scholarly journals Digital Phenotyping: an Epistemic and Methodological Analysis

2021 ◽  
Author(s):  
Simon Coghlan ◽  
Simon D’Alfonso
Keyword(s):  
Digital Phenotyping

Smartphone sensing of social interactions in people with and without schizophrenia

2020 ◽  
Author(s):  
Daniel Fulford ◽  
Jasmine Mote ◽  
Rachel Gonzalez ◽  
Samuel Abplanalp ◽  
Yuting Zhang ◽  
...  
Keyword(s):  
Social Behavior ◽  
Social Functioning ◽  
Social Activity ◽  
Network Size ◽  
Social Impairment ◽  
Schizophrenia Spectrum Disorders ◽  
Social Network Size ◽  
Digital Phenotyping ◽  
Ecological Momentary

Social impairment is a cardinal feature of schizophrenia spectrum disorders (SZ). Smaller social network size, diminished social skills, and loneliness are highly prevalent. Existing, gold-standard assessments of social impairment in SZ often rely on self-reported information that depends on retrospective recall and detailed accounts of complex social behaviors. This is particularly problematic in people with SZ given characteristic cognitive impairments and reduced insight. Ecological Momentary Assessment (EMA; repeated self-reports completed in the context of daily life) allows for the measurement of social behavior as it occurs in vivo, yet still relies on participant input. Momentary characterization of behavior using smartphone sensors (e.g., GPS, microphone) may also provide ecologically valid indicators of social functioning. In the current study we tested associations between both active (e.g., EMA-reported number of interactions) and passive (GPS-based mobility, conversations captured by microphone) smartphone-based measures of social activity and measures of social functioning and loneliness to examine the promise of such measures for understanding social impairment in SZ. Our results indicate that passive markers of mobility were more consistently associated with EMA measures of social behavior in controls than in people with SZ. Furthermore, dispositional loneliness showed associations with mobility metrics in both groups, while general social functioning was less related to these metrics. Finally, interactions detected in the ambient audio were more tied to social functioning in SZ than in controls. Findings speak to the promise of smartphone-based digital phenotyping as an approach to understanding objective markers of social activity in people with and without schizophrenia.

Listening Like a Computer: Attentional Tensions and Mechanized Care in Psychiatric Digital Phenotyping

2021 ◽  
pp. 016224392110263
Author(s):  
Beth M. Semel
Keyword(s):  
Speech Sound ◽  
Smartphone Application ◽  
Subject Position ◽  
Research Subject ◽  
Semantic Meaning ◽  
Team Members ◽  
Phone Calls ◽  
Digital Phenotyping ◽  
Human Research Subject ◽  
Audio Recordings

This article explores negotiations over the humanistic versus mechanized components of care through an ethnographic account of digital phenotyping research. I focus on a US-based team of psychiatric and engineering professionals assembling a smartphone application that they hope will analyze minute changes in the sounds of speech during phone calls to predict when a user with bipolar disorder will have a manic or depressive episode. Contrary to conventional depictions of psychiatry as essentially humanistic, the discourse surrounding digital phenotyping positions the machine as a necessary addition to mental health care precisely because of its more-than-human sensory, attentional capacities. The bipolar research team likewise portrays their app as capable of pinpointing sonic signs of mental illness that humans, too distracted by semantic meaning, otherwise ignore. Nevertheless, the team members tasked with processing the team’s data (audio recordings of human research subject speech) must craft and perform a selectively attentive machinic subject position, which they call “listening like a computer”: a paradoxical mode of attention (to speech sound) and inattention (to speech meaning). By tracing the team’s discursive and on-the-ground enactments of care and attention as both humanistic and machinic, I tune a critical ear to the posthuman promises of digital phenotyping.

