Digital BiomarkersPub Date : 2023-09-19eCollection Date: 2023-01-01DOI: 10.1159/000533189
David Nobbs, Wojciech Piwko, Christopher Bull, Francesca Cormack, Teemu Ahmaniemi, Sebastian C Holst, Meenakshi Chatterjee, Walter Maetzler, Stefan Avey, Wan Fai Ng
{"title":"Regulatory Qualification of a Cross-Disease Digital Measure: Benefits and Challenges from the Perspective of IMI Consortium IDEA-FAST.","authors":"David Nobbs, Wojciech Piwko, Christopher Bull, Francesca Cormack, Teemu Ahmaniemi, Sebastian C Holst, Meenakshi Chatterjee, Walter Maetzler, Stefan Avey, Wan Fai Ng","doi":"10.1159/000533189","DOIUrl":"10.1159/000533189","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Summary: </strong>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.</p><p><strong>Key messages: </strong>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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"7 1","pages":"132-138"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71411121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital BiomarkersPub Date : 2023-09-11eCollection Date: 2023-01-01DOI: 10.1159/000533523
Nele Peerenboom, Suvekshya Aryal, Jennifer M Blankenship, Tracy Swibas, Yaya Zhai, Ieuan Clay, Kate Lyden
{"title":"The Case for the Patient-Centric Development of Novel Digital Sleep Assessment Tools in Major Depressive Disorder.","authors":"Nele Peerenboom, Suvekshya Aryal, Jennifer M Blankenship, Tracy Swibas, Yaya Zhai, Ieuan Clay, Kate Lyden","doi":"10.1159/000533523","DOIUrl":"10.1159/000533523","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Summary: </strong>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.</p><p><strong>Key messages: </strong>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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"7 1","pages":"124-131"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71411122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital BiomarkersPub Date : 2023-08-31eCollection Date: 2023-01-01DOI: 10.1159/000533188
Daphne Ter Huurne, Nina Possemis, Leonie Banning, Angélique Gruters, Alexandra König, Nicklas Linz, Johannes Tröger, Kai Langel, Frans Verhey, Marjolein de Vugt, Inez Ramakers
{"title":"Validation of an Automated Speech Analysis of Cognitive Tasks within a Semiautomated Phone Assessment.","authors":"Daphne Ter Huurne, Nina Possemis, Leonie Banning, Angélique Gruters, Alexandra König, Nicklas Linz, Johannes Tröger, Kai Langel, Frans Verhey, Marjolein de Vugt, Inez Ramakers","doi":"10.1159/000533188","DOIUrl":"10.1159/000533188","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>We included 94 participants from the memory clinic of the Maastricht University Medical Center+ (SCD <i>N</i> = 56 and MCI <i>N</i> = 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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"7 1","pages":"115-123"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71411124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital BiomarkersPub Date : 2023-08-25eCollection Date: 2023-01-01DOI: 10.1159/000530698
Carsten Langholm, Tobias Kowatsch, Sandra Bucci, Andrea Cipriani, John Torous
{"title":"Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research.","authors":"Carsten Langholm, Tobias Kowatsch, Sandra Bucci, Andrea Cipriani, John Torous","doi":"10.1159/000530698","DOIUrl":"10.1159/000530698","url":null,"abstract":"<p><p>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: <i>phone usage</i>, <i>messages usage</i>, <i>visits</i>, <i>device usage</i>, and <i>ambient light</i>. 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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"7 1","pages":"104-114"},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71411120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital BiomarkersPub Date : 2023-08-14eCollection Date: 2023-01-01DOI: 10.1159/000530953
Catherine Morgan, Alessandro Masullo, Majid Mirmehdi, Hanna Kristiina Isotalus, Ferdian Jovan, Ryan McConville, Emma L Tonkin, Alan Whone, Ian Craddock
{"title":"Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson's Disease Severity.","authors":"Catherine Morgan, Alessandro Masullo, Majid Mirmehdi, Hanna Kristiina Isotalus, Ferdian Jovan, Ryan McConville, Emma L Tonkin, Alan Whone, Ian Craddock","doi":"10.1159/000530953","DOIUrl":"10.1159/000530953","url":null,"abstract":"<p><strong>Introduction: </strong>Technology holds the potential to track disease progression and response to neuroprotective therapies in Parkinson's disease (PD). The sit-to-stand (STS) transition is a frequently occurring event which is important to people with PD. The aim of this study was to demonstrate an automatic approach to quantify STS duration and speed using a real-world free-living dataset and look at clinical correlations of the outcomes, including whether STS parameters change when someone withholds PD medications.