Untitled

Original Paper

Using Fitness Trackers and Smartwatches to Measure Physical

Activity in Research:Analysis of Consumer Wrist-Worn Wearables

André Henriksen

, MSc (Comp Sci), MBA; Martin Haugen Mikalsen

, BSc (Comp Sci); Ashenafi Zebene Woldaregay

MSc (Comp Eng), MSc (Telemed & e-Health); Miroslav Muzny

3,4

, MSc (Comp Sci); Gunnar Hartvigsen

, MSc

(Comp Sci), PhD; Laila Arnesdatter Hopstock

, RN, CRNA, MSc (Nursing), PhD; Sameline Grimsgaard

, MPH,

MD, PhD

Department of Community Medicine, University of Tromsø – The Arctic University of Norway, Tromsø, Norway

Department of Computer Science, University of Tromsø – The Arctic University of Norway, Tromsø, Norway

Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway

Spin-Off Company and Research Results Commercialization Center, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic

Department of Health and Care Sciences, University of Tromsø – The Arctic University of Norway, Tromsø, Norway

Corresponding Author:

André Henriksen, MSc (Comp Sci), MBA

Department of Community Medicine

University of Tromsø – The Arctic University of Norway

Postboks 6050 Langnes

Tromsø, 9037

Norway

Phone: 47 77644000

Email: [email protected]

Abstract

Background: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped

with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately

collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore,

data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of

diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects.

Objective: The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability

of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects,

compare common brands in terms of developer access to collected health data, and features to consider when deciding which

brand to use in future research.

Methods: We searched for devices and brand names in six wearable device databases. For each brand, we identified additional

devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we

mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature

Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects.

Finally, we investigated developer accessibility to the health data collected by identified brands.

Results: We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device.

Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support

increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common

sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and

Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as

often as other brands.

Conclusions: The wearable landscape is in constant change. New devices and brands are released every year, promising improved

measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons.

Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently

used in research projects, and even less are thoroughly validated.

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 1http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

(J Med Internet Res 2018;20(3):e110) doi: 10.2196/jmir.9157

KEYWORDS

motor activity; physical activity; fitness trackers; heart rate; photoplethysmography

Introduction

Background

The World Health Organization recommends 150 min of

moderate intensity physical activity (PA) each week for adults

and 60 min for children and adolescents [1]. However, 25% of

adults and more than 80% of adolescents do not achieve the

recommended PA targets [1]. Results from the Tromsø Study,

the longest running population study in Norway, shows that

only 30.4% of women and 22.0% of men reach the

recommended target [2].

Low PA is currently the fourth leading risk factor for mortality

worldwide [3]. Even though there is limited evidence that using

wearable fitness trackers will improve health [4,5], these devices

are still popular, and new fitness devices appear on the consumer

market regularly. In 2016, vendors shipped 102 million devices

worldwide, compared with 82 million in 2015 [6]. Fifty-seven

percent of these devices were sold by the top five brands: Fitbit,

Xiaomi, Apple, Garmin, and Samsung. The first quarter of 2017

shows an increase of 18% in devices sold, compared with the

same period in 2016 [7]. With a large number of available

devices and brands, it is difficult to navigate through an

ever-growing list of brands and devices with different

capabilities, price, and quality.

Available sensors and internal interpreting algorithms determine

device output. Sensor data are, in most devices, reduced to a

limited set of metrics before being transferred to the user’s

mobile phone. In addition, limited space affects how long the

device can collect data before such a transfer is needed. Data

are stored locally, and in many cases, uploaded to brand specific

or open cloud–based health repositories. Accessing these data

by third-party apps and comparing them is not always possible.

These interoperability challenges were recently identified in a

study by Arriba-Pérez et al [8]. They suggested ways to handle

these issues, but they did not make any brand or device

recommendations. Several studies have compared

activity-tracking wearables. As an example, Kaewkannate and

Kim [9] did a comparison of four popular fitness trackers in

2016. They compared devices objectively and subjectively.

Data were thoroughly collected, but because of the rapid release

of new devices, these four devices will be among the most

popular only for a relatively short time. A comparison of brands

is also of interest because brands from larger companies are,

compared with small start-ups and crowd funded brands, likely

to survive longer. In addition, it is of interest to know which

brands support the various available programming options.

Sanders et al [10] did a literature review on articles using

wearables for health self-monitoring and sedentary behavior

and PA detection. They reviewed various aspects of these

devices, but they gave no details about device sensor support

and suitability in research.

The objective of this study was to examine how the consumer

market for wearables has evolved, and analyze and summarize

available devices that can measure PA and heart rate (HR).

Moreover, we aim to identify brands that are used extensively

in research projects, and compare and consider their relevance

for future studies.

Sensors

A plethora of devices promises to measure PA in new and

improved ways. These devices use different sensors and

algorithms to calculate human readable metrics based on sensor

output. Traditional step counters use pedometers to detect daily

step counts. Although cheap and energy efficient, pedometers

are not as accurate as accelerometers, which is the current

standard for collecting PA data [11]. All modern fitness trackers

and smartwatches have an accelerometer. Compared with

research tools (eg, ActiGraph [12]), these devices are considered

less accurate for some measurements [13,14]. However, they

are generally less invasive, cheaper, have more functionality,

are more user-friendly, and are increasingly being used in

research. Most accelerometer-based fitness wearables measure

acceleration in three directions [15] and can be used to estimate

type of movement, count steps, calculate energy expenditure

(EE) and energy intensity, as well as estimate sleep patterns

and more. The validity and reliability of these metrics varies.

Evenson et al [14] did a review in 2015 and found high validity

for steps but low validity for EE and sleep. Furthermore, they

found reliability for steps, distance, EE, and sleep to be high

for some devices.

In addition, some wearables have gyroscopes, magnetometers,

barometers, and altimeters. A gyroscope can potentially increase

device accuracy by measuring gravitational acceleration, that

is, orientation and angular velocity, and better estimate which

activity type a person is performing [16]. A magnetometer is a

digital compass [15] and can improve motion tracking accuracy

by detecting the orientation of the device relative to magnetic

north. Magnetometers improve accuracy by compensating for

gyroscope drift, a problem with gyroscopes where the rotation

axis slowly drifts from the actual motion and must be restored

regularly. Accelerometers, gyroscopes, and magnetometers are

often combined into an inertial measurement unit (IMU). Most

mobile phones use IMUs to calculate orientation, and an

increasing number of fitness wearables include this unit to give

more accurate metrics. Barometers or altimeters detect changes

in altitude [15] and can be used to improve some metrics (eg,

EE), as well as report additional metrics (eg, climbed floors).

Photoplethysmography (PPG) is a relatively new technique in

wearables. PPG is an optical technique to estimate HR by

monitoring changes in blood volume beneath the skin [17]. A

light-emitting diode projects light onto the skin, which is

affected by the HR and reflected back to the sensor. However,

movement, ambient light, and tissue compression affect the

light, resulting in signal noise, and cleaning algorithms often

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 2http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

use accelerometer data to assist HR estimation [18]. There is

some evidence that gyroscopes could be used [19] to reduce

PPG signal noise, so we are likely to see more devices in the

future equipped with PPG sensors. To further enrich the PA

data collection, some devices have a built in global positioning

system (GPS) receiver. This is especially true for high-end

fitness trackers and sports watches specifically targeting

physically active people. With a GPS, it is possible to track

more data, including position, speed, and altitude.

Algorithms and Mobile Apps

Raw data from sensors must be converted into readable metrics

to be meaningful for the user. Many devices only display a

limited set of metrics directly on the device (eg, today’s step

count or current HR) and rely on an accompanying mobile app

to show the full range of available metrics (eg, historic daily

step count and detailed HR data). Although the physical sensors

in these devices are very similar, the algorithms that interpret

sensor output are unique for most vendors. These algorithms

are often company secrets, and they can be changed without

notice. In addition, the quality and supported features of the

accompanying mobile apps varies, and the total user experience

will therefore differ. Each additional sensor included in a device

can be used to add additional types of metrics for the user or

supply internal algorithms with additional data to improve

accuracy of already available metric types. However, additional

sensors affect price and power consumption.

Device Types

There are many similarities between different types of devices,

and they may be difficult to categorize. We will use the term

wearable in this paper as a common term for wrist-worn devices

that can track and share PA data with a mobile phone.

A smartwatch is a wrist-worn device that, mostly, acts as an

extension to a mobile phone and can show notifications and

track PA and related metrics. Modern smartwatches often

include a touch screen and can support advanced features and

display high resolution activity trends [15]. Fitness trackers (ie,

smart band or fitness band), normally worn on the wrist or hip,

are devices more dedicated to PA tracking. A fitness tracker is

typically cheaper than a smartwatch because of less expensive

hardware and often fewer sensors. Due to this, it generally also

has better battery life and a limited interface for displaying

tracking results [15].

Other terms are also used, for example, sports watch and GPS

watch, which can be considered merges between smartwatches

and fitness trackers. In addition, there are hybrid watches (ie,

hybrid smartwatches) that have a traditional clockwork and

analogue display that have been fitted with an accelerometer.

An accompanying mobile app is needed to access most data,

but daily step counts are often represented as an analogue gauge

on the watch face.

