactivity at the population level is mobile health technology. To date, there are nearly 7 billion mobile phone subscriptions worldwide, with the use of mobile devices reaching 90% in developing countries and 96% globally [
As of 2012, 84% of mobile phone owners had downloaded at least one app to their phone; 19% of those individuals had downloaded an app specifically related to tracking or managing a health-related behavior[
The majority of health-related apps currently available are developed on the traditional dissemination model. Generic, automated text messages are typically sent to individual users on a standardized time of day or week, or access to content is on a dedicated website in which users are referred to go read. While these apps provide valuable information to the user, such apps have neglected to integrate evidence-based strategies from established health behavior change theories [
Although rare to find, research-derived programs such as the Heart Exercise And Remote Technologies (HEART) mobile phone trial [
As demonstrated in the HEART trial, social cognitive theory is particularly well suited for mHealth interventions, as the tenets of the theory are grounded in (1) self-monitoring, (2) self-evaluation, and (3) modification of current behavior based on this self-reflection [
The success of self-regulation is partly dependent on the fidelity, consistency, and timeliness of self-monitoring [
While active engagement in self-monitoring and self-regulation are essential for the maintenance of health-related behavior, it is also imperative to provide individuals with personalized feedback on their behavior [
This pilot study sought to examine the utility of a theory-based exercise self-monitoring app for increasing independent exercise adherence over 8 weeks. It was hypothesized that the use of this app would result in (1) more frequent exercise bouts, (2) more frequent self-monitoring, (3) higher perceived self-management of exercise behavior, and (4) higher self-efficacy to self-monitor exercise behavior in comparison to individuals not using the app.
The study was approved by the University of British Columbia: Okanagan Research Ethics Board. A randomized experimental pilot study design was utilized with participants being randomly selected to one of two conditions—the experimental, app-use condition, or the control, no-app condition. Those randomized to the experimental condition used the app for the 8-week investigation, whereas during the same time period, those randomized to the control condition did not have access to the app. The research assistant met with all participants at both baseline and post-testing time points.
Participants were recruited from a local YMCA fitness facility by means of announcements in fitness classes, posters located throughout the facility, and an information booth in the lobby. In addition, front desk YMCA staff members were instructed to inform individuals about the study opportunity. Eligible participants were current facility members aged 19-70 years, with access to a mobile device. A total of 94 individuals expressed interest in participating. Following initial screening via email, 56 members were deemed eligible (see
Eligible participants provided written consent and subsequently completed baseline questionnaires. All participants then engaged in a goal-setting discussion using the
Each participant’s profile was created on the app within 24 hours, at which time he/she was prompted by a text message to sign in and begin monitoring exercise behavior. Participants were encouraged to monitor exercise behavior on a daily basis (ie, record exercise into the app), regardless of whether purposeful exercise was planned or completed that day—from here on referred to as
At the beginning of each week, participants were sent a message based on social cognitive theory. Messages ranged from 65 to 135 words in length, and were delivered via the app messaging system, to which users were alerted via a text message. These theory-based messages targeted the components of self-monitoring, verbal persuasion, performance accomplishment, and vicarious experience (see
In the event of three consecutive missed check-ins, app users were contacted by the research assistant via SMS text message. If this progressed to four consecutive missed check-ins, the research assistant phoned the participant to discuss any difficulties encountered.
Following goal development, participants in the control condition were encouraged to implement their newly developed goals over the following 8 weeks. Control condition participants did not receive any support from the research assistant throughout the 8-week duration of the study.
At the beginning of week 8, participants in both conditions were contacted via email to schedule a 30-minute follow-up interview for the following week. During this interview, participants completed the poststudy questionnaire.
Participants were asked to provide basic demographic information, including year of birth (see
On the fourth day of each week, a second message was sent through the app, delivering tailored feedback and support based on the participant’s personal performance that week. Daily performance was measured on a 5-star rating system (ie, 5 stars represented complete goal achievement for that day and 3 stars represented partial goal achievement for that day). An additional message was sent through the app if a participant failed to check in to the app on 2 consecutive days (see
Participant flow.
Overview of weekly theory-based messages to participants.
