Digital Biomarkers最新文献

Introduction: Preoperative physical functional assessments (i.e., assessments that measure capability to perform physical activity) are integral to estimate perioperative risk for older adults. However, these assessments are not routinely performed in-clinic prior to surgery. Walking cadence, or the number of steps walked in a specified amount of time (i.e., steps/min), measures activity intensity and may be able to identify high-risk patients prior to surgery. Smartphones can measure walking characteristics and guide patients through remote functional assessments. Here, we assess feasibility, acceptability, and accuracy of Walk Test, a smartphone application designed to measure walking cadence.

Methods: We performed a prospective cohort study of older adults prior to abdominal surgery and enrolled them remotely to perform at-home usual- and fast-paced walks with subsequent validation in-clinic. Each walk (usual- and fast-paced) was 2 min in duration. Feasibility was assessed if 80% of patients could perform all study procedures; acceptability was measured using the Post-Study Survey Usability Questionnaire (PSSUQ); accuracy of our approach was assessed with Lin's concordance coefficient (CCC). activPAL thigh worn accelerometer worn during the in-clinic walk served as a gold standard comparison. We used the CCC to compare the at-home and in-clinic walks as performed by Walk Test.

Results: We enrolled 41 participants (mean age 69 ± 5 years, 26 (63%) female); 88% (36/41) successfully completed entire study protocol including independent installation of the application, walk tests (at-home and in-clinic) and questionnaires. Median (interquartile range) overall score of PSSUQ was 1 (1, 1) indicating strong acceptability and usability. The Lin's CCC between the in-clinic activPAL and Walk Test for usual-paced walk was 0.97 (95% CI: 0.96, 0.99, p < 0.001) and for fast-paced walks 0.96 (95% CI: 0.93, 0.98, p < 0.001). The CCC between the at-home and in-clinic walks for usual-paced walks was 0.70 (95% CI: 0.53, 0.86) and for fast-paced walks was 0.46 (95% CI: 0.21, 0.72).

Conclusion: We successfully demonstrated the feasibility, acceptability and accuracy of Walk Test to measure walking cadence. Future work is needed to standardize walk test performance at-home to ensure consistency between in-clinic and at-home measures.

Introduction: Impaired walking performance significantly impacts the quality of life in individuals with Parkinson's disease (PD). This study aimed to examine the effects of medication "on" and "off" periods on walking performance, focusing on an alternative aspect of traditional gait analysis by assessing movement components or synergies (i.e., principal movements, PMs).

Methods: Principal component analysis was used to decompose kinematic marker data from 22 PD patients (64.1 ± 10.5 years) during self-selected speed overground walking into a set of PMs that cooperatively contribute to the locomotion task. Gait adaptation between medication periods was assessed using two PM-based variables: relative explained variance (rVAR) of the PM's position, reflecting movement structure, and root mean square (RMS) of the PM's acceleration, indicating movement acceleration magnitude and reflecting changes in force or speed.

Results: The on-medication condition increased the contribution (greater rVAR) of PM₂, representing the swing-phase movement component (p = 0.001), and enhanced movement acceleration magnitudes (greater RMS) in PM₄, characterizing the single-leg support phase coupled with trunk rotation (p = 0.026).

Conclusion: Although medication enhances propulsion by increasing the contribution of swing-phase movement components, thereby improving forward movement and walking efficiency, it may also lead to instability during the single-leg stance phase.

Introduction: Benign paroxysmal positional vertigo (BPPV) is a common cause of dizziness that is diagnosed by detecting nystagmus through positional maneuvers. Limited access to expert clinicians to correctly perform and interpret the eye movement findings of positional tests can hamper the diagnosis and delay the treatment. We aimed to assess the usability of a smartphone-based eye-tracking application (EyePhone) for self-recording eye movements during positional testing.

Methods: Healthy volunteers were enrolled and provided instructions to perform Dix-Hallpike and Supine Roll tests using the EyePhone application to record themselves. A study team member was instructed to observe the process without interfering. They recorded the time each section took and the accuracy of performing positional tests. Usability was assessed using the mHealth App Usability Questionnaire (MAUQ), and expert evaluation of recorded videos determined quality.

Results: All participants successfully performed the tests and recorded their eye movements. On average, after watching the instruction, it took participants 3 min 31 s to record the Dix-Hallpike test and 3 min 4 s to record the Supine Roll test. Nine participants completed Dix-Hallpike without major errors, and all completed the Supine Roll successfully. An expert review found that 95% of videos had clear eye visibility. Participants rated the app as easy to use and stated that they would use the app again.

Conclusion: We demonstrated the usability and feasibility of the EyePhone app for self-recording positional tests. This application offers the potential for remote BPPV diagnosis and improved patient access to care.

Introduction: The chair-rising test (CRT) is being widely used to assess lower body power. The test provides valuable information about functional capacity and other health outcomes. However, most centers use timing-based outcomes, which may compromise its suitability in younger people and fitter geriatric patients, and which may also introduce confounding effects of body height. We, therefore, aimed to compare the traditional use of timing-based outcome with digitally assessed measurements of neuromuscular power.

Methods: Data were collected from a longitudinal population-based study that examined changes in muscle and bone health. CRT and jumping mechanography were performed on a ground reaction force plate. In 346 people (age: 32-92 years), chair-rising rate (fCRT) was manually assessed, and peak chair-rising power (PCRT) and jumping power (PJMG) were computed. Statistical analyses targeted breakpoints in the relationships between fCRT, PCRT, and PJMG. Effects of age, body height, and sex were assessed with linear and partial regression analyses.

