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The increasing prevalence of smartphone apps to help people find different services raises the question of whether apps to help people find physical activity (PA) locations would help better prevent and control having overweight or obesity.
The aim of this paper is to determine and quantify the potential impact of a digital health intervention for African American women prior to allocating financial resources toward implementation.
We developed our Virtual Population Obesity Prevention, agent-based model of Washington, DC, to simulate the impact of a place-tailored digital health app that provides information about free recreation center classes on PA, BMI, and overweight and obesity prevalence among African American women.
When the app is introduced at the beginning of the simulation, with app engagement at 25% (eg, 25% [41,839/167,356] of women aware of the app; 25% [10,460/41,839] of those aware downloading the app; and 25% [2615/10,460] of those who download it receiving regular push notifications), and a 25% (25/100) baseline probability to exercise (eg, without the app), there are no statistically significant increases in PA levels or decreases in BMI or obesity prevalence over 5 years across the population. When 50% (83,678/167,356) of women are aware of the app; 58.23% (48,725/83,678) of those who are aware download it; and 55% (26,799/48,725) of those who download it receive regular push notifications, in line with existing studies on app usage, introducing the app on average increases PA and decreases weight or obesity prevalence, though the changes are not statistically significant. When app engagement increased to 75% (125,517/167,356) of women who were aware, 75% (94,138/125,517) of those who were aware downloading it, and 75% (70,603/94,138) of those who downloaded it opting into the app’s push notifications, there were statistically significant changes in PA participation, minutes of PA and obesity prevalence.
Our study shows that a digital health app that helps identify recreation center classes does not result in substantive population-wide health effects at lower levels of app engagement. For the app to result in statistically significant increases in PA and reductions in obesity prevalence over 5 years, there needs to be at least 75% (125,517/167,356) of women aware of the app, 75% (94,138/125,517) of those aware of the app download it, and 75% (70,603/94,138) of those who download it opt into push notifications. Nevertheless, the app cannot fully overcome lack of access to recreation centers; therefore, public health administrators as well as parks and recreation agencies might consider incorporating this type of technology into multilevel interventions that also target the built environment and other social determinants of health.
The increasing prevalence of smartphone apps to help people find different services (eg, Yelp and OpenTable to find restaurants, Fandango to find movie theaters, AllTrails to find hikes, GasBuddy to find gas stations, Expedia to find hotels, and Zillow to find homes and apartments) raises the question of whether apps to help people find physical activity (PA) locations (eg, ClassPass [
All authors’ institutions were included in the institutional review board approval (IRB #00004203) at Johns Hopkins as the study began while certain members of the research team (MCF, KJO, YA, MM, SMB, PTW, SS, SR, MSG, MD, KR, DH, RS, and BYL) were based at Johns Hopkins.
We used and further developed a Virtual Population Obesity Prevention, agent-based model of Washington, DC in 2020-2021 [
We represented each of the 167,356 African American women (aged 18-65 years) living in Washington, DC with a computer model–based agent. Each agent (ie, each African American woman in Washington, DC) has attributes for age, height, lean or fat mass, household location, work location, and income based on representative data for the region and population. Each agent also has an embedded metabolic model, which converts daily caloric intake and expenditure to corresponding lean or fat mass [
In each simulated day, women may participate in a recreation center class, depending on a number of factors (
A digital health app that helps locate and send reminders about recreation (rec) center classes. *Factors influenced by phone app.
In the model, we represent a digital health app that helps locate and send reminders about in-person recreation center classes to increase the agents’ likelihood of participation (
Since only a certain percentage of the population may be aware that the app is available, we varied the proportion of women across the population who, in a given scenario, were aware of the app, subsequently downloaded the app, and then opted into push notifications (25%-75%). This means, 25% (41,839/167,356) of women are aware of the app, 25% (10,460/41,841) of those who are aware download it, and 25% (2615/10,460) of those who download it receive regular push notifications from the app. We ranged this to 75% (125,517/167,356) of women aware of the app, 75% (94,138/125,517) of those who are aware download it, and 75% (70,603/94,138) of those who download it opt into the app’s push notifications. Varying the level of user engagement across a range can help identify the thresholds of app engagement that result in observable and statistically significant impacts on PA and weight.
We used the model of Washington, DC to simulate the impact of a digital health app on in-person recreation center class participation, recreation center class PA (minutes per week), subsequent changes in BMI, as well as the prevalence of obesity and the state of having overweight. Each simulation experiment consisted of running the model of Washington, DC and all 167,356 computer model–based agents, 10 times over 5 simulated years.