Week | Message type | Theoretical content |
1 | Introduction (establishing rapport) | Hi (insert name)! My name is (insert counselor’s name) and I am your virtual exercise counselor. I can’t wait to see the progress you make as you monitor and modify your behavior. I know you are super motivated and ready to kick start your exercise so let’s get you moving! Check in each day to report your activity and keep an eye on your message center. I’ll be checking in frequently to see how you’re doing. Feel free to contact me if you have any questions or concerns.☺ |
2 | Importance of self-monitoring | Hey (insert name)! Just wanted to say you’re doing a great job! You’re already 1 week into using this app and you have tracked your behavior each day! Keeping track of your behavior allows you, and me, to see what a great job you are doing, and helps remind you of your goals. It can also show us where improvements are needed or whether there are any patterns that are problematic. Some people say that keeping track of what exercise they have done is the hardest part—you are excelling in this and this is what will keep you accountable to your personal goals! Keep checking in everyday and let’s rock this! |
3 | Reminder of importance of self-monitoring, use of verbal persuasion, and self-set rewards | Week 2 down and look at how far you have come! You have now been tracking your exercise behavior for 2 weeks. Keep in mind that self-monitoring is the key to making lasting behavior changes. With this app, tracking your behavior is easy and you are showing yourself that you can do it. You are holding yourself to those goals that you care about so much—doesn’t it feel great? Now is a great time to plan a reward for yourself. Keep up the great work performing and monitoring your exercise—you can do it! |
4 | Performance accomplishment | (insert name)—Wow look at all you’ve done so far! Take a look at your progress graph—all of those green bars you’ve accumulated are proof that you are well on your way to achieving your goals! You really are using this app to its full potential and you are in control of your exercise. You are doing fantastically—keep up this great momentum. |
5 | Feedback tailored to participant’s goals/overcoming perceived barriers | Example: Barrier = family time |
6 | Establishing vicarious experience | Did you know that you are not the only one going through this program right now? There are 40 other facility members just like you that are monitoring their exercise, trying to achieve their personal exercise goals, and using this app to help them reach those goals. These individuals have been recording their bouts of exercise on the app, and have been overcoming their exercise barriers. So far, the app has been keeping people honest and committed to their exercise goals. |
7 | Self-monitoring feedback loop | It’s week 7! You’re doing such a fantastic job taking charge of your own exercise regime by consistently monitoring your behavior and achieving positive scores each day. Now is a good time to look back and search for patterns of when you typically find it most difficult to stick with your exercise regimen. This can give you clues on how to circumvent those less-than-optimal motivational days. Notice weekends are your weak point? Be sure to get all your exercise in during the week and take the weekends off on purpose! Finding AM workouts unbearable? Modify your nighttime routine so that getting up and out the door isn’t so hard. By seeking out problematic trends, you can revise your plans and will be more likely to succeed. |
8 | Final check-in | Today marks the final week of goal tracking for the study. Think about where you started and look at where you are now, the physical and mental barriers that you have been able to break down, and all about what you have learned about yourself. You are in control of your behavior and you are in the habit of self-monitoring. You should feel proud of the progress you’ve made. Now use this feeling to rock your last week of workouts and use this as you move forward. Great job! |
Exercise counselor intervention timeline.
Achievement | Intervention message |
Complete daily goal achievement | |
Partial daily goal achievement | You’ve had some challenges but you did it! Good job for facing your barriers and getting out there. Keep up the good work. |
Partial goal achievement on multiple days | Good job for checking into the app. I know that can be hard when barriers present themselves but you are aware of what is not working. I know you are able to reach those goals you set out to achieve. You can do this! |
Missed check-in for 2+ days | Hey (insert name)! Just checking in to see how it’s going! 2 days have gone by since you last checked in to the system. Every day is a new one so let’s get you back on track and start monitoring that exercise! The hardest part is checking in and keeping track of what you are doing. If you have any questions or concerns please do not hesitate to contact me. |
Mean age and body mass index of participants.
Participant characteristics | Control group | Experimental group |
Age (years), mean (SD) | 41.53 (10.90) | 37.45 (14.13) |
BMIa (kg/m2), mean (SD) | 25.87 (3.60) | 28.24 (6.50) |
aBMI: body mass index.
