Wearable Activity Trackers in Managing Routine Health and
Fitness of Indian Older Adults: Exploring Barriers to Usage
Pallavi Rao
Anirudha Joshi
pallavi.rao@nmims.edu
Mukesh Patel School of Technology Management &
Engineering, NMIMS
Mumbai, Maharashtra, India
anirudha@iitb.ac.in
IDC School of Design, IIT-Bombay
Mumbai, Maharashtra, India
ABSTRACT
1
Geriatrics research has highlighted the importance of managing
routine health and fitness for older adults to improve their quality
of life. Correspondingly, public health organizations have given
specific guidelines on recommended levels of physical activities
for this age group. Despite these efforts, many older adults do not
engage in physical activities. Fitness technologies such as wearable
activity trackers can motivate people to be regular in their physical
activities. However, the adoption and usage of these devices among
older adults remains low, especially in developing countries. We
present findings from a longitudinal qualitative study with five
older adults from India who use wearable activity trackers. We
wanted to understand how they currently use these devices and
what barriers they face in getting the most out of them. We trained
the participants to help them overcome some of these barriers.
Though initially, participants used their trackers only to track their
steps, they could learn to use these devices more proactively after
our training sessions. From the study findings, we suggest that
activity trackers incorporate short how-to videos in local languages,
speech input, voice prompts, personalized feedback on progress,
and Q&A chat-bots to help older adults overcome some barriers.
Aging is often accompanied by an increased risk of diseases and a
decline in regular activities [20]. In geriatrics research, much work
highlights the importance of managing routine health and fitness for
older adults to improve their quality of life. Studies have suggested
various measures concerning older adults’ physical fitness, diet,
nutrition, and related aspects essential for healthy aging. In the
presence of solid evidence linking physical inactivity to chronic
diseases and increased physical activity to lower mortality and
morbidity in older adults, public health organizations such as the
World Health Organization (WHO) has given specific guidelines on
the recommended level of physical activities for older adults [53].
However, evidence shows that many older adults do not engage in
these activities [29, 48].
As per the Indian Council of Medical Research (ICMR) report [7],
Noncommunicable Diseases (NCDs) (such as heart disease, stroke,
cancer, and diabetes) account for 63% of deaths in the country. The
report also states that stroke is India’s third leading cause of death
and the sixth leading cause of disability. About 1.29 million incidents of stroke and almost 700,000 deaths were reported in India in
2019. Moreover, a maximum number of first-ever stroke cases were
observed between 60 and 74 years. Physical inactivity, unhealthy
diet, obesity, and hypertension are some risk factors causing these
diseases (tobacco use, alcohol use etc., are other factors reported).
While we do not have data on the physical activity patterns specifically of Indian older adults, a recent large-scale survey conducted
in 2020 [41] showed that 57% of the surveyed population (20 to 70
years old) do not meet the recommended physical activity regimen
by WHO. This percentage was higher among people over 40 (63.4%)
than those under 40 (51.7%). Thus, it becomes important to promote
healthy behavior (such as improving personal habits and attitudes
to prevent diseases [40]) among Indian older adults.
There is growing research on using fitness technologies to promote healthy behavior [27, 48]. Many fitness technologies such
as mobile health apps and wearable activity trackers are designed
to motivate and help people live a fit and healthy lifestyle. They
can track information related to health and fitness, such as tracking steps, calorie intake, and heart rate, and could be proactively
used for reviewing, reminding, sharing, etc. Studies have shown
that continuous monitoring of physical activities motivates people
to be regular in their physical activities and effectively increase
daily physical activities [27, 29, 35, 42]. For older adults, fitness
technologies such as wearable activity trackers can improve access
to healthcare information and empower them to play an active role
in the self-management of their health [50]. Despite the potential
CCS CONCEPTS
• Human-centered computing → User studies.
KEYWORDS
Wearable Activity Trackers, Indian older adults, Health and Fitness,
Usage barriers
ACM Reference Format:
Pallavi Rao and Anirudha Joshi. 2022. Wearable Activity Trackers in Managing Routine Health and Fitness of Indian Older Adults: Exploring Barriers
to Usage. In Nordic Human-Computer Interaction Conference (NordiCHI ’22),
October 8–12, 2022, Aarhus, Denmark. ACM, New York, NY, USA, 11 pages.
https://doi.org/10.1145/3546155.3546645
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NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
© 2022 Association for Computing Machinery.
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https://doi.org/10.1145/3546155.3546645
INTRODUCTION
NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
benefits, the use of fitness technologies among older adults is low
[23, 24, 50, 52] including in India [43].
Talukder et al. [50] highlight the gaps in the research in technology for health and fitness and say that most of the studies on
wearable activity trackers are done with the younger population.
The few studies on older adults are predominantly from developed
and western countries (e.g., [37, 45]). Aspects such as lifestyle, diet,
and attitudes towards fitness vary by culture and economy. There is
little research on the use of fitness technologies by older adults from
developing countries, including India. While research is lacking,
the Indian wearables market saw record growth in the first quarter
of 2022, with shipments crossing 13.9 million units, according to
the research firm, International Data Corporation (IDC), India [9].
To enable older adults to use activity trackers effectively, it is
essential first to identify barriers that prevent usage [48]. Thus,
in this paper, we investigate the barriers Indian older adults face
while using wearable activity trackers, assess if these barriers can
be overcome with appropriate training, and offer recommendations for designing wearable activity trackers for this demographic.
Specifically, we try to investigate the following research questions:
(1) How do middle-class Indian older adults currently use activity trackers for managing routine health and fitness? What
are the barriers that prevent them from optimally using these
devices?
(2) Is it possible to remove some of these barriers through appropriate training and personalized support? and after removing
these barriers, is there any change in usage?
(3) What are the recommendations for designing wearable activity trackers for this population?
To address these research questions, over a period of eight months,
we conducted five sessions of interviews, each with five Indian
older adults who were either already using or were willing to use
a wearable activity tracker. After identifying the barriers for each
individual, we trained them on the use of activity trackers in this
period. The training was personalized to each user’s needs. This
paper makes the following contributions:
(1) We report findings related to how some older adults from
India used wearable activity trackers for managing their
health and fitness and what barriers they faced.
(2) We report on how we conducted personalized training sessions for our participants to help remove the barriers.
(3) Based on the findings, we suggest some features that could
be incorporated in wearable activity trackers suitable for
this user group.
2
LITERATURE REVIEW
In this section, we first discuss wearable activity trackers, their
functionalities, benefits, and some prior studies that identified problems with the existing trackers. Next, we review past studies on
identifying the enablers and barriers in the usage of activity trackers. Finally, we review some geriatrics studies and policies in the
Indian context and identify the research gaps.
By “wearable activity trackers”, we mean wrist-worn devices
that are currently popular and can help users track and share data
(such as the number of steps walked and heart rate) [21]. Every
year, new models of wearable activity trackers are released in the
Pallavi Rao and Anirudha Joshi
market [21]. The trackers are equipped with several sensors and are
accompanied by a smartphone app. These devices generally have
an inertial measurement unit (IMU) sensor that uses a combination of accelerometers and gyroscopes to monitor the movement of
limbs and track exercise [25]. Activity trackers may have additional
sensors, including GPS, heart rate, pulse oximeter, and optical sensors. Most trackers estimate the type and quantity of movement,
calculate energy expenditure and identify sleep patterns [21]. There
are some concerns that these devices track the movement of only
a specific body part and thus are inadequate to capture a diverse
set of exercises accurately [25]. However, most trackers can count
steps taken by the user to an acceptable level of accuracy and reliability [37]. Some reviews differentiate between smartwatches
and fitness trackers [37, 45]. Though the options offered by the
two are often similar, a smartwatch is generally more expensive
(USD 100 to 400) and can support advanced features such as built-in
BlueTooth (to connect to wireless headphones) and built-in mics
(for calls and voice assistants), and has a higher resolution display.
