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Physical activity apps are commonly used to increase levels of activity and health status. To date, the focus of research has been to determine the potential of apps to influence behavior, to ascertain the efficacy of a limited number of apps to change behavior, and to identify the characteristics of apps that users prefer.
The purpose of this study was to identify the mechanisms by which the use of physical activity apps may influence the users’ physical activity behavior.
This study used a cross-sectional survey of users of health-related physical activity apps during the past 6 months. An electronic survey was created in Qualtrics’ Web-based survey software and deployed on Amazon Mechanical Turk. Individuals who had used at least one physical activity app in the past 6 months were eligible to respond. The final sample comprised 207 adults living in the United States. 86.0% (178/207) of respondents were between the ages of 26 and 54 years, with 51.2% (106/207) of respondents being female. Behavior change theory informed the creation of 20 survey items relating to the mechanisms of behavior change. Respondents also reported about engagement with the apps, app likeability, and physical activity behavior.
Respondents reported that using a physical activity app in the past 6 months resulted in a change in their attitudes, beliefs, perceptions, and motivation. Engagement with the app (
The findings from this study provide an overview of the mechanisms by which apps may impact behavior. App developers may wish to incorporate these mechanisms in an effort to increase impact. Practitioners should consider the extent to which behavior change theory is integrated into a particular app when they consider making recommendations to others wishing to increase levels of physical activity.
Regular physical activity is effective in primary and secondary prevention of chronic diseases, including those relating to both physical and mental health outcomes [
Despite clear and consistent guidelines establishing standards for physical activity, only half of the adults worldwide meet the recommended levels [
Smartphone apps have emerged as a potential tool for individuals seeking to increase levels of physical activity in an effort to improve health status [
Despite the wide use of health-related physical activity apps by millions of Americans [
The focus of several recent studies has been to analyze the content of physical activity apps and identify the extent to which behavior change theory is integrated [
This study involved a cross-sectional survey designed to analyze the self-reported impact of using a physical activity app on the mechanisms of behavior change. This was done through a survey directed to individuals who had utilized at least one physical activity app within the last 6 months. The survey gathered information regarding mechanisms of behavior change informed by behavior change theory, app engagement and likeability, app usage, and self-reported physical activity behavior.
The study sample comprised respondents who were recruited through Amazon Mechanical Turk (MTurk) and Turk Prime. MTurk is a crowdsourcing Internet marketplace that enables individuals and businesses to coordinate the completion of tasks, of which surveys are a common variety.
The sample was limited to respondents who were 18 years of age or older and residents of the United States. A total of 251 respondents completed the survey. The results for 10 respondents were excluded from the final sample because they reported not having used a health-related physical activity app in the past 6 months. Additionally, only respondents who completed all of the survey items were included in the final sample, which included a total of 207 respondents.
An electronic survey constructed through Qualtrics’ Web-based survey software was used to collect data through MTurk. The survey was available in MTurk for approximately 2 weeks with a US $1 incentive. The incentive was increased to US $2 after 2 weeks, and the survey was relaunched with Turk Prime to improve the response rate. As part of relaunching the survey using Turk Prime, an authenticator was built into the survey to prevent repeat respondents. Compensation was entirely based upon completing the survey and not on the quality of the responses.
Demographic information was gathered and respondents were asked to report their age, race, ethnicity, sex, highest level of education obtained, and annual household income (
Respondents gave responses to a series of questions designed to measure the app’s impact on the mechanisms of behavior change. The questions were based on three prominent behavior change theories: social cognitive theory, the theory of planned behavior, and the health belief model. Items were developed to measure specific constructs within these theories. For example, “My belief that physical inactivity leads to disease” is a reflection of outcome expectancies, a fundamental construct of social cognitive theory. A full list of items and their corresponding theories are displayed in
A standard, 5-point Likert response scale was used to measure these items, ranging from Strongly disagree (−2) to Strongly agree (+2). A composite theory variable was constructed by summing the values of these 20 items, and the Cronbach alpha of this variable was .931. This variable provided a global assessment of the extent to which the apps impacted constructs believed to be important in influencing behavior change. A polytheoretical measure was determined to be in line with the viewpoint that multifactorial behaviors are too complex for any one single theory and may be best addressed with multiple theories [
Five items related to the likeability and engagement (actual items displayed in
Stata version 14 was used to calculate all statistics. Descriptive statistics were calculated for each of the demographic, app usage, theory, engagement, and behavior variables. Multiple regression analysis was used to identify factors associated with behavior change theory constructs as well as with physical activity behavior, after controlling for potentially confounding variables.
