Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review

Breasail, Mícheál Ó; Biswas, Bijetri; Smith, Matthew D; Mazhar, Khadimul Anam; Tenison, Emma; Cullen, Anisha; Lithander, Fiona E; Roudaut, Anne; Henderson, Emily

doi:10.3390/s21248261

Cited by 30 publications

(21 citation statements)

References 75 publications

(95 reference statements)

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“…Technologies to support older adults and their caregivers at home is an area of strong growth for design. Policy makers, providers, and family caregivers are looking to technological solutions and investing in artificial intelligence (AI) and other technologies that monitor activity and safety of older adults, with a particular focus on Alzheimer’s disease and related dementias (e.g., the AAL Joint Platform of the European Union and AGE-WELL Initiative in Canada) [ 25 , 69 , 91 , 127 , 129 , 138 ]. In the U.S., health care payment systems including Medicaid are now beginning to cover a range of technologies that have remote monitoring functions [ 17 , 28 ], foretelling significant growth as lack of third party reimbursement had impeded startups in the aging space [ 19 , 129 ].…”

Section: Background and Related Workmentioning

confidence: 99%

“…There is particular excitement about the potential capability of detecting cognitive change using predictive linguistic markers [ 18 , 112 ], which involves audio capture in the home. Location tracking is becoming ubiquitous [ 25 , 48 ], and the problem of social isolation during COVID is further spurring the evolution of telepresence [ 64 , 87 ]. These three categories of technologies represent distinct types of data collection that may be carried out passively with little room for direct control by the older adult.…”

Section: Background and Related Workmentioning

confidence: 99%

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Control Matters in Elder Care Technology: Evidence and Direction for Designing It In

Berridge

Zhou

Lazar

et al. 2022

Designing Interactive Systems Conference

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Studies fnd that older adults want control over how technologies are used in their care, but how it can be operationalized through design remains to be clarifed. We present fndings from a large survey (n=825) of a well-characterized U.S. online cohort that provides actionable evidence of the importance of designing for control over monitoring technologies. This uniquely large, age-diverse sample allows us to compare needs across age and other characteristics with insights about future users and current older adults (n=496 >64), including those concerned about their own memory loss (n=201). All fve control options, which are not currently enabled, were very or extremely important to most people across age. Findings indicate that comfort with a range of care technologies is contingent on having privacy-and other control-enabling options. We discuss opportunities for design to meet these user needs that demand course correction through attentive, creative work.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Control Matters in Elder Care Technology: Evidence and Direction for Designing It In

Berridge

Zhou

Lazar

et al. 2022

Designing Interactive Systems Conference

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“…The progression of PD also makes it important to look at PA over time in the same cohorts of individuals, and how it affects HRQOL, including falls and depression. The data acquired from sensors, such as accelerometers, have increased as the cost of these devices has progressively decreased [ 73 , 74 , 75 ], but lack standardization and links to health-related measures. One study found that involuntary movements may increase the measures of PA [ 76 ], and may need to be factored in.…”

Section: Physical Activity Measures Among People With Condition-speci...mentioning

confidence: 99%

Physical Activity and Public Health among People with Disabilities: Research Gaps and Recommendations

Heath

Levine

2022

IJERPH

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Physical activity has become an integral component of public health systems modeling the public health core functions of assessment, policy development, and assurance. However, people with disabilities have often not been included in public health efforts to assess, develop policies, or evaluate the impact of physical activity interventions to promote health and prevent disease among people with disabilities. Addressing the core function of assessment, current physical activity epidemiology, and surveillance among people with disabilities across the globe highlights the paucity of surveillance systems that include physical activity estimates among people with disabilities. The status of valid and reliable physical activity measures among people with condition-specific disabilities is explored, including self-report measures along with wearable devices, and deficiencies in measurement of physical activity. The core functions of policy development and assurance are described in the context of community-based intervention strategies to promote physical activity among people with disabilities. The identification of research gaps in health behavior change, policy, and environmental approaches to promoting physical activity among people with disabilities is explored, along with recommendations based on the principles of inclusive and engaged research partnerships between investigators and the members of the disability community.

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“…Therefore, there is a need for the development of accurate, reliable and sensitive tools for the quanti cation of gait and mobility in real-life (8,9). Digital health technology, including body-worn or wearable devices (WD), offers a way forward by providing digital outcomes to remotely measure and monitor gait (10,11), a fundamental component of mobility (12,13). Nonetheless, due to several persisting challenges in this eld, current tools and techniques are still in their infancy.…”

Section: Introductionmentioning

confidence: 99%

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Micó-Amigo

Bonci

Paraschiv-Ionescu

et al. 2022

Preprint

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Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices (WD) and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection (GSD), foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods: Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture (PFF), 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 hours in the real-world, using a WD worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the WD. We assessed and validated three algorithms for GSD, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results: We identified two cohort-specific top performing algorithms for GSD and CAD, and a single best for ICD and SL. GSD best algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (PFF). Algorithms’ performances were lower for short WBs; slower gait speeds (<0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. Conclusions: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findingsshowed that the choice of algorithm for estimation of GSD and CAD DMOs should be cohort-specific (e.g., slow walkers and with gait impairments). Short WB length and slow walking speed worsened algorithms’ performances. Trial registration: ISRCTN – 12246987.

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Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review

Cited by 30 publications

References 75 publications

Control Matters in Elder Care Technology: Evidence and Direction for Designing It In

Control Matters in Elder Care Technology: Evidence and Direction for Designing It In

Physical Activity and Public Health among People with Disabilities: Research Gaps and Recommendations

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

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