A fitness tracker is typically cheaper (USD 10 to 100) and may have
fewer sensors and a limited display. As a result, fitness trackers
often have the added advantage of a longer battery life [45]. For this
paper, we do not differentiate between smartwatches and fitness
trackers.
Even though activity trackers help track information related
to health and fitness and increase physical activities, critics have
identified problems with existing trackers. Though these technologies often include many behavior change techniques such as goal
setting, feedback, rewards, and social factors, it is not clear which
of these components are most effective and are used by consumers
[48]. The long-term health benefits of these technologies are not
known yet. Wozniak et al. [54] argued that though these technologies are gaining popularity, they struggle to offer long-term health
benefits because of their inability to provide engaging goals. Also,
it is still not well understood whether the behavior change techniques included in fitness technologies are sufficient for changing
behaviors over the long term [48].
Studies have tried to identify the factors which act as enablers in
the usage of activity trackers. For example, Rapp and Cena [44] discussed how the device’s data was tracked, managed, visualized, and
used, impacting people’s engagement with the device. Their study
was on new users of trackers, and they suggested design strategies
focusing on discovery (allowing users to explore) and playfulness
(using game elements in tracking). In another study, Lewis et al. [31]
found that motivational cues and general health information were
the most helpful aspects of the device. Additionally, personalized
feedback [39] can improve the perceived helpfulness of the device.
Neupane et al. [38] stressed the importance of incorporating game
elements for the continued use of the trackers and their apps. These
studies focused on young, educated, technology-savvy individuals,
not older adults. Some studies have also been done with older adults,
finding a different set of enablers. For example, Kononova et al. [27]
highlighted the importance of incorporating collaborative social
support (e.g., increased socialization via activity tracker network)
in trackers for long-term usage. As per the study by Li et al. [32],
using a wider variety of tracker functions, daily wearing it, having
higher education, and frequently exercising are found to be the
Wearable Activity Trackers
factors associated with older adults’ long-term use of activity trackers. In another study, Li et al. [33] showed that internal motivation,
self-discipline, and family members are enablers in using activity
trackers among older adults. Their study also showed that goal
setting and feedback were essential factors influencing usage.
Studies have also looked into some of the barriers to the usage
of activity trackers. Clawson et al. [13] investigated the abandoned
wearable activity trackers on craigslist (secondary sales in online
marketplaces) to understand the reasons for abandonment and
found that changes in the physical activities associated with the
device and mismatch between users’ expectations and the capabilities of the device are some of the reasons for abandonment. Some
other known barriers are lack of motivation, lack of interest in the
functionalities, forgetting to wear, and inconvenience of managing
the devices [13, 47]. Again, these studies are not conducted on older
adults. In one of the few studies conducted on older adults, Talukdar
et al. [50] found that resistance to change and technology anxiety
are some barriers to usage.
Most studies reviewed in this section on the enablers and barriers
to activity trackers were on young, educated, and technology-savvy
individuals (e.g., [13, 31, 38, 39, 44, 47]). Furthermore, the studies
focused on older adults (e.g., [27, 32, 33, 50]) were from the USA
and China. Among these, there was only one study [50] found on
the barriers to the usage of trackers. In order to enable older adults
to use activity trackers effectively, it is essential first to identify the
barriers that prevent usage [48].
Medical research in India has been predominantly dedicated
to the treatment of individual diseases, and there has not been
much focus on the process of aging and preventing or delaying
age-related diseases [12]. The demographics of India are shifting.
While it is still considered a “young” country, with falling birth
rates and increasing life expectancy, it will grow older soon enough
[1]. This shift will impose social and economic challenges, as an
increase in the lifespan will not coincide with an improved quality
of life for older adults. An increasing proportion of the country’s
resources will need to be allocated towards the medical care of
the aging population [12]. The government of India has initiated
various policies and programs for healthy aging [14]. Recognizing
the importance of technology (eHealth, mHealth, wearables, etc.) in
healthcare delivery, the national health policy of India advocated an
extensive deployment of digital tools for improving efficiency and
outcomes [3]. Under the National Program for the Healthcare of Elderly, the government is also promoting health education programs
using various communication channels to realize the importance
of physical exercise, healthy habits, and reduction of stress among
older adults [6].
However, there is much more to achieve in researching and developing interventions that can promote healthy aging. Encouraging
people to exercise and lead an active lifestyle over a sustained period can facilitate behavior change. The use of technology can help
overcome some of the challenges caused by the demographic shift
[10, 20]. In this paper, we aim to identify the barriers faced by the
middle-class older adults in India for effectively using wearable
activity trackers and explore ways to overcome them.
NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
3 METHOD
3.1 Participants and Devices
We recruited older adult participants from India through the social
network of the first author and snowball sampling. We reached out
to people in our social network who were above 60 years of age and
led an active and healthy lifestyle. Based on the approval by our
ethics committee, we excluded people who were not active, were
bed-ridden, people who were advised not to exercise, and those
who could only exercise under the supervision of a physiotherapist.
We explained to potential participants that we were looking for
those who were either already using an activity tracker or were
interested in buying one. During the study, we would act as enablers
by removing barriers they faced, if any. At the time of recruitment,
we informed participants that we do not intend to prescribe or
endorse any specific device. This study was not sponsored by the
manufacturers of any device, and there was no conflict of interest.
Further, we did not intend to encourage the participant to use an
activity tracker. No incentive was given to participate in the study. If
the participant needed to buy a tracker, we helped them by showing
them the options available and informing them about the pros and
cons of each option, thus helping them to make a purchase decision.
However, the participant paid for any purchases.
We reached out to around 45 potential participants. Out of these,
only four were already using a wearable activity tracker, and only
one participant was willing to buy one and were recruited. The rest
did not show any interest in purchasing a wearable activity tracker.
Incidentally, all the five participants were from the same social
circle and knew each other before our study. They were motivated
to join the study as they knew other participants well and could
socialize with them during the process.
Table 1 summarizes each participant’s gender, age, city, the device used, its cost, and usage duration. There were four female and
one male participant. Their age ranged from 62 to 74. Participants
come from educated, middle-class backgrounds in India and speak
Kannada at home. All participants have bachelor’s degrees. Four
participants had worked professionally and were now retired (two
had been teachers, and two had been bankers). Four participants
lived in Tier-I cities (Mumbai and Bangalore) and one in Tier-II
cities (Udupi)1 . All have sons/daughters who are working outside
India. Thus our participants lived independently, either alone or
with their spouse. Participants were generally healthy and active.
Two of them regularly take medications for blood pressure; one is
borderline diabetic but not on any medication.
Participants used various activity tracker devices, as listed. The
four participants who already had a device received it as a present
from their son/daughter. P5, interested in purchasing a tracker,
asked us to help her with the purchase. She chose a tracker similar
to P4, who happened to be her sister.
3.2
Interview and Training Sessions
We conducted five interviews with each participant over a period
of eight months. The focus of our study included understanding
participants’ current usage patterns, identifying the barriers they
faced while using these devices, removing these barriers through
1 Classification
of cities based on its population, Tier-I being the highest population.
NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
Pallavi Rao and Anirudha Joshi
Table 1: Details of the participants
User
Gender
Age
P1
P2
P3
P4
P5
F
F
M
F
F
62
67
69
71
74
City
Device used
Bangalore
Bangalore
Bangalore
Udupi
Mumbai
VerifitPro
GOQii earlier, now Fitbit Versa 2
Samsung Galaxy Watch Active2
Fitbit Inspire HR
Fitbit Inspire HR
personalized training, and assessing the persistence of the new
usage over time. We framed the interview questions around the
focus of the study. The first three interview sessions were conducted
individually with each participant in April and May 2021. The fourth
group interview was conducted with all five participants in June
2021. The fifth concluding interview was conducted individually
with each participant in December 2021. Except for a meeting with
one participant, whom we helped install a new device, all interviews
were virtual. In addition to the interviews, we created a WhatsApp
group (with consent) that included the first author and the five
participants before their first interview. We created this group to
support the interview logistics (the date, the time, and the meeting
details) and for participants to share their physical activity data.
It is relevant to note that India experienced a severe second
COVID-19 wave from March to May 2021. India also introduced
COVID-19 vaccines for adults over 60 from March 1, 2021. By August 2021, the restrictions were eased, and most adults over 60 had
had their two doses [8]. India had not experienced the Omicron
variant-led third wave and the corresponding restrictions until the
fifth interview. Thus, while the first four interviews were in the
shadow of restricted mobility, the restrictions had eased considerably by the time of the fifth interview. Below, we describe each
interview in detail.
In the first interview, we discussed their life context, trackers, and
fitness routine with the participants. This interview was conducted
on a phone call and took about half an hour for each participant.
This interview aimed to identify the barriers people faced while
using their trackers. During this interview, we realized that participants were unaware of several features of their trackers, such as
tracking sleep, sharing data, etc. Further, some participants faced
difficulties changing the initial settings (such as changing steps goal
and distance) or adding a new exercise. We describe these findings
in the Findings section below.
After the first interview, we created training videos showing how
to perform some of the tasks in the trackers that participants found
difficult. These tasks included tracking steps, checking sleep data,
changing the settings, adding exercises such as yoga (that were not
tracked automatically), and sharing fitness data over WhatsApp.
These training videos are not videos with physical exercises to
follow but tutorials for using the trackers. Some of these videos
were personalized for each participant based on their needs. These
training videos are described in more detail in the Findings section
below. We shared these videos with the participants and encouraged
them to perform these tasks and share the data over the WhatsApp
group. We were in regular contact with participants during this
period.
Cost (INR)
5,000
14,000
18,000
7,000
7,000
Duration
2 years
2 years
1 year
1 year
Started during the study
Once we shared the training videos with the participants, we conducted the second and the third interview sessions on WhatsApp
video calls. In these interviews, we observed participants perform
the tasks (as narrated in the training videos). During the interviews,
we could observe the tasks performed on the trackers. Other tasks
(which needed to be done on the app) could not be observed because their phone was busy with our video call. However, from
time to time, participants shared data (obtained after performing
tasks) in the WhatsApp group, which gave us insights into their
app usage. Participants were free to call us on the phone if they
had questions between interviews. Each interview session with
each participant took about 20 to 30 minutes. The entire process
(training and conducting interviews) took about two months (April
and May 2021).
In the fourth interview, we aimed to have an open discussion
among participants about the usage of trackers after two months
of observation and training. Hence, we did not conduct individual
sessions. Instead, we conducted a group interview with all the
participants. In this interview, we discussed the long-term usage of
trackers and the concerns they still had. We used a Zoom video call
for this group interview, which took about an hour. This interview
was conducted in June 2021.
Six months after the fourth interview, we conducted the fifth
interview individually with each participant. In this interview, we
focused on assessing the persistence of the new usage of trackers
over time. This interview was conducted over a phone call and
lasted about 20 minutes each. This interview was conducted in
December 2021. Table 2 summarizes the interview sessions conducted with the participants. This study was approved by the Ethics
Committee of our university.
Each interview was transcribed individually for analysis. The
data analysis followed the concept-driven coding procedure (also
called deductive coding) mentioned by Gibbs [16]. He suggests
that researchers should come up with coding themes from the
topics in the study schedule. Accordingly, we came up with the
coding categories from the focus of our study. The analysis also
led to new coding categories. Overall, our interview data led to
the formation of six coding categories: Onboarding, Initial Tracker
Usage, Barriers to Usage beyond Step-tracking, Virtual Training
Sessions, Immediate Effects of Training, and Long-term Use. We
explain this in the Findings section below.
Wearable Activity Trackers
NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
Table 2: Summary of Interview Sessions
4
No.
Type
Goal
Platform
Conducted in
Interview-1
Interview-2
Interview-3
Interview-4
Individual
Individual
Individual
Group
Individual
Telephone
WhatsApp Video
WhatsApp Video
Zoom video conferencing
Telephone
Apr 2021
May 2021
May 2021
June 2021
Interview-5
To understand initial tracker usage
To observe task performance
To observe task performance
To understand the usage of trackers
after the training sessions
To understand the usage of trackers
over a period of six months
FINDINGS
This section presents the findings organized in six categories generated from the data analysis. We present the categories in a chronological order because they also reflect the stages that users went
through in their journey.
4.1
Onboarding
Onboarding was the first and perhaps the most insurmountable
barrier for the participants. None of our participants had onboarded
an activity tracker by themselves. The four participants (P1, P2,
P3, P4), who were already using a tracker before our study, had
received it as a present from their son or daughter. Stressing the
importance of tracking their parent’s regular walking routine, the
son or the daughter bought the device as a present, set it up, and
gave them initial training on its usage.
P5 did not own an activity tracker at the start of our study but
was recruited because she wanted to purchase one. We presented
to her several available options. After reviewing them, she asked
us to help her purchase the one similar to her sister (Fitbit Inspire
HR). We purchased the device, set it up on her phone, and gave her
initial training, which included charging the device and checking
the daily steps. This was the only in-person meeting in the study.
Given that none of our participants could even attempt to start
using the device on their own, onboarding seems to be the hardest
of barriers. A related problem was when there was a change in
either the mobile phone (connected to the tracker) or in the tracker
itself. P1 recently changed her mobile phone (as the old smartphone
gave problems), and since then, she has not had the app connected
to the tracker installed on the new phone. She was using only the
tracker to track steps daily. Upon our request, she downloaded the
app from the play store with the help of a neighbor during the
study (no configuration was done). P2 had recently changed the
tracker before the study (as the old tracker gave some problems).
This time again, her daughter (who lives abroad) bought the device
online and sent it. However, unlike last time, her daughter could not
give an initial training. After getting the new tracker, P2 installed
the relevant app but did not know how to do the settings (such as
setting the daily step goal, sleep time etc.) in the newly installed
app.
4.2
Initial Tracker Usage
The four participants who were already using a tracker before the
study were using it only to track the number of steps they walked
Dec 2021
on the day. Everyone had a different daily step goal (or “target”, as
many of them called it) set in their trackers. This number ranged
from 6,000 steps to 10,000 steps per day. While setting a target seems
essential, none of the participants knew an appropriate target, given
their age and condition. The participant’s son/daughter initially
set the target (perhaps the default value), and the participant did
not change it after that. WHO recommends at least 30 minutes
of moderate-intensity activity five times per week for this age
group [53]. However, none of the participants were aware of these
guidelines.
Though participants were not aware of the appropriateness of
the target, reaching it was nevertheless important for all four participants who were already using the tracker before our study. P1
said that she feels demotivated whenever she does not reach the
target. The other three participants (P2, P3, and P4) were not particularly demotivated if they did not reach their targets. Sometimes
they could not reach their targets because of an ailment such as a
backache or bad weather (during the rainy season). However, they
felt happy when they could reach their daily target, motivating
them to continue their regular walks. Usually, participants split
their walking schedule into two to three small sessions (morning,
evening, and after dinner). They started doing this after using trackers, mainly because reaching the target in two to three sessions
was easier than in one go. By doing this, they limit the amount of
time being sedentary throughout the day, which goes well with the
WHO guidelines [53].
