1
|
Gala AS, Wilkins KB, Petrucci MN, Kehnemouyi YM, Velisar A, Trager MH, Bronte-Stewart HM. The digital signature of emergent tremor in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:147. [PMID: 39112485 PMCID: PMC11306561 DOI: 10.1038/s41531-024-00754-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Emergent tremor in Parkinson's disease (PD) can occur during sustained postures or movements that are different from action tremor. Tremor can contaminate the clinical rating of bradykinesia during finger tapping. Currently, there is no reliable way of isolating emergent tremor and measuring the cardinal motor symptoms based on voluntary movements only. In this study, we investigated whether emergent tremor during repetitive alternating finger tapping (RAFT) on a quantitative digitography (QDG) device could be reliably identified and distinguished from voluntary tapping. Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and the Movement Disorders Society - Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III). Visual identification of tremor during QDG-RAFT was labeled by an experienced movement disorders specialist. Two methods of identifying tremor were investigated: 1) physiologically informed temporal thresholds 2) XGBoost model using temporal and amplitude features of tapping. The XGBoost model showed high accuracy for identifying tremor (area under the precision-recall curve of 0.981) and outperformed temporal-based thresholds. Percent time duration of classifier-identified tremor showed significant correlations with MDS-UPDRS III tremor subscores (r = 0.50, p < 0.0001). There was a significant change in QDG metrics for bradykinesia, rigidity, and arrhythmicity after tremor strikes were excluded (p < 0.01). The results demonstrate that emergent tremor during QDG-RAFT has a unique digital signature and the duration of tremor correlated with the MDS-UPDRS III tremor items. When involuntary tremor strikes were excluded, the QDG metrics of bradykinesia and rigidity were significantly worse, demonstrating the importance of distinguishing tremor from voluntary movement when rating bradykinesia.
Collapse
Affiliation(s)
- Aryaman S Gala
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin B Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Anca Velisar
- The Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA
| | - Megan H Trager
- Columbia University College of Physicians and Surgeons, New York City, NY, USA
| | - Helen M Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, US.
| |
Collapse
|
2
|
Xing Y, Song B, Crouthamel M, Chen X, Goss S, Wang L, Shen J. Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy. SENSORS (BASEL, SWITZERLAND) 2024; 24:3364. [PMID: 38894155 PMCID: PMC11174528 DOI: 10.3390/s24113364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/16/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Abstract
Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD.
Collapse
Affiliation(s)
- Yunzhao Xing
- Statistical Innovation Group, AbbVie, North Chicago, IL 60064, USA
| | - Bolin Song
- Digital Science, AbbVie, North Chicago, IL 60064, USA
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, USA
| | | | - Xiaotian Chen
- Statistical Innovation Group, AbbVie, North Chicago, IL 60064, USA
| | - Sandra Goss
- Digital Science, AbbVie, North Chicago, IL 60064, USA
| | - Li Wang
- Statistical Innovation Group, AbbVie, North Chicago, IL 60064, USA
| | - Jie Shen
- Digital Science, AbbVie, North Chicago, IL 60064, USA
| |
Collapse
|
3
|
Birrer V, Elgendi M, Lambercy O, Menon C. Evaluating reliability in wearable devices for sleep staging. NPJ Digit Med 2024; 7:74. [PMID: 38499793 PMCID: PMC10948771 DOI: 10.1038/s41746-024-01016-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/18/2024] [Indexed: 03/20/2024] Open
Abstract
Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.
