Factors Associated with Older Adults’ Long-Term Use of Wearable Activity Trackers

7131 words (29 pages) Full Dissertation in Full Dissertations

06/06/19 Full Dissertations Reference this

Disclaimer: This work has been submitted by a student. This is not an example of the work produced by our Dissertation Writing Service. You can view samples of our professional work here.

Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.

Factors Associated with Older Adults’ Long-Term Use of Wearable Activity TrackersResults from a US Survey

ABSTRACT

Wearable activity trackers (WATs) have the potential to improve older adults’ health. Long-term usage of WATs could be especially beneficial to older adults in terms of self-monitoring and health management. We conducted a survey (N = 193) with a nationally representative sample of older adults (65+) to explore factors associated with long-term usage (>6 months) of WATs within this population. Having chronic condition(s) and competing with family members and friends decreased odds of long-term use, whereas being a female, receiving the WAT as a gift, forming the habit of using the WAT, and having a higher household income increased odds of long-term use. Adopting WATs because of health benefits or supporting others had no significant influence on long-term engagement. We discuss implications and limitations of these results.

Author Keywords

Wearable activity trackers; older adults; long-term use.

INTRODUCTION

Commercially available wearable devices have become a rapidly expanding health-focused industry [34].  Though terms vary, such as wireless activity+sleep wristband, fitness trackers, activity trackers, or wearables, they are usually referring to the same type of device that allows people to automatically track steps taken, sleep time and quality, distance walked, calories burned, heart rate, etc. (functions available depend on brand and model) by using sensors [34]. Users adopt these devices for personalized data generation and goal setting to reach and maintain a healthier body and lifestyle [8]. As wearable activity trackers’ (WATs) costs decrease, their popularity has increased. As of February, 2017, Fitbit, one of the dominant companies in this industry, has 23.2 million active users across the world, a 37% increase from 2015 [50]. A national survey in the United States in 2012 found that 69% of adults used a tracking device to track weight, food intake, or amount of exercise [14].

With an expanding market, research on activity trackers has increased. Research shows that WATs usually incorporate behavior change techniques, such as goal setting, social support, feedback, and rewards [35]. Given that theoretically driven behavior change techniques have been shown to be effective in promoting physical activity [45], there is great potential in utilizing those techniques via the adoption and continued use of WATs.

Older adults have more obstacles to participating in physical activity than younger adults; thus, they may need more support in developing and maintaining health behaviors. This issue is also crucial because older adults are projected to constitute more than 25% of the population in Europe and North America [42]. The population aging process is also accompanied by the increase in chronic diseases, such as cardiovascular diseases, obesity, and diabetes, which is projected to account for 75% of deaths worldwide and greatly burden the healthcare system [38]. As of 2013, only 7% of the older adults own an activity tracker [26].

Most of the research on WATs thus far has used convenience samples to understand the initial uses and adoptions of WATs, typically by recruiting a small group of participants and surveying their pre- and post- usage attitudes or collecting qualitative data through focus groups and interviews about their perceptions of activity trackers after a brief trial period. Other studies analyzed the design aspects of WATs, such as usability, behavior change techniques present in activity trackers, how people interact with their trackers, and corresponding design strategies [7,19,34]. Although research has examined initial uses and adoptions of WATs, few studies have identified the factors associated with long-term use. However, one of the main issues that has emerged with the usage of WATs is the large dropout rate after initial adoption [5,7,25,26]. Even though studies have found that abandonment does not translate into lack of utility [5], we propose that long-term engagement with trackers is especially important for vulnerable populations, such as older adults, to fully achieve the benefits of WATs.

This paper presents results from a survey of older adults who are current or previous WAT users. As one of the first research studies to examine older adults’ long-term use of WATs and its associated factors, we identified significant predictors that may contribute to the long-term use of WATs for older adults. We discuss implications for design and directions for future research.

RELATED WORK

Older Adults and WATs

Even though the ownership of WATs among older adults was 7% in 2013, it is continuing to gain popularity among this population. Researchers evaluated trackers’ feasibility and utility among older adults and found that 95% (N = 34) of the participants reduced waist circumference and increased step counts during the 12-week pilot study [37]. Using a randomized trial method for a sample of older women (N = 51), Cadmus-Bertram et al. [3] concluded that when compared to a group of pedometer users, those using a Fitbit were significantly more active. In addition, feasibility studies have demonstrated that older adults found trackers easy to use [37]. Therefore, WATs could be an innovative way to increase the amount of physical activity among older adults.

