Digital Biomarkers最新文献

Background: This commentary highlights the evolution of our understanding of physical function (PF) and key models/frameworks that have contributed to the current holistic understanding of PF, which encompasses not only a person's performance but also the environment and any adaptations an individual utilizes. This commentary also addresses how digital health tools can facilitate and complement the assessment of holistic PF and enable both objective and subjective input from the participant in their real-world environment. Lastly, we discuss how successful implementation of digital tools within clinical research requires patient input.

Summary: This commentary highlights how our understanding of PF has evolved to be more holistic.

Key messages: Inclusion of digital tools within clinical research can provide a path forward to holistically assess PF in a patient-focused manner.

Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.

Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.

Results: The research highlights the critical role of brain texture features, particularly in memory regions, for AD detection. Significant sex-specific differences are observed, with males showing significance in texture features in memory regions, volume in vision regions, and SUVR in speech regions, while females exhibit significance in texture features in memory and speech regions, and SUVR in vision regions. Additionally, the study analyzes how obesity affects features used in AD prediction models, clarifying its effects on speech and vision regions, particularly brain volume.

Conclusion: The findings contribute valuable insights into the effectiveness of feature fusion, sex-specific differences, and the impact of obesity on AD-related biomarkers, paving the way for future research in early AD detection strategies and cognitive impairment classification.

Introduction: Wrist-worn accelerometers can capture stepping behavior passively, continuously, and remotely. Methods utilizing peak detection, threshold crossing, and frequency analysis have been used to detect steps from wrist-worn accelerometer data, but it remains unclear how different approaches perform across a range of walking speeds and free-living activities. In this study, we evaluated the performance of four open-source methods for deriving step counts from wrist-worn accelerometry data, when applied to data from a range of structured locomotion and free-living activities. In addition, we assessed how modifying the parameters of these methods would affect their performance.

Methods: Twenty-one participants (ages 20-33) wore an ActiGraph CentrePoint Insight Watch (Actigraph, LLC) on their non-dominant wrist while completing structured locomotion activities in a motion capture laboratory and during a free-living period in a mock apartment. Criterion step counts were determined from motion capture heel-strike events and from StepWatch 3 (Modus Health, LLC) during the free-living period. Four open-source methods implementing different algorithmic approaches were applied to CPIW data to derive step counts. The quantity and timing of method-derived and criterion steps during each type of activity were then compared.

Results: In terms of performance during structured locomotion, methods that relied on a single parameter, such as peak detection or threshold crossing, demonstrated the lowest bias among those investigated. Furthermore, three of the four investigated methods overestimated step counts during slow walking and underestimated step counts during fast walking, while the last method consistently underestimated at least half of the recorded steps across all speeds. During free-living activities, the method relying on frequency analysis exhibited the lowest percent error of all methods. Finally, we found that the incorporation of a locomotion classifier, wherein steps were only estimated during identified locomotion periods, reduced error for two methods when applied to data across structured and free-living settings.

Conclusion: In studying the performance of different step-counting approaches across different settings, we found a tradeoff between performance during structured walking and that during free-living activities. These findings highlight the opportunity for novel, context-aware methods for accurate step counting across real-world settings.

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.