Better Understanding Wearable Activity Monitor Use and Non-Use Among Older Adults: A Qualitative Study

Abstract

We used qualitative methods to better understand wearable activity monitor (WAM) use and its relationship with physical activity (PA) among older adults. Four focus groups, stratified by current/past (“ever”) WAM users and “never” WAM users, were conducted with 57 participants aged ≥60 years. Data were coded with deductive and inductive approaches. Among ever WAM users, those with low pre-WAM PA levels reported higher PA related to WAM use while those with high pre-WAM PA levels reported they used WAM for tracking and monitoring their activity. Ever WAM users were motivated to increase PA through rewards and social support. Many never WAM users reported that WAM was unnecessary. WAM may be helpful for some older adults to increase their PA, particularly those who are motivated by setting and achieving PA goals. The results could help develop strategies to overcome barriers to WAM adoption and promote WAM use among older adults.

Keywords

health promotion physical activity qualitative methods technology

What this paper adds

• Few studies have examined wearable activity monitor (WAM) use among older adults, a growing segment of the population for whom physical activity (PA) has important health benefits.

• Our focus group results contribute to a growing body of literature by providing a framework for better understanding how WAM motivates some older adults to increase their PA and identifies facilitators and barriers to use among older adults.

Applications of study findings

• Regular physical activity (PA) has important health benefits for older adults.

• Wearable activity monitor (WAM) use has potential to be a powerful tool for increasing PA among some older adults.

• Since our results suggest that WAM serves to motivate some “less active” older adults to increase their PA, a targeted approach to engage less active older adults who are open to WAM use may be useful to increase PA among this group.

Background

Regular physical activity (PA) has important health benefits for older adults including healthy aging, prevention and management of many chronic diseases, reduced risk of falls, better cognitive function, preserving gait velocity and muscle strength, and reduced mobility decline (Blair & Wei, 2000; Gillespie et al., 2003; Laurin et al., 2001; PAGAC, 2018; Trudelle-Jackson & Jackson, 2018). From a societal perspective, PA is also a contributor to reduced health care costs (Carlson et al., 2015).

The Physical Activity Guidelines for Americans, 2nd edition (US PAG) recommends older adults be as physically active as their abilities and conditions allow and, whenever possible, engage in ≥150 minutes per week of moderate-intensity activity (PAGAC, 2018). Older adults have the lowest levels of PA compared to other adult age groups. About 4 in 10 (40.5%) U.S. adults aged 65 and older meet the aerobic US PAG compared to nearly 6 in 10 (58.8%) U.S. adults ages 25–44 (NHIS, 2018).

One potential strategy to increase PA is through use of wearable activity monitors (WAM). A recent systematic review and meta-analysis provides support for the use of WAM to increase PA in the general population (Brickwood et al., 2019). However, prior research found older adults have the lowest prevalence of current or past WAM use among adults, at approximately 18% among adults age 65 years and over, compared to approximately 30% of younger adults (Omura et al., 2017). Their lower adoption of WAM may be attributed to their attitudes, confidence, cost (i.e., financial, time, effort) (Allen & Christie, 2016; Anderson & Perrin, 2017; Tedesco et al., 2017; Tsai et al., 2015), and lack of support for maintenance and use (Kononova et al., 2019). However, older adults are moving toward more digitally connected lives (Anderson & Perrin, 2017) and may increase their willingness to accept WAM if barriers to use are addressed (Preusse et al., 2017). WAM may be useful among older adults to maintain or increase PA by helping them to set specific goals and providing them with social support from friends and family (Floegel et al., 2015; Kononova et al., 2019; Lyons et al., 2014; Oliveira et al., 2020; Wright et al., 2017). WAM can be helpful to evaluate and quantify daily steps and provide feedback to promote walking (Kononova et al., 2019; Le Hello et al., 2018), as achievement of step goals is associated with adherence of WAM use (Swartz et al., 2019). The devices are aimed at users who are already motivated to change their PA behavior (Patel et al., 2015). Among adults of all ages, there is evidence that use of WAM, along with goal setting and other behavioral strategies, can promote increased PA (PAGAC, 2018; Patel et al., 2015; Zytnick et al., 2021). A recent systematic review and meta-analysis found WAM use increases PA among adults ages 60 and older compared to non-WAM users (Oliveira et al., 2020).

Research exploring WAM use and PA among older adults is relatively limited, particularly compared to the breadth of research that has been conducted among younger adults. Although the sample sizes were small, several studies have found WAM may be helpful to support PA behavior change among older adults (Mercer et al., 2016; O’Brien et al., 2015). While past research has found a positive association between WAM use and PA among older adults, it has not established temporality (i.e., whether WAM use leads to an increase in physical activity or whether more active older adults use WAM to track their activity).