Long-Term Use of WATs and Associated Factors

Regular physical activity is associated with decreased risks of chronic illnesses, cardiovascular diseases, cancer and diabetes, as well as improved mental health and well-being, reduced fall risks, and a longer, independent life for older adults [12,24,43]. As a tool for promoting physical activity, long-term engagement with WATs is beneficial for motivating, supporting, and routinizing healthy behaviors [15]. This is especially important for older adults because they have lower levels of physical activity, and they have unique psychological, social, physical, and environmental barriers associated with engaging in physical activity compared to younger adults, such as decreased social ties, increased perceptions of difficulties, limited resources, and lowered self-efficacy [12]. Previous research reported an average 50% decrease in older adults’ adherence to exercise regimens 6-7 months after initiation [6,32]. As an accessible and widely available health tool, WATs integrate various behavior change techniques, including goal-setting, self-regulation, and social support [35], and they have been shown to promote physical activity in older adults in several small-scale interventions compared to pedometers or Short Message Service (SMS) [3,48].

Swift abandonment of health technologies such as WATs is due to various reasons, such as forgetting to put it on, data inaccuracy, dissatisfaction with physical design and aesthetics, lifestyle changes, health conditions, and cost of collecting and sharing data [5,7,25]. A large survey (N = 6223) found that more than half of individuals in different age groups discontinue using WATs within six months of adoption [26], marking six months as a dividing point for defining short-term and long-term use.

However, research focusing on older adults’ long-term use of WATs is extremely limited. A few studies evaluated the long-term change brought about by long-term use of WATs but did not focus on older adults [15,41]. By interviewing 30 participants who used WATs for at least 3 months, one study demonstrated that most of the participants agree that they made “durable” and “profound” changes regarding health behaviors due to the prolonged use of the device. Even though not all participants took advantage of the social features, some of them mentioned monitoring family members’ step counts to show caring and support over time [15]. After giving 18 participants (ages varied from 36 to 73) a Fitbit for seven months, another study found that those who were still wearing the device reported significant health and lifestyle changes, resulting in positive changes, such as weight loss and social connection [41].

WATs gather and visualize data about one’s physical activity and serves as both a regulator and a motivator in people’s decision-making regarding health-related behaviors [36]. The wide application of WATs is related to the change in the U.S. healthcare industry. Modern medicine is increasingly advocating for the collaborative efforts of doctors and patients and is emphasizing prevention and patients’ self-care by encouraging adherence to a healthy lifestyle [11]. The largest application of wearable technologies is in the healthcare industry [16]. One study that investigated users’ initial goals of adopting smart devices including WATs identified that most of the goals are related to mental and physical health. However, in achieving the health benefits, WATs users might have both intrinsic and extrinsic motivations. Li et al. [29] identified external stimulus that included supporting a family and friend, witnessing other’s success, or health care provider’s recommendation as one reason for older adults to start using the WATs, in contrast to the other intrinsically driven motivation that included becoming more active, monitoring diet and health, etc. Therefore, our first research question is:

RQ1: How is adopting WATs for health benefits based on either intrinsic or extrinsic motivations associated with long-term use of WATs?

Besides initial adoption, practices during actual usage could also play a role in influencing long-term usage. Research on determinants of physical activity identified social support as one of the strongest predictors in adults’ physical activity [2,9,46]. Competition, which is both a form of social support and a persuasion technique [9,15], is a commonly available and used feature of WATs. For example, Fitbit ranks users by their daily step counts. To achieve a higher rank, users must always wear the WAT and record their every movement [18]. Two qualitative studies on the long-term use of WATs or similar mobile tracking devices found that users have mixed reviews on this feature. One found that users agreed that competition with friends on step counts or minutes active provides greater motivation to exercise [15], but another found that users perceived differences among friend circles based on physical conditions and lifestyles and thus viewed comparison and competition to be unhelpful [20]. So far, there is no quantitative data on whether competition is useful for long-term engagement of trackers to the authors’ best knowledge. In evaluating whether this feature is facilitating long-term use or not, we ask our second research question:

RQ2: How is competition with family and friends associated with long-term use of WATs?

Another usage factor that may predict long-term use is habit formation. Habit is characterized by the consistency, repetition, and automaticity of the behavior, and stability of the context [23]. Habit of using WATs is intertwined with competition as competition requires users to wear the tracker constantly. WATs are also marketed as a cost-efficient way to encourage physical activity and maintain health by forming the habit of tracking [18]. The role of habit in predicting the continued information systems usage has been established in prior studies [30,44,47]. In one study examining reasons why people use smart devices, Lazar et al. [25] found that “developed routine of use” was one of the factors for continued usage of smart devices, among which the unobtrusive and less bulky WATs are the ones that are the easiest to form the habit of using. As for the older adult population, Kadylak et al. [21] suggested that the development of information and communication technology (ICT) habits decreased the importance of perceived benefits and ease of use in predicting older adults’ ICT use. Thus, in figuring out the importance of habit in influencing older adults’ long-term engagement with WATs, we ask our third research question:

RQ3: How is forming the habit of using WATs associated with long-term use of WATs?

