Background: Questionnaires are the current hallmark for quantifying social functioning in human clinical research. In this study, we compared self- and proxy-rated (caregiver and researcher)... Show moreBackground: Questionnaires are the current hallmark for quantifying social functioning in human clinical research. In this study, we compared self- and proxy-rated (caregiver and researcher) assessments of social functioning in Schizophrenia (SZ) and Alzheimer's disease (AD) patients and evaluated if the discrepancy between the two assessments is mediated by disease-related factors such as symptom severity.Methods: We selected five items from the WHO Disability Assessment Schedule 2.0 (WHODAS) to assess social functioning in 53 AD and 61 SZ patients. Caregiver- and researcher-rated assessments of social functioning were used to calculate the discrepancies between self-rated and proxy-rated assessments. Furthermore, we used the number of communication events via smartphones to compare the questionnaire outcomes with an objective measure of social behaviour.Results: WHODAS results revealed that both AD (p < 0.001) and SZ (p < 0.004) patients significantly overestimate their social functioning relative to the assessment of their caregivers and/or researchers. This overestimation is mediated by the severity of cognitive impairments (MMSE; p = 0.019) in AD, and negative symptoms (PANSS; p = 0.028) in SZ. Subsequently, we showed that the proxy scores correlated more strongly with the smartphone communication events of the patient when compared to the patient-rated questionnaire scores (self; p = 0.076, caregiver; p < 0.001, researcher-rated; p = 0.046).Conclusion: Here we show that the observed overestimation of WHODAS social functioning scores in AD and SZ patients is partly driven by disease-related biases such as cognitive impairments and negative symptoms, respectively. Therefore, we postulate the development and implementation of objective measures of social functioning that may be less susceptible to such biases. Show less
The use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated... Show moreThe use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated preprocessing framework for deriving behavioral phenotypes from smartphone-based location data is currently lacking. Here, we present a preprocessing framework consisting of methods that are validated in the context of geospatial data. This framework aims to generate context-enriched location data by identifying stationary, non-stationary, and recurrent stationary states in movement patterns. Subsequently, this context-enriched data is used to derive a series of behavioral phenotypes that are related to movement. By using smartphone-based location data collected from 245 subjects, including patients with schizophrenia, we show that the proposed framework is effective and accurate in generating context-enriched location data. This data was subsequently used to derive behavioral readouts that were sensitive in detecting behavioral nuances related to schizophrenia and aging, such as the time spent at home and the number of unique places visited. Overall, our results indicate that the proposed framework reliably preprocesses raw smartphone-based location data in such a manner that relevant behavioral phenotypes of interest can be derived. Show less
