Uncorrected Author Proof
IOS Press
Smart devices and healthy aging
1Sergio Vinciguerra
aand Manlio Vinciguerra
b,c,d,∗2
aUniversity of Turin, Turin, Italy
3
bInternational Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
4
cDepartment of Histology and Anatomy, Faculty of Medicine, Masaryk University, Brno, Czech Republic
5
dInstitute for Liver and Digestive Health, Division of Medicine, University College London, London, UK
6
Abstract. Chronic diseases, such as cardiovascular disease, cancers, chronic respiratory diseases, diabetes mellitus, and neurodegenerative disorders represent major global health problems to society, and their incidence and prevalence continue to increase. Chronic diseases share common risk factors, including socio-economic factors and co-morbidities and, importantly, their risk increases with age. The silent transition from health to disease with a late onset of symptoms can delay treatment and interventions. Healthcare-systems must thus evolve proactive rather than purely reactive approaches to care once symptoms appear. Many self-tracking technologies (based on wireless biosensors) are readily available to the general public that monitor and record personal bio-related data. These biosensors may be wearable, implanted in the body or installed on a device. The aim of this review is to discuss the current market and proven utility of wrist-worn devices, in improving and maintaining a healthy lifestyle. Optimizing the technological opportunities for monitoring good health has the potential to empower people and help many enjoy a high quality of life.
7 8 9 10 11 12 13 14 15 16
Keywords: Smart devices, health self-monitoring 17
1. Introduction 18
Chronic diseases listed by World Health
Orga-19
nization (WHO), including cardiovascular disease
20
(CVD), cancers, chronic respiratory diseases,
dia-21
betes mellitus (DM), and neurodegenerative
disor-22
ders represent a major global health problem [1–3].
23
Such chronic diseases are key contributors to the
24
global health burden, are a leading cause of
mortal-25
ity worldwide, and are increasing in incidence and
26
prevalence [1–4]. Chronic diseases share common
27
risk factors, including socio-economic factors and
28
co-morbidities and are closely associated with aging
29
[1–3, 5]. The links between over-nutrition and/or
30
nutrient scarcity with overweight/obesity,
hyperten-31
sion, stroke, coronary heart disease, diabetes and
32
cancer are well established [6, 7]. Characteristic
33
diets observed in those affected by chronic diseases
34
typically include low consumption of cereals and
35
∗Corresponding author: Dr. Manlio Vinciguerra, PhD - Center for Translational Medicine (CTM), International Clinical Research Center (ICRC), St. Anne’s University Hospital. Tel.: +420 511 158 277; E-mail: manlio.vinciguerra@fnusa.cz.
vegetables and high consumption of meats and sat- 36
urated oils/fats. This type of diet, combined with a 37
sedentary lifestyle and increased exposure to tobacco 38
and alcohol, has contributed to the rapid increase of 39
chronic diseases over recent decades. 40
The challenge for health-care professionals and 41
researchers in the 21st century is to understand the 42
complexity of chronic diseases and the silent tran- 43
sition from a healthy to diseased state due to a late 44
onset of symptoms, which can delay treatment and 45
effective intervention. As a society, we need to shift 46
toward a model of disease prevention: healthcare 47
and medicine must evolve a proactive system that 48
moves away from a purely reactive approach to care 49
once the symptoms appear. Indeed, many individ- 50
uals do pay attention to their bodily functions and 51
sensations, diet, body weight, drug consumption and 52
exercise patterns, in an attempt to achieve optimal 53
health or help manage an ongoing illness or disease. 54
Self-tracking devices have helped encourage such 55
proactive health-care behaviors, and have long been 56
recommended as good practice in personal healthcare 57
[8]. The implications of self-tracking on concepts 58
ISSN 2451-9480/16/$35.00 © 2017 – IOS Press and the authors. All rights reserved
Uncorrected Author Proof
such as self-identity, social relations and embodiment
59
have gained interest over recent years. This
inter-60
est stemmed in part from increasing coverage in the
61
mass media of the potential for new digital
technolo-62
gies to facilitate self-tracking in novel, fashionable
63
ways. In the most modern conception, self-tracking
64
devices typically refer to wireless bio-sensors,
medi-65
cal instruments or equipment that are accessible to
66
the general public to monitor and record personal
67
bio-related data. Such wireless bio-sensors may be
68
wearable, implanted in the body or even installed
69
in homes, cars, workplaces and other environments
70
[8–11]. Some healthcare applications and services
71
are digitally availability through mobile device
appli-72
cations. In this regard, the integration of wireless
73
medical sensor networks and radio-frequency
iden-74
tification (RFID) technology to healthcare systems
75
has helped drive the advancement and sophistication
76
of self-monitoring capabilities.
