Benefiting From Digital Use: Prospective Association of Internet Use With Knowledge and Preventive Behaviors Related to Alzheimer Disease in the Israeli Survey of Aging

Neter, Efrat; Chachashvili‐Bolotin, Svetlana; Erlich, Bracha; Ifrah, Kfir

doi:10.2196/25706

Cited by 6 publications

(4 citation statements)

References 44 publications

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“…Lastly, in line with previous literature [ 81 , 82 ], we found that Internet use was associated with an uptake of preventive behaviors. However, due to the limitations of the SHARE database, we could not investigate which specific digital activities are associated with enhanced uptake of preventive behaviors in our sample.…”

Section: Discussionsupporting

confidence: 92%

Ethnic Differences in Attitudes and Preventive Behaviors Related to Alzheimer’s Disease in the Israeli Survey of Aging

Neter

Chachashvili‐Bolotin

2022

IJERPH

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Objectives: To examine ethnic differences in attitudes and preventive behaviors related to Alzheimer’s Disease (AD) in Israel. Methods: A household representative sample included 1198 older adults (M age = 70.78, SD = 9.64) who participated in the Israeli branch of the Survey of Health, Aging, and Retirement in Europe (SHARE-Israel), collected during 2015 and 2017. Descriptions of the groups (long term Israeli Jews (LTIJ), immigrants from the Former Soviet Union (FSU) and Palestinian Citizens of Israel (PCI)) were computed, and hierarchical regressions tested whether group differences were maintained after controlling for demographic, human and economic resources, Internet use, and AD familiarity. Results: Attitudes towards AD were the most negative among FSU and more accepting among PCI while AD-related preventive behaviors were highest among FSU, lowest among PCI, with LTIJ between them. After including demographic, human and economic resources, and familiarity with AD, differences in AD-preventive behaviors significantly decreased. In contrast, differences in attitudes among the groups remained stable even after other variables were accounted for, so that PCI were the most accepting and FSU manifested greatest avoidance of contact with persons with AD. Conclusions: The findings provide directions for culturally sensitive psycho-educational and other interventions for both the public and healthcare providers.

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Section: Discussionsupporting

confidence: 92%

Ethnic Differences in Attitudes and Preventive Behaviors Related to Alzheimer’s Disease in the Israeli Survey of Aging

Neter

Chachashvili‐Bolotin

2022

IJERPH

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“…Moreover, research suggests that stigma against dementia is related to fear and a lack of understanding about the disease [ 20 , 46 ]. Consequently, there is a growing need for dementia education and awareness campaigns targeted toward digital media [ 47 ] and specifically toward Twitter and other social media platforms.…”

Section: Discussionmentioning

confidence: 99%

Using Twitter to Examine Stigma Against People With Dementia During COVID-19: Infodemiology Study

et al. 2022

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Background During the pandemic, there has been significant social media attention focused on the increased COVID-19 risks and impacts for people with dementia and their care partners. However, these messages can perpetuate misconceptions, false information, and stigma. Objective This study used Twitter data to understand stigma against people with dementia propagated during the COVID-19 pandemic. Methods We collected 1743 stigma-related tweets using the GetOldTweets application in Python from February 15 to September 7, 2020. Thematic analysis was used to analyze the tweets. Results Based on our analysis, 4 main themes were identified: (1) ageism and devaluing the lives of people with dementia, (2) misinformation and false beliefs about dementia and COVID-19, (3) dementia used as an insult for political ridicule, and (4) challenging stigma against dementia. Social media has been used to spread stigma, but it can also be used to challenge negative beliefs, stereotypes, and false information. Conclusions Dementia education and awareness campaigns are urgently needed on social media to address COVID-19-related stigma. When stigmatizing discourse on dementia is widely shared and consumed amongst the public, it has public health implications. How we talk about dementia shapes how policymakers, clinicians, and the public value the lives of people with dementia. Stigma perpetuates misinformation, pejorative language, and patronizing attitudes that can lead to discriminatory actions, such as the limited provision of lifesaving supports and health services for people with dementia during the pandemic. COVID-19 policies and public health messages should focus on precautions and preventive measures rather than labeling specific population groups.

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“…However, continuously monitoring these biomarkers is unfeasible as obtaining these data incurs either high cost (eg, MRI) or great inconvenience for the patients due to the invasive nature of the procedure [15][16][17]. Digital markers, however, that evaluate cognitive impairment by analyzing behavioral data collected from digital devices such as smartphones [18][19][20] or personal computers [21][22][23] could alleviate the above challenges and would provide an inexpensive, easy-to-use alternative. To this end, the development and validation of digital markers that allow for the reliable screening of MCI is an important research topic [17,24,25].…”

Section: Introductionmentioning

confidence: 99%

Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study

Kim,

Park,

Choi

et al. 2023

J Med Internet Res

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Background With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a “virtual kiosk test” for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment. Objective We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features. Methods A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test—developed by our group—where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls. Results In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=–3.44; P=.004), and a greater number of errors (t49=–3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score. Conclusions Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach.

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Benefiting From Digital Use: Prospective Association of Internet Use With Knowledge and Preventive Behaviors Related to Alzheimer Disease in the Israeli Survey of Aging

Cited by 6 publications

References 44 publications

Ethnic Differences in Attitudes and Preventive Behaviors Related to Alzheimer’s Disease in the Israeli Survey of Aging

Ethnic Differences in Attitudes and Preventive Behaviors Related to Alzheimer’s Disease in the Israeli Survey of Aging

Using Twitter to Examine Stigma Against People With Dementia During COVID-19: Infodemiology Study

Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study

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