Studies to date quantitatively examine the association between WAM use and PA, but to our knowledge there are no qualitative studies that examine older adults’ reasons for WAM use and perceptions about their impact on physical activity. The growing research base and theoretical benefits of WAM use for older adults warrant further exploration. As such, this qualitative research served to better understand, among older adults: (1) the relationship between WAM use and PA; (2) mechanisms through which WAM may increase PA; (3) facilitators of continued WAM use; (4) barriers to WAM adoption. Through our study objectives, we hypothesized that we could identify groups of older adults who may benefit from WAM use.

Methods

Study Design and Approach

Focus groups were stratified by WAM use status. To be an ever WAM user, participants were using or had used wrist-worn devices, health and fitness smartphone apps, or step counters.

Recruitment and Sampling

Focus group participants were recruited from the Rockville Senior Center (RSC) in Rockville, MD. Members, who pay a nominal fee to join, must be aged ≥60 years. The RSC appeals to different interests and physical capabilities, offering educational classes and workshops, arts programs, counseling services, and the facility itself has a fitness center, cafeteria, computer lab, library, and community garden. We limited our recruitment to RSC members (aged ≥60 years who spoke fluent English) due to their sizeable sample of members (∼1200) and diversity of their demographics and interests.

Discussion Guide

In accordance with best practices, a brainstorming session with key informants with topical expertise was convened to garner ideas for discussion guide questions (Krueger & Casey, 2015). Ideas were subsequently organized and culled based on relevancy to the research questions. We created semi-structured discussion guides tailored to each group of ever and never WAM user participants (Tables 1 and 2).

Table 1.

Sample Discussion Guide Questions by Major Topic for Ever Wearable Activity Monitor Users.

Major Topics	Questions
Determinants of WAM use and continued WAM use	What was your primary motivation for using an activity tracker?
	Tell us how you got your activity tracker
	Tell us about the ways, if any, that people in your social circle, such as family, friends, or colleagues influenced your decision to use an activity tracker?
	Tell us about the ways, if any, that the media, such as articles in magazines, influenced your decision to use an activity tracker
	Tell us how you got your activity tracker set up
	What were or are the primary reasons you kept using your activity tracker over time?
	Talk about the ways, if any, you used your tracker as part of a group, for example, by sharing results
	Describe the ways, if any, that the appearance and comfort of your activity tracker contributed to your continued use
	Tell us if there is anything about your activity tracker that you wish you could change
Temporal relationship between WAM and PA	How has your physical activity changed, if at all, from before to after using the activity tracker?
	Tell us if there have been any major changes in your life in the last 5 years served to either increase or decrease your physical activity? Examples may include retirement or a health condition
	Think about the metrics your activity tracker tracks, such as step counts, heart rate, or hours of sleep. When using your activity tracker, which metrics, if any, do you monitor or check?
	After checking the metrics on your activity tracker, describe to us in what ways, if any, that seeing the feedback affects you

WAM = wearable activity monitor; PA = physical activity.

Table 2.

Sample Discussion Guide Questions by Major Topic for Never Wearable Activity Monitor Users.

Major Topic	Questions
Determinants of non-wearable activity monitor use	Tell us what, if anything, you knew prior to this focus group about what activity trackers are used for
	To the best of your ability, tell us how you learned about activity trackers in general
	Share with us what, if anything, you’ve heard about activity trackers that is negative
	Describe to us in what ways you feel you have or have not been targeted in the media by activity tracker companies. By media, we mean anything you watched, read, or saw in magazines, tv, on websites, etc.
	Tell us about the ways, if any, that people in your social circle may have influenced you to not use an activity tracker
	What would you say is the main reason you do not use an activity tracker?
	If someone gave you an activity tracker to try out and use for a week, talk about your ability to get help in setting up your activity tracker from someone in your social circle, if you wanted the help
	If someone gave you an activity tracker to try out and use for a week, what are some of the issues you think you would face when trying to use it?

The Institutional Review Board (study #1810047) approved all aspects of the study.

Measures and Data Collection Procedures

Prior to the focus groups, participants completed a survey questionnaire collecting information on demographics, WAM use patterns, and time spent on various types of activity and non-activity. The question that was asked of ever WAM users to determine their pre-WAM activity level was: “Prior to using your activity tracker, would you consider yourself to be” with response options of “inactive,” “slightly active,” “moderately active,” “very active,” or “I prefer not to answer.” To determine the current activity level of participants (both ever and never WAM users), participants were asked “Would you currently consider yourself to be” with the same response choices as above. Participants were categorized as “sufficiently active” if they self-reported a moderate or very active level of activity.

