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Older adults may use wearable devices for various reasons, ranging from monitoring clinically relevant health metrics or detecting falls to monitoring physical activity. Little is known about how this population engages with wearable devices, and no qualitative synthesis exists to describe their shared experiences with long-term use.
This study aims to synthesize qualitative studies of user experience after a multi-day trial with a wearable device to understand user experience and the factors that contribute to the acceptance and use of wearable devices.
We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to
In total, we reviewed 20 papers; 2 evaluated fall detection devices, 1 tested an ankle-worn step counter, and the remaining 17 tested activity trackers. The duration of wearing ranged from 3 days to 24 months. The views of 349 participants (age: range 51-94 years) were synthesized. Four key concepts were identified and outlined: motivation for device use, user characteristics (openness to engage and functional ability), integration into daily life, and device features. Motivation for device use is intrinsic and extrinsic, encompassing many aspects of the user experience, and appears to be as, if not more, important than the actual device features. To overcome usability barriers, an older adult must be motivated by the useful purpose of the device. A device that serves its intended purpose adds value to the user’s life. The user’s needs and the support structure around the device—aspects that are often overlooked—seem to play a crucial role in long-term adoption. Our “line-of-argument” model describes how motivation, ease of use, and device purpose determine whether a device is perceived to add value to the user’s life, which subsequently predicts whether the device will be integrated into the user’s life.
The added value of a wearable device is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. The added value contributes to the successful integration of the device into the daily life of the user. Useful device features alone do not lead to continued use. A support structure should be placed around the user to foster motivation, encourage peer engagement, and adapt to the user’s preferences.
Wearable health monitoring devices have seen a rapid rise in capability and popularity over the last two decades. These small wireless devices can monitor movements, improve physical activity, and facilitate ageing-in-place. Wearable devices temporarily and noninvasively attach to a person without hindering their movement and are often intended to be worn continuously. Examples include activity trackers (eg, Fitbit and smartwatches), fall detection devices, electromyography patches, and smart clothing.
Although older adults are not core consumers of wearable devices, their use of digital health technologies is increasing [
Researchers have used a variety of methods to collect information from older adults regarding the acceptability of wearable devices: surveys [
Qualitative research methods are well suited to examine the user experience and may offer explanations for unexpected or anomalous findings in quantitative data [
We aim to apply a qualitative meta-synthesis process to the available qualitative data on older adults’ experiences with using wearable devices. Meta-synthesis is a form of interpretive synthesis that can be used in the review and evaluation of qualitative research studies. Our meta-synthesis is based on the principles of meta-ethnography [
Our overarching research question is “What is the experience of older adults who took part in multi-day wearable device trials and what factors contribute to acceptance and use?”
The inclusion and exclusion criteria (
Peer-reviewed studies (in English)
Published between January 2015 and January 2020
Experiences of older adults using wearable devices
Using a defined qualitative approach
Presenting distinct qualitative data and results
Qualitative data collected after the multi-day trial
Studies not in English or outside the time frame
Not focused on older populations
No primary qualitative data presented
No continuous, multi-day trial component
(“wearable technology” OR “wearable sensor” OR “inertia sensor” OR “wireless sensor” OR accelerometer OR “Micro-Electrical-Mechanical-System” OR Actigraphy OR “inertial measurement unit” OR “motion monitor” OR “movement sensor” OR “wearable interface*” OR “body worn” OR wearable OR “wireless monitoring system” OR “activity tracker*” OR “activity-tracking” OR “activity sensor*” OR “activity assessment*” OR “fall detection” OR “wireless sensor networks”) AND (“user preference*” OR “user experience*” OR “user needs” OR preference* OR “patient centered” OR qualitative OR “focus group” OR perception* OR understanding OR acceptance OR adoption OR usability OR perspective) AND (“older adults” OR older OR ageing OR Parkinson’s OR Alzheimer’s OR Dementia OR stroke OR chronic) NOT (invasive OR implant*)
KM exported the search results, removed duplicates, screened all titles and abstracts for inclusion, and reviewed eligible full-text articles against the inclusion or exclusion criteria (
Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart of study selection process.
Data items extracted included information about the publication (date, authors, and study aims), study process (design, methods, and analysis), participant characteristics, device types and features, trial length, and relevant primary qualitative data (themes and quotations).
