Frailty and technology: a systematic review of gait analysis in those with frailty

Frailty and Parkinson's disease: the role of diabetes mellitus

Parkinson's disease (PD) is a chronic neurodegenerative disease associated with a progressive loss of dopaminergic neurons, clinically characterized by motor and non-motor signs. Frailty is a clinical condition of increased vulnerability and negative health outcomes due to the loss of multiple physiological reserves. Chronic hyperglycemia and insulin resistance, which characterize diabetes mellitus (DM), have been reported to alter dopaminergic activity, increase the risk of PD, and influence the development of frailty. Even though diabetes may facilitate the development of frailty in patients with PD, this relationship is not established and a revision of the current knowledge is necessary. Furthermore, the synergy between DM, PD, and frailty may drive clinical complexity, worse outcomes, and under-representation of these populations in the research. In this review, we aimed to discuss the role of diabetes in the development of frailty among patients with PD. We summarized the clinical characteristics and outcomes of patients with concomitant DM, PD, and frailty. Finally, interventions to prevent frailty in this population are discussed.

Characteristics of cadence during continuous walking in daily life

Despite the acknowledged relationship between the usual (preferred) walking speed (UWS) and health, there is currently no practical method available to reliably and accurately detect slight changes in UWS. This study aimed to explore whether either of the following two phenomena occurs during continuous daily walking in various periods: (a) Similarity between the most frequent cadences in the two periods. (b) The occurrence of the most frequent cadence in at least one of the two periods during the other period, with a frequency close to that of the most frequent cadence. In August 2021, invitations to participate in the study were extended via email to participants that took part in the Japan COVID-19 and Society Internet Surveys (JACSIS). A mobile phone application that collected step data during continuous walking was provided to the participants, and data were collected from December 1, 2021, to January 31, 2022. While 1022 participants installed the phone application, only 505 had measurement data for ten days or more in each of the two months of the study duration. The cadence during continuous walking was automatically measured daily from 05:00 to 21:00. Most participants exhibited at least one of the phenomena mentioned above, confirming a common, notably frequent, invariant cadence over time. Overall, this method allows for the identification of minor reductions and lower bounds of decline in UWS. This study illustrates the potential for tracking a decreasing trend in UWS. Early detection of a downward trend permits individuals to take timely remedial action, as recovery is relatively easy, and the confirmation of even a slight recovery bolsters recovery motivation.

Interactive boxing-cycling on frailty and activity limitations in frail and prefrail older adults: A randomized controlled trial

BACKGROUND

Frailty is common among older adults, often associated with activity limitations during physical and walking tasks. The interactive boxing-cycling combination has the potential to be an innovative and efficient training method, and our hypothesis was that interactive boxing-cycling would be superior to stationary cycling in improving frailty and activity limitations in frail and prefrail older adults.

OBJECTIVE

To examine the impact of interactive boxing-cycling on frailty and activity limitations in frail and prefrail older adults compared to stationary cycling.

MATERIALS AND METHODS

A single-blinded randomized controlled trial. Forty-five participants who met at least one frailty phenotype criteria were randomly assigned to receive either interactive boxing-cycling (n = 23) or stationary-cycling (n = 22) for 36 sessions over 12 weeks. The interactive boxing-cycling was performed on a cycle boxer bike with an interactive boxing panel fixed in front of the bike. The primary outcomes were frailty status, including score and phenotypes. Secondary outcomes included activity limitations during physical and walking tasks. The pre- and post-intervention data of both groups were analyzed using a repeated measures two-way ANOVA.

RESULTS

Both types of cycling significantly improved frailty scores (p<0.001). Interactive boxing-cycling was more effective than stationary cycling in reversing the frailty phenotype of muscle weakness (p = 0.03, odds ratio 9.19) and demonstrated greater improvements than stationary cycling in arm curl (p = 0.002, η2=0.20), functional reach (p = 0.001, η2=0.22), and grip strength (p = 0.02, η2=0.12) tests. Additionally, interactive boxing-cycling exhibited a greater effect on gait speed (p = 0.02, η2=0.13) and gait variability (p = 0.01, η2=0.14) during dual-task walking.

