Abstract
Background:
Physical activity is associated with a lower risk of major adverse cardiovascular events, but few individuals achieve guideline recommended levels of physical activity. Strategies informed by behavioral economics increase physical activity, but their longer-term effectiveness is uncertain. We sought to determine the effect of behaviorally-designed gamification, loss-framed financial incentives, or the combination on physical activity compared with attention control over 12-month intervention and 6-month post-intervention follow-up periods.
Methods:
Between May 2019 and January 2024, participants with clinical ASCVD or 10-year risk of myocardial infarction, stroke, or cardiovascular death ≥ 7.5% by the pooled cohort equation were enrolled in a pragmatic randomized clinical trial. Participants received a wearable device to track daily steps, established a baseline, selected a step goal increase, and were randomly assigned to control (n = 151), behaviorally-designed gamification (n = 304), loss-framed financial incentives (n = 302), or gamification + financial incentives (n = 305). The trial’s primary outcome was change in mean daily steps from baseline through the 12-month intervention period.
Results:
A total of 1062 patients (mean [SD] age 67 [8], 61% female, 31% non-white) were enrolled. Compared with controls, participants had significantly greater increases in mean daily steps from baseline during the 12-month intervention in the gamification arm (adjusted difference, 538.0; 95% CI, 186.2-889.9; P = 0.0027), financial incentives arm (adjusted difference, 491.8; 95% CI, 139.6-844.1; P = 0.0062), and gamification + financial incentives arm (adjusted difference, 868.0; 95% CI, 516.3-1219.7; P < 0.0001). During 6-month follow-up, physical activity remained significantly greater in the gamification + financial incentives arm than in the control arm (adjusted difference, 576.2; 95% CI, 198.5-954; P = 0.0028) but was not significantly greater in the gamification (adjusted difference, 459.8; 95% CI, 82.0-837.6; P = 0.0171) or financial incentives (adjusted difference, 327.9; 95% CI, −50.2 to 706; P = 0.09) arms, after adjusting for multiple comparisons.
Conclusions:
Behaviorally-designed gamification, loss-framed financial incentives, and the combination of both increased physical activity compared with control over a 12-month intervention period, with the largest effect in gamification + financial incentives. These interventions could be a useful component of strategies to reduce cardiovascular risk in high-risk patients.
Clinical trial registration:
Keywords: exercise, cardiovascular diseases, behavioral economics, gamification, health behavior
INTRODUCTION
Higher levels of physical activity are associated with improved control of cardiovascular risk factors and reduced risk of major adverse cardiovascular events.1-5 Guidelines therefore recommend that adults obtain at least 150 minutes of moderate intensity physical activity weekly.6 However, only about 25% of adults in the United States achieve this level of physical activity.7,8
Behavioral economics is a field of study that uses concepts from economics and psychology to better understand and influence how individuals make decisions.9 Behavioral economic concepts relevant to motivation for engaging in healthy behaviors include immediacy (people are more motivated by immediate rewards than future rewards), endowment effects (whereby people put more value in something they already have relative to something they could attain), and loss-framing (people are more motivated when a situation is framed as a loss rather than a gain), and status quo bias (people avoid initiating change).10-12 In shorter-term studies, gamification- and financial incentive-based interventions leveraging these behavioral economic concepts have each increased physical activity more than control in patients with or at risk for atherosclerotic cardiovascular disease (ASCVD).13-15 In these studies, each participant assigned to gamification was awarded points each week, with a fraction of those points taken away each day that they did not meet their step goal. They progressed through levels each week based on the total points they retained from that week. Participants assigned to financial incentive arms were awarded a sum of money each week, with a fraction of the money taken away each day they did not meet their step goal. The degree to which these interventions increase physical activity over longer periods is uncertain, as is the relative efficacy of gamification versus financial incentives.
We therefore tested the effectiveness of behaviorally-designed gamification, loss-framed financial incentives, or both, compared with attention control, to increase physical activity over prolonged follow-up in individuals at high risk for major adverse cardiovascular events.
METHODS
The data that support the findings of this study are available from the corresponding authors upon reasonable request. Drs. Fanaroff and Volpp had full access to all the data in the study and take responsibility for the study’s integrity and the data analysis.
Behavioral Economic Approaches to Increase Physical Activity Among Patients with Elevated Risk for Cardiovascular Disease (BE ACTIVE) was a randomized clinical trial conducted from May 2019 through January 2024, consisting of a 2-week run-in period, a 12-month intervention period, and a 6-month follow-up period. Details of the study design have been published.16 The trial protocol (Supplemental Methods) was approved by the University of Pennsylvania Institutional Review Board. The study was conducted using Way to Health, a research technology platform at the University of Pennsylvania.17
Participants
Recruitment occurred from May 2019 through May 2022. Participants were eligible if they had established ASCVD or a 10-year risk of myocardial infarction, stroke, or cardiovascular death ≥ 7.5% as determined by the pooled cohort equation.18 Established ASCVD was identified from diagnoses entered in the problem list within the electronic health record (EHR); 10-year risk of ASCVD by the pooled cohort equation was calculated using discrete data fields within the EHR. For complete inclusion and exclusion criteria, see Table S1. Eligible participants were identified by EHR queries and contacted electronically with a link to the study website. Participants used the study website to create an account, provide informed consent, and complete baseline survey assessments. Eligible participants were mailed a wrist-worn wearable device (Fitbit Charge 3, 4, or 5), linked to the Way to Health platform for remote data collection.
Run-in Period and Randomization
The participants entered a 2-week run-in period, during which a baseline step count was estimated using the second week of data.13,19 Participants who did not complete the run-in phase or had baseline counts > 7500 steps/day were excluded from the trial. After the run-in period, each participant was informed of their baseline step count and asked to set a goal step increase of 33%, 40%, 50%, or a custom goal ≥ 1500 steps greater than their baseline. This approach was selected to allow participants personalized goal setting,14 while nudging them to choose an ambitious but achievable target.