Although not required in a meta-ethnography, we assessed study quality to facilitate the critical reading of each study to gauge its potential contribution to the analysis (see Table S2 in
The analysis (
Identifying knowledge gaps and the literature available for a synthesis and developing research questions
Defining the focus of the synthesis, locating relevant studies, and assessing the quality of the included studies
Active reading of the studies to understand context and to extract relevant data
Themes and concepts were identified in the “results” and “discussion” sections; authors’ interpretations were retained where possible; descriptive studies (without author-generated themes) were coded; and each theme was transferred to an index card along with contextual information, a narrative summary, and device characteristics.
Index cards (each containing an extracted theme) were juxtaposed and grouped into general categories, categories were refined and subcategories emerged, and key concepts were identified.
Returning to each study and comparing with the generated categories, using the context provided by the authors to re-evaluate the category placement of each index card, generating new index cards when the existing index cards do not represent the totality of the study results, and generating new subcategories and condensing others to better describe the results of the studies
Compiling the participant raw data, index cards, and categories to produce overarching concepts that describe the results of the translation process
Development of a line-of-argument synthesis and conceptual model
The themed index cards were physically juxtaposed and grouped into categories based on patterns of meaning, as related to the research question. Categories and subcategories were refined iteratively through constant comparison within and across studies. Each category was compared against each original study using (1) reciprocal translation (recognizing reoccurring themes or concepts across studies) and (2) refutational translation (recognizing dissimilar themes or concepts across studies, not explained by contextual factors). When all the data were collated and interpreted, several key concepts were defined (third order) and synthesized to develop an integrative “line-of-argument.”
We tracked the preferred and disliked device features throughout the analysis process. Where relevant, we used specific device features to support the key concepts. We summarized the preferred and disliked features, but no frequency analysis was performed because each study used different devices and not all studies included participant feedback on device features.
We reported our results in line with the eMERGe guidance, which has been described for use by researchers conducting meta-ethnography [
The database search returned 1971 results (
Of the 20 included records, 2 evaluated fall detection devices [
Articles included in the meta-synthesis and quality appraisal scores using the Evaluation Tool for Qualitative Studies.
Study | Method | Participants | Device | Trial duration | ETQSa [ |
Abouzahra and Ghasemaghaei [ |
Interview pre- and posttrialb Device data |
44 participants Aged 65-75 years |
Fitbit: ATc+SPd—wrist-worn |
1 week | 6 |
Batsis [ |
Surveys Interviewb |
8 participants Aged 65-80 years Rural; obese |
Fitbit: PDe+SP—waist clip |
4 weeks | 5 |
Demiris et al [ |
Interview (×2)b Fall or device log |
18 participants Aged ≥62 years Slight fall risk |
FDDf—clip or lanyard |
4 months | 8 |
Ehn et al [ |
Interviewb Follow-up meeting Activity diary |
8 participants Aged 75-90 years |
Withings Activité Pop: AT+SP—wrist-worn Jawbone UP3: AT+SP—wrist-worn |
9-10 days for each device | 10 |
Farina et al [ |
Device diary Questionnaire Dyadic interviewb |
26 participants Aged 65-90 years Alzheimer and dementia |
GENEactiv Original: AT—wrist-worn |
1 month | 8 |
Fausset et al [ |
Questionnaire—interviews pre- or posttrialg Daily diary |
8 participants Aged 61-69 years |
Striiv: PD—clip Fitbit: PD—clip Nike+FuelBand: AT—wrist-worn MyFitnessPal: web-based |
Randomly assigned one device for 2 weeks | 5 |
Floegel et al [ |
Interviewb |
27 participants Aged 62-90 years Heart failure requiring hospitalization |
Tractivity: AT+SP—ankle-worn |
1 month | 6 |
Hermanns et al [ |
Surveysg Interviewb |
5 participants Aged 65-81 years Stage I-IV Parkinson disease |
Fitbit: AT+tablet—wrist-worn |
12 weeks | 8 |
Kononova et al [ |
Focus groupa |
48 (nonusers, short-term, former, and long-term users) Aged 65-94 years |
Garmin Vivofit 2: AT—wrist-worn |
2-4 weeks | 9 |
Lee et al [ |
Adoption and usability surveys Biweekly interviewsb |
17 participants Aged 65-85 years |
Nokia Go: AT—wrist-worn |
14 weeks | 8 |
Mercer et al [ |
Questionnaire Focus groupsb |
32 participants Aged 52-84 years |
