Cite as: Kamel Boulos MN. Towards precision and accuracy digital public health: informed decision-making using novel community-level physical activity indicators from app data aggregates of user populations. Invited talk at the 2019 International Geoinformatics Week - Annual meeting of Geoinformatics in Sustainable Ecosystem and Society (GSES), Zhujiang Hotel, Guangzhou, China, 22-25 November 2019 URLs: http://gig.geoweek2019.org/ and http://gig.geoweek2019.org/main/news/8.html Abstract: Big data aggregates from popular mobile physical activity (PA) tracking gadgets and the apps/platforms they are paired to can provide unique population insights that could help public health authorities devise superior PA promotion interventions, better target them, and dynamically monitor their effect over time, making adjustments to the interventions as necessary.

1. Towards precision and accuracy digital
public health: informed decision-making
using novel community-level physical
activity indicators from app data aggregates
of user populations
Maged N. Kamel Boulos, MBBCh, PhD, SMIEEE
Professor of Digital Health Systems 教授，博士生导师,
Sun Yat-sen University, China
mnkboulos@sysu.edu.cn

2. Our ultimate goal: Precision and
Accuracy (Digital) Public Health
 Precision Public Health is a term first introduced by Khoury et al. in 2016 in their
paper entitled 'Precision Public Health for the Era of Precision Medicine'
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4915347/
 A misnomer; should have been called Accuracy Public Health, but Precision Public
Health sounds nicer. Actually, we need both in public health: precision and
accuracy, so the ideal term here would be Precision and Accuracy Public Health.

3. Physical activity is essential for good
health and disease prevention
 Physical activity is critical for achieving and maintaining good physical and mental health
and for the prevention and control of a wide-range of chronic diseases, including type 2
diabetes (and its many complications), heart disease, joint disease, and certain types
of cancer and dementia, but is not as good for calorie burning or for offsetting a bad,
sugar and calorie-laden diet,* despite the claims promoted by 'big food’ companies. In fact,
the "wrong" type of physical activity might sometimes result in weight gain, e.g., swimming
in cold water may stimulate appetite and prevent weight loss
(https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008277).
 Tying physical activity promotion messages to calorie burning for weight loss (as the sole
purpose of exercising) can be counterproductive and demotivating for some persons who
fail to lose weight with exercise, resulting in these people giving up completely on exercise
despite its critical importance for their health, e.g., a systematic review and meta-analysis
published in November 2019 found that any amount of running, even just once a week,
substantially reduces risk of early death from cardiovascular problems, cancer and other
causes and is clearly better than no running
(https://bjsm.bmj.com/content/early/2019/09/25/bjsports-2018-100493).
But not for calorie
burning and weight loss!
It takes at least half hour
of brisk walking to just
burn 2 McVitie's digestive
biscuits!
>
* Vox has a 2017 report intended for non-clinical lay people explaining this
further and drawing on research evidence from dozens of studies
https://www.vox.com/2016/4/28/11518804/weight-loss-exercise-myth-burn-calories

4. Location-specific factors
 Individual physical activity (PA) and its health gains are dependent on a number of
location-specific factors, including the availability, positioning and accessibility
of PA POIs (points of interest), such as local green spaces/parks and covered
sports spaces and gym facilities (especially important during temperature
extremes and heavy rain/snow seasons).
 Additional neighbourhood walkability indicators that affect PA levels and weight
gains of residents include pollution (air, light and sound/noise), crowdedness,
crime rates, traffic/road network safety, and even individual age, gender and
genetic predisposition differences.
 Planning appropriate (precision and accuracy) public health interventions for PA
promotion that are well targeted and effective (both health- and cost-wise)
requires careful understanding and consideration of all of the above factors, i.e.,
requires access to comprehensive data and insights about these factors.
Despite the ubiquity of the
10,000-daily-step advice
to be healthy, there is very
little medical evidence to
back up this assertion.
In fact, a more moderate
figure of between 6,000
& 7,000 steps/day
or 150 minutes
of moderate exercise/
week is recommended
in the peer reviewed
literature.
See, for example:
Kolb H, Martin S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. BMC Med.
2017;15(1):1-11.
Schutte N, Nederend I, Hudziak J, al. e. Heritability of the affective response to exercise and its correlation to exercise
behavior. Psychol Sport Exerc. 2017;31:139-48.
Bauman A, Reis R, Sallis J, Wells J, Loos R, Martin B. Correlates of physical activity: why are some people physically
active and others not? Lancet. 2012;380(9838):258-71.
On the complex
interplay of
environment, genes
& other factors in
health & disease
(twin pairs study)
https://harvardmagazine.co
m/2019/07/nature-nurture-
genetics
V