Upon arrival, participants completed an informed consent form. DZ served as the focus groups’ moderator and led the discussions while a research assistant took notes and handled administrative duties. After the 60 minute focus group, each participant received a $25 gift card.

Analysis

Recordings were professionally transcribed verbatim. We conducted a thematic analysis (Braun & Clarke, 2006) that took both a theoretical deductive and a data-driven inductive approach (Saldana, 2015). We generated a priori codes based on the discussion questions and developed more codes based on a review of the transcripts. With the assistance of NVivo 12 software (QSR International Pty Ltd.), we looked for themes by reviewing the codes for patterns, checking counts, visualizing the data by drawing diagrams to display how concepts are connected, and examining the data for contradictions and negative evidence. We ran matrix coding queries with a descriptive intent to determine whether themes differed by WAM status or activity level (Miles & Huberman, 1994). Themes were reviewed by the research team and revised as appropriate.

Results

Demographics of Focus Group Participants

Two groups were comprised of ever WAM users (n = 15 and n = 14) and two groups were comprised of never WAM users (n = 13 and n = 15) for a total of 57 participants. While characteristics of our ever and never WAM users were similar, ever WAM users were somewhat more likely than never WAM users to be younger, female, non-Hispanic Caucasian, have a college or graduate degree, have an annual household income of ≥$100,000, and include more retirees. Among ever WAM users, 65.5% perceived themselves to be sufficiently active, that is, they reported activity levels of moderate to very active prior to WAM use and 82.8% after WAM use. Among never WAM users, 82.1% rated themselves as sufficiently active. See Table 3 for characteristics of participants by WAM use status.

Table 3.

Characteristics of Ever and Never Wearable Activity Monitor User Focus Group Participants.

Participant Characteristics	Ever WAM Users (n = 29)		Never WAM Users (n = 28)		p-value^a
Participant Characteristics	N	%	N	%	p-value^a
Aged ≥72	20	69.0	23	82.1	.32
Female	25	86.2	19	67.9	.14
Non-Hispanic Caucasian	24	82.8	19	67.9	.25
College or graduate degree	21	72.4	17	60.7	.45
Annual household income ≥$100,000	9	31.0	6	21.4	.68
Retired	28	96.6	25	89.3	.29
Sufficiently active (i.e., activity level of moderate to very active) before WAM use (ever WAM users) or currently (never WAM users)^b	19	65.5	23	82.1	.22
Sufficiently active (i.e., activity level of moderate to very active) after WAM use^a	24	82.8	n/a	n/a	n/a

WAM = wearable activity monitor.

^aCalculated using two sample Z-tests.

^bBased on participant self-reported activity level.

Perceived Impact of WAM Use on PA among Ever WAM Users

Themes on WAM use and PA differed between ever WAM users who described themselves as sufficiently active before WAM use and those who described low PA level prior to WAM use. For those who were already active, there was a theme related to the lack of impact of WAM on PA. As they were already active, these participants acquired WAM for tracking and monitoring their PA. Some of these already active participants used WAM to see if they were “really doing as much as [they] thought.”

“Yeah, I [began to use a WAM because I] wanted to make sure I’m really walking enough, because I thought I was, but this way I really can make sure I’m doing at least two miles every day.” —Female, ever WAM user, “more active.”

Other participants use their WAM because they appreciate its ability to track “all these different things,” in reference to heart rate, swimming, walking, biking, and other activities.

On the other hand, a theme among those in the less active group was that WAM use had increased their PA. They reported several mechanisms through which this occurred. For example, several “less active” participants reported that WAM motivated them to meet their PA goals, whether in terms of steps, distance, heart rate, or pulse. Allowing them to monitor their activity, some participants could “step it up” if they were not achieving their PA goals.

“If I didn’t have the goal and the number, I would just sit and watch television, but I want to meet my goal. It’s a strange psychology.” —Male, ever WAM user, “less active.”

A couple “less active” participants noted that WAM served to motivate them to be less sedentary. Some WAMs have a “nudge” feature that vibrates if the user has been inactive for too long.

“I mostly used the inactivity tracker, where it tells me to get off the computer and move.” –Female, ever WAM user, “less active.”

Other “less active” participants were motivated by daily rewards WAM provided rewards for engaging in PA. One participant noted:

“I’m walking more, because I know at five miles fireworks go off [on my WAM and…] I realize internally I love it.” –Female, ever WAM user, “less active.”

Others noted that as they continued to engage in PA because of WAM, they noticed longer-term rewards related to the PA itself (e.g., increased energy, better ability to keep up with grandchildren, weight loss and muscle gain, improved health numbers, decreased pain). As one participant commented:

“[I am] addicted to [WAM]” because “I started with the losing weight, and I just…keep pushing myself to do more and more steps.” –Female, ever WAM user, “less active.”