Fitbit Zip: PD—clip Jawbone Up 24: AT—wrist-worn Misfit Shine: AT—wrist-worn or clip Withings Pulse: AT—wrist-worn PD—clip |
5 devices, each for ≥3 days (≥15 days total) | 8 |
Nguyen et al [ |
Focus groupsb |
14 participants Aged 51-64 years |
Fitbit One: PD—clip Jawbone Up 24: AT—wrist-worn Garmin Vivofit 2: AT—wrist-worn Garmin Vivosmart: AT—wrist-worn Garmin Vivoactive: AT—wrist-worn Polar A300: AT—wrist-worn |
Assigned 2 devices; 2 weeks per device, 4 weeks total | 8 |
Preusse et al [ |
Questionnaire; interviewh |
16 participants Aged 65-73 years |
MyFitnessPal: web-based Fitbit One: AT—wrist-worn+web-based |
28 days | 7 |
Puri et al [ |
Questionnaire Interview with samplei |
20 participants Aged 55-84 years |
Microsoft Band: AT—wrist-worn Mi Band: AT—wrist-worn |
Each device for 3 weeks, 6 weeks total | 9 |
Rosales et al [ |
Interviews (×2)h |
5 participants Aged ≥65 years Smartphone users |
Moto G 360: SW—wrist-worn |
12-24 months | 6 |
Schlomann et al [ |
Group discussionb |
6 participants Aged 67-78 years |
AT+SP—wrist-worn |
1 month | 6 |
Schlomann [ |
Interviews (×2)b,h |
6 participants Aged 60-78 years Smartphone users |
ViFit: AT—wrist-worn |
1 year | 6 |
Thilo et al [ |
Daily diary Focus grouph |
15 participants Aged 75-92 years History of falls |
FDD—torso patch |
9 days | 8 |
Thorpe et al [ |
Interviewh Sensor data |
6 participants Aged 65-78 years Dementia |
Sony SmartWatch 3: SW+SP—wrist-worn |
9 weeks | 6 |
Zhou et al [ |
Questionnaire Interviewb |
20 participants Aged 58-68 years |
37 Degree Technology: AT+SP—wrist-worn |
3 months | 6 |
aETQS: Evaluation Tool for Qualitative Studies; maximum score is 10.
bInductive analysis.
cAT: activity tracker.
dSP: smartphone.
ePD: pedometer.
fFDD: fall detection device.
gDescriptive analysis.
hDeductive analysis.
iDirected content analysis.
This study found four key concepts, comprising 12 subthemes that characterize the collective experience of trial participants (
Age-related physiology and comorbidities [
Physical limitations such as hand dexterity
Slower processing speeds in time of need
Inactive lifestyle does not warrant activity tracker
Sense of independence [
Confidence in abilities to remember procedures
Change in routine (battery life; attached to phone)
Subjective norm, not burden on family
Access to instructions and training
Exploration and use of device features [
Interest in diverse features and uses
Confidence to explore means maximized benefits
Technology experience means ability to troubleshoot
Instructions to overcome hurdles
Self-efficacy for technology [
Skill to control and manipulate technology
Perception of one’s own ability to use technology
Insecurities of using the system reduced usage
Awareness of physical activity levels [
Real versus perceived activity levels
Awareness is not the same as motivation
Awareness is a catalyst, not a creator of motivation
Internal influences [
Intrinsic motivation required for behavior change
Achieving personal goals is satisfying
Desire to improve health and fitness
Expectation-confirmation theory; if the device meets the user’s expectation, they may be more likely to adopt the device
Quantification and feedback [
Personalized goals and feedback can motivate
Data visualization helps to plan and monitor goals
Health data visualization connects user to the purpose of the device
Poor or absent feedback diminishes value
Emotions invoked by the device [
Connected to feedback
Relationship with the device
Negative feelings toward the device can lead to abandonment
Emotional attachment to an external motivator can be a positive driving force
Social capital and encouragement [
Social capital promotes continued use
Wearables as adjunct to social support
Peer support, interaction, and communication
Help with troubleshooting
An external influence; boost motivation
Promotion by health care staff [
Benefits of involvement by health sector
Motivated to use if part of the treatment plan
Input from care team to overcome barriers and meet goals
Ease of integration is determined by features, day-to-day function, purpose, and reliability of the device
Cumbersome or annoying design features hinder integration
Lack of desired features diminishes value of the device
The device cannot serve its function if it is unreliable and difficult to use
Reliability issues affect routine and can lead to stigma and embarrassment
Device issues reduce motivation and diminish the value of devices