P5, who had started using a tracker just two weeks before the
first interview, used it to track the steps walked every day. She was
not aiming to reach the target (we had kept it to 10,000 steps, the
default value in the app). However, later she felt inspired to do so
when she discovered the daily steps achieved by other participants
in our WhatsApp group. (Participants learned to share the data
after we trained them, as explained in the Virtual Training Session
below).
Apart from targets, these devices provide motivational cues such
as badges, cups, etc. Two participants (P1, P4) said they liked these
encouragements and felt rewarded. At the start of our study, participants had found another benefit of using trackers. COVID-19
related lockdown was reimposed in parts of the country before the
period of the first interview. Hence, the participants took walks
inside their house (or in their garden if they had one). They found
the trackers quite helpful because they could still track the steps
walked and the distance covered, which would have otherwise been
very difficult in these smaller spaces.
NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
4.3
Barriers to Usage beyond Step-tracking
In the first round of interviews, we realized that participants faced
many barriers to optimal use of their trackers and faced some
problems in regular usage. The participants did not use the device
for other services such as setting alarms and reminders, tracking
day/week/month summary data, tracking sleep, sharing the data,
etc. Three participants (P2, P4, P5) were tracking their steps in
the mobile app connected to their trackers. P1 was doing so until
recently when she changed her mobile phone. P3 was tracking his
steps in his smartwatch as the corresponding app was not installed
on his phone. Two participants (P2, P4) said they tried to explore
the app connected to the device. However, their usage was limited
to tracking the daily steps and calories burnt. Participants were
scared to explore more options because of their constant worry of
“data/settings getting lost”.
Further, while two participants (P1 and P4) wore their device
throughout the day, the three others wore it only during their walks.
This implies that a lot of their data (e.g., sleep data) was not tracked.
This behavior was not out of choice or due to data privacy concerns
but because the participants were unaware of the benefits of using
trackers 24x7. They felt that trackers should be used only to track
their steps while walking. Two participants (P2, P5) mentioned that
“trackers come in the way while doing other household chores”.
Participants were unaware of how they could change their device
settings in the connected apps or view information available only in
the app (i.e., not visible on the tracker). P2 explicitly mentioned that
even though she could see the steps walked on the device, she could
not see the distance covered. The distance estimate is displayed in
the app, not on the tracker. Also, she could not review her sleep
data. P1 said that though the tracker tracks the steps correctly, it
does not track her “yoga exercises” (which was incorrect because
the app did have this option, but it was quite deep down in the
menus). Thus, she was bothered that her tracker was not accurately
capturing all her physical activities.
Three participants (P1, P4, P5) complained about the small display of their trackers. They could not read it while walking, as
they did not wear or carry their reading glasses. Though the app
connected to the tracker had a larger display, they were reluctant to
use it because it was inconvenient to unlock the phone frequently
while walking. The other two participants (P2 and P3) did not have
this problem as they had smartwatches; the display was bigger, and
they could clearly see the steps they had walked.
4.4
Virtual Training Sessions
Four of our five participants (P1, P2, P4, P5) were interested in
learning to proactively use their devices beyond tracking daily
steps. Hence, we thought of training them. Initially, we sent them
existing YouTube videos on Fitbit, and VerifitPro [2, 4, 5]. However,
participants did not find these videos helpful. They described these
videos as “very lengthy”, “irrelevant”, or “not clear”. Another barrier
was that these were all in English, which our participants were
not very comfortable with. These videos contained many details
(from charging the device to showing all the features) that our
participants were not interested in and did not cater to specific
tasks they wanted to know.
Pallavi Rao and Anirudha Joshi
Given this situation, we decided to take a more nuanced approach.
One of the authors owned a Fitbit device, similar to the one used
by P2, P4, and P5 (though different models). Using the connected
app, we created five short videos for these tasks: changing the daily
steps target and distance target, sharing steps data with friends,
tracking sleep, sharing sleep data with friends, and adding a new
exercise. We used a screen recorder app to record these videos.
We described each task in Kannada (the language spoken by our
participants). Some of these videos were personalized for a specific
problem, while others were generic. For example, P2 had issues
with her initial settings (she could not track sleep and distance). So
we made a short video for this specific task and sent it only to her.
Each of the other four videos addressed a common barrier faced by
everyone, and it was sent to all three of them.
The authors did not own a VerifitPro device. Hence, to help P1,
we downloaded a video from YouTube [2] which explains several
settings. We edited, made it shorter and task-specific, and added a
voice-over in Kannada. In this way, we created a video that showed
how to track and share data and another that showed how to add a
new exercise, explicitly addressing the problems P1 had.
We could thus train four of our participants (P1, P2, P4, P5)
interested in learning proactive use of their devices. However, we
could not train P3. He did not have the corresponding app installed
on his phone. Moreover, he said he did not face any problems during
the first interview. Though he participated actively in the study,
he was not keen on learning new tasks. He was happy to use his
smartwatch mainly as a watch (to check the time) and track his
daily steps.
4.5
Immediate Effects of Training
The immediate effects of the training videos were mixed. Three
participants (P1, P2, P4) benefited from the videos and could immediately do more with their trackers. During the second and
third interview sessions, we realized they had learned many things
through the training videos. In the second interview session, we
observed that they still faced some difficulty while performing the
tasks (even after watching the videos). However, they could perform
the tasks with ease by the third interview.
P1 learned how to add yoga exercises to the list of exercises in
her tracker and track and share the data, including calories burnt
and exercise duration. P2 and P4 learned to change the app settings
to track distance and sleep. These participants frequently shared
data of their steps walked, distance covered, and calories burnt with
others in the WhatsApp group. Overall, these three participants
(P1, P2, P4) had learned to overcome some barriers and use their
trackers more proactively.
On the other hand, the training videos were not all successful.
Though we had sent these short videos to P5, she could not learn
much and did not try doing any tasks mentioned in the videos.
Unlike other participants, she had recently started using the device
and was not yet fully accustomed to the tracker or the app. It
is possible that eventually, she may learn and optimally use her
tracker, but this was not the immediate outcome after watching the
videos. Also, as mentioned, we could not train P3 at all, as he was
not keen on using his tracker for anything other than checking time
and tracking steps. Though both P3 and P5 did not directly benefit
Wearable Activity Trackers
from the training videos, later, they said they were “motivated” to
learn new things after regularly watching others (P1, P2, P4) share
data on the WhatsApp group.
During the interactions with participants, we realized that they
repeatedly required support to overcome problems. These problems
were mainly related to finding a menu option. We tried to solve some
of these problems through training videos. Participants learned best
from the personalized short videos with a voice-over in Kannada.
Even though our participants understand English (their phones
were set up in English), they preferred to listen to instructions in
Kannada.
4.6
Long-term Use
We conducted our last round of interviews in December 2021, six
months after we had trained the participants. In this interview, we
observed that sharing and socialization on activity data seemed to
have gone beyond our study, at least in one case. P1 had motivated
two others in her social circle to purchase trackers. Both are older
adults and have purchased a model similar to P1. Now, P1 regularly
shares and discusses her physical activities with them.
By this time, most of the COVID-19 restrictions had been lifted.
This had mixed responses from our participants. For example, P4
steadily increased her physical activities over this time. She felt
satisfied whenever the data showed that she walked well and slept
well in a particular week. On the other hand, two participants (P2
and P3) said they are not very regular in their physical activities
because of travel and social events (now that the COVID-19 restrictions had been lifted). However, P2 regularly tracked her physical
activities on the tracker. She is now aware of the days when she
missed her walking routine.