Digital Phenotyping Adherence, Feasibility, And Tolerability In Outpatients With Schizophrenia

2021 ◽  
Author(s):  
Ian M. Raugh ◽  
Sydney H. James ◽  
Cristina M. Gonzalez ◽  
Hannah C. Chapman ◽  
Alex S. Cohen ◽  
...  
Keyword(s):  
Digital Phenotyping

scholarly journals Cross-evaluation of social mining for classification of depressed online personas

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alina Trifan ◽  
José Luis Oliveira
Keyword(s):  
Transfer Learning ◽  
Early Stage ◽  
Post Partum ◽  
Evaluation Study ◽  
Continuous Increase ◽  
Digital Phenotyping ◽  
Public Datasets ◽  
The Impact ◽  
Social Mining

Abstract With the continuous increase in the use of social networks, social mining is steadily becoming a powerful component of digital phenotyping. In this paper we explore social mining for the classification of self-diagnosed depressed users of Reddit as social network. We conduct a cross evaluation study based on two public datasets in order to understand the impact of transfer learning when the data source is virtually the same. We further complement these results with an experiment of transfer learning in post-partum depression classification, using a corpus we have collected for the matter. Our findings show that transfer learning in social mining might still be at an early stage in computational research and we thoroughly discuss its implications.

Digital phenotyping of sleep patterns among heterogenous samples of Latinx adults using unsupervised learning

2021 ◽  
Author(s):  
Ipek Ensari ◽  
Billy A. Caceres ◽  
Kasey B. Jackman ◽  
Niurka Suero-Tejeda ◽  
Ari Shechter ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Sleep Patterns ◽  
Digital Phenotyping

Diagnostic Validity of the Smart Aging Serious Game: An Innovative Tool for Digital Phenotyping of Mild Neurocognitive Disorder

2021 ◽  
pp. 1-13
Author(s):  
Sara Isernia ◽  
Monia Cabinio ◽  
Sonia Di Tella ◽  
Stefania Pazzi ◽  
Federica Vannetti ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Digital Health ◽  
Roc Curves ◽  
Vascular Cognitive Impairment ◽  
Serious Game ◽  
Diagnostic Validity ◽  
Early Assessment ◽  
Digital Phenotyping ◽  
Diagnostic Sensitivity And Specificity ◽  
Selective Reminding Test

Background: The Smart Aging Serious Game (SASG) is an ecologically-based digital platform used in mild neurocognitive disorders. Considering the higher risk of developing dementia for mild cognitive impairment (MCI) and vascular cognitive impairment (VCI), their digital phenotyping is crucial. A new understanding of MCI and VCI aided by digital phenotyping with SASG will challenge current differential diagnosis and open the perspective of tailoring more personalized interventions. Objective: To confirm the validity of SASG in detecting MCI from healthy controls (HC) and to evaluate its diagnostic validity in differentiating between VCI and HC. Methods: 161 subjects (74 HC: 37 males, 75.47±2.66 mean age; 60 MCI: 26 males, 74.20±5.02; 27 VCI: 13 males, 74.22±3.43) underwent a SASG session and a neuropsychological assessment (Montreal Cognitive Assessment (MoCA), Free and Cued Selective Reminding Test, Trail Making Test). A multi-modal statistical approach was used: receiver operating characteristic (ROC) curves comparison, random forest (RF), and logistic regression (LR) analysis. Results: SASG well captures the specific cognitive profiles of MCI and VCI, in line with the standard neuropsychological measures. ROC analyses revealed high diagnostic sensitivity and specificity of SASG and MoCA (AUCs >  0.800) in detecting VCI versus HC and MCI versus HC conditions. A classification accuracy acceptable-to-excellent was found for MCI and VCI (HC versus VCI; RF: 90%, LR: 91%. HC versus MCI; RF: 75%; LR: 87%). Conclusion: SASG allows the early assessment of cognitive impairment through ecological tasks and potentially in a self-administered way. These features make this platform suitable for being considered a useful digital phenotyping tool, allowing a non-invasive and valid neuropsychological evaluation, with evident implications for future digital-health trails and rehabilitation.

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