CONCLUSION

In frail and prefrail older adults, interactive boxing-cycling effectively improves frailty but is not superior to stationary cycling. However, it is more effective at improving certain activity limitations.

REGISTRATION NUMBER

TCTR20220328001.

Identification of Potential Blood-Based Biomarkers for Frailty by Using an Integrative Approach

INTRODUCTION

Although frailty is a geriatric syndrome that is associated with disability, hospitalization, and mortality, it can be reversible and preventable with the appropriate interventions. Additionally, as the current diagnostic criteria for frailty include only physical, psychological, cognitive, and social measurements, there is a need for promising blood-based molecular biomarkers to aid in the diagnosis of frailty.

METHODS

To identify candidate blood-based biomarkers that can enhance current diagnosis of frailty, we conducted a comprehensive analysis of clinical data, messenger RNA-sequencing (RNA-seq), and aging-related factors using a total of 104 older adults aged 65-90 years (61 frail subjects and 43 robust subjects) in a cross-sectional case-control study.

RESULTS

We identified two candidate biomarkers of frailty from the clinical data analysis, nine from the RNA-seq analysis, and six from the aging-related factors analysis. By using combinations of the candidate biomarkers and clinical information, we constructed risk prediction models. The best models used combinations that included skeletal muscle mass index measured by dual-energy X-ray absorptiometry (adjusted p = 0.026), GDF15 (adjusted p = 1.46E-03), adiponectin (adjusted p = 0.012), CXCL9 (adjusted p = 0.011), or apelin (adjusted p = 0.020) as the biomarker. These models achieved a high area under the curve of 0.95 in an independent validation cohort (95% confidence interval: 0.79-0.97). Our risk prediction models showed significantly higher areas under the curve than did models constructed using only basic clinical information (Welch's t test p < 0.001).

CONCLUSION

All five biomarkers showed statistically significant correlations with components of the frailty diagnostic criteria. We discovered several potential biomarkers for the diagnosis of frailty. Further refinement may lead to their future clinical use.

Effects of mirror neuron activation therapies on functionality in older adults: Systematic review and meta-analysis

PURPOSE

To identify the effects of mirror neuron activation (MNAT) combined or not with physical exercise (PE) in healthy older adults, on functionality, balance, gait velocity and risk of falls.

METHODS

A systematic electronic search was performed in PubMed/MEDLINE, Cochrane, and Embase databases.

RESULTS

Thirteen randomized controlled trials were included in the qualitative analysis, and eleven in the quantitative analysis. All studies showed fair to high quality and the most frequent high-risk bias was "Blinding of participants and personnel". Compared to the control condition, higher improvement was shown in older people who received MNAT, on functionality (1.57 [0.57, 2.62], balance (1.95 [1.32, 2.572]), and gait velocity (1.20 [0.30, 2.11]). Compared to PE, MNAT combined with PE does not improve functionality. More studies are needed to assess MNAT effectiveness in the rest of the outcomes.

CONCLUSIONS

Neuron system activation through MNAT improves relevant abilities in older adults, with better results when including functional activities. However, the beneficial effects on these variables of adding MNAT to a PE program are controversial.

