35909718

Five Year Pediatric Use of a Digital Wearable Fitness Device:

Lessons from a Pilot Case Study

Kimayani D. Butte 1, 2

Amir Bahmani 2

Atul J. Butte 3

Xiao Li 2, 4 *

Michael P. Snyder 2 *

* These senior authors contributed equally.

1 The Harker School, 500 Saratoga Avenue, San Jose, CA USA 95129

2 Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305

3 University of California, San Francisco, Mission Hall, 550 16th Street, 4th Floor, Box 0110,

San Francisco, CA USA 94158-2549

4 Division of General Medical Sciences, School of Medicine, Case Western Reserve University,

OH 44106

Corresponding Author:

Professor Michael P. Snyder

Stanford University School of Medicine

300 Pasteur Drive, M-344A

Stanford, CA 94305 USA

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NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Abstract

Objectives: Wearable fitness devices are increasingly used by the general population, with new

applications being proposed and designed for healthy adults as well as adults with chronic

diseases. Fewer, if any, studies of these devices have been conducted in healthy adolescents

and teenagers, especially over a long period of time. The goal of this work was to document the

successes and challenges involved in 5 years of a wearable fitness device use in a pediatric

case study.

Materials and Methods: Comparison of five years of step counts and minutes asleep from a

teenaged girl and her father.

Results: At 60 months, this may be the longest reported pediatric study involving a wearable

fitness device, and the first simultaneously involving a parent and a child. We find step counts

to be significantly higher for both the adult and teen on school/work days, along with less sleep.

The teen walked significantly less towards the end of the 5 year study. Surprisingly, many of

the adult’s and teen’s sleeping and step counts were correlated, possibly due to coordinated

behaviors.

Discussion: We end with several recommendations for pediatricians and device manufacturers,

including the need for constant adjustments of stride length and calorie counts as teens are

growing.

Conclusion: With periodic adjustments for growth, this pilot study shows these devices can be

used for more accurate and consistent measurements in adolescents and teenagers over longer

periods of time, to potentially promote healthy behaviors.

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Background and Significance

Wearable fitness devices are increasingly used by the general population. A recent study

estimates that 19% of Americans currently use a wearable fitness device

and an additional 15% of Americans stated they no longer used a fitness tracker, which raises a

question on their long-term use. Forty million new wearable devices were sold in 2017

, and

the company Fitbit sold approximately 13.5 million new Fitbit devices in 2019

While many consumers likely use these devices to track general health and wellness

parameters, there have been several attempts to discover medical utility for these devices.

Gresham, et al, recently showed in a study of 37 patients with cancer that wearable fitness

devices could accurately capture stair climbing and steps with enough accuracy to correlate with

traditional performance status measures assessed by health providers

. Similarly, Speier, et.

al., showed that wearable fitness devices could be very useful in monitoring a patient’s health

remotely in order to diagnose health problems more quickly

. The study was conducted for only

90 days with 186 participants with ischemic heart disease. Li, et. al., demonstrated that fitness

trackers can detect illnesses caused by infectious diseases such as Lyme and respiratory viral

infections as well as other health conditions

. Finally, Rose, et. al., have used fitness trackers to

detect heart conditions, such as atrial fibrillation

One of the longest studies on the use of wearable fitness devices was by Jakicic, et al, who

studied their utility for weight loss in young adults (ages 18 to 35 years)

. The study

randomized 471 participants, of which 351 were still with the study and provided their updated

weights at 24 months. The study showed a significant decrease in weight over time, but

surprisingly with less weight loss in the intervention group using the wearable fitness devices.

Also interestingly, nearly a quarter of the participants did not complete the study over 2 years.

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Despite these setbacks, the research use of wearable fitness devices is forecast to continue to

expand, with over 500 biomedical publications specifically mentioning Fitbit. With the flagship

NIH All of Us Research Program studies now adopting wearable fitness data along with

electronic health record data and genomic data, research using these devices is likely to

increase

To date, the vast majority of research on wearable fitness devices has been conducted in

adults. Fewer studies have been performed in the pediatric population, though companies are

now targeting sales growth of digital wearable devices in this population

. The few pediatric

studies that have been performed have targeted cohorts with specific diseases. Bian et al

looked at self-reported sleep quality from 22 participants with asthma as well as self-reported

symptoms of asthma, and compared the reports to the participants’ Fitbit data to show that Fitbit

sleep quality is lower when more asthma symptoms occur

. Voss, et al., conducted a study

with 40 participants ages 10-18 with congenital heart disease. The study assessed the validity

of the Fitbit step count by testing the wrist-worn Fitbit Charge HR against the hip-worn

ActiGraph accelerometer and found that the Fitbits recorded more steps than the accelerometer

. The study also found that daily Fitbit step counts of over 12,500 steps would meet commonly

promoted physical activity guidelines of over 60 minutes of activity per day. A similar study by

Miropolsky, et al., on 13 young adult cancer survivors between ages 20 and 39 years suggested

a Fitbit device could provide major motivation to engage in physical activity

Studies on healthy adolescents and teenagers have been even more rare. Kerner, et al. studied

one-hundred participants from two schools using Fitbit devices for 8 weeks, along with the Fitbit

app. They found using Fitbits can initially encourage adolescents (ages 13-14) to exercise, but

the participants were eventually discouraged potentially because Fitbit might be setting

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unrealistic goals

. The non-personalized goal of 10,000 steps per day made participants feel

unmotivated and lazy if they did not achieve that goal, which discouraged them from exercising

. Also, the competitive aspect of the Fitbit app, such as the leaderboard rankings for who got

the most steps, discouraged those who did not get many steps and sometimes demotivated the

participants who participated in exercise just to get more steps than they usually do. Finally, the

authors did not report on the actual success rate of how many students used the Fitbit for the

full 8 weeks.

These studies show that using a wearable fitness device could be useful for tracking some

health indicators from pediatric-aged individuals, without needing regular physician evaluation.

Almost all of these studies involved the participants using devices for 8 weeks or fewer, a period

too short for many health indicators to significantly improve. Health benefits from wearable

fitness devices might be expected to require longer-term use which could lead to new

discoveries in long-term health effects of exercise on medical conditions and general health.

Objectives

The goal of this work was to document the successes and challenges involved in 5 years of a

wearable fitness device use in a pediatric case study. The findings from this study may have

implications for both encouraging healthy behavior in adolescents as well as recommendations

for wearable device manufactures to improve adoption by adolescents and adults.

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Materials and Methods

Ethics: Two authors (including the lead author) collected their own data as citizen-scientists,

with their own devices that they obtained commercially through Amazon.com, initiated the

analyses, then approached the Stanford investigators to enhance the analyses, and both of

these authors contributed to writing this manuscript. This citizen-science research project was

initiated outside of an academic institution, was run by the two authors with their own data, had

no Federal funding, and the research is not related to an FDA or EPA-regulated product, so as

such does not need to undergo Institutional Review Board review, as per Resnik

Regardless, the two data contributors joined Stanford University research protocol 56378

approved by the Stanford University Institutional Review Board, specifically allowing participants

at or over age 13 years to share their past and current Fitbit measurement data with Stanford

investigators for research purposes, with informed consent.

Two Fitbit One devices were purchased on January 1, 2013 and activated shortly thereafter.

The Fitbit One was designed to track steps walked along with pace, stairs climbed, sleep

duration and activity. Two participants (and co-authors here) simultaneously started to use the

devices to track these measurements. The female participant started use at age 10 years and 4

months, and continued through her teenage years. The adult male participant (father of the

younger individual) started use at age 43 years and 9 months. Neither had any significant prior

medical history at the initiation of use. Both intended the use of these devices for improving and

maintaining general health and wellness. The adult also intended to use the device to increase

his walking and help in weight loss. While these devices (or their subsequent versions) have

still been in use since January 1, 2013, this analysis only covers the 60 months of use after a

pattern of regular consistent use was seen, starting on June 1, 2013. In June 2018, both

participants elected to study their data together for research.