Wearable Usage Scenario

Wearables come forward as a new alternative to tracking PA

in research (compared with, eg, ActiGraph), especially when it

is desired to collect measurements for a prolonged period of

time. In an intervention study, continuous data collecting from

wearables would allow researchers to better track changes in

PA and adjust the intervention accordingly. Wearables can also

be used in epidemiological research as a tool for tracking PA

for an extended period. This could reveal detailed PA changes

in a population over time. In both scenarios, there are several

potential important requirements to consider when choosing a

device for the study, including usability, battery life, price,

accuracy, durability, look and feel, and data access possibilities.

Methods

Search Strategies

Brands, Devices, and Sensors

We searched six databases to create a list of relevant wearable

devices: The Queen’s University’s Wearable Device Inventory

[20], The Vandrico Wearables database [21], GsmArena [22],

Wearables.com [23], SpecBucket [24], and PrisGuide [25,26].

We only used publicly available information when comparing

devices. We did the search from May 15, 2017 to July 1, 2017.

We identified wearables in two steps. In step one, we identified

and searched the six defined databases. In step two, we extracted

all brands from the list of devices identified in step one and

examined brand websites for additional devices. If we found

the same device in several databases with conflicting

information, we manually identified the correct information

from the device’s official website or other online sources (eg,

Wikipedia and Google search). We removed duplicates and

devices not fitting the inclusion criteria.

Brand Usage in Research

We searched Ovid MEDLINE on September 30, 2017 to

determine how often the most relevant brands were used in

previous studies. For each search, we performed a keyword

search with no limitations set. We divided our findings into

validation and reliability studies and data collection studies.

To decide which brand to consider most relevant, we did two

sets of searches. In the first set, we created a brand-specific

keyword search for brands that were (1) One of the five most

sold brands in 2015 or 2016 or (2) Had released 10 or more

unique devices. From the resulting list of articles, we screened

title, abstract, and the method section. This screening was done

to (1) Exclude articles out of scope and (2) To identify additional

brands used in these studies. We compiled a list of these brands

and performed a second set of searches, one for each new

identified brand. Eleven brands were finally included. The

specific keyword search used for each brand is given in the

Results section where we summarize our findings.

We also searched the US National Library of Medicine database

of clinical studies through the ClinicalTrials website, using the

same 11 keyword searches, to determine brand usage in ongoing

projects. One author did the articles screening, as well as the

projects description screening in ClinicalTrials.

Brand Developer Possibilities

To determine how relevant a specific brand is when planning

a new research project, we reviewed the 11 identified brands

and considered available developer options, supported mobile

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 3http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

phone environments, and options for health data storage. We

especially reviewed availability of an application programming

interface (API) and a software development kit (SDK).

Information was collected from Google Play, Apple’s App

Store, and official brand websites. Information retrieval was

done in September 2017.

Inclusion and Exclusion Criteria

Brands, Devices, and Sensors

The study is limited to wrist-worn consumer devices that utilize

accelerometers to measure PA. Devices capable of collecting

HR from the wrist using an optical sensor were tagged as PPG

devices. Devices were tagged as GPS devices only if they had

a built-in GPS tracker. We only included devices meant for

personal use, designed to be worn continuously (24/7), and were

capable of sharing data with mobile phones through Bluetooth.

The wrist-worn limitation was added because hip-worn devices

are not normally worn during the night (ie, not 24/7). Only

devices released before July 1, 2017 were included. We excluded

hybrid watches because most hybrid vendors make a large

number of watch variations, with what seems to be the same

hardware. In addition, these watches are mostly available

through high-end suppliers of traditional watches, at a price

point that would prevent researchers from considering their use

in a large study.

Brand Usage in Research

Due to the large number of available brands, we limited our

search to include only the 11 brands already identified as

relevant. We excluded brands that are no longer available (ie,

company shut down). Review studies were also excluded.

Brand Developer Possibilities

When reviewing brand relevance in research, we only reviewed

developer capabilities for the 11 brands we had already included

in the list of relevant brands. We set the additional limitation

that the brand was used in at least one article in Ovid

MEDLINE.

Device Categorization, Data Collection, and Reporting

Categories

When collecting information about wearables, we categorized

them into three groups:

Smartwatches: a device was tagged as a smartwatch if

•

It supported mobile phone notifications, and the vendor

described it as a smart watch, or if

•

It had a touch screen and was not explicitly described

as a fitness tracker by the vendor.

Fitness trackers: we classified a device as a fitness tracker

•

Its main purpose was to track PA, or if

•

The vendor called it a fitness tracker, or if

•

The device did not support notifications from the

connected mobile phone (eg, incoming calls or texts).

Hybrid watches: to be considered a hybrid watch, the device

had to have an analogue clockwork with a built-in digital

accelerometer.

We collected the following variables for each device: brand

name, device name, year of release, country of origin, device

type (eg, fitness tracker), and whether they had a built-in

accelerometer, gyroscope, magnetometer, barometer or altimeter,

GPS, and PPG.

We looked at three aspects of the devices we identified and

reported under three categories:

Metrics and trends: in this category, we described the status

for available brands, devices, and sensors, as well as

reviewed trends in sensor availability over time.

Brand usage in research: in this category, we searched Ovid

MEDLINE and ClinicalTrials and determined which brands

are most used in a research setting.

Brand developer possibilities: in this category, we reviewed

software integration platforms and mobile platform support

for the most relevant brands.

Results

Relevant Devices

An overview of the device search process is given in Figure 1.

We found 572 devices by searching online and offline databases

and 131 additional devices by visiting the official websites for

each identified brand, totaling 703 devices. Removing duplicates

left 567 unique devices. These were screened for variation, that

is, the same device with different design. After excluding 41

because of variation, 526 remained and were screened for

eligibility. We removed 103 devices for not fitting the inclusion

criteria. The remaining 423 devices were included in the study.

Brands, Devices, and Sensors

Brands

We identified 423 unique wearables, distributed between 132

different brands. Almost half the brands (47.0%, 62/132) had

only one device. Moreover, 75.0% (99/132) of brands had three

or fewer devices, and 83.3% (110/132) had five or fewer

devices. Brands originated from 23 different countries, but the

United States (43.2%, 57/132) and China (16.7%, 22/132,

mainland China; 19.0%, 25/132, including Taiwan) represented

the largest number of brand origin. Each remaining country

represented between 0.8% (1/132) and 5.3% (7/132) of brands.

As the market has grown and wearable technology has become

increasingly popular, a number of new brands have appeared

on the market. In 2011, there were only three brands available.

There was a small increase in brand count in 2012 and 2013,

but in 2014, we saw the largest increase with 41 new brands.

The number of new brands started to decrease in 2015, with 36

new brands in 2015 and 23 in 2016. Only three new brands have

been introduced in 2017, but this number only represents the

first 6 months of 2017. The final count for 2017 will likely be

higher. An overview of the number of new brands that appeared

on the market between 2011 and 2017 is given in Figure 2. Note

that some companies are no longer active and, for 17 devices,

we could not determine release year.

Most brands only had a small number of wearables, but some

produced a lot more. The brand with most unique wearables

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 4http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

was Garmin (United States) with 40 different devices. No.1

(China) introduced the second highest number of wearables

with 19 devices. An overview of the release year of the 22 (out

of 132) brands that have released more than five devices is given

in Table 1. Seven out of these 22 brands originated in the United

States, five (six including Taiwan) originated in China, and two

originated in South Korea. All other countries are represented

only once. Some of these brands are no longer active (eg, Pebble

and Jawbone).

Devices

Three devices were released in 2011 (earliest year), seven in

2012, 30 in 2013, and 87 in 2014. The year with the highest

number of new wearables was 2015, with 121 new devices. In

2016, 120 new devices were released; the first year with a

decreasing number of new wearables. The number of new and

accumulated devices from 2011 to 2017 is summarized in Table

2. The last column (unknown) represents devices where we

could not identify the release year. The above numbers represent

the total number of new devices. If grouped into fitness trackers

and smartwatches, there is a small overrepresentation among

new smartwatches. Up until 2014, about half of devices were

smartwatches. In 2015 and 2016, smartwatches represented

59.3% (143/241) of new devices, whereas fitness trackers

represented 40.6% (98/241).

Sensors

The number of sensors included in new devices have increased

in the last few years. Since 2015, the order of the most common

sensors has consistently been PPG, GPS, gyroscope,

magnetometer, and barometer or altimeter. In addition, these

sensors have had a steady increase in availability in the same

period. For 2017, 71% (27/38) of new devices included a PPG

sensor, 50% (19/38) included a GPS, 39% (15/38) included a

gyroscope, 34% (13/38) included a magnetometer, and 32%

(12/38) included a barometer or altimeter. Figure 3 gives an

overview of the number of devices each year that includes each

sensor, in percent of total number of released devices that year.

Devices with more than one sensor are represented once for

each sensor it includes.

In total, since 2011, 38.5% (163/423) of wearables have only

been equipped with one sensor (accelerometer). Moreover,

29.8% (126/423) of devices had two sensors, 12.1% (51/423)

had three sensors, 11.1% (47/423) had four sensors, and 6.4%

(27/423) had five sensors. Only 2.1% (9/423) of devices had

all six sensors. In Table 3, these numbers are broken down by

sensor combination and year. Some sensor combinations do not

exist and are excluded.

Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart.

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 5http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

Figure 2. Number of new and aggregated available brands by year.

Table 1. Device count per year for brands with six or more wearables.