Preferred features (in no particular order): waterproof, step count, easy-to-read display format, GPS (security in case of a fall or getting lost), looks like a watch, comfortable location on the body (generally wrist or ankle), secure attachment, smaller, long battery life, fewer notifications, does not interfere with clothing, personalized notifications or alarms, thin and flexible band, simple attachment (easy to use with limited dexterity), comfortable to wear at night, easy to work (intelligibility), more diverse features, health-related features, tracks sleep, looks nice or cool, simple smartphone or tablet app, other activities that older adults may be doing, real-time feedback on app or device, smaller design, easy to synchronize, automatic logging of activity, goal tracking, view health information, help section, large and easy-to-press buttons, and easy to see (if falls on the floor)
Disliked features (in no particular order): looks like a medical device (aesthetics), frequent charging, auto-goal function, inaccuracy, having to wear in bed (if uncomfortable), not capturing all activities, large and rigid band, tethered to the smartphone, uncertainties about water damage and charging, complicated tablet or smartphone, no practical training, does not match clothes, difficult to put on, frequent alarms or notifications, difficult to interact with when on the ankle, not compatible with a smartphone, difficult to handle, and not suited for older adults
The key concepts (order not indicative of prominence or salience) are (1) openness to engage and functional ability of the user; (2) motivation for device use; (3) integration into daily life; and (4) device features.
First-order quotations (raw, primary data, ie, direct participant quotations) and second-order (authors’ interpretations of their primary data) interpretations were used to support the analysis of the
Certain age-related characteristics can impact users’ comfort with new technologies, such as hearing loss, limited dexterity, and low vision [
Participants saw that, for senior persons less vigorous than themselves, everyday use of the device could be difficult, cumbersome, and demanding: “It is more difficult for a person less alert than me maybe also using walking aids. It might be tough for them to register like this every day.” [
In addition to their actual technical skills, an older person’s perceptions of their technical abilities can be a barrier to adoption [
This was reflected by many of our participants in the comments they made about the devices—often relating that they were “not built with us in mind,” that they were created “for someone younger,” and that devices needed a more “tech-savvy” user. [
Low self-efficacy for technology can influence users’ attitudes and limit the
“I was of course a bit worried initially about not being able to handle it. That I would push the wrong button and things like that.” [
Insecurities can arise when users encounter usability issues or technical failures and do not have the experience of identifying or resolving the issue:
The participants had felt inexperienced in handling the technical devices and therefore had felt insecure on whether they were doing this correctly. In addition, there were occasions when the technology had not worked properly, and this made the users wonder if the problems experienced were because of incorrect handling. [
Individuals with higher self-efficacy for technology are more open to using wearable devices and exploring their features. Users with lower self-efficacy tend to require more support, clear instructions, and additional training to increase their sense of control and prevent device abandonment [
Many older adults have a desire to learn more about their health and are interested in various advanced features (which are not always available) [
Users are less open to engaging with a device that is burdensome or limits their independence. A user will not perceive themselves as independent if they have to rely on friends, family, or researchers to help them with device issues. In addition, a device that requires frequent charging will affect the user’s routine and limit the time that they can spend away from a power source [
“I’m sure it’s there [the support] but it means taking their time, and making my problem their problem. And that’s hard for me to do because of my own attitudes about independence I think. I really resent supervision, which is intrusive and demanding; kinds of stuff like that within the family.” [
For many users, a degree of motivation is required to realize behavior change or long-term use. This is not exclusive to activity trackers; some participants do not feel the need to wear a fall detection device, even if they are at risk of falling:
A participant who experienced four falls during the course of the trial explained he did not need the device as, “I don’t consider myself a faller.” [
Some users felt that they were too young to need a fall detection device currently:
“You know if were a high fall risk...but at the moment I don’t consider that. When I get old maybe.” [
Thus, to successfully incorporate a fall detection device into their life, the person must have a recognized need and personal desire to prioritize their safety.
