Digital Biomarkers最新文献

Background: The global need for rapid diagnostic methods for pathogen identification and antimicrobial susceptibility testing (AST) is underscored by the increasing bacterial resistance and limited therapeutic options, especially critical in sepsis management.

Summary: This review examines the aspects of the eHealth and mHealth in Antimicrobial Stewardship Programs (ASPs) to improve the treatment of infections and rational use of antimicrobials.

Key messages: The evolution from traditional phenotype-based methods to rapid molecular and mass spectrometry techniques has significantly decreased result turnaround times, improving patient outcomes. Despite advancements, the complex decision-making in antimicrobial therapy often exceeds the capacity of many clinicians, highlighting the importance of ASPs. These programs, integrating mHealth and eHealth, leverage technology to enhance healthcare services and patient outcomes, particularly in remote or resource-limited settings. However, the application of such technologies in antimicrobial management remains underexplored in hospitals. The development of platforms combining antimicrobial prescription data with pharmacotherapeutic algorithms and laboratory integration can significantly reduce costs and improve hospitalization times and mortality rates.

Introduction: Existing theories and empirical works link phone use with anxiety; however, most leverage subjective self-reports of phone use (e.g., validated questionnaires) that may not correspond well with true behavior. Moreover, most works linking phone use with anxiety do not interrogate associations within a temporal framework. Accordingly, the present study sought to investigate the utility of passively sensed phone use as a longitudinal predictor of anxiety symptomatology within a population particularly vulnerable to experiencing anxiety.

Methods: Using data from the GLOBEM study, which continuously collected longitudinal behavioral data from a college cohort of N = 330 students, weekly PHQ-4 anxiety subscale scores across 3 years (2019-2021) were paired with median daily phone use records from the 2 weeks prior to anxiety self-report completion. Phone use was operationalized through unlock duration which was passively curated via Apple's "Screen Time" feature. GPS-tracked location data was further utilized to specify whether an individual's phone use was at home or away from home. Within-individual and temporal associations between phone use and anxiety were modeled within an ordinal mixed-effects logistic regression framework.

Results: While there was no significant association between anxiety levels and either median total phone use or median phone use at home, participants in the top quartile of median phone use away from home were predicted to exhibit clinically significant anxiety levels 20% more frequently than participants in the bottom quartile during the first study year; however, this association weakened across successive years. Importantly, these associations remained after controlling for age, physical activity, sleep, and baseline anxiety levels and were not recapitulated when operationalizing phone use with unlock frequency.

Conclusions: These findings suggest that phone use may be leveraged as a means of mitigating or coping with anxiety in social situations outside the home, while pandemic-related developments may also have attenuated this behavior later in the study. Nevertheless, the present results suggest promise in interrogating a larger suite of objectively measured phone use behaviors within the context of social anxiety.

Introduction: Weakened facial movements are early-stage symptoms of amyotrophic lateral sclerosis (ALS). ALS is generally detected based on changes in facial expressions, but large differences between individuals can lead to subjectivity in the diagnosis. We have proposed a computerized analysis of facial expression videos to detect ALS.

Methods: This study investigated the action units obtained from facial expression videos to differentiate between ALS patients and healthy individuals, identifying the specific action units and facial expressions that give the best results. We utilized the Toronto NeuroFace Dataset, which includes nine facial expression tasks for healthy individuals and ALS patients.

Results: The best classification accuracy was 0.91 obtained for the pretending to smile with tight lips expression.

Conclusion: This pilot study shows the potential of using computerized facial expression analysis based on action units to identify facial weakness symptoms in ALS.

Introduction: The complex health, social, and economic consequences of tobacco smoking underscore the importance of incorporating reliable and scalable data collection on smoking status and habits into research across various disciplines. Given that smoking impacts voice production, we aimed to develop a gender and language-specific vocal biomarker of smoking status.

Methods: Leveraging data from the Colive Voice study, we used statistical analysis methods to quantify the effects of smoking on voice characteristics. Various voice feature extraction methods combined with machine learning algorithms were then used to produce a gender and language-specific (English and French) digital vocal biomarker to differentiate smokers from never-smokers.

Results: A total of 1,332‬ participants were included after propensity score matching (mean age = 43.6 [13.65], 64.41% are female, 56.68% are English speakers, 50% are smokers and 50% are never-smokers). We observed differences in voice features distribution: for women, the fundamental frequency F0, the formants F1, F2, and F3 frequencies and the harmonics-to-noise ratio were lower in smokers compared to never-smokers (p < 0.05) while for men no significant disparities were noted between the two groups. The accuracy and AUC of smoking status prediction reached 0.71 and 0.76, respectively, for the female participants, and 0.65 and 0.68, respectively, for the male participants.

Conclusion: We have shown that voice features are impacted by smoking. We have developed a novel digital vocal biomarker that can be used in clinical and epidemiological research to assess smoking status in a rapid, scalable, and accurate manner using ecological audio recordings.

Background: Wearable sensors have been heralded as revolutionary tools for healthcare. However, while data are easily acquired from sensors, users still grapple with questions about how sensors can meaningfully inform everyday clinical practice and research.

Summary: We propose a simple, comprehensive framework for utilizing sensor data in healthcare. The framework includes three key processes that are applied together or separately to (1) automate traditional clinical measures, (2) illuminate novel correlates of disease and impairment, and (3) predict current and future outcomes. We demonstrate applications of the Automate-Illuminate-Predict framework using examples from rehabilitation medicine.

