Journal: Digital Biomarkers

Abbreviation

Digit Biomark

Publisher

Karger

Journal Volumes

ISSN

2504-110X

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2017 - 201912020 - 20234
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Publications 1 - 5 of 5
  • Reverse Engineering of Digital Measures: Inviting Patients to the Conversation
    Item type: Journal Article
    Mueller, Arne; Nam, Julian; Ng, Wan Fai; et al. (2023)
    Digital Biomarkers
    Background: Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures. Summary: In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled “Reverse Engineering of Digital Measures,” was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools. Key Messages: In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.
  • How Much Evidence Is Enough? Research Sponsor Experiences Seeking Regulatory Acceptance of Digital Health Technology-Derived Endpoints
    Item type: Journal Article
    Perry, Brian; Kehoe, Lindsay; Swezey, Teresa; et al. (2023)
    Digital Biomarkers
    Introduction: Digital health technologies (DHTs) provide opportunities for real-time data collection and assessment of patient function. However, use of DHT-derived endpoints in clinical trials to support medical product labelling claims is limited. Methods: From November 2020 through March 2021, the Clinical Trials Transformation Initiative (CTTI) conducted a qualitative descriptive study using semi-structured interviews with sponsors of clinical trials that used DHT-derived endpoints. We aimed to learn about their experiences, including their interactions with regulators and the challenges they encountered. Using applied thematic analysis, we identified barriers to and recommendations for using DHT-derived endpoints in pivotal trials. Results: Sponsors identified five key challenges to incorporating DHT-derived endpoints in clinical trials. These included (1) a need for additional regulatory clarity specific to DHT-derived endpoints, (2) the official clinical outcome assessment qualification process being impractical for the biopharmaceutical industry, (3) a lack of comparator clinical endpoints, (4) a lack of validated DHTs and algorithms for concepts of interest, and (5) a lack of operational support from DHT vendors. Discussion/Conclusion: CTTI shared the interview findings with the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) and during a multi-stakeholder expert meeting. Based on these discussions, we provide several new and revised tools to aid sponsors in using DHT-derived endpoints in pivotal trials to support labelling claims.
  • Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research
    Item type: Journal Article
    Langholm, Carsten; Kowatsch, Tobias; Bucci, Sandra; et al. (2023)
    Digital Biomarkers
    The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following: phone usage, messages usage, visits, device usage, and ambient light. We compared data from these five new sensors from SensorKit to our current digital phenotyping data collection sensors to assess similarity and differences in both raw and processed data. We present sample data from all five of these new sensors. We also present sample data from current digital phenotyping sources and compare these data to SensorKit sensors when applicable. SensorKit offers great potential for health research. Many SensorKit sensors improve upon previously accessible features and produce data that appears clinically relevant. SensorKit sensors will likely play a substantial role in digital phenotyping. However, using these data requires advanced health app infrastructure and the ability to securely store high-frequency data.
  • Need for Digital Biomarkers in Musculoskeletal Trials
    Item type: Journal Article
    Goldhahn, Jörg (2017)
    Digital Biomarkers
    Pain and loss of function – both problems cause patients to visit a musculoskeletal specialist. Therefore, both lead symptoms should serve as a benchmark for new therapeutic approaches. New technologies generating digital biomarkers have the potential to significantly change musculoskeletal trials. However, more work is needed to agree upon data and variable standards, to improve user friendliness, and to ensure data integrity along the whole processing way. Therefore, rigorous and systematic testing of new technological approaches is required to establish new outcome variables suitable for musculoskeletal trials. Consortia of researchers working on similar technologies and outcome variables should collaborate from the beginning to enable comparing and pooling data. Early interaction with health authorities and regulatory bodies are necessary to pave the way for a widespread use of a new technology.
  • Predicting Subjective Recovery from Lower Limb Surgery Using Consumer Wearables
    Item type: Journal Article
    Karas, Marta; Marinsek, Nikki; Goldhahn, Jörg; et al. (2020)
    Digital Biomarkers
    Introduction: A major challenge in the monitoring of rehabilitation is the lack of long-term individual baseline data which would enable accurate and objective assessment of functional recovery. Consumer-grade wearable devices enable the tracking of individual everyday functioning prior to illness or other medical events which necessitate the monitoring of recovery trajectories. Methods: For 1,324 individuals who underwent surgery on a lower limb, we collected their Fitbit device data of steps, heart rate, and sleep from 26 weeks before to 26 weeks after the self-reported surgery date. We identified subgroups of individuals who self-reported surgeries for bone fracture repair (n = 355), tendon or ligament repair/reconstruction (n = 773), and knee or hip joint replacement (n = 196). We used linear mixed models to estimate the average effect of time relative to surgery on daily activity measurements while adjusting for gender, age, and the participant-specific activity baseline. We used a sub-cohort of 127 individuals with dense wearable data who underwent tendon/ligament surgery and employed XGBoost to predict the self-reported recovery time. Results: The 1,324 study individuals were all US residents, predominantly female (84%), white or Caucasian (85%), and young to middle-aged (mean age 36.2 years). We showed that 12 weeks pre- and 26 weeks post-surgery trajectories of daily behavioral measurements (steps sum, heart rate, sleep efficiency score) can capture activity changes relative to an individual’s baseline. We demonstrated that the trajectories differ across surgery types, recapitulate the documented effect of age on functional recovery, and highlight differences in relative activity change across self-reported recovery time groups. Finally, using a sub-cohort of 127 individuals, we showed that long-term recovery can be accurately predicted, on an individual level, only 1 month after surgery (AUROC 0.734, AUPRC 0.8). Furthermore, we showed that predictions are most accurate when long-term, individual baseline data are available. Discussion: Leveraging long-term, passively collected wearable data promises to enable relative assessment of individual recovery and is a first step towards data-driven intervention for individuals.
Publications 1 - 5 of 5