The majority of respondents were between the ages of 26 and 54 years, with 45.9% (95/207) reporting their age between 26 and 34 years, and 40.1% (83/207) reporting their ages between 35 and 54 years (Table 1). Concerning race and ethnicity, 82.1% (170/207) of respondents reported being white and 94.2% (195/207) of respondents reported being of a non-Hispanic/Latino ethnicity. Females comprised 51.2 % (106/207) of respondents. Whereas the levels of education varied from less than a high school education to a professional degree, 63.8% (132/207) of respondents had either a 4-year degree or some college (not graduated) education. When asked about the number of physical activity apps used in the past 6 months, 60.9% (126/207) of respondents reported using only 1 physical activity app, whereas 29.0% (60/207) reported using 2 physical activity apps. Regarding frequency of physical activity app use in the past 6 months, 41.0% (85/207) of respondents reported using the apps daily, whereas 48.3% (100/207) of respondents reported using the apps multiple times a week. The most commonly used apps as reported by study respondents were Fitbit and MyFitnessPal, with 22.2% (46/207) of respondents reporting using Fitbit and 17.4 % (36/207) of respondents reporting using MyFitnessPal.
A majority of respondents (58.0%, 120/207) reported “Strongly agree” that using the apps increased their desire to be healthy (Table 2). Similarly, 56.0% (116/207) strongly agreed that the apps increased their desire to be physically active. Respondents reported similar “Strongly agree” response rates for increased motivation, intention, goal setting desire, and ability to be physically active as a result of app use. A minority of respondents strongly agreed that the apps increased their belief that people important to them want them to be physically active (21.7%, 45/207), their knowledge of the diseases that are caused by physical inactivity (22.2%, 46/207), and their belief that physical inactivity leads to disease (24.6%, 51/207).
Demographics (N=207).
Demographics | n (%) | ||
18-25 | 17 (8.2) | ||
26-34 | 95 (45.9) | ||
35-54 | 83 (40.1) | ||
55-64 | 11 (5.3) | ||
65 or older | 1 (0.5) | ||
American Indian or Alaska Native | 1 (0.5) | ||
Asian | 16 (7.7) | ||
Black or African American | 18 (8.7) | ||
Native Hawaiian or Other Pacific Islander | 2 (1.0) | ||
White | 170 (82.1) | ||
Hispanic/Latino | 12 (5.8) | ||
Non-Hispanic/Latino | 195 (94.2) | ||
Male | 101 (48.8) | ||
Female | 106 (51.2) | ||
Less than high school | 2 (1.0) | ||
High school/General educational development | 27 (13.0) | ||
Some college (not graduated) | 56 (27.1) | ||
2-year college degree | 28 (13.5) | ||
4-year college degree | 76 (36.7) | ||
Master’s degree | 15 (7.3) | ||
Professional degree (JD, MD, etc) | 3 (1.5) | ||
Northeast | 36 (17.4) | ||
Midwest | 34 (16.4) | ||
South | 93 (44.9) | ||
West | 44 (21.7) | ||
Less than 30,000 | 47 (22.7) | ||
30,000-39,999 | 39 (18.8) | ||
40,000-49,999 | 23 (11.1) | ||
50,000-59,999 | 28 (13.5) | ||
60,000-69,999 | 15 (7.3) | ||
70,000-79,999 | 19 (9.2) | ||
80,000-89,999 | 12 (5.8) | ||
90,000-99,999 | 6 (2.9) | ||
100,000 or more | 18 (8.7) |
aAll values are in 2016 US dollars.
Responses to behavior change constructs (N=207). A composite behavior theory variable was computed by summing these variables, Cronbach alpha=.931.
