東華大學圖書館 |

Digital Screening of Dementia in Hong Kong.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Digital Screening of Dementia in Hong Kong./
作者:	Wong, Pui Fai.
面頁冊數:	1 online resource (165 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
標題:	Health sciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29282511click for full text (PQDT)
ISBN:	9798802755570

Digital Screening of Dementia in Hong Kong.
Wong, Pui Fai.

Digital Screening of Dementia in Hong Kong. - 1 online resource (165 pages)

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2022.

Includes bibliographical references

IntroductionDementia is one of the most common neurodegenerative disorders in people who aged 65 or above. Alzheimer's disease (AD) is one of the most common forms of dementia, characterized by cognitive decline, impaired daily activities, and neuro/behavioural abnormalities. Currently, there is no effective treatment to reverse the condition of AD. However, early detection of AD allows clinicians to have early planning for interventions that can help to slow down the progress of cognitive decline.Conventional AD screening tests commonly use pencil-and-paper form as screening tools, such as Mini-mental State Examination (MMSE), but these tests may still have several disadvantages. For example, healthcare professionals may give subjective scoring on the cognitive symptoms. In recent years, many digital screening tests were developed as a digital format to avoid subjective bias, such as the Digital Clock Drawing Test, but the use of digital tools to construct complex diagrams may also bring a new challenge for the screening participants. Therefore, the objectives of this study are to develop and validate a simple digital screening tool for AD and study the effectiveness the screening platform with consideration of the drawing behavior.MethodPatients were recruited from an AD clinic at the Prince of Wales Hospital. Healthy controls were recruited from the community centres and other non-AD clinics with normal cognitive functions at the Prince of Wales Hospital. Consents were obtained from all eligible participants. The cognitive levels of each participant were assessed by the Montreal Cognitive Assessment Test (MoCA) and they were instructed to draw a simple diagram, i.e. a pair of interlocking pentagons, on a digital screen of a smart tablet. The drawing behaviors such as drawing time and number of stops, were real-time captured in the digital platform. The completed image was also scored for correctness. Image features and drawing behaviors were used to derive the prediction model for AD.Participants were randomly split into derivation and validation cohorts in a ratio of 7:3. Logistic regression with backward elimination was used to derive the risk of AD, using image features and drawing behaviors. Different models were developed using image features and drawing behaviours. The features of image were further analyzed in pixel format. The models were validated with C-statistic to compare the diagnostic accuracy of AD.ResultsIn this study, 185 patients were clinically diagnosed with AD and 268 participants were classified as healthy controls with MoCA cutoff at 22/21. When dynamic cutoff was considered for the participants with different age and education levels, 443 participants were defined as healthy controls. When MoCA cutoff at 22/21, the diagnostic accuracy of the prediction model with image features and drawing behaviors model demonstrated a diagnostic accuracy of 90% (95% CI: 0.85-0.95). The results were comparable when the image features were interpreted in pixel format. However, using image features alone, the prediction model only shown diagnostic accuracy of 76% (95% CI: 0.69-0.83). When the heathy controls were defined as dynamic cutoff values of MoCA, the result shown a comparable diagnostic accuracy.ConclusionDrawing behaviour which is captured on a digital screen of a smart tablet showed an improved diagnostic performance for AD. The executive function when drawing a diagram can be incorporated for a better cognitive screening than the simple scoring for the correctness of drawing.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798802755570Subjects--Topical Terms:

3168359
Health sciences.
Subjects--Index Terms:

Digital screeningIndex Terms--Genre/Form:

542853
Electronic books.

Digital Screening of Dementia in Hong Kong.
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504 $a Includes bibliographical references
520 $a IntroductionDementia is one of the most common neurodegenerative disorders in people who aged 65 or above. Alzheimer's disease (AD) is one of the most common forms of dementia, characterized by cognitive decline, impaired daily activities, and neuro/behavioural abnormalities. Currently, there is no effective treatment to reverse the condition of AD. However, early detection of AD allows clinicians to have early planning for interventions that can help to slow down the progress of cognitive decline.Conventional AD screening tests commonly use pencil-and-paper form as screening tools, such as Mini-mental State Examination (MMSE), but these tests may still have several disadvantages. For example, healthcare professionals may give subjective scoring on the cognitive symptoms. In recent years, many digital screening tests were developed as a digital format to avoid subjective bias, such as the Digital Clock Drawing Test, but the use of digital tools to construct complex diagrams may also bring a new challenge for the screening participants. Therefore, the objectives of this study are to develop and validate a simple digital screening tool for AD and study the effectiveness the screening platform with consideration of the drawing behavior.MethodPatients were recruited from an AD clinic at the Prince of Wales Hospital. Healthy controls were recruited from the community centres and other non-AD clinics with normal cognitive functions at the Prince of Wales Hospital. Consents were obtained from all eligible participants. The cognitive levels of each participant were assessed by the Montreal Cognitive Assessment Test (MoCA) and they were instructed to draw a simple diagram, i.e. a pair of interlocking pentagons, on a digital screen of a smart tablet. The drawing behaviors such as drawing time and number of stops, were real-time captured in the digital platform. The completed image was also scored for correctness. Image features and drawing behaviors were used to derive the prediction model for AD.Participants were randomly split into derivation and validation cohorts in a ratio of 7:3. Logistic regression with backward elimination was used to derive the risk of AD, using image features and drawing behaviors. Different models were developed using image features and drawing behaviours. The features of image were further analyzed in pixel format. The models were validated with C-statistic to compare the diagnostic accuracy of AD.ResultsIn this study, 185 patients were clinically diagnosed with AD and 268 participants were classified as healthy controls with MoCA cutoff at 22/21. When dynamic cutoff was considered for the participants with different age and education levels, 443 participants were defined as healthy controls. When MoCA cutoff at 22/21, the diagnostic accuracy of the prediction model with image features and drawing behaviors model demonstrated a diagnostic accuracy of 90% (95% CI: 0.85-0.95). The results were comparable when the image features were interpreted in pixel format. However, using image features alone, the prediction model only shown diagnostic accuracy of 76% (95% CI: 0.69-0.83). When the heathy controls were defined as dynamic cutoff values of MoCA, the result shown a comparable diagnostic accuracy.ConclusionDrawing behaviour which is captured on a digital screen of a smart tablet showed an improved diagnostic performance for AD. The executive function when drawing a diagram can be incorporated for a better cognitive screening than the simple scoring for the correctness of drawing.
520 $a 背景在65 歲或以上人口中，認知障礙症是最常見的腦退化的慢性疾病。然而，阿滋海默氏症便是最常見的認知障礙症，其特徵是認知能力下降、日常活動受影響。目前，阿滋海默氏症沒有直接有效的藥物治療方案。若阿滋海默氏症能在早期發現，醫生可預早計劃非藥物的干預治療，以減緩認知能力衰退的情況。傳統的阿滋海默氏症篩查方法都是靠紙筆形式進行，例如簡短智能測驗 (MMSE)。但是傳統的篩查方法確實有點缺點，如檢測者以主觀的角度來評價測驗結果的成績。近年，越來越多的數碼化的認知篩選測試方案出現，如時鐘繪圖的實時表現測試都改善了傳統測試方法的缺點。有見及此，利用繪圖的實時反應來評估阿滋海默氏症的風險為本研究目標。方法阿滋海默氏症患者於威爾斯親王醫院的認知障礙症診所招募，而健康對照組則在社區長者中心及醫院的其他兩個非認知障礙症診所中招募。合資格的參與者需簽署同意書方可參與研究。每位參與者都接受了蒙特利爾認知評估 (MoCA)測試。完成後，他們需在智能平板電腦上完成數字繪圖測試。參與者需用手指來複製兩個重叠的五邊形。繪製時間及速度的相關繪圖行為在測試過程中實時採集。繪製完成後的圖像也被評分。所有圖像特徵和繪圖行為亦用於預測阿滋海默氏症的風險。參與者以 7:3 的比例隨機分為推導隊列和驗證隊列。使用逐步邏輯回歸來選出圖像特徵和繪圖行為作出模型估算，以推段阿滋海默氏症的風險。最後，模型測試阿滋海默氏症的準確度。以C-統計量作為評估。結果在這項研究中，邀請185名阿滋海默氏症臨床確診的患者及268名健康對照組參加者(以MoCA的22/21指標)參與。若使用非單一的MoCA指標，即根據不用年紀及性別而定立風險指標，健康對照組參加者人數為443名。當使用MoCA的 22/21指標，圖像特徵及繪圖行為模型的預測準確率達90% (95% CI:0.85-0.95)。若僅使用圖像特徵來預測像阿滋海默氏症的準確率只得76% (95% CI: 0.69-0.83)。當使用非單一的MoCA指標測，測試結果跟MoCA的22/21指標大致相近。結論在數碼平台上的繪圖行為證實可提高預測阿滋海默氏症的準確度。繪圖時的動作確實描述認知行為的反應，比較傳統的打分方法，可提升預測阿滋海默氏症的準確度。
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