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Liang W, Wang Y, Huang Q, Shang B, Su N, Zhou L, Rhodes RE, Baker JS, Duan Y. Adherence to 24-Hour Movement Guidelines Among Chinese Older Adults: Prevalence, Correlates, and Associations With Physical and Mental Health Outcomes. JMIR Public Health Surveill 2024; 10:e46072. [PMID: 38869941 PMCID: PMC11211711 DOI: 10.2196/46072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 03/28/2024] [Accepted: 05/14/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND It is known that 24-hour movement behaviors, including physical activity (PA), sedentary behavior (SB), and sleep, are crucial components affecting older adults' health. Canadian 24-hour movement guidelines for older adults were launched in 2020, emphasizing the combined role of these 3 movement behaviors in promoting older adults' health. However, research on the prevalence and correlates of guideline adherence and its associations with health-related outcomes is limited, especially among Chinese older adults. OBJECTIVE This study aimed to investigate the prevalence and correlates of meeting 24-hour movement guidelines among Chinese older adults. Furthermore, this study aimed to examine the associations of guideline adherence with older adults' physical and mental health outcomes. METHODS Using a stratified cluster random sampling approach, a total of 4562 older adults (mean age 67.68 years, SD 5.03 years; female proportion: 2544/4562, 55.8%) were recruited from the latest provincial health surveillance of Hubei China from July 25 to November 19, 2020. Measures included demographics, movement behaviors (PA, SB, and sleep), BMI, waist circumference, waist-hip ratio (WHR), percentage body fat (PBF), systolic and diastolic blood pressure, physical fitness, depressive symptoms, and loneliness. Generalized linear mixed models were employed to examine the associations between variables using SPSS 28.0 (IBM Corp). RESULTS Only 1.8% (83/4562) of participants met all 3 movement guidelines, while 32.1% (1466/4562), 3.4% (155/4562), and 66.4% (3031/4562) met the individual behavioral guidelines for PA, SB, and sleep, respectively. Participants who were older, were female, and lived in municipalities with lower economic levels were less likely to meet all 3 movement guidelines. Adhering to individual or combined movement guidelines was associated with greater physical fitness and lower values of BMI, waist circumference, WHR, PBF, depressive symptoms, and loneliness, with the exception of the relationship of SB+sleep guidelines with loneliness. Furthermore, only meeting SB guidelines or meeting both PA and SB guidelines was associated with lower systolic blood pressure. CONCLUSIONS This is the first study to investigate adherence to 24-hour movement guidelines among Chinese older adults with regard to prevalence, correlates, and associations with physical and mental health outcomes. The findings emphasize the urgent need for promoting healthy movement behaviors among Chinese older adults. Future interventions to improve older adults' physical and mental health should involve enhancing their overall movement behaviors and should consider demographic differences.
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Affiliation(s)
- Wei Liang
- School of Physical Education, Shenzhen University, Shenzhen, China
| | - Yanping Wang
- Department of Sport, Physical Education and Health, Hong Kong Baptist University, Hong Kong, China (Hong Kong)
| | - Qian Huang
- Fitness and Health Lab, Hubei Institute of Sport Science, Wuhan, China
| | - Borui Shang
- Department of Social Sciences, Hebei Sports University, Shijiazhuang, China
| | - Ning Su
- School of Physical Education, Shenzhen University, Shenzhen, China
| | - Lin Zhou
- School of Physical Education, Hebei Normal University, Shijiazhuang, China
| | - Ryan E Rhodes
- School of Exercise Science, Physical and Health Education, University of Victoria, Victoria, BC, Canada
| | - Julien Steven Baker
- Department of Sport, Physical Education and Health, Hong Kong Baptist University, Hong Kong, China (Hong Kong)
| | - Yanping Duan
- Department of Sport, Physical Education and Health, Hong Kong Baptist University, Hong Kong, China (Hong Kong)
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Boudreaux BD, Frederick GM, O'Connor PJ, Evans EM, Schmidt MD. Harmonization of three different accelerometers to classify the 24 h activity cycle. Physiol Meas 2024; 45:045003. [PMID: 38530322 DOI: 10.1088/1361-6579/ad37ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 03/26/2024] [Indexed: 03/27/2024]
Abstract
