Enhancing attention and memory of individuals at clinical high risk for psychosis with mHealth technology

Advancements and Future Directions in Prevention Based on Evaluation for Individuals With Clinical High Risk of Psychosis: Insights From the SHARP Study

BACKGROUND AND HYPOTHESIS

This review examines the evolution and future prospects of prevention based on evaluation (PBE) for individuals at clinical high risk (CHR) of psychosis, drawing insights from the SHARP (Shanghai At Risk for Psychosis) study. It aims to assess the effectiveness of non-pharmacological interventions in preventing psychosis onset among CHR individuals.

STUDY DESIGN

The review provides an overview of the developmental history of the SHARP study and its contributions to understanding the needs of CHR individuals. It explores the limitations of traditional antipsychotic approaches and introduces PBE as a promising framework for intervention.

STUDY RESULTS

Three key interventions implemented by the SHARP team are discussed: nutritional supplementation based on niacin skin response blunting, precision transcranial magnetic stimulation targeting cognitive and brain functional abnormalities, and cognitive behavioral therapy for psychotic symptoms addressing symptomatology and impaired insight characteristics. Each intervention is evaluated within the context of PBE, emphasizing the potential for tailored approaches to CHR individuals.

CONCLUSIONS

The review highlights the strengths and clinical applications of the discussed interventions, underscoring their potential to revolutionize preventive care for CHR individuals. It also provides insights into future directions for PBE in CHR populations, including efforts to expand evaluation techniques and enhance precision in interventions.

The effectiveness of telemedicine applications in mental health services: a meta-analysis study

AIM

This study was conducted to reveal the effectiveness of telemedicine applications in mental health services.

MATERIAL METHOD

For this meta-analysis study, data were obtained by searching PubMed, Web of Science, EBSCOhost, Google Scholar, and YÖK Thesis Center databases for the last 5 years in September-December 2023. After the review, 24 studies were included.

FINDINGS

In this meta-analysis, it was found that telemedicine interventions in mental health services decreased the depression level of individuals (SMD - 0.168, 95% CI - 0.315 to - 0.021; Z =  - 2.243; p < .05). In addition, it was determined that the duration of the intervention played a role in the effectiveness of telemedicine interventions applied to individuals. In addition, it was determined that the type of intervention, the country where the research was conducted, and the patient group did not play a role in the effectiveness of telemedicine interventions applied to individuals.

CONCLUSION

Telemedicine applications in mental health services can play an effective role in reducing the burden of chronic mental illness and improving patient outcomes.

Systematic review and meta-analysis of adverse events in clinical trials of mental health apps

Mental health apps are efficacious, yet they may pose risks in some. This review (CRD42024506486) examined adverse events (AEs) from mental health apps. We searched (May 2024) the Medline, PsycINFO, Web of Science, and ProQuest databases to identify clinical trials of mental health apps. The risk of bias was assessed using the Cochrane Risk of Bias tool. Only 55 of 171 identified clinical trials reported AEs. AEs were more likely to be reported in trials sampling schizophrenia and delivering apps with symptom monitoring technology. The meta-analytic deterioration rate from 13 app conditions was 6.7% (95% CI = 4.3, 10.1, I2 = 75%). Deterioration rates did not differ between app and control groups (OR = 0.79, 95% CI = 0.62-1.01, I2 = 0%). Reporting of AEs was heterogeneous, in terms of assessments used, events recorded, and detail provided. Overall, few clinical trials of mental health apps report AEs. Those that do often provide insufficient information to properly judge risks related to app use.

Dissociable Default Mode Network Connectivity Patterns Underlie Distinct Symptoms in Psychosis Risk

The Clinical High Risk (CHR) stage of psychosis is characterized by subthreshold symptoms of schizophrenia including negative symptoms, dysphoric mood, and functional deterioration. Hyperconnectivity of the default-mode network (DMN) has been observed in early schizophrenia, but the extent to which hyperconnectivity is present in CHR, and the extent to which such hyperconnectivity may underlie transdiagnostic symptoms, is not clear. As part of the Shanghai At-Risk for Psychosis (SHARP) program, resting-state fMRI data were collected from 251 young adults (158 CHR and 93 controls, M = 18.72, SD = 4.68, 129 male). We examined functional connectivity of the DMN by performing a whole-brain seed-to-voxel analysis with the MPFC as the seed. Symptom severity across a number of dimensions, including negative symptoms, positive symptoms, and affective symptoms were assessed. Compared to controls, CHRs exhibited significantly greater functional connectivity (p < 0.001 uncorrected) between the MPFC and 1) other DMN nodes including the posterior cingulate cortex (PCC), and 2) auditory cortices (superior and middle temporal gyri, STG/MTG). Furthermore, these two patterns of hyperconnectivity were differentially associated with distinct symptom clusters. Within CHR, MPFC-PCC connectivity was significantly correlated with anxiety (r= 0.23, p=0.006), while MPFC-STG/MTG connectivity was significantly correlated with negative symptom severity (r=0.26, p=0.001). Secondary analyses using item-level symptom scores confirmed a similar dissociation. These results demonstrate that two dissociable patterns of DMN hyperconnectivity found in the CHR stage may underlie distinct dimensions of symptomatology.