Activity trackers are often worn to monitor physical activity levels. Some participants were motivated to increase their activity when the device was introduced into their lives:
“I was motivated by the technology, that I freely admit.” [
Other participants were already motivated to increase their activities before using the device:
“The technology has no impact on my motivation, I am physically active anyway. I am on the verge of getting diabetes, that is what motivates me the most.” [
Both physically active and inactive older adults can lack the motivation to use a wearable device if neither has the desire to change their activity levels. Intrinsic motivation seems to be particularly powerful for users who are inactive but have a strong desire to change this; this group has room to improve, unlike very active people who are already at their desired activity level [
They believed that a wearable device can motivate them to improve their exercise level. This theme was more significant in seniors who did not exercise regularly and seniors with lower income. [
Long-term users emphasized the importance of internal motivation (Just do it) where activity trackers were serving as secondary facilitators.... [
Those who were already satisfied with their exercise levels saw no benefits from using the device [
Some older adults found that quantification of their activity can drive motivation [
Each user has a different goal, so the more personalized the feedback, the more effective the device:
Goal setting was perceived important for increasing active behaviour: a quantitative goal was helpful for the user by clarifying if the current activity level was too low. [
However, already-active individuals were not always affected by feedback:
“I did not change my exercise habits during the monitoring, I took the same walk as usual in the morning or in the afternoon. It is a goal I have and as a pensioner, I have plenty of time.” [
The feedback and features of the device must align with the goals of the user. Some users only need a push (eg, step target). Others have more detailed health monitoring goals (eg, heart rate, sleep, and quantification of multiple activities). People can feel disconnected from a device that does not provide adequate feedback; this can limit a device’s ability to help the user achieve their goals.
The importance of feedback is not limited to activity trackers. Fall detection devices provide feedback in the form of alerts and calls for help. Trial participants said they would like clear feedback about when alerts were activated, who that alert was notifying, and how they could disable the alert [
Using an activity tracker often leads to increased awareness of one’s activity levels, particularly for those who were previously inactive:
“My Fitbit allowed me to personalise my exercise. I learned new things about myself from the fitbit.” [
However, increased awareness does not necessarily motivate the user to exercise. The desire to increase exercise levels (before knowing one’s current level) and an achievable exercise goal were more motivating than awareness. Certainly, these devices can show how sedentary users are and remind them to meet their exercise goals but a person must already want to make a lifestyle change:
“It was more informative than motivating, because I had my own agenda that my doctor set out for me to do.” [
Thus, activity trackers were more often viewed as a catalyst rather than a creator of behavior change.
Feedback on activity can elicit strong emotions among users and can become attached to their results, experiencing positive affect when they meet their goals and negative affect when they do not:
“It was irritating when it is visible that I had been so damn lazy. But it is good to have (the technology).” [
Sometimes, users are more concerned with how they connect with a device than the specific output metrics, so that emotional meaning is valued more than actual gains. When a device elicits more positive feelings than negative feelings, users are more inclined to continue use.