Few studies explored the demographic characteristics associated with long-term use of WATs. In one national survey in Australia (N = 1349), which defined tracker as including pedometers, accelerometers, smartphone applications, and heart rate monitors, found that males, non-working participants, people with lower education, and being inactive is associated with lowered odds for tracker usage [1]. Carroll et al. [4] identified that older individuals, males, and individuals with lower education levels are less likely to adopt health apps. Higher education and higher annual income are associated with older adults’ (65+) increased use of digital health technologies, such as using the Internet to fill prescriptions, contacting a clinician, etc. [28]. Given this evidence, we ask our fourth research question:

RQ4: How are demographic characteristics, including gender, age, education, and household income associated with long-term use of WATs?

Higher rates of chronic conditions among older adults may interfere with usage of WATs since 1) certain types of chronic conditions, such as arthritis, can limit mobility [42], and 2) tracking data may be inaccurate for individuals with chronic conditions [34]. Thus, we are also interested in:

RQ5: How is having chronic condition(s) associated with long-term use of WATs?

Not all WAT users purchase their trackers, as about 30% of users received the tracker as a gift [27]. One study categorized those who purchased the tracker deliberately as the purposive group and those who bought it impulsively, received it as a gift, or bought it to support friends and family as the explorative group. This study found that purposive users are more likely to wear their WATs more frequently and for a longer period [19]. Thus, we ask:

RQ6: How is receiving a WAT as a gift associated with long-term use of WATs?

METHOD

To answer our research questions, we collected data from an online survey distributed to a nationally representative sample (N = 314) of older adults (ages 65 and older) on Qualtrics. Participants were either a previous or current WAT user.  Among the total sample, 241 were current users and 73 were previous users. Among the 241 current users, those who used the tracker for more than six months are labeled as long-term users (N = 142). Among the 73 previous users, those who used the tracker for six months or less (N = 51) are labeled as short-term users [26]. Participants who did not fit into the two categories were removed from the sample. Our final sample has 193 participants.

Measures

DependentVariable.As stated before, current users who reported that they have used a WAT for more than six months are long-term users, and previous users who reported that they had used a WAT for six months or less are short-term users (long-term user = 1, short-term user = 0).

Health benefits. We defined health benefits as participants’ agreement with using the tracker for health-related reasons. Participants rated their levels of agreement using a 4-point Likert scale anchored by 1 (strongly disagree) and 4 (strongly agree) on a series of questions on why they started using the WATs. The items related to intrinsically driven health benefits include: 1) To help me become more active, 2) To help me improve a chronic illness, disease, or health problem that I have, 3) To help me lose weight, 4) To help me monitor my health, 5) To help me monitor my diet. Cronbach’s alpha for the five items were .71. The items related to extrinsically driven health benefits include: 1) To support a family member or friend, 2) Someone I know has had great success using a tracker, 3) My doctor or another health care provider recommended that I use a tracker. Cronbach’s alpha for the three items were .69.

Competition.Participants were asked if they use their tracker to compete with family and friends (such as who can take the most steps each day). In the analyses, competition is a dichotomous indicator (i.e., 1 = Competing, 0 = Not competing).

Habit formation. Short-term and long-term WAT users responded to different questions for themeasure. Short-term users were asked for their levels of agreement to three statements regarding reasons for their discontinued WAT use: 1) I forgot to wear my tracker, 2) I never wore my tracker, and 3) I kept forgetting to charge my tracker. The response options were reverse coded; higher values indicate that participants perceived their habit formation to be more successful. Long-term users were directly asked to indicate their levels of agreement with two questions about their success with habit formation. The first question asked if continued WAT use resulted from “being used to wearing the tracker.” The second question asked a respondents’ levels of agreement to four statements: 1) The use of my tracker has become a habit for me, 2) I am addicted to using my tracker, 3) I want to use my tracker every day, and 4) Using my tracker has become natural to me. Short-term and long-term users’ responses were averaged and then combined to create the scale measure of habit formation from 1 (strongly disagree) to 4 (strongly agree). While current and previous WAT users responded to different questions, we estimated Cronbach alpha for short-term and long-term user questions items (α = 0.95).

Demographics.Gender is a dichotomous indicator (1 = Female, 0 = Male). Participants indicated their highest levels of education ranging from less than a high school degree to a doctorate degree (1 = Less than high school degree, 6 = Doctorate degree). Additionally, participants were asked about their total household yearly incomes (1 = Less than $10,000, 9 = $200,000 or more).

Chronic condition. Participants were asked “According to the National Center for Health Statistics, a chronic health condition/disease is one lasting 3 or more months and generally cannot be prevented or cured with medication. Do you currently have one or more chronic health conditions and/or diseases?” Participants responded Yes ( = 1) or No ( = 0).

Gift. We asked the participants whether they obtained their WATs as a gift or purchased it themselves (1 = Gift, 0 = Purchased).

