Using a mobile health device to manage severe mental illness in the community: What is the potential and what are the challenges?

Automated Digital Interventions and Smoking Cessation: Systematic Review and Meta-analysis Relating Efficiency to a Psychological Theory of Intervention Perspective

BACKGROUND

Smoking remains a highly significant preventable global public health problem. In this context, digital interventions offer great advantages in terms of a lack of biological side effects, possibility of automatic delivery, and consequent human resource savings relative to traditional interventions. Such interventions have been studied in randomized controlled trials (RCTs) but have not been systematically reviewed with the inclusion of text-based and multiplatform-based interventions. In addition, this area has not been evaluated from the perspective of the psychological theoretical basis of intervention.

OBJECTIVE

The aim of this paper is to assess the efficiency of digital interventions in RCT studies of smoking cessation and to evaluate the effectiveness of the strategies used for digital interventions.

METHODS

An electronic search of RCTs was conducted using PubMed, Embase, and the Cochrane Library by June 30, 2021. Eligible studies had to compare automated digital intervention (ADI) to the use of a self-help guideline or no intervention. Participants were current smokers (aged 16 years or older). As the main outcome, abstinence after endpoint was extracted from the studies. Systematic review and meta-analysis were conducted to assess the efficiency of ADIs. Metaregressions were conducted to assess the relationship between intervention theory and effectiveness.

RESULTS

A total of 19 trials (15,472 participants) were included in the analysis. The overall abstinence rate (95% CI) at the endpoint was 17.8% (17.0-18.7). The overall risk ratio of the intervention group compared to the controls at the endpoint was 17.8% (17.0-18.7). Cochrane risk-of-bias tool for randomized trials (ROB 2) suggested that most of the studies had a low risk of bias (56.3%). Psychological theory-related constructs or predictors, which refer to other theory-based concepts (rather than only behavioral theory) such as craving or anxiety, are associated with effectiveness.

CONCLUSIONS

This study found that ADI had a clear positive effect compared to self-help guidelines or to no intervention, and effectiveness was associated with theory-related constructs or predictors. ADIs should be promoted by policy makers and clinical practitioners to address the huge gap between the need for smoking cessation and availability of traditional treatment resources. Possible increases in ADI efficiency may be achieved by optimally integrating psychotherapeutic theories and techniques.

TRIAL REGISTRATION

PROSPERO CRD42021256593; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=256593.

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients' families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.

LinguAPP: An m-Health Application for Teledentistry Diagnostics

An Android/iOS application for low-cost mobile devices to aid in dental diagnosis through questionnaire and photos is presented in this paper. The main purposes of our app lie in the ease of use even for nonexperienced users, in the limited hardware requirements that allow a wide diffusion, and in the possibility to modify the questionnaire for different pathologies. This tool was developed in about a month at the beginning of the COVID-19 (SARS-CoV-2) pandemic and is still in use in Italy to allow support to patients without going to the hospital, if not strictly necessary.

The state of psychiatric research in the Asia Pacific region

INTRODUCTION

This study aims to review recent scientific publications and research output in the field of psychiatry, from a series of countries in the Asia-Pacific region (Australia, India, Indonesia, Iran, Lebanon, Malaysia, and Nepal), with a view to identify themes and similarities across regions, as well as to examine the barriers and challenges in mental health research faced by countries in the region.

METHODS

Seven psychiatrists from seven countries reviewed recent published and ongoing research in psychiatry in their respective nations, with respect to themes, as well as any barriers or challenges faced by mental health researchers.

RESULTS

While the seven nations included in this review vary in terms of research capabilities and economic development level, they share many similarities both in terms of research direction, and with regards to challenges faced. Limitations in the form of sociocultural differences from the West, and a lack of funding were some of the barriers identified.

DISCUSSION

Mental health research in the region has been progressing well. However, more varied research in the form of qualitative or economic studies are lacking, as are multi-center studies. The similar issues that nations face with regards to research could perhaps benefit from collaborative efforts and initiatives for the furtherance of research in the region.

Integrating a Mobile Health Device Into a Community Youth Mental Health Team to Manage Severe Mental Illness: Protocol for a Randomized Controlled Trial

BACKGROUND

Symptoms of mental illness are often triggered by stress, and individuals with mental illness are sensitive to these effects. The development of mobile health (mHealth) devices allows continuous recording of biometrics associated with activity, sleep, and arousal. Deviations in these measures could indicate a stressed state requiring early intervention. This paper describes a protocol for integrating an mHealth device into a community mental health team to enhance management of severe mental illness in young adults.

OBJECTIVE

The aim of this study is to examine (1) whether an mHealth device integrated into a community mental health team can improve outcomes for young adults with severe mental illness and (2) whether the device detects periods of mental health versus deterioration.

METHODS

This study examines whether physiological information from an mHealth device prevents mental deterioration when shared with the participant and clinical team versus with the participant alone. A randomized controlled trial (RCT) will allocate 126 young adults from community mental health services for 6 months to standard case management combined with an integrated mHealth device (ie, physiological information is viewed by both participant and case manager: unWIRED intervention) or an unintegrated mHealth device (ie, participant alone self-monitors: control). Participants will wear the Empatica Embrace2 device, which continuously records electrodermal activity and actigraphy (ie, rest and activity). The study also examines whether the Embrace2 can detect periods of mental health versus deterioration. A variety of measurements will be taken, including physiological data from the Embrace2; participant and case manager self-report regarding symptoms, functioning, and quality of life; chart reviews; and ecological momentary assessments of stress in real time. Changes in each participant's Clinical Global Impression Scale scores will be assessed by blinded raters as the primary outcome. In addition, participants and case managers will provide qualitative data regarding their experience with the integrated mHealth device, which will be thematically analyzed.

RESULTS

The study has received ethical approval from the Western Sydney Local Health District Human Research Ethics Committee. It is due to start in October 2020 and conclude in October 2022.

CONCLUSIONS

The RCT will provide insight as to whether an integrated mHealth device enables case managers and participants to pre-emptively manage early warning signs and prevent relapse. We anticipate that unWIRED will enhance early intervention by improving detection of stress and allowing case managers and patients to better engage and respond to symptoms.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000642987; https://www.anzctr.org.au/ACTRN12620000642987.aspx.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/19510.