Dataset Information

Survey on Acceptance of Passive Technology Monitoring for Early Detection of Cognitive Impairment.

ABSTRACT:

Introduction

Digital biomarkers may act as a tool for early detection of changes in cognition. It is important to understand public perception of technologies focused on monitoring cognition to better guide the design of these tools and inform patients appropriately about the associated risks and benefits. Health care systems may also play a role in the clinical, legal, and financial implications of such technologies.

Objective

To evaluate public opinion on the use of passive technology for monitoring cognition.

Methods

This was a one-time, Internet-based survey conducted in English and Spanish.

Results

Within the English survey distributed in the USA (n = 173), 58.1% of respondents would be highly likely to agree to passive monitoring of cognition via a smartphone application. Thirty-eight percent of those with a higher degree of experience with technology were likely to agree to monitoring versus 20% of those with less experience with technology (p = 0.003). Sixty-two percent of non-health-care professionals were likely to agree to monitoring versus 45% of health-care workers (p = 0.012). There were significant concerns regarding privacy (p < 0.01). We compared the surveys answered in Spanish in Costa Rica via logistic regression (n = 43, total n = 216), adjusting for age, education level, health-care profession, owning a smartphone, experience with technology, and perception of cognitive decline. Costa Rican/Spanish-speaking respondents were 7 times more likely to select a high probability of agreeing to such a technology (p < 0.01). English-speaking respondents from the USA were 5 times more likely to be concerned about the impact on health insurance (p = 0.001) and life insurance (p = 0.01).

Conclusions

Understanding public perception and ethical implications should guide the design of digital biomarkers for cognition. Privacy and the health-care system in which the participants take part are 2 major factors to be considered. It is the responsibility of researchers to convey the ethical and legal implications of cognition monitoring.

SUBMITTER: Josephy-Hernandez S

PROVIDER: S-EPMC7879311 | biostudies-literature | 2021 Jan-Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Survey on Acceptance of Passive Technology Monitoring for Early Detection of Cognitive Impairment.

Josephy-Hernandez Sylvia S Norise Catherine C Han Jee-Young JY Smith Kara M KM

Digital biomarkers 20201230 1

<h4>Introduction</h4>Digital biomarkers may act as a tool for early detection of changes in cognition. It is important to understand public perception of technologies focused on monitoring cognition to better guide the design of these tools and inform patients appropriately about the associated risks and benefits. Health care systems may also play a role in the clinical, legal, and financial implications of such technologies.<h4>Objective</h4>To evaluate public opinion on the use of passive tech ...[more]

PMID: 33615117

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Project description:BackgroundThere is a growing interest to support home-based dementia care via contactless monitoring (CM) technologies which do not require any body contact, and allow informal caregivers to remotely monitor the health and safety of people with dementia (PwD). However, sustainable implementation of CM technologies requires a better understanding of informal caregivers' acceptance. This study aimed to examine the (1) general acceptance of CM technology for home-based dementia care, (2) acceptance of different sensor types and use scenarios, and (3) differences between accepters and refusers of CM technology.MethodA cross-sectional online survey was conducted among n = 304 informal caregivers of community-dwelling PwD [Mean(SD) age = 58.5 (10.7)] in the Netherlands and Germany. The survey contained a textual and graphical introduction to CM technologies, as well as questions targeting (1) general acceptance of CM technology, (2) acceptance of seven different contactless sensor types, (3) acceptance of five different use scenarios, and (4) caregivers' own and their care recipients' personal characteristics. Data were examined using descriptive and bivariate analyses.ResultsParticipants' general acceptance of CM technology was slightly positive. We found significant differences in acceptability between contactless sensor types (p < .001). RF-based sensors (e.g., radar) and light sensors were considered most acceptable, whereas camera-based sensors and audio sensors (e.g., microphones, smart speakers) were seen as least acceptable for home-based dementia care. Furthermore, participants' acceptance of different use scenarios for CM technology varied significantly (p < .001). The intention to use CM technology was highest for detecting emergencies (e.g., falls, wandering), and lowest for predicting acute situations (e.g., fall prediction). Lastly, accepters and refusers of CM technology significantly differed regarding gender (p = .010), their relation with the PwD (p = .003), eHealth literacy (p = .025), personal innovativeness (p < .001), usage of safety technology (p = .002), and the PwD's type of cognitive impairment (p = .035) and housing situation (p = .023).ConclusionOur findings can inform the development and implementation of acceptable CM technology to support home-based dementia care. Specifically, we show which sensor types and use scenarios should be prioritized from the informal caregiver's view. Additionally, our study highlights several personal characteristics associated with informal caregivers' acceptance of CM technology that should be taken into account during implementation.