5. Precision and accuracy public health: using big data
to support informed public health decision-making
 Big data from popular mobile health and fitness apps/platforms and other sources, and the
population insights they provide could help public health authorities devise superior
interventions, better target them, and dynamically monitor their effect over time, making
adjustments to the interventions as necessary.

6. Precision and accuracy public health: supporting
informed public health decision-making
Desiderata for the ideal activity tracker/app platform
 The ideal activity tracker/app platform should expose a standardised API (Application
Programming Interface), with suitable individual privacy provisions/guarantees (see
later), data warehousing policies and data sharing arrangements, to enable public
health authorities to obtain geo-tagged population app data aggregates of PA levels and
trends over time amongst their target populations (indicators of community physical
activity level/status).
 Population PA indicators can include average steps or, additionally, if Google Fit is used,
average 'Move Minutes' and 'Heart Points', per person/per day in different age groups
and country regions, cities and city neighbourhoods; percentages of poor, average
and high performers in target population; also cumulative (total) population steps,
'Move Minutes' and 'Heart Points' per region, city and neighbourhood for a given
period.
Google Fit (https://www.google.com/fit/)
earned the 'blessings' of the World
Health Organisation (WHO) and the
American Heart Association (AHA).

7. Image courtesy of Google

8.  Other relevant population-level indicators related to heart rate variability (see
https://www.health.harvard.edu/blog/heart-rate-variability-new-way-track-well-2017112212789),
sedentary behaviour and sleep quality/patterns in the community can also be extracted
from fitness and PA app and gadget big data aggregates of user populations to provide
additional 'lifestyle intelligence' for designing better public health
interventions.
Indicators of community PA status (cont’d)
V Mi Fit app: heart rate and sleep
>
Roberts KC, Butler G,
Branchard B, et al. The
Physical Activity, Sedentary
Behaviour and Sleep (PASS)
Indicator Framework. Health
Promot Chronic Dis Prev Can.
2017 Aug;37(8):252-256. doi:
10.24095/hpcdp.37.8.04.
(Public Health Agency of
Canada)

9. Indicators of community PA status (cont’d)
 These indicators can be monitored and compared every few months to determine
population progress and trends over time — cf. population-level surges observed in
fitness tracker step counts on the Cardiogram platform (https://cardiogram.com/)
coinciding with the release of Pokémon Go mobile exergame app
(https://www.pokemongo.com/en-us/) in July 2016 (Pokémon Go in this example can be
considered as a gamified PA "population intervention").
 Indicators can also be normalized by population number to compare different regions,
cities and neighbourhoods.
 Further interesting patterns might emerge from user data aggregates that can help city
planners better understand and optimise the physical activity landscape of their city and
its population; for example, data might reveal that
residents of Neighbourhoods A and B are doing most
of their daily exercises in some walkable streets,
parks or public gym facilities in a different
Neighbourhood C.
https://www.washingtonpost.com/news/to-your-health/wp/2016/07/15/pokemon-
go-leading-to-a-population-level-surge-in-fitness-tracker-step-counts/
https://watchaware.com/post/17592/cariogram-users-exercise-more-with-
release-of-pokemon-go