Increasing interest in measuring key components of the 24 h activity cycle (24-HAC) [sleep, sedentary behavior (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA)] has led to a need for better methods. Single wrist-worn accelerometers and different self-report instruments can assess the 24-HAC but may not accurately classify time spent in the different components or be subject to recall errors.Objective. To overcome these limitations, the current study harmonized output from multiple complimentary research grade accelerometers and assessed the feasibility and logistical challenges of this approach.Approach. Participants (n= 108) wore an: (a) ActiGraph GT9X on the wrist, (b) activPAL3 on the thigh, and (c) ActiGraph GT3X+ on the hip for 7-10 d to capture the 24-HAC. Participant compliance with the measurement protocol was compared across devices and an algorithm was developed to harmonize data from the accelerometers. The resulting 24-HAC estimates were described within and across days.Main results. Usable data for each device was obtained from 94.3% to 96.7% of participants and 89.4% provided usable data from all three devices. Compliance with wear instructions ranged from 70.7% of days for the GT3X+ to 93.2% of days for the activPAL3. Harmonized estimates indicated that, on average, university students spent 34% of the 24 h day sleeping, 41% sedentary, 21% in LPA, and 4% in MVPA. These behaviors varied substantially by time of day and day of the week.Significance. It is feasible to use three accelerometers in combination to derive a harmonized estimate the 24-HAC. The use of multiple accelerometers can minimize gaps in 24-HAC data however, factors such as additional research costs, and higher participant and investigator burden, should also be considered.
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Affiliation(s)
- Benjamin D Boudreaux
- Columbia University Irving Medical Center, New York, NY 10032-3784, United States of America
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Tam W, Alajlani M, Abd-Alrazaq A. An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review. J Med Internet Res 2023; 25:e42950. [PMID: 37594791 PMCID: PMC10474516 DOI: 10.2196/42950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 03/13/2023] [Accepted: 04/14/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom. OBJECTIVE In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom. METHODS A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors. RESULTS Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis. CONCLUSIONS This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.
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Affiliation(s)
- William Tam
- Insitute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
| | - Mohannad Alajlani
- Insitute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
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Mansoubi M, Dawes J, Bhatia A, Vashisht H, Collett J, Greenwood DC, Ezekiel L, O'Connor D, Leveridge P, Rayner C, Read F, Sivan M, Tuckerbell I, Ward T, Delaney B, Muhlhausen W, Dawes H. Digital home monitoring for capturing daily fluctuation of symptoms; a longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study). BMJ Open 2023; 13:e071428. [PMID: 37553189 PMCID: PMC10414119 DOI: 10.1136/bmjopen-2022-071428] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 06/28/2023] [Indexed: 08/10/2023] Open
Abstract
INTRODUCTION A substantial proportion of COVID-19 survivors continue to have symptoms more than 3 months after infection, especially of those who required medical intervention. Lasting symptoms are wide-ranging, and presentation varies between individuals and fluctuates within an individual. Improved understanding of undulation in symptoms and triggers may improve efficacy of healthcare providers and enable individuals to better self-manage their Long Covid. We present a protocol where we aim to develop and examine the feasibility and usability of digital home monitoring for capturing daily fluctuation of symptoms in individuals with Long Covid and provide data to facilitate a personalised approach to the classification and management of Long Covid symptoms. METHODS AND ANALYSIS This study is a longitudinal prospective cohort study of adults with Long Covid accessing 10 National Health Service (NHS) rehabilitation services in the UK. We aim to recruit 400 people from participating NHS sites. At referral to study, 6 weeks and 12 weeks, participants will complete demographic data (referral to study) and clinical outcome measures, including ecological momentary assessment (EMA) using personal mobile devices. EMA items are adapted from the COVID-19 Yorkshire Rehabilitation Scale items and include self-reported activities, symptoms and psychological factors. Passive activity data will be collected through wrist-worn sensors. We will use latent class growth models to identify trajectories of experience, potential phenotypes defined by co-occurrence of symptoms and inter-relationships between stressors, symptoms and participation in daily activities. We anticipate that n=300 participants provide 80% power to detect a 20% improvement in fatigue over 12 weeks in one class of patients relative to another. ETHICS AND DISSEMINATION The study was approved by the Yorkshire & The Humber-Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Findings will be disseminated in peer-reviewed publications and presented at conferences. TRIAL REGISTRATION NUMBER ISRCTN15022307.
