A digital biomarker of diabetes from smartphone-based vascular signals.
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ABSTRACT:
SUBMITTER: Avram R
PROVIDER: S-EPMC8483886 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) - the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking - was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.
| S-EPMC7661535 | biostudies-literature
Project description:BackgroundSensor-based monitoring tools fill a critical gap in multiple sclerosis (MS) research and clinical care.ObjectiveThe aim of this study is to assess performance characteristics of the Floodlight Proof-of-Concept (PoC) app.MethodsIn a 24-week study (clinicaltrials.gov: NCT02952911), smartphone-based active tests and passive monitoring assessed cognition (electronic Symbol Digit Modalities Test), upper extremity function (Pinching Test, Draw a Shape Test), and gait and balance (Static Balance Test, U-Turn Test, Walk Test, Passive Monitoring). Intraclass correlation coefficients (ICCs) and age- or sex-adjusted Spearman's rank correlation determined test-retest reliability and correlations with clinical and magnetic resonance imaging (MRI) outcome measures, respectively.ResultsSeventy-six people with MS (PwMS) and 25 healthy controls were enrolled. In PwMS, ICCs were moderate-to-good (ICC(2,1) = 0.61-0.85) across tests. Correlations with domain-specific standard clinical disability measures were significant for all tests in the cognitive (r = 0.82, p < 0.001), upper extremity function (|r|= 0.40-0.64, all p < 0.001), and gait and balance domains (r = -0.25 to -0.52, all p < 0.05; except for Static Balance Test: r = -0.20, p > 0.05). Most tests also correlated with Expanded Disability Status Scale, 29-item Multiple Sclerosis Impact Scale items or subscales, and/or normalized brain volume.ConclusionThe Floodlight PoC app captures reliable and clinically relevant measures of functional impairment in MS, supporting its potential use in clinical research and practice.
| S-EPMC8961252 | biostudies-literature
Project description:Introduction Tobacco smoking is related with a substantial morbidity and mortality as well as with tremendous socioeconomic burden. Therefore, an early successful smoking cessation bears an enormous medical and socioeconomic importance. The gold standard of smoking cessation, a combination of behavioral und pharmacologic therapy reaches only few smokers every year and so, guideline-based, low-threshold and broadly available digital cessation support could considerably increase the annual cessation rate. With the digital cessation program "Nichtraucherhelden" such a guideline-based program is available in Germany since December 2016.Methods The program consists of 2 preparation days and 10 program days und contains the major features of common smoking cessation courses. The present study examined the participants from December 2016 to November 2019 (n = 2491) regarding acceptance, adherence and effectivity. 69 % were female and the average age was 46 years.Results On average cessation was attained after 19 days. One year after the program a cessation rate of 15 % was achieved. Surprisingly, the cessation rate was higher in more addictive compared to less addictive participants.Conclusion The study shows that a digital guideline-based cessation program is feasible and effectual. For confirmation a control and randomized study is required.
| S-EPMC10027441 | biostudies-literature
Project description:BackgroundThe introduction of new medical technologies such as sensors has accelerated the process of collecting patient data for relevant clinical decisions, which has led to the introduction of a new technology known as digital biomarkers.ObjectiveThis study aims to assess the methodological quality and quality of evidence from meta-analyses of digital biomarker-based interventions.MethodsThis study follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline for reporting systematic reviews, including original English publications of systematic reviews reporting meta-analyses of clinical outcomes (efficacy and safety endpoints) of digital biomarker-based interventions compared with alternative interventions without digital biomarkers. Imaging or other technologies that do not measure objective physiological or behavioral data were excluded from this study. A literature search of PubMed and the Cochrane Library was conducted, limited to 2019-2020. The quality of the methodology and evidence synthesis of the meta-analyses were assessed using AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) and GRADE (Grading of Recommendations, Assessment, Development, and Evaluations), respectively. This study was funded by the National Research, Development and Innovation Fund of Hungary.ResultsA total of 25 studies with 91 reported outcomes were included in the final analysis; 1 (4%), 1 (4%), and 23 (92%) studies had high, low, and critically low methodologic quality, respectively. As many as 6 clinical outcomes (7%) had high-quality evidence and 80 outcomes (88%) had moderate-quality evidence; 5 outcomes (5%) were rated with a low level of certainty, mainly due to risk of bias (85/91, 93%), inconsistency (27/91, 30%), and imprecision (27/91, 30%). There is high-quality evidence of improvements in mortality, transplant risk, cardiac arrhythmia detection, and stroke incidence with cardiac devices, albeit with low reporting quality. High-quality reviews of pedometers reported moderate-quality evidence, including effects on physical activity and BMI. No reports with high-quality evidence and high methodological quality were found.ConclusionsResearchers in this field should consider the AMSTAR-2 criteria and GRADE to produce high-quality studies in the future. In addition, patients, clinicians, and policymakers are advised to consider the results of this study before making clinical decisions regarding digital biomarkers to be informed of the degree of certainty of the various interventions investigated in this study. The results of this study should be considered with its limitations, such as the narrow time frame.International registered report identifier (irrid)RR2-10.2196/28204.