Results: Breakpoints were found at (fCRT = 0.778 Hz, PJMG = 35.2 Watt/kg, p < 0.001) and at (fCRT = 0.669 Hz, PCRT = 9.9 Watt/kg, p < 0.001). Slow chair-risers, defined by fCRT <0.669 Hz, were older than fast chair-risers (p < 0.001), albeit with a largely overlapping age range (fast chair-risers: 32-90 years, slow chair-risers: 32-92 years). Body height was correlated with fCRT (p < 0.001) and PCRT (p = 0.009) but not with PJMG (p = 0.59).

Conclusion: Timing-based CRT does not unequivocally reflect neuromuscular power. Its association with chair-rising power holds only in people who take more than 75 s for 5 stand-ups. For jumping power, the cutoff is at 6.4 s. Slow and fast chair-risers cannot be easily discerned by age. Bias by body height can substantially obscure age effects in timing-based CRT assessments. We conclude that chair-rising power represents a more universally applicable biomarker and is less influenced by body height compared to timing-based chair-rising assessments.

Duchenne muscular dystrophy (DMD) is a progressive neuromuscular disorder that impairs daily functioning and results in premature death. Current clinical assessments are widely used for characterizing functional impairment but have limitations due to their subjective and effort-based nature and because they only capture a snapshot of symptoms at a single point in time. Digital health technologies, such as wearable devices, allow continuous collection of movement and physiological data during daily life and could provide objective measures of the impact of DMD symptoms on daily functioning. For example, measurement of the 95th centile of stride velocity has recently gained endorsement by European regulators as an endpoint for evaluating functional changes in DMD, but the use of wearables for this purpose is just beginning. In this study, we present preliminary investigations of candidate digital biomarkers of functional impairment using real-world data and further explore the relationships between these parameters and established clinical assessments. We found nine candidate biomarkers for detecting DMD-related functional impairment, all exhibiting large to very large effect sizes in our sample of 14 boys with DMD and matched controls (9 DMDs, 5 controls, age 4-12 years). Each candidate biomarker was moderately or strongly associated with clinical measures of function in DMD. Six of the biomarkers are novel and/or understudied in DMD including objective measures of gait acceleration and variability; postural control immediately before and after a postural transition; and the smoothness of postural transitions. Notably, postural transition measures were more sensitive to DMD-related impairment than gait, activity, and cardiac measures. These results suggest that the quality of postural transitions could serve as a sensitive and objective measure of functional impairment in DMD and point toward the need for further exploration of these measures.

The physical basis of speech production in humans requires the coordination of multiple anatomical systems, where inhalation and exhalation of air through lungs is at the core of the phenomenon. Vocalization happens during exhalation, while inhalation typically happens between speech pauses. We use deep learning models to predict respiratory signals during speech-breathing, from which the respiration rate is estimated. Bilingual data from a large clinical study (N = 1,005) are used to develop and evaluate a multivariate time series transformer model with speech encoder embeddings as input. The best model shows the predicted respiration rate from speech within ±3 BPM for 82% of test subjects. A noise-aware algorithm was also tested in a simulated hospital environment with varying noise levels to evaluate the impact on performance. This work proposes and validates speech as a virtual sensor for respiration rate, which can be an efficient and cost-effective enabler for remote patient monitoring and telehealth solutions.

Background: Novel clinical measures assessed by a digital health technology tool require thresholds to interpret change over time, such as the minimal clinically important difference. Establishing such thresholds is a key component of clinical validation, facilitating understanding of relevant treatment effects.

Summary: Many of the approaches to derive interpretative thresholds for patient-reported outcomes can be applied to digital clinical measures. We present theoretical background to the use of interpretative thresholds, including the distinction between thresholds based on perceived importance versus measurement error, and thresholds for group- versus individual-level interpretations. We then review methods to estimate such thresholds, including anchor-based approaches. We illustrate the methods using data on cough frequency counts as measured by a wearable device in a clinical trial.

Key messages: This paper provides an overview of statistical methodologies to estimate thresholds for the interpretation of change.

Introduction: Wearable technologies can enhance measurements completed from home by participants in decentralized clinical trials. These measurements have shown promise in monitoring patient wellness outside the clinical setting. However, there are challenges in handling data and its interpretation when using consumer wearables, requiring input from statisticians and data scientists. This article describes three methods to estimate daily steps to address gaps in data from the Apple Watch in cancer patients and uses one of these methods in an analysis of the association between daily step count estimates and clinical events for these patients.

Methods: A cohort of 50 cancer patients used the DigiBioMarC app integrated with an Apple Watch for 28 days. We identified different gap types in watch data based on their length and context to estimate daily steps. Cox proportional hazards regression models were used to determine the association between step count and time to death or time to first clinical event. Decision tree modeling and participant clustering were also employed to identify digital biomarkers of physical activity that were predictive of clinical event occurrence and hazard ratio to clinical events, respectively.

Results: Among the three methods explored to address missing steps, the method that identified different step data gap types according to their duration and context yielded the most reasonable estimate of daily steps. Ten hours of waking time was used to differentiate between sufficient and insufficient measurement days. Daily step count on sufficient days was the most promising predictor of time to first clinical event (p = 0.068). This finding was consistent with participant clustering and decision tree analyses, where the participant clusters emerged naturally based on different levels of daily steps, and the group with the highest steps on sufficient days had the lowest hazard probability of mortality and clinical events. Additionally, daily steps on sufficient days can also be used as a predictor of whether a participant will have clinical events with an accuracy of 83.3%.

Conclusion: We have developed an effective way to estimate daily steps of consumer wearable data containing unknown data gaps. Daily step counts on days with sufficient sampling are a strong predictor of the timing and occurrence of clinical events, with individuals exhibiting higher daily step counts having reduced hazard of death or clinical events.

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.