Participants were then randomized 1:2:2:2 to attention control, gamification, financial incentives, or gamification + financial incentives, stratified by baseline step count (< 4000 steps, 4000-7000 steps, > 7000 steps), using an electronic number generator through the Way to Health platform. Treatment assignment was open-label, but patients were not explicitly informed about other treatment arms. Investigators, statisticians, and data analysts were blinded to arm assignments until the study and analysis were completed.
Interventions
Participants randomized to attention control received a text message each day for 18 months telling them whether they achieved their step goal on the prior day.
Participants in the gamification arm were entered into a game that leverages insights from behavioral economics to address barriers to behavior change, and that has increased physical activity in several prior shorter-duration studies.14-16,19 First, each participant signed a precommitment pledge to try their best to achieve their daily step goal, an approach shown to motivate behavior change.20 Second, at the start of each week, participants received 70 points. Each day, if the step goal was achieved, the participant retained his or her points; if the step goal was not achieved, they were informed that they had lost 10 points. This design was selected to leverage prospect theory, which has demonstrated that loss framing is more effective at motivating behavior change than gain framing.10 Third, at the end of each week, participants moved up or down levels (blue, bronze, silver, gold, platinum) based on points retained the previous week. Each participant began in the silver level to create a sense of achievable goals.21 Fourth, we leveraged the fresh start effect—the concept that individuals are more motivated for aspirational behavior around temporal landmarks like the start of a new week22—by awarding points at the start of each week. Every eight weeks, individuals in the blue and bronze levels were restarted back at silver, and they were offered a chance to adjust their step goal. This reduced the risk that participants would become discouraged if they initially set their goals too high. Fifth, each participant chose a family member or friend who received a weekly email with the participant’s progress. These supportive sponsors helped enhance social incentives to motivate participants toward their goal. Lastly, at the end of the intervention period, participants in the gold and platinum levels received a trophy recognizing their achievement.
In the financial incentives arm, participants were informed each week that $14 was placed in their virtual account. Each day, if the step goal was achieved, the balance remained. If the step goal was not achieved, the participant was informed that $2 was taken away from their account. This structure leveraged prospect theory, and had successfully increased physical activity in a previous study of shorter duration.13
In the gamification + financial incentives arm, participants received the interventions from both the gamification and financial incentives arms.
In all three intervention arms, participants had an 8-week ramp-up period during which goals were increased gradually from baseline to the target. After 12 months, participants no longer received their intervention but continued to receive a daily text message noting their step count from the day prior (as in the control arm) for an additional 6 months.
Outcome measures
The primary outcome was change in daily steps from baseline through the 12-month intervention period, excluding the 8-week ramp-up phase. Secondary outcomes included change in mean daily steps from baseline through the 6-month follow-up period, change in mean weekly minutes of moderate to vigorous physical activity (MVPA) from baseline through the intervention and follow-up periods and proportion of participant-weeks with ≥ 150 minutes MVPA during the intervention and follow-up periods. Step data was captured by the wearable devices, automatically transferred to participants’ internet-connected devices via Bluetooth, and then automatically uploaded to the Penn Way to Health platform. Consistent with the 2018 Physical Activity Guidelines for Americans, MVPA was defined as any minute during which a participant took at least 100 steps.23-25
Statistical analysis
The study was powered for six comparisons between arms using a common cutoff for the test statistics that ensures the familywise error rate is < 0.05.26 In the first phase, the 3 intervention arms were compared with the control arm. With 300 participants in each intervention arm, and 150 participants in the control arm, we estimated 93% power to detect a difference between arms of 1000 steps and 85% power to detect a difference of 750 steps, assuming a 10% drop-out rate. This approach assumed that the control arm had mean daily steps of 6000, a standard deviation (SD) of the difference between intervention and control arms of 2000 steps,14,15,19,27-29 a 10% dropout rate, and a conservative Bonferroni adjustment of the type I error rate with a 2-sided α of 0.017 to adjust for up to 3 comparisons. In the second phase, only intervention arms that were significantly different from the control arm were compared with each other, using a conservative Bonferroni adjustment of the type I error rate with a 2-sided α of 0.017.
All randomly assigned patients were included in the intention-to-treat analysis.
Data are missing for any day that the participant did not use the wearable device or upload data. For the main analysis, we used multivariate imputation by chained equations for days with missing step values or values < 1000 steps/day, as in prior work, because daily step values < 1000 may not reflect full data capture.14,15,30,31 The following determinants were included in the imputation model: study arm, calendar month (fitted as a nominal variable), week of study, baseline daily steps, age, sex, race/ethnicity, educational level, marital status, household income level, self-reported health, and established atherosclerotic cardiovascular disease. We performed 20 sets of imputations and combined results using Rubin’s standard rules.32 We performed two sensitivity analyses using collected data without imputation (days with missing step count or step count < 1000 are excluded); in the first sensitivity analysis, only days with missing step counts were excluded, and in the second we excluded days with missing step count or step count < 1000.
The primary analysis fit generalized linear mixed effect regression models to evaluate changes in daily steps and weekly minutes of MVPA, adjusting for each participant’s baseline measure, time from study start (as a continuous measure), calendar month, and participant random effects to account for repeated measures. As a sensitivity analysis, we produced fully adjusted models, adding age, gender, race, education, marital status, income, self-reported health, and body mass index to the main adjusted model. For change in steps and weekly minutes MVPA, we assumed a normal distribution and obtained difference in steps between arms for the intervention and follow-up periods as least squared means. We report the effectiveness of the intervention for the primary outcome overall and key subgroups. To estimate the adjusted difference in odds that a participant would have ≥ 150 minutes of MVPA in a given week, we used generalized logistic mixed effect regression models, including the same covariates as in models evaluating other outcomes.
Statistical analyses were performed using SAS version 9.4 (SAS Institute) in February 2024.
RESULTS
Of 101,664 patients offered enrollment, 2188 completed informed consent and baseline surveys, and were mailed a wearable device. Of these, 1062 completed the baseline run-in period with step count < 7500 and were randomized to attention control (n = 151), behaviorally-designed gamification (n = 304), loss-framed financial incentives (n = 302), or gamification + loss-framed financial incentives (n = 305) (Figure 1). Demographics and clinical characteristics were similar between the groups (Table 1). The mean (SD) age of the cohort was 66.7 (8.1), 60.5% were women, and 25.0% were Black. Mean (SD) baseline daily step count was 5081 (1585), mean (SD) daily minutes of MVPA was 5.8 (7.5), and mean (SD) goal step count increase was 1867 (817).
