E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures

Digital phenotyping for mental health based on data analytics: A systematic literature review

Even though mental health is a human right, mental disorders still affect millions of people worldwide. Untreated and undertreated mental health conditions may lead to suicide, which generates more than 700,000 deaths annually around the world. The broad adoption of smartphones and wearable devices allowed the recording and analysis of human behaviors in digital devices, which might reveal mental health symptoms. This analysis constitutes digital phenotyping research, referring to frequent and constant measurement of human phenotypes in situ based on data from smartphones and other personal digital devices. Therefore, this article presents a systematic literature review providing a computer science view on data analytics for digital phenotyping in mental health. This study reviewed 5,422 articles from ten academic databases published up to September 2024, generating a final list of 74 studies. The investigated databases are ACM, IEEE Xplore, PsycArticles, PsycInfo, Pubmed, Science Direct, Scopus, Springer, Web of Science, and Wiley. We investigated ten research questions, considering explored data, employed devices, and techniques for data analysis. This review also organizes the application domains and mental health conditions, data analytics techniques, and current research challenges. This study found a growing research interest in digital phenotyping for mental health in recent years. Current approaches still present a high dependence on self-reported measures of mental health status, but there is evidence of the employment of smartphones for leveraging passive data collection. Traditional machine learning techniques are the main explored strategies for analyzing the large amount of collected data. In this regard, published approaches deeply focused on data analysis, generating opportunities concerning the implementation of resources for assisting individuals suffering from mental disorders.

The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact

Digital phenotyping collects health data digitally, supporting early disease diagnosis and health management. This paper systematically reviews the diversity of research methods in digital phenotyping and its clinical benefits, while also focusing on its importance within the P4 medicine paradigm and its core role in advancing its application in biobanks. Furthermore, the paper envisions the continued clinical benefits of digital phenotyping, driven by technological innovation, global collaboration, and policy support.

Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review

OBJECTIVES

We aimed to examine the predictive accuracy of functioning, relapse or remission among patients with psychotic disorders, using machine learning methods. We also identified specific features that were associated with these clinical outcomes.

DESIGN

The methodology of this review was guided by the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy.

DATA SOURCES

CINAHL, EMBASE, PubMed, PsycINFO, Scopus and ScienceDirect were searched for relevant articles from database inception until 21 November 2024.

ELIGIBILITY CRITERIA

Studies were included if they involved the use of machine learning methods to predict functioning, relapse and/or remission among individuals with psychotic spectrum disorders.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers screened the records from the database search. Risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies tool from Cochrane. Synthesised findings were presented in tables.

RESULTS

23 studies were included in the review, which were mostly conducted in the west (91%). Predictive summary area under the curve values for functioning, relapse and remission were 0.63-0.92 (poor to outstanding), 0.45-0.95 (poor to outstanding), 0.70-0.79 (acceptable), respectively. Logistic regression and random forest were the best performing algorithms. Factors influencing outcomes included demographic (age, ethnicity), illness (duration of untreated illness, types of symptoms), functioning (baseline functioning, interpersonal relationships and activity engagement), treatment variables (use of higher doses of antipsychotics, electroconvulsive therapy), data from passive sensor (call log, distance travelled, time spent in certain locations) and online activities (time of use, use of certain words, changes in search frequencies and length of queries).

CONCLUSION

Machine learning methods show promise in the prediction of prognosis (specifically functioning, relapse and remission) of mental disorders based on relevant collected variables. Future machine learning studies may want to focus on the inclusion of a broader swathe of variables including ecological momentary assessments, with a greater amount of good quality big data covering longer longitudinal illness courses and coupled with external validation of study findings.

PROSPERO REGISTRATION NUMBER

The review was registered on PROSPERO, ID: CRD42023441108.

A systematic review of passive data for remote monitoring in psychosis and schizophrenia

There is increasing use of digital tools to monitor people with psychosis and schizophrenia remotely, but using this type of data is challenging. This systematic review aimed to summarise how studies processed and analysed data collected through digital devices. In total, 203 articles collecting passive data through smartphones or wearable devices, from participants with psychosis or schizophrenia were included in the review. Accelerometers were the most common device (n = 115 studies), followed by smartphones (n = 46). The most commonly derived features were sleep duration (n = 50) and time spent sedentary (n = 41). Thirty studies assessed data quality and another 69 applied data quantity thresholds. Mixed effects models were used in 21 studies and time-series and machine-learning methods were used in 18 studies. Reporting of methods to process and analyse data was inconsistent, highlighting a need to improve the standardisation of methods and reporting in this area of research.