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Affiliation(s)
- Maedeh Mansoubi
- NIHR Exeter Biomedical Research Center, Medical School, Faculty of Health and Life sciences, University of Exeter, Exeter, UK
- Medical School, University of Exeter, Exeter, UK
- Department of Public Health and Sport Sciences, University of Exeter, Exeter, UK
| | - Joanna Dawes
- Medical School, University of Exeter, Exeter, UK
| | | | | | - Johnny Collett
- Department of Sport, Health and Social Work, Oxford Brookes University, Oxford, UK
| | - Darren C Greenwood
- Academic Department of Rehabilitation Medicine, University of Leeds, Leeds, UK
| | - Leisle Ezekiel
- School of Health Sciences, University of Southampton, Southampton, UK
| | | | - Phaedra Leveridge
- Department of Public Health and Sport Sciences, University of Exeter, Exeter, UK
| | - Clare Rayner
- Patient Advisory Group (PAG) Representative, Leeds, UK
| | - Flo Read
- Department of Health and Community Sciences, University of Exeter, Exeter, UK
| | - Manoj Sivan
- Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, UK
| | | | - Tomas Ward
- Insight SFI Research Centre for Data Analytics, Dublin City University, Dublin, Ireland
| | - Brendan Delaney
- Faculty of Medicine, Department of Surgery & Cancer, Imperial College, London, UK
| | | | - Helen Dawes
- NIHR Exeter Biomedical Research Center, Medical School, Faculty of Health and Life sciences, University of Exeter, Exeter, UK
- Medical School, University of Exeter, Exeter, UK
- Department of Public Health and Sport Sciences, University of Exeter, Exeter, UK
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Dooley EE, Winkles JF, Colvin A, Kline CE, Badon SE, Diaz KM, Karvonen-Gutierrez CA, Kravitz HM, Sternfeld B, Thomas SJ, Hall MH, Gabriel KP. Method for Activity Sleep Harmonization (MASH): a novel method for harmonizing data from two wearable devices to estimate 24-h sleep-wake cycles. JOURNAL OF ACTIVITY, SEDENTARY AND SLEEP BEHAVIORS 2023; 2:8. [PMID: 37694170 PMCID: PMC10492590 DOI: 10.1186/s44167-023-00017-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/02/2023] [Indexed: 09/12/2023]
Abstract
Background Daily 24-h sleep-wake cycles have important implications for health, however researcher preferences in choice and location of wearable devices for behavior measurement can make 24-h cycles difficult to estimate. Further, missing data due to device malfunction, improper initialization, and/or the participant forgetting to wear one or both devices can complicate construction of daily behavioral compositions. The Method for Activity Sleep Harmonization (MASH) is a process that harmonizes data from two different devices using data from women who concurrently wore hip (waking) and wrist (sleep) devices for ≥ 4 days. Methods MASH was developed using data from 1285 older community-dwelling women (ages: 60-72 years) who concurrently wore a hip-worn ActiGraph GT3X + accelerometer (waking activity) and a wrist-worn Actiwatch 2 device (sleep) for ≥ 4 days (N = 10,123 days) at the same time. MASH is a two-tiered process using (1) scored sleep data (from Actiwatch) or (2) one-dimensional convolutional neural networks (1D CNN) to create predicted wake intervals, reconcile sleep and activity data disagreement, and create day-level night-day-night pairings. MASH chooses between two different 1D CNN models based on data availability (ActiGraph + Actiwatch or ActiGraph-only). MASH was evaluated using Receiver Operating Characteristic (ROC) and Precision-Recall curves and sleep-wake intervals are compared before (pre-harmonization) and after MASH application. Results MASH 1D CNNs had excellent performance (ActiGraph + Actiwatch ROC-AUC = 0.991 and ActiGraph-only ROC-AUC = 0.983). After exclusions (partial wear [n = 1285], missing sleep data proceeding activity data [n = 269], and < 60 min sleep [n = 9]), 8560 days were used to show the utility of MASH. Of the 8560 days, 46.0% had ≥ 1-min disagreement between the devices or used the 1D CNN for sleep estimates. The MASH waking intervals were corrected (median minutes [IQR]: -27.0 [-115.0, 8.0]) relative to their pre-harmonization estimates. Most correction (-18.0 [-93.0, 2.0] minutes) was due to reducing sedentary behavior. The other waking behaviors were reduced a median (IQR) of -1.0 (-4.0, 1.0) minutes. Conclusions Implementing MASH to harmonize concurrently worn hip and wrist devices can minimizes data loss and correct for disagreement between devices, ultimately improving accuracy of 24-h compositions necessary for time-use epidemiology.