Previously less active WAM users also described being motivated through social support. Many participants who tracked steps taken in a day walked extra in the evenings to achieve their step goals. Friends and spouses often played a role in the extra activity, sometimes as partners in evening walks or, for those who were apart, as the “friendly competition” in a contest to see who could get more daily steps.

“I just gave my sister one [WAM] for her birthday. And now, she calls me every day to tell me how many steps she’s done.” –Female, ever WAM user, “less active.”

Facilitators of Continued Use of WAM among Ever WAM Users

Among both less active and more active WAM users, a major theme was that they sustained WAM use over time. Only a few individuals contradicted this theme and phased out use or lost and never replaced their WAM. Most ever WAM users said they had been using WAM for more than a year and continued to use it on all or most days. There were three subthemes describing facilitators to sustained use: usability, appearance, and cost.

WAM users found their technology useable, either because they felt they had sufficient support or because the technology itself was very easy. Many described having accessible support from friends or family, or else online support from a community of WAM users. Particularly among Fitbit^TM users, there was agreement that the technology itself was very easy to use:

“At least the Fitbit^TM was pretty menu-driven. Once you get the app set up on your phone, it takes you through step by step.” –Male, ever WAM user, “more active.”

While appearance was not a primary reason for continued WAM use, it was mentioned several times as an advantage that helped keep the technology relevant to them. Ever WAM users thought WAM “kept them current” and they enjoyed the attention of curious folks who asked about their WAM. Some thought their WAMs were “very stylish” and they liked being able to change the band for different occasions or seasons:

“I treated myself last summer to the ventilated band in a pretty color, which is a little bit cooler” –Female, ever WAM user, “less active.”

For some ever WAM users, the expense of WAM served as motivation to continue using WAM. They wanted to get value from the WAM and not waste the money spent:

“If you spend the money to have it, to me, that makes me want to use it.” –Female, ever WAM user, “more active.”

While it did not seem to have a major impact on continued use, some ever WAM users described some negative aspects of WAM. Several noted inaccurate or unreliable readings. For example, one ever WAM user cited taking a known 4 mile walk where WAM only tracked 2.7 miles. Another ever WAM user walked with his wife, who was also wearing a WAM, and they had vastly different step counts:

“Well, my wife and I both use one and go on walks together, and we have completely different readings. She seems to walk a hell of a lot more than me.” –Male, ever WAM user, “less active.”

Several participants wearing wrist-worn WAMs were frustrated that steps taken on grocery shopping trips did not get counted while their hands were stationary pushing the cart. Lastly, participants who used a smartphone app as their WAM, by design, were unable to count steps without the phone being on their body.

Reasons for Non-Adoption of WAM among Never WAM Users

A major theme among never WAM users was that WAM was unnecessary. For one, they preferred other methods of tracking PA, such as how their clothes fit or the metrics provided by exercise machines. Further, those who set PA goals did them in terms of metrics that did not need WAM to track, like number of exercise classes taken per week or number of walks taken in a day.

Second, some participants lacked desire to measure their PA at all while others had no interest in increasing their PA. WAM would not be a useful tool for either of these groups of people.

“I just want to say, a lot of us might want to do less. I am not interested in doing more physical activity than I’m already doing.” –Female, never WAM user, “more active.”

“I wouldn’t really care to have [WAM] particularly. I mean, I do what [PA] I would do, and I try to do more. But I don’t feel that I need to categorize every darn thing I do.” –Female, never WAM user, “more active.”

Another theme among never users was that there were challenges and barriers to using WAM that were perceived as difficult to overcome—including the inconvenience of WAM use, the difficulty in setting up and using WAM, and its cost. Those who thought WAM was inconvenient were not looking to add anything else to their already full list of responsibilities:

“I think that having to wear [WAM], remember to put it on every day, and the wrist-worn ones you have to charge every -- I’m charging an iPhone, I’m charging an iPad, I’m charging a computer. I’m tired of charging. I don’t know, I think it’s inconvenient.” –Female, never WAM user, “less active.”

Some lacked confidence in their ability set up and use WAM:

“I think it’s, the technology is intimidating to me, like, is it hard to set up, or how to use it… Just, is it going to be challenging to set it up, not use it, but to set it up?” –Female, never WAM user, “more active.”

A barrier mentioned by a few never WAM users was cost. This was perceived as a deterrent, especially for something they basically considered unnecessary. For example, one participant said:

“So, you put out all that money and then you sort of use it in the beginning. But then I could see myself not using it, so then I’ve spent that money and I don’t use it. It just goes in my junk drawer.” –Female, never WAM user, “less active.”