</p><p><strong>Methods: </strong>Eighty-five hours of video data were collected from 24 participants staying in pairs for 5-day periods in a naturalistic setting. Skeleton joints were extracted from the video data; the head trajectory was estimated and used to estimate the STS parameters of duration and speed.</p><p><strong>Results: </strong>3.14 STS transitions were seen per hour per person on average. Significant correlations were seen between automatic and manual STS duration (Pearson rho - 0.419, <i>p</i> = 0.042) and between automatic STS speed and manual STS duration (Pearson rho - 0.780, <i>p</i> < 0.001). Significant and strong correlations were seen between the gold-standard clinical rating scale scores and both STS duration and STS speed; these correlations were not seen in the STS transitions when the participants were carrying something in their hand(s). Significant differences were seen at the cohort level between control and PD participants' ON medications' STS duration (U = 6,263, <i>p</i> = 0.018) and speed (U = 9,965, <i>p</i> < 0.001). At an individual level, only two participants with PD became significantly slower to STS when they were OFF medications; withholding medications did not significantly change STS duration at an individual level in any participant.</p><p><strong>Conclusion: </strong>We demonstrate a novel approach to automatically quantify and ecologically validate two STS parameters which correlate with gold-standard clinical tools measuring disease severity in PD.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"7 1","pages":"92-103"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/40/91/dib-2023-0007-0001-530953.PMC10425718.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10022613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital BiomarkersPub Date : 2023-08-09eCollection Date: 2023-01-01DOI: 10.1159/000531054
Bohdana Ratitch, Andrew Trigg, Madhurima Majumder, Vanja Vlajnic, Nicole Rethemeier, Richard Nkulikiyinka
{"title":"Clinical Validation of Novel Digital Measures: Statistical Methods for Reliability Evaluation.","authors":"Bohdana Ratitch, Andrew Trigg, Madhurima Majumder, Vanja Vlajnic, Nicole Rethemeier, Richard Nkulikiyinka","doi":"10.1159/000531054","DOIUrl":"10.1159/000531054","url":null,"abstract":"<p><strong>Background: </strong>Assessment of reliability is one of the key components of the validation process designed to demonstrate that a novel clinical measure assessed by a digital health technology tool is fit-for-purpose in clinical research, care, and decision-making. Reliability assessment contributes to characterization of the signal-to-noise ratio and measurement error and is the first indicator of potential usefulness of the proposed clinical measure.</p><p><strong>Summary: </strong>Methodologies for reliability analyses are scattered across literature on validation of PROs, wet biomarkers, etc., yet are equally useful for digital clinical measures. We review a general modeling framework and statistical metrics typically used for reliability assessments as part of the clinical validation. We also present methods for the assessment of agreement and measurement error, alongside modified approaches for categorical measures. We illustrate the discussed techniques using physical activity data from a wearable device with an accelerometer sensor collected in clinical trial participants.</p><p><strong>Key messages: </strong>This paper provides statisticians and data scientists, involved in development and validation of novel digital clinical measures, an overview of the statistical methodologies and analytical tools for reliability assessment.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"7 1","pages":"74-91"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/90/b8/dib-2023-0007-0001-531054.PMC10425717.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10017660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital BiomarkersPub Date : 2023-07-28eCollection Date: 2023-01-01DOI: 10.1159/000531224
Meelis Lootus, Lulu Beatson, Lucas Atwood, Theo Bourdais, Sandra Steyaert, Chethan Sarabu, Zeenia Framroze, Harriet Dickinson, Jean-Christophe Steels, Emily Lewis, Nirav R Shah, Francesca Rinaldo