Project description:Purpose: The early detection of Mild Cognitive Impairment (MCI) is essential in aging societies where dementia is becoming a common manifestation among the elderly. Thus our aim is to develop a decision tree to discriminate individuals at risk of MCI among non-institutionalized elderly users of community pharmacy. A more clinically and patient-oriented role of the community pharmacist in primary care makes the dispensation of medication an adequate situation for an effective, rapid, easy, and reproducible screening of MCI. Methods: A cross-sectional study was conducted with 728 non-institutionalized participants older than 65. A total of 167 variables were collected such as age, gender, educational attainment, daily sleep duration, reading frequency, subjective memory complaint, and medication. Two screening tests were used to detect possible MCI: Short Portable Mental State Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE). Participants classified as positive were referred to clinical diagnosis. A decision tree and predictive models are presented as a result of applying techniques of machine learning for a more efficient enrollment. Results: One hundred and twenty-eight participants (17.4%) scored positive on MCI tests. A recursive partitioning algorithm with the most significant variables determined that the most relevant for the decision tree are: female sex, sleeping more than 9 h daily, age higher than 79 years as risk factors, and reading frequency. Moreover, psychoanaleptics, nootropics, and antidepressants, and anti-inflammatory drugs achieve a high score of importance according to the predictive algorithms. Furthermore, results obtained from these algorithms agree with the current research on MCI. Conclusion: Lifestyle-related factors such as sleep duration and the lack of reading habits are associated with the presence of positive in MCI test. Moreover, we have depicted how machine learning provides a sound methodology to produce tools for early detection of MCI in community pharmacy. Impact of findings on practice: The community of pharmacists provided with adequate tools could develop a crucial task in the early detection of MCI to redirect them immediately to the specialists in neurology or psychiatry. Pharmacists are one of the most accessible and regularly visited health care professionals and they can play a vital role in early detection of MCI.

Project description:IntroductionThe gradual changes over the decades in the longevity and ageing of European society as a whole can be directly related to the prolonged decline in the birth rate and increase in the life expectancy. According to the WHO, there is an increased risk of dementia or other cognitive disorders as the population ages, which have a major impact on public health. Mild cognitive impairment (MCI) is described as a greater than expected cognitive decline for an individual's age and level of education, but that does not significantly interfere with activities of daily living. Patients with MCI exhibit a higher risk of dementia compared with others in the same age group, but without a cognitive decline, have impaired walking and a 50% greater risk of falling.The urban lifestyle and advent of smartphones, mobility and immediate access to all information via the internet, including health information, has led to a totally disruptive change in most general aspects.This systematic review protocol is aimed at evaluating the effectiveness of technology-based interventions in the detection, prevention, monitoring and treatment of patients at risk or diagnosed with MCI.Methods and analysisThis review protocol follows the recommendations of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols reporting guidelines. The search will be performed on MEDLINE (PubMed), CENTRAL, CINAHL Plus, ISI Web of Science and Scopus databases from 2010 to 2020. Studies of interventions either randomised clinical trials or pre-post non-randomised quasi-experimental designs, published in English and Spanish will be included. Articles that provide relevant information on the use of technology and its effectiveness in interventions that assess improvements in early detection, prevention, follow-up and treatment of the patients at risk or diagnosed with MCI will be included.Ethics and disseminationEthics committee approval not required. The results will be disseminated in publications and congresses.

Dataset Information

Survey on Acceptance of Passive Technology Monitoring for Early Detection of Cognitive Impairment.

Introduction

Objective

Methods

Results

Conclusions

Publications

Survey on Acceptance of Passive Technology Monitoring for Early Detection of Cognitive Impairment.

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