Total

Unknown2017201620152014201320122011CountryBrand

4041311651United StatesGarmin

914211United StatesFitbit

821311United StatesMisfit

7151United StatesLifeTrak

6141United StatesiFit

63111United StatesJawbone

61311United StatesPebble

19595ChinaNo. 1

9252ChinaOmate

9252ChinaZeblaze

81331ChinaHuawei

711221ChinaOumax

8422TaiwanMobile Action

124161South KoreaSamsung

72113South KoreaLG

7511EnglandWorldSim

1122421FinlandPolar

624GermanyTechnaxx

743ItalyAwatch

752JapanEpson

7412NetherlandsTomTom

181764SwitzerlandMyKronoz

Total brand count for the United States=7, China and Taiwan=6, and South Korea=2. All other countries are represented only once.

Table 2. Number of new and accumulated devices by year.

Unknown2017201620152014201320122011Devices

1738120121873073New

42340636824812740103Accumulated

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 6http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

Figure 3. Percentage of devices released each year, supporting each sensor. GPS: global positioning system; PPG: photoplethysmography.

Brand Usage in Research

The top five vendors in 2015 [27] and 2016 [6], in sold units,

were Fitbit, Xiaomi, Apple, Garmin, and Samsung. Brands with

more than 10 unique wearables include Garmin, No.1,

MyKronoz, Samsung, and Polar. These eight, and additional

brands identified during the MEDLINE search and ClinicalTrials

search, were considered. We did not find any publications or

active clinical trials that used devices from No.1 or MyKronoz.

Devices from Basis, BodyMedia, Pebble, Jawbone, Microsoft,

and Nike were also used in some of the identified studies, but

these brands do no longer produce wearables within the scope

of this paper and were excluded from further analysis.

The MEDLINE search resulted in 81 included studies that we

divided into two groups: (1) validation and reliability studies

and (2) data collection studies. Studies where wearable output

was compared with existing research instruments known to give

accurate results (eg, ActiGraph) or with direct observation, as

well as studies where several wearables were compared with

each other for accuracy or reliability, were classified as

validation and reliability studies. Studies where wearables were

used as a tool for intervention or observation, to collect data on

PA, HR, EE, sleep, or other available metrics, were classified

as data collection studies. Out of these 81 studies, 61 were

classified as validation and reliability studies, whereas 20 were

classifies as data collection studies.

Fitbit devices were used in 54 studies [9,13,28-79]. Out of these,

40 studies were validation or reliability studies. In 22 of the

studies, one or more Garmin devices were used

[32,33,46,49,50,62,77-92]. Of these, 18 were validation or

reliability studies. Eight studies used Apple devices

[29,30,35,49,62,79,93,94]. Six of these were validation or

reliability studies. All studies using devices from Misfit, Polar,

Withings, Mio, Samsung, PulseOn, TomTom, and Xiaomi were

validation or reliability studies. Misfit devices were used in 12

studies [9,36,42,43,46,61-63,85,95-97]; Polar devices were used

in 6 studies [36,43,46,62,98,99]; Withings [63,85,89,100,101],

Mio [29,30,54,102,103], and Samsung [29,30,58,62,96] devices

were used in 5 studies; PulseOn devices were used in 4 studies

[29,104-106]; TomTom devices were used in 2 studies [54,79];

and Xiaomi devices were used in 1 study [96].

From ClinicalTrials, we found that the vast majority of ongoing

projects use, or are planning to use, Fitbit devices. All other

devices were mentioned in three or less projects, whereas Fitbit

devices were mentioned in 31 studies. A summary of these

studies and projects is given in Table 4. We further grouped the

validation and reliability studies into five categories. A total of

31 studies focused on step counts or distance, 15 studies

researched EE, 15 studies measured HR, 10 studies measured

sleep, and 7 studies collected other metrics. Multimedia

Appendix 1 gives an overview of articles found in MEDLINE,

which brands they included in the study, and which of the five

categories they are grouped into.

Brand Developer Possibilities

Next, we considered developer possibilities for the 11 brands

already identified as most relevant in research: Apple, Fitbit,

Garmin, Mio, Misfit, Polar, PulseOn, Samsung, TomTom,

Withings, and Xiaomi. All brands had an app in the Apple App

Store and could connect to the iPhone. Except for the Apple

Watch, all other brands had an app in Google Play and could

be used with Android phones.

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 7http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

Table 3. Number and percentage of devices supporting a specific group of sensors, by year.

2017201620152014201320122011Sensors

4 (11)37 (30.8)50 (41.3)40 (46)16 (53)5 (71)2 (67)Accelerometer (Acc), n (%)

Acc + 1 sensor, n (%)

10 (26)27 (22.5)11 (9.1)9 (10)1 (3)1 (14)

PPG

3 (2.5)15 (12.4)9 (10)2 (7)1 (33)

GPS

1 (3)4 (3.3)9 (7.4)3 (3)1 (3)Gyroscope (Gyro)

3 (2.5)1 (1)2 (7)1 (14)Magnetometer (Mag)

2 (5)1 (0.8)1 (1)Barometer (Bar)

Acc + 2 sensors, n (%)

3 (8)6 (5)7 (5.8)1 (3)GPS + PPG

1 (3)5 (4.2)5 (4.1)4 (5)Gyro + PPG

2 (1.7)2 (1.7)1 (1)Gyro + GPS

2 (1.7)1 (0.8)1 (3)Bar + PPG

1 (0.8)2 (2)Gyro + Mag

1 (0.8)1 (1)1 (3)Mag + GPS

1 (0.8)Mag + PPG

1 (1)Gyro + Bar

2 (2)Bar + GPS

Acc + 3 sensors, n (%)

1 (3)2 (1.7)3 (2.5)3 (3)1 (3)Gyro + Mag + GPS

1 (3)3 (2.5)2 (1.7)4 (5)Gyro + Mag + PPG

1 (3)4 (3.3)2 (2)3 (10)Mag + Bar + GPS

1 (3)6 (5)1 (1)Gyro + GPS + PPG

2 (5)2 (1.7)Bar + GPS + PPG

1 (3)1 (0.8)Mag + GPS + PPG

2 (1.7)Gyro + Bar + PPG

1 (0.8)Gyro + Mag + Bar

Acc + 4 sensors, n (%)

4 (3.3)3 (2.5)1 (3)Mag + Bar + GPS + PPG

3 (8)3 (2.5)1 (1)Gyro + Mag + GPS + PPG

1 (3)4 (3.3)2 (1.7)Gyro + Bar + GPS + PPG

2 (5)1 (0.8)Gyro + Mag + Bar + GPS

1 (0.8)1 (1)Gyro + Mag + Bar + PPG

Acc + 5 sensors, n (%)

4 (11)2 (1.7)2 (1.7)1 (1)All sensors

38120121873073Total, n

PPG: photoplethysmography.

GPS: global positioning system.

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 8http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

Table 4. Number of identified articles in Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials.

ClinicalTrialsMEDLINE

MEDLINE

search term

Brand

Data collection

studies

Validation or reliability

studies

Data collection studies

(total article count=20)

Validation or reliability

studies

(total article

count=61)

3011440Fitbit AND (Alta OR Blaze OR

Charge OR Flex OR Surge)

Fitbit

21418Garmin AND (Approach OR D2

OR Epix OR Fenix OR Forerunner

OR Quatix OR Swim OR Tactix

OR Vivo*)

Garmin

10012Misfit AND (Flare OR Flash OR

Link OR Ray OR Shine OR Va-

por)

Misfit

1126Apple watchApple

3106Polar AND (“Polar Loop” OR

M200 OR M4?0 OR M600 OR

V800 OR A3?0)

Polar

2005WithingsWithings

2105Mio Alpha OR Mio Fuse OR Mio

Slice

Mio

2005Samsung Gear NOT “Gear VR”

NOT Oculus

Samsung

1004PulseOnPulseOn

102TomTomTomTom

1001XiaomiXiaomi

MEDLINE: Medical Literature Analysis and Retrieval System Online.

Number of validation or reliability studies in MEDLINE.

Number of data collection studies in MEDLINE.

Number of validation or reliability studies in ClinicalTrials.

Number of data collection studies in ClinicalTrials.

Three brands supported Windows Phone: Fitbit, Garmin, and

Misfit. Apple Health and Google Fit are the two most common

open cloud health repositories. Mio, Misfit, Polar, Withings,

and Xiaomi, were the only brands that automatically

synchronized fitness data to both of these repositories through

these open APIs. The Apple Watch only synchronized

automatically to the Apple Health repository. Seven out of 11

brands had a private cloud repository with an accompanying

API, which allows third-party apps to access these data. Five

brands had an SDK, which makes it possible to create custom

programs to communicate with the device or create watch faces

that can run on the device.

The Apple Watch was the only device running on watchOS.

Three brands had at least one device running on Android Wear.

The remaining seven brands used a custom system. A summary

of all attributes for each brand is given in Table 5. Not all

devices for a specific brand support all features. In addition,

this is a snapshot of the status of these attributes, which are

likely to change over time as new devices and brands expand

their capabilities. The Apple Watch development environment

is called WatchKit SDK and can be used to write apps for the

Apple Watch [107]. Apple’s health storage solution is called

Apple Health. A variety of different data types can be stored

here and accessed by third-party developers through the

HealthKit API [108]. Access to any of these services requires

enrollment in the Apple Developer Program, which currently

costs US $99 per year.

Fitbit offers three major SDKs (Device API, Companion API,

and Settings API) for developing apps for Fitbit devices. In

addition, Fitbit offers the Web API that can be used to access

Fitbit cloud-stored fitness data. The Web API exposes six types

of data: PA, HR, location, nutrition, sleep, and weight [109].

