Highlights
- •Dementia is an important issue with an ever greater impact on society.
- •Wearable devices are now widely discussed as useful pragmatic tools in neurodegenerative diseases.
- •Inertial wearables can quantify postural control and gait to provide useful digital biomarkers in dementia.
- •Lack of standards and lack of access to large data sets are limiting the use of wearables in modern medicine.
Abstract
Dementia is a critically important issue due to its wide impact on health services
as well as its personal and societal costs. Limitations exist for current dementia
protocols, and there are calls to introduce modern technology that facilitates the
addition of digital biomarkers to routine clinical practice. Wearable technology (wearables)
are nearly ubiquitous in everyday life, gathering discrete and continuous digital
data on habitual activities, but their utility in modern medicine remains low. Due
to advances in data analytics, wearables are now commonly discussed as pragmatic tools
to aid the diagnosis and treatment of a range of neurological disorders. Inertial
sensor-based wearables are one such technology; they offer a low-cost approach to
quantify routine movements that are fundamental to normal activities of daily living,
most notably postural control and gait. Here, we provide a narrative review of how
wearables are providing useful postural control and gait data to facilitate the capture
of digital markers to aid dementia research. We outline the history of wearables,
from their humble beginnings to their current use beyond the clinic, and explore their
integration into modern systems, as well as the ongoing standardisation and regulatory
efforts to integrate their use in clinical trials.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to MaturitasAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Prevalence of dementia subtypes: a 30-year retrospective survey of neuropathological reports.Arch. Gerontol. Geriatr. 2009; 49: 146-149
- Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity.Npj Digit. Med. 2019; 2: 9
- What can gait tell us about dementia? Review of epidemiological and neuropsychological evidence.Gait Posture. 2017; 53: 215-223
- The impact of mild cognitive impairment on gait and balance: a systematic review and meta-analysis of studies using instrumented assessment.Gerontology. 2017; 63: 67-83
- Risk factors for mild cognitive impairment, dementia and mortality: the sydney memory and ageing study.J. Am. Med. Dir. Assoc. 2017; 18: 388-395
- Research Roundtable.([cited 2019 22 May]; Available from:)2019www.alz.org/research/for_researchers/partnerships/research_roundtable
- Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer’s disease clinical trials.Alzheimers Dement (N Y). 2018; 4: 234-242
- Large-scale physical activity data reveal worldwide activity inequality.Nature. 2017; 547: 336-339
- Big data vs accurate data in health research: large-scale physical activity monitoring, smartphones, wearable devices and risk of unconscious bias.Med. Hypotheses. 2018; 119: 32-36
- Rivastigmine for gait stability in patients with Parkinson’s disease (ReSPonD): a randomised, double-blind, placebo-controlled, phase 2 trial.Lancet Neurol. 2016; 15: 249-258
- Fusion Motion Capture: Can technology be used to optimise alpine ski racing technique?.Impact of Technology on Sports Ii. 2008: 825-831
- Wavelet-based sit-to-stand detection and assessment of fall risk in older people using a wearable pendant device.IEEE Trans. Biomed. Eng. 2017; 64: 1602-1607https://doi.org/10.1109/TBME.2016.2614230
- Barometric pressure and triaxial accelerometry-based falls event detection.IEEE Trans. Neural Syst. Rehabil. Eng. 2010; 18: 619-627
- New methods to monitor stair ascents using a wearable pendant device reveal how behavior, fear, and frailty influence falls in octogenarians.IEEE Trans. Biomed. Eng. 2015; 62: 2595-2601
- Context focused older adult mobility and gait assessment.Conf. Proc. IEEE Eng. Med. Biol. Soc. 2015; 2015: 6943-6946
- iCap: instrumented assessment of physical capability.Maturitas. 2015; 82: 116-122
- ISway: a sensitive, valid and reliable measure of postural control.J. Neuroeng. Rehabil. 2012; 9 (p. 59-59)
- Increasing the use of mobile technology-derived endpoints in clinical trials.Clin. Trials. 2018; 15: 313-315
- Monitoring human health behaviour in one’s living environment: a technological review.Med. Eng. Phys. 2014; 36: 147-168
- Direct measurement of human movement by accelerometry.Med. Eng. Phys. 2008; 30: 1364-1386