Validation consisted of comparing different model-generated metrics to observed values to determine if the model was representing what was occurring. For example, when we ran simulation runs, we saw that, on average, 2.1% (3514/167,356) of women were participating in recreation center classes daily compared to the observed 3.8% from the 2017 American Time Use Survey [
Physical activity, overweight, obesity, BMI outcomes by baseline probability to exercise for different scenarios (eg, with and without digital health app).
Simulation scenarios at each baseline probability to exercise | Percent of population exercising at recreation centers, mean (95% CI) | Average number of physical activity min/week, mean (95% CI) | Overweight prevalence, mean (95% CI) | Obesity prevalence, mean (95% CI) | Average BMI, mean (95% CI) | Average BMI among women with obesity, mean (95% CI) | ||||||||
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No digital health app | 58.66 (54.65-62.67) | 36.97 (34.45-39.50) | 24.44 (23.91-24.97) | 56.10 (54.56-57.64) | 30.16 (29.86-30.45) | 34.20 (34.00-34.41) | |||||||
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25%-25%-25%a | 58.91 (54.87-62.94) | 37.26 (34.71-39.81) | 24.42 (23.88-24.96) | 56.09 (54.53-57.65) | 30.15 (29.86-30.45) | 34.21 (34.00-34.43) | ||||||
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50%-50%-50%b | 61.09 (56.92-65.26) | 39.83 (37.12-42.54) | 24.45 (23.91-24.98) | 55.67 (54.15-57.19) | 30.07 (29.78-30.36) | 34.16 (33.94-34.37) | ||||||
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75%-75%-75%c | 65.10 (60.64-69.56) | 44.45 (41.41-47.50) | 24.70 (24.21-25.20) | 54.68 (53.12-56.25) | 29.90 (29.60-30.19) | 34.04 (33.83-34.26) | ||||||
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No digital health app | 58.67 (54.66-62.68) | 52.84 (49.23-56.45) | 25.52 (25.04-26.01) | 52.75 (51.06-54.43) | 29.56 (29.27-29.86) | 33.81 (33.61-34.01) | |||||||
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25%-25%-25%a | 58.92 (54.89-62.94) | 53.25 (49.61-56.89) | 25.54 (25.04-26.04) | 52.62 (50.91-54.33) | 29.56 (29.26-29.86) | 33.83 (33.62-34.05) | ||||||
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50%-50%-50%b | 61.17 (56.99-65.35) | 56.98 (53.09-60.88) | 26.24 (25.68-26.80) | 51.25 (49.47-53.04) | 29.44 (29.14-29.74) | 33.83 (33.62-34.03) | ||||||
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75%-75%-75%c | 65.10 (60.64-69.55) | 63.52 (59.18-67.87) | 27.72 (27.05-28.40) | 48.66 (46.75-50.56) | 29.23 (28.92-29.54) | 33.82 (33.63-34.02) | ||||||
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No digital health app | 78.30 (72.96-83.64) | 86.33 (80.43-92.22) | 27.88 (26.36-29.39) | 44.42 (41.63-47.20) | 28.38 (28.06-28.70) | 33.00 (32.80-33.21) | |||||||
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25%-25%-25%a | 78.22 (72.86-83.58) | 86.88 (80.93-92.83) | 28.24 (26.81-29.67) | 43.90 (41.27-46.52) | 28.38 (28.05-28.71) | 33.08 (32.84-33.31) | ||||||
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50%-50%-50%b | 78.25 (72.90-83.60) | 92.17 (85.89-98.45) | 28.57 (27.17-29.96) | 42.63 (40.03-45.23) | 28.24 (27.91-28.57) | 33.10 (32.86-33.34) | ||||||
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75%-75%-75%c | 78.29 (72.95-83.63) | 101.41 (94.48-108.33) | 29.40 (28.15-30.66) | 40.27 (37.75-42.78) | 28.00 (27.65-28.34) | 33.15 (32.91-33.40) |
a25% (41,839/167,356) aware of the app, 25% (10,460/41,839) of those who are aware download the app, and 25% (2615/10,460) of those who download it receive notifications.
b50% (83,678/167,356) aware of the app, 50% (48,725/83,678) of those who are aware download the app, and 50% (26,799/48,725) of those who download it receive notifications.
c75% (125,517/167,356) aware of the app, 75% (94,138/125,517) of those who are aware download app, and 75% (70,603/94,138) of those who download it receive app notifications.
Percent of women exercising with and without the mobile app within each ward in Washington, DC.
With lower levels of user engagement with the mobile phone app, that is 25% aware of app (41,839/167,356), 25% of those aware download app (10,460/41,839), and 25% (2615/10,460) of those who download it receive app notifications, the app had a negligible and nonsignificant impact on the additional minutes of PA (<1 minute), on the additional percent of women who ever exercise (0.2% [335/167,356];
Increasing user engagement to approximately 50% (eg, 50% aware [83,678/167,356], 58.23% [48,725/83,678] of those who are aware download the app [
The percent of women attending at least one recreation center class over the course of the simulation shows additional gains between when the baseline probability to exercise is between 10% (10/100; 2.43% [4067/167,356], 95% CI –4.24% to 9.1%) and 25% (25/100; 2.5% [4184/167,356]; 95% CI –4.2% to 9.2%). When the baseline probability to exercise is 50% (50/100), the percent of women exercising at least once hits a ceiling of 78% (130,538/167,356) (increase of 0.05% [84/167,356]; 95% CI –8.68% to 8.77%), due to the location and accessibility of recreation centers for some women. Thus, at lower probabilities to exercise (eg, 10%-25%), the app is more effective at increasing the number of women participating in at least one recreation center class (
Further increasing app engagement to 75%, with 75% (125,517/167,356) of women aware of the app, 75% (94,138/125,517) of those who are aware downloading the app, and 75% (70,603/94,138) of those who download it opting into the app’s push notifications resulted in statistically significant gains to PA and reductions in obesity prevalence. For example, weekly PA increased by 10.7 (95% CI 4.2-17.2) minutes per week, and obesity prevalence decreased by an absolute 4.09% (6,845/167,356; 95% CI 1.2%-7.0%) with 25% baseline exercise probability (
Impact of mobile app on physical activity, BMI, as well as overweight and obesity prevalence at each baseline probability to exercise. Rec: recreation.
The results varied substantially by ward. For example, at 25% (25/100) baseline probability to exercise (assuming 50% [83,678/167,356] aware, 50% [48,725/83,678] of those who are aware downloading the app, and 50% [26,799/48,725] of those who download it receiving app notifications), Ward 6 had the highest absolute increase in average PA minutes per week (4.85, 95% CI 4.58-5.11), and the greatest reduction in average BMI (–0.15 kg/m2, 95% CI –0.19 to –0.11). However, Ward 7 had the lowest (3.39, 95% CI 3.24-3.53) increase in PA minutes per week and the smallest reduction in BMI (–0.09 kg/m2; 95% CI –0.12 to –0.06). Changes in overweight and obesity prevalence also varied between wards and decreased by as much as 2.6% (539/20,739; 95% CI 2.3%-2.9%) in Ward 6, where participation in recreation center classes was highest and as little as 1.9% (622/32,729; 95% CI 1.7%-2.1%) in Ward 7 (25% baseline exercise probability).