RESULTS

A summary of participants’ demographic information is presented in Table 1. Among the 193 participants, age ranged from 66 to 89 years (M = 69.5, SD = 6.2), 61% were female, 52% of the participants had an education beyond a Bachelor’s degree or above, 60% had an annual household income between $35,000 to $149,999, and 45.6% of the older adults in the sample experienced at least one chronic condition. The average time of WAT use for long-term users was a year, and the average time of WAT use for short-term users was three months. Fitbit was the most commonly used WAT brand (76.2%). Table 2 showed participants’ agreements with using WATs because of intrinsically or extrinsically driven health benefits, competition, habit formation, and percentage of participants experiencing at least one chronic condition. As shown, 40.4% received the WAT as a gift, and 47.1% engaged in competition with family and friends. Participants agreed more with using the WAT for health benefits and with the success in forming the habit of using a WAT, and they disagreed more with using the WAT because of supporting a family member or friend, other’s success, or doctor’s recommendation.

Age Mean = 69.5, SD = 6.2
Gender Female (61.1%)

Male     (38.9%)

Annual household income Less than $10,000 (1.0%)

$10,000 to $24,999 (7.3%)

$25,000 to $34,999 (9.8%)

$35,000 to $49,999 (15.5%)

$50,000 to $74,999 (23.8%)

$75,000 to $99,999 (18.7%)

$100,000 to $149,999 (17.6%)

$150,000 to $199,999 (3.1%)

$200,000 or more (3.1%)

Education Less than high school degree (0.5%)

High school graduate or GED (14.5%)

Some college (33.2%)

Bachelor’s degree (27.5%)

Master’s degree (19.7%)

Doctorate degree (4.7%)

WAT brand Fitbit (76.2%)

Garmin (12.4%)

Apple (5.2%)

Misfit (2.1%)

Jawbone (1.0%)

Nike (1.0%)

Table 1. Demographics of the participants (N = 193)

User type Long-term user (73.6%)

Short-term user (26.4%)

Health benefits (Intrinsically driven) Mean = 2.73 SD = 0.56
Health benefits (Extrinsically driven) Mean = 2.07 SD = 0.64
Engaged in competition with family and friends Yes (47.1%)

No  (52.9%)

Habit formation Mean = 3.20 SD = 0.57
Chronic condition Yes (45.6%)

No  (54.4%)

Received it as a gift Yes (40.4%)

No  (59.6%)

Table 2. Descriptive statistics (N = 193)

Table 3 displayed results of the logistic regression with user type as the dependent variable. The model predicted whether the participant was a long-term user significantly (X2(12) = 111.36, p < .001) and explained 40% of the variance in long-term engagement (McFadden’s Adj R2 = 0.40). McFadden’s pseudo R2 can be viewed as an approximate variance in the outcome accounted for by the independent variables, in this case, long-term engagement. It is usually smaller than R2 and a value of .2 to .4 is considered an excellent fit [33]. In this case, the value of 0.40 suggests a great fit.

For RQ1, adopting WATs for health benefits based on either intrinsic motivations (b = 0.32, p > .05) or extrinsic motivations (b = 0.54, p > .05) did not turn out to have a significant relationship with long-term use.  For RQ2, engaging in competition with family and friends was significantly related to long-term usage (b = -3.80, p < .001). Comparing to those who did not compete, competition decreased the odds of becoming a long-term user by 98%. Chi-square test showed that short-term users are more likely to be engaging in competition than long-term users (X2 (1) = 38.41, p < .001). For RQ3, developing the habit of using the WAT significantly predicted long-term usage (b = 2.11, p < .001). Every unit increase in a participant’s agreement with his or her success in habit formation increased the odds of becoming a long-term user by 726%. Compared to short-term users, on average, long-term users agreed more with their success in forming the habit (M = 3.29, SD = 0.5 vs. M= 2.94, SD = 0.6; t (191) = -3.92, p < .001).

For RQ4 on demographic characteristics, annual household income predicted long-term usage (b = 0.64, p < .01). Every unit increase in household income increased the odds of becoming a long-term user by 89%. On average, long-term users have higher annual household incomes than short-term users (M = 5.43, SD = 1.64 vs. M = 4.35, SD = 1.78; t (191) = -3.93, p < .001). Gender was also predictive of long-term engagement (b = 1.47, p < .05). Comparing to being a male, being a female increased the odds of becoming a long-term user by 334%. Chi-square test also showed that females are more likely to become long-term users (X2 (1) = 9.46, p < .01). Education (b = 0.39, p > .05) and age (b = 0.04, p > .05) were found to have no significant effects on the long-term use of WATs.

For RQ5, having one or more chronic conditions did not bode well for long-term usage. The relationship between having a chronic condition and long-term engagement was statistically significant (b = -1.33, p < .01), moreover, results showed that having a chronic condition decreased the odds of becoming a long-term user by 74%. Chi-square test also showed that short-term users are more likely to be suffering from chronic conditions (X2 (1) = 14.82, p < .001). For RQ6, receiving the WAT as a gift was predictive of long-term usage (b = 1.60, p < .01). Comparing to those who purchased the WAT by themselves, receiving it as a gift increased the odds of becoming a long-term user by 399%. Long-term users were more likely to obtain the tracker as a gift (X2 (1) = 3.48, p < .1).