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Fitbit enables the downloading of raw level device data, by registering through their Application

Programming Interface (API). Using the API ID number that Fitbit provided, a short program

was written in R to access the Fitbit data, serially downloading blocks of daily step and sleep

data representing every 100 days, to cover the entire 60 months. No outlier measurements

were removed. Sleep amounts in minutes are assigned to the wake up day. Weekend nights

were defined as those leading into a Saturday or Sunday, which are days with no school.

School days were defined as weekdays that were not within a set of five long holiday breaks:

one week mid-winter break in February, one week spring break in March or April, ten week

summer break in June-August, Thanksgiving one week break in November, and two week

winter break between December and January.

Height data for the teen, needed to calculate stride length, was downloaded from her own

electronic medical records, with height measurements made and documented by a physician

assistant or pediatrician.

Analysis was performed using Google Sheets, Minitab Express, R [version 4.0.2 (2020-06-22)],

and RStudio [version 1.3.1073]. R packages used include ggplot2 [version 3.3.2], tidyverse

[version 1.3.0], and corrr [version 0.4.2].

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Results

We wanted to examine the patterns and challenges associated with long term adolescent use of

wearable fitness data. Sixty months of step and sleep data, collected from the Fitbit wearable

fitness devices of a father and daughter (Table 1) were downloaded and analyzed. School for

the teen and work for the adult was generally in session from Monday morning through Friday

afternoon and the teen’s school year consistently ran from late August to early June. For the

first year of the study, the teen had to was required to complete a “mile run” once per year. In

the subsequent 3 years in middle school, the teenager was required to complete a “mile run”

once every month. In the final year of the study (which was during high school), there were no

daily physical education classes, only lower intensity yoga without mandatory walking or

running.

[Table 1 inserted here]

The teen was noted to have more missing measurements than the adult, including a 193 day

gap in measurements in 2016. However, more than 85% of the possible 1826 days of step

counting were available for both individuals. Fewer sleep measurements were made than step

measurements by both individuals, likely due to the need to remember to manually activate and

deactivate the Fitbit One sleep timer before and after sleeping.

[Figure 1 inserted here]

With over 1500 days of step counts available for both individuals, some clear differences are

notable. For the teen, step counts significantly dropped over the five years (Figure 1A, negative

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correlation of date versus steps r = -0.517, p = 1.20 x 10

-107

), whereas step counts only slightly

dropped the same period for the adult (Figure 1B, negative correlation of date versus steps r = -

0.066, p = 0.005). On average, the adult walked significantly more steps than the teen over the

5 years (adult mean 7757.2, standard deviation 2850.7; teen mean 6568.3, standard deviation

3685.9, t-test p < 2.2 x 10

-16

). Interestingly, both individuals walked significantly less on

weekend days (Sundays and Saturdays) than on weekdays, but the difference was more

pronounced in the teen (Figure 1C and 1D, teen t-test p < 2.2 x 10

-16

, adult t-test p = 0.006).

[Figure 2 inserted here]

We further investigated the teen’s step counts. Using six years of school calendars, we

determined the specific dates of five yearly recurring holidays (one week mid-winter break in

February, one week spring break in March or April, ten week summer break from June through

August, Thanksgiving one week break in November, and two week winter break from between

December and January). Approximately 30% of the available step count measurements could

be classified as occurring during one of these holidays (teen: 472 of 1566 measurements; adult:

571 of 1823 measurements). The teen walked significantly more on school days compared to

non-school days (combining holiday and weekend days, Figure 2A, 2105.9 more steps on

average, t-test p = 8.17 x 10

-31

). Interestingly, the adult showed no significant difference in step

counts between school days and non-school days. The teen made fewest steps in February

and March compared to August through October (Figure 2B), and there was significant

variability in the steps across the months (Chi-square test p = 3.71 x 10

-67

). To test the effect of

seasonality, we fit a linear regression model on the teen’s step counts, with parameters

representing whether a day was a weekend, in one of the five holiday periods, the year of the

study (first through fifth), and the month of the year. All of these parameters were highly

significant in the fit regression model (Table 2).