Collapse
Affiliation(s)
- Vera Birrer
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Carlo Menon
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
| |
Collapse
|
4
|
Bronte-Stewart H, Gala A, Wilkins K, Pettruci M, Kehnemouyi Y, Velisar A, Trager M. The digital signature of emergent tremor in Parkinson's disease. RESEARCH SQUARE 2023:rs.3.rs-3467667. [PMID: 37961117 PMCID: PMC10635351 DOI: 10.21203/rs.3.rs-3467667/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Emergent tremor in Parkinson's disease (PD) can occur during sustained postures or movement that is different from action tremor. Tremor can contaminate the clinical rating of bradykinesia during finger tapping. Currently, there is no reliable way of isolating emergent tremor and measuring the cardinal motor symptoms based on voluntary movements only. Objective Investigate whether emergent tremor during repetitive alternating finger tapping (RAFT) on a quantitative digitography (QDG) device can be reliably identified and distinguished from voluntary tapping. Methods Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and the Movement Disorders Society - Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III). Visual identification of tremor during QDG-RAFT was labelled by an experienced movement disorders specialist. Two methods of identifying tremor were investigated: 1) physiologically-informed temporal thresholds 2) XGBoost model using temporal and amplitude features of tapping. Results The XGBoost model showed high accuracy for identifying tremor (area under the precision-recall curve of 0.981) and outperformed temporal-based thresholds. Percent time duration of classifier-identified tremor showed significant correlations with MDS-UPDRS III tremor subscores (r = 0.50, P < 0.0001). There was a significant change in QDG metrics for bradykinesia, rigidity and arrhythmicity after tremor strikes were excluded (p < 0.01). Conclusions Emergent tremor during QDG-RAFT has a unique digital signature and the duration of tremor correlated with the MDS-UPDRS III tremor items. When involuntary tremor strikes were excluded, the QDG metrics of bradykinesia and rigidity were significantly worse, demonstrating the importance of distinguishing tremor from voluntary movement when rating bradykinesia.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Megan Trager
- Columbia University College of Physicians and Surgeons
| |
Collapse
|
5
|
Cesnakova L, Meadows K, Avey S, Barrett J, Calimlim B, Chatterjee M, Goss S, Keyloun KR, Lambert J, Northcott CA, Patalano F, Sirbu D, Begolka WS, Thyssen N, Zorman S, Goldsack JC. A patient-centred conceptual model of nocturnal scratch and its impact in atopic dermatitis: A mixed-methods study supporting the development of novel digital measurements. SKIN HEALTH AND DISEASE 2023; 3:e262. [PMID: 37799371 PMCID: PMC10549806 DOI: 10.1002/ski2.262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/09/2023] [Accepted: 06/01/2023] [Indexed: 10/07/2023]
Abstract
Background Emerging digital measures and clinical outcome assessments (COAs) leveraging digital health technologies (DHTs) could address the need for objective, quantitative measures of symptoms of atopic dermatitis (AD), such as nocturnal scratching. Development of such measures needs to be supported by evidence reflecting meaningfulness to patients. Objectives To assess nocturnal scratching as a concept of interest associated with meaningful aspects of health of patients with AD (adults and children); and to explore patient-centred considerations for novel COAs measuring nocturnal scratch using DHTs. Methods Phase 1 evaluated disease impacts on everyday life and the lived experience with nocturnal scratching through qualitative interviews of AD patients and caregivers. Phase 2 deployed a quantitative survey to a sample of AD patients as well as caregivers. Results Four cohorts with various AD severity levels participated in Phase 1: (1) adults with AD (n = 15), (2) their caregivers/spouses/partners (n = 6), (3) children with AD (n = 14), and (4) their adult caregivers (n = 14). Findings were used to develop a conceptual model for nocturnal scratching as a potential concept of interest. The Phase 2 survey was completed by 1349 of 27640 invited adults with AD and caregivers of children with AD. The most burdensome aspects of AD reported were itchy skin and scratching. Overall, ∼65% of participants reported nocturnal scratching ≥1 day/week, resulting in ∼1-1.4 h of sleep lost per night. In all, 85%-91% of respondents considered it at least somewhat valuable that a treatment reduces night-time scratching. About 50% reported willingness to use technology to this end and ∼25% were unsure. Conclusion Our results represented by the conceptual model confirm that nocturnal scratch is a concept of interest related to meaningful aspects of health for patients with AD and therefore is worth being captured as a distinct outcome for clinical and research purposes. DHTs are suitable tools presenting an important measurement opportunity to assess and evaluate occurrence, frequency, and other parameters of nocturnal scratching as a disease biomarker or COA of treatment efficacy.