77
Patient-centered activity monitoring for the
self-78
management of chronic health conditions has proven
79
effective in the post-surgical recovery of patients with
80
cardiac diseases or diabetes [12], but smart devices
81
for self-monitoring also have the potential to support
82
health in everyday living to prevent the insurgence of
83
chronic diseases. The aim of this review is to
high-84
light the current market offer, potential and proven
85
utility of smart devices (in particular wrist bands)
86
in prolonging the functional and cognitive capacity
87
of the general population. Optimizing opportunities
88
for good health has the potential to empower people,
89
particularly the elderly, to enjoy an independent and
90
high quality of life, with great social and economic
91
benefits.
92
2. Smart devices: A growing market 93
The elderly population represents a huge untapped
94
market for technology companies producing smart
95
devices. According to 2010 US census data,∼13%
96
of the US population is aged ≥65 years; this
per-97
centage is estimated to increase to ∼19% by 2030
98
[13]. An area of technological invention that holds
99
particular promise for this age group is wearable
tech-100
nology (wearables). Wearables are currently used by
101
>20% of US citizens. Many wearables encourage and
102
support healthy lifestyles, such as smart wear
devel-103
oped by top brands to help optimize workout routines.
104
Wearables that foster healthy aging and independent
105
living, such as nanotechnologic “smart clothes” that
106
monitor health and remind subjects to take their med- 107
ications, have the potential to fill the wardrobes of the 108
elderly. In fact, market analysts predict that medical 109
applications will soon account for the largest share in 110
the smart textile industry, reaching $843 million by 111
2021 [14]. 112
Wearables, such as activity trackers, are a good 113
example of the Internet of Things (IoT), which is 114
the network of physical devices, home appliances, 115
and other items embedded with electronics, software, 116
sensors, actuators, and have network connectivity to 117
enable these objects to collect and exchange data. The 118
healthcare IoT market segment is poised to hit $117 119
billion by 2020, and currently involves big indus- 120
trial players such as IBM, Cisco, Microsoft, Google, 121
Amazon, and GE. Thousands of devices and gad- 122
gets are available that have the potential to help one 123
live a healthier mental, physical and emotional life 124
[15, 16], but it is beyond the scope of this review to 125
discuss them all. Popular application choices include 126
those that monitor sleep quality (Pebble Time and 127
the Android Sleep App), blood pressure (Nokia’s 128
Withings watch and Viatom’s CheckMe™), exercise 129
(Gymwatch®fitness tracker) and that aid meditation 130
(InteraXon’s Muse™ head band) and stress reduction 131
(PIP). Here, we focus exclusively on the wrist-worn 132
trackers for sleep, calorie intake and fitness, as a 133
proof-of-concept that smart devices can help main- 134
tain a healthy span in the general population. 135
Fitbit®is the best known fitness tracker brand on
136
the market, and it comes in many forms. Fitbit® is 137
a touch-screen wristwatch that not only tracks steps 138
and sleep, but also alerts the user to incoming phone 139
calls and text messages, monitors heart rate with a 140
built-in optical heart-rate monitor and tracks outdoor 141
activity via GPS. Fitbit® is easy to use and con- 142
nects the user to a wider community that shares the 143
same activities and allows them to compete in a play- 144
ful manner. Other widely used wrist-worn trackers 145
include the Xiaomi MI Band 2, Garmin Vivosmart 3, 146
Samsung Gear Fit2 and the Apple Watch S1. Table 1 147
summarizes the features of these devices (including 148
steps, calories, distance, clock, sleep tracker, wire- 149
less, battery/charge lasts, continuous hearth rate, GPS 150
tracking, multi-sport detector, music control, style or 151
mode of wearing and price) and economic indicators 152
(such as the number of units sold, the market share 153
and the year-over-year growth), based on the Amazon 154
online US market. Fitbit®achieves the most units sold 155
and has the biggest market share worldwide, there is 156
large variation in price between the various available 157
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Table 1
Main features of wrist-worn devices, average price and sales (indicative, based on Amazon online US market
Most popular health Fitbit Xiaomi Mi Garmin Samsung Apple Others
monitoring devices Charge 2 Band 2 Vivosmart 3 Gear Frt 2 Watch SI
Steps Yes Yes Yes Yes Yes
Calories Yes Yes Yes Yes Yes
Distance Yes Yes Yes Yes Yes
Clock Yes Yes Yes Yes Yes
Sleep Tracker Yes Yes Yes Yes No
Wireless Yes Yes Yes Yes Yes
Battery/Charge lasts 7–10 Days 20 Days 6 Days 3 Days l8h
Continuous Hearth rate Yes Yes Yes Yes Yes
GPS Tracking Yes Yes No Yes No
Multi-sport Yes No No Yes Yes
Music Control Yes No Yes Yes Yes
Style Wristband Wristband Wristband Wristband Smartwatch
Price∗ $148.95 $36,98 $139.99 $177.99 $276.55
Fourth Quarter 2016 Units Shipped∗∗ 6.5 5.2 2.1 1.9 4.6 13.6
Fourth Quarter 2016 Market Share∗∗ 19.2 15.2 6.2 5.6 13.6 40.1
Fourth Quarter 2015 Units Shipped∗∗ $.4 2.6 2.2 1.4 4.1 10.3
Fourth Quarter 2015 Market Share∗∗ 29% 9.10% 7.60% 4.70% 14.10% 35%
Year-Over-Year Growth∗∗ –22.70% 96.20% –4% 37.90% 13% 32.10%
∗Amazon online US market.∗∗All products Fibit, Xiaomi, Garmin, Samsung, Apple and other.
