Wearable Computing for Dementia Patients

Kaenampornpan, Manasawee; Khai, Nguyen Duc; Kawattikul, Khanabhorn

doi:10.1007/978-3-030-44044-2_3

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…However, van Heek et al [39] focused on design perspectives including gathering of data, data access, and storage duration, as well as perceived benefits and barriers, in order to integrate caregivers' perspectives into design of technologies. Our results align with those of other recent studies [38][39][40][41][42][43][44][45][46] showing that there is a greater likelihood for adoption and use of fall alert wearables among care recipients with dementia, which is assumedly negotiated by the caregiver as a result of care recipient incapacity.…”

Section: Principal Findingssupporting

confidence: 92%

Attitudes Toward Technology and Use of Fall Alert Wearables in Caregiving: Survey Study

Dahlke¹,

Lee²,

Smith³

et al. 2021

JMIR Aging

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Background Wearable technology for fall alerts among older adult care recipients is one of the more frequently studied areas of technology, given the concerning consequences of falls among this population. Falls are quite prevalent in later life. While there is a growing amount of literature on older adults’ acceptance of technology, less is known about how caregivers’ attitudes toward technology can impact care recipients’ use of such technology. Objective The objective of our study was to examine associations between caregivers’ attitudes toward technology for caregiving and care recipients’ use of fall alert wearables. Methods This study examined data collected with an online survey from 626 caregivers for adults 50 years and older. Adapted from the technology acceptance model, a structural equation model tested the following prespecified hypotheses: (1) higher perceived usefulness of technologies for caregiving would predict higher perceived value of and greater interest in technologies for caregiving; (2) higher perceived value of technologies for caregiving would predict greater interest in technologies for caregiving; and (3) greater interest in technologies for caregiving would predict greater use of fall alert wearables among care recipients. Additionally, we included demographic factors (eg, caregivers’ and care recipients’ ages) and caregiving context (eg, caregiver type and caregiving situation) as important predictors of care recipients’ use of fall alert wearables. Results Of 626 total respondents, 548 (87.5%) with all valid responses were included in this study. Among care recipients, 28% used fall alert wearables. The final model had a good to fair model fit: a confirmatory factor index of 0.93, a standardized root mean square residual of 0.049, and root mean square error of approximation of 0.066. Caregivers’ perceived usefulness of technology was positively associated with their attitudes toward using technology in caregiving (b=.70, P<.001) and interest in using technology for caregiving (b=.22, P=.003). Greater perceived value of using technology in caregiving predicted greater interest in using technology for caregiving (b=.65, P<.001). Greater interest in using technology for caregiving was associated with greater likelihood of care recipients using fall alert wearables (b=.27, P<.001). The caregiver type had the strongest inverse relationship with care recipients’ use of fall alert wearables (unpaid vs paid caregiver) (b=–.33, P<.001). Conclusions This study underscores the importance of caregivers’ attitudes in care recipients’ technology use for falls management. Raising awareness and improving perception about technologies for caregiving may help caregivers and care recipients adopt and better utilize technologies that can promote independence and enhance safety.

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Section: Principal Findingssupporting

confidence: 92%

Attitudes Toward Technology and Use of Fall Alert Wearables in Caregiving: Survey Study

Dahlke¹,

Lee²,

Smith³

et al. 2021

JMIR Aging

View full text Add to dashboard Cite

show abstract

“…Furthermore, the process of collecting physiological data from those individuals who have received a diagnosis of dementia using wearables can be difficult as the cognitive impairment can lead to problems in the collection of the data, especially in the severe stages of the disease [91]. One reason for this which has been reported is that the subject may remove the device due to discomfort or unfamiliarity with the device [10].…”

Section: Data Search Resultsmentioning

confidence: 99%

Comparison of Subjective and Physiological Stress Levels in Home and Office Work Environments

Harper

Ghali

Khan

2022

Lecture Notes in Computer Science

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“…People with dementia are classed as vulnerable adults due to their mental impairment [20], which can often make it more difficult to acquire ethical approval to conduct research which includes them as subjects [21]. Furthermore, physiological data collection from people with dementia using wearables can be difficult as the cognitive impairment can lead to problems in collecting the data, especially in the severe stages but also often in mild to moderate dementia [22]. This could be because the subject removes the device either due to discomfort or because the device is unfamiliar to them [8], or because the subject forgets to put a data collection device on [23,24].…”

Section: Data Search Resultsmentioning

confidence: 99%

Challenges in Data Capturing and Collection for Physiological Detection of Dementia-Related Difficulties and Proposed Solutions

Harper

Ghali

Hussain

et al. 2021

Intelligent Computing Theories and Application

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Dementia is a neurodegenerative disease which leads to the individual experiencing difficulties in their daily lives. Often these difficulties cause a large amount of stress, frustration and upset in the individual, however identifying when the difficulties are occurring or beginning can be difficult for caregivers, until the difficulty has caused problematic behavior or undeniable difficulty to the person with dementia. Therefore, a system for identifying the onset of dementia-related difficulties would be helpful in the management of dementia. Previous work highlighted wearable computing-based systems for analyzing physiological data as particularly promising. In this paper, we outline the methodology used to perform a systematic search for a relevant dataset. However, no such dataset was found. As such, a methodology for collecting such a dataset and making it publicly available is proposed, as well as for using it to train classification models that can predict difficulties from the physiological data. Several solutions to overcome the lack of available data are identified and discussed: data collection experiments to collect novel datasets; anonymization and pseudonymization to remove all identifiable data from the dataset; and synthetic data generation to produce a larger, anonymous training dataset. In conclusion, a combination of all the identified methods should ideally be employed in future solutions. Future work should focus on the conductance of the proposed experiment and the sharing of the collected data in the manner proposed, with data ideally being collected from as many people as possible with as many different types of dementia as possible.

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Wearable Computing for Dementia Patients

Cited by 5 publications

References 8 publications

Attitudes Toward Technology and Use of Fall Alert Wearables in Caregiving: Survey Study

Attitudes Toward Technology and Use of Fall Alert Wearables in Caregiving: Survey Study

Comparison of Subjective and Physiological Stress Levels in Home and Office Work Environments

Challenges in Data Capturing and Collection for Physiological Detection of Dementia-Related Difficulties and Proposed Solutions

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