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[Table 2 inserted here]

On average, the teen walked 9103.1 steps per day during the first year of the study, and only

3646.8 steps per day during the final year of the study, or a drop of 60%. The increasing

heights for the growing teen were used to better understand the context for this marked drop in

step counts. Fourteen height measurements were downloaded for the teen, with measurements

available between ages 2 and 16 years. A cubic spline was then fit to these measurements,

and a height of 144.9 cm was estimated for the teen on June 1, 2013, the starting date for this

study. A height of 162.6 cm (measured close to the date) was used for May 31, 2018, the

ending date for this analysis. The teen’s height is estimated to have increased 17.7 cm (or

12.2%) during the analysis period of this study, and thus only a 12.2% increase in stride length

over the course of the study

16,17

[Figure 3 inserted here]

Fewer days of sleep measurements were available for both individuals, but comparisons were

still possible with over 800 nights of data available. The teen slept slightly less towards the end

of the 5 years, compared to the start (Figure 3A, correlation of date versus minutes asleep r = -

0.10, p = 0.003). The adult showed no significant change in sleep over the 5 years (Figure 3B,

correlation of date versus minutes asleep r = 0.036, p = 0.152, not significant). Similar to the

step counts, differences were observed on weekends. Both individuals slept significantly longer

over nights when the morning fell on a weekend day. The teen slept an average of 92.7

minutes longer in weekends (Figure 3C, green weekday mean 457.3, orange weekend mean

550.0, t-test p = 7.23 x 10

-29

) whereas the adult slept 61 minutes longer (Figure 3D, green

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weekday mean 345.5, orange weekend mean 406.3, t-test p = 1.5 x 10

-36

). Overall, the teen

slept an average of 120.1 minutes longer per day than the adult.

[Figure 4 inserted here]

Given the father and daughter living as a family in the same home, we then compared step

counts and sleeping on the same days. Surprisingly, the number of steps walked by the teen

and adult were correlated (Figure 4A, n = 1589 points, Pearson r = 0.2, p = 4.92 x 10

-16

). The

step count correlation was even stronger when considering only the weekend days (n = 449

points, Pearson r = 0.4, p = 1.25 x 10

-18

), suggesting coordinated walking behaviors on the

weekend. Interestingly, the amount of sleep was also correlated between the father and

daughter (Figure 4B, n = 707 points, Pearson r = 0.23, p = 1.32 x 10

-9

), but much weaker when

considering only the weekend days (n = 189 points, Pearson r = 0.17, p = 0.023), suggesting

the daughter’s and father’s sleep were less coordinated on the weekends. In general, both the

teen and adult slept more and walked less on non-school days (Figure 4A and B, in orange)

than on school days (green).

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Discussion

We describe here a 5-year longitudinal case study of a wearable fitness device in a teenage

female, comparing these measurements to her adult father, using the same type of device

during the same time period. To our knowledge, this is the longest reported pediatric study

involving a wearable fitness device, and the first simultaneously involving a parent and a child.

We describe five specific findings here. First, the teen appears to be generally walking less

than the adult (who was purposefully trying to walk more for weight loss), and progressively

walking less during the 5 years. Second, both the adult and teen walk significantly less on

weekends and holidays. Third, the teen female sleeps more than the adult and has more

variation in sleep, but both the teen and adult sleep more on weekends. Fourth, on average,

the teenager slept more than the adult. Sleep differences may have been due to travel by the

adult and/or the fact that short naps are often not recorded by these devices, which needed

manual triggers to start the timer. Fifth, interestingly, the number of steps walked and minutes

slept by the teen and adult are correlated. The correlation in step counts may be due to

common activities (e.g. shopping) or longer walks (e.g. hikes) performed together as a family.

Similarly, sleep correlations were likely due to shared behavior: if the teen worked late to

complete homework, one of the two parents often stayed awake with her.