  Coefficient (SE) Percent change in Odds
Constant -13.32(3.87)
Health benefits (Intrinsically driven) 0.32(0.59) 38%
Health benefits (Extrinsically driven) 0.54(0.46) 71%
Competition

(Compete = 1)

-3.80(0.69) *** -98%***
Habit formation 2.11(0.53) *** 726%***
Chronic condition

(Yes = 1)

-1.33(0.50) ** -74% **
Gift (Yes = 1) 1.60(0.57) ** 399%**
Age 0.04(0.04) 4%
Gender (Female = 1) 1.47(0.58) * 334%*
Annual household income 0.64(0.19) ** 89%**
Education 0.39(0.27) 48%
McFadden’s Adj R2 0.40
Cox-Snell R2 0.44
Note:

Model X2(10) = 111.24, p < 0.001

* p < 0.05, ** p < 0.01, *** p < 0.001

N=193

Table 3. Logistic regression on factors associated with long-term use (N = 193)

DISCUSSION

Building upon previous qualitative studies that explored long-term use of new technologies, in particular, wearables, the current study quantitatively examined factors contributing to the long-term use of WATs using an online, nationally representative sample of older adults aged 65 and older. Habit formation, being female, having high annual household income, and receiving a WAT as a gift were found to be positively related to long-term use, while engaging in competition through WAT and having chronic health conditions were negatively related to the dependent variable. This section discusses the findings in relation to the existing literature and implications for designing and implementing technologies for long-term use.

One of the surprising findings was that competition with family and friends through WATs decreased the odds of long-term use. There are inconsistent findings regarding competition for long-term engagement [15,20]. Some prior research suggests that social competition is fun [39] and may increase long-term engagement [15]. However, for specific demographic groups, competition may not be motivating but rather demotivating because these individuals may be the low-performing ones in these competitions. For seniors, when they compete with family and friends, especially family members who are younger and healthier, the chance that they will lose in such competitions is high. In fact, some prior qualitative findings are consistent with our finding that competition is either demotivating [20,31] or serves as a double-edged sword and only works for certain individuals [40]. Social competition has been adopted in many fitness apps such as Nike+, myfitnesspal, RunKeeper, and mapmyrun. Anecdotal evidence suggests that these apps are motivating partly because most of the users are already quite active and fit. Competition may help elevate them to the next level. However, when designing a wearable and app for seniors, who are generally less active and may have chronic conditions, instead of competition, support groups focusing on normative influence or cooperation may be better, at least for some particular population of older adults [10].

Naturally, habit formation is associated with long-term use. Once individuals have formed the habit of using WATs, in other words, when they start using WATs automatically, without the need of consciously thinking about it, they are more likely to use it for a long time. This suggests that in order to determine strategies for long-term WAT use, we should identify how they form habits. Research on habit formation indicates several factors that facilitate new habits, such as setting a goal for a new behavior, engaging in simple day-to-day actions that will get individuals closer to their goal, a consistent plan of when and where to perform the action, cues of time and location that can trigger the behavior, and consistent repetition [17,23,49]. Having the above-mentioned factors built in the design of the WAT, its companion app, and other technologies is essential to promote long-term use.

Being female and having higher household income were found to be associated with long-term use of WAT. This finding is consistent with previous research on technology adoption (e.g., adoption of health apps) [4,13,22]. It is important to note that this study focused not on technology adoption but on technology long-term use, which is thought to be its novel contribution to the existing literature. Taken together, empirical evidence suggests that gender and income are predictive of WAT adoption as well as its continued use. The finding also indicates that special design consideration may be needed for the male population to increase adoption and encourage long-term use.

Having chronic diseases decreased the odds of long-term use of WAT as chronic conditions limit movement. Nevertheless, people with chronic conditions could benefit from long-term WAT use for self-monitoring and motivating physical activity. How to design WATs for older adults who have chronic conditions deserves further investigation.

We found that receiving the WAT as a gift also increased the odds of long-term use. It is possible that a gift represents a caring social contact that wishes the receiver to be healthy. The gift may potentially motivate older adults to use the WAT as a way to show appreciation for the gift-givers. Hence, a WAT as a gift can be indicative of a strong social support network that increases chances of engaging with health technology for a long time. This finding calls for further exploration of the relationship between social support and long-term WATs use.

There are no significant effects of adopting the tracker for health benefits for long-term use, regardless of intrinsically or extrinsically driven motivations. These findings could offer evidence on how the initial motivations for adoption are not necessarily predicative of long-term engagement.

Limitations and Future Work

We acknowledge several limitations of this work. Firstly, the operationalization of habit formation is a combination of long-term and short-term users’ responses to the different questions we asked among these two groups. Even though the Cronbach’s alpha for long-term and short-term users’ measurement of habit reached .95, it is not ideal to have two sets of questions measuring the same construct for these two type of users. Nevertheless, habit formation is shown to be the most potent predictor for long-term usage. Given the magnitude of its effects, we suggest that further study should focus on understanding the specific process of habit formation to better utilize its mechanisms in future interventions. Secondly, our data come from a Qualtrics panel of older adults who already have some experience with WATs, which means that they are relatively sophisticated internet users, in good health, and are open to new technologies, so this might not be a typical sample of older adults. Future work should include a larger sample size with to verify the findings.