Collapse
Affiliation(s)
| | | | - Stefan Avey
- Janssen Research & Development LLCRaritanNew JerseyUSA
| | - Judy Barrett
- Health Outcomes Insights Ltd.FaringdonOxfordshireUK
| | | | | | | | | | | | | | | | | | | | | | - Sylvain Zorman
- Novartis Pharma AGBaselSwitzerland
- ActiGraph LLCPensacolaFlordiaUSA
| | | |
Collapse
|
6
|
Padmanabha A, Choudhary S, Majidi C, Erickson Z. A multimodal sensing ring for quantification of scratch intensity. COMMUNICATIONS MEDICINE 2023; 3:115. [PMID: 37726377 PMCID: PMC10509275 DOI: 10.1038/s43856-023-00345-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 08/04/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND An objective measurement of chronic itch is necessary for improvements in patient care for numerous medical conditions. While wearables have shown promise for scratch detection, they are currently unable to estimate scratch intensity, preventing a comprehensive understanding of the effect of itch on an individual. METHODS In this work, we present a framework for the estimation of scratch intensity in addition to the detection of scratch. This is accomplished with a multimodal ring device, consisting of an accelerometer and a contact microphone, a pressure-sensitive tablet for capturing ground truth intensity values, and machine learning algorithms for regression of scratch intensity on a 0-600 milliwatts (mW) power scale that can be mapped to a 0-10 continuous scale. RESULTS We evaluate the performance of our algorithms on 20 individuals using leave one subject out cross-validation and using data from 14 additional participants, we show that our algorithms achieve clinically-relevant discrimination of scratching intensity levels. By doing so, our device enables the quantification of the substantial variations in the interpretation of the 0-10 scale frequently utilized in patient self-reported clinical assessments. CONCLUSIONS This work demonstrates that a finger-worn device can provide multidimensional, objective, real-time measures for the action of scratching.
Collapse
Affiliation(s)
- Akhil Padmanabha
- Robotics Institute, Carnegie Mellon University, Forbes Avenue, Pittsburgh, 15213, PA, USA.
| | - Sonal Choudhary
- Department of Dermatology, University of Pittsburgh Medical Center, Fifth Avenue, Pittsburgh, 15213, PA, USA
| | - Carmel Majidi
- Robotics Institute, Carnegie Mellon University, Forbes Avenue, Pittsburgh, 15213, PA, USA
- Mechanical Engineering, Carnegie Mellon University, Forbes Avenue, Pittsburgh, 15213, PA, USA
| | - Zackory Erickson
- Robotics Institute, Carnegie Mellon University, Forbes Avenue, Pittsburgh, 15213, PA, USA
| |
Collapse
|
7
|
Ji J, Venderley J, Zhang H, Lei M, Ruan G, Patel N, Chung YM, Giesting R, Miller L. Assessing nocturnal scratch with actigraphy in atopic dermatitis patients. NPJ Digit Med 2023; 6:72. [PMID: 37100893 PMCID: PMC10133290 DOI: 10.1038/s41746-023-00821-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 04/04/2023] [Indexed: 04/28/2023] Open
Abstract
Nocturnal scratch is one major factor leading to impaired quality of life in atopic dermatitis (AD) patients. Therefore, objectively quantifying nocturnal scratch events aids in assessing the disease state, treatment effect, and AD patients' quality of life. In this paper, we describe the use of actigraphy, highly predictive topological features, and a model-ensembling approach to develop an assessment of nocturnal scratch events by measuring scratch duration and intensity. Our assessment is tested in a clinical setting against the ground truth obtained from video recordings. The new approach addresses unmet challenges in existing studies, such as the lack of generalizability to real-world applications, the failure to capture finger scratches, and the limitations in the evaluation due to imbalanced data in the current literature. Furthermore, the performance evaluation shows agreement between derived digital endpoints and the video annotation ground truth, as well as patient-reported outcomes, which demonstrated the validity of the new assessment of nocturnal scratch.
Collapse
Affiliation(s)
- Ju Ji
- Eli Lilly & Company, INc., Indianapolis, IN, USA.
| | | | - Hui Zhang
- Eli Lilly & Company, INc., Indianapolis, IN, USA
| | - Mengjue Lei
- Eli Lilly & Company, INc., Indianapolis, IN, USA
| | | | - Neel Patel
- Eli Lilly & Company, INc., Indianapolis, IN, USA
| | - Yu-Min Chung
- Eli Lilly & Company, INc., Indianapolis, IN, USA
| | | | - Leah Miller
- Eli Lilly & Company, INc., Indianapolis, IN, USA
| |
Collapse
|
8
|
Todorov A, Torah R, Wagih M, Ardern-Jones MR, Beeby SP. Electromagnetic Sensing Techniques for Monitoring Atopic Dermatitis-Current Practices and Possible Advancements: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3935. [PMID: 37112275 PMCID: PMC10144024 DOI: 10.3390/s23083935] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 03/29/2023] [Accepted: 04/04/2023] [Indexed: 06/19/2023]
Abstract
Atopic dermatitis (AD) is one of the most common skin disorders, affecting nearly one-fifth of children and adolescents worldwide, and currently, the only method of monitoring the condition is through an in-person visual examination by a clinician. This method of assessment poses an inherent risk of subjectivity and can be restrictive to patients who do not have access to or cannot visit hospitals. Advances in digital sensing technologies can serve as a foundation for the development of a new generation of e-health devices that provide accurate and empirical evaluation of the condition to patients worldwide. The goal of this review is to study the past, present, and future of AD monitoring. First, current medical practices such as biopsy, tape stripping and blood serum are discussed with their merits and demerits. Then, alternative digital methods of medical evaluation are highlighted with the focus on non-invasive monitoring using biomarkers of AD-TEWL, skin permittivity, elasticity, and pruritus. Finally, possible future technologies are showcased such as radio frequency reflectometry and optical spectroscopy along with a short discussion to provoke research into improving the current techniques and employing the new ones to develop an AD monitoring device, which could eventually facilitate medical diagnosis.
Collapse
Affiliation(s)
- Alexandar Todorov
- Centre of Flexible Electronics and E-Textiles, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;
| | - Russel Torah
- Centre of Flexible Electronics and E-Textiles, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;
| | - Mahmoud Wagih
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK;
| | - Michael R. Ardern-Jones
- Clinical Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 1DU, UK;
| | - Steve P. Beeby
- Centre of Flexible Electronics and E-Textiles, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;
| |
Collapse
|
9
|
Hamidi Shishavan H, Garza J, Henning R, Cherniack M, Hirabayashi L, Scott E, Kim I. Continuous physiological signal measurement over 24-hour periods to assess the impact of work-related stress and workplace violence. APPLIED ERGONOMICS 2023; 108:103937. [PMID: 36462453 DOI: 10.1016/j.apergo.2022.103937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/30/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
Work-related stress has long been recognized as an essential factor affecting employees' health and wellbeing. Repeated exposure to acute occupational stressors puts workers at high risk for depression, obesity, hypertension, and early death. Assessment of the effects of acute stress on workers' wellbeing usually relies on subjective self-reports, questionnaires, or measuring biometric and biochemical markers in long-cycle time intervals. This study aimed to develop and validate the use of a multiparameter wearable armband for continuous non-invasive monitoring of physiological states. Two worker populations were monitored 24 h/day: six loggers for one day and six ICU nurses working 12-hr shifts for one week. Stress responses in nurses were highly correlated with changes in heart rate variability (HRV) and pulse transit time (PTT). A rise in the low-to high-frequency (LF/LH) ratio in HRV was also coincident with stress responses. HRV on workdays decreased compared to non-work days, and PTT also exhibited a persistent decrease reflecting increased blood pressure. Compared to loggers, nurses were involved in high-intensity work activities 45% more often but were less active on non-work days. The wearable technology was well accepted by all worker participants and yielded high signal quality, critical factors for long-term non-invasive occupational health monitoring.