Figure 1: Participant flow.
Participants in all arms were provided with a wearable device, which they connected to the research platform and used to track steps, establish baseline measures of physical activity, and select a step goal increase. Participants in the control arm received regular feedback from the wearable device and its smartphone application but no other interventions. Participants in the intervention arms participated in the interventions automatically during the first 52 weeks and then had no interventions other than daily reports of their step counts during the 26-week follow-up period.
Table 1:
Baseline characteristics
| Control (n = 151) |
Gamification (n = 304) |
Financial incentives (n = 302) |
Gamification + Financial incentives (n = 305) |
|
|---|---|---|---|---|
| Age (mean, SD) | 66.6 (8) | 67.2 (8) | 66.4 (8.2) | 66.6 (8.2) |
| Female sex (n, %) | 97 (64.2%) | 197 (64.8%) | 173 (57.3%) | 175 (57.4%) |
| Race/ethnicity (n, %) | ||||
| White non-Hispanic | 103 (68.2%) | 214 (70.4%) | 202 (66.9%) | 218 (71.5%) |
| Black non-Hispanic | 40 (26.5%) | 76 (25%) | 82 (27.2%) | 68 (22.3%) |
| Asian/Pacific Islander non-Hispanic | 7 (4.6%) | 5 (1.6%) | 7 (2.3%) | 9 (3%) |
| Hispanic | 1 (0.7%) | 4 (1.3%) | 7 (2.3%) | 4 (1.3%) |
| Other | 0 (0%) | 5 (1.6%) | 4 (1.3%) | 6 (2%) |
| Education (n, %) | ||||
| Some high school or less | 2 (1.3%) | 1 (0.3%) | 3 (1%) | 4 (1.3%) |
| High school graduate | 13 (8.6%) | 14 (4.6%) | 19 (6.3%) | 6 (2%) |
| Some college or specialized training | 31 (20.5%) | 69 (22.7%) | 57 (18.9%) | 58 (19%) |
| College graduate | 105 (69.5%) | 220 (72.4%) | 223 (73.8%) | 237 (77.7%) |
| Marital status (n, %) | ||||
| Single | 27 (17.9%) | 48 (15.8%) | 40 (13.2%) | 44 (14.4%) |
| Married | 91 (60.3%) | 189 (62.2%) | 189 (62.6%) | 195 (63.9%) |
| Other | 33 (21.9%) | 67 (22%) | 73 (24.2%) | 66 (21.6%) |
| Annual household income (n, %) | ||||
| < $50,000 | 48 (31.8%) | 61 (20.1%) | 73 (24.2%) | 61 (20%) |
| $50-100,000 | 52 (34.4%) | 109 (35.9%) | 106 (35.1%) | 106 (34.8%) |
| > $100,000 | 51 (33.8%) | 134 (44.1%) | 123 (40.7%) | 138 (45.2%) |
| Self-reported health status (n, %) | ||||
| Excellent | 11 (7.3%) | 23 (7.6%) | 11 (3.6%) | 21 (6.9%) |
| Very good | 56 (37.1%) | 96 (31.6%) | 95 (31.5%) | 88 (28.9%) |
| Good | 60 (39.7%) | 145 (47.7%) | 149 (49.3%) | 147 (48.2%) |
| Fair | 23 (15.2%) | 37 (12.2%) | 45 (14.9%) | 47 (15.4%) |
| Poor | 1 (0.7%) | 3 (1%) | 2 (0.7%) | 2 (0.7%) |
| Prior wearable device use (n, %) | 106 (70.2%) | 213 (70.1%) | 206 (68.2%) | 214 (70.2%) |
| BMI (mean, SD) | 31.4 (7.3) | 31.0 (6.3) | 31.5 (7.0) | 31.1 (6.5) |
| Diabetes (n, %) | 37 (24.5%) | 65 (21.4%) | 66 (21.9%) | 76 (24.9%) |
| Hyperlipidemia (n, %) | 77 (51%) | 163 (53.6%) | 170 (56.3%) | 155 (50.8%) |
| Hypertension (n, %) | 93 (61.6%) | 188 (61.8%) | 192 (63.6%) | 184 (60.3%) |
| Current smoking (n, %) | 11 (7.3%) | 10 (3.3%) | 11 (3.6%) | 11 (3.6%) |
| Prior myocardial infarction (n, %) | 4 (2.6%) | 6 (2%) | 8 (2.6%) | 7 (2.3%) |
| Stroke (n, %) | 2 (1.3%) | 4 (1.3%) | 7 (2.3%) | 5 (1.6%) |
| Heart failure (n, %) | 4 (2.6%) | 6 (2%) | 5 (1.7%) | 5 (1.6%) |
| Chronic obstructive pulmonary disease (n, %) | 5 (3.3%) | 4 (1.3%) | 10 (3.3%) | 9 (3%) |
| Chronic kidney disease (n, %) | 4 (2.6%) | 8 (2.6%) | 10 (3.3%) | 15 (4.9%) |
| Baseline daily steps (mean, SD) | 4980 (1554) | 4958 (1556) | 5018 (1579) | 5081 (1585) |
| Step goal selection (n, %) | ||||
| 33% increase from baseline | 51 (33.8%) | 92 (30.3%) | 95 (31.5%) | 114 (37.4%) |
| 40% increase from baseline | 26 (17.2%) | 37 (12.2%) | 45 (14.9%) | 56 (18.4%) |
| 50% increase from baseline | 21 (13.9%) | 72 (23.7%) | 61 (20.2%) | 55 (18%) |
| Custom goal | 53 (35.1%) | 103 (33.9%) | 101 (33.4%) | 80 (26.2%) |
| Step goal increase from baseline (mean, SD) | 1855 (867) | 1890 (854) | 1890 (829) | 1826 (742) |
SD, standard deviation; BMI, body mass index
During the intervention and follow-up periods, step data was missing or < 1000 on 18.8% of participant-days, similar to previous physical activity intervention studies (Table S2).14,15 A total of 954 participants (89.8%) completed the entire 18-month study. There were 776 adverse events reported in 600 participants; 23 participants dropped out of the trial due to adverse events (Table S3).