Utility of Consumer-Grade Wearable Devices for Inferring Physical and Mental Health Outcomes in Severe Mental Illness: Systematic Review

BACKGROUND

Digital wearable devices, worn on or close to the body, have potential for passively detecting mental and physical health symptoms among people with severe mental illness (SMI); however, the roles of consumer-grade devices are not well understood.

OBJECTIVE

This study aims to examine the utility of data from consumer-grade, digital, wearable devices (including smartphones or wrist-worn devices) for remotely monitoring or predicting changes in mental or physical health among adults with schizophrenia or bipolar disorder. Studies were included that passively collected physiological data (including sleep duration, heart rate, sleep and wake patterns, or physical activity) for at least 3 days. Research-grade actigraphy methods and physically obtrusive devices were excluded.

METHODS

We conducted a systematic review of the following databases: Cochrane Central Register of Controlled Trials, Technology Assessment, AMED (Allied and Complementary Medicine), APA PsycINFO, Embase, MEDLINE(R), and IEEE XPlore. Searches were completed in May 2024. Results were synthesized narratively due to study heterogeneity and divided into the following phenotypes: physical activity, sleep and circadian rhythm, and heart rate.

RESULTS

Overall, 23 studies were included that reported data from 12 distinct studies, mostly using smartphones and centered on relapse prevention. Only 1 study explicitly aimed to address physical health outcomes among people with SMI. In total, data were included from over 500 participants with SMI, predominantly from high-income countries. Most commonly, papers presented physical activity data (n=18), followed by sleep and circadian rhythm data (n=14) and heart rate data (n=6). The use of smartwatches to support data collection were reported by 8 papers; the rest used only smartphones. There was some evidence that lower levels of activity, higher heart rates, and later and irregular sleep onset times were associated with psychiatric diagnoses or poorer symptoms. However, heterogeneity in devices, measures, sampling and statistical approaches complicated interpretation.

CONCLUSIONS

Consumer-grade wearables show the ability to passively detect digital markers indicative of psychiatric symptoms or mental health status among people with SMI, but few are currently using these to address physical health inequalities. The digital phenotyping field in psychiatry would benefit from moving toward agreed standards regarding data descriptions and outcome measures and ensuring that valuable temporal data provided by wearables are fully exploited.

TRIAL REGISTRATION

PROSPERO CRD42022382267; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=382267.

Psychotic relapse prediction via biomarker monitoring: a systematic review

Background

Associating temporal variation of biomarkers with the onset of psychotic relapse could help demystify the pathogenesis of psychosis as a pathological brain state, while allowing for timely intervention, thus ameliorating clinical outcome. In this systematic review, we evaluated the predictive accuracy of a broad spectrum of biomarkers for psychotic relapse. We also underline methodological concerns, focusing on the value of prospective studies for relapse onset estimation.

Methods

Following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, a list of search strings related to biomarkers and relapse was assimilated and run against the PubMed and Scopus databases, yielding a total of 808 unique records. After exclusion of studies related to the distinction of patients from controls or treatment effects, the 42 remaining studies were divided into 5 groups, based on the type of biomarker used as a predictor: the genetic biomarker subgroup (n = 4, or 9%), the blood-based biomarker subgroup (n = 15, or 36%), the neuroimaging biomarker subgroup (n = 10, or 24%), the cognitive-behavioral biomarker subgroup (n = 5, or 12%) and the wearables biomarker subgroup (n = 8, or 19%).

Results

In the first 4 groups, several factors were found to correlate with the state of relapse, such as the genetic risk profile, Interleukin-6, Vitamin D or panels consisting of multiple markers (blood-based), ventricular volume, grey matter volume in the right hippocampus, various functional connectivity metrics (neuroimaging), working memory and executive function (cognition). In the wearables group, machine learning models were trained based on features such as heart rate, acceleration, and geolocation, which were measured continuously. While the achieved predictive accuracy differed compared to chance, its power was moderate (max reported AUC = 0.77).

Discussion

The first 4 groups revealed risk factors, but cross-sectional designs or sparse sampling in prospective studies did not allow for relapse onset estimations. Studies involving wearables provide more concrete predictions of relapse but utilized markers such as geolocation do not advance pathophysiological understanding. A combination of the two approaches is warranted to fully understand and predict relapse.