A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups

Falls and frailty status are often associated with a decline in physical capacity and multifactorial assessment is highly recommended. Based on the functional and biomechanical parameters measured during clinical tests with an accelerometer integrated into smart eyeglasses, the purpose was to characterize a population of older adults through an unsupervised analysis into different physical performance groups. A total of 84 participants (25 men and 59 women) over the age of sixty-five (age: 74.17 ± 5.80 years; height: 165.70 ± 8.22 cm; body mass: 68.93 ± 13.55 kg) performed a 30 s Sit-to-Stand test, a six-minute walking test (6MWT), and a 3 m Timed Up and Go (TUG) test. The acceleration data measured from the eyeglasses were processed to obtain six parameters: the number of Sit-to-Stands, the maximal vertical acceleration values during Sit-to-Stand movements, step duration and length, and the duration of the TUG test. The total walking distance covered during the 6MWT was also retained. After supervised analyses comparison (i.e., ANOVAs), only one of the parameters (i.e., step length) differed between faller groups and no parameters differed between frail and pre-frail participants. In contrast, unsupervised analysis (i.e., clustering algorithm based on K-means) categorized the population into three distinct physical performance groups (i.e., low, intermediate, and high). All the measured parameters discriminated the low- and high-performance groups. Four of the measured parameters differentiated the three groups. In addition, the low-performance group had a higher proportion of frail participants. These results are promising for monitoring activities in older adults to prevent the decline of physical capacities.

Turning and sitting movements during timed up and go tests predict deterioration of physical function in middle-aged adults

BACKGROUND

Deterioration of physical function in middle-aged adults is a significant challenge that can lead to increased risk of future falls. However, a screening method for the functional decline in middle-aged adults has not been established.

RESEARCH QUESTION

To evaluate the relationship between biomechanical parameters assessed by the timed up and go test (TUG) and locomotive syndrome (LS) in middle-aged adults.

METHODS

The inclusion criterion was: under 65 years of age. A total of 97 volunteers (mean age 51.1 years) participated in this study. An LS test was performed, including a 2-step test, a stand-up test, and a 25-question Geriatric Locomotive Function Scale. The TUG was measured using inertial measurement units (IMUs) at comfortable and fast speeds. We then determined the minimum values for anterior-posterior acceleration and angular velocity around the medial-lateral axis, as well as the maximum values of angular velocity around the vertical axis for the upper trunk and sacrum in a TUG phase.

RESULTS

Angular velocity around the vertical axis for upper trunk and sacrum were significantly smaller in LS than non-LS in the turn phase of both speed conditions. For the fast speed condition, the minimum anterior-posterior acceleration for sacrum was greater in LS than in the non-LS condition for the stand-to-sit phase. Angular velocity around the vertical axis for turning and anterior-posterior acceleration from sitting were associated with detection of LS.

SIGNIFICANCE

Turning and sitting movements during TUG should be observed using IMU to screen for physical function decline in middle aged adults.

Movement patterns during gait initiation in older adults with various stages of frailty: a biomechanical analysis

BACKGROUND

Gait initiation is challenging for older individuals with poor physical function, particularly for those with frailty. Frailty is a geriatric syndrome associated with increased risk of illness, falls, and functional decline. This study examines whether spatial and temporal parameters of gait initiation differ between groups of older adults with different levels of frailty, and whether fear of falling, and balance ability are correlated with the height of lifting the food during gait initiation.

METHODS

Sixty-one individuals aged > 65 years, classified by Fried frailty phenotype, performed five self-paced gait initiation trials. Data was collected using a three-dimensional passive optical motion capture system, consisting of 10 cameras with the ability to perceive reflective markers, and two force plates. The total duration of gait initiation and the duration of its four sub-phases, the first step length, and the maximum foot clearance during the first step were derived, and compared statistically between groups. Additionally, an association analysis was conducted between foot clearance and fear of falling, and confidence in balance in older individuals.

RESULTS

Frail individuals had significantly longer unloading durations, and total durations of gait initiation compared to non-frail older adults. Additionally, they had shorter first step lengths compared to non-frail older adults. Pre-frail older adults also showed shorter steps compared to the non-frail group. However, there were no significant differences between groups for the maximum foot clearance during the first step. Nevertheless, the maximum foot clearance of older individuals correlated significantly with their fear of falling and confidence in balance.

CONCLUSION

Older adults with reduced physical function and signs of frailty mainly display longer duration of gait initiation and decreased first step length compared to non-frail older adults. The release phase is decreased as the double support phase is prolonged in frail patients. This information can guide the development of specialized exercise programs to improve mobility in this challenging motion between static and dynamic balance.

