An aging focused unobtrusive and Privacy-Preserving Digital Behaviorome

Abstract

Digital measures are increasingly used as objective health measures in remote-monitoring settings. In addition to their use in purely clinical research, such as in clinical trials, one promising application area for sensor-derived digital measures is in technology-assisted ageing and ageing-related research. In this context, digital measures may be used to measure the risk of certain adverse events such as falls, and also to provide novel research insights into ageing and ageing-related conditions, like cognitive impairment. While major emphasis has been placed on deriving one or more digital measures from wearable devices, a more holistic approach inspired by systems biology that leverages large, non-exhaustive sets of digital measures may prove highly beneficial. Such an approach would be useful if combined with modern big data approaches like machine learning. As such, extensive sets of digital measures, which may be referred to as digital behavioromes, could help characterise new phenotypes in deep phenotyping efforts. These measures could also assist in the discovery of novel digital biomarkers or in the creation of digital clinical outcome assessments. While clinical research into digital measures focuses primarily on measures derived from wearable devices, proven technology used for long-term remote monitoring of older adults is generally contactless, unobtrusive, and privacy-preserving. In this context, we introduce and describe a digital behaviorome: a large, non-exhaustive set of digital measures based entirely on contactless, unobtrusive, and privacy-preserving sensor technologies. We also demonstrate how such a behaviorome can be used to build digital clinical outcome assessments that are relevant to ageing and derived from machine learning. These outcomes included fall risk, frailty, mild cognitive impairment, and late-life depression. With the exception of late-life depression, all digital outcome assessments demonstrated a promising ability (ROC AUC ≥ 0.7) to discriminate between positive and negative health outcomes, often in the range of comparable work with wearable devices. Finally, we highlight the possibility of using these digital behaviorome-based outcome assessments to discover novel potential digital biomarkers for each outcome. Here, we found reasonable contributors but also some potentially interesting new candidates regarding fall risk and mild cognitive impairment.

Distinct eye movement patterns to complex scenes in Alzheimer’s disease and Lewy body disease

“…Second, quantifying complex patterns of altered eye movements can support screening and differentiation for dementia subtypes, as has already been demonstrated for several other disorders (Anderson and MacAskill, 2013;Tseng et al, 2013). Note that behavioral measures collected using digital health technologies have become increasingly used in clinical trials of neurological disorders (Masanneck et al, 2023) and are expected to be used as non-invasive, relatively inexpensive, and easily measurable behavioral biomarkers (Kourtis et al, 2019;Schütz et al, 2022;Chen et al, 2023). In this context, along with other behavioral data such as speech, gait, and drawing (Mc Ardle et al, 2019;Pieruccini-Faria et al, 2021;Yamada et al, 2021Yamada et al, , 2022a, eye-tracking data a prospective example (Anderson and MacAskill, 2013;Tseng et al, 2013;Itti, 2015;Kourtis et al, 2019;Valliappan et al, 2020).…”