New European privacy regulation: Assessing the impact for digital medicine innovations

Mulder, Trix; Jagesar, Raj; Klingenberg, Aline; Bonnici, Jeanne Pia Mifsud; Kas, Martien

doi:10.1016/j.eurpsy.2018.07.003

Cited by 10 publications

(10 citation statements)

References 5 publications

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“…These sensors can be used to collect a high-resolution trace of behavioral data, which can then be used to derive relevant behavioral markers. This method is increasingly referred to as "digital phenotyping" 5 or "passive behavioral monitoring" 6 . Recent studies are already starting to reveal the clinical potential of the approach in the context of neuropsychiatric research [7][8][9][10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

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A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data

et al. 2020

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The use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated preprocessing framework for deriving behavioral phenotypes from smartphone-based location data is currently lacking. Here, we present a preprocessing framework consisting of methods that are validated in the context of geospatial data. This framework aims to generate contextenriched location data by identifying stationary, non-stationary, and recurrent stationary states in movement patterns. Subsequently, this context-enriched data is used to derive a series of behavioral phenotypes that are related to movement. By using smartphone-based location data collected from 245 subjects, including patients with schizophrenia, we show that the proposed framework is effective and accurate in generating context-enriched location data. This data was subsequently used to derive behavioral readouts that were sensitive in detecting behavioral nuances related to schizophrenia and aging, such as the time spent at home and the number of unique places visited. Overall, our results indicate that the proposed framework reliably preprocesses raw smartphone-based location data in such a manner that relevant behavioral phenotypes of interest can be derived.

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Section: Introductionmentioning

confidence: 99%

“…To achieve these goals, we collected location data by using a passive behavioral monitoring application called BEHAPP 6,17 in three different samples. First, we collected data in a relatively small sample of healthy individuals (sample 1; n = 10) to optimize and evaluate the efficiency of the preprocessing procedures.…”

Section: Introductionmentioning

confidence: 99%

A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data

et al. 2020

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“…Throughout the study duration, it tapped into various sources of behavioral data including communication events, phone usage logs (e.g., WhatsApp, Facebook), geographic location data, and Wi-Fi sensor data. Importantly, BeHapp is a research instrument that adheres to strict requirements toward privacy and informed consent that are common to formal scientific research projects involving human subjects ( 25 ). The data is end-to-end encrypted and the service consistently applies the principle of least privileges and zero trust.…”

Section: Methodsmentioning

confidence: 99%

“…The mean age was 31.2 (range: 20-63) years, and the mean BMI was 24.8 (range: 16.9-47.7) kg/m 2 . The mean (range) of HAM-D total score at screening was 20.4 (17)(18)(19)(20)(21)(22)(23)(24)(25) for the patients, and it was 1.2 (0-3) for the healthy controls. The mean (range) of C-SSRS total score at screening was 32.1 (0-77) for the patients, and it was 1.1 (0-8) for the healthy controls.…”

Section: Subject Demographics and Psychometric Scoresmentioning

confidence: 96%

A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression

et al. 2021

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Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted.Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression.Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception.Results: Our data analysis was organized by technology – to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression.Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications.

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“…Regulatory frameworks such as the European General Data Protection Regulation (GDPR), in effect since 2018, must also be considered, given their important impact on technology development [23]. The GDPR expects initiatives involved with personal data to carry out a data protection impact assessment (DPIA) [24]. In a DPIA, we evaluate data sensitivity and how these data must be handled to prevent unauthorized access to and loss of personal data.…”

Section: Securitymentioning

confidence: 99%

Requirements and Operational Guidelines for Secure and Sustainable Digital Phenotyping: Design and Development Study

Jagesar¹,

Vorstman²,

Kas³

2021

J Med Internet Res

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Background Digital phenotyping, the measurement of human behavioral phenotypes using personal devices, is rapidly gaining popularity. Novel initiatives, ranging from software prototypes to user-ready research platforms, are innovating the field of biomedical research and health care apps. One example is the BEHAPP project, which offers a fully managed digital phenotyping platform as a service. The innovative potential of digital phenotyping strategies resides among others in their capacity to objectively capture measurable and quantitative components of human behavior, such as diurnal rhythm, movement patterns, and communication, in a real-world setting. The rapid development of this field underscores the importance of reliability and safety of the platforms on which these novel tools are operated. Large-scale studies and regulated research spaces (eg, the pharmaceutical industry) have strict requirements for the software-based solutions they use. Security and sustainability are key to ensuring continuity and trust. However, the majority of behavioral monitoring initiatives have not originated primarily in these regulated research spaces, which may be why these components have been somewhat overlooked, impeding the further development and implementation of such platforms in a secure and sustainable way. Objective This study aims to provide a primer on the requirements and operational guidelines for the development and operation of a secure behavioral monitoring platform. Methods We draw from disciplines such as privacy law, information, and computer science to identify a set of requirements and operational guidelines focused on security and sustainability. Taken together, the requirements and guidelines form the foundation of the design and implementation of the BEHAPP behavioral monitoring platform. Results We present the base BEHAPP data collection and analysis flow and explain how the various concepts from security and sustainability are addressed in the design. Conclusions Digital phenotyping initiatives are steadily maturing. This study helps the field and surrounding stakeholders to reflect upon and progress toward secure and sustainable operation of digital phenotyping–driven research.

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New European privacy regulation: Assessing the impact for digital medicine innovations

Cited by 10 publications

References 5 publications

A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data

A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data

A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression

Requirements and Operational Guidelines for Secure and Sustainable Digital Phenotyping: Design and Development Study

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