Key messages: Automate-Illuminate-Predict provides a universal approach to extract clinically meaningful data from wearable sensors. This framework can be applied across the care continuum to enhance patient care and inform personalized medicine through accessible, noninvasive technology.

Introduction: Essential tremor is a common movement disorder. Numerous validated clinical rating scales exist to quantify essential tremor severity by employing rater-dependent visual observation but have limitations, including the need for trained human raters and the lack of precision and sensitivity compared to technology-based objective measures. Other continuous objective methods to quantify tremor amplitude have been developed, but frequently provide unitless measures (e.g., tremor power), limiting real-world interpretability. We propose a novel algorithm to measure kinetic tremor amplitude using digital spiral drawings, applying the V3 framework (sensor verification, analytical validation, and clinical validation) to establish reliability and clinical utility.

Methods: Archimedes spiral drawings were recorded on a digitizing tablet from participants (n = 7) enrolled in a randomized placebo control double-blinded crossover pilot trial evaluating the efficacy of oral cannabinoids in reducing essential tremor. We developed an algorithm to calculate the mean and maximum tremor amplitude derived from the spiral tracings. We compared the digitally measured tremor amplitudes to manual measurement to evaluate sensor reliability, determined the test-retest reliability of the digital output across two short-interval repeated measures, and compared the digital measure to kinetic tremor severity graded using The Essential Tremor Rating Assessment Scale (TETRAS) score for spiral drawings.

Results: This algorithm for automated assessment of kinetic tremor amplitude from digital spiral tracings demonstrated a high correlation with manual spot measures of tremor amplitude, excellent test-retest reliability, and a high correlation with human ratings of the TETRAS score for spiral drawing severity when the tremor severity was rated "slight tremor" or worse.

Discussion: This digital measure provides a simple and clinically relevant evaluation of kinetic tremor amplitude that shows promise as a potential future endpoint for use in clinical trials of essential tremor.

Introduction: The Digital Measures Development: Core Measures of Sleep project, led by the Digital Medicine Society (DiMe), emphasizes the importance of sleep as a cornerstone of health and the need for standardized measurements of sleep and its disturbances outside the laboratory. This initiative recognizes the complex relationship between sleep and overall health, addressing it as both a symptom of underlying conditions and a consequence of therapeutic interventions. It aims to fill a crucial gap in healthcare by promoting the development of accessible, nonintrusive, and cost-effective digital tools for sleep assessment, focusing on factors important to patients, caregivers, and clinicians.

Methods: A central feature of this project was an expert workshop conducted on April 19th, 2023. The workshop convened stakeholders from diverse backgrounds, including regulatory, payer, industry, academic, and patient groups, to deliberate on the project's direction. This gathering focused on discussing the challenges and necessities of measuring sleep across various therapeutic areas, aiming to identify broad areas for initial focus while considering the feasibility of generalizing these measures where applicable. The methodological emphasis was on leveraging expert consensus to guide the project's approach to digital sleep measurement.

Results: The workshop resulted in the identification of seven key themes that will direct the DiMe Core Digital Measures of Sleep project and the broader field of sleep research moving forward. These themes underscore the project's innovative approach to sleep health, highlighting the complexity of omni-therapeutic sleep measurement and identifying potential areas for targeted research and development. The discussions and outcomes of the workshop serve as a roadmap for enhancing digital sleep measurement tools, ensuring they are relevant, accurate, and capable of addressing the nuanced needs of diverse patient populations.

Conclusion: The Digital Medicine Society's Core Measures of Sleep project represents a pivotal effort to advance sleep health through digital innovation. By focusing on the development of standardized, patient-centric, and clinically relevant digital sleep assessment tools, the project addresses a significant need in healthcare. The expert workshop's outcomes underscore the importance of collaborative, multi-stakeholder engagement in identifying and overcoming the challenges of sleep measurement. This initiative sets a new precedent for the integration of digital tools into sleep health research and practice, promising to improve outcomes for patients worldwide by enhancing our understanding and measurement of sleep.

Introduction: Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health.

Methods: As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached.

Results: Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females.

Conclusion: Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health.

Introduction: Amyotrophic lateral sclerosis (ALS) can affect various eye movements, making eye tracking a potential means for disease monitoring. In this study, we evaluated the feasibility of ALS patients self-recording their eye movements using the "EyePhone," a smartphone eye-tracking application.

Methods: We prospectively enrolled ten participants and provided them with an iPhone equipped with the EyePhone app and a PowerPoint presentation with step-by-step recording instructions. The goal was for the participants to record their eye movements (saccades and smooth pursuit) without the help of the study team. Afterward, a trained physician administered the same tests using video-oculography (VOG) goggles and asked the participants to complete a questionnaire regarding their self-recording experience.

Results: All participants successfully completed the self-recording process without assistance from the study team. Questionnaire data indicated that participants viewed self-recording with EyePhone favorably, considering it easy and comfortable. Moreover, 70% indicated that they prefer self-recording to being recorded by VOG goggles.

Conclusion: With proper instruction, ALS patients can effectively use the EyePhone to record their eye movements, potentially even in a home environment. These results demonstrate the potential for smartphone eye-tracking technology as a viable and self-administered tool for monitoring disease progression in ALS, reducing the need for frequent clinic visits.