Devices can lead to stigma and embarrassment when drawing attention to the public:
“It’s when they don’t say anything you wonder kinda what they’re looking at, cause they do take notice of it.” [
False alarms from fall detection devices can lead to disruptions in public [
“...what I was wearing was sheer, and would show this light which everybody was curious about, and it just didn’t look good with, I didn’t want to wear it.” [
For many users, the social network around the device is key to its continued use [
“Meeting with others in the sense of: did they experience the same thing? Do they need encouragement? Can something they’re doing encourage me to alter behaviours?” [
Peers can also help troubleshoot issues and provide hints for maximizing user benefits [
Long-term users indicated social support to be the main motivational factor, with the focus on building relationships around daily activity routines. Long-term users were better prepared to modify the social environment around them to maintain an active lifestyle, receive positive feedback, and seek accountability from others. [
For those using a device for medical purposes, the input and encouragement of a health professional can be important for adoption and continued use. Learning about the device from professionals can help overcome barriers to adoption and ultimately meet their goals:
“But if someone can guide you through it, I think any of them, once you start using them you would probably use it. But I wouldn’t go to Best Buy I wouldn’t have thought to go to best buy. If it’s for my health, I would think to go to a pharmacy.” [
To be successfully integrated into the user’s life, the device must not only be acceptable and reliable, it must also be perceived by the user to add value to their life. The ease of integration is often determined by the purpose and features of a device and the reliability of the device’s functions. Certain design features, such as appearance, weight, material, dimensions, and comfort, are particularly important. If the wearable device mimics a device already in the user’s life (eg, wristwatch), it can be seamlessly integrated:
“Then, it becomes a habit. And this is precisely what happened to me with my watch [reference to the AT].” [
If the device does not have the user’s desired features (eg, swimming, activity history, or GPS tracking), the user’s perceived value of the device may be low:
“Really, after the bloom got off the rose, I didn’t like anything about it.” [
Conversely, users may tolerate design faults if they value the device.
A common barrier to acceptance is its unreliability. When someone cannot rely on a device to serve its purpose or give accurate feedback, it loses value, and the motivation to wear the device can wane. This is evident in the authors’ conclusions:
Some participants...questioned whether the result was correct. This reduced their motivation for being monitored. [
It is also evident in the raw participant data:
“...We began to think that it wasn’t accurate, so it lost its appeal.” [
Critically, the device should not negatively affect the user’s routine. Frequent charging, not being waterproof, being tethered to a smartphone, and being difficult to put on and take off are examples of features that can disrupt a user’s routine [
Participants across the 20 included studies generally preferred devices that have the following features: waterproof, small in size, comfortable (especially if worn at night), aesthetically pleasing (fashionable; not like a medical device), with an easy-to-read display, a long battery life, and a thin, flexible band (if worn on the wrist). They enjoyed using device features that counted their steps, tracked their location using GPS, automatically logged their activity, measured health parameters (heart rate, blood pressure, or sleep), updated them on activity goals, automatically contacted help in the event of a fall, and synchronized automatically with their other devices. They like devices that are easy to attach, are secure, do not interfere with clothing, and are easy to handle.
The participants disliked devices that were inaccurate, required frequent charging, were uncomfortable, tethered to a smartphone, were difficult to attach, were not compatible with their smartphone, were not suited for older users, or do not capture all of their daily activities. They especially disliked devices without adequate instructions to help them troubleshoot issues or turn off annoying alarms.
The first-order (quotations), second-order (individual themes extracted from each paper), and third-order (key concept) interpretations were considered as a whole to develop a line-of-argument synthesis.
The experience of integrating a device into everyday life is a dynamic process of assessing the added value of the device and is influenced by a range of interrelated intrinsic (internal motivation, functional ability, interest, and openness) and extrinsic (external motivation, training, device characteristics, functionality, and feedback) factors. Many factors influence whether an older adult sees a device as worth wearing, and the appraisal and balance of these factors tell the user whether the device adds value to their life.
We developed a line of argument to describe the factors that influence successful integration (
Conceptual model developed from the line-of-argument synthesis.
User motivation is key. Without motivation (eg, symptom monitoring), the user will view the device as just another piece of technology. On the basis of the data collated in this synthesis, we found that older adults do not adopt new technologies because of their novelty. We found that motivation comes in two forms: intrinsic and extrinsic motivation. They influence both the user’s initial reason to adopt a device and to sustain its use.
Intrinsic motivation describes a user’s personal connection to a device. Initially, the user must be motivated to make a change in their life that will be supported by adopting a wearable device (eg, increasing physical activity or detecting falls). Intrinsic motivation is often required for individuals to adopt a device initially. The device itself does not create motivation; many users commented that while being able to view their daily step count is interesting, it does not spur them to change their physical activity habits unless they are already motivated to do so. Intrinsic motivation is also important for a device to add long-term value to a user’s life. It is necessary to overcome some of the usability hurdles that users face when they adopt a device. It also fuels continued use as the initial novelty wears off.