Finally, across the two focus groups with never WAM users, two participants had never heard of WAM prior to the focus group. When asked what, if anything, they had known about WAM, those participants responded:

“I had no idea. The only exercise I do is when I come here [to the RSC].” –Female, never WAM user, “less active.”

“I was not familiar [with WAM]…but [now that I am participating in this focus group] I know a little bit more.” –Male, never WAM user, “more active.”

Those individuals who never heard of WAM were older and less technologically connected (e.g., did not have an email address, did not have a smartphone) and were not aware of family or friends who used the devices.

Discussion

We used qualitative methods to explore the relationship between WAM use and PA, facilitators of continued WAM, and reasons for non-adoption of WAM. Based on the participants’ first-hand experience using WAM, our results suggested some older adults who were less active prior to WAM use increased their PA because of WAM, while those who were more active prior to WAM used WAM to track and monitor their PA without WAM impacting their PA. There were multiple mechanisms through which ever WAM users were motivated by WAM and continued to use it with only limited critiques of its use. Non-adoption among never WAM users was largely related to considering it unnecessary.

Our results suggest that some less active older adults use WAM and increase their PA, while more active older adults use WAM to help maintain their PA levels. These results support findings from another WAM focus group study conducted among older adults. They found that long-term ever WAM users continued to use WAM because they were motivated by the long-term health benefits and the social support it provides (Kononova et al., 2019). The authors concluded that future research is needed to further explore and quantify reasons behind WAM use maintenance, and facilitators and barriers to use, the usefulness of which our study reinforces.

Past research found an association between rewards and an increased likelihood of a behavior (Judah et al., 2018), which aligns with our participants’ description of doing increased PA to reach their PA goal. Participants are motivated to reach their daily PA goals (e.g., steps taken) and see the “fireworks emoji” reward as a result. And while a participant may be motivated by the emoji, the PA cycle was reinforced with multiple participants describing their health improvements (e.g., weight loss, improved ability to keep up with grandchildren) as a result of their increased PA because of WAM. The more rewarding a behavior, the greater the reinforcement, resulting in greater gains for the behavior to become a habit (Judah et al., 2018).

Other studies have also found social support and competition to be important factors in increasing engagement and adherence of PA behaviors (Sallis & Owen, 1999; Sullivan & Lachman, 2016), as well as engagement strategies that influence behavior, such as social competition (Patel et al., 2015). Our participants spoke of the positive effect that friendly step competitions, support for WAM troubleshooting, and taking extra walks with spouses had on their level of PA.

Research supports our finding that older adults can master WAM use fairly easily (O'Brien et al., 2015). While WAM has traditionally targeted young, sporty, technologically-inclined individuals (Tedesco et al., 2017), the landscape of offerings to older adults is beginning to change. For example, the Apple Watch Series 4 was released in late 2018 and was designed to detect falls, which is a feature beneficial for older adults with balance issues. Designing WAMs that take into consideration the differences in ability among users will continue to facilitate WAM use among older adults.

As for the cost, some of our participants found price to be a motivator for use, which aligns with past research about the motivational difference between an individual who purchases a WAM themselves versus someone who receives one as a gift (Sullivan & Lachman, 2016). The former individual may be more motivated to make healthy changes to their behavior and therefore be willing to make the financial investment in WAM (Sullivan & Lachman, 2016).

Some WAM users perceived their devices to be inaccurate. A study found WAM accurately measured step counts among older community-dwelling adults (Straiton et al., 2018); however, a different study found WAM has less utility among older adults with slow walking speeds or gait disorders (Lauritzen et al., 2013). Differences in the findings may be attributed to the selection of older adult participants and the types of WAM used. Since inaccuracies may be a barrier to WAM acceptance (Preusse et al., 2017), selecting a WAM that is designed for the needs of older adults may be important for long-term WAM adherence.

Non-adoption of WAM among never WAM users stemmed from their feeling that WAM is unnecessary. If all barriers to WAM use were removed, some older adults would still not use WAM because they are not motivated by the purpose of the device (Moore et al., 2021). Use of WAM requires older adults to have favorable intentions to use the features of WAM, instead of a desire to track their activity in other forms that are not measured by WAM (Preusse et al., 2017). There has been a rise in recent research reporting increased acceptance of WAM use among older adults (Mercer et al., 2016; Tedesco et al., 2017), but more research is needed to better understand in what ways older adults who find WAM unnecessary instead either track their activity without using WAM or lack a desire to track their activity using WAM at all. Our study found some older adults use metrics like how their clothes fit or rely on quantification from exercise machines, but we can only speculate about the association between these non-WAM metrics and physical activity level. Many of our never WAM users are physically active without the use of WAM, but choose to measure their activity in a way that is helpful and goal-oriented for them, as opposed to a metric commonly measured by WAM.