| S-EPMC9813819 | biostudies-literature
Project description:BackgroundSensors and digital devices have revolutionized the measurement, collection, and storage of behavioral and physiological data, leading to the new term digital biomarkers.ObjectiveThis study aimed to investigate the scope of clinical evidence covered by systematic reviews (SRs) of randomized controlled trials involving digital biomarkers.MethodsThis scoping review was organized using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. With the search limited to English publications, full-text SRs of digital biomarkers included randomized controlled trials that involved a human population and reported changes in participants' health status. PubMed and the Cochrane Library were searched with time frames limited to 2019 and 2020. The World Health Organization's classification systems for diseases (International Classification of Diseases, Eleventh Revision), health interventions (International Classification of Health Interventions), and bodily functions (International Classification of Functioning, Disability, and Health [ICF]) were used to classify populations, interventions, and outcomes, respectively.ResultsA total of 31 SRs met the inclusion criteria. The majority of SRs studied patients with circulatory system diseases (19/31, 61%) and respiratory system diseases (9/31, 29%). Most of the prevalent interventions focused on physical activity behavior (16/31, 52%) and conversion of cardiac rhythm (4/31, 13%). Looking after one's health (physical activity; 15/31, 48%), walking (12/31, 39%), heart rhythm functions (8/31, 26%), and mortality (7/31, 23%) were the most commonly reported outcomes. In total, 16 physiological and behavioral data groups were identified using the ICF tool, such as looking after one's health (physical activity; 14/31, 45%), walking (11/31, 36%), heart rhythm (7/31, 23%), and weight maintenance functions (7/31, 23%). Various digital devices were also studied to collect these data in the included reviews, such as smart glasses, smartwatches, smart bracelets, smart shoes, and smart socks for measuring heart functions, gait pattern functions, and temperature. A substantial number (24/31, 77%) of digital biomarkers were used as interventions. Moreover, wearables (22/31, 71%) were the most common types of digital devices. Position sensors (21/31, 68%) and heart rate sensors and pulse rate sensors (12/31, 39%) were the most prevalent types of sensors used to acquire behavioral and physiological data in the SRs.ConclusionsIn recent years, the clinical evidence concerning digital biomarkers has been systematically reviewed in a wide range of study populations, interventions, digital devices, and sensor technologies, with the dominance of physical activity and cardiac monitors. We used the World Health Organization's ICF tool for classifying behavioral and physiological data, which seemed to be an applicable tool to categorize the broad scope of digital biomarkers identified in this review. To understand the clinical value of digital biomarkers, the strength and quality of the evidence on their health consequences need to be systematically evaluated.
| S-EPMC9641516 | biostudies-literature
Project description:ObjectiveThe amyotrophic lateral sclerosis (ALS) trial outcome measures are clinic based. Active and passive smartphone data can provide important longitudinal information about ALS progression outside the clinic.MethodsWe used Beiwe, a research platform for smartphone-based digital phenotyping, to collect active (self-report ALSFRS-R surveys and speech recordings) and passive (phone sensors and logs) data from patients with ALS for approximately 24 weeks. In clinics, at baseline and every 3 months, we collected vital capacity, ALSFRS-R, and ALS-CBS at enrollment, week 12, and week 24. We also collected ALSFRS-R by telephone at week 6.ResultsBaseline in-clinic ALSFRS-R and smartphone self-report correlation was 0.93 (P < 0.001). ALSFRS-R slopes were equivalent and within-subject standard deviation was smaller for smartphone-based self-report (0.26 vs. 0.56). Use of Beiwe afforded weekly collection of speech samples amenable to a variety of analyses, and we found mean pause time to increase by 0.02 sec per month across the sample.InterpretationSmartphone-based digital phenotyping in people with ALS is feasible and informative. Self-administered smartphone ALSFRS-R scores correlate highly with clinic-based ALSFRS-R scores, have low variability, and could be used in clinical trials. More research is required to fully analyze speech recordings and passive data, and to identify optimal digital markers for use in future ALS clinical trials.
