An Aging Focused Unobtrusive and Privacy-Preserving Digital Behaviorome

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.

Key Measurement Concepts and Appropriate Clinical Outcome Assessments in Pediatric Achondroplasia Clinical Trials

Background: This study aimed to identify fit-for-purpose clinical outcome assessments (COAs) to evaluate physical function and social and emotional well-being in clinical trials enrolling a pediatric population with achondroplasia. Qualitative interviews lasting 90 minutes were conducted in the US with children/adolescents with achondroplasia and/or their caregivers. Interviews utilized concept elicitation methodology to explore experiences and priorities for treatment outcomes. Cognitive debriefing methodology explored relevance and understanding of selected COAs. Results: Interviews (N=36) were conducted with caregivers of children age 0–2 years (n=8) and 3–7 years (n=7) and child/caregiver dyads with children age 8–11 years (n=15) and 12–17 years (n=6). Children/caregivers identified pain, short stature, impacts on physical functioning (e.g. reach), and impacts on well-being (e.g. negative attention/comments) as key bothersome aspects of achondroplasia. Caregivers considered an increase in height (n=9/14, 64%) and an improvement in limb proportion (n=11/14, 71%) as successful treatment outcomes. The Childhood Health Assessment Questionnaire (CHAQ) and Quality of Life in Short Stature Youth (QoLISSY-Brief) were cognitively debriefed. CHAQ items evaluating activities, reaching, and hygiene were most relevant. QoLISSY-Brief items evaluating reaching, height bother, being treated differently, and height preventing doing things others could were most relevant. The CHAQ and QoLISSY-Brief instructions, item wording, response scales/options and recall period were well understood by caregivers and adolescents age 12–17. Some children aged 8–11 had difficulty reading, understanding, or required caregiver input. Feedback informed minor amendments to the CHAQ and the addition of a seven-day recall period to the QoLISSY-Brief. These amendments were subsequently reviewed and confirmed in N=12 interviews with caregivers of children age 0–11 (n=9) and adolescents age 12–17 (n=3).Conclusions: Achondroplasia impacts physical functioning and well-being. An increase in height and improvement in limb proportion are considered to be important treatment outcomes, but children/adolescents and their caregivers expect that a successful treatment should also improve important functional outcomes such as reach. The CHAQ (adapted for achondroplasia) and QoLISSY-Brief are relevant and appropriate measures of physical function and emotional/social well-being for pediatric achondroplasia trials; patient-report is recommended for age 12–17 years and caregiver-report is recommended for age 0–11 years.