Construct or mechanism of changea | n (%) | ||||
Strongly disagree | Somewhat disagree | Neither agree nor disagree | Somewhat agree | Strongly agree | |
My belief that physical inactivity leads to disease (outcome expectations)b | 11 (5.3) | 39 (18.8) | 32 (15.5) | 74 (35.8) | 51 (24.6) |
My belief that diseases related to physical inactivity are harmful (outcome expectancies)b | 8 (3.9) | 26 (12.6) | 36 (17.4) | 65 (31.4) | 72 (34.8) |
My belief that being physically active can prevent disease (behavioral belief)c | 2 (1.0) | 14 (6.8) | 27 (13.0) | 85 (41.1) | 85 (41.1) |
My belief that physical activity is important in preventing disease (behavioral belief)c | 2 (1.0) | 15 (7.3) | 24 (11.6) | 86 (41.6) | 80 (38.7) |
My ability to be physically active (self-efficacy)b | 1 (0.5) | 8 (3.9) | 14 (6.8) | 80 (38.7) | 104 (50.2) |
My confidence that I can be physically active (self-efficacy)b | 1 (0.5) | 5 (2.4) | 8 (3.7) | 104 (50.2) | 89 (43.0) |
My motivation to be physically active (behavioral attitudes)c | 0 (0) | 3 (1.5) | 10 (4.8) | 79 (4.8) | 115 (55.6) |
My desire to be physically active (behavioral attitudes)c | 0 (0) | 1 (0.5) | 17 (8.2) | 73 (35.3) | 116 (56.0) |
My intentions to be physically active (behavioral intention)c | 0 (0) | 1 (0.5) | 13 (6.3) | 81 (39.1) | 112 (54.1) |
My attitudes about the importance of physical activity in preventing disease (behavioral attitudes)c | 1 (0.5) | 13 (6.3) | 25 (12.1) | 87 (42.0) | 81 (39.1) |
My belief that people important to me want me to be physically active (subjective norm)c | 9 (4.4) | 31 (15.0) | 50 (24.2) | 72 (34.8) | 45 (21.7) |
My perception that many other people are physically active (situational perception)b | 8 (3.9) | 29 (14.0) | 39 (18.8) | 71 (34.3) | 60 (29.0) |
My knowledge of ways in which I can be physically active (knowledge)b | 2 (1.0) | 14 (6.8) | 15 (7.3) | 95 (45.9) | 81 (39.1) |
My knowledge of the diseases that are caused by physical inactivity (knowledge)b | 15 (7.3) | 43 (20.8) | 36 (17.4) | 67 (32.4) | 46 (22.2) |
My awareness of the benefits of being physically active (perceived benefits)d | 1 (0.5) | 8 (3.9) | 22 (10.6) | 88 (42.5) | 88 (42.5) |
My desire to be healthy (behavioral attitudes)c | 0 (0) | 1 (0.5) | 12 (5.8) | 74 (35.8) | 120 (58.0) |
The social support I have received for being physically active (reinforcement)b | 7 (3.4) | 35 (16.9) | 45 (21.8) | 67 (32.4) | 53 (35.6) |
The positive feedback I have received for being physically active (reinforcement)b | 7 (3.4) | 21 (10.1) | 38 (18.4) | 80 (38.7) | 61 (29.5) |
My desire to set goals to be physically active (attitude toward behavior)b | 0 (0) | 1 (0.5) | 10 (4.8) | 87 (42.0) | 109 (52.7) |
My ability to achieve my physical activity goals (self-efficacy)b | 1 (0.5) | 3 (1.5) | 11 (5.3) | 91 (44.0) | 101 (48.8) |
aAll theory questions in the survey were preceded by this statement: “Now think about the physical activity/exercise apps that you have used in the past 6 months. Using the apps has increased”:
bSocial cognitive theory.
cTheory of planned behavior.
dHealth belief model.
Responses to likeability and engagement items (N=207). A composite engagement variable was computed by summing these variables, Cronbach alpha=.890.
Itema | n (%) | ||||
Strongly disagree | Somewhat disagree | Neither agree nor disagree | Somewhat agree | Strongly agree | |
The app was useful. | 0 (0) | 1 (0.5) | 4 (1.9) | 67 (32.4) | 135 (65.2) |
The app was easy to use. | 0 (0) | 1 (0.5) | 3 (1.5) | 64 (30.9) | 139 (67.2) |
I enjoyed using the app. | 0 (0) | 2 (1.0) | 13 (6.3) | 68 (32.9) | 124 (59.9) |
I liked the app. | 0 (0) | 0 (0) | 7 (3.4) | 72 (34.8) | 128 (61.8) |
I would recommend the app to others. | 0 (0) | 1 (0.5) | 5 (2.4) | 72 (34.8) | 129 (62.3) |
aAll engagement questions in the survey were preceded by this statement: “Considering the apps that you have used in the past 6 months”:
Regarding the app likeability and engagement (
More than half of respondents strongly agreed that apps influenced frequency (58.5%, 121/207) and consistency (58.9%, 122/207) of physical activity (
App engagement (
Responses to physical activity behavior items (N=207). A composite behavior change variable was computed by summing these variables, Cronbach alpha=.854.