The passage of time seemed to have made people more familiar
with their activity trackers. P5, who was new to activity trackers
and had not learnt a lot from our training sessions six months ago,
had meanwhile learned to track her steps (after re-watching the
training videos we had shared with her). She said that there was
also an increase in her physical activities. She was unsure whether
it was because of the tracking or because she had started to go
outdoors for walks. However, she had not yet learned to share
her data, so we could not verify this. Meanwhile, even P3 became
interested in tracking the missed days on his smartwatch. He was
the only one who was not interested in learning new features in
the early part of the study. However, during the fifth interview, he
showed interest in learning how to track his steps (over a period)
and sleep data on his smartwatch.
Participants (P1, P2, P4, P5) said they re-referred to our training
videos whenever they faced old problems (such as tracking the
sleep data). Meanwhile, some new issues had “emerged” during this
period. For example, P1 and P2 could not track their sleep data on
some days. They said that the data is shown on some days and not
on other days. P4 could not share any of the data now as the “share
option is missing” in her app. We speculate that it could be because
of an update in the app version; however, she could not confirm
this. New training sessions were now required to solve these issues.
All participants intended to continue using their tracker in the
future. They felt it was pretty helpful in tracking their physical
NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
activities. P4 said, “using the tracker has become a habit now”. However, they discussed some concerns regarding change/upgrades
in the technology. Though they have learned many things from
our videos, they were slightly anxious about how it would be in
the future, as technology keeps changing. Participants also raised
some questions about their trackers. For example, they were still
unsure of the correct step goal for their age and health conditions.
Further, they could not interpret some of the information given in
the trackers. For example, after P4 learned to track and share her
sleep data from our training, she now wanted to know more about
“REM, Light, and Deep sleep” and what her “sleep score” meant. All
participants felt that there was too much information that they
could not fully comprehend.
5
BARRIERS TO USAGE AND DESIGN
RECOMMENDATIONS
Even if an older adult wants to purchase an activity tracker, it is not
easy to decide which device might be suitable. Trackers come in
a wide price range, and people are not currently familiar with the
different sensors they include. While four of our participants got
the trackers from their son or daughter, P5 purchased the device
with our help. Even though we showed various models available
in the market, she chose the one similar to her sister because of
the social reference. Similarly, P1 has influenced two others in
her network to purchase a device similar to hers. These incidents
highlight how opaque these devices are for a first-time buyer. Given
the broad price range of these trackers, it is not clear which one
to purchase. People make decisions based on what someone in
their social network owns. Hence, it is recommended that these
trackers are more transparent about their features, functionalities,
and, importantly, suitability to an age group. It will help consumers,
especially first-time buyers who might not have a social reference,
make a better choice.
Even after older adults purchase an activity tracker, onboarding
is also difficult. Our study findings show that onboarding is the most
challenging barrier to adopting trackers. Each of our participants
needed help to set up their devices before use. This does not seem
to be the problem only for beginners. When some experienced
participants changed their phone or tracker, they could not do the
settings independently. A possible solution for this would be an
option to automatically synchronize the tracker data and settings
in the newly installed app and device and allow cross-device / app
data exchange. These features will ensure hassle-free use of the
tracker and the connected app.
Once onboarded, we found that older adults use their trackers
mainly to track the number of steps they walk each day. During
the COVID-19 restrictions, they found the trackers particularly
useful because they could easily track the number of steps they
walked indoors. Walking in constrained areas, particularly inside a
house, might get monotonous. Activity trackers, on the other hand,
enabled our participants to meet their daily step goals even in the
more constrained locations by keeping track of the steps. While
this is all good, it remains a limited usage compared to a tracker’s
potential and the older adult’s needs.
Participants appreciated that trackers could help them monitor
their exercise over time and give them motivational messages. These
NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
findings are similar to the study by [29, 35, 42] on continuous
monitoring and by [31] on motivational messages, though these
studies were not conducted on older adults. Motivational cues given
on smartphones improved physical activity behaviors in patients
suffering from a chronic obstructive pulmonary disease (COPD)
[49], and chronic low back pain [15]. Motivational cues could be a
valuable component of activity trackers that aim to improve active
behavior. Research is needed to determine which motivational cues
effectively increase the activity behaviors of Indian older adults.
During the interview sessions, we observed that our participants
needed more personalized help on the phone when they were stuck
with a problem. Trackers (and the connected apps) come with a
plethora of options, and it is not easy for older adults to navigate
through these and find a solution to their problems. This shows
that older adults will require ongoing support to use their trackers. They find it challenging to learn on their own and need help.
Features such as discovery and playfulness were suggested by [44]
for designing trackers for young adults. Due to aging, the decline
in cognitive, physical, and sensory functioning might make it difficult for older adults to use activity trackers like younger users. We
observed that our participants are not explorers, though they are
good at learning what they find useful. Some studies have focused
on designing better instructional resources to assist older adults.
For example, Hagiya et al. [18] proposed a tutoring system for text
entry on smartphones that provided instructions about the next
operation. With some training videos, we found that our participants could learn to track other aspects of their wellness, including
sleep and exercises such as yoga. Similarly, incorporating short
how-to videos demonstrating various types of task performance
would help older adults solve some of the problems they face while
using trackers. A related problem was that updates to the app threw
older adults off. A potential solution would be to have long-term
support (LTS) versions of the trackers and the connected apps [43].
This might significantly help older adults because learning and
remembering how to use new products is one of the main problems
they face while using any technology.
In our study, older adults could learn to share their progress
with friends from personalized training videos. In turn, this inspired other older adults to lead a more active life and learn to
use the trackers for themselves. Social support (such as having
someone to walk or exercise with or having a friend or a family
member who supports them to be active) is known to increase
engagement, adherence, and completion in physical activity interventions [30, 46, 47, 51], and it is especially important for increasing
physical activities in older adults [26]. Incorporating social support
in activity trackers more directly could enable widespread usage in
older adult communities [27].
We also found that the participants went back to the training
videos after several months. This shows that, while older adults
may forget how to do a task, training videos can help both immediately and in the long term. The videos did need a fair amount of
personalization in terms of what the participants wanted to know.
As has been found earlier [43], many older adults are not proactive
foragers of information; they depend on coming across information
serendipitously.
Activity trackers available in the market do not usually come
with built-in video-based instructions. Perhaps it is assumed that
Pallavi Rao and Anirudha Joshi
the use is intuitive. One of the primary barriers to device adoption
among older adults is a lack of knowledge [27]. Activity tracker
manufacturers may consider incorporating short clips of videobased instructions if the target users are older adults. Further, in
our study, videos needed localization in terms of language. While
the participants had their phones set up in English (which is fairly
representative of most middle-class, educated Indian phone users),
they faced difficulties following English videos in foreign accents.
While phone interfaces are preferably set up in English, content
is better comprehended in the mother tongue. Future apps and
operating systems could support such a dual-language setup.
We observed that having a specific daily step target motivated
our participants to lead an active lifestyle. On the other hand, not
achieving a daily target could demotivate some. It may be a good
idea to incorporate a margin around the target as suggested in [22].
These margins could be set for weekly, monthly, and yearly targets.
Doing this might lower the burden of strict goal achievement on a
given day, and the motivation could still be kept intact. Currently,
the apps focus on the step targets. Additional targets could be set
in terms of the number of 30 move-minutes recommendations by
WHO and the number of weeks in which the user could do so on
five occasions [53]. These weekly, monthly, and yearly achievements could be made more visible to reduce the pressure of daily
achievements further and emphasize long-term health goals.
