Digital Biomarkers最新文献

Background: Fatigue is a prominent symptom in many diseases and is strongly associated with impaired daily function. The measurement of daily function is currently almost always done with questionnaires, which are subjective and imprecise. With the recent advances of digital wearable technologies, novel approaches to evaluate daily function quantitatively and objectively in real-life conditions are increasingly possible. This also creates new possibilities to measure fatigue-related changes of daily function using such technologies.

Summary: This review examines which digitally assessable parameters in immune-mediated inflammatory and neurodegenerative diseases may have the greatest potential to reflect fatigue-related changes of daily function.

Key messages: Results of a standardized analysis of the literature reporting about perception-, capacity-, and performance-evaluating assessment tools indicate that changes of the following parameters: physical activity, independence of daily living, social participation, working life, mental status, cognitive and aerobic capacity, and supervised and unsupervised mobility performance have the highest potential to reflect fatigue-related changes of daily function. These parameters thus hold the greatest potential for quantitatively measuring fatigue in representative diseases in real-life conditions, e.g., with digital wearable technologies. Furthermore, to the best of our knowledge, this is a new approach to analysing evidence for the design of performance-based digital assessment protocols in human research, which may stimulate further systematic research in this area.

Introduction: Image-based machine learning holds great promise for facilitating clinical care; however, the datasets often used for model training differ from the interventional clinical trial-based findings frequently used to inform treatment guidelines. Here, we draw on longitudinal imaging of psoriasis patients undergoing treatment in the Ultima 2 clinical trial (NCT02684357), including 2,700 body images with psoriasis area severity index (PASI) annotations by uniformly trained dermatologists.

Methods: An image-processing workflow integrating clinical photos of multiple body regions into one model pipeline was developed, which we refer to as the "One-Step PASI" framework due to its simultaneous body detection, lesion detection, and lesion severity classification. Group-stratified cross-validation was performed with 145 deep convolutional neural network models combined in an ensemble learning architecture.

Results: The highest-performing model demonstrated a mean absolute error of 3.3, Lin's concordance correlation coefficient of 0.86, and Pearson correlation coefficient of 0.90 across a wide range of PASI scores comprising disease classifications of clear skin, mild, and moderate-to-severe disease. Within-person, time-series analysis of model performance demonstrated that PASI predictions closely tracked the trajectory of physician scores from severe to clear skin without systematically over- or underestimating PASI scores or percent changes from baseline.

Conclusion: This study demonstrates the potential of image processing and deep learning to translate otherwise inaccessible clinical trial data into accurate, extensible machine learning models to assess therapeutic efficacy.

Introduction: Changes in speech can act as biomarkers of cognitive decline in Alzheimer's disease (AD). While shorter speech samples would promote data collection and analysis, the minimum length of informative speech samples remains debated. This study aims to provide insight into the effect of sample length in analyzing longitudinal recordings of spontaneous speech in AD by comparing the original random length, 5- and 1-minute-long samples. We hope to understand whether capping the audio improves the accuracy of the analysis, and whether an extra 4 min conveys necessary information.

Methods: 110 spontaneous speech samples were collected from decades of Youtube videos of 17 public figures, 9 of whom eventually developed AD. 456 language features were extracted and their text-length-sensitivity, comparability, and ability to capture change over time were analyzed across three different sample lengths.

Results: Capped audio files had advantages over the random length ones. While most extracted features were statistically comparable or highly correlated across the datasets, potential effects of sample length should be acknowledged for some features. The 5-min dataset presented the highest reliability in tracking the evolution of the disease, suggesting that the 4 extra minutes do convey informative data.

Conclusion: Sample length seems to play an important role in extracting the language feature values from speech and tracking disease progress over time. We highlight the importance of further research into optimal sample length and standardization of methods when studying speech in AD.

Introduction: Head impulse, nystagmus, and test of skew (HINTS) is more accurate for the early diagnosis of occipital fossa stroke than magnetic resonance imaging. However, the head impulse test (HIT) is relatively challenging to perform, as it is subjective. Herein, we developed a prototype video HIT (vHIT) system using an iPhone (Apple, Cupertino, CA, USA) that is compact, easy to operate, and analyzable by our iPhone application.

Methods: The iPhone-vHIT and a vHIT using EyeSeeCam (Interacoustics, Eden Prairie, NM, USA) were performed on a healthy man in his 30s and on a patient with vestibular neuritis who visited the Mejiro University Ear Institute Clinic. For the iPhone-vHIT, eye movements were detected by analyzing high-speed videos captured using an iPhone camera, and head movements were followed using an iPhone gyro sensor. An iPhone fixation brace was used to capture the video without any blurring.

Results: The iPhone-vHIT system obtained vHIT waveforms similar to those of the EyeSeeCam-vHIT system in the healthy man and the patient with vestibular neuritis. The iPhone-vHIT system effectively detected the reduced vestibulo-ocular reflex gain in patients with vestibular neuritis. The iPhone-vHIT system at 120 frames per second was less sensitive to catch-up saccades than the EyeSeeCam.

Conclusion: vHIT systems using a smartphone have been reported but are currently unavailable. At present, the iPhone-vHIT application in this study is the only available smartphone-based vHIT system for screening of peripheral vestibular dysfunction. We believe that the prototype iPhone-vHIT with a high-speed camera will be clinically used to perform the vHIT, even though it only examines the lateral semicircular canal.