Itema | n (%) | ||||
Strongly disagree | Somewhat disagree | Neither agree nor disagree | Somewhat agree | Strongly agree | |
My actual goal setting to be physically active | 1 (0.5) | 2 (1.0) | 6 (2.9) | 102 (49.3) | 96 (46.4) |
My frequency of physical activity | 1 (0.5) | 0 (0) | 9 (4.4) | 76 (36.7) | 121 (58.5) |
My intensity of physical activity | 0 (0) | 14 (6.8) | 24 (11.6) | 82 (39.6) | 87 (42.0) |
My consistency in being physically active | 0 (0) | 3 (1.5) | 7 (3.4) | 75 (36.2) | 122 (58.9) |
aAll theory questions in the survey were preceded by this statement: “Now think about the physical activity/exercise apps that you have used in the past 6 months. Using the apps has increased”:
Regression analysis and behavior change theory (N=207).
Variable | Coefficient (Standard error) | ||
App engagement | .23 (0.04) | 6.22 | <.001 |
Frequency of app use | .39 (0.17) | 2.27 | .03 |
Price | .46 (0.18) | 2.54 | .01 |
Age | .05 (0.11) | 0.46 | .65 |
Gender | .22 (0.16) | 1.31 | .19 |
Education | .01 (0.06) | 0.11 | .91 |
Regression analysis and physical activity (N=207).
Variable | Coefficient (Standard error) | ||
Theory | .21 (0.028) | 7.52 | <.001 |
App engagement | .40 (0.074) | 5.45 | <.001 |
Frequency of app use | −.01 (0.067) | −0.02 | .99 |
Price | −.01 (0.07) | −0.17 | .86 |
Age | .01 (0.04) | 0.27 | .79 |
Gender | −.06 (0.06) | −0.98 | .33 |
Education | .019 (0.023) | 0.83 | .41 |
The purpose of this study was to identify mechanisms of behavior change that are impacted by using a physical activity app. Second, we sought to explore the relationship between mechanisms of behavior change and self-reported actual changes in physical activity behaviors. The majority of respondents reported that apps had a favorable impact on their perceptions, attitudes, and beliefs. Physical activity apps certainly resulted in a marked increase in their desire to be healthy and motivation to be physically active. A recent review of app-based interventions reported that the method for increasing motivation to be physically active may include providing prompt and timely feedback to the user [
An association between the frequency of use and reported impact on the theory-based mechanisms of behavior change was observed. The exact reason for this relationship is not known. Some possible explanations may include the user-friendly nature of the apps that impacted theoretical constructs, increased user motivation, or higher user satisfaction. In a recent study of physical activity app users, respondents valued receiving push prompts and feedback [
Higher priced apps in this study were more likely to have a positive influence on the mechanisms of change, including constructs such as respondents’ attitudes, beliefs, and perceptions. A similar finding has also been reported in other studies of health and fitness apps, where apps with a higher cost were evaluated to have greater potential for influencing behavior change [
This study makes two unique contributions to extant literature. First is the identification of the theory-based mechanisms that are most impacted by using a physical activity app. Second is the connection between the theory-based mechanisms of change and physical activity behavior. The significant findings of the latter provide at least some validation of the major findings of this study. If there had been no association between the mechanisms of change and physical activity behavior, the study findings would be of little practical significance. In light of this finding, future efforts could focus on development-related questions to determine the most effective way to integrate and impact these theory-based mechanisms of change.
The limitations of this study should be considered when interpreting the findings. First, respondents in this study had limited diversity regarding race, ethnicity, education, and income. Respondents in this study were primarily white, educated, and had higher income status. This limitation is likely a reflection of the demographic using mTurk’s Web-based surveying system, which also tends to mirror these demographic characteristics [
The purpose of this study was to investigate the mechanisms by which changes in physical activity might occur when using a physical activity app. Findings indicate that increased engagement and use may be related to mechanisms of behavior change informed by behavior change theory. Furthermore, these mechanisms of change appear to be related to physical activity. Those wishing to develop physical activity apps may consider ways to integrate these mechanisms of change. Additionally, practitioners in search of apps for recommendation to improve physical activity behaviors should consider apps with an emphasis on these theory-informed mechanisms with more confidence, as they may be more likely to result in behavior change.
Amazon Mechanical Turk
None declared.