While we have shown that device use for more than 5 years is possible, we note special efforts

that could be taken to enhance continued accurate use in the pediatric setting. First, the

number of steps needed to traverse a distance obviously depends on the stride length. If an

exact stride length is not entered, Fitbit estimates the length given the sex and height of the user

using a proprietary formula, which is multiplied by the step count to estimate the distance

walked. However, teenagers gain significant height as they go through puberty, and if the stride

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length is not periodically readjusted, a shorter total distance walked may be misestimated. For

example, an average 10-year-old female at the 50%ile for height at 138 cm would have an

estimated stride length of 57 cm, while the same female 5 years later at the 50%ile for height at

162 cm would have an estimated stride length of 67 cm or 17.5% longer

17,19,20

. Thus, 5000

steps walked at age 15 years cover 2.08 miles, whereas 5000 steps for a 10-year-old would

cover 1.77 miles. However, in this study, we show that the 60% decrease in step counts seen

over the five years greatly exceeds the teen’s 12.2% estimated increase in stride length.

Given that teenager heights change over time, if the teen does not know (or remember) to

periodically change their height in the settings, then the Fitbit may miscalculate the number of

steps and thus distance walked. Similarly, the nutritional needs of teenagers change over time,

and if teens are using calorie counting or diet-related features, the nutritional goals will need to

be periodically adjusted to keep the advice safe and accurate. The device itself should

periodically prompt for such updates, perhaps on the teen’s birthday.

In addition, if teenagers are visiting their primary care physician for regular preventative health,

the physician could remind or offer to update these body-related settings. But currently, most

pediatricians do not have any easy access to the data from the wearable fitness devices of their

patients. Ideally, they would have the data and tools to help provide targeted fitness advice

(e.g. make sure to walk more on weekends), beyond just adjusting the settings.

Despite the obviously small sample size, it is still possible to draw some early conclusions to

potentially inform recommendations. We noted several other issues for wearable device use in

the pediatric age range. The teenage female had a contiguous gap in measurements of 7

months. For many years, there was no reminder or warning issued when measurements were

not being synchronized from the device. This could easily happen if the synchronization device

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(i.e. a smartphone or tablet or home computer) is upgraded or restored from a backup.

Measurements will be lost if devices are not synchronized periodically. For the Fitbit One used

here, the on-device memory only stores 30 days of measurements

. Users should ideally

receive a warning if device measurements have not been synchronized to their phones after

several days. Fitbit reportedly made an improvement to address this problem in March 2018

However, to our knowledge, parents still do not get notifications when their child’s device is not

syncing. This should be addressed before long term pediatric use is recommended.

Similarly, teens (or potentially even younger users) will need to remember to periodically charge

the device. This habit is harder to adopt, as these devices are worn while sleeping, when other

devices (such as phones) are typically charged. Alternative times for consistent charging merit

exploration (e.g. while bathing). If the teen has different devices to track steps and sleep, each

device will need to be charged when it is not in use.

In this study, we noted the teenager completed fewer steps than the adult. This may be due to

a deliberate effort of the adult deliberately to walk at his workplace, whereas the teenager sits in

the classroom for most of the day and only walked between classes. However, we noted both

individuals walked more during these school/workdays compared to weekend days or holidays.

Employers and schools could design layouts that enhance walking activity during the day.

School course schedules are difficult to arrange logistically, but consecutive classes could be

separated away from each other on campus, thereby requiring students to walk more between

classes. In the workplace, conference calls via mobile devices could be encouraged, thus

potentially enabling walking during the calls.

The motivating factors used by the companies making wearable fitness devices can also be

tailored for teens. Smartphone-based reminders to exercise could be tailored around

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teenagers' unique schedules, with bursts of activity potentially clustered around sports

schedules and with periods of less exercise during exam periods.

Anecdotally, the teen started to use the Fitbit One device because she saw her father, the adult

in this study, using his device, and because she wanted to be healthier. The prime reason she

used the device was to measure her sleep each night, as she could not easily estimate it just by

looking at a clock. She could also look at the Fitbit app to see how often she woke up during the

night and judge her sleep quality. Instead of relying on qualitative assessments of her lifestyle,

she could get quantitative assessments for both her exercise and sleep. As a separate reason,

she also used her device because she could see her step count change over time. For example,

she could see when she was walking fewer steps per day in middle and high school than she

had walked in elementary school. However, the step count was not the primary motivator for

continued use of the device. She continued use of the device it because it was easy to

integrate into her lifestyle and did not disrupt her daily routine. She developed a habit of

charging the device when bathing.