Introduction: Exposure to light fundamentally influences human physiology and behaviour by synchronising our biological clock to the external light-dark cycle and controlling melatonin production. In addition to well-controlled laboratory studies, more naturalistic approaches to examining these "non-visual" effects of light have been developed in recent years. As naturalistic light exposure is quite unlike well-controlled stimulus conditions in the laboratory, it is critical to measure light exposure in a person-referenced way, the "spectral diet." To this end, light loggers have been developed to capture personalised light exposure. As an alternative to light sensors integrated into wrist-worn actimeters, pendants, or brooch-based light loggers, a recently developed wearable light logger laterally attached to spectacle frames enables the measurement of biologically relevant quantities in the corneal plane.

Methods: Here, we examine the usability and acceptability of using the light logger in an undergraduate student sample (n = 18, mean±1SD: 20.1 ± 1.7 years; 9 female; Oxford, UK) in real-world conditions during a 24-h measurement period. We probed the acceptability of the light logger using rating questionnaires and open-ended questions.

Results: Our quantitative results show a modest acceptability of the light logger. A thematic analysis of the open-ended questions reveals that the form factor of the device, in particular, size, weight, and stability, and reactions from other people to the wearer of the light logger, were commonly mentioned aspects.

Conclusion: In sum, the results indicate the miniaturisation of light loggers and "invisible" integration into extant everyday objects as key areas for future technological development, facilitating the availability of light exposure data for developing personalised intervention strategies in both research, clinical and consumer contexts.

Background: Innovative Medicines Initiative (IMI) consortium IDEA-FAST is developing novel digital measures of fatigue, sleep quality, and impact of sleep disturbances for neurodegenerative diseases and immune-mediated inflammatory diseases. In 2022, the consortium met with the European Medicines Agency (EMA) to receive advice on its plans for regulatory qualification of the measures. This viewpoint reviews the IDEA-FAST perspective on developing digital measures for multiple diseases and the advice provided by the EMA.

Summary: The EMA considered a cross-disease measure an interesting and arguably feasible concept. Developers should account for the need for a strong rationale that the clinical features to be measured are similar across diseases. In addition, they may expect increased complexity of study design, challenges when managing differences within and between disease populations, and the need for validation in both heterogeneous and homogeneous populations.

Key messages: EMA highlighted the challenges teams may encounter when developing a cross-disease measure, though benefits potentially include reduced resources for the technology developer and health authority, faster access to innovation across different therapeutic fields, and feasibility of cross-disease comparisons. The insights included here can be used by project teams to guide them in the development of cross-disease digital measures intended for regulatory qualification.

Background: Depression imposes a major burden on public health as the leading cause of disability worldwide. Sleep disturbance is a core symptom of depression that affects the vast majority of patients. Nonetheless, it is frequently not resolved by depression treatment and may even be worsened through some pharmaceutical interventions. Disturbed sleep negatively impact patients' quality of life, and persistent sleep disturbance increases the risk of recurrence, relapse, and even suicide. However, the development of novel treatments that might improve sleep problems is hindered by the lack of reliable low-burden objective measures that can adequately assess disturbed sleep in this population.

Summary: Developing improved digital measurement tools that are fit for use in clinical trials for major depressive disorder could promote the inclusion of sleep as a focus for treatment, clinical drug development, and research. This perspective piece explores the path toward the development of novel digital measures, reviews the existing evidence on the meaningfulness of sleep in depression, and summarizes existing methods of sleep assessments, including the use of digital health technologies.

Key messages: Our objective was to make a clear call to action and path forward for the qualification of new digital outcome measures which would enable assessment of sleep disturbance as an aspect of health that truly matters to patients, promoting sleep as an important outcome for clinical development, and ultimately ensure that disturbed sleep will not remain the forgotten symptom of depression.

Introduction: We studied the accuracy of the automatic speech recognition (ASR) software by comparing ASR scores with manual scores from a verbal learning test (VLT) and a semantic verbal fluency (SVF) task in a semiautomated phone assessment in a memory clinic population. Furthermore, we examined the differentiating value of these tests between participants with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). We also investigated whether the automatically calculated speech and linguistic features had an additional value compared to the commonly used total scores in a semiautomated phone assessment.

Methods: We included 94 participants from the memory clinic of the Maastricht University Medical Center+ (SCD N = 56 and MCI N = 38). The test leader guided the participant through a semiautomated phone assessment. The VLT and SVF were audio recorded and processed via a mobile application. The recall count and speech and linguistic features were automatically extracted. The diagnostic groups were classified by training machine learning classifiers to differentiate SCD and MCI participants.

Results: The intraclass correlation for inter-rater reliability between the manual and the ASR total word count was 0.89 (95% CI 0.09-0.97) for the VLT immediate recall, 0.94 (95% CI 0.68-0.98) for the VLT delayed recall, and 0.93 (95% CI 0.56-0.97) for the SVF. The full model including the total word count and speech and linguistic features had an area under the curve of 0.81 and 0.77 for the VLT immediate and delayed recall, respectively, and 0.61 for the SVF.

Conclusion: There was a high agreement between the ASR and manual scores, keeping the broad confidence intervals in mind. The phone-based VLT was able to differentiate between SCD and MCI and can have opportunities for clinical trial screening.

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.