Fitbit also has a solution for accessing high-resolution step and

HR data (ie, intraday data), granted on a case by case basis.

There is no cost for developing with the Fitbit SDKs or API.

There are two generations of programmable Garmin wearables

[110]. The Connect IQ SDK can be used by both generations,

but devices using the newer Connect IQ 2 generation support

more features. Development with this SDK is free. Garmin also

offers a cloud-based Web API, Garmin Connect, which allows

third-party apps to access users’cloud-based fitness data. Access

to this API costs US $5000 (one-time license). In addition,

Garmin maintains a separate Health API intended to be used

by companies for wellness improvement of their employees.

This API is free but requires a manual approval from Garmin.

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 9http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

Table 5. Brand environment, integration, and development support.

XiaomiWithingsTomTomSamsungPulseOnPolarMisfitMioGarminFitbitAppleFeature

Supported platform

✓✓✓✓✓✓✓✓✓✓Android

✓✓✓✓✓✓✓✓✓✓✓iPhone

✓✓✓Windows phone

Integration

✓✓✓✓✓✓Automatic synchronization

to Apple Health

✓✓✓✓✓Automatic synchronization

to Google Fit

✓✓✓✓✓✓✓Private cloud storage

✓✓✓✓✓✓✓✓

Cloud storage API

✓✓✓✓✓

Developer SDK

Watch system

✓✓✓Android Wear

✓watchOS (Apple)

✓✓✓✓✓✓✓Custom

API: application programming interface.

SDK: software development kit.

The Misfit developer ecosystem consists of three SDKs (Sleep

SDK, Link SDK, and Device SDK) [111]. The Misfit Device

SDK is the major SDK for developing apps for and

communication with Misfit devices. This SDK is only available

on request. Misfit also offers the Misfit Scientific Library that

can be used to access Misfits proprietary sensor algorithms

directly. This library is also only available on request. In

addition, the Misfit Cloud API is used to access users’data from

the Misfit cloud server. All SDKs and the API are free.

Polar does not offer a separate SDK. Polar devices can integrate

with Google Fit and Apple Health and deposits collected data

there [112]. This data are accessed using Google Fit APIs and

Apple HealthKit APIs. In addition, data are uploaded to Polar’s

cloud storage, which is accessible by third-party developers

through the AccessLink API. Besides PA data (steps, EE, and

sleep), basic training data are also stored here. Access to

AccessLink is free.

Development for a Samsung smartwatch is done using the Tizen

SDK (Samsung smartwatch operating system is called Tizen).

The Samsung Health SDK platform consists of two parts: Data

SDK and Service SDK. Together these can be used to store and

access health data collected from internal and external sensors,

as well as third-party apps running on a Samsung watch or a

mobile phone. Development using any of these services is free

[113].

TomTom offers the Sports Cloud API for accessing data

collected from TomTom devices. The API provides four types

of data: PA (eg, exercises bouts), HR, tracking (eg, steps and

EE), and physiology (eg, weight). Access to the API is free

[114].

Nokia acquired Withings in 2016, and the original Withings

API is now available as the Nokia Health API. Besides PA and

sleep measurements, the API also gives access to intraday PA

data. Nokia must manually approve access to this high-resolution

activity API. The API is free [115].

Summarizing Results

Which features are most important when considering devices

for a research project will depend on the purpose and design of

the study. It is therefore not possible to identify one brand as

the best brand in all circumstances. However, we have tried to

quantify various aspects of a brand to identify and summarize

their benefits.

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 10http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

Table 6. Brand summary.

No. 1MyKronozXiaomiTomTomPulseOnMioWithingsSamsungPolarAppleMisfitGarminFitbitBrand

191837132121138409

Devices

12455568122254

MEDLINE

12455566121840Validation or

reliability

1422161021Steps

221343410Energy ex-

penditure

1245214147Heart rate

21418Sleep

1243Other

111322421331

ClinicalTrials

✓✓✓✓✓✓

SDK

✓✓✓✓✓✓✓✓

API

✓✓✓✓✓✓

Apple Health

✓✓✓✓✓

Google Fit

Number of unique devices.

MEDLINE: Medical Literature Analysis and Retrieval System Online. Number of articles in MEDLINE.

Number of validation or reliability studies in MEDLINE, grouped by metric (step, EE, HR, sleep, and others).

Number of active projects in ClinicalTrials.

Supports an SDK for third-party software implementation.

API: application programming interface. Supports an API for developer access to data cloud.

Supports automatic synchronization to Apple Health data cloud.

Supports automatic synchronization to Google Fit data cloud.

We used eight categories in this custom comparison, which we

suggest to consider before deciding on a brand for any research

project:

Device count: a higher number of available devices make

it possible to pick a device that is more tailored to the study.

Article count: a higher number of articles in Ovid

MEDLINE indicate usage in previous studies.

Validation or reliability count: a high number of validation

or reliability studies provides knowledge about device and

brand accuracy.

ClinicalTrials count: a high number of active projects in

ClinicalTrials indicate brand relevance.

SDK support: brands that allows third-party programs to

run on their devices or communicate directly with the

device, by offering an SDK, adds more possibilities for

customization.

API support: brands that allows third-party programs to

access the data cloud repository, by offering API access,

adds more possibilities for health data collection and

retrieval.

Apple Health: brands supporting automatic synchronization

to Apple Health allow usage of Apple HealthKit API.

Google Fit: brands supporting automatic synchronization

to Google Fit allow usage of Google Fit API.

A consensus between authors was reached to include these

specific categories because we think together they indicate how

often a specific brand has been used in the past and will be used

in the future, and they show which options are available for data

extraction. These are not the only possible categories, and each

category will not be equally important for all studies.

Table 6 gives a summary of these categories for each brand. A

transposed Excel (Microsoft) version for dynamic sorting is

given in Multimedia Appendix 2. We have divided MEDLINE

validation and reliability studies into subgroups, making it easier

to compare brands for specific study purposes.

Discussion

Availability and Trends

The number of new brands increased every year from 2011 to

2014, but from 2015 to 2016, we saw a decrease in the number

of new brands. The number of new devices also increased from

2011 to 2015, with a slight reduction in 2016. Many new and

existing companies have tried to enter the wearable market

during these years. Some have become popular, whereas others

are no longer available. The number of new devices in the first

two quarters of 2017 seems low, and there is a small indication

that the number of new brands and devices released each year

is declining. During the data collection phase, we also identified

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 11http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

a large number of hybrid watches. Although we did not report

on these, this relatively new branch of wearables has grown in

popularity. The Fossil group, representing 19 brands, recently

announced they would launch more than 300 hybrid watches

and smartwatches in 2017 [116]. Most of these will be hybrids,

and 2017 may see the highest number of new hybrids released

to date.

We only found nine devices that support all five sensors

considered in this study. Among the 11 most relevant brands,

only Fitbit Surge, Garmin Forerunner 935, Garmin Quatix 5,

Samsung Gear S, and TomTom Adventure fall in this category.

Most devices (68%) support only one sensor, in addition to the

accelerometer. These numbers indicate that sensor count is not

the main argument when choosing a device for personal use. In

addition to the accelerometer, the most common sensors are

PPG and GPS, regardless of sensor count. One reason for this

may be that the added benefit of having these sensors, in a

fitness setting, is very clear. Accelerometers can be used for

step counting, PA intensity, exercise detection, and other

well-understood metrics, whereas the added benefit of a

gyroscope may be less intuitive. The added convenience of

using a PPG compared with a pulse chest strap, or no HR

detection at all, is also easy to understand. Adding a GPS also

adds some easy-to-understand benefits, where tracking progress

on a map and the possibility to detect speed is the most obvious.

Magnetometers and barometers or altimeters may not be sensors

that most people consider relevant for PA, although they can

be used to enhance accuracy of EE and other metrics.

Brand Usage in Research

In the MEDLINE literature search, we found 81 studies that

used one or more of the 11 brands we identified as most relevant

in research. Out of these, 61 were validation or reliability

studies. The remaining 20 studies used wearable devices as data

collection instruments to measure PA, HR, EE, sleep, or other

metrics. Fitbit was used in twice as many validation or reliability

studies as any other brand. This has likely contributed to the

high number of studies where Fitbit was used as the only

instrument for health data collection. The same trend will likely

continue in future publications because numbers from

ClinicalTrials for active projects shows an overrepresentation

of Fitbit-enabled projects. Of the brands currently available, the

five most often used in research projects are Fitbit, Garmin,

Misfit, Apple, and Polar. In addition, these brands have all

existed for several years and have either released a large number

of unique devices or shipped a large number of total devices.

As such, they are likely to stay on the market for the near future.

A high article count, high number of validation or reliability

studies, or high number of studies in ClinicalTrials for a specific

brand does not automatically imply validity or reliability. It

does, however, show researcher interest in these brands.

Implication for Practice

Table 6 is a good starting point when considering brands for a

new research project. Article count, validation or reliability

study count, and ClinicalTrials count together indicate brand

dependability. Larger numbers indicate how relevant, usable,

and valid previous researchers have found each brand to be. In

projects where it is relevant, SDK support allows programmatic

interaction directly with the device. API support allows storage

in, and access to, a brand-specific cloud-based health data

repository. Apple Health and Google Fit support are alternative

solutions for storing and accessing health data in an open cloud

repository. For projects that require multiple brand support,

using open solutions reduces the need to implement specific

software for each brand. SDK, API, Apple Health, and Google

Fit must be supported on both the brand and device level,

however.