Groups of Older Adults Who May Benefit from WAM Use

The relationship between WAM use and PA among older adults appears to be complex. Since our results suggest that WAM serves to motivate some “less active” older adults to increase their PA, a targeted approach to engage less active older adults who are open to WAM use may be useful to increase PA among this group. Specifically, the results of this study suggest that interventions to increase WAM use should target: (1) Less active never WAM users who are intrinsically motivated to increase their PA for improved health. They are willing to receive support to overcome barriers to WAM use in order to achieve their PA goals. (2) Less active older adults who are unaware of WAM’s existence. They cannot reap potential benefits of WAM without knowing about the tool. While our focus groups had a low prevalence of unaware individuals, we suspect there may be a higher prevalence in the general population of older adults since our participants likely opted to participate if they had at least some interest in the topic. To our knowledge, no studies have been conducted specifically on awareness of WAM among older adults, but the literature shows, for example, that rural areas have lower levels of adoption and use of technology (Salemink et al., 2017), whereas our study participants resided in suburban regions.

Study Strengths and Limitations

Strengths of our study included achieving participant diversity by PA level, age range, sex, race, education, and annual household income and use of a single focus group moderator across all four focus groups. The moderator attempted to minimize any potential effects of her own biases by adhering to the discussion guide questions as closely as possible. The study benefited from stratifying focus groups by ever and never WAM users to allow for different perspectives based on WAM use. Lastly, our study is unique in that it distinguishes between groups of never WAM users who may benefit from WAM use.

Some limitations should be considered in interpreting the results of this work. First, as with any type of qualitative research, the views of older adults who were not in our sample were not represented. For example, everyone in our study lives in relatively similar types of communities in a Maryland suburb and has access to resources such as the senior center where focus groups took place. Perspectives from residents of rural communities and those who have limited physical mobility were not represented in this study. Had we been able to broaden our target population to older adults without access to these resources and who are not connected to the Senior Center, we may have yielded different findings related to WAM use and physical activity. For example, in communities without a Senior Center to frequent, where older adults feel uneasy about their personal safety, traffic safety, or physical accessibility to places where they can engage in activity, older adults may not feel supported in pursuing positive health behaviors (Ottoni et al., 2021). Second, social desirability bias is an inherent limitation of focus group research although we made efforts to encourage candid conversations. With any self-report of information, responses could be skewed. Sometimes when reporting physical activity level, participant self-reports are higher than what is actually measured on activity tracker devices (Olds et al., 2019). But we have no reason to suspect that the potential inflation of physical activity level would differ by ever versus never WAM user. Additional bias may have been introduced when asking participants about their self-reported physical activity level and their WAM use; it is possible their reporting of current WAM status affected their retrospective reporting of PA.

Conclusions

Emerging literature found WAM use has potential to be a powerful tool for increasing PA among some older adults. Our focus group results contribute to a growing body of literature by providing a framework for better understanding how WAM motivates some older adults to increase their PA and identifies facilitators and barriers to use among older adults. Using WAM as a tool to increase PA is especially important as public health practitioners look for opportunities to help mitigate the low prevalence of PA among older adults (BRFSS, 2016; NCHS, 2014; Paul et al., 2015).

Supplemental Material

Supplemental Material - Better Understanding Wearable Activity Monitor Use and Non-Use Among Older Adults: A Qualitative Study

Supplemental Material for Better Understanding Wearable Activity Monitor Use and Non-Use Among Older Adults: A Qualitative Study by Deena Zytnick, Sara C. Folta, Kieran F. Reid, and Virginia R. Chomitz in Journal of Applied Gerontology

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is supported by Mathew and Brenda B. Ross Initiative on Aging.

Ethical Approval

Social Behavioral & Educational Research Institutional Review Board at Tufts University: Exempt status for IRB study # 1807021

ORCID iD

Deena Zytnick

Supplemental Material

Supplemental material for this article is available online.

References

Allen

L. N.

Christie

G. P.

(2016). The emergence of personalized health technology. Journal of Medical Internet Research, 18(5), Article e99. https://doi.org/10.2196/jmir.5357

Anderson

Perrin

(2017). Tech adoption climbs among older adults. Pew Research Center. http://www.pewinternet.org/2017/05/17/tech-adoption-climbs-among-older-adults/

Blair

S. N.

Wei

(2000). Sedentary habits, health, and function in older women and men. American Journal of Health Promotion, 15(1), 1–8. https://doi.org/10.4278/0890-1171-15.1.1

Braun

Clarke

(2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

BRFSS . (2016). Percent of adults who engage in no leisure-time physical activity. Centers for Disease Control and Prevention. https://chronicdata.cdc.gov/Nutrition-Physical-Activity-and-Obesity/Percent-of-Adults-who-engage-in-no-leisure-time-ph/8mz7-s5ke