10. Sweatcoins! (serving both as incentivized intervention and as indicator)
 Population 'sweatcoin' levels, e.g., per neighbourhood, can also be
used. Sweatcoins are a kind of 'digital currency' that can be spent on
goods and services or sent to family and friends.
 The app behind them, Sweatcoin (https://sweatco.in/), harnesses
daily living physical activity monitoring (with phone or fitness tracker).
The more steps a user takes the more sweatcoins s/he can earn.
One feature that stands out with the rewards structure is the
philanthropic possibility, in that users can also donate their sweatcoins
to charities.
 As of mid-November 2019, their website states that more than 6 billion
(1012) steps have been converted to date, and one of their goals is to
make sweatcoins universally accepted as a form of payment.
 At the end, the choice of app(s)/platform(s) to forge data sharing
agreements with, and aggregate user population data from, will
depend on which app(s)/platform(s) are adopted the most by users/by
the majority of our target populations.
 At the time of writing, Google Fit remains one of the most ubiquitous
platforms worldwide and certainly much more adopted that Sweatcoin,
but programmes can also be launched to actively promote Sweatcoin
or other apps/platforms among target populations.

11. Indicators of community PA status
(cont’d)
 Location-tagging of app data aggregates (e.g., by
neighbourhood or city) should be straightforward and
readily available, as most PA and fitness mobile apps
today require 'location permission' and use it to log
user's home base location, fetch corresponding weather
data, map walking, running and cycling routes, etc.
 Location is derived from GPS and other location
services (Wi-Fi, Bluetooth [beacons] and cellular
network) on smartphones, as well as GPS on smart
bands/smartwatches that are equipped with one.
 Moreover, many smart bands/smartwatches without
GPS offer a 'connected GPS' mode for tracking location
(waypoints), distance and pace using the GPS on the
smartphone (app) they are paired to.

12. Indicators of community PA status (cont’d)
 In the same vein, Fillekes et al. (2019) are trying to develop a comprehensive set of GPS-
based indicators reflecting the multidimensional nature of daily mobility for applications
in health and aging research.
 As far as sensor/device accuracy is practically concerned, in population aggregates, we
are looking for crude trends over time (e.g., overall population PA levels, and whether
they are increasing or decreasing or remaining unchanged over time/in different
seasons of the year, etc.), so inaccuracies and differences between different sensors
and device models/brands should not pose a big issue, and most over- and
underestimates will offset each other in sufficiently large samples.
^ Fillekes MP, Giannouli E, Kim EK, Zijlstra W, Weibel R. Towards a comprehensive set of GPS-based indicators reflecting the
multidimensional nature of daily mobility for applications in health and aging research. Int J Health Geogr. 2019 Jul
24;18(1):17. doi:10.1186/s12942-019-0181-0.
User tip:
Always use a reputable
smartwatch/smart band
brand and avoid the
no-name, cheap clones
on the market, which
often have low grade
or even fake (no)
sensors inside and
cannot sync their data
to common platforms
such as Google Fit.

13. Not all PA types are equal!
^ https://www.telegraph.co.uk/science/2019/08/01/swimming-cold-
water-may-stimulate-appetite-prevent-weight-loss/
 Recent work by Lin et al. (2019) suggests that regular
jogging, followed by mountain climbing, walking, power
walking, certain types of dancing, and long yoga practices
were helpful in maintaining desirable weight ranges in
individuals who are genetically predisposed to obesity.
 But the same study found that cycling, stretching exercises,
swimming and Dance Dance Revolution (a digital exergame
series) did not counteract the genetic effects on obesity.
^
https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008277

