Remote Digital Phenotyping in Serious mental Illness: Focus on Negative Symptoms, Mood Symptoms, and Self-Awareness

Issues and developments related to assessing function in serious mental illness

Dialogues in Clinical Neuroscience ◽

10.31887/dcns.2016.18.2/dvelligan ◽

2016 ◽

Vol 18 (2) ◽

pp. 135-144 ◽

Cited By ~ 2

Keyword(s):

Mental Illness ◽

Clinical Trials ◽

Cognitive Impairment ◽

Serious Mental Illness ◽

Daily Activity ◽

Functional Outcomes ◽

Negative Symptoms ◽

Functional Disability ◽

Assessment Instruments ◽

Novel Treatments

Serious mental illness (SMI) results in functional disability that imposes a significant burden on individuals, caregivers, and society. Development of novel treatments is under way in an effort to improve the illness domains of cognitive impairment and negative symptoms and subsequently to improve functional outcomes. The assessment of functional outcomes in SMI faces a number of challenges, including the proliferation of assessment instruments and the differential prioritization of functional goals among stakeholder groups. Functional assessments relying on self- and informant report present a number of limitations. Identifying alternative strategies to assess functioning that are reliable, valid, and sensitive to change is necessary for use in clinical trials. Measures of functional capacity have been proposed for clinical trials investigating compounds to treat cognitive impairment in schizophrenia. Alternative approaches employing effort-based decision making or daily activity recording using instruments such as the Daily Activity Report may be more appropriate for studies focused on improving negative symptoms.

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Deriving symptom networks from digital phenotyping data in serious mental illness

BJPsych Open ◽

10.1192/bjo.2020.94 ◽

2020 ◽

Vol 6 (6) ◽

Author(s):

Ryan Hays ◽

Matcheri Keshavan ◽

Hannah Wisniewski ◽

John Torous

Keyword(s):

Mental Illness ◽

Lived Experience ◽

Serious Mental Illness ◽

Temporal Dynamics ◽

Transition Probabilities ◽

Generative Models ◽

State Transitions ◽

Poor Sleep ◽

Preventative Care ◽

Digital Phenotyping

Background Symptoms 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. Aims In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis. Method Self-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. Results The 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. Conclusions Using 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.

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Schizophrenia Spectrum and First-Episode Psychosis

Understanding the Mental Health Problems of Children and Adolescents ◽

10.1093/oso/9780190927844.003.0015 ◽

2021 ◽

pp. 229-246

Author(s):

Kirstin Painter ◽

Maria Scannapieco

Keyword(s):

Mental Illness ◽

Serious Mental Illness ◽

Mental Health Professionals ◽

Negative Symptoms ◽

Real Life ◽

Signs And Symptoms ◽

Autism Spectrum ◽

First Episode ◽

Class Discussion ◽

Research Findings

Schizophrenia is a serious mental illness that results in strange thinking, abnormal feelings, and/or odd behaviors. It affects approximately 1 percent of the population. This chapter examines the genetics of schizophrenia and presents the research findings of brain studies, including a longitudinal study of children with early-onset schizophrenia. Next, the chapter provides an overview of the signs and symptoms of schizophrenia and discusses diagnosing schizophrenia in children and adolescents. An overview of autism spectrum disorder, while not considered a serious mental illness by most mental health professionals, is included in this chapter due to its shared genetic components and overlap in its presentation of symptoms with schizophrenia. The chapter ends with useful tools for assessing for the positive and negative symptoms of schizophrenia followed by real-life case studies and questions for class discussion.

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