An IoT-based cognitive impairment detection device: A newly proposed method in older adults care—choice reaction time-device development and data-driven validation DOI Creative Commons
Cristian Vizitiu, Vera Stara, Luca Antognoli

et al.

Digital Health, Journal Year: 2024, Volume and Issue: 10

Published: Jan. 1, 2024

Background Research shows that older adults' performance on choice reaction time (CRT) tests can predict cognitive decline. A simple CRT tool could help detect mild impairment (MCI) and preclinical dementia, allowing for further stratification of disorders on-site or via telemedicine. Objective The primary objective was to develop a testing device protocol differentiate between two categories: (a) subjective decline (SCD) non-amnestic (na-MCI), (b) amnestic (a-MCI) multiple-domain a-MCI (a-MCI-MD). Methods pilot study in Italy Romania with 35 adults (ages 61–85) assessed function using the Mini-Mental State Examination (MMSE) color response task. Reaction time, accuracy, demographics were recorded, machine learning classifiers analyzed differences dementia screen deficits. Results Moderate correlations found MMSE score both mean accuracy rate. There significant difference groups’ blue light, but not any other colors SVM RUSBoosted trees have best prediction capabilities among tested classifier algorithms, presenting an rate 77.1%. Conclusions effectively differentiates capacities adults, facilitating early diagnosis neurocognitive diseases also identify impairments from stressors like dehydration sleep deprivation. This highlights potential portable devices monitoring function, including SCD MCI.

Language: Английский

Declines in social–emotional skills in college students during the COVID-19 pandemic DOI Creative Commons
Janine Cerutti, Keith B. Burt, Robert W. Moeller

et al.

Frontiers in Psychology, Journal Year: 2024, Volume and Issue: 15

Published: July 15, 2024

Introduction The present study investigated whether social–emotional skills in first year college students differed before and after the coronavirus disease (COVID-19) lockdowns. Methods Participants ( N = 1,685) consisted of (mean age 18.53 years) selected from a broader cohort enrolled longitudinal on mental health at liberal arts colleges United States. In cohort-sequential design, participants completed an online survey assessing January 2018, 2019, 2020, 2022. Using analysis covariance, we examined mean differences between who were years (January 2018–2020) lockdowns 2022), controlling for sociodemographic variables. Results post-lockdown group scored significantly lower emotional control expressivity marginally higher social sensitivity compared to pre-lockdown group. No social/emotional or detected. Discussion These findings indicate that COVID-19 impaired some, but not all, students. Addressing may help reduce burden.

Language: Английский

Citations

3
An IoT-based cognitive impairment detection device: A newly proposed method in older adults care—choice reaction time-device development and data-driven validation DOI Creative Commons
Cristian Vizitiu, Vera Stara, Luca Antognoli

et al.

Digital Health, Journal Year: 2024, Volume and Issue: 10

Published: Jan. 1, 2024

Background Research shows that older adults' performance on choice reaction time (CRT) tests can predict cognitive decline. A simple CRT tool could help detect mild impairment (MCI) and preclinical dementia, allowing for further stratification of disorders on-site or via telemedicine. Objective The primary objective was to develop a testing device protocol differentiate between two categories: (a) subjective decline (SCD) non-amnestic (na-MCI), (b) amnestic (a-MCI) multiple-domain a-MCI (a-MCI-MD). Methods pilot study in Italy Romania with 35 adults (ages 61–85) assessed function using the Mini-Mental State Examination (MMSE) color response task. Reaction time, accuracy, demographics were recorded, machine learning classifiers analyzed differences dementia screen deficits. Results Moderate correlations found MMSE score both mean accuracy rate. There significant difference groups’ blue light, but not any other colors SVM RUSBoosted trees have best prediction capabilities among tested classifier algorithms, presenting an rate 77.1%. Conclusions effectively differentiates capacities adults, facilitating early diagnosis neurocognitive diseases also identify impairments from stressors like dehydration sleep deprivation. This highlights potential portable devices monitoring function, including SCD MCI.

Language: Английский

Citations

0