Extrinsic motivation is another important contributor to device adoption and added value. This includes factors such as training, technical support, promotion, support from health professionals, peer support, and device feedback. Initially, extrinsic motivation influences device adoption through the practicalities of acquiring and setting up the device and learning how to use its features. Older adults are often asked to adopt a device for fall detection or as part of a treatment or health regime. This form of extrinsic motivation often leads to device adoption but may not contribute to added value if other extrinsic factors (eg, technical and peer support) are not present. Technical support was frequently cited as a crucial extrinsic motivator, both initially and over time. Good technical support connotes added value because it gives the user the confidence to explore the device’s features and supports integration into the user’s life. Peer support is another important extrinsic motivation factor that contributes to both device adoption and added value. It comes in many forms and is unique for each user, but its importance is universal. Social support encourages older adults to adopt wearable devices and motivates continued use. Conversely, reliance on social support (eg, having to bother someone for assistance) limits the user’s independence and could be a barrier to continued use. Together, the factors of intrinsic and extrinsic motivation influence whether an individual will adopt a device and whether the device will continue to add value to their life.
The purpose of the device (fall detection, step count, etc) is the main reason why older adults adopt it, and it is what initially draws a user to that specific device. Unlike those of a younger generation, older adults do not tend to use new technology simply because they have fun features. They view devices as tools and expect them to serve their purposes with accuracy and reliability. The purpose of a device (and its features) is key to adoption. Older adults are unlikely to adopt a device that does not fulfill pre-existing needs. The purpose of the device also adds value and facilitates integration as the user expands their relationship with the device. Upon adoption, the user evaluates whether, and to what extent, the device serves its intended purpose. A device that continues to serve its intended purpose (or serves additional purposes as the user becomes more familiar with its features) is perceived to add value, leading to integration as the user relies on that device to fulfill an important need in their life.
Along with motivation and purpose, ease of use also predicts added value. This is defined as the degree to which a device is free of physical and mental effort for users. Specific device features (eg, battery life and touch screen menus) influence ease of use, as do general features such as access to simple instructions and the amount of interaction required. An easy-to-use device adds value by reducing the burden of using the device. This allows users to focus on their motivators and the fundamental purpose of the device.
Added value to life is the ultimate contributor to successful integration into daily life. The added value is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. When the negatives outweigh the positives, the device will most likely not be integrated into everyday life.
This is the first study to systematically review and synthesize the qualitative literature on older adults’ experiences with wearable devices. This meta-synthesis collated the experiences of 349 trial participants and presented the key factors that influence user acceptance and adherence. These factors include intrinsic and extrinsic motivation to use the device; the purpose of the device and how it relates to the user’s expectations and needs; and the ease of use and functional ability of the user. The user’s appraisal of these factors determines the level of value added by the device to the life of each user.
Motivation for device use comes in two forms (intrinsic and extrinsic) and encompasses many aspects of the user experience. According to our line-of-argument synthesis, motivation influences both device adoption and added value. Motivation seems to be as, if not more, important for older adults than the actual device features. Moreover, the user’s needs and the support structure around the device—aspects that are often overlooked—seem to play a crucial role in long-term adoption.
