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Hourly step recommendations to achieve daily goals for working and older adults

Health and Fitness

Hourly step recommendations to achieve daily goals for working and older adults

G. Ang, C. S. Tan, et al.

Discover how daily step goals can be more effectively achieved! This research by Gregory Ang, Chuen Seng Tan, Nicole Lim, Jeremy Tan, Falk Müller-Riemenschneider, Alex R. Cook, and Cynthia Chen analyzes 24-hour step data from the National Steps Challenge™. Find out how older adults excel in step counts and get insights on achieving the 10,000-step goal by the right hour!

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Playback language: English
Introduction
Global physical inactivity rates remain high despite established links between increased physical activity and reduced chronic disease risk. Financial incentives, facilitated by wearable technology, offer a promising approach to promote physical activity. Wearable technology's widespread adoption enables the collection of high-resolution data, such as hourly step counts, allowing for personalized interventions. While many studies examine the impact of incentives on physical activity, the intraday influence of incentive thresholds is understudied. This study addresses this gap by analyzing high-resolution step count data from a large-scale incentivized physical activity program to understand how participants accumulate steps throughout the day and to provide personalized recommendations for achieving daily step goals. The National Steps Challenge™ Season 3 (NSC3) in Singapore provided a unique opportunity to examine this due to its large sample size, use of wearable technology, and tiered incentive structure. Understanding the intraday patterns of physical activity is crucial for designing effective and timely interventions aimed at improving population health.
Literature Review
Existing literature extensively documents the positive correlation between physical activity and reduced risk of chronic diseases like type 2 diabetes and stroke. Despite these benefits, physical inactivity persists at alarming rates globally. Studies have shown that financial incentives can effectively encourage physical activity, and the use of wearable technology enhances the ability to personalize these incentives based on individual progress. However, research focusing on how these incentives influence behavior within a day, especially in real-world settings beyond controlled trials, remains limited. This research gap highlights the need for studies that analyze detailed intraday activity patterns to inform the design of personalized interventions, such as prompts and nudges, aimed at maximizing the effectiveness of physical activity initiatives. The current study builds on this existing knowledge by investigating the hourly step patterns of a large population participating in a real-world incentivized program, providing insights that can inform the design of more effective interventions.
Methodology
This retrospective observational study used data from the National Steps Challenge™ Season 3 (NSC3) in Singapore. NSC3 incentivized participants to achieve daily step goals (5000, 7500, and 10,000 steps) with varying HealthPoints rewards, exchangeable for cash vouchers. The study included hourly step count data from 3075 participants (excluding those in the Personal Pledge) representing 52,346 participant-days between January 8th and March 31st, 2018. Data preprocessing involved imputing missing 30-minute step counts with zero and aggregating 30-minute data into hourly counts. Participants with incomplete demographic information were excluded. The primary outcome measure was the hourly step count trajectory. Covariates included self-reported demographics (sex, age, height, weight), categorized into age groups (17-29, 30-39, 40-49, 50-59, ≥60), and BMI groups based on Asian cut-offs. Statistical analysis involved a two-part model: a logistic regression to model the probability of having positive step counts and a gamma regression to model the positive step counts. The model was stratified by weekday/weekend and incorporated the cumulative step counts up to the previous hour as a covariate. Bayesian inference was used with Markov Chain Monte Carlo (MCMC) for parameter estimation, assessing convergence using the Gelman-Rubin diagnostic. All analyses were performed in R.
Key Findings
The study sample consisted of 3075 participants (40.4% male) with a mean age of 44.2 years. The mean daily step count was 10,400 steps (SD = 6100). Significant peaks in daily step counts were observed at the incentive thresholds (5000, 7500, 10,000 steps). Hourly step counts exhibited distinct patterns on weekdays, with peaks during typical commuting and lunch hours (7-9 AM, 12-2 PM, 6-8 PM). Weekend patterns showed later peaks and lower overall step counts. Older adults (≥60) showed higher hourly step counts than younger adults (30-59) until 6 PM on weekdays. A key finding revealed that accumulating at least 7000 steps by 7 PM was strongly associated with achieving the 10,000-step goal. The two-part model effectively captured the overall trend in cumulative step counts, showing a pronounced peak at 10,000 steps. Further analysis provided recommendations for hourly step counts needed to reach daily goals (5000, 7500, and 10,000 steps) at different times of day, stratified by age, BMI, and sex. These recommendations varied across demographic groups, suggesting the potential for personalized interventions. For instance, participants aged 30-39 required more steps by 10 p.m. to achieve the 10,000 step goal compared to participants aged 60 and above. Participants with normal BMI required more steps compared to those with an obese BMI.
Discussion
The findings suggest that the incentivized step goals significantly influenced participants' behavior, as evidenced by the peaks in step counts at the threshold levels. The hourly step count patterns reflect typical daily routines, with peaks coinciding with commuting and lunch breaks. The study's recommendations for achieving daily step goals, stratified by demographic characteristics, highlight the potential for personalized interventions tailored to individuals' lifestyles and activity patterns. The strong association between reaching 7000 steps by 7 PM and achieving the 10,000-step goal suggests that interventions focusing on the evening hours could be particularly effective. These personalized interventions could involve timely prompts or nudges delivered through wearable devices, encouraging individuals to maximize physical activity during their most active periods. The varying step count requirements across demographic groups emphasize the importance of considering individual differences when designing such interventions. The study supports the idea that interventions targeting specific times of the day and considering individual characteristics can be more effective than general interventions.
Conclusion
This study provides valuable insights into the intraday step count patterns of a large population participating in an incentivized physical activity program. The findings highlight the effectiveness of tiered incentive structures in motivating participants to reach step goals, as well as the importance of considering demographic factors when designing personalized interventions. The hourly step count recommendations, stratified by demographic characteristics, provide a foundation for future research on the efficacy of timely prompts and nudges delivered through wearable devices. Future research should experimentally test the impact of these personalized interventions on achieving step goals in diverse populations. By considering individual factors and optimizing the timing of interventions, we can develop more effective strategies to promote physical activity and improve population health.
Limitations
As a retrospective observational study, NSC3 is subject to selection bias, and the absence of a control group limits causal inferences. The study's focus on intraday step patterns did not examine changes in daily step counts. The generalizability of findings might be limited to the Singaporean population, as factors like climate and public transportation systems influence physical activity levels. The arbitrary nature of the 10,000-step goal also warrants consideration, as future physical activity guidelines may shift towards time-based recommendations.
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