There are several limitations to this study. Obviously, only two individuals were studied here.

Others have already been listed above, such as some missing data due to the lack of any

device-issued warning. Fewer days of sleep measurements were made, compared to step

counts. The Fitbit One needs to be worn while in bed, and requires a button to be manually

held down as one is ready to sleep. The same button must then be held down right after waking

up to stop the timer. It was common for both individuals to forget to perform either of these two

manual steps. A more automated way to determine sleep start and end would be ideal and

would enhance accuracy. It is possible that some of these missing measurements might have

biased the analysis, if the data were not missing at random.

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Conclusion

Regardless of the limitations, this pilot case study shows that wearable fitness devices can be

useful in tracking the long-term health of both adults and teenagers, and we hope to see more

studies conducted on the long-term use of these devices.

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Acknowledgements

We would like to thank The Harker School for providing the Minitab Express software that was

used in analyzing the data. We would also like to thank Chris Spenner (Harker School) for

research mentorship and Dr. Tarangini Deshpande (NuMedii, Inc.) for comments on the

manuscript.

Author contributions

Conceptualization, K.D.B., A.J.B., and M.P.S.; Software, A.J.B.; Analysis, K.D.B., A.B., X.L.;

Resources, M.P.S.; Writing, K.D.B., A.J.B., X.L., and M.P.S.; Visualization, K.D.B., and A.J.B.;

Supervision, X.L., and M.P.S.

Competing interests

Michael Snyder is a cofounder and is on the scientific advisory board of Personalis, Filtircine,

SensOmics, Qbio, January, Mirvie, Oralome, and Proteus. He is also on the scientific advisory

board of Genapsys and Jupiter. Atul Butte is a co-founder and consultant to Personalis and

NuMedii; consultant to Samsung, Mango Tree Corporation, and in the recent past, 10x

Genomics, Helix, Pathway Genomics, and Verinata (Illumina); has served on paid advisory

panels or boards for Geisinger Health, Regenstrief Institute, Gerson Lehman Group,

AlphaSights, Covance, Novartis, Genentech, Merck, and Roche; is a shareholder in Personalis

and NuMedii; is a minor shareholder in Apple, Facebook, Alphabet (Google), Microsoft,

Amazon, Snap, 10x Genomics, Illumina, CVS, Nuna Health, Assay Depot, Vet24seven,

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Regeneron, Sanofi, Royalty Pharma, AstraZeneca, Moderna, Biogen, Paraxel, and Sutro, and

several other non-health related companies and mutual funds; and has received honoraria and

travel reimbursement for invited talks from Johnson and Johnson, Roche, Genentech, Pfizer,

Merck, Lilly, Takeda, Varian, Mars, Siemens, Optum, Abbott, Celgene, AstraZeneca, AbbVie,

Westat, and many academic institutions, medical or disease specific foundations and

associations, and health systems. Atul Butte receives royalty payments through Stanford

University, for several patents and other disclosures licensed to NuMedii and Personalis. Atul

Butte’s research has been funded by NIH, Northrup Grumman (as the prime on an NIH

contract), Genentech, Johnson and Johnson, FDA, Robert Wood Johnson Foundation, Leon

Lowenstein Foundation, Intervalien Foundation, Priscilla Chan and Mark Zuckerberg, the

Barbara and Gerson Bakar Foundation, and in the recent past, the March of Dimes, Juvenile

Diabetes Research Foundation, California Governor’s Office of Planning and Research,

California Institute for Regenerative Medicine, L’Oreal, and Progenity. The other authors

declare no competing interests.

Data Availability

The sleep and step count data for the adult and teen used in this analysis are publicly available

through Github: https://github.com/kimibutte/Fitbit-Data.

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Figure Legends

Figure 1: A. and B. Steps per day over time for the teenager and adult, respectively, with green

representing school days and orange as non-school days (i.e. weekends and weeklong

holidays). C. and D. Steps per day on each day of the week for the teenager and adult,

respectively, with green representing weekdays and orange as weekends (Saturday and

Sunday).