Psychotherapy 2.0 - Application context and effectiveness of sensor technology in psychotherapy with children and adolescents: A systematic review

Background

E-mental health applications have been increasingly used in the psychotherapeutic care of patients for several years. State-of-the-art sensor technology could be used to determine digital biomarkers for the diagnosis of mental disorders. Furthermore, by integrating sensors into treatment, relevant contextual information (e.g. field of gaze, stress levels) could be made transparent and improve the treatment of people with mental disorders. An overview of studies on this approach would be useful to provide information about the current status quo.

Methods

A systematic review of the use of sensor technology in psychotherapy for children and adolescents was conducted with the aim of investigating the use and effectiveness of sensory technology in psychotherapy treatment. Five databases were searched for studies ranging from 2000 to 2023. The study was registered by PROSPERO (CRD42023374219), conducted according to Cochrane recommendations and used the PRISMA reporting guideline.

Results

Of the 38.560 hits in the search, only 10 publications met the inclusion criteria, including 3 RCTs and 7 pilot studies with a total of 257 subjects. The study population consisted of children and adolescents aged 6 to 19 years with mental disorders such as OCD, anxiety disorders, PTSD, anorexia nervosa and autistic behavior. The psychotherapy methods investigated were mostly cognitive behavioral therapy (face-to-face contact) with the treatment method of exposure for various disorders. In most cases, ECG, EDA, eye-tracking and movement sensors were used to measure vital parameters. The heterogeneous studies illustrate a variety of potential useful applications of sensor technology in psychotherapy for adolescents. In some studies, the sensors are implemented in a feasible approach to treatment.

Conclusion

Sensors might enrich psychotherapy in different application contexts.However, so far there is still a lack of further randomized controlled clinical studies that provide reliable findings on the effectiveness of sensory therapy in psychotherapy for children and adolescents. This could stimulate the embedding of such technologies into psychotherapeutic process.https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023374219, identifier [CRD42023374219].

Potential Increasing Trend in Schizophrenia Relapse Prevention in the Past 40 Years: A Bibliometric Analysis

OBJECTIVES

Schizophrenia is an organic disease and a severe mental disorder with a relatively high risk of relapse. The rising rate of schizophrenia relapse has motivated researchers and academics to innovate and develop interventions aimed at relapse prevention. This bibliometric study sought to examine the publication trends in schizophrenia relapse prevention from 1973 to 2023, assess the contribution of international collaborations across various journals, identify the most influential authors and articles, and forecast future developments in this field.

METHODS

The study included 683 articles obtained from the Scopus database, analyzed using VOSviewer software, and visualized with Tableau.

RESULTS

Reports of schizophrenia relapse prevention strategies have increased significantly over the last 3 decades. However, fluctuations persist, as evidenced by the annual number of publications ranging from 25 to 40 within the past 5 years. Nevertheless, this increasing trend underscores the sustained interest in this area of research. Regarding contribution size, the United States produced the largest volume of publications on this subject. John M. Kane authored the most articles, while Stefan Leucht exhibited the highest h-index. Frequently used keywords in this field include "relapse AND schizophrenia" AND "prevention."

CONCLUSIONS

These results represent an important reference for determining the current state of research on schizophrenia relapse prevention and future research directions.

Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities

Preventing relapse in schizophrenia improves long-term health outcomes. Repeated episodes of psychotic symptoms shape the trajectory of this illness and can be a detriment to functional recovery. Despite early intervention programs, high relapse rates persist, calling for alternative approaches in relapse prevention. Predicting imminent relapse at an individual level is critical for effective intervention. While clinical profiles are often used to foresee relapse, they lack the specificity and sensitivity needed for timely prediction. Here, we review the use of speech through Natural Language Processing (NLP) to predict a recurrent psychotic episode. Recent advancements in NLP of speech have shown the ability to detect linguistic markers related to thought disorder and other language disruptions within 2-4 weeks preceding a relapse. This approach has shown to be able to capture individual speech patterns, showing promise in its use as a prediction tool. We outline current developments in remote monitoring for psychotic relapses, discuss the challenges and limitations and present the speech-NLP based approach as an alternative to detect relapses with sufficient accuracy, construct validity and lead time to generate clinical actions towards prevention.