Daily step counts
Unadjusted mean daily step counts by week and study arm are shown in Figure 2a; change from baseline is shown in Figure S1. Participants in the control arm had a mean (SD) increase in daily steps of 1418 (1753) from baseline through the main intervention period; participants in the gamification, financial incentives, and gamification + financial incentives arms had mean (SD) increases in daily steps from baseline of 1954 (1706), 1915 (1903), and 2297 (1842), respectively, over the same time period. Over the post-intervention follow-up period, participants in the control, gamification, financial incentives, and gamification + financial incentives arms had a mean (SD) increase in daily steps from baseline of 1245 (1879), 1708 (1882), 1576 (2062), and 1831 (1891), respectively.
Figure 2: Unadjusted Mean Daily Steps and Weekly Minutes Moderate to Vigorous Physical Activity, by Week and Study Arm.
Mean daily steps using imputed data over the study period are shown in panel A; weekly minutes of moderate to vigorous physical activity are shown in panel B. MVPA, moderate to vigorous physical activity
During the main intervention period, compared with attention control, participants had greater increase in mean daily steps from baseline in the gamification (adjusted difference 538.0; 95% CI 186.2-889.9; p = 0.0027), financial incentives (adjusted difference 491.8; 95% CI 139.6-844.1; p = 0.0062), and gamification + financial incentives arms (adjusted difference 868.0; 95% CI 516.3-1219.7; p < 0.001) (Table 2). During the post-intervention follow-up period, the adjusted differences in daily steps from baseline compared with control were 459.8 (95% CI 82.0-837.6, p = 0.0171) for gamification, 327.9 (95% CI −50.2-706.0, p = 0.09) for financial incentives, and 576.2 (95% CI 198.5-954.0; p = 0.0028) for gamification + financial incentives, Results were similar in the fully adjusted model and in sensitivity analyses that used collected data without multiple imputation (Tables S4 and S5). Gamification + financial incentives was superior to financial incentives (adjusted difference 376.4; 95% CI 89.3-663.1; p = 0.0102) during the main intervention period; there were otherwise no differences between intervention arms (Table S6).
Table 2:
Daily step outcomes
| Variable | Control | Gamification | Financial incentives | Gamification + financial incentives |
|---|---|---|---|---|
| Mean steps per day, baseline (SD) | 4980 (1554) | 4958 (1556) | 5018 (1579) | 5081 (1585) |
| Mean steps per day, weeks 9-52 (main intervention period) (SD) | 6398 (2437) | 6912 (2382) | 6933 (2570) | 7378 (2597) |
| Unadjusted mean change from baseline, steps per day, weeks 9-52 (main intervention period) (SD) | 1418 (1753) | 1954 (1706) | 1915 (1903) | 2297 (1842) |
| Main adjusted model | ||||
| Difference versus control (95% CI) | -- | 538.0 (186.2, 889.9) | 491.8 (139.6, 844.1) | 868.0 (516.3, 1219.7) |
| P value | -- | 0.0027 | 0.0062 | <.0001 |
| Fully adjusted model | ||||
| Difference versus control (95% CI) | -- | 574.4 (223.2, 925.6) | 493.1 (142.4, 843.8) | 869.5 (518.1, 1220.9) |
| P value | -- | 0.0013 | 0.0059 | <.0001 |
| Mean steps per day, weeks 53-78 (post-intervention follow-up period) (SD) | 6225 (2437) | 6666 (2470) | 6594 (2601) | 6912 (2567) |
| Unadjusted mean change from baseline, steps per day, weeks 53-78 (post-intervention follow-up period) | 1245 (1879) | 1708 (1882) | 1576 (2062) | 1831 (1891) |
| Main adjusted model | ||||
| Difference versus control (95% CI) | -- | 459.8 (82.0, 837.6) | 327.9 (−50.2, 706.0) | 576.2 (198.5, 954) |
| P value | -- | 0.0171 | 0.0892 | 0.0028 |
| Fully adjusted model | ||||
| Difference versus control (95% CI) | -- | 531.8 (153.8, 909.9) | 362.6 (−14.8, 740.1) | 612 (233.6, 990.5) |
| P value | -- | 0.0058 | 0.0597 | 0.0015 |
Covariates in the main adjusted model included baseline measure, time, calendar-month fixed effects (fitted as a nominal variable), and participant random effects to account for repeated measures. Fully adjusted models included all elements of the main adjusted model, plus age, gender, race, education, marital status, income, self-reported health, and body mass index. To account for multiple comparisons in the primary analysis, p < 0.017 is defined as statistically significant. SD, standard deviation; CI, confidence interval
Treatment effect by subgroup interactions for key subgroups are shown for the intervention and post-intervention follow-up periods in Figures S2 and S3.
Minutes of moderate to vigorous physical activity
Unadjusted mean weekly minutes of MVPA by week and study arm are shown in Figure 2b; change from baseline is shown in Figure S4. Participants in the control arm had a mean (SD) increase in weekly minutes of MVPA of 39.6 (63.8) from baseline through the main intervention period; participants in the gamification, financial incentives, and gamification + financial incentives arms had a mean (SD) increased weekly minutes of MVPA from baseline by 54.7 (74.0), 56.6 (75.6), and 65.4 (73.1), respectively, over the same time period. Over the post-intervention follow-up period, participants in the control, gamification, financial incentives, and gamification + financial incentives arms had a mean (SD) increase in weekly minutes MVPA from baseline of 37.3 (62.3), 50.7 (72.8), 50.9 (70.5), and 57.6 (69.0), respectively.