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Figure 2: A. Distribution of steps per day for the teenager on school days (green) and non-

school days (orange; weekends and weeklong holidays). B. Average and standard deviation of

teenager steps for each month of the year.

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Figure 3: A. and B. Minutes asleep per day over time for the teenager and adult, respectively,

with green representing school days and orange as non-school days (i.e. weekends and

weeklong holidays). C. and D. Minutes asleep per day on each day of the week for the teenager

and adult, respectively, with green representing weekdays and orange as weekends (Saturday

and Sunday).

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Figure 4: A. Steps per day for the teenager against the number of steps on identical days for the

adult, with green representing school days and orange as non-school days. B. Minutes asleep

per night for the teenager against the minutes of sleep per night for the adult, on identical days,

with green representing school days and orange as non-school days.

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Table Headings

Table 1: Data collected over 60 months from the teen and adult.

Table 2: Linear regression model fit on the 1826 step counts from the teen, with variables

representing whether a day was a weekend, in one of the five holiday periods, the year of the

study (first through fifth), and the month of the year.

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Table 1

Teen Female

Adult Male

Starting age 10 years 4 months (ending

4th grade)

43 years 9 months

Available step measurements

(days)

1566 (85.8%) 1823 (99.8%)

Missing step measurements

(days)

260 (14.2%) 3 (0.16%)

Available sleep

measurements (days)

828 (45.3%) 1548 (84.8%)

Missing sleep measurements

(days)

998 (54.7%) 278 (25.2%)

Steps per day, mean

(standard deviation)

6568.3 (3685.9) 7757.2 (2850.7)

Minutes sleep per night,

mean (standard deviation)

482.6 (112.1) 362.5 (85.7)

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Table 2

Variable

linear regression

model

Coefficient

ANOVA p

value

Intercept 11215.0

Day is a weekend day – 2194.91 < 2.2 x 10

Day during one of the five holiday periods – 996.75 < 2.2 x 10

Every year of the study past the first year

(First year 0, second year 1, …)

– 1299.89 1.953 x 10

Month (1 for January, 12 for December) – 107.01 1.425 x 10

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5000

10000

15000

20000

25000

2014 2015 2016 2017 2018

Date

Steps

Teen, number of points: 1566

Date vs. Steps

5000

10000

15000

20000

25000

2014 2015 2016 2017 2018

Date

Steps

Adult, number of points: 1823

Date vs. Steps

5000

10000

15000

20000

25000

Sunday Monday Tuesday WednesdayThursday Friday Saturday

Day of Week

Steps

Teen, number of points: 1566

Weekday vs. Steps

5000

10000

15000

20000

25000

Sunday Monday Tuesday WednesdayThursday Friday Saturday

Day of Week

Steps

Adult, number of points: 1823

Weekday vs. Steps

Figure 1

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5000

10000

15000

20000

25000

FALSE TRUE

School day

Steps

Teen, number of points: 1566

School day vs. Steps

2500

5000

7500

10000

12500

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Mean Steps

Teen, number of points: 1566

Month vs. Steps, Across All Five Years

Figure 2

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250

500

750

1000

2014 2015 2016 2017 2018

Date

Sleep (min)

Teen, number of points: 828

Date vs. Sleep

250

500

750

1000

2014 2015 2016 2017 2018

Date

Sleep (min)

Adult, number of points: 1548

Date vs. Sleep

250

500

750

1000

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

Day of Week

Sleep (min)

Teen, number of points: 828

Weekday vs. Sleep

250

500

750

1000

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

Day of Week

Sleep (min)

Adult, number of points: 1548

Weekday vs. Sleep

Figure 3

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Pearson r = 0.2

p = 4.92e−16

5000

10000

15000

20000

0 10000 20000

Adult Steps

Teen Steps

Steps, number of points: 1589

Adult Male vs. Teen Female, Steps Per Day

Pearson r = 0.23

p = 1.32e−09

250

500

750

0 250 500 750 1000

Adult Sleep (min)

Teen Sleep (min)

Sleep, number of points: 707

Adult Male vs. Teen Female, Time Asleep Per Day

Figure 4

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