A high brand device count makes it easier to find a device that

best supports the study needs. In addition to available sensors

(ie, metrics), validation, and previous usage in research, several

other potential relevant criteria exist, including price,

availability, phone environment support, affiliated app features,

look and feel, battery life, build quality or robustness, water

resistance, connectivity, and usability.

Figure 4. Criteria to consider when choosing brand or device. API: application programming interface; SDK: software development kit.

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 12http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

Furthermore, projects that need programmatic access to the

wearable or stored health data should especially consider SDK

or API features and ease of use, as well as privacy and security.

Figure 4 gives a summary of criteria to consider when selecting

brand and device.

Limitations

We visited all the brands’ websites to find additional devices,

but several sites did not contain any information about

discontinued devices. The release year of a device was rarely

available on device webpages, and we had to search for reviews

and other sources to find this information. The level of detail

in device hardware specifications varied. Some vendors did not

specify which sensor they included in their devices and only

mentioned which features the device had. In some cases, the

sensor could be derived from this information, but in other cases,

we had to find this information elsewhere. Wikipedia was also

used to collect sensor support and release year for some devices.

This open editable encyclopedia is not necessarily always

updated with correct information. For these reasons, there may

be some inaccuracies in reported sensor support and release

year. We did not collect information about device

discontinuation. Reported numbers for total available devices

does, therefore, not reflect the numbers of devices that currently

can be store bought but rather the number of unique devices

that have existed at some point.

Conclusions

In the last few years, we have seen a large increase in available

brands and wearable devices, and more devices are released

with additional sensors. However, for activity tracking, some

sensors are more relevant than others are. In this study, we have

focused on sensor support, health data cloud integration, and

developer possibilities; because we find these to be most relevant

for collection of PA data in research. However, deciding which

wearable to use will depend on several additional factors.

The wearable landscape is constantly changing as new devices

are released and as new vendors enter or leave the market, or

are acquired by larger vendors. What currently are considered

relevant devices and brands will therefore change over time,

and each research project should carefully consider which brand

and device to use. As a tool for future research, we have defined

a checklist of elements to consider when making this decision.

Acknowledgments

The authors would like to thank Vandrico Solutions Inc for giving them API access to their database. They would also like to

thank Steven Richardson and Debra Mackinnon at Queen’s University for giving them an excerpt from their offline wearable

database. The publication charges for this study have been funded by a grant from the publication fund of the University of

Tromsø – The Arctic University of Norway.

Conflicts of Interest

None declared.

Multimedia Appendix 1

List of MEDLINE articles included in the results for "Brand usage in research".

[XLSX File (Microsoft Excel File), 24KB-Multimedia Appendix 1]

Multimedia Appendix 2

Summary of the most important categories to consider when selecting a wearable brand for research.

[XLSX File (Microsoft Excel File), 13KB-Multimedia Appendix 2]

References

1. World Health Organization. Physical activity URL: http://www.who.int/mediacentre/factsheets/fs385/en [accessed

2017-09-11] [WebCite Cache ID 6tOYSnXaX]

2. Emaus A, Degerstrøm J, Wilsgaard T, Hansen BH, Dieli-Conwright CM, Furberg AS, et al. Does a variation in self-reported

physical activity reflect variation in objectively measured physical activity, resting heart rate, and physical fitness? Results

from the Tromso study. Scand J Public Health 2010 Nov;38(5 Suppl):105-118. [doi: 10.1177/1403494810378919] [Medline:

21062845]

3. World Health Organization. 2017. Global Strategy on Diet, Physical Activity and Health URL: http://www.who.int/

dietphysicalactivity/pa/en [accessed 2017-09-11] [WebCite Cache ID 6tOXoFxbF]

4. Finkelstein EA, Haaland BA, Bilger M, Sahasranaman A, Sloan RA, Nang EE, et al. Effectiveness of activity trackers with

and without incentives to increase physical activity (TRIPPA): a randomised controlled trial. Lancet Diabetes Endocrinol

2016 Dec;4(12):983-995. [doi: 10.1016/S2213-8587(16)30284-4] [Medline: 27717766]

5. Jakicic JM, Davis KK, Rogers RJ, King WC, Marcus MD, Helsel D, et al. Effect of wearable technology combined with

a lifestyle intervention on long-term weight loss: the IDEA randomized clinical trial. J Am Med Assoc 2016 Sep

20;316(11):1161-1171. [doi: 10.1001/jama.2016.12858] [Medline: 27654602]

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 13http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

6. IDC. Wearables Aren't Dead, They're Just Shifting Focus as the Market Grows 16.9% in the Fourth Quarter, According to

IDC URL: https://www.idc.com/getdoc.jsp?containerId=prUS42342317 [accessed 2018-03-01] [WebCite Cache ID

6xZyWGaxT]

7. IDC. Xiaomi and Apple Tie for the Top Position as the Wearables Market Swells 17.9% During the First Quarter URL:

https://www.idc.com/getdoc.jsp?containerId=prUS42707517 [accessed 2018-03-01] [WebCite Cache ID 6xZypbatQ]

8. de Arriba-Perez F, Caeiro-Rodríguez M, Santos-Gago JM. Collection and processing of data from wrist wearable devices

in heterogeneous and multiple-user scenarios. Sensors (Basel) 2016 Sep 21;16(9) [FREE Full text] [doi: 10.3390/s16091538]

[Medline: 27657081]

9. Kaewkannate K, Kim S. A comparison of wearable fitness devices. BMC Public Health 2016 May 24;16:433 [FREE Full

text] [doi: 10.1186/s12889-016-3059-0] [Medline: 27220855]

10. Sanders JP, Loveday A, Pearson N, Edwardson C, Yates T, Biddle SJ, et al. Devices for self-monitoring sedentary time or

physical activity: a scoping review. J Med Internet Res 2016 May 04;18(5):e90 [FREE Full text] [doi: 10.2196/jmir.5373]

[Medline: 27145905]

11. Corder K, Brage S, Ekelund U. Accelerometers and pedometers: methodology and clinical application. Curr Opin Clin

Nutr Metab Care 2007 Sep;10(5):597-603. [doi: 10.1097/MCO.0b013e328285d883] [Medline: 17693743]

12. ActiGraph Corp. ActiGraph URL: http://actigraphcorp.com/ [accessed 2017-09-11] [WebCite Cache ID 6tOYrA9Ke]

13. Reid RE, Insogna JA, Carver TE, Comptour AM, Bewski NA, Sciortino C, et al. Validity and reliability of Fitbit activity

monitors compared to ActiGraph GT3X+ with female adults in a free-living environment. J Sci Med Sport 2017

Jun;20(6):578-582. [doi: 10.1016/j.jsams.2016.10.015] [Medline: 27887786]

14. Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity

trackers. Int J Behav Nutr Phys Act 2015 Dec 18;12:159 [FREE Full text] [doi: 10.1186/s12966-015-0314-1] [Medline:

26684758]

15. Richardson S, Mackinnon D. Left to their own devices? Privacy implications of wearable technology in Canadian workplaces

URL: http://www.sscqueens.org/publications/left-to-their-own-devices [accessed 2018-03-01] [WebCite Cache ID

6xZzwLAN6]

16. Wagenaar RC, Sapir I, Zhang Y, Markovic S, Vaina LM, Little TD. Continuous monitoring of functional activities using

wearable, wireless gyroscope and accelerometer technology. Conf Proc IEEE Eng Med Biol Soc 2011;2011:4844-4847.

[doi: 10.1109/IEMBS.2011.6091200] [Medline: 22255423]

17. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 2007

Mar;28(3):R1-39. [doi: 10.1088/0967-3334/28/3/R01] [Medline: 17322588]

18. Kim SH, Ryoo DW, Bae C. Adaptive noise cancellation Using accelerometers for the PPG signal from forehead. Conf Proc

IEEE Eng Med Biol Soc 2007;2007:2564-2567. [doi: 10.1109/IEMBS.2007.4352852] [Medline: 18002518]

19. Casson AJ, Vazquez Galvez A, Jarchi D. Gyroscope vs. accelerometer measurements of motion from wrist PPG during

physical exercise. ICT Express 2016 Dec;2(4):175-179. [doi: 10.1016/j.icte.2016.11.003]

20. Richardson SM, Mackinnon D. Queen's University. 2017. Wearable Device Inventory, Queen's University.

21. Vandrico. 2017. The wearables database URL: http://vandrico.com/wearables/ [accessed 2017-07-08] [WebCite Cache ID

6roNvEjdE]

22. GSM Arena. 2017. URL: http://www.gsmarena.com/results.php3?sFormFactors=8 [accessed 2017-07-08] [WebCite Cache

ID 6roO1WCdv]

23. Wearables. 2017. Wearables.Com: Helping you make sense of wearable tech URL: http://www.wearables.com/devices/

[accessed 2017-07-08] [WebCite Cache ID 6roO94rOu]

24. Smartwatches. 2017. SpecBucket: Smart Watches URL: https://smartwatches.specbucket.com/ [accessed 2017-07-08]

[WebCite Cache ID 6roOBKrxr]

25. Prisguiden. 2017. Smartklokke URL: https://prisguiden.no/kategorier/smartklokke [accessed 2017-07-08] [WebCite Cache

ID 6roOHtXzf]