CONCLUSION

WATs can improve older adults’ health by encouraging and motivating them to stay active [18], thus decrease their risks for premature death and other forms of diseases that are known to be associated with low physical activity [43]. Understanding long-term use of WATs among older adults is important given that WATs are likely to be abandoned quickly after initial adoption [5,7,25,26], and such abandonment harmed their potential in supporting long-term health behavior change. This paper presents the first quantitative and national representative survey on older adults’ long-term use of WATs and its associated factors. Findings could be used to inspire WAT designs that afford long-term use and to offer helpful strategies for future interventions with WATs among older adults.

REFERENCES

1. Stephanie Alley, Stephanie Schoeppe, Diana Guertler, Cally Jennings, Mitch J. Duncan, and Corneel Vandelanotte. 2016. Interest and preferences for using advanced physical activity tracking devices: results of a national cross-sectional survey. BMJ open 6, 7: e011243.

2. Adrian E. Bauman, Rodrigo S. Reis, James F. Sallis, Jonathan C. Wells, Ruth JF Loos, Brian W. Martin, Lancet Physical Activity Series Working Group, and others. 2012. Correlates of physical activity: why are some people physically active and others not? The lancet 380, 9838: 258–271. http://dx.doi.org/10.1016/S0140-6736(12)60735-1

3. Lisa A. Cadmus-Bertram, Bess H. Marcus, Ruth E. Patterson, Barbara A. Parker, and Brittany L. Morey. 2015. Randomized Trial of a Fitbit-Based Physical Activity Intervention for Women. American Journal of Preventive Medicine 49, 3: 414–418. https://doi.org/10.1016/j.amepre.2015.01.020

4. Jennifer K Carroll, Anne Moorhead, Raymond Bond, William G LeBlanc, Robert J Petrella, and Kevin Fiscella. 2017. Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach. Journal of Medical Internet Research 19, 4: e125. https://doi.org/10.2196/jmir.5604

5. 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 (UbiComp ’15), 647–658. https://doi.org/10.1145/2750858.2807554

6. A. F. Cooper, G. Jackson, J. Weinman, and R. Horne. 2002. Factors associated with cardiac rehabilitation attendance: a systematic review of the literature. Clinical Rehabilitation 16, 5: 541–552. https://doi.org/10.1191/0269215502cr524oa

7. Daniel A. Epstein, Monica Caraway, Chuck Johnston, An Ping, James Fogarty, and Sean A. Munson. 2016. Beyond Abandonment to Next Steps: Understanding and Designing for Life After Personal Informatics Tool Use. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16), 1109–1113. https://doi.org/10.1145/2858036.2858045

8. Kelly R. Evenson, Michelle M. Goto, and Robert D. Furberg. 2015. Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity; London 12. 18;12:159. doi: 10.1186/s12966-015-0314-1.

9. Amy A Eyler, Ross C Brownson, Rebecca J Donatelle, Abby C King, David Brown, and James F Sallis. 1999. Physical activity social support and middle- and older-aged minority women: results from a US survey. Social Science & Medicine 49, 6: 781–789. https://doi.org/10.1016/S0277-9536(99)00137-9

10. Chloe Fan, Jodi Forlizzi, and Anind Dey. 2012. Considerations for technology that support physical activity by older adults. In Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility, 33–40. Retrieved June 28, 2017 from http://dl.acm.org/citation.cfm?id=2384923

11. Cara Bailey Fausset, Tracy L. Mitzner, Chandler E. Price, Brian D. Jones, Brad W. Fain, and Wendy A. Rogers. 2013. Older Adults’ Use of and Attitudes toward Activity Monitoring Technologies. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57, 1: 1683–1687. https://doi.org/10.1177/1541931213571374

12. Theresa A. Floegel, Peter R. Giacobbi, Jr., Joseph M. Dzierzewski, Adrienne T. Aiken-Morgan, Beverly Roberts, Christina S. McCrae, Michael Marsiske, and Matthew P. Buman. 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

13. Susannah Fox and Maeve Duggan. 2012. Mobile Health 2012. Pew Research Center: Internet, Science & Tech. Retrieved September 19, 2017 from http://www.pewinternet.org/2012/11/08/mobile-health-2012/

14. Susannah Fox and Maeve Duggan. 2013. Tracking for Health. Pew Research Center: Internet, Science & Tech. Retrieved April 30, 2017 from http://www.pewinternet.org/2013/01/28/tracking-for-health/

15. Thomas Fritz, Elaine M. Huang, Gail C. Murphy, and Thomas Zimmermann. 2014. Persuasive Technology in the Real World: A Study of Long-term Use of Activity Sensing Devices for Fitness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14), 487–496. https://doi.org/10.1145/2556288.2557383

16. Yiwen Gao, He Li, and Yan Luo. 2015. An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems 115, 9: 1704–1723. https://doi.org/10.1108/IMDS-03-2015-0087

17. Benjamin Gardner, Phillippa Lally, and Jane Wardle. 2012. Making health habitual: the psychology of “habit-formation” and general practice. The British Journal of General Practice 62, 605: 664–666. https://doi.org/10.3399/bjgp12X659466

18. James N. Gilmore. 2016. Everywear: The quantified self and wearable fitness technologies. new media & society 18, 11: 2524–2539.

