Wearable Technology for High-Frequency Cognitive and Mood Assessment in Major Depressive Disorder: Longitudinal Observational Study

Cormack, Francesca; McCue, Maggie; Taptiklis, Nick; Skirrow, Caroline; Glazer, Emilie; Panagopoulos, Elli; Schaik, Tempest van; Fehnert, Ben; King, James; Barnett, Jennifer H.

doi:10.2196/12814

Cited by 57 publications

(57 citation statements)

References 56 publications

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“…Other areas of interest are the assessment of memory loss in the eldery (Cantab Mobile, Cambridge Cognition Ltd) and seizure monitoring (Embrace, Empatica Srl.) 26 , 27 .…”

Section: Resultsmentioning

confidence: 99%

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

Dhunnoo²,

2020

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At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.

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“…Other areas of interest are the assessment of memory loss in the eldery (Cantab Mobile, Cambridge Cognition Ltd) and seizure monitoring (Embrace, Empatica Srl.) 26 , 27 .…”

Section: Resultsmentioning

confidence: 99%

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

Dhunnoo²,

2020

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“…The CANTAB’s n-back task has recently been adapted as part of the Cognition Kit app to be delivered via the Apple watch ( ) in 30 mild-to-moderate depression participants (aged 19-63 years) ( 34 ). Participants were required to complete the n-back three times per day, in addition to mood surveys.…”

Section: Resultsmentioning

confidence: 99%

Implementing Remote Memory Clinics to Enhance Clinical Care During and After COVID-19

et al. 2020

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Social isolation is likely to be recommended for older adults due to COVID-19, with ongoing reduced clinical contact suggested for this population. This has increased the need for remote memory clinics, we therefore review the literature, current practices and guidelines on organizing such remote memory clinics, focusing on assessment of cognition, function and other relevant measurements, proposing a novel pathway based on three levels of complexity: simple telephone or video-based interviews and testing using available tests (Level 1), digitized and validated methods based on standard pen-and-paper tests and scales (Level 2), and finally fully digitized cognitive batteries and remote measurement technologies (RMTs, Level 3). Pros and cons of these strategies are discussed. Remotely collected data negates the need for frail patients or carers to commute to clinic and offers valuable insights into progression over time, as well as treatment responses to therapeutic interventions, providing a more realistic and contextualized environment for data-collection. Notwithstanding several challenges related to internet access, computer skills, limited evidence base and regulatory and data protection issues, digital biomarkers collected remotely have significant potential for diagnosis and symptom management in older adults and we propose a framework and pathway for how technologies can be implemented to support remote memory clinics. These platforms are also well-placed for administration of digital cognitive training and other interventions. The individual, societal and public/private costs of COVID-19 are high and will continue to rise for some time but the challenges the pandemic has placed on memory services also provides an opportunity to embrace novel approaches. Remote memory clinics' financial, logistical, clinical and practical benefits have been highlighted by COVID-19, supporting their use to not only be maintained when social distancing legislation is lifted but to be devoted extra resources and attention to fully potentiate this valuable arm of clinical assessment and care.

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“…Digital assessment has also illuminated novel insights into the nature and course of psychological and psychiatric disorders. High-frequency assessment of cognition and mood via wearable devices among persons with major depressive disorder has been shown to be feasible and valid over an extended period [ 62 ]. Behavioral indicators passively collected through a mobile sensing platform (e.g., the sum of outgoing calls, a count of unique numbers texted, the dynamic variation of voice, speaking rate) have been shown to predict symptoms of depression and PTSD [ 63 ].…”

Section: The State Of the Science Of Digital Health Data-driven Appromentioning

confidence: 99%

Digital health data-driven approaches to understand human behavior

Marsch¹

2020

Neuropsychopharmacol.

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Advances in digital technologies and data analytics have created unparalleled opportunities to assess and modify health behavior and thus accelerate the ability of science to understand and contribute to improved health behavior and health outcomes. Digital health data capture the richness and granularity of individuals’ behavior, the confluence of factors that impact behavior in the moment, and the within-individual evolution of behavior over time. These data may contribute to discovery science by revealing digital markers of health/risk behavior as well as translational science by informing personalized and timely models of intervention delivery. And they may help inform diagnostic classification of clinically problematic behavior and the clinical trajectories of diagnosable disorders over time. This manuscript provides a review of the state of the science of digital health data-driven approaches to understanding human behavior. It reviews methods of digital health assessment and sources of digital health data. It provides a synthesis of the scientific literature evaluating how digitally derived empirical data can inform our understanding of health behavior, with a particular focus on understanding the assessment, diagnosis and clinical trajectories of psychiatric disorders. And, it concludes with a discussion of future directions and timely opportunities in this line of research and its clinical application.

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Wearable Technology for High-Frequency Cognitive and Mood Assessment in Major Depressive Disorder: Longitudinal Observational Study

Cited by 57 publications

References 56 publications

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database

Implementing Remote Memory Clinics to Enhance Clinical Care During and After COVID-19

Digital health data-driven approaches to understand human behavior

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