Wrist-to-Tibia/Shoe Inertial Measurement Results Translation Using Neural Networks

Most of the established gait evaluation methods use inertial sensors mounted in the lower limb area (tibias, ankles, shoes). Such sensor placement gives good results in laboratory conditions but is hard to apply in everyday scenarios due to the sensors' fragility and the user's comfort. The paper presents an algorithm that enables translation of the inertial signal measurements (acceleration and angular velocity) registered with a wrist-worn sensor to signals, which would be obtained if the sensor was worn on a tibia or a shoe. Four different neural network architectures are considered for that purpose: Dense and CNN autoencoders, a CNN-LSTM hybrid, and a U-Net-based model. The performed experiments have shown that the CNN autoencoder and U-Net can be successfully applied for inertial signal translation purposes. Estimating gait parameters based on the translated signals yielded similar results to those obtained based on shoe-sensor signals.

Step length is associated with comprehensive frailty status in community-dwelling older people

AIM

To examine spatial-temporal gait parameters associated with comprehensive frailty status in community-dwelling, independent older people.

METHODS

This cross-sectional study included 225 older people (≥65 years) living independently in the community. The Kihon Checklist was used to assess comprehensive frailty status, and participants were classified as robust, pre-frailty, or frailty. A sheet-type plantar pressure sensor was used to evaluate the following gait parameters, which were extracted at the usual and fast pace: gait speed, cadence, stride time, step length-to-height ratio (step length/height), step width, stance duration, double-support time, and variability of each gait parameter. Ordinal logistic regression analysis adjusted for confounding factors was performed to determine the association between gait parameters and frailty status. In addition, the ability to discriminate frailty status was evaluated by receiver operating characteristic (ROC) curve analysis for gait parameters that were significantly associated with frailty status.

RESULTS

Frailty status was pre-frailty in 79 (35.1%) and frailty in 30 (13.3%) participants. Ordinal logistic regression analysis showed a significant association of step length/height (%) at both usual and fast pace with frailty status, even after adjustment for confounding factors (usual pace: odds ratio [OR] = 0.93 [95% confidence interval, CI: 0.86-0.99]; fast pace: OR = 0.93 [95% CI: 0.87-0.99]). ROC curve analysis identified step length/height at fast pace in women as the best discriminator between frailty and non-frailty (area under the curve 0.69, cut-off value 43.4%, sensitivity 50%, specificity 82%).

CONCLUSIONS

Step length appears to be a useful gait parameter for discriminating frailty status in community-dwelling, independent older people. Geriatr Gerontol Int 2024; 24: 18-24.

Spatiotemporal gait parameter fluctuations in older adults affected by mild cognitive impairment: comparisons among three cognitive dual-task tests

BACKGROUNDS

Gait disorder is associated with cognitive functional impairment, and this disturbance is more pronouncedly when performing additional cognitive tasks. Our study aimed to characterize gait disorders in mild cognitive impairment (MCI) under three dual tasks and determine the association between gait performance and cognitive function.

METHODS

A total of 260 participants were enrolled in this cross-sectional study and divided into MCI and cognitively normal control. Spatiotemporal and kinematic gait parameters (31 items) in single task and three dual tasks (serial 100-7, naming animals and words recall) were measured using a wearable sensor. Baseline characteristics of the two groups were balanced using propensity score matching. Important gait features were filtered using random forest method and LASSO regression and further described using logistic analysis.

RESULTS

After matching, 106 participants with MCI and 106 normal controls were recruited. Top 5 gait features in random forest and 4 ~ 6 important features in LASSO regression were selected. Robust variables associating with cognitive function were temporal gait parameters. Participants with MCI exhibited decreased swing time and terminal swing, increased mid stance and variability of stride length compared with normal control. Subjects walked slower when performing an extra dual cognitive task. In the three dual tasks, words recall test exhibited more pronounced impact on gait regularity, velocity, and dual task cost than the other two cognitive tests.

CONCLUSION

Gait assessment under dual task conditions, particularly in words recall test, using portable sensors could be useful as a complementary strategy for early detection of MCI.

Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders

BACKGROUND

Early recognition of older people at risk of undesirable clinical outcomes is vital in preventing future disabling conditions. Here, we report the prognostic performance of an electronic frailty index (eFI) in comparison with traditional tools among nonfrail and prefrail community-dwelling older adults. The study is to investigate the predictive utility of a deficit-accumulation eFI in community elders without overt frailty.

METHODS

Participants aged 65-80 years with a Clinical Frailty Scale of 1-3 points were recruited and followed for 2 years. The eFI score and Fried's frailty scale were determined by using a semiautomated platform of self-reported questionnaires and objective measurements which yielded cumulative deficits and physical phenotypes from 80 items of risk variables. Kaplan-Meier method and Cox proportional hazards regression were used to analyze the severity of frailty in relation to adverse outcomes of falls, emergency room (ER) visits and hospitalizations during 2 years' follow-up.

RESULTS

A total of 427 older adults were evaluated and dichotomized by the median FI score. Two hundred and sixty (60.9%) and 167 (39.1%) elders were stratified into the low- (eFI ≤ 0.075) and the high-risk (eFI > 0.075) groups, respectively. During the follow-up, 77 (47.0%) individuals developed adverse events in the high-risk group, compared with 79 (30.5%) in the low-risk group (x2, p = 0.0006). In multivariable models adjusted for age and sex, the increased risk of all three events combined in the high- vs. low-risk group remained significant (adjusted hazard ratio (aHR) = 3.08, 95% confidence interval (CI): 1.87-5.07). For individual adverse event, the aHRs were 2.20 (CI: 1.44-3.36) for falls; 1.67 (CI: 1.03-2.70) for ER visits; and 2.84 (CI: 1.73-4.67) for hospitalizations. Compared with the traditional tools, the eFI stratification (high- vs. low-risk) showed better predictive performance than either CFS rating (managing well vs. fit to very fit; not discriminative in hospitalizations) or Fried's scale (prefrail to frail vs. nonfrail; not discriminative in ER visits).

CONCLUSION

The eFI system is a useful frailty tool which effectively predicts the risk of adverse healthcare outcomes in nonfrail and/or prefrail older adults over a period of 2 years.

Cerebral Cortex Activation and Gait Performance between Healthy and Prefrail Older Adults during Cognitive and Walking Tasks

Pre-frailty is a transitional stage between health and frailty. Previous studies have demonstrated that individuals with pre-frailty experience declines in cognitive and gait performances compared with healthy individuals. However, the basic neural mechanism underlying this needs to be clarified. In this cross-sectional study, twenty-one healthy older adults and fifteen with pre-frailty underwent three conditions, including a single cognitive task (SC), single walking task (SW), and dual-task (DT), while cortical hemodynamic reactions were measured using functional near-infrared spectroscopy (fNIRS). The prefrail group (PG) showed a significantly lower activation of the left dorsolateral prefrontal cortex (L-DLPFC) than the healthy group (HG) when performing SC (p < 0.05). The PG showed a significantly lower Timed Up and Go test and step speed than the HG during SW (p < 0.05). The coefficient of variation (CV) of the step length of the PG was significantly higher than that of the HG when performing DT (p < 0.05). No significant correlation in cerebral cortex activation and gait parameters in the HG when performing SW and DT was noted (p > 0.05). Participants of the PG with a higher oxygenated area in the left anterior prefrontal cortex (L-APFC) had a lower step frequency during SW (r = -0.533, p = 0.041), and so did the following indicators of the PG during DT: L-APFC and step speed (r = -0.557, p = 0.031); right anterior prefrontal cortex and step speed (r = -0.610, p = 0.016); left motor cortex and step speed (r = -0.674, p = 0.006); step frequency (r = -0.656, p = 0.008); and step length (r = -0.535, p = 0.040). The negative correlations between the cerebral cortex and gait parameters of the PG indicated a neural compensatory effect of pre-frailty. Therefore, older adults with pre-frailty promote prefrontal activation to compensate for the impaired sensorimotor systems.

Frailty Syndrome as a Transition from Compensation to Decompensation: Application to the Biomechanical Regulation of Gait

Most gait parameters decrease with age and are even more importantly reduced with frailty. However, other gait parameters exhibit different or even opposite trends for aging and frailty, and the underlying reason is unclear. Literature focuses either on aging, or on frailty, and a comprehensive understanding of how biomechanical gait regulation evolves with aging and with frailty seems to be lacking. We monitored gait dynamics in young adults (19-29 years, n = 27, 59% women), middle-aged adults (30-59 years, n = 16, 62% women), and non-frail (>60 years, n = 15, 33% women) and frail older adults (>60 years, n = 31, 71% women) during a 160 m walking test using the triaxial accelerometer of the Zephyr Bioharness 3.0 device (Zephyr Technology, Annapolis, MD, USA). Frailty was evaluated using the Frail Scale (FS) and the Clinical Frailty Scale (CFS). We found that in non-frail older adults, certain gait parameters, such as cadence, were increased, whereas other parameters, such as step length, were decreased, and gait speed is maintained. Conversely, in frail older adults, all gait parameters, including gait speed, were decreased. Our interpretation is that non-frail older adults compensate for a decreased step length with an increased cadence to maintain a functional gait speed, whereas frail older adults decompensate and consequently walk with a characteristic decreased gait speed. We quantified compensation and decompensation on a continuous scale using ratios of the compensated parameter with respect to the corresponding compensating parameter. Compensation and decompensation are general medical concepts that can be applied and quantified for many, if not all, biomechanical and physiological regulatory mechanisms of the human body. This may allow for a new research strategy to quantify both aging and frailty in a systemic and dynamic way.

A review of quality-of-life in elderly osteoarthritis

INTRODUCTION

Osteoarthritis (OA) is the commonest joint disease in the world. Although aging is not invariably associated with OA, aging of the musculoskeletal system increases susceptibility to OA. Pain and reduced function due to OA, negatively impact health-related quality of life (HRQoL) in the elderly.

AREAS COVERED

We searched PubMed and Google Scholar with search term "osteoarthritis' combined with terms 'elderly' 'ageing' 'healthrelated quality of life' 'burden' "prevalence 'hip osteoarthritis' 'knee osteoarthritis' 'hand osteoarthritis' to identify relevant articles. This article discusses the global impact and joint-specific burden due to OA and the challenges in assessment of HRQoL in elderly with OA. We further describe some HRQoL determinants that particularly impact elderly persons with OA. These determinants include physical activity, falls, psychosocial consequences, sarcopaenia, sexual health, and incontinence. The usefulness of physical performance measures, as an adjunct to assessing HRQoL is explored. The review concludes by outlining strategies to improve HRQoL.

EXPERT OPINION

Assessment of HRQoL in elderly with OA is mandatory if effective interventions/treatment are to be instituted. But existent HRQoL assessments have shortcomings when used in elderly§. It is recommended that determinants of QoL which are unique to the elderly, be examined with greater detail and weightage in future studies.

Relationship between Muscle Strength and Gait Parameters in Healthy Older Women and Men

Maintaining sufficient muscle strength is fundamental to prevent a decline in basic physical functions such as gait, and is therefore a prerequisite for a healthy independent life in older people. However, the relationship between gait parameters and the strength of single muscle groups is reported with inconclusive results. The objective of this study was to analyze the relationship of strength of nine single muscle groups of lower and upper leg muscles as well as handgrip strength for gait parameters in older adults. Sixty-nine independently living older adults participated in the study. Maximum ankle plantar- and dorsiflexion, knee flexion and extension, as well as hip abduction, adduction, flexion, and extension strength, were measured using an isokinetic dynamometer. Additionally, hand grip strength measured via a hand dynamometer was obtained. Walking gait parameters were recorded with a 3D motion capture system on an instrumented treadmill. The relationships between multiple strength and gait variables were analyzed by Pearson's correlation coefficient. Linear regression analyses were performed to identify the predictive ability of muscle strength (normalized to body weight) for gait speed, stride time, stance time, stride length and step width. Multiple significant weak to moderate positive ([r = 0.343, p = 0.047]-[r = 0.538, p = 0.002]) and negative ([r = -0.340, p = 0.046]-[r = 0.593, p = 0.001]) correlations that were unequally distributed between both sexes were detected. Significant regression models explained ([r² = 16.6%, p = 0.015]-[r² = 44.3 %, p = 0.003]) and ([r² = 21.8%, p = 0.022]-[r² = 36.1%, p = 0.044]) of the gait parameter variations for men and women, respectively. The results suggest a sex-specific relevance of single muscle groups for all gait parameters. This may be attributed to anatomical differences and it is important to prevent strength-related changes in gait parameters.

Spatio-Temporal Gait Parameters of Hospitalized Older Patients: Comparison of Fallers and Non-Fallers

Gait disorders are predisposing factors for falls. They are accessible to rehabilitation and can be analyzed using tools that collect spatio-temporal parameters of walking, such as the GAITRite^® mat. The objective of this retrospective study was to find differences between the spatio-temporal parameters in patients who had fallen compared to patients who did not fall in a population of older patients hospitalized in acute geriatrics department. Patients over 75 years were included. For each patient, spatio-temporal parameters were collected using the GAITRite^® mat. The patients were divided into two groups according to whether they had a history of fall. The spatio-temporal parameters were compared between the two groups and in relation to the general population. Sixty-seven patients, with an average age of 85.9 ± 6 years, were included. The patients had comorbidities, cognitive impairment and were polymedicated. The mean walking speed was 51.4 cm/s in non-fallers group and 47.3 cm/s in fallers group (p = 0.539), indicating pathological walking in comparison with the general population of the same age (average 100 cm/s). No association was found between the spatio-temporal parameters and fall, probably linked to many confounding factors such as the pathogenicity of walking of our patients and their comorbidities.

Does texting while walking affect spatiotemporal gait parameters in healthy adults, older people, and persons with motor or cognitive disorders? A systematic review and meta-analysis

BACKGROUND

Smartphone use during postural-locomotor tasks is an everyday activity for individuals of all ages in diverse environmental situations and with various health conditions. Nevertheless, the use of smartphones during walking is responsible for many accidents.

RESEARCH QUESTION

This systematic review and meta-analysis examined spatiotemporal gait parameters during the dual-task situation "texting + gait" versus isolated gait task (single task) in adult persons (>18 years).

METHODS

Electronic database searches were performed in PubMed, Embase, CINHAL, and LISSA. Two examiners assessed the eligibility and quality of appraisal with the Downs and Black checklist. The standardized mean difference (SMD) with 95 % confidence intervals was calculated to compare single- and dual-task situations. The pooled estimates of the overall effect were computed using a random or fixed effects method, and forest plots were generated.

RESULTS AND SIGNIFICANCE

A total of 25 studies were included. All studies included healthy adults, with four studies including older persons and three including people with pathological conditions. The walking task was with (N = 4) and without (N = 21) obstacles and in laboratory (N = 21) or ecological conditions (N = 7). The quality scores were 6-8/16 for eight studies, 9-12/16 for seven studies, and more than 12/16 for three studies. During the "texting + gait" tasks, the meta-analysis highlighted a significant impairment of gait speed, step and stride length, cadence, and double and single support (p < 0.05). The spatiotemporal parameters of gait were systematically altered during the texting task regardless of the population and test conditions. However, the quality of the studies is moderate, and few studies have been conducted for people with motor deficiencies. The impact of texting on walking should be better considered to develop prevention actions.