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Affiliation(s)
- Erin E. Dooley
- Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - J. F. Winkles
- Epidemiology Data Center, The University of Pittsburgh, Pittsburgh, PA, USA
| | - Alicia Colvin
- Department of Epidemiology, The University of Pittsburgh, Pittsburgh, PA, USA
| | - Christopher E. Kline
- Department of Health and Human Development, The University of Pittsburgh, Pittsburgh, PA, USA
| | - Sylvia E. Badon
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Keith M. Diaz
- Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY, USA
| | | | - Howard M. Kravitz
- Department of Psychiatry and Behavioral Sciences and Department of Preventive Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Barbara Sternfeld
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - S. Justin Thomas
- Department of Psychiatry and Behavioral Neurobiology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Martica H. Hall
- Department of Psychiatry, School of Medicine, The University of Pittsburgh, Pittsburgh, PA, USA
| | - Kelley Pettee Gabriel
- Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL, USA
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Kristiansson E, Fridolfsson J, Arvidsson D, Holmäng A, Börjesson M, Andersson-Hall U. Validation of Oura ring energy expenditure and steps in laboratory and free-living. BMC Med Res Methodol 2023; 23:50. [PMID: 36829120 PMCID: PMC9950693 DOI: 10.1186/s12874-023-01868-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/16/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living. METHODS Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count. RESULTS Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (- 0.12 ± 0.28 MET), standing (- 0.27 ± 0.33 MET), fast walk (- 0.82 ± 1.92 MET) and very fast run (- 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: - 0.34-0.26; TEE: 362-494 kcal; AEE: - 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits. CONCLUSION This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count.
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Affiliation(s)
- Emilia Kristiansson
- Center for Health and Performance, Department of Food and Nutrition, and Sport Science Faculty of Education, University of Gothenburg, Gothenburg, Sweden
- Institute of Neuroscience and Physiology, Department of Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jonatan Fridolfsson
- Center for Health and Performance, Department of Food and Nutrition, and Sport Science Faculty of Education, University of Gothenburg, Gothenburg, Sweden
| | - Daniel Arvidsson
- Center for Health and Performance, Department of Food and Nutrition, and Sport Science Faculty of Education, University of Gothenburg, Gothenburg, Sweden
| | - Agneta Holmäng
- Institute of Neuroscience and Physiology, Department of Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Mats Börjesson
- Center for Health and Performance, Department of Food and Nutrition, and Sport Science Faculty of Education, University of Gothenburg, Gothenburg, Sweden
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Ulrika Andersson-Hall
- Institute of Neuroscience and Physiology, Department of Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
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Tam W, Alajlani M, Abd-alrazaq A. An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review (Preprint).. [DOI: 10.2196/preprints.42950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND
The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.
OBJECTIVE
In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.
METHODS
A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.
RESULTS
Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.
CONCLUSIONS
This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.
CLINICALTRIAL
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