scholarly journals Deriving symptom networks from digital phenotyping data in serious mental illness

BJPsych Open ◽  
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.

scholarly journals Loneliness and its Association with Health Behaviors in People with a Lived Experience of a Serious Mental Illness

2020 ◽  
Author(s):  
Karen L. Fortuna ◽  
Joelle Ferron ◽  
Cynthia L. Bianco ◽  
Meghan M. Santos ◽  
Ashley Williams ◽  
...  
Keyword(s):  
Mental Illness ◽  
Health Behaviors ◽  
Lived Experience ◽  
Serious Mental Illness

scholarly journals Ambulatory vocal acoustics, temporal dynamics, and serious mental illness.

2019 ◽  
Vol 128 (2) ◽  
pp. 97-105 ◽  
Author(s):  
Alex S. Cohen ◽  
Taylor L. Fedechko ◽  
Elana K. Schwartz ◽  
Thanh P. Le ◽  
Peter W. Foltz ◽  
...  
Keyword(s):  
Mental Illness ◽  
Serious Mental Illness ◽  
Temporal Dynamics ◽  
Vocal Acoustics

scholarly journals Engaging Older Veterans With Serious Mental Illness in Physical Activity: In-Person, Remote, and Hybrid Models

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 205-206
Author(s):  
Sera Havrilla ◽  
Alicia Lucksted ◽  
Deborah Medoff ◽  
Karen Fortuna ◽  
Amanda Peeples ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Mental Illness ◽  
Lived Experience ◽  
Serious Mental Illness ◽  
Complex Care ◽  
Care Needs ◽  
Coaching Intervention ◽  
Peer Specialists ◽  
Complex Care Needs

Abstract Older adults with serious mental illness (SMI) have complex care needs across medical, psychiatric, cognitive, and social domains. This growing population exhibits high levels of medical comorbidity and sedentariness. Innovative interventions that promote holistic recovery for this group are needed, especially in the context of the COVID-19 pandemic. Peer Education on Exercise for Recovery (PEER) is a peer coaching intervention, delivered by VA Peer Specialists (Veterans with lived experience of mental illness), to promote exercise and physical activity among older adults with SMI. This paper will present on three different models of PEER: fully in-person, fully remote, and a hybrid model with both in-person and remote elements. Preliminary data indicates that PEER is (1) engaging and well-liked, (2) associated with greater sustained increases in physical activity compared to an active control, and (3) can lead to sustained physical activity increases that are resilient to situational constraints such as physical distancing.

scholarly journals The relationship between hopelessness and risk factors for early mortality in people with a lived experience of a serious mental illness

2020 ◽  
Vol 18 (4) ◽  
pp. 369-382
Author(s):  
Karen L. Fortuna ◽  
Maria Venegas ◽  
Cynthia L. Bianco ◽  
Bret Smith ◽  
John A. Batsis ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mental Illness ◽  
Lived Experience ◽  
Serious Mental Illness ◽  
Early Mortality ◽  
The Relationship

scholarly journals Digital Peer Support Mental Health Interventions for People With a Lived Experience of a Serious Mental Illness: Systematic Review (Preprint)

2019 ◽  
Author(s):  
Karen L Fortuna ◽  
John A Naslund ◽  
Jessica M LaCroix ◽  
Cynthia L Bianco ◽  
Jessica M Brooks ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Lived Experience ◽  
Peer Support ◽  
Serious Mental Illness ◽  
Methodological Quality ◽  
Clinical Effectiveness ◽  
Health Interventions ◽  
Health Conditions ◽  
Inclusion Criteria

BACKGROUND Peer support is recognized globally as an essential recovery service for people with mental health conditions. With the influx of digital mental health services changing the way mental health care is delivered, peer supporters are increasingly using technology to deliver peer support. In light of these technological advances, there is a need to review and synthesize the emergent evidence for peer-supported digital health interventions for adults with mental health conditions. OBJECTIVE The aim of this study was to identify and review the evidence of digital peer support interventions for people with a lived experience of a serious mental illness. METHODS This systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedures. The PubMed, Embase, Web of Science, Cochrane Central, CINAHL, and PsycINFO databases were searched for peer-reviewed articles published between 1946 and December 2018 that examined digital peer support interventions for people with a lived experience of a serious mental illness. Additional articles were found by searching the reference lists from the 27 articles that met the inclusion criteria and a Google Scholar search in June 2019. Participants, interventions, comparisons, outcomes, and study design (PICOS) criteria were used to assess study eligibility. Two authors independently screened titles and abstracts, and reviewed all full-text articles meeting the inclusion criteria. Discrepancies were discussed and resolved. All included studies were assessed for methodological quality using the Methodological Quality Rating Scale. RESULTS Thirty studies (11 randomized controlled trials, 2 quasiexperimental, 15 pre-post designs, and 2 qualitative studies) were included that reported on 24 interventions. Most of the studies demonstrated feasibility, acceptability, and preliminary effectiveness of peer-to-peer networks, peer-delivered interventions supported with technology, and asynchronous and synchronous technologies. CONCLUSIONS Digital peer support interventions appear to be feasible and acceptable, with strong potential for clinical effectiveness. However, the field is in the early stages of development and requires well-powered efficacy and clinical effectiveness trials. CLINICALTRIAL PROSPERO CRD42020139037; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID= 139037

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