{"id":910,"date":"2024-10-05T09:05:16","date_gmt":"2024-10-05T08:05:16","guid":{"rendered":"https:\/\/igormatias.com\/?p=910"},"modified":"2026-02-02T13:02:26","modified_gmt":"2026-02-02T13:02:26","slug":"providemus-alz-ubiquitous-screening-of-preclinical-alzheimers-disease-with-consumer-grade-technologies","status":"publish","type":"post","link":"https:\/\/igormatias.com\/pt\/2024\/10\/05\/providemus-alz-ubiquitous-screening-of-preclinical-alzheimers-disease-with-consumer-grade-technologies\/","title":{"rendered":"Providemus alz: Ubiquitous screening of Preclinical Alzheimer’s Disease with consumer-grade Technologies"},"content":{"rendered":"

Abstract: <\/span>This article introduces a study to facilitate the early detection of preclinical Alzheimer\u2019s disease using consumer-grade technologies. It uses a smartphone app and a hybrid smartwatch to monitor cognition over time by collecting a comprehensive set of technology-reported, patient-reported, and performance-reported outcomes. This project focuses on continuous and passive data collection, highlighting the potential of ubiquitous devices to monitor cognitive changes in naturalistic settings. This article details the tools, study design, and metrics used to provide a scalable early mental health monitoring solution. Preliminary findings suggest the effectiveness of the data collection tools, demonstrating the promise of ubiquitous computing in advancing mental health research and facilitating early intervention strategies. The study has the potential to significantly impact public health by providing a scalable and non-invasive method for early detection of cognitive impairment, thereby enabling timely interventions and better management of Alzheimer\u2019s disease. Additionally, it follows an open data policy, ensuring that the collected data is available to other researchers for further analysis and use, promoting transparency and collaboration in the scientific community. This research underscores the need for innovative approaches to develop reliable, scalable solutions for early detection and monitoring of cognitive changes, contributing significantly to the growing field of ubiquitous technologies for mental health.<\/p>\n

Igor Matias1<\/sup>, Matthias Kliegel2<\/sup>, Katarzyna Wac1<\/sup><\/p>\n

[1<\/sup>Quality of Life Technologies Lab, 2<\/sup>Cognitive Aging Lab], University of Geneva, Switzerland<\/p>\n

In 2024 Companion of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UBICOMP).<\/span><\/p>\n

Article: here<\/a><\/p>\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Abstract: Wearable devices are a useful and widely used source of continuous and temporal dependant data. In contrast to the traditional clinical environment, these devices allow time series data collection in an individual\u2019s daily living environ- ment. However, missing data can occur while using them. Many techniques have been applied to solve these data gaps; nonetheless, missing time series data poses extra challenges, such as maintaining the temporal dependency. In this article, we addressed the forecast of sleep trackers data (sleeping heart rate (HR) and time asleep) for 2 main reasons: (1) to design models capable of accurately forecasting missing data from those devices, and (2) to apply those models to empower sleep interventions that may increase its quality, by forecasting future sleep events. We collected wearables data over 290 days (per individual) from 12 participants using a smartwatch and made this dataset publicly available. We then explored several hyperparameters of 2 Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). We further elaborated and compared the performance of 3 approaches to training those RNNs. Although similar performance, slightly more accurate results were obtained after training a GRU network on an entire population\u2019s dataset, which was able to forecast the average, minimum, and maximum sleeping HR with a root-mean-squared error (RMSE) of 4.4 (\u00b1 1.4), 4.9 (\u00b1 2.6), and 12.1 (\u00b1 4.0) beats per minute, respectively. However, the total time asleep was impossible to forecast with low error.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11],"tags":[],"class_list":["post-910","post","type-post","status-publish","format-standard","hentry","category-conference"],"gutentor_comment":0,"yoast_head":"\nProvidemus alz: Ubiquitous screening of Preclinical Alzheimer's Disease with consumer-grade Technologies<\/title>\n<meta name=\"description\" content=\"Abstract: Wearable devices are a useful and widely used source of continuous and temporal dependant data. 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