| S-EPMC6529832 | biostudies-literature
Project description:Drug development for mood disorders can greatly benefit from the development of robust, reliable, and objective biomarkers. The incorporation of smartphones and wearable devices in clinical trials provide a unique opportunity to monitor behavior in a non-invasive manner. The objective of this study is to identify the correlations between remotely monitored self-reported assessments and objectively measured activities with depression severity assessments often applied in clinical trials. 30 unipolar depressed patients and 29 age- and gender-matched healthy controls were enrolled in this study. Each participant's daily physiological, physical, and social activity were monitored using a smartphone-based application (CHDR MORE™) for 3 weeks continuously. Self-reported depression anxiety stress scale-21 (DASS-21) and positive and negative affect schedule (PANAS) were administered via smartphone weekly and daily respectively. The structured interview guide for the Hamilton depression scale and inventory of depressive symptomatology-clinical rated (SIGHD-IDSC) was administered in-clinic weekly. Nested cross-validated linear mixed-effects models were used to identify the correlation between the CHDR MORE™ features with the weekly in-clinic SIGHD-IDSC scores. The SIGHD-IDSC regression model demonstrated an explained variance (R2) of 0.80, and a Root Mean Square Error (RMSE) of ± 15 points. The SIGHD-IDSC total scores were positively correlated with the DASS and mean steps-per-minute, and negatively correlated with the travel duration. Unobtrusive, remotely monitored behavior and self-reported outcomes are correlated with depression severity. While these features cannot replace the SIGHD-IDSC for estimating depression severity, it can serve as a complementary approach for assessing depression and drug effects outside the clinic.
| S-EPMC10620211 | biostudies-literature
Project description:Multidimensional integrative data analysis of digital phenotyping is crucial for elucidating the pathologies of multifactorial and heterogeneous diseases, such as the dry eye (DE). This crowdsourced cross-sectional study explored a novel smartphone-based digital phenotyping strategy to stratify and visualize the heterogenous DE symptoms into distinct subgroups. Multidimensional integrative data were collected from 3,593 participants between November 2016 and September 2019. Dimension reduction via Uniform Manifold Approximation and Projection stratified the collected data into seven clusters of symptomatic DE. Symptom profiles and risk factors in each cluster were identified by hierarchical heatmaps and multivariate logistic regressions. Stratified DE subgroups were visualized by chord diagrams, co-occurrence networks, and Circos plot analyses to improve interpretability. Maximum blink interval was reduced in clusters 1, 2, and 5 compared to non-symptomatic DE. Clusters 1 and 5 had severe DE symptoms. A data-driven multidimensional analysis with digital phenotyping may establish predictive, preventive, personalized, and participatory medicine.
| S-EPMC8688467 | biostudies-literature
Project description:In this article, various 50 Ω driver circuits for digital signals are compared and evaluated. These circuits can drive different logic families, even over longer cables, with fast rise and fall times of a few nanoseconds. A fully assembled test board and all measurement results are presented. The design files, source codes and measurement results can be freely downloaded from Mendeley Data. Does it make a difference if a solderless breadboard is used instead of a printed circuit board with solid ground plane? Apart from considerable noise on the breadboard's supply rail, the overall signal quality is better than one might expect. The test board is not only useful for evaluating driver circuits but can also be used directly as a simple pulse generator. Some of the driver circuits presented here show faster rise and fall times than many commercial function generators.
| S-EPMC8342968 | biostudies-literature
Project description:Background:Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker-based prognostic models and focused on generalizability and robustness of the models. Method:We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi-site, 40-month prospective study collecting data in memory clinics, general practitioner offices, and home environments. Results:Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance. Conclusion:Digital biomarker prognostic models can be a useful tool to assist large-scale population screening for the early detection of cognitive impairment and patient monitoring over time.
| S-EPMC7437401 | biostudies-literature