3. Impact of smart devices on healthy aging 158
Commercial self-monitoring wrist-worn devices
159
are increasingly popular in both the consumer and
160
medical markets. In 2015, 232 million wearable
161
electronic devices were sold worldwide, and sales
162
increased by 18.4% in 2016 [17]. With this increase
163
in popularity, behavioral change interventions are
164
becoming more evident [18–22]. However, with their
165
increasing popularity, questions as to the validity,
166
accuracy and reliability of these devices become
167
increasingly more important. A recent study
evalu-168
ated such commercially available devices for clinical
169
purposes [23]. Here, four activity trackers and one
170
sleep tracker were evaluated based on step count
171
validity, in a cohort of 22 healthy volunteers asked
172
to complete a walking test [23]. The study found
173
that some self-monitoring devices are better suited
174
than others for measuring step count at slow
walk-175
ing speeds. The Fitbit® and the Garmin Vivofit 2
176
showed the lowest average systematic error
percent-177
age; however, the standard deviations of the Fitbit®
178
were significantly lower than the Garmin Vivofit 2,
179
making this device the most reliable for use in
slow-180
walking populations [23]. Another study examined
181
the accuracy of heart rate and energy expenditure
182
measures made at different exercise intensities by
183
three popular wrist-worn activity monitors [24]. A
184
cohort of 62 participants wore the Apple Watch,
185
Fitbit®, or Garmin Forerunner 225 and their
valid-186
ity was assessed using a heart-rate chest strap and 187
a metabolic cart. The participants completed a 10- 188
minute seated baseline assessment; separate 4-minute 189
stages of light-, moderate-, and vigorous-intensity 190
treadmill exercises; and a 10-minute seated recov- 191
ery period. The highest measurement error for all 192
devices occurred during the light and moderate phys- 193
ical activity stages [24]. The Apple Watch provided 194
the most accurate measure of heart rate, followed by 195
Fitbit® and then the Garmin Forerunner 225 [24]. 196
Although these results seemed to indicate the most 197
favorable outcomes for the Apple Watch, this device 198
measured significantly lower heart rates during light 199
and moderate physical activity compared to the other 200
devices. Fitbit®produced reasonably accurate results 201
during moderate physical activity but measured lower 202
heart-rate readings during vigorous physical activity 203
compared to the other devices. Finally, Garmin Fore- 204
runner 225 read accurately during vigorous physical 205
activity but measured significantly higher heart rate 206
readings at all other intensities compared to the other 207
devices [24]. 208
Several randomized controlled trials on the utility 209
of these popular wrist-worn activity trackers, and in 210
particular Fitbit®, on health outcomes across various 211
age ranges of the general population have been con- 212
ducted or are ongoing. The list below is not intended 213
to be exhaustive but highlights the potential range of 214
applications of wrist-worn devices on improving a 215
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3.1. Completed trials
217
A randomized controlled trial by Smith Lillehei et
218
al. compared the effectiveness of lavender
(Lavan-219
dula angustifolia) and sleep hygiene versus sleep
220
hygiene alone on sleep quantity and quality, and
221
aimed to determine whether any positive effects of
222
lavender could be sustained for 2 weeks [25]. The
223
study cohort included 79 college students with
self-224
reported sleep issues and their usual sleep settings
225
were maintained. The intervention took place over
226
five nights; the experimental group wore a patch
227
with lavender essential oil and the control group
228
wore a blank patch [25]. Sleep quantity was
mea-229
sured using a Fitbit® tracker and a sleep diary,
230
and sleep quality was measured using the
Pitts-231
burgh Sleep Quality Index (PSQI) and the NIH
232
Patient-Reported Outcomes Measurement
Informa-233
tion System (PROMIS) sleep disturbance short form.