During the main intervention period, compared with control, the adjusted differences from baseline in weekly minutes MVPA were 15.4 (95% CI 1.2-29.6; p = 0.0333) for gamification, 17.1 (95% CI 2.9-31.4; p = 0.0183) for financial incentives, and 26.4 (95% CI 12.2-40.7; p = 0.0003) for gamification + financial incentives (Table 3). During the post-intervention follow-up period, compared with control, the adjusted differences from baseline in weekly minutes MVPA were 10.8 (95% CI −3.0-24.6; p = 0.12) for gamification, 8.0 (95% CI −5.9-21.8; p = 0.26) for financial incentives, and 12.5 (95% CI −1.4-26.3; p = 0.08) for gamification + financial incentives. There were no differences between any two intervention arms over the main intervention and post-intervention follow-up periods (Table S7). Results were similar in the fully adjusted model.
Table 3:
Weekly minutes moderate to vigorous physical activity
| Variable | Control | Gamification | Financial incentives | Gamification + financial incentives |
|---|---|---|---|---|
| Mean minutes MVPA/week, baseline (SD) | 37.1 (54) | 39.8 (51.8) | 38.7 (54) | 44.3 (59.5) |
| Mean minutes MVPA/week, weeks 9-52 (SD) (main intervention period) | 76.7 (77.2) | 94.5 (82.2) | 95.3 (87.4) | 109.6 (97.9) |
| Unadjusted mean change from baseline, minutes MVPA/week, weeks 9-52 (main intervention period) (SD) | 39.6 (63.8) | 54.7 (74.0) | 56.6 (75.6) | 65.4 (73.1) |
| Main adjusted model | ||||
| Difference versus control (95% CI) | -- | 15.4 (1.2, 29.6) | 17.1 (2.9, 31.4) | 26.4 (12.2, 40.7) |
| P value | -- | 0.0333 | 0.0183 | 0.0003 |
| Fully adjusted model | ||||
| Difference versus control (95% CI) | -- | 16.2 (2.2, 30.3) | 18.1 (4.1, 32.2) | 26.7 (12.7, 40.8) |
| P value | -- | 0.0238 | 0.0114 | 0.0002 |
| Mean minutes MVPA/week, weeks 53-78 (post-intervention follow-up period) (SD) | 70.6 (74.9) | 83.5 (83.7) | 79.9 (83.1) | 88.8 (86.6) |
| Unadjusted mean change from baseline, minutes MVPA/week, weeks 53-78 (post-intervention follow-up period) (SD) | 37.3 (62.3) | 50.7 (72.8) | 50.9 (70.5) | 57.6 (69.0) |
| Main adjusted model | ||||
| Difference versus control (95% CI) | -- | 10.8 (−3.0, 24.6) | 8.0 (−5.9, 21.8) | 12.5 (−1.4, 26.3) |
| P value | -- | 0.1254 | 0.2582 | 0.0771 |
| Fully adjusted model | ||||
| Difference versus control (95% CI) | -- | 13.3 (−0.3, 27) | 10.7 (−3, 24.3) | 14.5 (0.8, 28.1) |
| P value | -- | 0.0557 | 0.1246 | 0.0383 |
Covariates in the main adjusted model included baseline measure, time, calendar-month fixed effects (fitted as a nominal variable), and participant random effects to account for repeated measures. Fully adjusted models included all elements of the main adjusted model, plus age, gender, race, education, marital status, income, self-reported health, and body mass index. To account for multiple comparisons in the primary analysis, p < 0.017 is defined as statistically significant. MVPA, moderate to vigorous physical activity; SD, standard deviation; CI, confidence interval
The unadjusted proportion of participant-weeks with ≥ 150 minutes of MVPA during the main intervention period was 0.16 for control, 0.23 for gamification, 0.24 for financial incentives, and 0.27 for gamification + financial incentives. During the post-intervention follow-up period, these levels were 0.14 for control, 0.18 for gamification and financial incentives, and 0.20 for gamification + financial incentives. Participants in the gamification + financial incentives arm had greater odds of a week with ≥ 150 minutes of MVPA than those in the control arm during the main intervention period (Table S8). There were otherwise no differences between intervention arms and control or between intervention arms (Table S9).
DISCUSSION
In this trial of adults at high risk for major adverse cardiovascular events, interventions with gamification, financial incentives, and gamification + financial incentives each significantly increased physical activity compared with attention control over a 12-month intervention period. This effect was sustained over a 6-month post-intervention follow-up period in the group randomized to gamification + financial incentives, and with a non-significant trend toward higher mean daily steps over post-intervention follow-up in the group randomized to gamification. Gamification + financial incentives also increased weekly minutes of MVPA more than control over the intervention period, with trends toward significant increases with gamification and financial incentives alone. These findings, from one of the longest and largest trials of a fully home-based physical activity promotion intervention yet conducted, have important implications for the design and implementation of physical activity programs in clinical practice.
First, the gamification and financial incentive interventions leveraged principles from behavioral economics, including precommitment, loss aversion, the endowment effect, goal gradients, and the fresh start effect, as well as lessons from completed clinical trials. This study confirms the effectiveness of these strategies for physical activity promotion in a cohort of adults at high risk for major adverse cardiovascular events. Though hybrid home- and center-based interventions have increased physical activity in older adults over long-term follow-up,33 leveraging behavioral insights facilitated the design of effective fully home-based interventions that do not rely on clinical personnel. As such, these interventions could be deployed at scale within a health system, identifying and inviting eligible patients using electronic health record algorithms, and using automated feedback and incentive delivery.
Second, no previous fully home-based physical activity promotion interventions had durations longer than 24 weeks or total follow-up longer than 36 weeks.13-15,19,29,34 During our 12-month intervention, physical activity declined slowly among participants in all arms; however, it declined more slowly in the intervention arms and remained higher in all intervention arms compared with control. During the post-intervention follow-up period, when participants in all arms received no intervention other than daily reports of their step counts, physical activity declined further, but remained higher than control in the gamification and gamification + financial incentives arms. Importantly, the increase in physical activity was in comparison to attention control, which involved goal-setting and daily text messages, and was itself associated with substantially increased physical activity. This observed increase in physical activity in the attention control arm might to some degree reflect the increase in physical activity that could be achieved by a motivated patient who bought and used a wearable fitness tracker, though participants also received daily text messages, which generated social accountability from participants knowing were being observed. In this context, a conservative interpretation is that the difference between interventions and control reflects the additive value of a health system-based program to increase physical activity in a motivated patient who purchased a wearable fitness tracker on their own. The change from baseline through the main intervention and post-intervention follow-up periods in the intervention arms might be conceptualized as the effect that a health system-based program would have on physical activity versus usual care. Compared with baseline, the mean increase in daily steps at the end of the 18-month study period was > 1500 in all 3 intervention arms, and the mean increase in weekly minutes of MVPA was > 40, indicating the potential for this intervention to have a large, sustained effect on physical activity versus usual care if deployed in routine clinical practice. This suggests that a successful long-term intervention can change participants’ habitual level of physical activity, with maintenance of a higher level of physical activity after the intervention is withdrawn.
Third, this study evaluated the relative effectiveness of gamification versus financial incentives versus gamification + financial incentives for physical activity promotion. There were no significant differences in physical activity outcomes between gamification and financial incentives. A formal cost-effectiveness analysis is forthcoming, but gamification may be preferable in many contexts for financial reasons and because there are no additional costs to continuing it indefinitely, potentially mitigating the risk of challenges with maintaining gains in physical activity after intervention withdrawal. Engagement remained high over the 12-month intervention, suggesting the feasibility of an indefinite intervention. Gamification + financial incentives was significantly more effective than financial incentives alone and with a non-significant trend toward greater effectiveness gamification alone. The effectiveness of the gamification + financial incentives intervention may have been due to a response in a higher proportion of patients, as some patients may have been motivated by gamification and others by financial incentives. Future studies should examine the effectiveness of tailored physical activity interventions, delivering gamification to patients likely to respond to this intervention and reserving financial incentives for patients who are unlikely to (or fail to) respond to gamification.
In observational studies, increased physical activity is associated with a lower risk of all-cause mortality and major adverse cardiovascular events, including myocardial infarction and stroke.35-41 Using data derived from these observational studies, a long-term increase in daily steps by ~ 10% from a baseline of 5000 per day, as observed in all 3 intervention arms, would be associated with a 6% lower risk of all-cause mortality and a 10% lower risk of cardiovascular mortality,41 highlighting the clinical relevance of the increase in physical activity achieved in this trial. The fact that much of the increase in physical activity persisted after withdrawal of the intervention suggests that this type of intervention could reduce cardiovascular events, and this should be tested in clinical trials designed to test these hypotheses directly.
Limitations
First, though inclusion criteria were broad, participants voluntarily chose to enroll in the study, and may not be representative of all patients that were eligible and contacted to offer enrollment. The self-selecting nature of the cohort is inherent to clinical trials, but it may also limit generalizability. In this trial, approximately 1% of patients contacted and offered the opportunity to participate were ultimately randomized. Second, we evaluated physical activity using step counts and minutes of MVPA, and we did not collect data on other measures of functional status or clinical outcomes. Dedicated trials are needed to determine whether the effects on physical activity observed in this trial translate to benefits in cardiovascular outcomes, functional capacity, or other patient-centered measures. Third, we captured physical activity data using Fitbit devices, which were selected for this pragmatic clinical trial because they are widely available, easy for participants to use, and could be feasibly implemented if similar interventions were deployed in clinical practice. Though concerns have been raised regarding the accuracy of Fitbit devices relative to research-grade accelerometers or other criterion standards,42,43 all participants received the same device, comparisons were between randomized groups, and any inaccuracy would not lead to systematic bias. Lastly, the trial was powered to detect a 750-step difference between groups in mean daily steps during the main intervention period and was not powered to detect changes in MVPA or changes from baseline through the post-intervention follow-up period. With four arms and three main comparisons, we used a conservative Bonferroni correction to establish thresholds for statistical significance. The observed differences between intervention and control arms in daily steps over the post-intervention follow-up period and in weekly minutes MVPA over the intervention and post-intervention follow-up periods may be clinically relevant even if not statistically significant, and larger trials, or trials with fewer comparator arms, will be necessary to definitively determine the effect of similar interventions on those outcomes.
CONCLUSIONS
In this trial enrolling adults at high risk for major adverse cardiovascular events, interventions with gamification, financial incentives, and gamification + financial incentives each significantly increased daily steps compared with an attention control group over a 12-month intervention period. The difference remained statistically significant over a 6-month follow-up period in the gamification + financial incentives intervention. These interventions could be a useful component of strategies to reduce cardiovascular risk in high-risk patients.
Supplementary Material
CLINICAL PERSPECTIVES.
What is new?
Gamification and financial incentive interventions designed with concepts from behavioral economic theory substantially increased physical activity compared with attention control over 12-month follow-up.
The combination of gamification and financial incentives led to the greatest increase in physical activity over the 12-month intervention period, which was sustained over 6-month post-intervention follow-up
What are the clinical implications?
Interventions based on behavioral economic concepts are more effective than attention control for increasing physical activity over prolonged intervention and follow-up durations.
These scalable interventions could be a useful strategy to increase physical activity and reduce cardiovascular events in patients at high cardiovascular risk.
Acknowledgements:
We thank the patients who participated in the study. We thank the Data Safety and Monitoring Board: Philip Greenland, MD; William Yancy, MD; and Judy Zhong, PhD. Members of the Data Safety and Monitoring Board were compensated for serving in this role.
Sources of Funding:
BE ACTIVE was funded by a grant from the National Institutes of Health (R61/R33HL141440) to Drs. Volpp and Fanaroff.
Role of the Funder/Sponsor:
The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
NON-STANDARD ABBREVIATIONS AND ACRONYMS
- BE ACTIVE
Behavioral Economic Approaches to Increase Physical Activity Among Patients with Elevated Risk for Cardiovascular Disease
- ASCVD
atherosclerotic cardiovascular disease
- EHR
electronic health record
- MVPA
moderate to vigorous physical activity
- SD
standard deviation
Footnotes
Conflict of interest Disclosures: Dr. Volpp is a co-owner of a behavioral economics consulting firm, VAL Health. All other authors report no relevant conflicts of interest.