Brickwood

K. J.

Watson

O’Brien

Williams

A. D.

(2019). Consumer-based wearable activity trackers increase physical activity participation: Systematic review and meta-analysis. JMIR Mhealth and Uhealth, 7(4), Article e11819. https://doi.org/10.2196/11819

Carlson

S. A.

Fulton

J. E.

Pratt

Yang

Adams

E. K.

(2015). Inadequate physical activity and health care expenditures in the United States. Progress in Cardiovascular Diseases, 57(4), 315–323. https://doi.org/10.1016/j.pcad.2014.08.002

Floegel

T. A.

Giacobbi

P. R.

Jr Dzierzewski

J. M.

Aiken-Morgan

A. T.

Roberts

McCrae

C. S.

Marsiske

Buman

M. P.

(2015). Intervention markers of physical activity maintenance in older adults. American Journal of Health Behavior, 39(4), 487–499. https://doi.org/10.5993/ajhb.39.4.5

Gillespie

L. D.

Gillespie

W. J.

Robertson

M. C.

Lamb

S. E.

Cumming

R. G.

Rowe

B. H.

(2003). Interventions for preventing falls in elderly people. The Cochrane Database of Systematic Reviews, 4, Cd000340. https://doi.org/10.1002/14651858.cd000340

10.

Judah

Gardner

Kenward

M. G.

DeStavola

Aunger

(2018). Exploratory study of the impact of perceived reward on habit formation. BMC Psychology, 6(1), 62. https://doi.org/10.1186/s40359-018-0270-z

11.

Kononova

Kamp

Bowen

Rikard

R. V.

Cotten

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), Article e9832. https://doi.org/10.2196/mhealth.9832

12.

Krueger

R. A.

Casey

M. A.

(2015). Focus groups: A practical guide for applied research (5th ed.). Sage.

13.

Laurin

Verreault

Lindsay

MacPherson

Rockwood

(2001). Physical activity and risk of cognitive impairment and dementia in elderly persons. Archives of Neurology, 58(3), 498–504. https://doi.org/10.1001/archneur.58.3.498

14.

Lauritzen

Muñoz

Luis Sevillano

Civit

(2013). The usefulness of activity trackers in elderly with reduced mobility: A case study. Studies in Health Technology and Informatics, 192(1), 759–762. https://doi.org/10.3233/978-1-61499-289-9-759

15.

Le Hello

Trombert

Morel

Chieh

Brouard

Boissier

(2018). Performance analysis of walking of 10,000 regular users of a connected activity tracker. Journal de Médecine Vasculaire, 43(4), 231–237. https://doi.org/10.1016/j.jdmv.2018.04.001

16.

Lyons

E. J.

Lewis

Z. H.

Mayrsohn

B. G.

Rowland

J. L.

(2014). Behavior change techniques implemented in electronic lifestyle activity monitors: A systematic content analysis. Journal of Medical Internet Research, 16(8), Article e192. https://doi.org/10.2196/jmir.3469

17.

Mercer

Giangregorio

Schneider

Chilana

Grindrod

(2016). Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: A mixed-methods evaluation. JMIR Mhealth and Uhealth, 4(1), Article e7. https://doi.org/10.2196/mhealth.4225

18.

Miles

M. B.

Huberman

A. M.

(1994). Qualitative data analysis: An expanded sourcebook. SAGE.

19.

Moore

O'Shea

Kenny

Barton

Tedesco

Sica

Crowe

Alamäki

Condell

Nordström

Timmons

(2021). Older adults’ experiences with using wearable devices: Qualitative systematic review and meta-synthesis [review]. JMIR Mhealth and Uhealth, 9(6), Article e23832. https://doi.org/10.2196/23832

20.

NCHS . (2014). National health interview survey: Survey description. Centers for Disease Control and Prevention. ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2013/srvydesc.pdf

21.

NHIS . (2018). The national health interview survey early release program. https://www.cdc.gov/nchs/nhis/releases.htm. https://public.tableau.com/profile/nhis#!/vizhome/FIGURE7_2/Dashboard7_2

22.

O'Brien

Troutman-Jordan

Hathaway

Armstrong

Moore

(2015). Acceptability of wristband activity trackers among community dwelling older adults. Geriatric Nursing, 36(2 Suppl), S21–S25. https://doi.org/10.1016/j.gerinurse.2015.02.019

23.

Olds

T. S.

Gomersall

S. R.

Olds

S. T.

Ridley

(2019). A source of systematic bias in self-reported physical activity: The cutpoint bias hypothesis. Journal of Science and Medicine in Sport, 22(8), 924–928. https://doi.org/10.1016/j.jsams.2019.03.006

24.