Purposeful exercise behavior was measured using the Godin Leisure Time Exercise Questionnaire (GLTEQ) [
Assessing the frequency of self-monitoring throughout the 8-week study duration required condition-specific measures. At baseline, all participants’ self-reported the frequency of their self-monitoring exercise behavior over the past 7 days. Postintervention, frequency of self-monitoring exercise behavior among the app users (ie, experimental condition) was assessed using the total number of completed app check-ins, averaged over the 8-week duration of the study. Participants in the control condition were asked to provide an average weekly self-monitoring frequency over the previous 8 weeks.
Self-management of exercise was measured using six items from Hallam and Petosa’s [
Self-efficacy to self-monitor (SESM) exercise was assessed using three items. Participants rated their confidence to self-monitor exercise bouts on an 11-point Likert scale ranging from 0% (
Data were analyzed using SPSS Statistics version 21 (IBM Corp). A series of independent
A total of 56 adults—mean age 40 years (SD 13); mean BMI 26.8 kg/m2 (SD 5.3)—participated in this study, with 36% of participants having achieved a university level degree or higher, and 50% working either part time or full time. A total of 28 individuals out of 56 (50%) were randomized to the experimental condition—mean age 38 years (SD 14); mean BMI 28.2 kg/m2 (SD 6.5)—and 28 out of 56 (50%) were randomized to the control condition—mean age 42 years (SD 11); mean BMI 25.9 kg/m2 (SD 3.6). In total, 41 out of 56 participants (73%) provided follow-up data 8-weeks postintervention (see
There were no statistical differences in demographic, dependent, or independent variables between conditions at baseline with the exception of current exercise self-monitoring behavior. At baseline, participants in the control condition (mean 1.89, SD 2.28) reported a higher frequency of self-monitoring in the past 7 days than participants in the experimental condition (mean 0.52, SD 1.61;
A repeated-measures ANOVA examining self-reported exercise frequency revealed no main effect for time (
A repeated-measures ANOVA examining self-monitoring frequency showed a main effect for time (
A repeated-measures ANOVA examining self-management of exercise behavior revealed no main effect for time (
Participant demographic characteristics.
Participant characteristics | Control group |
Experimental group |
|||
Sex (female) | 16 (57) | 16 (57) | |||
Education | |||||
Less than high school | 0 (0) | 0 (0) | |||
High school | 5 (18) | 5 (18) | |||
Apprenticeship, trades, or diploma | 2 (7) | 3 (11) | |||
College | 4 (14) | 4 (14) | |||
University diploma or degree | 7(25) | 7 (25) | |||
Postgraduate degree | 0 (0) | 2 (7) | |||
Occupation | |||||
Working full time | 8 (29) | 9 (32) | |||
Working part time | 4 (14) | 3 (11) | |||
Working occasionally/contract work | 1 (4) | 1 (4) | |||
Student | 0 (0) | 4 (14) | |||
Retired | 1 (4) | 2 (7) | |||
Other | 4 (14) | 2 (7) |
aOnly 18 out of 28 control group participants completed these measures.
bOnly 21 out of 28 experimental group participants completed these measures.
Self-reported exercise and self-monitoring frequency.
Category | Control group, average frequency/7 days (SD) | Experimental group, average frequency/7 days (SD) | ||
Preintervention | Postintervention | Preintervention | Postintervention | |
Exercise engagement | 4.92 (2.91) | 4.74 (3.70) | 5.14 (3.14) | 7.24 (3.40) |
Self-monitoring frequency | 1.89 (2.28) | 1.95 (2.58) | 0.52 (1.61) | 6.00 (0.93) |
Self-management of exercise behavior.
Category | Control group, average frequency/7 days (SD) | Experimental group, average frequency/7 days (SD) | ||
Preintervention | Postintervention | Preintervention | Postintervention | |
Self-management | 3.34 (0.82) | 3.16 (0.32) | 3.42 (0.86) | 3.29 (0.29) |
Self-efficacy to self-monitor exercise.
