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Social behavior assessment in cognitively impaired older adults using a passive and remote smartphone application
Alzheimer's & Dementia
Abstract
Background
In Alzheimer’s Disease (AD), loss of social interactions has a major impact on well‐being. Therefore, AD patients would benefit from early detection of symptoms of social withdrawal. Current measurement techniques such as questionnaires are subjective and rely on recall, in contradiction to smartphone applications, which measure social behavior passively and objectively. Here, we examine social interactions through passive remote monitoring with the smartphone application BEHAPP in cognitively impaired participants. This study aims to investigate (1) the association between demographic characteristics and BEHAPP outcome variables in cognitively normal (CN) older adults, (2) if social behavior as measured using the passive smartphone app BEHAPP is impaired in cognitively impaired (CI) participants compared to subjects with subjective cognitive decline (SCD), and CN older adults. In addition, we explored in a subset of individuals the association between BEHAPP outcomes and neuropsychiatric symptoms.
Method
CN (n=209), SCD (n=55) and CI (n=22) participants, older than 45 years, installed the BEHAPP app on their own Android smartphone for 7‐42 days. CI participants had a clinical diagnosis of mild cognitive impairment or AD‐type dementia. The app continuously measured communication events, application usage and location. Neuropsychiatric Inventory (NPI) total scores were available from 20 SCD and 22 CI participants.
Result
We found that older cognitively healthy participants called less frequently and made less use of apps. No sex effects were found. Linear models corrected for age, sex and education showed that compared to the CN and SCD groups, CI participants called less unique contacts and contacted the same contacts relatively more often (Figure 1). They also made less use of apps, visited less unique places and traveled less far from home. Higher total NPI scores were associated with more unique stay points and further travelling. Similar behavior patterns were found when correcting for multiple comparisons.
Conclusion
Cognitively impaired individuals show reduced social activity, as measured by the smartphone application BEHAPP. Neuropsychiatric symptoms seemed only marginally associated with social behavior as measured with BEHAPP. This research shows that a passive and remote smartphone application is able to objectively and passively measure altered social behavior in a cognitively impaired population.
... This implies the importance of evaluating surrogate social cognition markers of neurodegeneration, as no reliable cognitive ToM tasks exist. For this purpose, the passive analysis of smartphone data, such as the frequency of phone calls and mobile applications, facilitated the identification of patients with cognitive impairment from age-and education-matched healthy individuals thus far 135 . For instance, those with cognitive impairment had less usage activity and contacted the same people more frequently. ...
Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson’s and Alzheimer’s disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as “bio-psycho-social” conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.
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