Some of our study participants could not see the steps walked
(during their walks) because of the small display of trackers. Voice
prompts could reduce the need to keep checking the small display
of the trackers while exercising. Some of the connected apps have
voice prompt facility. However, they are not easily accessible and
thus were not used sufficiently. Besides voice prompts, voice input
could also be helpful for older adults. To fully use the connected
apps’ functions, it is essential to know text input, which the participants found difficult. Voice input is preferred for people without
hearing impairments [17]. Past studies have shown that voice inputs have a low barrier of entry compared to other input methods
for older adults [28, 55]. Though voice input could be useful, it also
presents some difficulties for older adults. Studies have focused on
designing efficient voice inputs to assist older adults. For example,
Hagiya et al. [17] designed a tutoring system (for smartphones)
that improved the efficiency of voice input for novice older adults.
More research is needed in designing voice inputs for older adults
in activity trackers.
In the final round of interviews, we discovered that while the
usage of the tracker increased, our participants now had new questions. They wanted to know more about other information given by
their trackers, such as sleep scores or REM. One possible solution to
support such developing curiosity would be to incorporate a Q&A
chatbot (either voice, text, or a combination) to help resolve such
doubts. The chatbot could also act as a personal fitness trainer.
Table 3 summarizes the barriers that we uncovered through our
study and the design recommendations that we suggest to overcome
these barriers.
6
DISCUSSION
To enable older adults use fitness technologies, it was essential
first to identify barriers that prevented usage. As an initial step, in
Wearable Activity Trackers
NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
Table 3: Barriers older adults face while using trackers and Design recommendations to overcome the barriers
No.
Barrier
Design recommendation
Helps in
1
Not able to decide which tracker to
purchase
Making a purchase decision
2
Change in the tracker/phone
3
4
Not able to perform tasks
Continuous version updates
5
Usage of English in training videos
Transparency in showing the
features, functionalities and
suitability
Synchronisation of old data
with the new device
Short how-to videos
LTS version of trackers & connected apps
Language localization
6
Not able to achieve the step goal
everyday
Apps
do
not
provide
weekly/monthly/yearly achievements of physical activities
Not able to see the steps walked
(while walking)
Difficulty in inputting text
Regular usage problems
7
8
9
10
Hassle free usage
Margin along with the step goal
Regular training
Minimising disruptions due to
version updates
Supporting a multilingual culture
Keeping motivation intact
Weekly/monthly/yearly
achievement summaries
Emphasise long-term health
goals over daily achievement
Voice prompts
Appropriate feedback
Voice input
Q&A chatbot
Easy input mechanism
Solving doubts + other functionalities, long-term usage
this paper, we tried to understand how middle-class Indian older
adults currently use activity trackers. We identified the barriers that
prevented them from optimally using activity trackers and explored
whether and how these barriers could be removed through training.
Additionally, we found opportunities to design activity trackers for
Indian older adults to manage their routine health and fitness.
During the recruitment, we realized that older adults were unaware of the benefits of fitness technologies. Among the 45 middleclass Indian older adults we approached for this study, only four
were using an activity tracker, and only one was interested in purchasing one. The rest were unaware of what trackers could do
and said that these devices are suitable only for “youngsters”. This
echoes findings from [43], where older adults reportedly said that
such devices were suitable for “youngsters” and “marathon runners”. The noteworthy point is that none of the older adults we
approached seemed to bother about the cost of the trackers (we had
consolidated a list of trackers available in the market and discussed
their cost and basic functionalities with them). This was perhaps
because we approached people who were mainly from a middleclass background and the trackers we mentioned are available at
what they consider to be an affordable price.
Indian culture is renowned for being “collectivist”. The joint
family has historically been the favored family structure in Indian
culture [36]. Traditionally, family ties have been given much importance. When children become adults, they are expected to be
responsible for taking care of their elderly parents. However, owing
to the changes in the social structure (such as drifting away from
the extended family and relocation to metropolitan regions or other
cities/countries), many older adults are living independently. Most
of the 45 older adults we approached also live independently, either
alone or with their spouses. Their children have migrated to other
cities / abroad in search of jobs. Many of them seemed motivated
toward fitness mainly because they wished to lead an independent
life. However, most of them are not regular in their exercise routine
and do not keep track of their missing schedule. While there seems
to be a desire to exercise regularly, activity trackers can help track
such exercise over the long term and help people gain control.
6.1
Limitations
We acknowledge the limitations of our study. While we did multiple
interviews with each user over eight months, we had a small sample
of just five users. There is a fair amount of diversity visible even in
our small sample. More diversity may exist in the wider population,
and more extensive studies are required. Nevertheless, it was not
easy to enroll people in this study. Even though we reached out
to many more middle-class older adults, most of them did not
have a wearable activity tracker and did not show any interest
in purchasing one. They thought that such devices were “not for
them”. We hope that findings from studies such as ours can help
design better trackers and change this perception. Further, our study
was eight months long. While this may be considered reasonably
longitudinal for a study in the Human Computer Interaction (HCI)
field, even longer studies are needed to conclude whether changes
in behaviors were sustained and whether these in fact led to healthy
outcomes.
Due to the COVID-19 restrictions and to avoid in-person interaction, we conducted studies virtually. This included phone calls,
WhatsApp video calls, and Zoom. Social research has been conducted online for many years (e.g., [34]); thus, our experience is
not particularly new. Even so, observational studies are generally
conducted face-to-face, mainly because of the need to observe participants perform the tasks. While this was a limitation in our
NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
study, the focus was on training the participants, which was doable
through virtual meetings. We conducted the virtual studies and
training sessions without many problems. Our participants showed
much interest and remained enthusiastic throughout. This could
be attributed to the recent increase in mobile phone usage. Both
WhatsApp and Zoom were pre-installed on our participants’ mobile phones, and they regularly used these two platforms for video
calls. We acknowledge that these skills imply that our participants
were at the higher end of the current technology-abilities spectrum
among older adults in India. Correspondingly, other users may face
different problems, and in all likelihood, those may be harder to
overcome.
While virtual studies imposed some limitations, they also enabled us to reach out to a group of users who were spread out
geographically. People who agreed to participate recommended
others, and their recommendations helped in recruiting people who
were socially connected (though geographically dispersed). This
allowed us to conduct a study with reasonably high external validity. Social networks that older adults care about, and would like to
share their data with, could often be geographically dispersed.
7
CONCLUSION
Wearable activity trackers are commonly marketed to the younger
population and are not designed and developed based on the needs
and preferences of the aging population. However, these technologies can be viewed as assistive technologies that help older
adults monitor their physical activities and lead healthy life [33].
As Kononova et al. [27] say, activity trackers may be an effective
technology to encourage physical activity among older adults, especially those who have never tried it before. Our participants found
that activity trackers helped them to meet their daily step goals
even in constrained locations during COVID restrictions. Activity
trackers have great potential in promoting behavior change, encouraging physical activities, and maintaining the health of aging
individuals [11, 19].
To help older adults get going with activity trackers, it is crucial
to address the barriers they face and the limitations of the current activity trackers. Our study findings showed that older adults
need continuous training and personalized support while using the
trackers. This was true for all the participants, irrespective of the
trackers they used. Beyond training, we found opportunities for
incorporating “socialization”, “personalization”, and “localization”
into the design of activity trackers. Additionally, we saw prospects
for more widespread use of speech input and voice-based feedback
in activity trackers and their corresponding applications. There are
opportunities to provide more nuanced and personalized feedback
on progress and achievements and set clearer and comprehensible
goals for exercise and lifestyle. There are opportunities to explain
scientific terminology such as “VO2 max” and “REM sleep” in simpler language and to make them more actionable. We hope our
findings will help design more suitable activity trackers and corresponding apps for older adults.
ACKNOWLEDGMENTS
Authors are grateful to all the participants for their time and enthusiasm throughout the interview and training sessions.
Pallavi Rao and Anirudha Joshi
REFERENCES
[1] 2015. World Population Prospects. Retrieved Jan 22, 2022 from https://population.
un.org/wpp/publications/files/key_findings_wpp_2015.pdf
[2] 2017. BFit Sport: Setting up the VeryFitPro App. Retrieved May 10, 2021 from
https://www.youtube.com/watch?v=2az6Kh5N-Tg
[3] 2017. National Health Policy. Retrieved Feb 07, 2021 from https://www.nhp.gov.
in/nhpfiles/national_health_policy_2017.pdf
[4] 2020. Fitbit Versa 3 Setup (and Onboarding). Retrieved May 10, 2021 from
https://www.youtube.com/watch?v=NFl4cG6kwdM
[5] 2020. How to use the Fitbit Inspire. Retrieved May 10, 2021 from https://www.
youtube.com/watch?v=lSzCfZWLhFs
[6] 2021. National Health Policy. Retrieved Feb 07, 2021 from https://nhm.gov.in/
index1.php?lang=1&level=2&sublinkid=1046&lid=605
[7] 2021. Stroke incidence and mortality: A report of the population based stroke
registries, India. Retrieved Jan 21, 2022 from https://ncdirindia.org/All_Reports/
pbsrbook/resources/PBSR_Report_2021.pdf
[8] 2022. Co-WIN: Winning Over COVID-19. Retrieved Jan 18, 2022 from https:
//dashboard.cowin.gov.in/
[9] Shivani Anand. 2022. India Wearables Market Records Double-digit Growth in
1Q22, Shipping 13.9 Million Units, Says IDC India. Retrieved July 15, 2022 from
https://www.idc.com/getdoc.jsp?containerId=prAP49123222
[10] Kabita Barua, Madhur Borah, Chandana Deka, and Rana Kakati. 2017. Morbidity
pattern and health-seeking behavior of elderly in urban slums: A cross-sectional
study in Assam, India. Journal of family medicine and primary care 6, 2 (2017),
345.
[11] Katie-Jane Brickwood, Greig Watson, Jane O’Brien, and Andrew D Williams.
2019. Consumer-based wearable activity trackers increase physical activity
participation: systematic review and meta-analysis. JMIR mHealth and uHealth
7, 4 (2019), e11819.
[12] G. Chawla. 2019. Healthy Aging Research in India. Journal of experimental
research on human growth & aging 2 (2019).
[13] James Clawson, Jessica A Pater, Andrew D Miller, Elizabeth D Mynatt, and
Lena Mamykina. 2015. No longer wearing: investigating the abandonment of
personal health-tracking technologies on craigslist. In Proceedings of the 2015
ACM international joint conference on pervasive and ubiquitous computing. 647–
658.
[14] Jinkook Lee David E. Bloom, T. V. Sekher. 2021. Longitudinal Aging Study in
India (LASI): new data resources for addressing aging in India. Nature Aging 1
(2021).
[15] Marit GH Dekker-van Weering, Miriam MR Vollenbroek-Hutten, and Hermie J
Hermens. 2012. Do personalized feedback messages about activity patterns
stimulate patients with chronic low back pain to change their activity behavior
on a short term notice? Applied psychophysiology and biofeedback 37, 2 (2012),
81–89.
[16] G Gibbs. 2008. Analyzing qualitative data. Sage publications.
[17] Toshiyuki Hagiya, Keiichiro Hoashi, and Tatsuya Kawahara. 2018. Voice Input Tutoring System for Older Adults Using Input Stumble Detection. In 23rd
International Conference on Intelligent User Interfaces (Tokyo, Japan) (IUI ’18).
Association for Computing Machinery, New York, NY, USA, 415–419. https:
//doi.org/10.1145/3172944.3172995
[18] Toshiyuki Hagiya, Toshiharu Horiuchi, and Tomonori Yazaki. 2016. Typing
Tutor: Individualized Tutoring in Text Entry for Older Adults Based on Input
Stumble Detection. In Proceedings of the 2016 CHI Conference on Human Factors
in Computing Systems (San Jose, California, USA) (CHI ’16). Association for
Computing Machinery, New York, NY, USA, 733–744. https://doi.org/10.1145/
2858036.2858455
[19] Sheri J Hartman, Sandahl H Nelson, and Lauren S Weiner. 2018. Patterns of Fitbit
use and activity levels throughout a physical activity intervention: exploratory
analysis from a randomized controlled trial. JMIR mHealth and uHealth 6, 2
(2018), e8503.
[20] Jorunn L Helbostad, Beatrix Vereijken, Clemens Becker, Chris Todd, Kristin
Taraldsen, Mirjam Pijnappels, Kamiar Aminian, and Sabato Mellone. 2017. Mobile
health applications to promote active and healthy ageing. Sensors 17, 3 (2017),
622.
[21] André Henriksen, Martin Haugen Mikalsen, Ashenafi Zebene Woldaregay,
Miroslav Muzny, Gunnar Hartvigsen, Laila Arnesdatter Hopstock, and Sameline
Grimsgaard. 2018. Using fitness trackers and smartwatches to measure physical
activity in research: analysis of consumer wrist-worn wearables. Journal of
medical Internet research 20, 3 (2018), e9157.
[22] Gyuwon Jung, Jio Oh, Youjin Jung, Juho Sun, Ha-Kyung Kong, and Uichin Lee.
2021. “Good Enough!”: Flexible Goal Achievement with Margin-based Outcome
Evaluation. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–15.
[23] Khan Kalimullah and Donthula Sushmitha. 2017. Influence of design elements
in mobile applications on user experience of elderly people. Procedia computer
science 113 (2017), 352–359.
Wearable Activity Trackers
[24] Shwetambara Kekade, Chung-Ho Hseieh, Md Mohaimenul Islam, Suleman Atique,
Abdulwahed Mohammed Khalfan, Yu-Chuan Li, and Shabbir Syed Abdul. 2018.
The usefulness and actual use of wearable devices among the elderly population.
Computer methods and programs in biomedicine 153 (2018), 137–159.
[25] Rushil Khurana. 2019. The past, the present, and the future of fitness tracking.
XRDS: Crossroads, The ACM Magazine for Students 25, 4 (2019), 30–33.
[26] Abby C King, Dan Stokols, Emily Talen, Glenn S Brassington, and Richard
Killingsworth. 2002. Theoretical approaches to the promotion of physical activity:
forging a transdisciplinary paradigm. American journal of preventive medicine 23,
2 (2002), 15–25.
[27] Anastasia Kononova, Lin Li, Kendra Kamp, Marie Bowen, RV Rikard, Shelia
Cotten, and Wei Peng. 2019. The use of wearable activity trackers among older
adults: focus group study of tracker perceptions, motivators, and barriers in the
maintenance stage of behavior change. JMIR mHealth and uHealth 7, 4 (2019),
e9832.
[28] Jarosław Kowalski, Anna Jaskulska, Kinga Skorupska, Katarzyna Abramczuk,
Cezary Biele, Wiesław Kopeć, and Krzysztof Marasek. 2019. Older Adults and
Voice Interaction: A Pilot Study with Google Home. In Extended Abstracts of the
2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland
Uk) (CHI EA ’19). Association for Computing Machinery, New York, NY, USA,
1–6. https://doi.org/10.1145/3290607.3312973
[29] Liliana Laranjo, Ding Ding, Bruno Heleno, Baki Kocaballi, Juan C Quiroz,
Huong Ly Tong, Bahia Chahwan, Ana Luisa Neves, Elia Gabarron, Kim Phuong
Dao, et al. 2021. Do smartphone applications and activity trackers increase
physical activity in adults? Systematic review, meta-analysis and metaregression.
British Journal of Sports Medicine 55, 8 (2021), 422–432.
[30] Dan Ledger and Daniel McCaffrey. 2014. Inside wearables: How the science of
human behavior change offers the secret to long-term engagement. Endeavour
Partners 200, 93 (2014), 1.
[31] Zakkoyya H Lewis, Lauren Pritting, Anton-Luigi Picazo, and Milagro JeanMarieTucker. 2020. The utility of wearable fitness trackers and implications for increased engagement: An exploratory, mixed methods observational study. Digital
health 6 (2020), 2055207619900059.
[32] Lin Li, Wei Peng, Anastasia Kononova, Marie Bowen, and Shelia R Cotten. 2020.
Factors associated with older adults’ long-term use of wearable activity trackers.
Telemedicine and e-Health 26, 6 (2020), 769–775.
[33] Lin Li, Wei Peng, Anastasia Kononova, Kendra Kamp, and Shelia Cotten. 2021.
Rethinking Wearable Activity Trackers as Assistive Technologies: A Qualitative Study on Long-Term Use. In Proceedings of the 54th Hawaii International
Conference on System Sciences. 3923.
Doing a Fieldwork in a Pandemic.
Re[34] Deborah Lupton. 2021.
trieved August 21, 2021 from https://docs.google.com/document/d/
1clGjGABB2h2qbduTgfqribHmog9B6P0NvMgVuiHZCl8/edit#
[35] Suliman Mansi, Stephan Milosavljevic, Steve Tumilty, Paul Hendrick, Chris Higgs,
and David G Baxter. 2015. Investigating the effect of a 3-month workplace-based
pedometer-driven walking programme on health-related quality of life in meat
processing workers: a feasibility study within a randomized controlled trial. BMC
public health 15, 1 (2015), 1–12.
[36] Y Nandan. 1980. Typology and Analysis of the Asian-Indian Family." In The New
Ethnics: Asian Indians in the United States, ed. P. Saran and E. Eames. New York:
Praeger. (1980).
[37] M Benjamin Nelson, Leonard A Kaminsky, D Clark Dickin, and ALEXANDER H
Montoye. 2016. Validity of Consumer-Based Physical Activity Monitors for
Specific Activity Types. Medicine and science in sports and exercise 48, 8 (2016),
1619–1628.
[38] Aatish Neupane, Derek Hansen, Anud Sharma, Jerry Alan Fails, Bikalpa Neupane,
and Jeremy Beutler. 2020. A Review of Gamified Fitness Tracker Apps and
Future Directions. In Proceedings of the Annual Symposium on Computer-Human
Interaction in Play. 522–533.
[39] Kwok Ng, Jorma Tynjälä, and Sami Kokko. 2017. Ownership and use of commercial physical activity trackers among Finnish adolescents: cross-sectional study.
JMIR mHealth and uHealth 5, 5 (2017), e61.
[40] World Health Organization. 2002. The world health report 2002: reducing risks,
promoting healthy life. World Health Organization.
[41] Vivek Podder, Raghuram Nagarathna, Akshay Anand, Suchitra S Patil, Amit Kumar Singh, and Hongasandra Ramarao Nagendra. 2020. Physical activity patterns
in India stratified by zones, age, region, BMI and implications for COVID-19: a
nationwide study. Annals of Neurosciences 27, 3-4 (2020), 193.
[42] Anna Puig-Ribera, Judit Bort-Roig, Angel M González-Suárez, Iván MartínezLemos, Maria Giné-Garriga, Josep Fortuño, Joan C Martori, Laura Muñoz-Ortiz,
Raimon Milà, Jim McKenna, et al. 2015. Patterns of impact resulting from a ‘sit
less, move more’web-based program in sedentary office employees. PloS one 10,
4 (2015), e0122474.
[43] Pallavi Rao and Anirudha Joshi. 2020. Design Opportunities for Supporting
Elderly in India in Managing their Health and Fitness Post-COVID-19. In IndiaHCI’20: Proceedings of the 11th Indian Conference on Human-Computer Interaction.
34–41.
NordiCHI ’22, October 8–12, 2022, Aarhus, Denmark
[44] Amon Rapp and Federica Cena. 2016. Personal informatics for everyday life:
How users without prior self-tracking experience engage with personal data.
International Journal of Human-Computer Studies 94 (2016), 1–17.
[45] S Richardson and D Mackinnon. 2017. Left to their own devices? Privacy implications of wearable technology in Canadian workplaces. Surveillance Studies
Centre (2017).
[46] Liza S Rovniak, Lan Kong, Melbourne F Hovell, Ding Ding, James F Sallis,
Chester A Ray, Jennifer L Kraschnewski, Stephen A Matthews, Elizabeth Kiser,
Vernon M Chinchilli, et al. 2016. Engineering online and in-person social networks for physical activity: a randomized trial. Annals of Behavioral Medicine 50,
6 (2016), 885–897.
[47] Patrick C Shih, Kyungsik Han, Erika Shehan Poole, Mary Beth Rosson, and
John M Carroll. 2015. Use and adoption challenges of wearable activity trackers.
IConference 2015 proceedings (2015).
[48] Alycia N Sullivan and Margie E Lachman. 2017. Behavior change with fitness
technology in sedentary adults: a review of the evidence for increasing physical
activity. Frontiers in public health 4 (2017), 289.
[49] Monique Tabak, Harm Op den Akker, and Hermie Hermens. 2014. Motivational
cues as real-time feedback for changing daily activity behavior of patients with
COPD. Patient education and counseling 94, 3 (2014), 372–378.
[50] Md Shamim Talukder, Golam Sorwar, Yukun Bao, Jashim Uddin Ahmed, and
Md Abu Saeed Palash. 2020. Predicting antecedents of wearable healthcare
technology acceptance by elderly: A combined SEM-Neural Network approach.
Technological Forecasting and Social Change 150 (2020), 119793.
[51] Christina R Victor, Annabelle Rogers, Alison Woodcock, Carole Beighton,
Derek G Cook, Sally M Kerry, Steve Iliffe, Peter Whincup, Michael Ussher, and
Tess J Harris. 2016. What factors support older people to increase their physical
activity levels? An exploratory analysis of the experiences of PACE-Lift trial
participants. Archives of Gerontology and Geriatrics 67 (2016).
[52] Shengzhi Wang, Khalisa Bolling, Wenlin Mao, Jennifer Reichstadt, Dilip Jeste,
Ho-Cheol Kim, and Camille Nebeker. 2019. Technology to support aging in
place: Older adults’ perspectives. In Healthcare, Vol. 7. Multidisciplinary Digital
Publishing Institute, 60.
[53] WHO WHO. 2010. Global recommendations on physical activity for health.
Geneva World Heal Organ 60 (2010).
[54] Paweł W Woźniak, Przemysław Piotr Kucharski, Maartje MA de Graaf, and Jasmin
Niess. 2020. Exploring Understandable Algorithms to Suggest Fitness Tracker
Goals that Foster Commitment. In Proceedings of the 11th Nordic Conference on
Human-Computer Interaction: Shaping Experiences, Shaping Society. 1–12.
[55] Randall Ziman and Greg Walsh. 2018. Factors Affecting Seniors’ Perceptions of
Voice-Enabled User Interfaces. In Extended Abstracts of the 2018 CHI Conference
on Human Factors in Computing Systems (Montreal QC, Canada) (CHI EA ’18).
Association for Computing Machinery, New York, NY, USA, 1–6. https://doi.
org/10.1145/3170427.3188575