481. Predicting Mood Disturbance Severity in Bipolar Subjects with Mobile Phone Keystroke Dynamics and Metadata

Zulueta, John; Piscitello, Andrea; Rasic, Mladen; Easter, Rebecca; Babu, Pallavi; Langenecker, Scott A.; McInnis, Melvin G.; Ajilore, Olusola; Nelson, Peter; Ryan, Kelly A.; Leow, Alex D.

doi:10.1016/j.biopsych.2017.02.965

Cited by 4 publications

(2 citation statements)

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“…In bipolar disorder (BPD), there has been substantial progress in the development of digital-phenotyping techniques for condition monitoring and relapse detection. Changes in location and activity patterns, 12,23–25 keyboard interaction dynamics, 26,27 social phone utilization metrics 24,25,28 (such as calls placed and received) and voice 26,29 (for example, captured from phone calls) have been used, alongside active measures, to predict both manic and depressive states. Relapse is common in BPD, with 70% of individuals experiencing deterioration or recurrence within5 years of a manic episode.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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

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Section: Introductionmentioning

confidence: 99%

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

2019

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“…Clinically valuable applications have been identified in depression and anxiety, suicidality, drug and alcohol disorders, ageing and dementia, and neurological disease. Through digital phenotyping [2,3] (or personal sensing [4]), behavioral signals, sensor data, and self-reported information gathered through smartphones, wearable sensors and smart home devices can be combined to elucidate the nature and clinical status of health conditions, such as depression [5,6], anxiety [7], and bipolar disorder [8][9][10][11][12][13]. These signals also promise better insight into the earliest signs of mental disorders, such as changes in sleep, social behavior, and cognitive function, and raise the prospect of robust, individual-level risk stratification and prediction [14].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications (Preprint)

Barnett¹,

Huckvale²,

Christensen³

et al. 2019

Preprint

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UNSTRUCTURED In this viewpoint we describe the architecture of, and design rationale for, a new software platform designed to support the conduct of digital phenotyping research studies. These studies seek to collect passive and active sensor signals from participants' smartphones for the purposes of modelling and predicting health outcomes, with a specific focus on mental health. We also highlight features of the current research landscape that recommend the coordinated development of such platforms, including the significant technical and resource costs of development, and we identify specific considerations relevant to the design of platforms for digital phenotyping. In addition, we describe trade-offs relating to data quality and completeness versus the experience for patients and public users who consent to their devices being used to collect data. We summarize distinctive features of the resulting platform, InSTIL (Intelligent Sensing to Inform and Learn), which includes universal (ie, cross-platform) support for both iOS and Android devices and privacy-preserving mechanisms which, by default, collect only anonymized participant data. We conclude with a discussion of recommendations for future work arising from learning during the development of the platform. The development of the InSTIL platform is a key step towards our research vision of a population-scale, international, digital phenotyping bank. With suitable adoption, the platform will aggregate signals from large numbers of participants and large numbers of research studies to support modelling and machine learning analyses focused on the prediction of mental illness onset and disease trajectories.

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A Systematic Literature Review on Latest Keystroke Dynamics Based Models

et al. 2022

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The purpose of this study is to conduct a comprehensive evaluation and analysis of the most recent studies on the implications of keystroke dynamics (KD) patterns in user authentication, identification, and the determination of useful information. Another aim is to provide an extensive and up-to-date survey of the recent literature and potential research directions to understand the present state-of-the-art methodologies in this particular domain that are expected to be beneficial for the KD research community. From January 1st, 2017 to March 13th, 2022, the popular six electronic databases have been searched using a search criterion ("keystroke dynamics" OR "typing pattern") AND ("authentication" OR "verification" OR "identification"). With this criterion, a total of nine thousand three hundred forty-eight results, including duplicates, were produced. However, one thousand five hundred forty-seven articles have been chosen after removing duplicates and preliminary screening. Due to insufficient information, only one hundred twentyseven high-quality quantitative research articles have been included in the article selection process. We compared and summarised several factors with multiple tables to comprehend the various methodologies, experimental settings, and findings. In this study, we have identified six unique KD-based designs and presented the status of findings toward an effective solution in authentication, identification, and prediction. We have also discovered considerable heterogeneity across studies in each KD-based design for desktops and smartphones separately. Finally, this paper found a few open research challenges and provided some indications for a deeper understanding of the issues and further study.

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481. Predicting Mood Disturbance Severity in Bipolar Subjects with Mobile Phone Keystroke Dynamics and Metadata

Cited by 4 publications

References 0 publications

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

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

Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications (Preprint)

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

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