Towards a youth mental health paradigm: a perspective and roadmap

Molecular Psychiatry, Journal Year: 2023, Volume and Issue: 28(8), P. 3171 - 3181

Published: Aug. 1, 2023

Abstract Most mental disorders have a typical onset between 12 and 25 years of age, highlighting the importance this period for pathogenesis, diagnosis, treatment ill-health. This perspective addresses interactions risk protective factors brain development as key pillars accounting emergence psychopathology in youth. Moreover, we propose that novel approaches towards early diagnosis interventions are required reflect evolution emerging psychopathology, service models, knowledge exchange science practitioners. Taken together, transformative intervention paradigm research clinical care could significantly enhance health young people initiate shift prevention severe disorders.

Language: Английский

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From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression

Neuroscience & Biobehavioral Reviews, Journal Year: 2024, Volume and Issue: 158, P. 105541 - 105541

Published: Jan. 11, 2024

Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was investigate current features methods used in MDD. We searched PubMed, PsycINFO, Embase, Scopus Web Science (10/11/2023) articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk bias assessed using several sources. Studies were compared within analysis goals (correlating depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) included. achieved moderate performance. Common themes included challenges from complex missing data (leading a risk bias), lack external validation. made progress towards relating phenotypes clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may beneficial patient monitoring. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Framework: https://osf.io/s7ay4

Language: Английский

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

JMIR Mental Health, Journal Year: 2025, Volume and Issue: 12, P. e65143 - e65143

Published: Jan. 7, 2025

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 illness (SMI); however, roles of consumer-grade devices are not well understood. Objective This study aims examine utility data from consumer-grade, digital, (including smartphones wrist-worn devices) remotely monitoring predicting changes in adults schizophrenia bipolar disorder. Studies were included that collected physiological sleep duration, heart rate, wake patterns, activity) at least 3 days. Research-grade actigraphy methods physically obtrusive excluded. Methods We conducted a systematic review following databases: Cochrane Central Register Controlled Trials, Technology Assessment, AMED (Allied Complementary Medicine), APA PsycINFO, Embase, MEDLINE(R), IEEE XPlore. Searches completed May 2024. Results synthesized narratively due heterogeneity divided into phenotypes: activity, circadian rhythm, rate. Overall, 23 studies reported 12 distinct studies, mostly using centered relapse prevention. Only 1 explicitly aimed address outcomes SMI. In total, over 500 participants SMI, predominantly high-income countries. Most commonly, papers presented activity (n=18), followed by rhythm (n=14) rate (n=6). The use smartwatches support collection 8 papers; rest used only smartphones. There was some evidence lower levels higher rates, later irregular onset times associated psychiatric diagnoses poorer symptoms. However, measures, sampling statistical approaches complicated interpretation. Conclusions Consumer-grade wearables show ability detect digital markers indicative status but few currently these inequalities. phenotyping field psychiatry would benefit moving toward agreed standards regarding descriptions outcome measures ensuring valuable temporal provided fully exploited. Trial Registration PROSPERO CRD42022382267; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=382267

Language: Английский

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The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality

World Psychiatry, Journal Year: 2025, Volume and Issue: 24(2), P. 156 - 174

Published: May 15, 2025

The expanding domain of digital mental health is transitioning beyond traditional telehealth to incorporate smartphone apps, virtual reality, and generative artificial intelligence, including large language models. While industry setbacks methodological critiques have highlighted gaps in evidence challenges scaling these technologies, emerging solutions rooted co‐design, rigorous evaluation, implementation science offer promising pathways forward. This paper underscores the dual necessity advancing scientific foundations increasing its real‐world applicability through five themes. First, we discuss recent technological advances phenotyping, intelligence. Progress this latter area, specifically designed create new outputs such as conversations images, holds unique potential for field. Given spread then evaluate supporting their utility across various contexts, well‐being, depression, anxiety, schizophrenia, eating disorders, substance use disorders. broad view field highlights need a generation more rigorous, placebo‐controlled, studies. We subsequently explore engagement that hamper all tools, propose solutions, human support, navigators, just‐in‐time adaptive interventions, personalized approaches. analyze issues, emphasizing clinician engagement, service integration, scalable delivery finally consider ensure innovations work people thus can bridge disparities, reviewing on tailoring tools historically marginalized populations low‐ middle‐income countries. Regarding augment extend care, conclude models positively impact care if deployed correctly.

Language: Английский

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

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 27, 2025

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

Language: Английский

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Validity of a smartphone application for self-monitoring psychiatric symptoms in patients with schizophrenia

Digital Health, Journal Year: 2025, Volume and Issue: 11

Published: Jan. 1, 2025

Objective Despite increasing research on digital technologies for psychiatric disorders, studies specifically examining self-monitoring of symptoms with smartphone applications by patients schizophrenia remain limited. This study aims to evaluate the validity and reliability using a application among at Mindlink, community-based early intervention center. Methods Fifty-three young spectrum disorders participated. They rated their across five domains—delusions, hallucinations, anxiety, depression, perceived stress—using an 11-point Likert scale baseline, 1 week, 8 weeks, 16 weeks. Test–retest was assessed intraclass correlation coefficients (ICCs) between baseline 1-week ratings. Concurrent determined correlating app-based ratings established self-report clinician-administered scales, including Eppendorf Schizophrenia Inventory, Hamilton Program Voices Questionnaire, Beck Depression Generalized Anxiety Disorder-7, Perceived Stress Scale. The accuracy app's depression rating receiver operating characteristic (ROC) analysis. Results ICCs test–retest were high all symptom domains, ranging from 0.741 0.876 ( p < 0.001). Significant correlations observed formal assessments time points. ROC analysis single-item self-ratings app yielded area under curve 0.829 = 0.002), indicating good accuracy. Conclusion demonstrates that key stress is valid reliable schizophrenia. These findings support potential enhance management enable detection relapse in this population.

Language: Английский

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Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample

Acta Psychiatrica Scandinavica, Journal Year: 2024, Volume and Issue: 151(3), P. 388 - 400

Published: May 28, 2024

Clinical assessment of mood and anxiety change often relies on clinical or self-reported scales. Using smartphone digital phenotyping data resulting markers behavior (e.g., sleep) to augment symptom scores offers a scalable potentially more valid method understand changes in patients' state. This paper explores the potential using combination active passive sensors context smartphone-based assess two distinct cohorts patients preliminary reliability validity this method. Participants from different cohorts, each n = 76, one with diagnoses depression/anxiety other schizophrenia, utilized mindLAMP collect surveys mood/anxiety), along consisting (geolocation, accelerometer, screen state) for at least 1 month. anomaly detection algorithms, we assessed if statistical anomalies could predict mood/anxiety as measured via surveys. The model was reliably able 4 points greater depression by PHQ-9 GAD-8 both patient populations, an area under ROC curve 0.65 0.80 respectively. For GAD-7, these AUCs were maintained when predicting significant 7 days advance. Active alone predicted around 52% 75% variability schizophrenia populations These results indicate feasibility transdiagnostic cohorts. across groups, countries, sites (India US) suggest may offer reliable approach change. Future work should emphasize prospective application methods.

Language: Английский

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Real-world predictors of relapse in patients with schizophrenia and schizoaffective disorder in a large health system

Schizophrenia, Journal Year: 2024, Volume and Issue: 10(1)

Published: Feb. 29, 2024

Abstract Schizophrenia is often characterized by recurring relapses, which are associated with a substantial clinical and economic burden. Early identification of individuals at the highest risk for relapse in real-world treatment settings could help improve outcomes reduce healthcare costs. Prior work has identified few consistent predictors schizophrenia, however, studies to date have been limited insurance claims data or small patient populations. Thus, this study used large sample health systems electronic record (EHR) analyze relationships between patient-level factors model set that can be identify increased prevalence relapse, severe preventable reality schizophrenia. This retrospective, observational cohort utilized EHR extracted from largest Midwestern U.S. non-profit system relapse. The included patients diagnosis schizophrenia (ICD-10 F20) schizoaffective disorder F25) who were treated within October 15, 2016, December 31, 2021, received care least 12 months. A episode was defined as an emergency room inpatient encounter pre-determined behavioral health-related ICD code. Patients’ baseline characteristics, comorbidities utilization described. Modified log-Poisson regression (i.e. log Poisson robust variance estimation) analyses estimate across ultimately adjusted predicting Among 8119 unique study, 2478 (30.52%) experienced 5641 (69.48%) no Patients primarily male (54.72%), White Non-Hispanic Latino (54.23%), Medicare (51.40%), had diagnoses substance use (19.24%), overweight/obesity/weight gain (13.06%), extrapyramidal symptoms (48.00%), lipid metabolism (30.66%), hypertension (26.85%), diabetes (19.08%). Many differences comorbidities, revealed relapsed did not Through building, final all significant following variables: insurance, age, race/ethnicity, diagnosis, symptoms, number encounters, prior relapses episodes, long-acting injectable prescriptions written. Prevention priority care. Challenges related historical knowledge offers variables conceivably construct algorithms models proactively monitor demographic, comorbidity, medication, parameters place modify approaches avoid future

Language: Английский

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

Schizophrenia Research, Journal Year: 2024, Volume and Issue: 266, P. 205 - 215

Published: Feb. 29, 2024

Language: Английский

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Digital Phenotyping in Adults with Schizophrenia: A Narrative Review

Current Psychiatry Reports, Journal Year: 2023, Volume and Issue: 25(11), P. 699 - 706

Published: Oct. 20, 2023

Language: Английский

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