Towards remote digital phenotyping of cognition in schizophrenia

Lifestyles and Quality of Life of People with Mental Illness During the COVID-19 Pandemic

The COVID-19 pandemic has had a significant impact on the quality of life (QoL), daily lifestyle, and mental health of people suffering from a mental disorder. This study aimed to investigate the effects of the prolongation of the COVID-19 emergency on QoL and lifestyles in a sample of 100 outpatients at the Psychiatry Unit in Palermo University Hospital, Italy. QoL was measured through the 12-item Short Form Survey and the COV19-Impact on Quality of Life. Lifestyle changes during the pandemic were measured through the lifestyle change questionnaire. The majority of participants reported a great impact of COVID-19 on the QoL, and almost half reported worsened lifestyles. Worsened lifestyles were predictive of both poor mental and physical health related QoL. These results suggest that people with mental illness need interventions targeting lifestyles, and the mental health service in Italy should adjust to the ongoing pandemic, developing virtual treatments.

Remote cognitive assessment in severe mental illness: a scoping review

Many individuals living with severe mental illness, such as schizophrenia, present cognitive deficits and reasoning biases negatively impacting clinical and functional trajectories. Remote cognitive assessment presents many opportunities for advancing research and treatment but has yet to be widely used in psychiatric populations. We conducted a scoping review of remote cognitive assessment in severe mental illness to provide an overview of available measures and guide best practices. Overall, 34 studies (n = 20,813 clinical participants) were reviewed and remote measures, psychometrics, facilitators, barriers, and future directions were synthesized using a logic model. We identified 82 measures assessing cognition in severe mental illness across 11 cognitive domains and four device platforms. Remote measures were generally comparable to traditional versions, though psychometric properties were infrequently reported. Facilitators included standardized procedures and wider recruitment, whereas barriers included imprecise measure adaptations, technology inaccessibility, low patient engagement, and poor digital literacy. Our review identified several remote cognitive measures in psychiatry across all cognitive domains. However, there is a need for more rigorous validation of these measures and consideration of potentially influential factors, such as sex and gender. We provide recommendations for conducting remote cognitive assessment in psychiatry and fostering high-quality research using digital technologies.

Smoking Cessation Apps for People with Schizophrenia: How Feasible Are m-Health Approaches?

Background: The large health disparities among those diagnosed with schizophrenia urgently need to be addressed. These disparities are partially caused by adverse health behaviours such as smoking. Smoking cessation apps vary in efficacy across various populations, and there are concerns regarding the accessibility and usability of apps for people with schizophrenia. Objective: This review identifies and examines the feasibility of using apps for smoking cessation in people with schizophrenia. Methods: A non-systematic narrative literature review of smoking cessation apps for individuals with schizophrenia was performed. Results: Eight studies were included in this review. Conclusion: Smoking cessation apps can be acceptable and feasible, but may need to be tailored to the needs of people with schizophrenia. Key messages: (1) Smoking cessation apps could be acceptable and feasible for use in people with schizophrenia; (2) Lack of motivation was perceived as the main potential barrier with regard to people with schizophrenia engaging with smoking cessation apps; (3) In order to improve motivation of people diagnosed with schizophrenia, apps could include games, rewards, and/or social support; (4) Smoking cessation apps with a simple interface seem to be beneficial for this population; (5) Apps may need to be tailored to consider this population's mental health needs.

Knowledge and Response to the COVID-19 Pandemic in People With Severe Mental Illness in Bangladesh and Pakistan: A Cross-Sectional Survey

BACKGROUND

People with severe mental illnesses (SMIs) are likely to face disproportionate challenges during a pandemic. They may not receive or be able to respond to public health messages to prevent infection or to limit its spread. Additionally, they may be more severely affected, particularly in low- and middle-income countries.

METHODS

We conducted a telephone survey (May-June 2020) in a sample of 1,299 people with SMI who had attended national mental health institutes in Bangladesh and Pakistan before the pandemic. We collected information on top worries, socioeconomic impact of the pandemic, knowledge of COVID-19 (symptoms, prevention), and prevention-related practices (social distancing, hygiene). We explored the predictive value of socio-demographic and health-related variables for relative levels of COVID-19 knowledge and practice using regularized logistic regression models.

FINDINGS

Mass media were the major source of information about COVID-19. Finances, employment, and physical health were the most frequently mentioned concerns. Overall, participants reported good knowledge and following advice. In Bangladesh, being female and higher levels of health-related quality of life (HRQoL) predicted poor and better knowledge, respectively, while in Pakistan being female predicted better knowledge. Receiving information from television predicted better knowledge in both countries. In Bangladesh, being female, accessing information from multiple media sources, and better HRQoL predicted better practice. In Pakistan, poorer knowledge of COVID-19 prevention measures predicted poorer practice.

CONCLUSION

Our paper adds to the literature on people living with SMIs and their knowledge and practices relevant to COVID-19 prevention. Our results emphasize the importance of access to mass and social media for the dissemination of advice and that the likely gendered uptake of both knowledge and practice requires further attention.