19. Rúben Gouveia, Evangelos Karapanos, and Marc Hassenzahl. 2015. How Do We Engage with Activity Trackers?: A Longitudinal Study of Habito. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’15), 1305–1316. https://doi.org/10.1145/2750858.2804290

20. Xinning Gui, Yu Chen, Clara Caldeira, Dan Xiao, and Yunan Chen. 2017. When Fitness Meets Social Networks: Investigating Fitness Tracking and Social Practices on WeRun. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ’17), 1647–1659. https://doi.org/10.1145/3025453.3025654

21. T. Kadylak, J. Francis, S. R. Cotten, R. V. Rikard, R. LaRose, T. Makki, and H. Tsai. 2016. Extending the STAM: The role of habit in the acceptance of technology. Gerontechnology 15, suppl: 39s. https://doi.org/10.4017/gt.2016.15.s.612.00

22. Paul Krebs and Dustin T. Duncan. 2015. Health App Use Among US Mobile Phone Owners: A National Survey. JMIR mHealth and uHealth 3, 4: e101. https://doi.org/10.2196/mhealth.4924

23. Phillippa Lally and Benjamin Gardner. 2013. Promoting habit formation. Health Psychology Review 7, sup1: S137–S158. https://doi.org/10.1080/17437199.2011.603640

24. Jonas Lauritzen, Adolfo Muñoz, Jose Luis Sevillano, and Anton Civit. 2013. The usefulness of activity trackers in elderly with reduced mobility: a case study. Studies in Health Technology and Informatics 192: 759–762.

25. Amanda Lazar, Christian Koehler, Joshua Tanenbaum, and David H. Nguyen. 2015. Why We Use and Abandon Smart Devices. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’15), 635–646. https://doi.org/10.1145/2750858.2804288

26. Dan Ledger and Daniel McCaffrey. Inside Wearables: How the science of human behavior change offers the secret to long-term engagement. Retrieved September 11, 2017 from https://blog.endeavour.partners/inside-wearable-how-the-science-of-human-behavior-change-offers-the-secret-to-long-term-engagement-a15b3c7d4cf3

27. Dan Ledger and Daniel McCaffrey. Inside Wearables: How the science of human behavior change offers the secret to long-term engagement – Part 2. Retrieved from https://digitalintelligencetoday.com/wp-content/uploads/2015/11/2014-Inside-Wearables-Part-2-July-2014.pdf

28. David M. Levine, Stuart R. Lipsitz, and Jeffrey A. Linder. 2016. Trends in Seniors’ Use of Digital Health Technology in the United States, 2011-2014. JAMA 316, 5: 538–540. https://doi.org/10.1001/jama.2016.9124

29. Lin Li, Wei Peng, Kendra Kamp, Marie Bowen, Shelia R. Cotten, R. V. Rikard, and Anastasia Kononova. 2017. Poster: Understanding Long-Term Adoption of Wearable Activity Trackers Among Older Adults. In Proceedings of the 2017 Workshop on Wearable Systems and Applications, 33–34. Retrieved July 3, 2017 from http://dl.acm.org/citation.cfm?id=3089819

30. Moez Limayem, Sabine Gabriele Hirt, and Christy MK Cheung. 2007. How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly: 705–737.

31. James J. Lin, Lena Mamykina, Silvia Lindtner, Gregory Delajoux, and Henry B. Strub. 2006. Fish’N’Steps: Encouraging Physical Activity with an Interactive Computer Game. In Proceedings of the 8th International Conference on Ubiquitous Computing (UbiComp’06), 261–278. https://doi.org/10.1007/11853565_16

32. Kathleen A. Martin and Adrienne R. Sinden. 2001. Who Will Stay and Who Will Go? A Review of Older Adults’ Adherence to Randomized Controlled Trials of Exercise. Journal of Aging and Physical Activity 9, 2: 91–114. https://doi.org/10.1123/japa.9.2.91

33. Daniel McFadden. Quantitative methods for analyzing travel behavior of individuals: some recent developments. . Institute of Transportation Studies, University of California, 1977. Retrieved September 19, 2017 from https://core.ac.uk/download/pdf/6448852.pdf

34. Kathryn Mercer, Lora Giangregorio, Eric Schneider, Parmit Chilana, Melissa Li, and Kelly 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: e7. https://doi.org/10.2196/mhealth.4225

