Dataset Information

Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia.

ABSTRACT: Digital phenotyping, or the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices and smartphones, in particular, holds great potential for behavioral monitoring of patients. However, realizing the potential of digital phenotyping requires understanding of the smartphone as a scientific data collection tool. In this pilot study, we detail a procedure for estimating data quality for phone sensor samples and model the relationship between data quality and future symptom-related survey responses in a cohort with schizophrenia. We find that measures of empirical coverage of collected accelerometer and GPS data, as well as survey timing and survey completion metrics, are significantly associated with future survey scores for a variety of symptom domains. We also find evidence that specific measures of data quality are indicative of domain-specific future survey outcomes. These results suggest that for smartphone-based digital phenotyping, metadata is not independent of patient-reported survey scores, and is therefore potentially useful in predicting future clinical outcomes. This work raises important questions and considerations for future studies; we explore and discuss some of these implications.

SUBMITTER: Torous J

PROVIDER: S-EPMC6550248 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia.

Torous John J Staples Patrick P Barnett Ian I Sandoval Luis R LR Keshavan Matcheri M Onnela Jukka-Pekka JP

NPJ digital medicine 20180406

Digital phenotyping, or the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices and smartphones, in particular, holds great potential for behavioral monitoring of patients. However, realizing the potential of digital phenotyping requires understanding of the smartphone as a scientific data collection tool. In this pilot study, we detail a procedure for estimating data quality for phone sensor samples and model the relationship ...[more]

PMID: 31304300

Similar Datasets

Project description:BackgroundSymptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms.AimsIn this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis.MethodSelf-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains.ResultsThe three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups.ConclusionsUsing a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

Project description:Loneliness is a highly prevalent experience in schizophrenia. Theoretical models developed in the general population propose that loneliness is tantamount to a feeling of being unsafe, is accompanied by enhanced environmental threat perception, and leads to poor physical, emotional, and cognitive functioning. Previous research has reported that loneliness is associated with poorer physical and emotional health in schizophrenia; however, few studies have directly compared loneliness and its correlates in persons with schizophrenia and non-psychiatric comparison subjects. The purpose of the current study was to evaluate similarities and differences in the construct of loneliness, the equivalency of the measurement of this construct, and similarities and differences in the pattern of external correlates of loneliness between schizophrenia and non-psychiatric comparison groups. The third version of the University of California, Los Angeles Loneliness Scale (UCLA-3) was administered to 116 individuals with schizophrenia or schizoaffective disorder and 106 non-psychiatric comparison subjects. Additional clinical and positive psychological measures were collected, as well as demographic characteristics of the two groups. Multiple groups confirmatory factor analysis revealed that the UCLA-3 was best characterized by a bifactor model in which all items loaded on a general loneliness dimension as well as one of two orthogonal method factors reflecting item wording in both groups. Furthermore, the UCLA-3 exhibited invariant measurement of these latent constructs across groups. Mean levels of loneliness were nearly a standard deviation higher in the schizophrenia group. Nonetheless, the overall pattern and strength of correlates were largely similar across groups, with loneliness being positively associated with depression, anxiety, and perceived stress, and negatively correlated with mental well-being, happiness, and resilience. Subtle differences in correlates of age, optimism, and satisfaction with life were found. Overall, loneliness appears to be distinct from other schizophrenia-related deficits and operates similarly across schizophrenia and NC groups, suggesting that theoretical models of loneliness developed in the general population may generalize to schizophrenia.

Dataset Information

Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia.

Publications

Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure