Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety

npj Digital Medicine, Journal Year: 2019, Volume and Issue: 2(1)

Published: Sept. 6, 2019

The use of data generated passively by personal electronic devices, such as smartphones, to measure human function in health and disease has significant research interest. Particularly psychiatry, objective, continuous quantitation using patients' own devices may result clinically useful markers that can be used refine diagnostic processes, tailor treatment choices, improve condition monitoring for actionable outcomes, early signs relapse, develop new intervention models. If a principal goal digital phenotyping is clinical improvement, needs attend now factors will help or hinder future adoption. We identify four opportunities directed toward this goal: exploring intermediate outcomes underlying mechanisms; focusing on purposes are likely practice; anticipating quality safety barriers adoption; the potential personalized medicine arising from integration interventions. Clinical relevance also means explicitly addressing consumer needs, preferences, acceptability ultimate users There risk that, without considerations, benefits delayed not realized because approaches feasible application healthcare, evidence required support commissioning, developed. Practical steps accelerate agenda include further development technology platforms scalability equity, establishing shared repositories common standards, fostering multidisciplinary collaborations between stakeholders (including patients), computer scientists, researchers.

Language: Английский

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Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Sensors, Journal Year: 2021, Volume and Issue: 21(10), P. 3461 - 3461

Published: May 16, 2021

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, subsequent management these conditions. The objective this review is identify critically appraise most recent smart wearable technologies used depression, anxiety, stress, physiological process(es) linked their detection. MEDLINE, CINAHL, Cochrane Central, PsycINFO databases were studies which utilised detect or stress. included articles that assessed anxiety unanimously heart rate variability (HRV) parameters detection with latter better detected by HRV electroencephalogram (EGG) together. Electrodermal activity was studies, high accuracy detection; however, questionable reliability. Depression found be largely using specific EEG signatures; detecting depression are not currently available on market. This systematic highlights average many commercially accurate compared variability, electrodermal activity, possibly respiratory rate.

Language: Английский

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Psychiatric diagnosis and treatment in the 21st century: paradigm shifts versus incremental integration

World Psychiatry, Journal Year: 2022, Volume and Issue: 21(3), P. 393 - 414

Published: Sept. 8, 2022

Psychiatry has always been characterized by a range of different models and approaches to mental disorder, which have sometimes brought progress in clinical practice, but often also accompanied critique from within without the field. Psychiatric nosology particular focus debate recent decades; successive editions DSM ICD strongly influenced both psychiatric practice research, led assertions that psychiatry is crisis, advocacy for entirely new paradigms diagnosis assessment. When thinking about etiology, many researchers currently refer biopsychosocial model, this approach received significant critique, being considered some observers overly eclectic vague. Despite development evidence-based pharmacotherapies psychotherapies, current evidence points treatment gap research-practice health. In paper, after considering we discuss proposed novel perspectives recently achieved prominence may significantly impact research future: neuroscience personalized pharmacotherapy; statistical nosology, assessment research; deinstitutionalization community health care; scale-up psychotherapy; digital phenotyping therapies; global task-sharing approaches. We consider extent transitions practices reflect hype or hope. Our review indicates each contributes important insights allow hope future, provides only partial view, any promise paradigm shift field not well grounded. conclude there crucial advances that, despite progress, considerable need further improvements intervention; such will likely be specific shifts rather incremental iterative integration.

Language: Английский

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Digital health tools for the passive monitoring of depression: a systematic review of methods

npj Digital Medicine, Journal Year: 2022, Volume and Issue: 5(1)

Published: Jan. 11, 2022

The use of digital tools to measure physiological and behavioural variables potential relevance mental health is a growing field sitting at the intersection between computer science, engineering, clinical science. We summarised literature on remote measuring technologies, mapping methodological challenges threats reproducibility, identified leading signals for depression. Medical science databases were searched January 2007 November 2019. Published studies linking depression objective data obtained from smartphone wearable device sensors in adults with unipolar healthy subjects included. A descriptive approach was taken synthesise study methodologies. included 51 found reproducibility transparency arising failure provide comprehensive descriptions recruitment strategies, sample information, feature construction determination handling missing data. characterised by small sizes, short follow-up duration great variability quality reporting, limiting interpretability pooled results. Bivariate analyses show consistency statistically significant associations features sleep, physical activity, location, phone Machine learning models predictive value aggregated features. Given pitfalls combined literature, these results should be purely as starting point hypothesis generation. Since this research ultimately aimed informing practice, we recommend improvements reporting standards including consideration generalisability such wider diversity samples, thorough methodology bias numerous

Language: Английский

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Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring

Frontiers in Digital Health, Journal Year: 2021, Volume and Issue: 3

Published: April 7, 2021

Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for monitoring mental health. Variations parameters such as movement, sleep duration, heart rate, electrocardiogram, skin temperature, etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone, call logs can be utilized to identify voice features social activities indicative depressive symptoms, physiological factors rate conductance used detect stress anxiety Therefore, a wide range devices comprising variety have developed capture these behavioral translate them into phenotypes states related Such systems aim behaviors that consequence an underlying alteration, hence, raw sensor captured converted define markers, through machine learning. However, due complexity passive relationships not simple need well-established. Furthermore, intrapersonal interpersonal differences considered when interpreting data. Altogether, combining practical mobile wearable right analysis algorithms provide useful tool management The current review aims comprehensively present critically discuss all available smartphone-based, wearable, environmental detecting relation treatment and/or most common health conditions.

Language: Английский

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Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review

JMIR Mental Health, Journal Year: 2018, Volume and Issue: 6(2), P. e9819 - e9819

Published: Dec. 15, 2018

Background Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating innovative therapeutic program treatment-resistant schizophrenia. The exploits information from mobile phones wearable sensors behavioral tracking to support intervention administration. Objective To systematically review original studies on sensor-based mHealth apps uncovering associations between sensor data symptoms of psychiatric disorders in order the m-RESIST approach assess effectiveness monitoring therapy. Methods A systematic English-language literature, according Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web Science, Cochrane Central Register Controlled Trials databases. Studies published September 1, 2009, 30, 2018, were selected. Boolean search operators iterative combination terms applied. Results reporting quantitative collected use and/or sensors, where that associated clinical outcomes, included. total 35 identified; most them investigated bipolar disorders, depression, depression symptoms, stress, while only a few addressed persons schizophrenia, depression. Conclusions Although demonstrated association their usability settings not yet fully assessed needs be scrutinized more thoroughly.

Language: Английский

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Innovations in research and clinical care using patient‐generated health data

CA A Cancer Journal for Clinicians, Journal Year: 2020, Volume and Issue: 70(3), P. 182 - 199

Published: April 20, 2020

Patient-generated health data (PGHD), or health-related gathered from patients to help address a concern, are used increasingly in oncology make regulatory decisions and evaluate quality of care. PGHD include self-reported treatment histories, patient-reported outcomes (PROs), biometric sensor data. Advances wireless technology, smartphones, the Internet Things have facilitated new ways collect during clinic visits daily life. The goal current review was provide an overview clinical, regulatory, technological, analytic landscape as it relates research begins with rationale for described by US Food Drug Administration, Institute Medicine, other scientific organizations. evidence base clinic-based remote symptom monitoring using is described, emphasis on PROs. An presented approaches digital phenotyping device-based, real-time assessment biometric, behavioral, self-report, performance Analytic opportunities regarding envisioned context big artificial intelligence medicine. Finally, challenges solutions integration into clinical care presented. electronic medical record PROs data, analysis large complex sets, potential workflow redesign. In addition, there currently more limited use relative Despite these challenges, benefits them likely be integrated

Language: Английский

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Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors

Frontiers in Psychiatry, Journal Year: 2020, Volume and Issue: 11

Published: Dec. 18, 2020

Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance assessing depressive severity by using behavioral physiological features obtained from wristband smartphone sensors. Method: Participants were thirty-one individuals with Major Depressive Disorder (MDD). The protocol included 8 weeks monitoring through six in-person clinical interviews during which was assessed 17-item Hamilton Depression Rating Scale (HDRS-17). Results: wore right left wrist 92 94% time respectively. Three machine-learning models estimating developed–one combining wearable sensors, one including only smartphones, sensors–and evaluated in two different scenarios. Correlations between models' estimate HDRS scores clinician-rated ranged moderate high (0.46 [CI: 0.42, 0.74] 0.7 0.66, 0.74]) had accuracy Mean Absolute Error ranging 3.88 ± 0.18 4.74 1.24. time-split scenario model smartphones performed best. ten most predictive mobile related phone engagement, activity level, skin conductance, heart rate variability. Conclusion: Monitoring MDD patients following a assessment feasible provide an changes Future studies should further examine best symptoms strategies enhance accuracy.

Language: Английский

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Technology and mental health: The role of artificial intelligence

European Psychiatry, Journal Year: 2018, Volume and Issue: 55, P. 1 - 3

Published: Oct. 28, 2018

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Language: Английский

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Short-term prediction of suicidal thoughts and behaviors in adolescents: Can recent developments in technology and computational science provide a breakthrough?

Journal of Affective Disorders, Journal Year: 2019, Volume and Issue: 250, P. 163 - 169

Published: March 6, 2019

Language: Английский

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