Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia
| Autor: | Patrick Staples, Luis Sandoval, Jukka-Pekka Onnela, John Torous, Matcheri S. Keshavan, Ian Barnett |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Schizophrenia (object-oriented programming) Medicine (miscellaneous) Health Informatics lcsh:Computer applications to medicine. Medical informatics Article 03 medical and health sciences 0302 clinical medicine Health Information Management Phone Human behaviour Signs and symptoms Human phenotype Data collection Statistics Scientific data Data science 3. Good health 030227 psychiatry Computer Science Applications Variety (cybernetics) Metadata Data quality Cohort lcsh:R858-859.7 Software 030217 neurology & neurosurgery |
| Zdroj: | npj Digital Medicine, Vol 1, Iss 1, Pp 1-9 (2018) NPJ Digital Medicine |
| ISSN: | 2398-6352 |
| Popis: | 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. Digital phenotyping: assessing data quality in schizophrenia A pilot study shows that smartphone-collected data from patients with schizophrenia could be used to infer their mental-health status. Using smartphones as scientific data gathering tools holds great promise for understanding some of the behavioral features of psychiatric disorders and could provide an early indication of worsening symptoms. However, few studies have assessed the quality of the collected data, and thus the accuracy of clinical outcome prediction. Patrick Staples at the Harvard T. H. Chan School of Public Health in Boston, MA, and colleagues examined the relationship between data quality and future symptom-related survey responses in 16 patients with schizophrenia. They found that smartphone sensor data as well as phone-use metrics related to the completion of symptom-related surveys were significantly associated with survey results, highlighting the clinical relevance of this approach. |
| Databáze: | OpenAIRE |
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