26. Prisguiden. 2017. Aktivitetsarmband URL: https://prisguiden.no/kategorier/aktivitetsarmband [accessed 2017-07-08]

[WebCite Cache ID 6roOJt6HJ]

27. Business Wire. 2016. The Worldwide Wearables Market Leaps 126.9% in the Fourth Quarter and 171.6% in 2015 URL:

https://www.businesswire.com/news/home/20160223005496/en/Worldwide-Wearables-Market-Leaps-126.9-Fourth-Quarter

[accessed 2008-03-01] [WebCite Cache ID 6xa0wRrvV]

28. Balto JM, Kinnett-Hopkins DL, Motl RW. Accuracy and precision of smartphone applications and commercially available

motion sensors in multiple sclerosis. Mult Scler J Exp Transl Clin 2016;2:2055217316634754 [FREE Full text] [doi:

10.1177/2055217316634754] [Medline: 28607720]

29. Shcherbina A, Mattsson CM, Waggott D, Salisbury H, Christle JW, Hastie T, et al. Accuracy in wrist-worn, sensor-based

measurements of heart rate and energy expenditure in a diverse cohort. J Pers Med 2017 May 24;7(2) [FREE Full text]

[doi: 10.3390/jpm7020003] [Medline: 28538708]

30. Wallen MP, Gomersall SR, Keating SE, Wisløff U, Coombes JS. Accuracy of heart rate watches: implications for weight

management. PLoS One 2016;11(5):e0154420 [FREE Full text] [doi: 10.1371/journal.pone.0154420] [Medline: 27232714]

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 14http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

31. Chow JJ, Thom JM, Wewege MA, Ward RE, Parmenter BJ. Accuracy of step count measured by physical activity monitors:

The effect of gait speed and anatomical placement site. Gait Posture 2017 Sep;57:199-203. [doi:

10.1016/j.gaitpost.2017.06.012] [Medline: 28666177]

32. Chen MD, Kuo CC, Pellegrini CA, Hsu MJ. Accuracy of wristband activity monitors during ambulation and activities.

Med Sci Sports Exerc 2016 Dec;48(10):1942-1949. [doi: 10.1249/MSS.0000000000000984] [Medline: 27183123]

33. Duncan M, Murawski B, Short CE, Rebar AL, Schoeppe S, Alley S, et al. Activity trackers implement different behavior

change techniques for activity, sleep, and sedentary behaviors. Interact J Med Res 2017 Aug 14;6(2):e13 [FREE Full text]

[doi: 10.2196/ijmr.6685] [Medline: 28807889]

34. Sprint G, Cook D, Weeks D, Dahmen J, La Fleur A. Analyzing sensor-based time series data to track changes in physical

activity during inpatient rehabilitation. Sensors (Basel) 2017 Sep 27;17(10) [FREE Full text] [doi: 10.3390/s17102219]

[Medline: 28953257]

35. Chowdhury EA, Western MJ, Nightingale TE, Peacock OJ, Thompson D. Assessment of laboratory and daily energy

expenditure estimates from consumer multi-sensor physical activity monitors. PLoS One 2017;12(2):e0171720 [FREE Full

text] [doi: 10.1371/journal.pone.0171720] [Medline: 28234979]

36. Mercer K, Li M, Giangregorio L, Burns C, Grindrod K. Behavior change techniques present in wearable activity trackers:

a critical analysis. JMIR Mhealth Uhealth 2016;4(2):e40 [FREE Full text] [doi: 10.2196/mhealth.4461] [Medline: 27122452]

37. Gaudet J, Gallant F, Bélanger M. A bit of fit: minimalist intervention in adolescents based on a physical activity tracker.

JMIR Mhealth Uhealth 2017 Jul 06;5(7):e92 [FREE Full text] [doi: 10.2196/mhealth.7647] [Medline: 28684384]

38. Jacobsen RM, Ginde S, Mussatto K, Neubauer J, Earing M, Danduran M. Can a home-based cardiac physical activity

program improve the physical function quality of life in children with fontan circulation? Congenit Heart Dis

2016;11(2):175-182. [doi: 10.1111/chd.12330] [Medline: 26879633]

39. Ridgers ND, Timperio A, Brown H, Ball K, Macfarlane S, Lai SK, et al. A cluster-randomised controlled trial to promote

physical activity in adolescents: the Raising Awareness of Physical Activity (RAW-PA) Study. BMC Public Health 2017

Jan 04;17(1):6 [FREE Full text] [doi: 10.1186/s12889-016-3945-5] [Medline: 28052773]

40. Li LC, Sayre EC, Xie H, Clayton C, Feehan LM. A community-based physical activity counselling program for people

with knee osteoarthritis: feasibility and preliminary efficacy of the track-OA study. JMIR Mhealth Uhealth 2017 Jun

26;5(6):e86 [FREE Full text] [doi: 10.2196/mhealth.7863] [Medline: 28652228]

41. Dondzila C, Garner D. Comparative accuracy of fitness tracking modalities in quantifying energy expenditure. J Med Eng

Technol 2016 Aug;40(6):325-329. [doi: 10.1080/03091902.2016.1197978] [Medline: 27280592]

42. Bellone GJ, Plano SA, Cardinali DP, Chada DP, Vigo DE, Golombek DA. Comparative analysis of actigraphy performance

in healthy young subjects. Sleep Sci 2016;9(4):272-279 [FREE Full text] [doi: 10.1016/j.slsci.2016.05.004] [Medline:

28154740]

43. Bai Y, Welk GJ, Nam YH, Lee JA, Lee JM, Kim Y, et al. Comparison of consumer and research monitors under

semistructured settings. Med Sci Sports Exerc 2016;48(1):151-158. [doi: 10.1249/MSS.0000000000000727] [Medline:

26154336]

44. Lee HA, Lee HJ, Moon JH, Lee T, Kim MG, In H, et al. Comparison of wearable activity tracker with actigraphy for sleep

evaluation and circadian rest-activity rhythm measurement in healthy young adults. Psychiatry Investig 2017

Mar;14(2):179-185 [FREE Full text] [doi: 10.4306/pi.2017.14.2.179] [Medline: 28326116]

45. Chu AH, Ng SH, Paknezhad M, Gauterin A, Koh D, Brown MS, et al. Comparison of wrist-worn Fitbit Flex and waist-worn

ActiGraph for measuring steps in free-living adults. PLoS One 2017;12(2):e0172535 [FREE Full text] [doi:

10.1371/journal.pone.0172535] [Medline: 28234953]

46. Brooke SM, An HS, Kang SK, Noble JM, Berg KE, Lee JM. Concurrent validity of wearable activity trackers under

free-living conditions. J Strength Cond Res 2017 Apr;31(4):1097-1106. [doi: 10.1519/JSC.0000000000001571] [Medline:

27465631]

47. Block VJ, Lizée A, Crabtree-Hartman E, Bevan CJ, Graves JS, Bove R, et al. Continuous daily assessment of multiple

sclerosis disability using remote step count monitoring. J Neurol 2017 Feb;264(2):316-326. [doi: 10.1007/s00415-016-8334-6]

[Medline: 27896433]

48. Morhardt DR, Luckenbaugh A, Goldstein C, Faerber GJ. Determining resident sleep during and after call with commercial

sleep monitoring devices. Urology 2017 Aug;106:39-44. [doi: 10.1016/j.urology.2017.03.059] [Medline: 28502597]

49. Dooley EE, Golaszewski NM, Bartholomew JB. Estimating accuracy at exercise intensities: a comparative study of

self-monitoring heart rate and physical activity wearable devices. JMIR Mhealth Uhealth 2017 Mar 16;5(3):e34 [FREE

Full text] [doi: 10.2196/mhealth.7043] [Medline: 28302596]

50. Leth S, Hansen J, Nielsen OW, Dinesen B. Evaluation of commercial self-monitoring devices for clinical purposes: results

from the Future Patient Trial, Phase I. Sensors (Basel, Switzerland) 2017;17(1):1-11.

51. Bian J, Guo Y, Xie M, Parish AE, Wardlaw I, Brown R, et al. Exploring the association between self-reported asthma

impact and Fitbit-derived sleep quality and physical activity measures in adolescents. JMIR Mhealth Uhealth 2017 Jul

25;5(7):e105 [FREE Full text] [doi: 10.2196/mhealth.7346] [Medline: 28743679]

52. Coughlin SS, Hatzigeorgiou C, Anglin J, Xie D, Besenyi GM, De Leo G, et al. Healthy lifestyle intervention for adult clinic

patients with type 2 diabetes mellitus. Diabetes Manag (Lond) 2017;7(2):197-204 [FREE Full text] [Medline: 28794802]

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 15http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

53. Åkerberg A, Koshmak G, Johansson A, Lindén M. Heart rate measurement as a tool to quantify sedentary behavior. Stud

Health Technol Inform 2015;211:105-110. [Medline: 25980855]

54. Stahl SE, An HS, Dinkel DM, Noble JM, Lee JM. How accurate are the wrist-based heart rate monitors during walking

and running activities? Are they accurate enough? BMJ Open Sport Exerc Med 2016;2(1):1-7 [FREE Full text] [doi:

10.1136/bmjsem-2015-000106]

55. Alinia P, Cain C, Fallahzadeh R, Shahrokni A, Cook D, Ghasemzadeh H. How accurate is your activity tracker? A

comparative study of step counts in low-intensity physical activities. JMIR Mhealth Uhealth 2017 Aug 11;5(8):e106 [FREE

Full text] [doi: 10.2196/mhealth.6321] [Medline: 28801304]