Validation of an ecological momentary assessment to measure processing speed and executive function in schizophrenia

Cognitive impairments are a core feature of schizophrenia that have negative impacts on functional outcomes. However, it remains challenging to assess these impairments in clinical settings. Smartphone apps provide the opportunity to measure cognitive impairments in an accessible way; however, more research is needed to validate these cognitive assessments in schizophrenia. We assessed the initial accessibility, validity, and reliability of a smartphone-based cognitive test to measure cognition in schizophrenia. A total of 29 individuals with schizophrenia and 34 controls were included in the analyses. Participants completed the standard pen-and-paper Trail Making Tests (TMT) A and B, and smartphone-based versions, Jewels Trail Tests (JTT) A and B, at the single in-lab visit. Participants were asked to complete the JTT remotely once per week for three months. We also investigated how subjective sleep quality and mood may affect cognitive performance longitudinally. In-lab and remote JTT scores moderately and positively correlated with in-lab TMT scores. Moderate test-retest reliability was observed across the in-lab, first remote, and last remote completion times of the JTT. Additionally, individuals with schizophrenia had significantly lower performance compared to controls on both the in-lab JTT and TMT. Self-reported mood had a significant effect on JTT A performance over time but no other significant relationships were found remotely. Our results support the initial accessibility, validity and reliability of using the JTT to measure cognition in schizophrenia. Future research to develop additional smartphone-based cognitive tests as well as with larger samples and in other psychiatric populations are warranted.

Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia: protocol for the HOPE-S observational study

INTRODUCTION

The course of schizophrenia illness is characterised by recurrent relapses which are associated with adverse clinical outcomes such as treatment-resistance, functional and cognitive decline. Early identification is essential and relapse prevention remains a primary treatment goal for long-term management of schizophrenia. With the ubiquity of devices such as smartphones, objective digital biomarkers can be harnessed and may offer alternative means for symptom monitoring and relapse prediction. The acceptability of digital sensors (smartphone and wrist-wearable device) and the association between the captured digital data with clinical and health outcomes in individuals with schizophrenia will be examined.

METHODS AND ANALYSIS

In this study, we aim to recruit 100 individuals with schizophrenia spectrum disorders who are recently discharged from the Institute of Mental Health (IMH), Singapore. Participants are followed up for 6 months, where digital, clinical, cognitive and functioning data are collected while health utilisation data are obtained at the 6 month and 1 year timepoint from study enrolment. Associations between digital, clinical and health outcomes data will be examined. A data-driven machine learning approach will be used to develop prediction algorithms to detect clinically significant outcomes. Study findings will inform the design, data collection procedures and protocol of future interventional randomised controlled trial, testing the effectiveness of digital phenotyping in clinical management of individuals with schizophrenia spectrum disorders.

ETHICS AND DISSEMINATION

Ethics approval has been granted by the National Healthcare Group (NHG) Domain Specific Review Board (DSRB Reference no.: 2019/00720). The results will be published in peer-reviewed journals and presented at conferences.

TRIAL REGISTRATION NUMBER

NCT04230590.

Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review

Digital phenotyping-the moment-by-moment quantification of human phenotypes in situ using data related to activity, behavior, and communications, from personal digital devices, such as smart phones and wearables-has been gaining interest. Personalized health information captured within free-living settings using such technologies may better enable the application of patient-generated health data (PGHD) to provide patient-centered care. The primary objective of this scoping review is to characterize the application of digital phenotyping and digitally captured active and passive PGHD for outcome measurement in surgical care. Secondarily, we synthesize the body of evidence to define specific areas for further work. We performed a systematic search of four bibliographic databases using terms related to "digital phenotyping and PGHD," "outcome measurement," and "surgical care" with no date limits. We registered the study (Open Science Framework), followed strict inclusion/exclusion criteria, performed screening, extraction, and synthesis of results in line with the PRISMA Extension for Scoping Reviews. A total of 224 studies were included. Published studies have accelerated in the last 5 years, originating in 29 countries (mostly from the USA, n = 74, 33%), featuring original prospective work (n = 149, 66%). Studies spanned 14 specialties, most commonly orthopedic surgery (n = 129, 58%), and had a postoperative focus (n = 210, 94%). Most of the work involved research-grade wearables (n = 130, 58%), prioritizing the capture of activity (n = 165, 74%) and biometric data (n = 100, 45%), with a view to providing a tracking/monitoring function (n = 115, 51%) for the management of surgical patients. Opportunities exist for further work across surgical specialties involving smartphones, communications data, comparison with patient-reported outcome measures (PROMs), applications focusing on prediction of outcomes, monitoring, risk profiling, shared decision making, and surgical optimization. The rapidly evolving state of the art in digital phenotyping and capture of PGHD offers exciting prospects for outcome measurement in surgical care pending further work and consideration related to clinical care, technology, and implementation.

COVID-19 and Severe Mental Illness: Impact on patients and its relation with their awareness about COVID-19

COVID-19 outbreak has promoted many public health measures in the general population. However, its impact on a vulnerable population with severe mental illness (SMI) is less addressed. Aim of this study was to determine the impact of COVID -19 to patients with SMI and identify its relation with their COVID-19 knowledge. A cross-sectional telephonic survey among 132 patients with SMI who were clinically stable before the COVID-19 pandemic was conducted. A 23 item interview proforma comprising of self-reported knowledge related to COVID-19 by patients and their illness and treatment status from their caregivers. Eleven patients were completely not aware of the ongoing COVID-19 pandemic. Three fourth of patients were not worried about getting COVID-19 and lacks adequate knowledge to identify symptoms. Two-third of patients lacked adequate knowledge of precautionary measures against COVID-19. One out of five patients lacked knowledge of the mode of transmission and stopped their psychiatric treatment. Thirty percent showed features of relapse of symptoms during this lockdown period. In multivariate regression analysis, patients from lower socioeconomic status, low literacy levels, with inadequate social support showed less knowledge related to COVID-19. Mental health services which target this vulnerable population during early disaster reduce the burden to the community.