Collapse
Affiliation(s)
- Hossein Hamidi Shishavan
- Department of Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA; Department of Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA.
| | - Jennifer Garza
- Department of Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA.
| | - Robert Henning
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, 06269, USA.
| | - Martin Cherniack
- Center for the Promotion of Health in the New England Workplace, University of Connecticut, USA.
| | - Liane Hirabayashi
- Northeast Center for Occupational Health and Safety in Agriculture, Forestry, and Fishing, Bassett Medical Center, NY, 13326, USA.
| | - Erika Scott
- Northeast Center for Occupational Health and Safety in Agriculture, Forestry, and Fishing, Bassett Medical Center, NY, 13326, USA.
| | - Insoo Kim
- Department of Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA; Department of Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA.
| |
Collapse
|
10
|
Ryser F, Gassert R, Werth E, Lambercy O. A novel method to increase specificity of sleep-wake classifiers based on wrist-worn actigraphy. Chronobiol Int 2023:1-12. [PMID: 36938627 DOI: 10.1080/07420528.2023.2188096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
The knowledge of the distribution of sleep and wake over a 24-h day is essential for a comprehensive image of sleep-wake rhythms. Current sleep-wake scoring algorithms for wrist-worn actigraphy suffer from low specificities, which leads to an underestimation of the time staying awake. The goal of this study (ClinicalTrials.gov Identifier: NCT03356938) was to develop a sleep-wake classifier with increased specificity. By artificially balancing the training dataset to contain as much wake as sleep epochs from day- and nighttime measurements from 12 subjects, we optimized the classification parameters to an optimal trade-off between sensitivity and specificity. The resulting sleep-wake classifier achieved high specificity of 80.4% and sensitivity of 88.6% on the balanced dataset containing 3079.9 h of actimeter data. In the validation on night sleep of separate adaptation recordings from 19 healthy subjects, the sleep-wake classifier achieved 89.4% sensitivity and 64.6% specificity and estimated accurately total sleep time and sleep efficiency with a mean difference of 12.16 min and 2.83%, respectively. This new, device-independent method allows to rid sleep-wake classifiers from their bias towards sleep detection and lay a foundation for more accurate assessments in everyday life, which could be applied to monitor patients with fragmented sleep-wake rhythms.
Collapse
Affiliation(s)
- Franziska Ryser
- Rehabilitation Engineering Laboratory, ETH Zurich, Zurich, Switzerland.,Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Sleep and Health Zurich (SHZ), University of Zurich, Zurich, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, ETH Zurich, Zurich, Switzerland
| | - Esther Werth
- Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Sleep and Health Zurich (SHZ), University of Zurich, Zurich, Switzerland
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, ETH Zurich, Zurich, Switzerland
| |
Collapse
|
11
|
de-la-Fuente-Robles YM, Ricoy-Cano AJ, Albín-Rodríguez AP, López-Ruiz JL, Espinilla-Estévez M. Past, Present and Future of Research on Wearable Technologies for Healthcare: A Bibliometric Analysis Using Scopus. SENSORS (BASEL, SWITZERLAND) 2022; 22:8599. [PMID: 36433195 PMCID: PMC9696945 DOI: 10.3390/s22228599] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/30/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
Currently, wearable technology is present in different fields that aim to satisfy our needs in daily life, including the improvement of our health in general, the monitoring of patient health, ensuring the safety of people in the workplace or supporting athlete training. The objective of this bibliometric analysis is to examine and map the scientific advances in wearable technologies in healthcare, as well as to identify future challenges within this field and put forward some proposals to address them. In order to achieve this objective, a search of the most recent related literature was carried out in the Scopus database. Our results show that the research can be divided into two periods: before 2013, it focused on design and development of sensors and wearable systems from an engineering perspective and, since 2013, it has focused on the application of this technology to monitoring health and well-being in general, and in alignment with the Sustainable Development Goals wherever feasible. Our results reveal that the United States has been the country with the highest publication rates, with 208 articles (34.7%). The University of California, Los Angeles, is the institution with the most studies on this topic, 19 (3.1%). Sensors journal (Switzerland) is the platform with the most studies on the subject, 51 (8.5%), and has one of the highest citation rates, 1461. We put forward an analysis of keywords and, more specifically, a pennant chart to illustrate the trends in this field of research, prioritizing the area of data collection through wearable sensors, smart clothing and other forms of discrete collection of physiological data.