234
The experimental group demonstrated better sleep
235
quality at post-intervention and 2-week follow-up,
236
compared to the control (sleep-hygiene-only) group.
237
Interestingly, sleep quantity did not differ between
238
groups [25]. This study highlighted the utility of
239
wrist-worn smart devices for monitoring certain sleep
240
parameters.
241
Several clinical trials of tracking food intake or
242
recording the number of steps made per day using
243
a pedometer have resulted in successful weight loss
244
[26, 27]. However, the impact of food management
245
applications on permanent behavioral change toward
246
diet and activity is doubtful [28, 29]. In fact, a
ran-247
domized clinical trial conducted at the University of
248
Pittsburgh that enrolled 471 participants, found that
249
among young adults with a body mass index between
250
25 and <40, the addition of a wearable technology
251
device to a standard behavioral intervention resulted
252
in less weight loss over a 24-month period compared
253
to a standard intervention alone [21].
254
Data suggest that wrist-worn devices are
effec-255
tive because users like them. Another randomized
256
controlled trial assessed the feasibility and efficacy
257
of integrating the Fitbit® tracker with a related
258
website resource, in improving physical activity
259
in postmenopausal women [19]. A cohort of 51
260
postmenopausal overweight/obese women was
ran-261
domized to a 16-week web-based self-monitoring
262
intervention or a comparison group. The web-based
263
tracking group received a Fitbit® and an
instruc-264
tional session, and the comparison group received
265
a standard pedometer. All participants were asked
266
to perform 2.5 h/week moderate-to-vigorous physical
267
activity, and the physical activity outcomes were 268
then measured. Interestingly, almost 100% of the 269
web-based tracking group wore the wrist tracker and 270
liked it [19]. The wrist-worn smart device was well- 271
accepted in this sample of postmenopausal women 272
and strikingly, it was associated with increased phys- 273
ical activity at 16 weeks. Relative to baseline, those 274
wearing a Fitbit®increased their number of steps per 275
day by 789± 2 [19]. Conversely, those carrying a 276
simple pedometer did not show a significant increase 277
in physical activity [19]. 278
3.2. Ongoing trials 279
The Australian project Raising Awareness of Phys- 280
ical Activity (RAW-PA) aims to study the impact 281
of Fitbit® Flex on the daily physical activity levels 282
(moderate to vigorous intensity) of 300 teenagers 283
[30]. Specifically, RAW-PA is a 12 week multi- 284
component intervention that will examine whether 285
a wearable activity tracker combined with behavior 286
change resources is effective at promoting physical 287
activity in inactive adolescents who are attending 288
schools in socioeconomically disadvantaged areas 289
[30]. RAW-PA is anticipated to provide insights 290
into how such technologies are used by adolescents, 291
which at present is unclear. 292
A German clinical trial is evaluating the effec- 293
tiveness of two web-based interventions in initiating 294
and maintaining regular physical activity in adults 295
aged 65–75 years compared to a delayed interven- 296
tion group [31]. The participants have been randomly 297
assigned to one of three study arms: a) participants 298
receive access to a web-based intervention for 10 299
weeks, which allows them to track their weekly 300
physical activity (subjective self-monitoring); b) par- 301
ticipants receive access to the web-based intervention 302
for 10 weeks and in addition, will track their physi- 303
cal activity using Fitbit®(objective self-monitoring); 304
c) participants receive access to the intervention 305
implemented in the first study arm after completion 306
of the 12-week follow-up in the other two groups 307
(delayed intervention). Using an integrated approach 308
(including community meetings and psychological 309
assessment), it is hoped that this study will provide 310
answers regarding the effectiveness of web-based 311
interventions in promoting the maintenance of reg- 312
ular physical activity in persons aged 65–75 years 313
[31]. 314
The DOREMI project (Decrease of cOgnitive 315
Uncorrected Author Proof
empowerment in lifestyle Management and social
317
Inclusion), coordinated by Prof. Oberdan Parodi
318
(CNR, Milano), combines multidisciplinary research
319
in the areas of serious games, social networking,
320
Wireless Sensor Network, activity recognition and
321
contextualization and behavioral pattern analysis
322
[32]. By recording and monitoring information about
323
the use of adopted lifestyle protocols, DOREMI
324
aims to track user performance over long periods and
325
provide early warning of signs of malnutrition and
326
physical and cognitive deterioration. The DOREMI
327
system utilizes four core technologies and three
328
applications. These technologies include: “Smart
329
Carpet”, a Wii-based balance board for daily weight
330
assessment; an Android tablet containing the three
331
applications; a wrist-worn DOREMI bracelet that