Category | Control group, average perceived % self-efficacy to |
Experimental group, average perceived % self-efficacy to |
||
Preintervention | Postintervention | Preintervention | Postintervention | |
Self-efficacy to |
80.70 | 81.05 | 83.71 | 84.70 |
A repeated-measures ANOVA examining SESM was conducted, revealing no main effect for time (
This preliminary pilot study investigated the utility of a theory-based self-monitoring app for improving exercise adherence. To our knowledge, this study is the first to integrate behavior change theory in an app, using personalized goals and interaction with a virtual exercise counselor for the promotion of exercise behavior. Findings provide preliminary evidence that, after 8 weeks, individuals with access to such an app engage in a higher frequency of exercise behavior in comparison to individuals who did not have the app. Specifically, app users reported engaging in 7.2 bouts of exercise per week after 2 months, whereas individuals without use of the app reported engaging in 4.7 bouts of exercise per week at this time period. Although this did not reach statistical significance (
Findings from this pilot study also provide partial support for our secondary hypothesis that use of a theory-based self-monitoring app will result in a higher frequency of self-monitoring in comparison to individuals without access to an app. From baseline to 8 weeks later, app users’ self-monitoring frequency increased from less than one event per week, to an average of six self-monitoring events per week. Self-reported self-monitoring of exercise behavior was unchanged from baseline to post-testing in the control condition (see
Despite increases in both exercise and self-monitoring behavior, our hypotheses that use of the app would result in an improved self-management of exercise behavior, or self-efficacy to self-monitor exercise behavior was not supported. Use of the app did not result in a significant effect on self-management of exercise from pre- to post-testing time points (see
It is also possible the items used to measure self-regulation did not adequately measure the construct within the context of exercise. Although Hallam and Petosa’s [
The integration of theory into the development of a self-monitoring app was the primary strength of this pilot study. To date, principles from theories of health behavior have been used sparingly within mHealth apps [
This study is not without limitations. Given the nature of this investigation acting as a pilot trial, and in working with an entrepreneurial developer, a power calculation was not performed out of logistics in working with our industry partner and recruitment time constraints. Recruitment for this study was limited to one fitness facility due to restrictions placed by the app industry partner, resulting in limited power to detect group differences as well as an inability to conduct more complex analyses to better understand why potential differences existed (eg, mediation and multiple mediation). These findings may not be generalizable to individuals who are not able to afford fitness facility memberships; however, it should be noted that the facility utilized in this study offers subsidized memberships based on gross income. Given the general recruitment criteria (ie, 19-70 years of age, access to a mobile phone device), the heterogeneity of our sample may have weakened our ability to draw concise conclusions and apply them to the general population as not all participants were new to exercise and may have had prior experience with mHealth app technology. Self-monitoring behavior was the secondary focus of this intervention. While app users’ self-monitoring frequency was calculated using data from the app, the self-monitoring frequency of participants in the control condition (ie, no app) was based on self-report data, as the control aspect of this study design prohibited measurement via an app of these participants. The use of self-report data is inherent to recall bias [
Wearable devices (eg, fitness trackers, pedometers, and accelerometers) have become sophisticated, with continued development of technology bringing credibility to such devices. Continued research on the development of mHealth devices could help to establish users' trust in the integration of technology (eg, mobile phone apps and wearable devices) to monitor health behaviors. Overall, an enhanced trust in the use of technology could have a meaningful effect on the ability of a device to impact the health of the public in general, as well as specialized populations [
A total of 8 weeks of mHealth app use resulted in increased exercise and self-monitoring behavior, providing some support for the use of a self-monitoring app to increase adherence to exercise and self-monitoring of exercise behavior. This study protocol also demonstrates the feasibility of incorporating theory-based messages into existing mHealth apps, although the inclusion of such content did not lead to anticipated changes in self-efficacy to self-monitor or self-management of exercise behavior. Multiple inoculations of theory-based messages may be needed for sizable changes to be made in these constructs. Future research is warranted to understand the long-term efficacy of an mHealth app and its effect on exercise and self-monitoring behavior.
analysis of variance
body mass index
Godin Leisure Time Exercise Questionnaire
Heart Exercise And Remote Technologies
self-efficacy to self-monitor
specific, measureable, attainable, relevant, and time-bound
short message service
The study idea was conceived by ECV and MEJ. ECV was responsible for the collection of data, performing data analysis and interpretation, and the writing and editing of the manuscript. MEJ was responsible for overseeing all aspects of the study, working with the industry mHealth app development team, contributing to data interpretation, and editing of the manuscript. NDO assisted with conceptualization of the study idea and review of the manuscript.
None declared.