Understanding the barriers and factors associated with consumer adoption of wearable technology devices in managing personal health

Sergueeva, Ksenia; Shaw, Norman; Lee, Seung Hwan

doi:10.1002/cjas.1547

Cited by 50 publications

(56 citation statements)

References 69 publications

(135 reference statements)

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“…The study finds Indian youth perceive the performance of the smart wearables should meet their expectation which corroborates with several earlier studies (Talukder et al, (2019); Sergueeva et al, (2019) Francis, (2019; Zhou and Chen (2018); Choi et al, (2017); Krey et al, (2016).…”

Section: Discussionsupporting

confidence: 90%

“…There is a trend towards fitness and wellness as a habit, given the stressful work environment and lifestyle disorders, particularly in the knowledge industry. Hence, the results finds that habit significantly impacts the behavioural intentions of the youth about the use of smart wearable devices, which supports earlier studies by Sergueeva et al (2019); Zhou and Chen (2018); Yuan et al (2015).…”

Section: Discussionsupporting

confidence: 89%

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Is a Smarter Generation in the Offing?

Ghosh

2022

International Journal of Technology and Human Interaction

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The study explored the critical determinants of behavioral intention and willingness to use smart wearable devices among Indian youth. Data were collected using a survey instrument on the youth aged between 20-30 years (N=262) in India. The results showed, performance expectancy, hedonic motivation, and habits impacted the behavioural intention of the youth. Facilitating conditions impacted the youth’s willingness to use. Behavioural intentions of the youth impacted their willingness to use smart wearable devices. Male youth moderated the effect of performance expectancy on behavioural intentions. Gender and Purpose of-Use moderated the effect of hedonic motivation and habits on behavioural intention and behavioural intention on willingness to use. Only health and fitness moderated the effect of facilitating condition on willingness to use smart wearable devices among youth. Though the study is limited to the Indian context, its implications can benefit the wearable commerce providers in designing, positioning, and marketing the products in developing economies.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 89%

Is a Smarter Generation in the Offing?

Ghosh

2022

International Journal of Technology and Human Interaction

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“…Concerns have also been raised about the accuracy of data gathered by wearables in people of color [ 18 ]. Technology acceptance of new wearable devices remains another significant barrier [ 19 ]. Despite the forecasted growth, use of these devices has reportedly slowed [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey

2020

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Background Despite the growing popularity of wearable health care devices (from fitness trackes such as Fitbit to smartwatches such as Apple Watch and more sophisticated devices that can collect information on metrics such as blood pressure, glucose levels, and oxygen levels), we have a limited understanding about the actual use and key factors affecting the use of these devices by US adults. Objective The main objective of this study was to examine the use of wearable health care devices and the key predictors of wearable use by US adults. Methods Using a national survey of 4551 respondents, we examined the usage patterns of wearable health care devices (use of wearables, frequency of their use, and willingness to share health data from a wearable with a provider) and a set of predictors that pertain to personal demographics (age, gender, race, education, marital status, and household income), individual health (general health, presence of chronic conditions, weight perceptions, frequency of provider visits, and attitude towards exercise), and technology self-efficacy using logistic regression analysis. Results About 30% (1266/4551) of US adults use wearable health care devices. Among the users, nearly half (47.33%) use the devices every day, with a majority (82.38% weighted) willing to share the health data from wearables with their care providers. Women (16.25%), White individuals (19.74%), adults aged 18-50 years (19.52%), those with some level of college education or college graduates (25.60%), and those with annual household incomes greater than US $75,000 (17.66%) were most likely to report using wearable health care devices. We found that the use of wearables declines with age: Adults aged >50 years were less likely to use wearables compared to those aged 18-34 years (odds ratios [OR] 0.46-0.57). Women (OR 1.26, 95% CI 0.96-1.65), White individuals (OR 1.65, 95% CI 0.97-2.79), college graduates (OR 1.05, 95% CI 0.31-3.51), and those with annual household incomes greater than US $75,000 (OR 2.6, 95% CI 1.39-4.86) were more likely to use wearables. US adults who reported feeling healthier (OR 1.17, 95% CI 0.98-1.39), were overweight (OR 1.16, 95% CI 1.06-1.27), enjoyed exercise (OR 1.23, 95% CI 1.06-1.43), and reported higher levels of technology self-efficacy (OR 1.33, 95% CI 1.21-1.46) were more likely to adopt and use wearables for tracking or monitoring their health. Conclusions The potential of wearable health care devices is under-realized, with less than one-third of US adults actively using these devices. With only younger, healthier, wealthier, more educated, technoliterate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers, and health care policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society.

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“…The ndings of the present study are consistent with past studies in their impact on behavioral intention. Also, comparatively the recent work by Sergueeva et al (154) reported that habit is a signi cant determinant of wearable adoption. Therefore, the focus on the role of habit on behavioral intention would be worthwhile for mHealth providers' service.…”

Section: Discussionmentioning

confidence: 92%

Use or Not Use? The Role of Trust and Habit in mHealth Ser-vices Use Intention Among Older Population in Hong Kong

Liu

Sorwar

Rahman

et al. 2022

Preprint

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Background: Evidence from literature suggests that mobile health (mHealth) services can potentially improve healthcare outcomes among elderly population. Hence, the government of Hong Kong has recently taken several community and IT services initiatives for training older people on how to enhance their abilities and interest in using mHealth technology. Although mHealth services have been widely implemented worldwide, their adoption and use by elderlies are very low including those in Hong Kong. This study aims to understand key factors influencing mHealth use intention among the elderly Chinese population in Hong Kong. Methods: We extended the Unified Theory of Acceptance and Use of Technology (UTAUT2) as the basis of our conceptual framework. We applied Partial Least Squares path modeling method to conduct the Structural Equation Model (SEM) technique that allows measuring the theoretical validity of any conceptual framework. A convenience and snowball sampling methods were used to recruit community-dwelling older people aged 65 or above. In total, 201 valid responses were used for testing the theoretical validity of the proposed conceptual framework. Results: The primary finding shows that the widely used UTAUT2 model falls short in explaining mHealth service acceptance behavior in the elderly population. We further propose a simplified model, the Healthcare Technology Service Acceptance (HTSA) model, to understand the formation of mHealth service acceptance behavior. The findings show that trust is an important component of technology service acceptance intention behavior that was missing in the UTAUT2 model. The results also show that several antecedent factors (i.e., social influence, government policy, and service quality) are critical in forming technology trust beliefs. Conclusions: The study shows that the HTSA model can better explain mHealth acceptance behavior than the UTAUT2 model. This study advances knowledge in the mHealth technology adoption domain by proposing a simplified new version of the UTAUT2 model for understanding healthcare technology service acceptance and use intention among the elderly population. The findings of the study provide valuable information to the Hong Kong government and healthcare organizations for a wider adoption of mHealth services specially in older populations. "

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Understanding the barriers and factors associated with consumer adoption of wearable technology devices in managing personal health

Cited by 50 publications

References 69 publications

Is a Smarter Generation in the Offing?

Is a Smarter Generation in the Offing?

Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey

Use or Not Use? The Role of Trust and Habit in mHealth Ser-vices Use Intention Among Older Population in Hong Kong

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