332
collects patient metrics and sends them to a
central-333
ized home-based station; and ∼10 environmental
334
home-installed sensors to assess life-style habits
335
and level of socialization. Each application focuses
336
on a specific aging-related issue: fitness, cognition
337
via a series of games, and diet. In a revolutionary
338
approach, clinicians can remotely check the diaries
339
and, if necessary, modify the participant’s habits
340
to promote health (in accordance with the general
341
guidelines promoted by project) [33]. A pilot study
342
cohort included 32 people aged between 65 and 80
343
years, who participated in 3-month trial in either the
344
UK or Italy. These participants were characterized
345
in terms of their physical activity (including daily
346
steps/meters travelled measured with the DOREMI
347
bracelet), hemodynamic and biochemical parameters
348
(including blood pressure, lipid profile and glycemic
349
index), caloric intake and balance at baseline and at
350
the end of the trial. In addition, they were stimulated
351
to perform an indoor physical activity protocol
mon-352
itored by the DOREMI bracelet, invited to fill in a
353
diet e-diary to receive nutritional advice provided by
354
an expert, and tested for balance (via the DOREMI
355
smart balance board. The DOREMI system has
356
been tested for 3 years and the project was recently
357
completed; we are now awaiting the publication of its
358
first results. Thus far, DOREMI participants at both
359
UK and Italian test sites reported an overall increase
360
in physical activity and a significant improvement in
361
hemodynamics and in dietary habits.
362
The VISTERA project, powered by the
Euro-363
pean Institute for Systems Biology and Medicine
364
(EISBM), aims to encourage the transition from
reac-365
tive to proactive methods of health and wellness
366
management based on the principles of
Sys-367
tems P4 (Predictive, Preventive, Personalized and
368
Participatory) Medicine [35, 36]. The study, based 369
in France (and in collaboration with other European 370
partners) uses individual active engagement in health- 371
care monitoring techniques, such as: participant 372
follow-up over time, participants access to a compre- 373
hensive dataset, data analysis using advanced compu- 374
tational tools and participant access to a personalized 375
dashboard for health and wellness coaching [34]. 376
Over the course of the project, self-measurements of 377
heart and respiration rate, sleep quality, weight, blood 378
pressure and calorie uptake will be made using wrist- 379
worn devices. By monitoring the participants over a 380
long period of time, it is hoped that actionable rec- 381
ommendations to maintain health and wellness can be 382
made, and early detection warnings of events indica- 383
tive of risk or transition to disease can be provided. 384
The expectation of the VISTERA Project is that by 385
monitoring up to billions of individuals over the next 386
25 years will trigger a reversal of the escalating costs 387
of healthcare management and drug and diagnostic 388
development in just one generation. All partners of 389
the VISTERA Project are committed to respect the 390
privacy of the participants and will implement strin- 391
gent measures to ensure compliance with national and 392
international regulations on personal data protection. 393
This project is very ambitious and still in its infancy. 394
4. Conclusions and perspectives 395
Wrist-worn smart devices have clinical utility, but 396
they are under-utilized in the healthcare industry. 397
People wanting to use technology to live a health- 398
ier life are rushing to buy a wearable. We advocate 399
that wearables are an excellent option to help take 400
control of health and life style at the individual level. 401
However, wearables can currently only show data, 402
which will not be actionable if it is not known how 403
and what should be changed. Wearables should not 404
completely replace the traditional methods of scoring 405
mental, physical and emotional health parameters, 406
but should be complementary. Self-monitoring helps 407
people understand how their own body works, and 408
encourages vigilance about health. At the clinical 409
level, incorporating smart wearable sensors into the 410
routine care of patients with a chronic disease could 411
enhance physician-patient relationships, increase the 412
autonomy and involvement of patients in their own 413
healthcare and provide novel remote monitoring tech- 414
niques that will revolutionize healthcare management 415
Uncorrected Author Proof
Acknowledgments417
We would like to thank Dr. Charles Auffray
418
(EISBM, Lyon, France) for inspiration and fruitful
419
discussions, and Insight Editing London for
proof-420
reading the manuscript.
421
In loving memory of Prof. Domenico Vinciguerra
422
(1937–2015).
423
Financial support 424
This work was supported by the European
425
Social Fund and European Regional Development
426
Fund - Project MAGNET (grant no. CZ.02.1.01/
427
0.0/0.0/15 003/0000492 to M.V.).
428
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