Technology and Mental Health: State of the Art for Assessment and Treatment

Harvey, Philip D.; Depp, Colin A.; Rizzo, Albert; Strauss, Gregory P.; Spelber, David; Carpenter, Linda L.; Kalin, Ned H.; Krystal, John H.; McDonald, William M.; Nemeroff, Charles B.; Rodriguez, Carolyn I.; Widge, Alik S.; Torous, John

doi:10.1176/appi.ajp.21121254

Cited by 23 publications

(18 citation statements)

References 156 publications

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“…To date, studies of clinical validation have been scarce, as although digital phenotyping allows for an extensive and broad range of personal data, demonstrating its use and validity in the clinic is challenging especially when many outcomes are personalised. This is particularly so for passive data measures as, unlike standardised diagnostic interview or questionnaire-based measures which are used routinely in clinical trials and outcome management, agreed standards have not been defined for clinical validation of passive digital phenotyping 19. In addition, standardised approaches have not been uniformly applied to assess how advanced analytical methods, including machine learning algorithms, could be used to reduce the complexity of active and passive phenotyping data to deliver clinically actionable predictive models 20.…”

Section: Presentationmentioning

confidence: 99%

“…This combination is important as although digital phenotyping data may have high reliability, as noted its clinical validity has been sparingly investigated. The transdiagnostic approach may therefore address the challenge of trying to directly associate a new digital signal with a biological endpoint, as although in some areas the results are promising, there have also been examples of inconsistencies in associating particular metrics (such as sleep measures or screen time) with mental health disorders 19. One approach would be new prospective studies, but an alternative or complementary strategy could also be to focus more urgently on developing and implementing agreed standards for measuring and reporting digital phenotyping 2.…”

Section: Presentationmentioning

confidence: 99%

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Digital mental health: challenges and next steps

et al. 2023

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Digital innovations in mental health offer great potential, but present unique challenges. Using a consensus development panel approach, an expert, international, cross-disciplinary panel met to provide a framework to conceptualise digital mental health innovations, research into mechanisms and effectiveness and approaches for clinical implementation. Key questions and outputs from the group were agreed by consensus, and are presented and discussed in the text and supported by case examples in an accompanying appendix. A number of key themes emerged. (1) Digital approaches may work best across traditional diagnostic systems: we do not have effective ontologies of mental illness and transdiagnostic/symptom-based approaches may be more fruitful. (2) Approaches in clinical implementation of digital tools/interventions need to be creative and require organisational change: not only do clinicians and patients need training and education to be more confident and skilled in using digital technologies to support shared care decision-making, but traditional roles need to be extended, with clinicians working alongside digital navigators and non-clinicians who are delivering protocolised treatments. (3) Designing appropriate studies to measure the effectiveness of implementation is also key: including digital data raises unique ethical issues, and measurement of potential harms is only just beginning. (4) Accessibility and codesign are needed to ensure innovations are long lasting. (5) Standardised guidelines for reporting would ensure effective synthesis of the evidence to inform clinical implementation. COVID-19 and the transition to virtual consultations have shown us the potential for digital innovations to improve access and quality of care in mental health: now is the ideal time to act.

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Section: Presentationmentioning

confidence: 99%

Section: Presentationmentioning

confidence: 99%

Digital mental health: challenges and next steps

et al. 2023

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“…One reason for this combination is to improve the transferability of training effects in specific settings. Compared to CT alone, CT combined with supported employment programs has been shown to generate larger training effects in vocational settings in other populations ( 67 ). This combined protocol may also be beneficial to AD/HD with poor vocational skills.…”

Section: Not Only Computerized Ctmentioning

confidence: 99%

Perspectives on heterogeneity-informed cognitive training for attention-deficit/hyperactivity disorder

Zhang

2023

Front. Psychiatry

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Attention-deficit/hyperactivity disorder (AD/HD) is a heterogeneous neurodevelopmental condition, posing a severe threat to quality of life. Pharmacological therapies are the front-line treatment; however, their shortages encourage the development of alternative treatments for AD/HD. One promising method of developing alternative treatments is cognitive training (CT). A CT-based therapy was recently approved by the US Food and Drug Administration. However, due to heterogeneity in AD/HD, a CT protocol is unlikely to provide a one-size-fits-all solution for all patients with AD/HD. Therefore, this article highlights key aspects that need to be considered to further develop CT protocols for AD/HD, regarding training content, timing, suitability, and delivery mode. The perspectives presented here contribute to optimizing CT as an alternative option for treating AD/HD.

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“…Digital phenotyping data can be grouped into ‘active’ and ‘passive’ data collection methods. 5 Active data collection requires users to complete a task such as a survey or a video ‘selfie’, whereas passive approaches involve unobtrusive data collection that occurs automatically (e.g. via sensors in wearables).…”

Section: The Limitations Of Rating Scalesmentioning

confidence: 99%

“…Several active and passive digital phenotyping measures have shown promise as measures of negative symptoms, including geolocation (GPS coordinate data that show location and location changes), accelerometry (measures of movements in three dimensions) and – using audio and video recordings – natural language processing and automated analysis of facial expressions and vocal characteristics. 5 There is preliminary evidence that the five negative symptoms can be distinguished by digital measures.…”

Section: The Limitations Of Rating Scalesmentioning

confidence: 99%