56. Winslow BD, Nguyen N, Venta KE. Improved mental acuity forecasting with an individualized quantitative sleep model.

Front Neurol 2017;8:160 [FREE Full text] [doi: 10.3389/fneur.2017.00160] [Medline: 28487671]

57. Spiotta AM, Fargen KM, Denham SL, Fulton ME, Kellogg R, Young E, et al. Incorporation of a physical education and

nutrition program into neurosurgery: a proof of concept pilot program. Neurosurgery 2016 Oct;79(4):613-619. [doi:

10.1227/NEU.0000000000001358] [Medline: 27465847]

58. Modave F, Guo Y, Bian J, Gurka MJ, Parish A, Smith MD, et al. Mobile device accuracy for step counting across age

groups. JMIR Mhealth Uhealth 2017 Jun 28;5(6):e88 [FREE Full text] [doi: 10.2196/mhealth.7870] [Medline: 28659255]

59. Dominick GM, Winfree KN, Pohlig RT, Papas MA. Physical activity assessment between consumer- and research-grade

accelerometers: a comparative study in free-living conditions. JMIR Mhealth Uhealth 2016 Sep 19;4(3):e110 [FREE Full

text] [doi: 10.2196/mhealth.6281] [Medline: 27644334]

60. Schoenfelder E, Moreno M, Wilner M, Whitlock KB, Mendoza JA. Piloting a mobile health intervention to increase physical

activity for adolescents with ADHD. Prev Med Rep 2017 Jun;6:210-213 [FREE Full text] [doi: 10.1016/j.pmedr.2017.03.003]

[Medline: 28373931]

61. Kooiman TJ, Dontje ML, Sprenger SR, Krijnen WP, van der Schans CP, de Groot M. Reliability and validity of ten consumer

activity trackers. BMC Sports Sci Med Rehabil 2015;7:24 [FREE Full text] [doi: 10.1186/s13102-015-0018-5] [Medline:

26464801]

62. Fokkema T, Kooiman TJ, Krijnen WP, van der Schans CP, de Groot M. Reliability and validity of ten consumer activity

trackers depend on walking speed. Med Sci Sports Exerc 2017 Apr;49(4):793-800. [doi: 10.1249/MSS.0000000000001146]

[Medline: 28319983]

63. Mantua J, Gravel N, Spencer RM. Reliability of sleep measures from four personal health monitoring devices compared

to research-based actigraphy and polysomnography. Sensors (Basel) 2016 Dec 05;16(5) [FREE Full text] [doi:

10.3390/s16050646] [Medline: 27164110]

64. Chen JL, Guedes CM, Cooper BA, Lung AE. Short-term efficacy of an innovative mobile phone technology-based

intervention for weight management for overweight and obese adolescents: pilot study. Interact J Med Res 2017 Aug

02;6(2):e12 [FREE Full text] [doi: 10.2196/ijmr.7860] [Medline: 28768612]

65. Reichardt LA, Aarden JJ, van Seben R, van der Schaaf M, Engelbert RH, Bosch JA, Hospital-ADL study group. Unravelling

the potential mechanisms behind hospitalization-associated disability in older patients; the Hospital-Associated Disability

and impact on daily Life (Hospital-ADL) cohort study protocol. BMC Geriatr 2016 Mar 05;16:59 [FREE Full text] [doi:

10.1186/s12877-016-0232-3] [Medline: 26945587]

66. Laranjo L, Lau AY, Martin P, Tong HL, Coiera E. Use of a mobile social networking intervention for weight management:

a mixed-methods study protocol. Br Med J Open 2017 Jul 12;7(7):e016665 [FREE Full text] [doi:

10.1136/bmjopen-2017-016665] [Medline: 28706104]

67. Pumper MA, Mendoza JA, Arseniev-Koehler A, Holm M, Waite A, Moreno MA. Using a Facebook group as an adjunct

to a pilot mHealth physical activity intervention: a mixed methods approach. Stud Health Technol Inform 2015;219:97-101.

[Medline: 26799887]

68. Cook JD, Prairie ML, Plante DT. Utility of the Fitbit Flex to evaluate sleep in major depressive disorder: a comparison

against polysomnography and wrist-worn actigraphy. J Affect Disord 2017 Aug 01;217:299-305. [doi:

10.1016/j.jad.2017.04.030] [Medline: 28448949]

69. Jo E, Lewis K, Directo D, Kim MJ, Dolezal BA. Validation of biofeedback wearables for photoplethysmographic heart

rate tracking. J Sports Sci Med 2016 Sep;15(3):540-547 [FREE Full text] [Medline: 27803634]

70. Floegel TA, Florez-Pregonero A, Hekler EB, Buman MP. Validation of consumer-based hip and wrist activity monitors in

older adults with varied ambulatory abilities. J Gerontol A Biol Sci Med Sci 2016 Jun 02;72(2):229-236. [doi:

10.1093/gerona/glw098] [Medline: 27257217]

71. Alharbi M, Bauman A, Neubeck L, Gallagher R. Validation of Fitbit-Flex as a measure of free-living physical activity in

a community-based phase III cardiac rehabilitation population. Eur J Prev Cardiol 2016 Feb 23;23(14):1476-1485. [doi:

10.1177/2047487316634883] [Medline: 26907794]

72. Diaz KM, Krupka DJ, Chang MJ, Shaffer JA, Ma Y, Goldsmith J, et al. Validation of the Fitbit One® for physical activity

measurement at an upper torso attachment site. BMC Res Notes 2016 Apr 12;9:213 [FREE Full text] [doi:

10.1186/s13104-016-2020-8] [Medline: 27068022]

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 16http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

73. Sushames A, Edwards A, Thompson F, McDermott R, Gebel K. Validity and reliability of Fitbit Flex for step count,

moderate to vigorous physical activity and activity energy expenditure. PLoS One 2016;11(9):e0161224 [FREE Full text]

[doi: 10.1371/journal.pone.0161224] [Medline: 27589592]

74. Kang SG, Kang JM, Ko KP, Park SC, Mariani S, Weng J. Validity of a commercial wearable sleep tracker in adult insomnia

disorder patients and good sleepers. J Psychosom Res 2017 Jun;97:38-44. [doi: 10.1016/j.jpsychores.2017.03.009] [Medline:

28606497]

75. Voss C, Gardner RF, Dean PH, Harris KC. Validity of commercial activity trackers in children with congenital heart disease.

Can J Cardiol 2017 Jun;33(6):799-805. [doi: 10.1016/j.cjca.2016.11.024] [Medline: 28347581]

76. Nelson MB, Kaminsky LA, Dickin DC, Montoye AH. Validity of consumer-based physical activity monitors for specific

activity types. Med Sci Sports Exerc 2016 Aug;48(8):1619-1628. [doi: 10.1249/MSS.0000000000000933] [Medline:

27015387]

77. Treacy D, Hassett L, Schurr K, Chagpar S, Paul S, Sherrington C. Validity of different activity monitors to count steps in

an inpatient rehabilitation setting. Phys Ther 2017;97(5):581-588.

78. Huang Y, Xu J, Yu B, Shull PB. Validity of FitBit, Jawbone UP, Nike+ and other wearable devices for level and stair

walking. Gait Posture 2016;48:36-41. [doi: 10.1016/j.gaitpost.2016.04.025]

79. Gillinov S, Etiwy M, Wang R, Blackburn G, Phelan D, Gillinov AM, et al. Variable accuracy of wearable heart rate monitors

during aerobic exercise. Med Sci Sports Exerc 2017 Aug;49(8):1697-1703. [doi: 10.1249/MSS.0000000000001284]

[Medline: 28709155]

80. Woodman JA, Crouter SE, Bassett Jr DR, Fitzhugh EC, Boyer WR. Accuracy of consumer monitors for estimating energy

expenditure and activity type. Med Sci Sports Exerc 2017;49(2):371-377. [Medline: 27580155]

81. Bronikowski M, Bronikowska M, Glapa A. Do they need goals or support? A report from a goal-setting intervention using

physical activity monitors in youth. Int J Environ Res and Public Health 2016;13(9):1-12. [Medline: 27649219]

82. Jones AP, Coombes EG, Griffin SJ, van Sluijs EM. Environmental supportiveness for physical activity in English

schoolchildren: a study using Global Positioning Systems. Int J Behav Nutr Phys Act 2009;6:42. [Medline: 19615073]

83. Mooney R, Quinlan LR, Corley G, Godfrey A, Osborough C, Olaighin G. Evaluation of the Finis Swimsense® and the

Garmin SwimTM activity monitors for swimming performance and stroke kinematics analysis. PloS One

2017;12(2):e0170902. [Medline: 28178301]

84. An HS, Jones GC, Kang SK, Welk GJ, Lee JM. How valid are wearable physical activity trackers for measuring steps?

Eur J Sport Sci 2017 Apr;17(3):360-368. [doi: 10.1080/17461391.2016.1255261] [Medline: 27912681]

85. Ehrler F, Weber C, Lovis C. Influence of pedometer position on pedometer accuracy at various walking speeds: a comparative

study. J Med Internet Res 2016 Oct 06;18(10):e268 [FREE Full text] [doi: 10.2196/jmir.5916] [Medline: 27713114]

86. Ammann R, Taube W, Neuhaus M, Wyss T. The influence of the gait-related arm swing on elevation gain measured by

sport watches. J Hum Kinet 2016;51:53-60. [doi: 10.1515/hukin-2015-0170]

87. Eyre EL, Duncan MJ, Birch SL, Cox V, Blackett M. Physical activity patterns of ethnic children from low socio-economic

environments within the UK. J Sports Sci 2015;33(3):232-242. [Medline: 24998418]

88. Schaffer SD, Holzapfel SD, Fulk G, Bosch PR. Step count accuracy and reliability of two activity tracking devices in people

after stroke. Physiother Theory Pract 2017;33(10):788-796. [Medline: 28777710]

89. O'Connell S, ÓLaighin G, Kelly L, Murphy E, Beirne S, Burke N, et al. These shoes are made for walking: sensitivity

performance evaluation of commercial activity monitors under the expected conditions and circumstances required to

achieve the international daily step goal of 10,000 steps. PLoS One 2016;11(5):e0154956 [FREE Full text] [doi:

10.1371/journal.pone.0154956] [Medline: 27167121]

90. Jennings CA, Berry TR, Carson V, Culos-Reed SN, Duncan MJ, Loitz CC, et al. UWALK: the development of a

multi-strategy, community-wide physical activity program. Transl Behav Med 2017;7(1):16-27. [doi:

10.1007/s13142-016-0417-5]

91. Price K, Bird SR, Lythgo N, Raj IS, Wong JY, Lynch C. Validation of the Fitbit One, Garmin Vivofit and Jawbone UP

activity tracker in estimation of energy expenditure during treadmill walking and running. J Med Eng Technol

2017;41(3):208-215. [Medline: 27919170]

92. Claes J, Buys R, Avila A, Finlay D, Kennedy A, Guldenring D, et al. Validity of heart rate measurements by the Garmin

Forerunner 225 at different walking intensities. J Med Eng Technol 2017;41(6):480-485. [doi:

10.1080/03091902.2017.1333166]

93. O'Brien A, Schlosser RW, Shane HC, Abramson J, Allen AA, Flynn S, et al. Brief report: just-in-time visual supports to

children with autism via the Apple watch®: a pilot feasibility study. J Autism Dev Disord 2016 Dec;46(12):3818-3823.

[doi: 10.1007/s10803-016-2891-5] [Medline: 27573856]

94. Cook S, Stauffer JC, Goy JJ, Graf D, Puricel S, Frobert A, et al. Heart rate never lies: interventional cardiologist and Braude's

quote revised. Open Heart 2016;3(1):e000373 [FREE Full text] [doi: 10.1136/openhrt-2015-000373] [Medline: 26835145]

95. Mercer K, Giangregorio L, Schneider E, Chilana P, Li M, Grindrod K. Acceptance of commercially available wearable

activity trackers among adults aged over 50 and with chronic illness: a mixed-methods evaluation. JMIR mHealth uHealth

2016;4(1):e7 [FREE Full text] [doi: 10.2196/mhealth.4225] [Medline: 26818775]

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 17http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

96. El-Amrawy F, Nounou MI. Are currently available wearable devices for activity tracking and heart rate monitoring accurate,

precise, and medically beneficial? Healthc Inform Res 2015 Oct;21(4):315-320 [FREE Full text] [doi:

10.4258/hir.2015.21.4.315] [Medline: 26618039]

97. Ferguson T, Rowlands AV, Olds T, Maher C. The validity of consumer-level, activity monitors in healthy adults worn in

free-living conditions: a cross-sectional study. Int J Behav Nutr Phys Act 2015;12:42 [FREE Full text] [doi:

10.1186/s12966-015-0201-9] [Medline: 25890168]

98. Hernández-Vicente A, Santos-Lozano A, De Cocker K, Garatachea N. Validation study of Polar V800 accelerometer. Ann

Transl Med 2016 Aug;4(15):278 [FREE Full text] [doi: 10.21037/atm.2016.07.16] [Medline: 27570772]

99. Giles D, Draper N, Neil W. Validity of the Polar V800 heart rate monitor to measure RR intervals at rest. Eur J Appl Physiol

2016 Mar;116(3):563-571 [FREE Full text] [doi: 10.1007/s00421-015-3303-9] [Medline: 26708360]

100. Gruwez A, Libert W, Ameye L, Bruyneel M. Reliability of commercially available sleep and activity trackers with manual

switch-to-sleep mode activation in free-living healthy individuals. Int J Med Inform 2017 Jun;102:87-92. [doi:

10.1016/j.ijmedinf.2017.03.008] [Medline: 28495352]

101. O'Connell S, ÓLaighin G, Quinlan LR. When a step is not a step! Specificity analysis of five physical activity monitors.

PLoS One 2017;12(1):e0169616 [FREE Full text] [doi: 10.1371/journal.pone.0169616] [Medline: 28085918]

102. Parak J, Korhonen I. Evaluation of wearable consumer heart rate monitors based on photopletysmography. Conf Proc IEEE

Eng Med Biol Soc 2014;2014:3670-3673. [doi: 10.1109/EMBC.2014.6944419] [Medline: 25570787]

103. Spierer DK, Rosen Z, Litman LL, Fujii K. Validation of photoplethysmography as a method to detect heart rate during rest

and exercise. J Med Eng Technol 2015;39(5):264-271. [doi: 10.3109/03091902.2015.1047536] [Medline: 26112379]

104. Parak J, Uuskoski M, Machek J, Korhonen I. Estimating heart rate, energy expenditure, and physical performance with a

wrist photoplethysmographic device during running. JMIR mHealth uHealth 2017 Jul 25;5(7):e97 [FREE Full text] [doi:

10.2196/mhealth.7437] [Medline: 28743682]

105. Delgado-Gonzalo R, Parak J, Tarniceriu A, Renevey P, Bertschi M, Korhonen I. Evaluation of accuracy and reliability of

PulseOn optical heart rate monitoring device. Conf Proc IEEE Eng Med Biol Soc 2015 Aug;2015:430-433. [doi:

10.1109/EMBC.2015.7318391] [Medline: 26736291]

106. Parak J, Tarniceriu A, Renevey P, Bertschi M, Delgado-Gonzalo R, Korhonen I. Evaluation of the beat-to-beat detection

accuracy of PulseOn wearable optical heart rate monitor. Conf Proc IEEE Eng Med Biol Soc 2015 Aug;2015:8099-8102.

[doi: 10.1109/EMBC.2015.7320273] [Medline: 26738173]

107. Apple. 2017. WatchOS - Apple Developer URL: https://developer.apple.com/watchos [accessed 2017-09-19] [WebCite

Cache ID 6tamfJinU]

108. Apple. 2017. HealthKit - Apple Developer URL: https://developer.apple.com/healthkit/ [accessed 2017-09-13] [WebCite

Cache ID 6tRfiSKVw]

109. Fitbit. 2017. Fitbit Developer URL: https://dev.fitbit.com/ [accessed 2017-09-22] [WebCite Cache ID 6tfGRUrmR]

110. Garmin. Garmin Developers URL: https://developer.garmin.com/ [accessed 2017-09-13] [WebCite Cache ID 6tRfk6phg]

111. Misfit. Misfit Developer Toolkit URL: https://build.misfit.com/ [accessed 2017-09-13] [WebCite Cache ID 6tRgRKOAN]

112. Polar. Polar AccessLink URL: https://www.polar.com/en/connect_with_polar/polar_accesslink [accessed 2017-09-19]

[WebCite Cache ID 6tamdLu6Q]

113. Samsung. Samsung Health - Samsung Developers URL: http://developer.samsung.com/health [accessed 2017-09-13]

[WebCite Cache ID 6tRftLAHj]

114. TomTom. TomTom Developer Portal URL: https://developer.tomtom.com/ [accessed 2017-09-13] [WebCite Cache ID

6tRgHGW0A]

115. Nokia. Nokia Health API Developer Documentation URL: https://developer.health.nokia.com/api [accessed 2017-09-13]

[WebCite Cache ID 6tRgHmHhR]

116. PR Newswire Association. Fossil Group Reimagines the Watch URL: https://www.prnewswire.com/news-releases/

fossil-group-reimagines-the-watch-300427943.html [accessed 2018-03-01] [WebCite Cache ID 6xa1Z8fI6]

Abbreviations

API: application programming interface

EE: energy expenditure

GPS: global positioning system

HR: heart rate

IMU: inertial measurement unit

MEDLINE: Medical Literature Analysis and Retrieval System Online

PA: physical activity

PPG: photoplethysmography

SDK: software development kit

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 18http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX

Edited by G Eysenbach; submitted 17.11.17; peer-reviewed by J Sanders, P Wark, K Winfree, R Fallahzadeh, C Fernández; comments

to author 07.12.17; revised version received 18.12.17; accepted 06.01.18; published 22.03.18

Please cite as:

Henriksen A, Haugen Mikalsen M, Woldaregay AZ, Muzny M, Hartvigsen G, Hopstock LA, Grimsgaard S

Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables

J Med Internet Res 2018;20(3):e110

URL: http://www.jmir.org/2018/3/e110/

doi: 10.2196/jmir.9157

PMID: 29567635

Arnesdatter Hopstock, Sameline Grimsgaard. Originally published in the Journal of Medical Internet Research (http://www.jmir.org),

22.03.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License

(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,

provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic

information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be

included.

J Med Internet Res 2018 | vol. 20 | iss. 3 | e110 | p. 19http://www.jmir.org/2018/3/e110/

(page number not for citation purposes)

Henriksen et alJOURNAL OF MEDICAL INTERNET RESEARCH

XSL

•

RenderX