Our simulation model of African American women in Washington, DC, and their use of a place-tailored digital health app to help identify recreation center classes shows that the app does not result in substantive population-wide health effects at lower levels of app engagement (eg, 25% of women are aware of the app, 25% of those aware of the app download it, and 25% of those who download it receive regular push notifications from the app). When 50% of women are aware of the app, 58.23% of those who are aware download the app, and 55% of those who download it receive regular push notifications from the app, there are observable changes in PA and weight across the population, but the impact is not statistically significant. For the app to result in statistically significant increases in PA and reductions to obesity prevalence over 5 years, there needs to be at least 75% of women who are aware of the app, 75% of those aware of the app downloading it, and 75% of those who download it opting into the app’s push notifications. Thus, we demonstrated the minimum levels of engagement needed at the outset of a mobile phone app campaign (approximately 50% aware of the app, 50% of those who are aware download the app, and 50% of those who download it receive app notifications, assuming reductions in use over the first 3 months) to observe a change in PA and weight across the population. Studies have shown how perceived usefulness of an app, user-friendliness, backing from health care professionals, and continued engagement impact app usage [
Further, our results show that a place-tailored app is more likely to be successful in increasing PA in those who already have a higher likelihood to exercise. While the results showed that the app was successful at encouraging individuals who have a low baseline probability (eg, 10% [10/100] and 25% [25/100]) to exercise to attend at least one new class over the course of the simulated period, this alone was not enough to drive a sustained change in regular exercise. The app did a better job at increasing the average duration of PA each week as baseline probability to exercise increased. This indicates that improving knowledge of recreation center classes, while important, should be coupled with interventions to help overcome personal and social barriers (eg, limited social support for PA or time constraints) that determine baseline exercise probability [
Regardless of user engagement with the app, place-tailored digital health apps need to be combined with increasing physical access to recreation centers to see greater than additive effects in PA and subsequent health outcomes. There is a limit to a place-tailored app’s impact because some individuals cannot access recreation centers due to the distance and lack of transportation (eg, access to car) from their home location. As shown in our results, there are clear disparities in the success of the app in improving health outcomes in neighborhoods with greater access to recreation centers (with nearly a 1.4-fold increase in the use of recreation center classes in these neighborhoods [eg, Ward 6]) compared to neighborhoods with less accessible recreation centers (eg, Ward 7), even with 75% of women who are aware of the app, 75% of those aware of the app download it, and 75% of those who download it opt into the app’s push notifications. Past studies have shown that lower-income neighborhoods in many cities around the United States have less accessible PA locations and recreation centers [
Our results also show that it takes time for the effect of the place-tailored mobile app to fully manifest (>2 years). In general, 1 year is not enough time to see an impact on BMI and overweight and obesity prevalence, as population-level effects on weight and subsequent health benefits accrue over years. This shows the need to continuously measure the value of intervention programs over a period of several years, since reductions in overweight and obesity prevalence may not be demonstrated immediately, and effective interventions may wrongly be deemed unsuccessful if evaluated too early. Accounting for this ramp-up period is important, as it can also take time for a new technology to be adopted and used. Our results show that the speed of the reduction in overweight and obesity prevalence in the population increases year after year as adoption rates increase, revealing a potential opportunity to increase momentum as more users adopt similar place-tailored digital health technology.
In addition to being able to simulate extended periods of time, another benefit of simulation modeling is that it can be adapted and refined over time. For example, simulation modeling can be used in conjunction with clinical trials [
All models are simplifications of reality and cannot account for all possible factors that may affect PA decision-making. Our model included a few simplifying assumptions. For example, we did not account for objective accessibility to a recreation center near a woman’s workplace and used the objective accessibility near the home as a proxy. In addition, since we wanted to demonstrate how to design an app that harnesses geographic location and the value of such an app, our study focused on the app locating and reminding individuals about in-person classes, rather than web-based classes. However, such an app may offer similar benefits for web-based classes such as reminding individuals about when classes are scheduled and what equipment is needed, while reducing potential geographic barriers to exercise. We also assumed that in-person classes are available (eg, not during a public health emergency such as the COVID-19 pandemic). When determining body weight changes for each woman, we assumed that compensatory eating did not occur. Our model simulated behavior of and used data specific to Washington, DC African American women, which may limit generalizability to other populations or geographic areas.
Our study shows that a digital health app that helps identify recreation center classes does not result in substantive population-wide health effects at lower levels of app engagement (eg, 25% of women who are aware of the app, 25% of those who are aware of the app download it, and 25% of those who download it receive regular push notifications from the app). For the app to result in statistically significant increases in PA and reductions to obesity prevalence over 5 years, there needs to be at least 75% of women aware of the app, 75% of those aware of the app download it, and 75% of those who download it opt into the app’s push notifications. Even so, the app cannot fully overcome lack of access to recreation centers, and therefore, public health administrators as well as parks and recreation agencies might consider incorporating this type of technology into multilevel interventions that also target the built environment and other social determinants of health.
Supplementary materials.
physical activity
This project was supported by the National Institutes of Health (NIH) Intramural Research Program via grants ZIA HL006168, ZIA HL006225, ZIA HL006252, ZIA MD000010, and ZIA MD000020. The Social Determinants of Obesity and Cardiovascular Risk Laboratory is funded by the Division of Intramural Research at the National Heart, Lung, and Blood Institute (NHLBI) and the Intramural Research Program of the National Institute on Minority Health and Health Disparities (NIMHD). The Socio-Spatial Determinants of Health (SSDH) Laboratory is supported by the Intramural Research Program, NIMHD, NIH, and by the NIH Distinguished Scholars Programs.
This project was also supported by the
None declared.