Presentation given at the 27th European meeting on hypertension and cardiovascular protection (ESH), Milan, June 16 2017

1. Mobile health apps for exercise monitoring:
where do we stand?
Enrico G Caiani, PhD
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
enrico.caiani@polimi.it

2. Cesare Alippi
No conflict of interest

3. Cesare Alippi
Mobile devices and healthcare apps
The use of mobile devices that
allow data collection in real time is
increasing ubiquitously, empowering
individuals to assume a more
active role in monitoring and
managing their chronic conditions
and therapeutic regimens, as well as
their health and wellness.
The rapid adoption of connected mobile devices could potentially
enable the shift from a sickcare system to a preventative care
system, with big potential savings at stake.
Mobile devices offer the possibility to monitor and track health
parameters, thanks to externally-connected devices or to
embedded sensors technology.
enrico.caiani@polimi.it

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Fitness wearable
Fitness wearables, which include wristbands, smart garments, chest
straps, sports watches and other fitness monitors, continue to
increase in popularity, with a forecast for wearable devices worldwide
of 322 Millions of Units in 2017 (+17%) [Garner, 2016]:
44 millions
(+26%)
67 millions
(+32%)
27 millions
(+12%)
7 millions
(-38%)
5 millions
(+400%)
enrico.caiani@polimi.it

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Pysical inactivity and mobility assessment
Physical inactivity is an independent risk factor for
chronic disease and disability and is estimated to
result in 3.2 million deaths world-wide each year
[Alwan A. Global status report on noncommunicable diseases 2010],
while regular physical activity has been
associated with health improvements in many
populations [Aadahl M, et al. Prev Med. 2009].
Goal: to assess mobility in the free-living environment as health
and wellness measure.
Step counting is one of the most commonly used
measures of physical activity [Thomas JR, et al. Research
methods in physical activity 2005]. One of the main issues
associated with step counts as a physical activity
measure is that high accuracy is needed.
enrico.caiani@polimi.it

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Activity trackers: pedometers
The use of a pedometer has been associated with significant
increases in physical activity and significant decreases in body mass
index and blood pressure [Bravata DM, JAMA 2007].
Smartphone apps as pedometer generally
results in underestimation (up to 30%) of
measured parameters in both light and
moderate intensity activity, among both normal-
weight and obese group.
[Konharn K et al, J Phys Act Health 2016]
enrico.caiani@polimi.it

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Activity trackers: MEMS 3-axis accelerometer
[Fortune E et al, Med Eng Phys. 2014]
Gold standard: video data
N =12 (25-55 yo)
walking/jogging trials in a
straight line over an 8.5 m
walkway
Result as median (IQR)
92% (8%) 92% (36%)
93% (22%) 33% (35%)
FitBit (Ankle)Custom
FitBit (Waist) Nike Wristband
 Difficulty in testing in
free-living environment
 Accuracy varies with
device used
 Accuracy varies with
speed: lower at slow and
high walking speeds
enrico.caiani@polimi.it

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Optical heart rate monitor: wrist-worn devices
The ability to measure physical activity through wrist-worn devices
provides an opportunity for cardiovascular medicine. However, the
accuracy of commercial devices is largely unknown.
[Shcherbina A et al, J Pers Med 2017]
Heart Rate Energy expenditure (EE)
• HR with <5%acceptable error under controlled laboratory conditions
(Apple Watch lowest, Samsung Gear S2 highest)
• no wrist-worn monitoring devices report EE with <20% error
• darker skin tone, larger wrist circumference, and higher BMI worsen.
Median % error
N=60
enrico.caiani@polimi.it

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Optical heart rate monitor: earbuds
Direct light to the region
between the anti-tragus and
concha of the ear, where a
bank of expanding arterioles
is present.
Main challenge: finding the right balance of fit and comfort.
Better estimate of EE (2SD: 14%) BUT need for more validation studies!
[Dondzila C and Garner D, J Med Eng Technol. 2016]
enrico.caiani@polimi.it

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ECG heart rate monitor
 Proper wearing
 No moisture, dry air
 Static electricity
produced by synthetic
shirts
 Electrical Interference
(powerlines, train lines,
etc…)
HR spikes
Chest straps
Wrist ECG 10 sec registration only
(Kardia band, Alivecor)
Compression sleeve Komodo
Technologies
enrico.caiani@polimi.it

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Apps for Exercise monitoring
Day-by-day increasing number of apps relating to
physical activity in “Health and fitness” category of
App Stores!
To promote behavioural change, people need to keep
motivated over time and habits must be changed.
This requires the use of behavioural change theories as the
basis for designing tailored motivation and feedback messages,
and linking to behaviours monitored by the sensor system to
provide behavioural feedback loops.
enrico.caiani@polimi.it

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Behavioural changes through apps
Top-ranked 200 apps screened
[Conroy et al, Am J Prev Med 2014]
Behaviour change coded using
“CALO-RE” taxonomy from app
description
Median of 4 behaviour change techniques per app
Provide instruction on how to perform behavior 66%
Model/demonstrate the behavior 53%
Provide feedback on performance 50%
Goal setting—behavior 38%
Plan social support/change 37%
Information about others’ approval 28%
Apps focused on physical
activity motivation, with emphasis
on social- and self regulation
of physical activity (54%)
Apps focused on physical activity
education, characterized by
providing instruction on how to perform
the behavior (46%)
enrico.caiani@polimi.it

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Behavioural changes through apps
Forming intentions is rarely sufficient for changing
behavior, and further motivational support is often
needed for people to implement their intentions.
Surprisingly, the most well-established technique for
bridging the intention-behavior gap, action planning,
was relatively rare (4%) in descriptions of the top-
ranked physical activity apps.
[Conroy et al, Am J Prev Med 2014]
Knowledge about how to practice a desired
health behavior is a necessary precursor to
behavior change because it contributes to task
self-efficacy, which facilitates the formation of
intentions to be physically active.
enrico.caiani@polimi.it

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Behavioural changes through apps
In 19/27 studies, proved efficacy to
improve sedentary behaviour and promoting
physical activity for app-based interventions,
with multi-component interventions
(SMS, phone coaching and motivational
emails) more effective than standalone app
interventions [Schoeppe S et al. Int J Behav Nutr
Phys Act 2016].
The average attrition rate (i.e., participant loss to follow-up) was 17%
(lower compared to rates of 23–27% found in web-based
interventions).
enrico.caiani@polimi.it

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Conclusion
Mobile apps and wearable technology offer the possibility to
monitor and track health parameters BUT not medical devices.
Choice of wearable device is critical, based on require accuracy of
parameter of interest (steps, HR, EE): need for comparative
research.
Choice of the app-based intervention could influence the
effectiveness of the behavioral change, based on the embedded
technique: need for proper clinical coaching.
Need for mHealth interventions focused on older people’s needs
and preferences, where facilitators are independence,
understanding, and visibility, while barriers are complexity, limited
usability, and ineffectiveness.
enrico.caiani@polimi.it

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Join us!
Acknowledgements
enrico.caiani@polimi.it