REFERENCES
- 1.Sattelmair J, Pertman J, Ding EL, Kohl HW, Haskell W, Lee I-M. Dose response between physical activity and risk of coronary heart disease: a meta-analysis. Circulation. 2011;124:789–795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380:219–229. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, Hu FB. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA. 2012;307:1273–1283. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Artinian NT, Fletcher GF, Mozaffarian D, Kris-Etherton P, Van Horn L, Lichtenstein AH, Kumanyika S, Kraus WE, Fleg JL, Redeker NS, Meininger JC, Banks J, Stuart-Shor EM, Fletcher BJ, Miller TD, Hughes S, Braun LT, Kopin LA, Berra K, Hayman LL, Ewing LJ, Ades PA, Durstine JL, Houston-Miller N, Burke LE, American Heart Association Prevention Committee of the Council on Cardiovascular Nursing. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association. Circulation. 2010;122:406–441. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Ding D, Lawson KD, Kolbe-Alexander TL, Finkelstein EA, Katzmarzyk PT, van Mechelen W, Pratt M, Lancet Physical Activity Series 2 Executive Committee. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388:1311–1324. [DOI] [PubMed] [Google Scholar]
- 6.Centers for Disease Control and Prevention. Physical Activity Data & Statistics [Internet]. Centers for Disease Control and Prevention. 2021. [cited 2021 May 16];Available from: https://www.cdc.gov/physicalactivity/data/index.html [Google Scholar]
- 7.Andersen LB, Mota J, Di Pietro L. Update on the global pandemic of physical inactivity. Lancet. 2016;388:1255–1256. [DOI] [PubMed] [Google Scholar]
- 8.Elgaddal N, Karmarow Ellen A., Reuben Cynthia. Physical Activity Among Adults Aged 18 and Over: United States, 2020 [Internet]. 2022. [cited 2024 Mar 25];Available from: https://www.cdc.gov/nchs/data/databriefs/db443.pdf [PubMed] [Google Scholar]
- 9.Volpp Kevin G., Loewenstein George, Asch David A. Behavioral Econonmics and Health (Chapter 468). In: Harrison’s Principles of Internal Medicine. New York: McGraw-Hill; 2018. [Google Scholar]
- 10.Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision under Risk. Econometrica. 1979;47:263–291. [Google Scholar]
- 11.Kahneman D, Knetsch JL, Thaler RH. Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias. J Econ Perspect. 1991;5:193–206. [Google Scholar]
- 12.Keren G, Roelofsma P. Immediacy and Certainty in Intertemporal Choice. Organ Behav Hum Decis Process. 1995;63:287–297. [Google Scholar]
- 13.Chokshi NP, Adusumalli S, Small DS, Morris A, Feingold J, Ha YP, Lynch MD, Rareshide CA, Hilbert V, Patel MS. Loss-framed financial incentives and personalized goal-setting to increase physical activity among ischemic heart disease patients using wearable devices: the ACTIVE REWARD randomized trial. J Am Heart Assoc. 2018;7:e009173. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Patel MS, Bachireddy C, Small DS, Harrison JD, Harrington TO, Oon AL, Rareshide CAL, Snider CK, Volpp KG. Effect of Goal-Setting Approaches Within a Gamification Intervention to Increase Physical Activity Among Economically Disadvantaged Adults at Elevated Risk for Major Adverse Cardiovascular Events: The ENGAGE Randomized Clinical Trial. JAMA Cardiol. 2021;6:1387–1396. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Patel MS, Small DS, Harrison JD, Fortunato MP, Oon AL, Rareshide CAL, Reh G, Szwartz G, Guszcza J, Steier D, Kalra P, Hilbert V. Effectiveness of Behaviorally Designed Gamification Interventions With Social Incentives for Increasing Physical Activity Among Overweight and Obese Adults Across the United States: The STEP UP Randomized Clinical Trial. JAMA Intern Med. 2019;179:1624–1632. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Fanaroff AC, Patel MS, Chokshi N, Coratti S, Farrady D, Norton L, Rareshide CA, Zhu J, Szymczak JE, Russell LB, Small DS, Volpp KGM. A Randomized Controlled Trial of Gamification, Financial Incentives, or Both to Increase Physical Activity Among Patients with Elevated Risk for Cardiovascular Disease: Rationale and Design of the BE ACTIVE Study. Am Heart J. 2023;260:82–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Asch DA, Volpp KG. On the Way to Health. LDI Issue Brief. 2012;17:1–4. [PubMed] [Google Scholar]
- 18.Muntner P, Colantonio LD, Cushman M, Goff DC Jr, Howard G, Howard VJ, Kissela B, Levitan EB, Lloyd-Jones DM, Safford MM. Validation of the atherosclerotic cardiovascular disease Pooled Cohort risk equations. JAMA. 2014;311:1406–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Patel MS, Benjamin EJ, Volpp KG, Fox CS, Small DS, Massaro JM, Lee JJ, Hilbert V, Valentino M, Taylor DH, Manders ES, Mutalik K, Zhu J, Wang W, Murabito JM. Effect of a Game-Based Intervention Designed to Enhance Social Incentives to Increase Physical Activity Among Families: The BE FIT Randomized Clinical Trial. JAMA Intern Med. 2017;177:1586–1593. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Rogers T, Milkman KL, Volpp KG. Commitment devices: using initiatives to change behavior. JAMA. 2014;311:2065–2066. [DOI] [PubMed] [Google Scholar]
- 21.Cane J, O’Connor D, Michie S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement Sci. 2012;7:37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Dai H, Milkman KL, Riis J. Put Your Imperfections Behind You: Temporal Landmarks Spur Goal Initiation When They Signal New Beginnings. Psychol Sci. 2015;26:1927–1936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Marshall SJ, Levy SS, Tudor-Locke CE, Kolkhorst FW, Wooten KM, Ji M, Macera CA, Ainsworth BE. Translating physical activity recommendations into a pedometer-based step goal: 3000 steps in 30 minutes. Am J Prev Med. 2009;36:410–415. [DOI] [PubMed] [Google Scholar]
- 24.Tudor-Locke C, Sisson SB, Collova T, Lee SM, Swan PD. Pedometer-determined step count guidelines for classifying walking intensity in a young ostensibly healthy population. Can J Appl Physiol. 2005;30:666–676. [DOI] [PubMed] [Google Scholar]
- 25.Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, George SM, Olson RD. The Physical Activity Guidelines for Americans. JAMA. 2018;320:2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Follmann DA, Proschan MA, Geller NL. Monitoring pairwise comparisons in multi-armed clinical trials. Biometrics. 1994;50:325–336. [PubMed] [Google Scholar]
- 27.Patel MS, Asch DA, Rosin R, Small DS, Bellamy SL, Eberbach K, Walters KJ, Haff N, Lee SM, Wesby L, Hoffer K, Shuttleworth D, Taylor DH, Hilbert V, Zhu J, Yang L, Wang X, Volpp KG. Individual Versus Team-Based Financial Incentives to Increase Physical Activity: A Randomized, Controlled Trial. J Gen Intern Med. 2016;31:746–754. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Patel MS, Asch DA, Rosin R, Small DS, Bellamy SL, Heuer J, Sproat S, Hyson C, Haff N, Lee SM, Wesby L, Hoffer K, Shuttleworth D, Taylor DH, Hilbert V, Zhu J, Yang L, Wang X, Volpp KG. Framing Financial Incentives to Increase Physical Activity Among Overweight and Obese Adults: A Randomized, Controlled Trial. Ann Intern Med. 2016;164:385–394. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Patel MS, Volpp KG, Rosin R, Bellamy SL, Small DS, Fletcher MA, Osman-Koss R, Brady JL, Haff N, Lee SM, Wesby L, Hoffer K, Shuttleworth D, Taylor DH, Hilbert V, Zhu J, Yang L, Wang X, Asch DA. A Randomized Trial of Social Comparison Feedback and Financial Incentives to Increase Physical Activity. Am J Health Promot. 2016;30:416–424. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Bassett DR, Wyatt HR, Thompson H, Peters JC, Hill JO. Pedometer-measured physical activity and health behaviors in U.S. adults. Med Sci Sports Exerc. 2010;42:1819–1825. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Kang M, Rowe DA, Barreira TV, Robinson TS, Mahar MT. Individual information-centered approach for handling physical activity missing data. Res Q Exerc Sport. 2009;80:131–137. [DOI] [PubMed] [Google Scholar]
- 32.Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley; 2004. [Google Scholar]
- 33.Pahor M, Guralnik JM, Ambrosius WT, Blair S, Bonds DE, Church TS, Espeland MA, Fielding RA, Gill TM, Groessl EJ, King AC, Kritchevsky SB, Manini TM, McDermott MM, Miller ME, Newman AB, Rejeski WJ, Sink KM, Williamson JD, for the LIFE study investigators. Effect of Structured Physical Activity on Prevention of Major Mobility Disability in Older Adults: The LIFE Study Randomized Clinical Trial. JAMA. 2014;311:2387–2396. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Sardi L, Idri A, Fernández-Alemán JL. A systematic review of gamification in e-Health. J Biomed Inform. 2017;71:31–48. [DOI] [PubMed] [Google Scholar]
- 35.Lee I-M, Shiroma EJ, Kamada M, Bassett DR, Matthews CE, Buring JE. Association of Step Volume and Intensity With All-Cause Mortality in Older Women. JAMA Intern Med. 2019;179:1105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Paluch AE, Bajpai S, Bassett DR, Carnethon MR, Ekelund U, Evenson KR, Galuska DA, Jefferis BJ, Kraus WE, Lee I-M, Matthews CE, Omura JD, Patel AV, Pieper CF, Rees-Punia E, Dallmeier D, Klenk J, Whincup PH, Dooley EE, Pettee Gabriel K, Palta P, Pompeii LA, Chernofsky A, Larson MG, Vasan RS, Spartano N, Ballin M, Nordström P, Nordström A, Anderssen SA, Hansen BH, Cochrane JA, Dwyer T, Wang J, Ferrucci L, Liu F, Schrack J, Urbanek J, Saint-Maurice PF, Yamamoto N, Yoshitake Y, Newton RL, Yang S, Shiroma EJ, Fulton JE, Steps for Health Collaborative. Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. Lancet Public Health. 2022;7:e219–e228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Paluch AE, Gabriel KP, Fulton JE, Lewis CE, Schreiner PJ, Sternfeld B, Sidney S, Siddique J, Whitaker KM, Carnethon MR. Steps per Day and All-Cause Mortality in Middle-aged Adults in the Coronary Artery Risk Development in Young Adults Study. JAMA Netw Open. 2021;4:e2124516. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Saint-Maurice PF, Troiano RP, Bassett DR, Graubard BI, Carlson SA, Shiroma EJ, Fulton JE, Matthews CE. Association of Daily Step Count and Step Intensity With Mortality Among US Adults. JAMA. 2020;323:1151. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Dwyer T, Pezic A, Sun C, Cochrane J, Venn A, Srikanth V, Jones G, Shook R, Sui X, Ortaglia A. Objectively Measured Daily Steps and Subsequent Long Term All-Cause Mortality: The Tasped Prospective Cohort Study. PloS one. 2015;10:e0141274. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Tudor-Locke C, Bassett DR Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004;34:1–8. [DOI] [PubMed] [Google Scholar]
- 41.del Pozo Cruz B, Ahmadi MN, Lee I-M, Stamatakis E. Prospective Associations of Daily Step Counts and Intensity With Cancer and Cardiovascular Disease Incidence and Mortality and All-Cause Mortality. JAMA Intern Med. 2022;182:1139–1148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Chevance G, Golaszewski NM, Tipton E, Hekler EB, Buman M, Welk GJ, Patrick K, Godino JG. Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis. JMIR Mhealth Uhealth. 2022;10:e35626. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Waddell A, Birkett S, Broom D, McGregor G, Harwood AE. Validating the Fitbit Charge 4© wearable activity monitor for use in physical activity interventions. J Sci Med Sport. 2024;S1440-2440(24)00024-0. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.