- A review of data mining using big data in health informatics.J. Big Data. 2014; 1: 2
- Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study.PLoS One. 2017; 12 (p. e0169649)
- Identifying dementia outcomes in UK Biobank: a validation study of primary care, hospital admissions and mortality data.Eur. J. Epidemiol. 2019; 34: 557-565https://doi.org/10.1007/s10654-019-00499-1
- Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.Neuroimage. 2018; 166: 400-424
- Comparison between clinical gait and daily-life gait assessments of fall risk in older people.Geriatr. Gerontol. Int. 2017; 17: 2274-2282
- Spatio-temporal and kinematic gait analysis in patients with Frontotemporal dementia and Alzheimer’s disease through 3D motion capture.Gait Posture. 2017; 52: 312-317
- Big data are coming to psychiatry: a general introduction.Int. J. Bipolar Disord. 2015; 3 (p. 21-21)
- Challenges of big data analysis.Sci. Rev. 2014; 1: 293-314
- Big data to smart data in Alzheimer’s disease: the brain health modeling initiative to foster actionable knowledge.Alzheimers Dement. 2016; 12: 1014-1021
- Predictive big data analytics: a study of parkinson’s disease using large, complex, heterogeneous, incongruent, multi-source and incomplete observations.PLoS One. 2016; 11 (p. e0157077)
- Big data analytics: computational intelligence techniques and application areas.Technol. Forecast. Soc. Change. 2018; https://doi.org/10.1016/j.techfore.2018.03.024
- Is dementia research ready for big data approaches?.BMC Med. 2015; 13 (p. 145-145)
- DemaWare2: integrating sensors, multimedia and semantic analysis for the ambient care of dementia.Pervasive Mob. Comput. 2017; 34: 126-145
- Multi-modal activity recognition from egocentric vision, semantic enrichment and lifelogging applications for the care of dementia.J. Vis. Commun. Image Represent. 2018; 51: 169-190
- The design of intelligent in-home assistive technologies: assessing the needs of older adults with dementia and their caregivers.Gerontechnology. 2011; 10: 169-182
- Vestibular function assessment using the NIH Toolbox.Neurology. 2013; 80: S25-31
- A comparison of accelerometry and center of pressure measures during computerized dynamic posturography: a measure of balance.Gait Posture. 2011; 33: 594-599
- Human balance and posture control during standing and walking.Gait Posture. 1995; 3: 193-214
- Gait and cognition: a complementary approach to understanding brain function and the risk of falling.J. Am. Geriatr. Soc. 2012; 60: 2127-2136
- Does the evaluation of gait quality during daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings.Neurorehabil. Neural Repair. 2013; 27: 742-752
- Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach.J. Gerontol. A Biol. Sci. Med. Sci. 2013; 68: 820-827
- Moving forward on gait measurement: toward a more refined approach.Mov. Disord. 2013; 28: 1534-1543
- Towards holistic free-living assessment in Parkinson's disease: unification of gait and fall algorithms with a single accelerometer.2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2016
- Postural stability in older adults with Alzheimer disease.Phys. Ther. 2017; 97: 290-309
- Postural stability analysis with inertial measurement units in Alzheimer’s disease.Dement. Geriatr. Cogn. Dis. Extra. 2014; 4: 22-30
- Compensatory postural adjustments in an oculus virtual reality environment and the risk of falling in Alzheimer’s disease.Dement. Geriatr. Cogn. Dis. Extra. 2016; 6: 252-267
- Sensor-based balance training with motion feedback in people with mild cognitive impairment.J. Rehabil. Res. Dev. 2016; 53: 945-958
- Application of machine learning in postural control kinematics for the diagnosis of Alzheimer’s disease.Comput. Intell. Neurosci. 2016; 2016 (p. 3891253)
- Gait and balance analysis for patients with Alzheimer’s disease using an inertial-sensor-based wearable instrument.IEEE J. Biomed. Health Inform. 2014; 18: 1822-1830
- The brain map of gait variability in aging, cognitive impairment and dementia-a systematic review.Neurosci. Biobehav. Rev. 2017; 74: 149-162
- The trajectory of gait speed preceding mild cognitive impairment.Arch. Neurol. 2010; 67: 980-986
- Motoric cognitive risk syndrome and the risk of dementia.J. Gerontol. A Biol. Sci. Med. Sci. 2013; 68: 412-418
- Motor and cognitive trajectories before dementia: results from gait and brain study.J. Am. Geriatr. Soc. 2018; 66: 1676-1683
- Gait speed and decline in gait speed as predictors of incident dementia.J. Gerontol. A Biol. Sci. Med. Sci. 2017; 72: 655-661