Our bottom-up inductive qualitative synthesis supports the findings of existing theory-bound models of technology acceptance. It was not intended from the outset that our conceptual model would tie in with quantitative models such as the Technology Acceptance Model (TAM) and the value-based adoption model (VAM). We felt that the TAM and VAM could be used to structure and contextualize our findings. These models use quantitative surveys to test hypotheses about factors that predict the intention to use. For example, the TAM shows that perceived ease of use, perceived usefulness, and attitude toward the system predict intention to use [
Originally designed to describe the acceptance of information services in organizations [
The TAM and VAM inspired the recent development of a smart wearables acceptance model for older adults by Li et al [
Our review points to age-related factors that can influence acceptance, such as experience with technology and openness to engage. A systematic review of factors influencing acceptance of technology for ageing-in-place found a similar phenomenon and discussed the effect of age and chronic illness on the acceptance of vital sign monitoring systems [
When designing future wearable device acceptance models for older adults, researchers should consider the multiple stages of device use that follow the initial “intention to use.” Furthermore, in the user experience, concepts such as added value become relevant, which may have a different set of predictors than the initial intention to use. In the development of the Senior Technology Acceptance and Adoption Model (STAM), Renaud and van Biljon [
Our study proposed several predictors that could be tested as a part of future model development studies. First, motivation is key and seems to be a constant driving force throughout the user experience. Researchers should question users on both intrinsic and extrinsic motivators to see if these factors predict integration. Second, the purpose of the device (and whether the user aligns with that purpose) should be investigated as a predictor of device adoption and added value. Finally, ease of use should be considered within the context of older adults, as done by Yu-Huei [
Researchers play a role in validating (or refuting) the findings of this review. Older adults require a specific wearable device acceptance model because they are a distinct population from the individuals used to develop the TAM and other wearable device acceptance models [
For clinical trial researchers, we stress the need to provide extrinsic motivation for their participants by conveying the importance of the device. They should also provide training and technical support to facilitate ease of use. Clinicians using wearable devices in their private practice can provide extrinsic motivation by clearly explaining the device’s purpose and the meaning of measurements to their patients. They should also provide encouragement and technical support. They can support intrinsic motivation by learning about their patients’ health goals and desires to use a wearable device.
Although not explicitly stated, several of the included studies used a user-centered design approach [
Measures such as practicing reflexivity and using 2 reviewers maximized the quality of this meta-synthesis. The authors are an experienced multidisciplinary team (geriatric medicine, psychology, epidemiology, and engineering) with expertise in qualitative approaches. By following the eMERGe reporting guidance (see Table S1 in
The content of our results and the line-of-argument conceptual model were contingent on the data collected from the broad inclusion criteria. This is both a strength and a weakness of qualitative syntheses; it affords reviewers the flexibility to uncover new ideas but it dictates which questions can be answered. Some studies lacked rich descriptions and interpretations of their findings or did not provide sufficient context (about the sample, the device, or the procedures). This limited the contributions of some studies to the meta-synthesis [
All studies included in this review were published in English and conducted in Western countries. The findings may not represent countries with different cultures, access to wearables, and income levels. Several studies included short trial periods and a few participants. Each study evaluated a different device (or set of devices), which restricted comparisons between studies. Future research would benefit from long-term trials using in-depth qualitative methods to evaluate the drivers of acceptance and adherence. Future research must also include the views of older adults who use wearable devices as part of clinical care, not just a research trial. An alternative set of predictors might be relevant to participants who use a device for a specific health purpose.
This review found that several key factors influence the acceptance and use of wearable devices by older adults. These include intrinsic and extrinsic motivation for device use, ease of use, device purpose, and perceived added value to the user’s life. Designers, clinicians, and researchers should be aware that useful device features alone do not lead to continued use. To overcome the usability barriers (eg, limited technical ability), an older adult must be motivated to use a device because it serves a useful purpose. A support structure should be placed around the user that fosters motivation, encourages engagement with peers, and adapts to the user’s preferences. Future research should evaluate our conceptual model by validating our proposed predictors and conducting long-term wearable device trials that use qualitative methods to comprehensively address the multiple stages of device use and the many factors that contribute to adherence.
Evaluation tool for the qualitative studies quality appraisal checklist.
The Meta-Ethnography Reporting Guidance checklist.
Evaluation Tool for Qualitative Studies
Smart Sensor Devices for Rehabilitation and Connected Healthy
Meta-Ethnography Reporting Guidance
Senior Technology Acceptance and Adoption Model
Technology Acceptance Model
value-based adoption model
This review was cofunded by the European Regional Development Fund under Ireland’s European Structural and Investment Fund Programme 2014-2020. This work was supported in part by the Interreg Norther Periphery and Artic Programme funded project SENDoc (Smart sENsor Devices fOr rehabilitation and Connected health). Aspects of this publication were supported by Enterprise Ireland and Abbvie Inc under grant IP 2017 0625.
KM, EO, and S Timmons conceived the study. KM and EO guided the study methodology and execution. KM and LK collected and analyzed the data. KM drafted the manuscript. All authors (KM, EO, LK, JB, S Tedesco, MS, CC, AA, JC, AN, and S Timmons) critically reviewed and provided intellectual input to the manuscript. All authors read and approved the final manuscript.
None declared.