Collapse
|
12
|
Ke Wang W, Cesnakova L, Goldsack JC, Dunn J. Defining digital measurement of scratching during sleep, or “Nocturnal Scratch”: A review of the literature (Preprint). J Med Internet Res 2022; 25:e43617. [PMID: 37071460 PMCID: PMC10155092 DOI: 10.2196/43617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/22/2022] [Accepted: 03/25/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Digital sensing solutions represent a convenient, objective, relatively inexpensive method that could be leveraged for assessing symptoms of various health conditions. Recent progress in the capabilities of digital sensing products has targeted the measurement of scratching during sleep, traditionally referred to as nocturnal scratching, in patients with atopic dermatitis or other skin conditions. Many solutions measuring nocturnal scratch have been developed; however, a lack of efforts toward standardization of the measure's definition and contextualization of scratching during sleep hampers the ability to compare different technologies for this purpose. OBJECTIVE We aimed to address this gap and bring forth unified measurement definitions for nocturnal scratch. METHODS We performed a narrative literature review of definitions of scratching in patients with skin inflammation and a targeted literature review of sleep in the context of the period during which such scratching occurred. Both searches were limited to English language studies in humans. The extracted data were synthesized into themes based on study characteristics: scratch as a behavior, other characterization of the scratching movement, and measurement parameters for both scratch and sleep. We then developed ontologies for the digital measurement of sleep scratching. RESULTS In all, 29 studies defined inflammation-related scratching between 1996 and 2021. When cross-referenced with the results of search terms describing the sleep period, only 2 of these scratch-related papers also described sleep-related variables. From these search results, we developed an evidence-based and patient-centric definition of nocturnal scratch: an action of rhythmic and repetitive skin contact movement performed during a delimited time period of intended and actual sleep that is not restricted to any specific time of the day or night. Based on the measurement properties identified in the searches, we developed ontologies of relevant concepts that can be used as a starting point to develop standardized outcome measures of scratching during sleep in patients with inflammatory skin conditions. CONCLUSIONS This work is intended to serve as a foundation for the future development of unified and well-described digital health technologies measuring nocturnal scratching and should enable better communication and sharing of results between various stakeholders taking part in research in atopic dermatitis and other inflammatory skin conditions.
Collapse
Affiliation(s)
- Will Ke Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | | | | | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States
- The Duke Clinical Research Institute, Durham, NC, United States
| |
Collapse
|
13
|
Psaltos DJ, Mamashli F, Adamusiak T, Demanuele C, Santamaria M, Czech MD. Wearable-Based Stair Climb Power Estimation and Activity Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:6600. [PMID: 36081058 PMCID: PMC9459813 DOI: 10.3390/s22176600] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 06/15/2023]
Abstract
Stair climb power (SCP) is a clinical measure of leg muscular function assessed in-clinic via the Stair Climb Power Test (SCPT). This method is subject to human error and cannot provide continuous remote monitoring. Continuous monitoring using wearable sensors may provide a more comprehensive assessment of lower-limb muscular function. In this work, we propose an algorithm to classify stair climbing periods and estimate SCP from a lower-back worn accelerometer, which strongly agrees with the clinical standard (r = 0.92, p < 0.001; ICC = 0.90, [0.82, 0.94]). Data were collected in-lab from healthy adults (n = 65) performing the four-step SCPT and a walking assessment while instrumented (accelerometer + gyroscope), which allowed us to investigate tradeoffs between sensor modalities. Using two classifiers, we were able to identify periods of stair ascent with >89% accuracy [sensitivity = >0.89, specificity = >0.90] using two ensemble machine learning algorithms, trained on accelerometer signal features. Minimal changes in model performances were observed using the gyroscope alone (±0−6% accuracy) versus the accelerometer model. While we observed a slight increase in accuracy when combining gyroscope and accelerometer (about +3−6% accuracy), this is tolerable to preserve battery life in the at-home environment. This work is impactful as it shows potential for an accelerometer-based at-home assessment of SCP.
Collapse
|
14
|
Adamowicz L, Christakis Y, Czech MD, Adamusiak T. SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing. JMIR Mhealth Uhealth 2022; 10:e36762. [PMID: 35353039 PMCID: PMC9073613 DOI: 10.2196/36762] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/28/2022] [Accepted: 03/29/2022] [Indexed: 01/30/2023] Open
Abstract
Wearable inertial sensors are providing enhanced insight into patient mobility and health. Significant research efforts have focused on wearable algorithm design and deployment in both research and clinical settings; however, open-source, general-purpose software tools for processing various activities of daily living are relatively scarce. Furthermore, few studies include code for replication or off-the-shelf software packages. In this work, we introduce SciKit Digital Health (SKDH), a Python software package (Python Software Foundation) containing various algorithms for deriving clinical features of gait, sit to stand, physical activity, and sleep, wrapped in an easily extensible framework. SKDH combines data ingestion, preprocessing, and data analysis methods geared toward modern data science workflows and streamlines the generation of digital endpoints in “good practice” environments by combining all the necessary data processing steps in a single pipeline. Our package simplifies the construction of new data processing pipelines and promotes reproducibility by following a convention over configuration approach, standardizing most settings on physiologically reasonable defaults in healthy adult populations or those with mild impairment. SKDH is open source, as well as free to use and extend under a permissive Massachusetts Institute of Technology license, and is available from GitHub (PfizerRD/scikit-digital-health), the Python Package Index, and the conda-forge channel of Anaconda.
Collapse
Affiliation(s)
- Lukas Adamowicz
- Digital Medicine and Translational Imaging, Pfizer Inc, Cambridge, MA, United States
| | - Yiorgos Christakis
- Digital Medicine and Translational Imaging, Pfizer Inc, Cambridge, MA, United States
| | - Matthew D Czech
- Digital Medicine and Translational Imaging, Pfizer Inc, Cambridge, MA, United States
| | - Tomasz Adamusiak
- Digital Medicine and Translational Imaging, Pfizer Inc, Cambridge, MA, United States
| |
Collapse
|
15
|
Di J, Demanuele C, Kettermann A, Karahanoglu FI, Cappelleri JC, Potter A, Bury D, Cedarbaum JM, Byrom B. Considerations to address missing data when deriving clinical trial endpoints from digital health technologies. Contemp Clin Trials 2021; 113:106661. [PMID: 34954098 DOI: 10.1016/j.cct.2021.106661] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 11/23/2021] [Accepted: 12/18/2021] [Indexed: 11/25/2022]
Abstract
Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold. High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspective in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.
Collapse
Affiliation(s)
- Junrui Di
- Pfizer Inc., United States of America.
| | | | | | | | | | | | | | - Jesse M Cedarbaum
- Yale University School of Medicine, United States of America; Coeruleus Clinical Sciences LLC, United States of America
| | - Bill Byrom
- Signant Health, United States of America
| |
Collapse
|
16
|
Wimalasena NK, Milner G, Silva R, Vuong C, Zhang Z, Bautista DM, Woolf CJ. Dissecting the precise nature of itch-evoked scratching. Neuron 2021; 109:3075-3087.e2. [PMID: 34411514 PMCID: PMC8497439 DOI: 10.1016/j.neuron.2021.07.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/10/2021] [Accepted: 07/26/2021] [Indexed: 01/17/2023]
Abstract
Itch is a discrete and irritating sensation tightly coupled to a drive to scratch. Acute scratching developed evolutionarily as an adaptive defense against skin irritants, pathogens, or parasites. In contrast, the itch-scratch cycle in chronic itch is harmful, inducing escalating itch and skin damage. Clinically and preclinically, scratching incidence is currently evaluated as a unidimensional motor parameter and believed to reflect itch severity. We propose that scratching, when appreciated as a complex, multidimensional motor behavior, will yield greater insight into the nature of itch and the organization of neural circuits driving repetitive motor patterns. We outline the limitations of standard measurements of scratching in rodent models and present new approaches to observe and quantify itch-evoked scratching. We argue that accurate quantitative measurements of scratching are critical for dissecting the molecular, cellular, and circuit mechanisms underlying itch and for preclinical development of therapeutic interventions for acute and chronic itch disorders.
Collapse
Affiliation(s)
- Nivanthika K Wimalasena
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA; Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - George Milner
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
| | - Ricardo Silva
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Cliff Vuong
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Zihe Zhang
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA; Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Diana M Bautista
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Hellen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.
| | - Clifford J Woolf
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA; Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA.
| |
Collapse
|