{"title":"Development and Assessment of an Artificial Intelligence-Based Tool for Ptosis Measurement in Adult Myasthenia Gravis Patients Using Selfie Video Clips Recorded on Smartphones.","authors":"Meelis Lootus, Lulu Beatson, Lucas Atwood, Theo Bourdais, Sandra Steyaert, Chethan Sarabu, Zeenia Framroze, Harriet Dickinson, Jean-Christophe Steels, Emily Lewis, Nirav R Shah, Francesca Rinaldo","doi":"10.1159/000531224","DOIUrl":"10.1159/000531224","url":null,"abstract":"<p><strong>Introduction: </strong>Myasthenia gravis (MG) is a rare autoimmune disease characterized by muscle weakness and fatigue. Ptosis (eyelid drooping) occurs due to fatigue of the muscles for eyelid elevation and is one symptom widely used by patients and healthcare providers to track progression of the disease. Margin reflex distance 1 (MRD1) is an accepted clinical measure of ptosis and is typically assessed using a hand-held ruler. In this work, we develop an AI model that enables automated measurement of MRD1 in self-recorded video clips collected using patient smartphones.</p><p><strong>Methods: </strong>A 3-month prospective observational study collected a dataset of video clips from patients with MG. Study participants were asked to perform an eyelid fatigability exercise to elicit ptosis while filming \"selfie\" videos on their smartphones. These images were collected in nonclinical settings, with no in-person training. The dataset was annotated by non-clinicians for (1) eye landmarks to establish ground truth MRD1 and (2) the quality of the video frames. The ground truth MRD1 (in millimeters, mm) was calculated from eye landmark annotations in the video frames using a standard conversion factor, the horizontal visible iris diameter of the human eye. To develop the model, we trained a neural network for eye landmark detection consisting of a ResNet50 backbone plus two dense layers of 78 dimensions on publicly available datasets. Only the ResNet50 backbone was used, discarding the last two layers. The embeddings from the ResNet50 were used as features for a support vector regressor (SVR) using a linear kernel, for regression to MRD1, in mm. The SVR was trained on data collected remotely from MG patients in the prospective study, split into training and development folds. The model's performance for MRD1 estimation was evaluated on a separate test fold from the study dataset.</p><p><strong>Results: </strong>On the full test fold (<i>N</i> = 664 images), the correlation between the ground truth and predicted MRD1 values was strong (<i>r</i> = 0.732). The mean absolute error was 0.822 mm; the mean of differences was -0.256 mm; and 95% limits of agreement (LOA) were -0.214-1.768 mm. Model performance showed no improvement when test data were gated to exclude \"poor\" quality images.</p><p><strong>Conclusions: </strong>On data generated under highly challenging real-world conditions from a variety of different smartphone devices, the model predicts MRD1 with a strong correlation (<i>r</i> = 0.732) between ground truth and predicted MRD1.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"7 1","pages":"63-73"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5b/23/dib-2023-0007-0001-531224.PMC10399113.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9954353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meghan Lukac, Hannah Luben, Anne E Martin, Zachary Simmons, A. Geronimo
{"title":"Spatial-Temporal Analysis of Gait in Amyotrophic Lateral Sclerosis Using Foot-Worn Inertial Sensors: An Observational Study","authors":"Meghan Lukac, Hannah Luben, Anne E Martin, Zachary Simmons, A. Geronimo","doi":"10.1159/000530067","DOIUrl":"https://doi.org/10.1159/000530067","url":null,"abstract":"Introduction: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that alters gait and increases the risk of falls. The current model of care involves in-person multidisciplinary clinic visits to, in part, assess alterations in gait, evaluate safety, and make recommendations for management. Clinic visits, however, are relatively infrequent, and multidisciplinary evaluations can be physically demanding for patients. To better understand how gait changes over time in those with ALS and enable healthcare providers to properly respond to these changes, remote monitoring of functional mobility would be advantageous. Methods: The objective of this study was to remotely track long-term changes in walking speed using wearable inertial measurement units (IMUs). Nine ALS patients and 6 healthy controls submitted twice-weekly home walking recordings for 24 and 4 weeks, respectively. An IMU data processing method was developed and validated against laboratory-measured walking speed. Results: For both ALS patients and healthy controls, home walking speed was less than clinic walking speed by an average of 0.19 m/s (p = 0.0024). Over 24 weeks, home walking speed significantly decreased for 5 of 9 ALS patients at an average of −0.021 m/s/months (p = 0.005). Those who eventually transitioned to using assistive device (AD) while on the study demonstrated a greater decrease in walking speed than those who did not. Conclusions: Remote longitudinal gait monitoring of ALS patients is feasible with the use of an IMU. Decreases in walking speed were detected in the majority of patients, most strongly in those who eventually transitioned to an AD. Home walking speed may more accurately represent the walking abilities of ALS patients in their real-life environments, a finding which further supports the case for remote monitoring in ALS.","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139372580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital BiomarkersPub Date : 2023-05-12eCollection Date: 2023-01-01DOI: 10.1159/000529899