14. Segmentation by PA and PA facility types
< Mi Fit
 Ideally, PA tracking in populations and PA mapping of facilities and
opportunities in the neighbourhood environment should also be
segmented by type of activity, e.g., walking, cycling and swimming,
taking into consideration the different genetic makeups, as well as other
relevant profiles and characteristics of population subgroups.
 If we want to deliver effective and truly individualized PA advice and
coaching to different population subgroups, and plan existing, and optimize
future, community PA facilities and spaces accordingly, we have to include
this sort of detail resolution about current opportunities and uptake of
different PA types by different population subgroups in the neighbourhood,
since not all PA types are equally effective in different types of individuals.
>
A step towards future automatic segmentation of PA type and intensity: Screenshot of Mi Fit app (Huami/Xiaomi,
China - https://play.google.com/store/apps/details?id=com.xiaomi.hm.health - version 4.0.11 – November 2019) is
enlisting the help of its users to manually tag different types of daily activities and sports as they are carrying them
out, so that the corresponding fitness band sensor data patterns (e.g., from the tri-axial accelerometer, tri-axial
gyroscope and PPG (photoplethysmography) heart rate sensors in Xiaomi/Huami Mi Smart Band 4) can be learned by
the platform. Moreover, the Xiaomi/Huami Mi Smart Band 4 itself (https://www.mi.com/global/mi-smart-band-4) has
an on-screen 'Workout' menu where users can select the type of sports activity they are about to start and then tell
the band when they have finished their selected sports session (by pressing an on-screen 'Stop' button on the band).
Hopefully, future versions of these fitness trackers and their corresponding software platforms will support auto-
matic segmentation of physical activity types without manual user input. It is noteworthy that Mi Fit is compatible
with the Google Fit platform and can upload its user data to the latter over the cloud.

15. The new ‘Apple Heart and Movement Study’
 The most direct follow-up to Apple’s 400,000-person Heart Study conducted with
Stanford (https://www.nejm.org/doi/full/10.1056/NEJMoa1901183), Apple’s new
study launched in November 2019 will look at heart rate and rhythm data collected
via a user’s Apple Watch, alongside movement and workout data collected on the
Watch and phone (500,000 participants over the next five years).
 Researchers will
study how factors
that affect heart
health are related
to deterioration in
mobility or overall
health, with the
ultimate goal of
developing more
and better
preventative tools.
https://www.mobihealthnews.com/ne
ws/north-america/apple-rolls-out-
research-one-app-three-new-
longitudinal-studies
https://apps.apple.com/us/app/id1
463884356
Other Apple/Apple
Watch big data studies
within the same
programme include a
study on the effects
of noise exposure on
hearing.

16. Addressing privacy concerns
 Data privacy and security issues are real and serious.
A recent Strava fitness data from troops wearing their
wearable technology, and their associated 'heatmaps',
revealed secret military bases and troop movement.
If these exposed data were available to the public,
and given to opposing forces, it could have put military
and civilians at risk of being attacked.
 Privacy concerns extend beyond sharing user location
updates with cloud services. In fact, if we were to
remove all location data from a user's profile, there
would still be other possible ways to reveal the real
identity of that user.
In a recent study, Kupin et al. (https://link.springer.com/chapter/10.1007/978-3-030-05710-7_5)
collected data at dozens of time points in virtual reality environments (think digital
exergaming here), and could identify an individual with 90% accuracy.
Just like postcode, IP address and voiceprint, virtual reality and augmented reality
tracking data should be treated as potential 'personally identifiable information'
since they can be used to uncover a person's identity, either alone or combined
with other personal identifying details.
https://www.wired.com/story/strava-heat-map-
military-bases-fitness-trackers-privacy/
Privacy breaches also take
the form of pestering, highly
personalised/location-based ads
being served based on behavioural
tracking of individual users.

17. Addressing ethical and privacy concerns
 The public needs to be educated and reassured about the nature of big data
aggregates, their purpose and value in public health, and that adequate provisions
are in place to ensure they do not expose any individual user identifying bits that
can be traced back to specific, named persons.
 Governing legislation and regulations might need to be revisited.
https://www.cnbc.com/2019/11/13/privacy-consumer-groups-seek-to-block-google-
fitbit-deal-citing-antitrust-and-privacy-concerns.html
https://www.politico.com/news/2019/11/15/lawmakers-call-for-hipaa-updates-following-googles-data-deal-071088 >

18. For more information
 Kamel Boulos MN, Yang SP. Mobile physical activity planning and tracking: a brief
overview of current options and desiderata for future solutions. mHealth. 2019;5: in
press.
Journal URL: http://mhealth.amegroups.com (online ISSN: 2306-9740, indexed by PubMed & PubMed Central)