35. Kathryn Mercer, Melissa Li, Lora Giangregorio, Catherine Burns, and Kelly Grindrod. 2016. Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis. JMIR mHealth and uHealth 4, 2: e40. https://doi.org/10.2196/mhealth.4461

36. Tracy L. Mitzner, Sara E. McBride, Laura H. Barg-Walkow, and Wendy A. Rogers. 2013. Self-Management of Wellness and Illness in an Aging Population. Reviews of Human Factors and Ergonomics 8, 1: 277–333. https://doi.org/10.1177/1557234X13492979

37. Tara O’Brien, Meredith Troutman-Jordan, Donna Hathaway, Shannon Armstrong, and Michael Moore. 2015. Acceptability of wristband activity trackers among community dwelling older adults. Geriatric Nursing 36, 2: S21–S25. https://doi.org/10.1016/j.gerinurse.2015.02.019

38. World Health Organization. 2002. The world health report 2002: reducing risks, promoting healthy life. World Health Organization. Retrieved September 19, 2017 from http://books.google.com/books?hl=en&lr=&id=epuQi1PtY_cC&oi=fnd&pg=PR9&dq=%22illustration+by+Laura+de%22+%22of+people+and+their+physicians+and+politicians+throughout+history.+It+can+be%22+%22from+the+planet+and+have+reduced+the+risk+to+individuals+and%22+&ots=N3E3eXHcOn&sig=r8UIqXt8-kcMSjAKVrSQ_UTQE7A

39. Wei Peng and Julia Crouse. 2013. Playing in parallel: the effects of multiplayer modes in active video game on motivation and physical exertion. Cyberpsychology, Behavior and Social Networking 16, 6: 423–427. https://doi.org/10.1089/cyber.2012.0384

40. Wei Peng, Shaheen Kanthawala, Shupei Yuan, and Syed Ali Hussain. 2016. A qualitative study of user perceptions of mobile health apps. BMC public health 16, 1: 1158. https://doi.org/10.1186/s12889-016-3808-0

41. M. Randriambelonoro, Y. Chen, and P. Pu. 2017. Can Fitness Trackers Help Diabetic and Obese Users Make and Sustain Lifestyle Changes? Computer 50, 3: 20–29. https://doi.org/10.1109/MC.2017.92

42. Salvatore Tedesco, John Barton, and Brendan O’Flynn. 2017. A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry. Sensors 17, 6: 1277. https://doi.org/10.3390/s17061277

43. Teresa Seeman and Xinguang Chen. 2002. Risk and protective factors for physical functioning in older adults with and without chronic conditions: MacArthur Studies of Successful Aging. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences 57, 3: S135-144.

44. A. Soontornwat, S. Funilkul, and U. Supasitthimethee. 2016. Essential social attributes and Habit in fitness mobile applications usage to motivate a physical activity. In 2016 International Computer Science and Engineering Conference (ICSEC), 1–6. https://doi.org/10.1109/ICSEC.2016.7859955

45. 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. https://doi.org/10.3389/fpubh.2016.00289

46. Bert N. Uchino. 2004. Social Support and Physical Health : Understanding the Health Consequences of Relationships. Yale University Press, New Haven, US. Retrieved October 25, 2016 from http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10170042

47. Viswanath Venkatesh, James YL Thong, and Xin Xu. 2012. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. Retrieved March 25, 2017 from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2002388

48. Julie B. Wang, Lisa A. Cadmus-Bertram, Loki Natarajan, Martha M. White, Hala Madanat, Jeanne F. Nichols, Guadalupe X. Ayala, and John P. Pierce. 2015. Wearable Sensor/Device (Fitbit One) and SMS Text-Messaging Prompts to Increase Physical Activity in Overweight and Obese Adults: A Randomized Controlled Trial. Telemedicine Journal and E-Health: The Official Journal of the American Telemedicine Association 21, 10: 782–792. https://doi.org/10.1089/tmj.2014.0176

49. Wendy Wood and David T. Neal. 2007. A new look at habits and the habit-goal interface. Psychological Review 114, 4: 843–863. https://doi.org/10.1037/0033-295X.114.4.843

50. 2017. Fitbit Reports $574M Q416 and $2.17B FY16 Revenue, Sells 6.5M devices in Q416 and 22.3M devices in FY16. Retrieved April 26, 2017 from http://www.businesswire.com/news/home/20170222006497/en/Fitbit-Reports-574M-Q416-2.17B-FY16-Revenue

Cite This Work

To export a reference to this article please select a referencing stye below:

Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.

Related Services

View all

DMCA / Removal Request

If you are the original writer of this essay and no longer wish to have the essay published on the UK Essays website then please:

McAfee SECURE sites help keep you safe from identity theft, credit card fraud, spyware, spam, viruses and online scams Prices from
£29

Undergraduate 2:2 • 250 words • 7 day delivery

Order now

Delivered on-time or your money back

Rated 4.1 out of 5 by
Reviews.co.uk Logo (21 Reviews)

Get help with your dissertation