Pilot study to test the feasibility and clinical efficacy of a psychosocial care programme for patients with psychosis in low-resource settings

BACKGROUND

Home-based psychosocial care has the potential to improving outcomes in patients with schizophrenia and related disorders (SCZ). There is lack of India data for such care in early psychosis. We developed the "Saksham" programme, a bespoke self-managed home-based psychosocial care model, available in two formats: manual-based and mobile-application based. With the anticipated success of recruitment of early psychosis cases in our setting, we plan to test the such intervention in this population in future trials.

AIM

To assess the feasibility of the Saksham programme intervention in people with SCZ and its clinical efficacy as an adjunct to treatment as usual.

METHODS

Seventy-five patient-caregiver pairs (total n=150) were recruited. Patients received either: treatment-as-usual (TAU) (n=25), manual-based Saksham intervention+TAU (n=25), or app-based Saksham intervention+TAU (n=25). Feasibility (i.e. acceptability, practicality, demand, implementation and integration) was assessed at three-months. Participants were assessed for psychopathology, illness-severity, cognition, functioning, disability, and caregiver-coping at baseline, one-month, and three-month. The percentage changes over time were compared across three groups.

RESULTS

More found the mobile application-based intervention acceptable and easy-to-use than the manual-based intervention (92 % vs 68 %, and 76 % vs 68 %, respectively). Psychopathology and caregiver-burden improved significantly in all three groups (p<0.05). Cognition, disability, functioning, and caregiver burden improved significantly in the two Saksham intervention groups, with greater improvement in the Saksham app group (p<0.05).

CONCLUSION

Home-based intervention is feasible and acceptable in a low-resource setting, with preliminary evidence for effectiveness. These findings need corroboration with randomised controlled trials in early psychosis to ameliorate course of illness.

Accessibility and availability of smartphone apps for schizophrenia

App-based interventions have the potential to enhance access to and quality of care for patients with schizophrenia. However, less is known about the current state of schizophrenia apps in research and how those translate to publicly available apps. This study, therefore, aimed to review schizophrenia apps offered on marketplaces and research literature with a focus on accessibility and availability. A search of recent reviews, gray literature, PubMed, and Google Scholar was conducted in August 2022. A search of the U.S. Apple App Store and Google Play App Store was conducted in July 2022. All eligible studies and apps were systematically screened/reviewed. The academic research search produced 264 results; 60 eligible studies were identified. 51.7% of research apps were built on psychosis-specific platforms and 48.3% of research apps were built on non-specific platforms. 83.3% of research apps offered monitoring functionalities. Only nine apps, two designed on psychosis-specific platforms and seven on non-specific platforms were easily accessible. The search of app marketplaces uncovered 537 apps; only six eligible marketplace apps were identified. 83.3% of marketplace apps only offered psychoeducation. All marketplace apps lacked frequent updates with the average time since last update 1121 days. There are few clinically relevant apps accessible to patients on the commercial marketplaces. While research efforts are expanding, many research apps are unavailable today. Better translation of apps from research to the marketplace and a focus on sustainable interventions are important targets for the field.

Digital technology for addressing cognitive impairment in recent-onset psychosis: A perspective

Cognitive impairments in psychosis negatively impact functional recovery and quality of life. Existing interventions for improving cognitive impairment in recent-onset psychosis show inconsistent treatment efficacy, small effects, suboptimal engagement and limited generalizability to daily life functioning. In this perspective we explore how digital technology has the potential to address these limitations in order to improve cognitive and functional outcomes in recent-onset psychosis. Computer programs can be used for standardized, automated delivery of cognitive remediation training. Virtual reality provides the opportunity for learning and practicing cognitive skills in real-world scenarios within a virtual environment. Smartphone apps could be used for notification reminders for everyday tasks to compensate for cognitive difficulties. Internet-based technologies can offer psychoeducation and training materials for enhancing cognitive skills. Early findings indicate some forms of digital interventions for cognitive enhancement can be effective, with well-established evidence for human-supported computer-based cognitive remediation in recent-onset psychosis. Emerging evidence regarding virtual reality is favorable for improving social cognition. Overall, blending digital interventions with human support improves engagement and effectiveness. Despite the potential of digital interventions for enhancing cognition in recent-onset psychosis, few studies have been conducted to date. Implementation challenges affecting application of digital technologies for cognitive impairment in recent-onset psychosis are sustained engagement, clinical integration, and lack of quality in the commercial marketplace. Future opportunities lie in including motivational frameworks and behavioral change interventions, increasing service engagement in young people and lived experience involvement in digital intervention development.

Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study

BACKGROUND

There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables.

OBJECTIVE

This study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP.

METHODS

The Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code.

RESULTS

With a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources-based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques.

CONCLUSIONS

The LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions.