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      Analyzing the Use of Accelerometers as a Method of Early Diagnosis of Alterations in Balance in Elderly People: A Systematic Review

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          Abstract

          Alterations of balance are a growing public health problem as they affect one in three adults over the age of 65, and one in two over the age of 80. Identifying the factors that affect postural stability is essential in designing specific interventions to maintain the independence and mobility of older people. The aim of this review was to understand the use of accelerometers in order to assess the balance in older people. Analyzing the most appropriate evaluation methodology and protocolizing it will optimize the processes of early identification of balance alterations. However, quantitative assessment methods of balance are usually limited to a laboratory environment, a factor that can be overcome by accelerometers. A systematic search was carried out across eight databases where accelerometers were employed to assess balance in older people. Articles were excluded if they focused on sensor design and did not measure balance or apply the technology on targeted participants. A total of 19 articles were included for full-text analysis, where participants took part in the balance evaluation monitored by accelerometers. The analysis of spatio-temporal parameters and the magnitude of the accelerations recorded by the devices were the most common study variables. Accelerometer usage has potential to positively influence interventions based on physical exercise to improve balance and prevent falls in older people.

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          Most cited references97

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          Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test.

          This study examined the sensitivity and specificity of the Timed Up & Go Test (TUG) under single-task versus dual-task conditions for identifying elderly individuals who are prone to falling. Fifteen older adults with no history of falls (mean age=78 years, SD=6, range=65-85) and 15 older adults with a history of 2 or more falls in the previous 6 months (mean age=86.2 years, SD=6, range=76-95) participated. Time taken to complete the TUG under 3 conditions (TUG, TUG with a subtraction task [TUGcognitive], and TUG while carrying a full cup of water [TUGmanual]) was measured. A multivariate analysis of variance and discriminant function and logistic regression analyses were performed. The TUG was found to be a sensitive (sensitivity=87%) and specific (specificity=87%) measure for identifying elderly individuals who are prone to falls. For both groups of older adults, simultaneous performance of an additional task increased the time taken to complete the TUG, with the greatest effect in the older adults with a history of falls. The TUG scores with or without an additional task (cognitive or manual) were equivalent with respect to identifying fallers and nonfallers. The results suggest that the TUG is a sensitive and specific measure for identifying community-dwelling adults who are at risk for falls. The ability to predict falls is not enhanced by adding a secondary task when performing the TUG.
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            Usefulness of the Berg Balance Scale in stroke rehabilitation: a systematic review.

            In a recent study of 655 physical therapists working with a stroke population, the Berg Balance Scale (BBS) was identified as the most commonly used assessment tool across the continuum of stroke rehabilitation. Given the widespread popularity of the BBS, it is important to critically appraise the BBS for its use with a stroke population. The purposes of this study were to conduct a systematic review of the psychometric properties of the BBS specific to stroke and to identify strengths and weaknesses in its usefulness for stroke rehabilitation. Twenty-one studies examining the psychometric properties of the BBS with a stroke population were retrieved. Internal consistency was excellent (Cronbach alpha=.92-.98) as was interrater reliability (intraclass correlation coefficients [ICCs]=.95-.98), intrarater reliability (ICC=.97), and test-retest reliability (ICC=.98). Sixteen studies focused on validity and generally found excellent correlations with the Barthel Index, the Postural Assessment Scale for Stroke Patients, Functional Reach Test, the balance subscale of Fugl-Meyer Assessment, the Functional Independence Measure, the Rivermead Mobility Index (except for weight shift and step-up items), and gait speed. Berg Balance Scale scores predicted length of stay, discharge destination, motor ability at 180 days poststroke, and disability level at 90 days, but these scores were not predictive of falls. Eight studies focused on responsiveness; all reported moderate to excellent sensitivity. Three studies found floor or ceiling effects. The BBS is a psychometrically sound measure of balance impairment for use in poststroke assessment. Given the floor and ceiling effects, clinicians may want to use the BBS in conjunction with other balance measures.
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              Scaling gait data to body size

              At L Hof (1996)
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                09 September 2019
                September 2019
                : 19
                : 18
                : 3883
                Affiliations
                [1 ]Faculty of Physical Therapy, University of Vigo; Campus a Xunqueira, s/n, 36156 Pontevedra, Spain
                [2 ]Faculty of Education and Sport Sciences, University of Vigo; Campus a Xunqueira, s/n, 36156 Pontevedra, Spain
                Author notes
                [* ]Correspondence: jlsoidan@ 123456uvigo.es
                Author information
                https://orcid.org/0000-0001-8795-6731
                Article
                sensors-19-03883
                10.3390/s19183883
                6767154
                31505828
                be758d60-3496-43d6-aa78-6e3ad22c39a4
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 02 August 2019
                : 06 September 2019
                Categories
                Review

                Biomedical engineering
                wearables,kinematics,sensors,motion analysis,postural balance
                Biomedical engineering
                wearables, kinematics, sensors, motion analysis, postural balance

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