- Slow gait speed is associated with executive function decline in older people with mild to moderate dementia: a one year longitudinal study.Arch. Gerontol. Geriatr. 2017; 73: 148-153
- Gait phenotype from mild cognitive impairment to moderate dementia: results from the GOOD initiative.Eur. J. Neurol. 2016; 23: 527-541
- The role of executive function and attention in gait.Mov. Disord. 2008; 23: 329-342
- Executive function and gait in older adults with cognitive impairment.J. Gerontol. A Biol. Sci. Med. Sci. 2008; 63: 1350-1355
- The effect of walking path configuration on gait in adults with Alzheimer’s dementia.Gait Posture. 2018; 64: 226-229
- The relationship between gait dynamics and future cognitive decline: a prospective pilot study in geriatric patients.Int. Psychogeriatr. 2018; 30: 1301-1309
- The complexity of daily life walking in older adult community-dwelling fallers and non-fallers.J. Biomech. 2016; 49: 1420-1428
- Ambulatory fall-risk assessment: amount and quality of daily-life gait predict falls in older adults.J. Gerontol. A Biol. Sci. Med. Sci. 2015; 70: 608-615
- Whole-day gait monitoring in patients with Alzheimer’s disease: a relationship between attention and gait cycle.J Alzheimers Dis Rep. 2017; 1: 1-8
- Gait in mild Alzheimer’s disease: feasibility of multi-center measurement in the clinic and home with body-worn sensors: a pilot study.J. Alzheimers Dis. 2018; 63: 331-341
- Measurement of accelerometry-based gait parameters in people with and without dementia in the field: a technical feasibility study.Methods Inf. Med. 2013; 52: 319-325
- Sensor-derived physical activity parameters can predict future falls in people with dementia.Gerontology. 2014; 60: 483-492
- Older people with dementia have reduced daily-life activity and impaired daily-life gait when compared to age-sex matched controls.J. Alzheimer Dis. 2019; (In Press)
- Accelerometer-based gait assessment: pragmatic deployment on an international scale.2016 IEEE Statistical Signal Processing Workshop (SSP). 2016
- Delivering home healthcare through a Cloud-based Smart Home Environment (CoSHE).Future Gener. Comput. Syst. 2018; 81: 129-140
- Dementia prevention, intervention, and care.Lancet. 2017; 390: 2673-2734
- Utilizing a wristband sensor to measure the stress level for people with dementia.Sensors. 2016; 16: 1989
- Reliability of wearable sensors to detect agitation in patients with dementia: a pilot study.Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology. 2018;
- A real-time linked dataspace for the internet of things: enabling “pay-as-you-go” data management in smart environments.Future Gener. Comput. Syst. 2019; 90: 405-422
- Wearables, implants, and internet of things: the technology needs in the evolving landscape.IEEE Trans. Multi-Scale Comput. Syst. 2016; 2: 123-128
- What does big data mean for wearable sensor systems?: contribution of the IMIA wearable sensors in healthcare WG.Yearb. Med. Inform. 2014; 9: 135-142
- Use of mobile devices to measure outcomes in clinical research, 2010–2016: a systematic literature review.Digit. Biomark. 2018; 2: 11-30
- Analysis of free-living gait in older adults with and without parkinson’s disease and with and without a history of falls: identifying generic and disease-specific characteristics.J. Gerontol. A Biol. Sci. Med. Sci. 2017; (p. glx254-glx254)
- Isosorbide mononitrate in heart failure with preserved ejection fraction.N. Engl. J. Med. 2015; 373: 2314-2324
- A systematic review of feasibility studies promoting the use of mobile technologies in clinical research.Npj Digit. Med. 2019; (In Press)
- Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia.Alzheimers Dement. 2018; 14: 1216-1231
- Preferred placement and usability of a smart textile system vs. inertial measurement units for activity monitoring.Sensors (Basel). 2018; 18
- A review of in-body biotelemetry devices: implantables, ingestibles, and injectables.IEEE Trans. Biomed. Eng. 2017; 64: 1422-1430
- Using smart socks and rhythmic haptic cues to stimulate the foot arch can reduce gait variabiltiy during a freezing of gait elicitation task.First International Motor Impairment Conference. 2018;
- Feasibility and effects of home-based smartphone-delivered automated feedback training for gait in people with Parkinson’s disease: a pilot randomized controlled trial.Parkinsonism Relat. Disord. 2016; 22: 28-34
Article info
Publication history
Published online: May 24, 2019
Accepted:
May 23,
2019
Received in revised form:
May 22,
2019
Received:
March 12,
2019
Identification
Copyright
© 2019 Elsevier B.V. All rights reserved.