Nilgün Çınar, Sude Aslan Kendirli, Miruna Florentina Ateş, Ezgi Yakupoğlu, Ebru Akbuğa, Naci Emre Bolu, F. Karalı, T. Okluoğlu, Nazlı Gamze Bülbül, Elif Bayindir, Kamil Tolga Atam, Enis Hisarlı, Sarp Akgönül, Oğulcan Bagatır, Emre Sahiner, Bora Orgen, Türker Ahmet Hasan Sahiner
{"title":"Validity and Reliability Study of Online Cognitive Tracking Software (BEYNEX)","authors":"Nilgün Çınar, Sude Aslan Kendirli, Miruna Florentina Ateş, Ezgi Yakupoğlu, Ebru Akbuğa, Naci Emre Bolu, F. Karalı, T. Okluoğlu, Nazlı Gamze Bülbül, Elif Bayindir, Kamil Tolga Atam, Enis Hisarlı, Sarp Akgönül, Oğulcan Bagatır, Emre Sahiner, Bora Orgen, Türker Ahmet Hasan Sahiner","doi":"10.3233/adr-230117","DOIUrl":null,"url":null,"abstract":"Background: Detecting cognitive impairment such as Alzheimer’s disease early and tracking it over time is essential for individuals at risk of cognitive decline. Objective: This research aimed to validate the Beynex app’s gamified assessment tests and the Beynex Performance Index (BPI) score, which monitor cognitive performance across seven categories, considering age and education data. Methods: Beynex test cut-off scores of participants (n = 91) were derived from the optimization function and compared to the Montreal Cognitive Assessment (MoCA) test. Validation and reliability analyses were carried out with data collected from an additional 214 participants. Results: Beynex categorization scores showed a moderate agreement with MoCA ratings (weighted Cohen’s Kappa = 0.48; 95% CI: 0.38–0.60). Calculated Cronbach’s Alpha indicates good internal consistency. Test-retest reliability analysis using a linear regression line fitted to results yielded R∧2 of 0.65 with a 95% CI: 0.58, 0.71. Discussion: Beynex’s ability to reliably detect and track cognitive impairment could significantly impact public health, early intervention strategies and improve patient outcomes.","PeriodicalId":73594,"journal":{"name":"Journal of Alzheimer's disease reports","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Alzheimer's disease reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/adr-230117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Detecting cognitive impairment such as Alzheimer’s disease early and tracking it over time is essential for individuals at risk of cognitive decline. Objective: This research aimed to validate the Beynex app’s gamified assessment tests and the Beynex Performance Index (BPI) score, which monitor cognitive performance across seven categories, considering age and education data. Methods: Beynex test cut-off scores of participants (n = 91) were derived from the optimization function and compared to the Montreal Cognitive Assessment (MoCA) test. Validation and reliability analyses were carried out with data collected from an additional 214 participants. Results: Beynex categorization scores showed a moderate agreement with MoCA ratings (weighted Cohen’s Kappa = 0.48; 95% CI: 0.38–0.60). Calculated Cronbach’s Alpha indicates good internal consistency. Test-retest reliability analysis using a linear regression line fitted to results yielded R∧2 of 0.65 with a 95% CI: 0.58, 0.71. Discussion: Beynex’s ability to reliably detect and track cognitive impairment could significantly impact public health, early intervention strategies and improve patient outcomes.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
