The antecedents and results of seniors’ use of activity tracking wearable devices

Abouzahra, Mohamed; Ghasemaghaei, Maryam

doi:10.1016/j.hlpt.2019.11.002

Cited by 33 publications

(29 citation statements)

References 13 publications

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“…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 Multimedia Appendix 1 [9,[13][14][15][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] for the checklist). We used the Evaluation Tool for Qualitative Studies (ETQS) [45], as this provides detailed instructions on applying the evaluation criteria, unlike the CASP (Critical Appraisals Skills Programme) tool [46,47].…”

Section: Data Extraction and Quality Appraisalmentioning

confidence: 99%

“…In others, participants were randomly assigned to one of several devices [34,35]. The views of 349 participants (age: range 51-94 years) were synthesized, including those with previous breast cancer [32], obesity [36,37], resolving heart failure [30,37], Parkinson disease [38], dementia [15,39], and walking aids [9,29] and those who were fully independent and healthy [9,13,14,29,[31][32][33][34][35]37,[40][41][42][43][44]. Table 1 summarizes the results of the data extraction process.…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…Certain age-related characteristics can impact users' comfort with new technologies, such as hearing loss, limited dexterity, and low vision [13,14]. Older users may be slower to process new information and therefore require simple, visible instructions:…”

Section: Age-related Physiology or Comorbiditiesmentioning

confidence: 99%

“…Researchers have used a variety of methods to collect information from older adults regarding the acceptability of wearable devices: surveys [6,7], wear time [1], diaries [8,9], interviews [9], and focus groups [10,11]. Some studies collected information about general preferences regarding device design [11]; others allowed participants to interact with several devices before asking about preferred design features [12], in which participants used a wearable device for multiple days and then provided feedback [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Older Adults’ Experiences With Using Wearable Devices: Qualitative Systematic Review and Meta-synthesis

Moore¹,

O'Shea²,

Kenny³

et al. 2021

JMIR Mhealth Uhealth

108

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Background 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. Objective 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. Methods We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to older adults and wearable devices. A meta-synthesis methodology was used. We extracted themes from primary studies, identified key concepts, and applied reciprocal and refutational translation techniques; findings were synthesized into third-order interpretations, and finally, a “line-of-argument” was developed. Our overall goal was theory development, higher-level abstraction, and generalizability for making this group of qualitative findings more accessible. Results 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. Conclusions 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.

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Section: Data Extraction and Quality Appraisalmentioning

confidence: 99%

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Section: Age-related Physiology or Comorbiditiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Older Adults’ Experiences With Using Wearable Devices: Qualitative Systematic Review and Meta-synthesis

Moore¹,

O'Shea²,

Kenny³

et al. 2021

JMIR Mhealth Uhealth

108

View full text Add to dashboard Cite

show abstract

“…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. We examine predictors related to individual health [ 37 ], technology self-efficacy [ 36 , 38 ], personal demographics [ 32 ], and attitudes towards fitness or exercise [ 35 ] as well as their associations with the use of wearable health care devices. Our research model, with all the predictor variables, is shown in Figure 1 .…”

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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Annoyed to Discontinue: Factors Influencing (Dis)Continuance of Using Activity Tracking Wearables

Fietkiewicz

Ilhan

2021

Lecture Notes in Computer Science

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The antecedents and results of seniors’ use of activity tracking wearable devices

Cited by 33 publications

References 13 publications

Older Adults’ Experiences With Using Wearable Devices: Qualitative Systematic Review and Meta-synthesis

Older Adults’ Experiences With Using Wearable Devices: Qualitative Systematic Review and Meta-synthesis

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

Annoyed to Discontinue: Factors Influencing (Dis)Continuance of Using Activity Tracking Wearables

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