Improving Psychiatry Services with Artificial Intelligence: Opportunities and Challenges

Mental disorders are a critical global public health problem due to their increasing prevalence, rising costs, and significant economic burden. Despite efforts to increase the mental health workforce in Türkiye, there is a significant shortage of psychiatrists, limiting the quality and accessibility of mental health services. This review examines the potential of artificial intelligence (AI), especially large language models, to transform psychiatric care in the world and in Türkiye. AI technologies, including machine learning and deep learning, offer innovative solutions for the diagnosis, personalization of treatment, and monitoring of mental disorders using a variety of data sources, such as speech patterns, neuroimaging, and behavioral measures. Although AI has shown promising capabilities in improving diagnostic accuracy and access to mental health services, challenges such as algorithmic biases, data privacy concerns, ethical implications, and the confabulation phenomenon of large language models prevent the full implementation of AI in practice. The review highlights the need for interdisciplinary collaboration to develop culturally and linguistically adapted AI tools, particularly in the Turkish context, and suggests strategies such as fine-tuning, retrieval-augmented generation, and reinforcement learning from human feedback to increase AI reliability. Advances suggest that AI can improve mental health care by increasing diagnostic accuracy and accessibility while preserving the essential human elements of medical care. Current limitations need to be addressed through rigorous research and ethical frameworks for effective and equitable integration of AI into mental health care. Keywords: Artificial İntelligence, Health, Large Language Model, Machine Learning, Psychiatry.

Early myopericarditis diagnosed in a 31-year-old patient using smartwatch technology: A case report

INTRODUCTION

Smartwatches, wrist-mounted devices with computing capacity able to connect with other devices via short-range wireless networking, are today commonly used by the general population to monitor their health status using specific applications. Currently, these devices offer new possibilities in remote health care monitoring and integration with other applications, through alert notifications, collection of personal data by a variety of sensors and the storage of these data. Several companies are introducing smartwatches with "health status" monitoring software with multiple functions, i.e. electrocardiogram (ECG) sensors. Recently, detection of atrial fibrillation based on heart rate monitoring by optical sensors resulted to be feasible and reliable when using the Apple Watch® and its corresponding application. Indeed, previous case reports highlighted its sensitivity in detecting morphological changes typical of the Acute Coronary Syndrome.

CASE REPORT

We report the case of a healthcare worker, who experienced chest pain and diffuse myalgia, detected ECG alterations in the ST segment, and reached the Emergency Department Myopericarditis was diagnosed and treated promptly to prevent complications.

DISCUSSION

Acute viral myocarditis and pericarditis are clinical conditions, usually characterized by 21 a benign course that does not require medical evaluation. However, ventricular arrhythmias are also common in viral myocarditis, and the latter is associated with a large proportion of sudden cardiac deaths in the young population without previous structural heart disease. In this case report, smartwatch technology allowed the preventive implementation of interventions against potentially life-threatening complications. Further developments in smartwatch technology could lead to more sensitive and specific diagnostic algorithms for conditions that require immediate medical intervention.

Psychotic Relapse Prediction in Schizophrenia Patients Using A Personalized Mobile Sensing-Based Supervised Deep Learning Model

Mobile sensing-based modeling of behavioral changes could predict an oncoming psychotic relapse in schizophrenia patients for timely interventions. Deep learning models could complement existing non-deep learning models for relapse prediction by modeling the latent behavioral features relevant to prediction. However, given the inter-individual behavioral differences, model personalization might be required. In this work, we propose RelapsePredNet, a Long Short-Term Memory (LSTM) neural network-based model for relapse prediction. The model is personalized for a particular patient by using data from patients most similar to the given patient based on their demographics or baseline mental health scores. RelapsePredNet was compared with a deep learning-based anomaly detection model for relapse prediction. Additionally, we investigated if RelapsePredNet could complement ClusterRFModel (a random forest model leveraging clustering and template features proposed in prior work) in a fusion model. The CrossCheck dataset consisting of continuous mobile sensing data obtained from 63 schizophrenia patients, each monitored for up to a year, was used for our evaluations. RelapsePredNet outperformed the deep learning-based anomaly detection for relapse prediction with an F2 score of 0.21 and 0.52 in the full test set and the Relapse Test Set (consisting of data from patients who have had relapse only), respectively, representing a 29.4% and 38.8% improvement. Patients' social functioning scale (SFS) score was found to be the best personalization metric to define patient similarity. RelapsePredNet complemented the ClusterRFModel as it improved the F2 score by 26.1% with a fusion model, resulting in an F2 score of 0.30 in the full test set.