Oliveira

J. S.

Sherrington

R. Y. Z.

Franco

M. R.

Tiedemann

(2020). Effect of interventions using physical activity trackers on physical activity in people aged 60 years and over: A systematic review and meta-analysis. British Journal of Sports Medicine, 54(20), 1188–1194. https://doi.org/10.1136/bjsports-2018-100324

25.

Omura

J. D.

Carlson

S. A.

Paul

Watson

K. B.

Fulton

J. E.

(2017). National physical activity surveillance: Users of wearable activity monitors as a potential data source. Preventive Medicine Reports, 5(C), 124–126. https://doi.org/10.1016/j.pmedr.2016.10.014

26.

Ottoni

C. A.

Sims-Gould

Winters

(2021). Safety perceptions of older adults on an urban greenway: Interplay of the social and built environment. Health and Place, 70, 102605. https://doi.org/10.1016/j.healthplace.2021.102605

27.

PAGAC . (2018). 2018 physical activity Guidelines advisory committee scientific report. https://health.gov/paguidelines/second-edition/report/pdf/PAG_Advisory_Committee_Report.pdf

28.

Patel

M. S.

Asch

D. A.

Volpp

K. G.

(2015). Wearable devices as facilitators, not drivers, of health behavior change. JAMA, 313(5), 459–460. https://doi.org/10.1001/jama.2014.14781

29.

Paul

Carlson

S. A.

Carroll

D. D.

Berrigan

Fulton

J. E.

(2015). Walking for transportation and leisure among U.S. Adults—National health interview survey 2010. Journal of Physical Activity & Health, 12(Suppl 10 1), S62–S69. https://doi.org/10.1123/jpah.2013-0519

30.

Preusse

K. C.

Mitzner

T. L.

Fausset

C. B.

Rogers

W. A.

(2017). Older adults’ acceptance of activity trackers. Journal of Applied Gerontology, 36(2), 127–155. https://doi.org/10.1177/0733464815624151

31.

Saldana

(2015). Thinking qualitatively: Methods of mind. SAGE.

32.

Salemink

Strijker

Bosworth

(2017). Rural development in the digital age: A systematic literature review on unequal ICT availability, adoption, and use in rural areas. Journal of Rural Studies, 54, 360–371. https://doi.org/10.1016/j.jrurstud.2015.09.001

33.

Sallis

J. F.

Owen

(1999). Physical activity and behavioral medicine. Sage.

34.

Straiton

Alharbi

Bauman

Neubeck

Gullick

Bhindi

Gallagher

(2018). The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: A systematic review. Maturitas, 112, 85–93. https://doi.org/10.1016/j.maturitas.2018.03.016

35.

Sullivan

A. N.

Lachman

M. E.

(2016). Behavior change with fitness technology in sedentary adults: A review of the evidence for increasing physical activity. Frontiers in Public Health, 4(1), 289. https://doi.org/10.3389/fpubh.2016.00289

36.

Swartz

M. C.

Lewis

Z. H.

Swartz

M. D.

Martinez

Lyons

E. J.

(2019). Brief report: Active ingredients for adherence to a tracker-based physical activity intervention in older adults. Journal of Applied Gerontology, 38(7), 1023–1034. https://doi.org/10.1177/0733464817739350

37.

Tedesco

Barton

O’Flynn

(2017). A review of activity trackers for senior citizens: Research perspectives, commercial landscape and the role of the insurance industry. Sensors (Basel), 17(6), 1277. https://doi.org/10.3390/s17061277

38.

Trudelle-Jackson

Jackson

A. W.

(2018). Do older adults who meet 2008 physical activity guidelines have better physical performance than those who do not meet? Journal of Geriatric Physical Therapy, 41(3), 180–185. https://doi.org/10.1519/jpt.0000000000000118

39.

Tsai

H. S.

Shillair

Cotten

S. R.

Winstead

Yost

(2015). Getting grandma online: Are tablets the answer for increasing digital inclusion for older adults in the U.S. Experimental Gerontology, 41(10), 695–709. https://doi.org/10.1080/03601277.2015.1048165

40.

Wright

S. P.

Hall Brown

T. S.

Collier

S. R.

Sandberg

(2017). How consumer physical activity monitors could transform human physiology research. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 312(3), R358–R367. https://doi.org/10.1152/ajpregu.00349.2016

41.

Zytnick

Kumar

G. S.

Folta

S. C.

Reid

K. F.

Tybor

Chomitz

V. R.

(2021). Wearable activity monitor use is associated with the aerobic physical activity guidelines and walking among older adults. American Journal of Health Promotion, 35(5), 679–687. https://doi.org/10.1177/0890117120985834

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