Anomaly detection to predict relapse risk in schizophrenia

Henson, Philip; D’Mello, Ryan; Vaidyam, Aditya; Keshavan, Matcheri S.; Torous, John

doi:10.1038/s41398-020-01123-7

Cited by 49 publications

(30 citation statements)

References 19 publications

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“…More specifically, Barnett et al 147 followed 17 patients with psychosis using a passive monitoring app installed on their smartphone for up to three months, and identified anomalies in mobility patterns and social behavior in the two weeks prior to relapse. A further study in 83 patients with psychosis using digital markers found similar results 30 . This was also observed in a study (N=60) using a neural network approach 148 .…”

Section: Evidence For Digital Psychiatry Within Specific Contextssupporting

confidence: 69%

“…For instance, relapse risk in schizophrenia may be foreseen by "anomaly detection", which involves the use of smartphone sen sor data to monitor divergences of an individual's behavioral patterns compared to his/her personal baseline. Preliminary studies in small samples have found reasonable sensitivity and specificity from applying this approach to date 30 .…”

Section: Smartphone Sensor Data and Digital Phenotypingmentioning

confidence: 95%

“…Recent reviews suggest that “most studies still only scratch the surface of advanced smartphone capabilities” 26 , and less than 2% of apps on the commercial marketplaces appear to leverage digital phenotyping potential 27 . Still, recent studies are employing digital phenotyping methods across diverse mental disorders 28‐30 , and research interest in this field is expanding at a rapid pace.…”

Section: Tools and Technologiesmentioning

confidence: 99%

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The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality

et al. 2021

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As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies -such as smartphone apps, virtual reality, chatbots, and social media -have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions -including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders -as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.

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Section: Evidence For Digital Psychiatry Within Specific Contextssupporting

confidence: 69%

Section: Smartphone Sensor Data and Digital Phenotypingmentioning

confidence: 95%

Section: Tools and Technologiesmentioning

confidence: 99%

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The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality

et al. 2021

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“…The error components are then used to build Hotelling's T-squared test statistic and identify anomalies. Henson et al [25] applied the method and achieved 89% sensitivity and 75% specificity for predicting relapse in schizophrenia in a cohort of 126 participants followed by 3 to 12 months.…”

Section: Introductionmentioning

confidence: 99%

Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data

Liu¹,

Onnela²

2022

Preprint

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To detect aberrant human behaviors from large volume of passive data collected by smartphones in real time, we propose an online anomaly detection method using Hotelling’s T-squared test. The test statistic is a weighted average, with more weight on the between-individual component when there are little data available for the individual and more weight on the within-individual component when the data are adequate. The algorithm takes only O(1) run time in each update and the required memory usage is fixed after a pre-specified number of updates. The performance of the proposed method, in terms of accuracy, sensitivity and specificity, is consistently better than or equal to the offline method that it builds upon depending on the sample size of the individual data.

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“…The implementation of data collections during inpatient treatment for schizophrenia could help to quantify symptom development and treatment response. This would allow for the partial decryption of the disorder and identify patients that are at great risk to relapse (Henson et al, 2021;Torous et al, 2018). Ideally, this data is assessed digitally as it can be stored directly in the patient's electronic medical record and the clinical staff has a direct access to the patients' All rights reserved.…”

Section: Introductionmentioning

confidence: 99%

Symptom Monitoring based on Digital Data Collection During Inpatient Treatment of Schizophrenia Spectrum Disorders – a Feasibility Study

Herpertz

Richter

Barkhau

et al. 2021

Preprint

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Background: Digital acquisition of risk factors and symptoms based on patients self-reports represents a promising, cost-efficient and increasingly prevalent approach for standardized data collection in psychiatric clinical routine. While the feasibility of digital data collection has been demonstrated across a range of psychiatric disorders, studies investigating digital data collection in schizophrenia spectrum disorder patients are scarce. Hence, up to now our knowledge about the acceptability and feasibility of digital data collection in patients with a schizophrenia spectrum disorder remains critically limited. Objective: The objective of this study was to explore the acceptance towards and performance with digitally acquired assessments of risk and symptom profiles in patients with a schizophrenia spectrum disorder in comparison with patients with an affective disorder. Methods: We investigated the acceptance, the required support and the data entry pace of patients during a longitudinal digital data collection system of risk and symptom profiles using self-reports on tablet computers throughout inpatient treatment in patients with a schizophrenia spectrum disorder. As a benchmark comparison, findings in patients with schizophrenia spectrum disorder were evaluated in direct comparison with findings in affective disorder patients. The influence of sociodemographic data and clinical characteristics on the assessment was explored. The study was performed at the Department of Psychiatry at the University of Muenster between February 2020 and February 2021. Results: Of 82 patients diagnosed with a schizophrenia spectrum disorder who were eligible for inclusion 59.8% (n=49) agreed to participate in the study of whom 54.2% (n=26) could enter data without any assistance. Inclusion rates, drop-out rates and subjective experience ratings did not differ between patients with a schizophrenia spectrum disorder and patients with an affective disorder. Out of all participating patients, 98% reported high satisfaction with the digital assessment. Patients with a schizophrenia spectrum disorder needed more support and more time for the assessment compared to patients with an affective disorder. The extent of support of patients with a schizophrenia spectrum disorder was predicted by age, whereas the feeling of self-efficacy predicted data entry pace. Conclusion: Our results indicate that, although patients with a schizophrenia spectrum disorder need more support and more time for data entry than patients with an affective disorder, digital data collection using patients self-reports is a feasible and well-received method. Future clinical and research efforts on digitized assessments in psychiatry should include patients with a schizophrenia spectrum disorder and offer adequate support to reduce digital exclusion of these patients.

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Anomaly detection to predict relapse risk in schizophrenia

Cited by 49 publications

References 19 publications

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality

Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data

Symptom Monitoring based on Digital Data Collection During Inpatient Treatment of Schizophrenia Spectrum Disorders – a Feasibility Study

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