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

Abstract

Background: The widespread use of physical activity trackers enables the collection of high-resolution health data, such as hourly step counts, to evaluate health promotion programmes. We aim to investigate how participants meet their daily step goals.

Methods: We used 24-h steps data from the National Steps Challenge^TM Season 3, wherein participants were rewarded with vouchers when achieving specified goals of 5000, 7500, and 10,000 steps per day. We extracted data from 3075 participants' including a total of 52,346 participant-days. We modelled the hourly step counts using a two-part model, in which the distribution for step counts was allowed to depend on the sum of step counts up to the previous hour and participant demographics.

Results: Participants have a mean age of 44.2 years (standard deviation = 13.9), and 40.4% are males. We show that on weekdays, the hourly mean step counts among participants aged 60 and above are higher than participants aged 30 to 59 from the start of the day till 6 p.m. We also find that participants who accumulate at least 7000 steps by 7 p.m. are associated with higher success of achieving 10,000 steps.

Conclusions: We provide recommendations on the hourly targets to achieve daily goals, based on different participants' characteristics. Future studies could experimentally test if prompts and nudges at the recommended times of day could promote reaching step goals.

Fig. 1. Distribution of daily and hourly step counts.

a Histogram of daily step counts. b Mean hourly step counts for each day of the week. c Proportion of zero hourly step counts for each day of the week. d Histogram of daily step counts of participants aged 30–59 against 60 and above. HP: HealthPoints. Walking 5000, 7500, or 10,000 steps a day earns 10, 25, or 40 HealthPoints, respectively. The different shadings correspond to the HealthPoints earned. e Mean hourly step counts of participants aged 30–59 against 60 and above for weekdays and weekends. f Proportion of zero hourly step counts of participants aged 30–59 against 60 and above for weekdays and weekends. Number of participant-days (n). Age 17+: 52,346. Age 30–59: 37,622. Age 60+: 9271. Mon: 7534. Tue: 7554. Wed: 7538. Thu: 7563. Fri: 7432. Sat: 7612. Sun: 7113. Weekday, age 30–59: 27,007. Weekday, age 60+: 6677. Weekend, age 30–59: 10,615. Weekend, age 60+: 2594.

Fig. 2. Model fit to the data.

a Model and data for cumulative steps from the start of the day on weekdays. b Model and data for cumulative steps from the start of the day on weekends. The blue-shaded regions denote the 10–25–75–90 percentiles of the data while the solid and dotted lines denote the 10–25–75–90 percentiles of the posterior predictive distribution from the model. In the case where the model fits perfectly to the data, we should expect that the solid lines overlap the edges of the dark blue regions and the dotted lines overlap the edges of the light blue regions (see the legend where the 10–25–75–90 percentiles overlap perfectly).

Fig. 3. Estimated mean step counts in the next hour.

a Weekday for 6 p.m., 8 p.m., and 10 p.m. b Weekend for 6 p.m., 8 p.m., and 10 p.m. c Weekday for 7 p.m., 9 p.m., and 11 p.m. d Weekend for 7 p.m., 9 p.m., and 11 p.m. The figure shows the predicted mean step counts (y-axis) and the 95% credible intervals (CI) on the mean step counts for the rest of the day, adjusting for age group, BMI group and sex. The posterior sample size is 20,000. Further details are provided in the Supplementary Method. The bars denote the 95% CIs, and the dots denote the mean. The means and the 95% CIs were plotted assuming the participant had accumulated total step counts in intervals of 500 steps at the start of the hour from 0 to 12,500 steps (x-axis). The daily step goals (diagonal lines) were 5000, 7500, and 10,000 steps.