Vicki Sandys, Lavleen Bhat, Emer O'Hare, Anna Ninan, Kevin Doyle, Shane Kelly, Peter Conlon, Donal Sexton, Colin Edwards, Paul McAleese, Conall O'Seaghdha
{"title":"Pilot Study of a Wearable Hydration Monitor in Haemodialysis Patients: Haemodialysis Outcomes & Patient Empowerment Study 02.","authors":"Vicki Sandys, Lavleen Bhat, Emer O'Hare, Anna Ninan, Kevin Doyle, Shane Kelly, Peter Conlon, Donal Sexton, Colin Edwards, Paul McAleese, Conall O'Seaghdha","doi":"10.1159/000529899","DOIUrl":"10.1159/000529899","url":null,"abstract":"<p><strong>Introduction: </strong>We aimed to assess the validity and reproducibility of a wearable hydration device in a cohort of maintenance dialysis patients.</p><p><strong>Methods: </strong>We conducted a prospective, single-arm observational study on 20 haemodialysis patients between January and June 2021 in a single centre. A prototype wearable infrared spectroscopy device, termed the Sixty device, was worn on the forearm during dialysis sessions and nocturnally. Bioimpedance measurements were performed 4 times using the body composition monitor (BCM) over 3 weeks. Measurements from the Sixty device were compared with the BCM overhydration index (litres) pre- and post-dialysis and with standard haemodialysis parameters.</p><p><strong>Results: </strong>12 out of 20 patients had useable data. Mean age was 52 ± 12.4 years. The overall accuracy for predicting pre-dialysis categories of fluid status using Sixty device was 0.55 [K = 0.00; 95% CI: -0.39-0.42]. The accuracy for the prediction of post-dialysis categories of volume status was low [accuracy = 0.34, K = 0.08; 95% CI: -0.13-0.3]. Sixty outputs at the start and end of dialysis were weakly correlated with pre- and post-dialysis weights (<i>r</i> = 0.27 and <i>r</i> = 0.27, respectively), as well as weight loss during dialysis (<i>r</i> = 0.31), but not ultrafiltration volume (<i>r</i> = 0.12). There was no difference between the change in Sixty readings overnight and the change in Sixty readings during dialysis (mean difference 0.09 ± 1.5 kg), [<i>t</i>(39) = 0.38, <i>p</i> = 0.71].</p><p><strong>Conclusion: </strong>A prototype wearable infrared spectroscopy device was unable to accurately assess changes in fluid status during or between dialysis sessions. In the future, hardware development and advances in photonics may enable the tracking of interdialytic fluid status.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"7 1","pages":"18-27"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9841625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital BiomarkersPub Date : 2023-05-12eCollection Date: 2023-01-01DOI: 10.1159/000530413
Ieuan Clay, Nele Peerenboom, Dana E Connors, Steven Bourke, Alison Keogh, Katarzyna Wac, Tova Gur-Arie, Justin Baker, Christopher Bull, Andrea Cereatti, Francesca Cormack, Damien Eggenspieler, Luca Foschini, Raluca Ganea, Peter M A Groenen, Nicole Gusset, Elena Izmailova, Christoph M Kanzler, Lada Leyens, Kate Lyden, Arne Mueller, Julian Nam, Wan-Fai Ng, David Nobbs, Foteini Orfaniotou, Thanneer Malai Perumal, Wojciech Piwko, Anja Ries, Alf Scotland, Nick Taptiklis, John Torous, Beatrix Vereijken, Shuai Xu, Laurenz Baltzer, Thorsten Vetter, Jörg Goldhahn, Steven C Hoffmann
{"title":"Reverse Engineering of Digital Measures: Inviting Patients to the Conversation.","authors":"Ieuan Clay, Nele Peerenboom, Dana E Connors, Steven Bourke, Alison Keogh, Katarzyna Wac, Tova Gur-Arie, Justin Baker, Christopher Bull, Andrea Cereatti, Francesca Cormack, Damien Eggenspieler, Luca Foschini, Raluca Ganea, Peter M A Groenen, Nicole Gusset, Elena Izmailova, Christoph M Kanzler, Lada Leyens, Kate Lyden, Arne Mueller, Julian Nam, Wan-Fai Ng, David Nobbs, Foteini Orfaniotou, Thanneer Malai Perumal, Wojciech Piwko, Anja Ries, Alf Scotland, Nick Taptiklis, John Torous, Beatrix Vereijken, Shuai Xu, Laurenz Baltzer, Thorsten Vetter, Jörg Goldhahn, Steven C Hoffmann","doi":"10.1159/000530413","DOIUrl":"10.1159/000530413","url":null,"abstract":"<p><strong>Background: </strong>Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures.</p><p><strong>Summary: </strong>In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled \"Reverse Engineering of Digital Measures,\" was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools.</p><p><strong>Key messages: </strong>In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"7 1","pages":"28-44"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9852819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}