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.

Assessment Tools of Biopsychosocial Frailty Dimensions in Community-Dwelling Older Adults: A Narrative Review

Frailty is a complex interplay between several factors, including physiological changes in ageing, multimorbidities, malnutrition, living environment, genetics, and lifestyle. Early screening for frailty risk factors in community-dwelling older people allows for preventive interventions on the clinical and social determinants of frailty, which allows adverse events to be avoided. By conducting a narrative review of the literature employing the International Narrative Systematic Assessment tool, the authors aimed to develop an updated framework for the main measurement tools to assess frailty risks in older adults, paying attention to use in the community and primary care settings. This search focused on the biopsychosocial domains of frailty that are covered in the SUNFRAIL tool. The study selected 178 reviews (polypharmacy: 20; nutrition: 13; physical activity: 74; medical visits: 0; falls: 39; cognitive decline: 12; loneliness: 15; social support: 5; economic constraints: 0) published between January 2010 and December 2021. Within the selected reviews, 123 assessment tools were identified (polypharmacy: 15; nutrition: 15; physical activity: 25; medical visits: 0; falls: 26; cognitive decline: 18; loneliness: 9; social support: 15; economic constraints: 0). The narrative review allowed us to evaluate assessment tools of frailty domains to be adopted for multidimensional health promotion and prevention interventions in community and primary care.

Association of Muscle Mass, Muscle Strength, and Muscle Function with Gait Ability Assessed Using Inertial Measurement Unit Sensors in Older Women

Aging-related muscle atrophy is associated with decreased muscle mass (MM), muscle strength (MS), and muscle function (MF) and may cause motor control, balance, and gait pattern impairments. This study determined associations of three speed-based gait variables with loss of MM, MS, and MF in older women. Overall, 432 older women aged ≥65 performed appendicular skeletal muscle, handgrip strength, and five times sit-to-stand test to evaluate MM, MS, and MF. A gait test was performed at three speeds by modifying the preferred walking speed (PWS; slower walking speed (SWS); faster-walking speed (FWS)) on a straight 19 m walkway. Stride length (SL) at PWS was significantly associated with MM. FWS and coefficient of variance (CV) of double support phase (DSP) and DSP at PWS showed significant associations with MS. CV of step time and stride time at SWS, FWS, and single support phase (SSP) at PWS showed significant associations with MF. SL at PWS, DSP at FWS, CV of DSP at PWS, stride time at SWS, and CV of SSP at PWS showed significant associations with composite MM, MS, and MF variables. Our study indicated that gait tasks under continuous and various speed conditions are useful for evaluating MM, MS, and MF.

Predicting frailty in older adults using vocal biomarkers: a cross-sectional study

Background

Frailty is a common issue in the aging population. Given that frailty syndrome is little discussed in the literature on the aging voice, the current study aims to examine the relationship between frailty and vocal biomarkers in older people.

Methods

Participants aged ≥ 60 years visiting geriatric outpatient clinics were recruited. They underwent frailty assessment (Cardiovascular Health Study [CHS] index; Study of Osteoporotic Fractures [SOF] index; and Fatigue, Resistance, Ambulation, Illness, and Loss of weight [FRAIL] index) and were asked to pronounce a sustained vowel /a/ for approximately 1 s. Four voice parameters were assessed: average number of zero crossings (A1), variations in local peaks and valleys (A2), variations in first and second formant frequencies (A3), and spectral energy ratio (A4).

Results

Among 277 older adults, increased A1 was associated with a lower likelihood of frailty as defined by SOF (odds ratio [OR] 0.84, 95% confidence interval [CI] 0.74–0.96). Participants with larger A2 values were more likely to be frail, as defined by FRAIL and CHS (FRAIL: OR 1.41, 95% CI 1.12–1.79; CHS: OR 1.38, 95% CI 1.10–1.75). Sex differences were observed across the three frailty indices. In male participants, an increase in A3 by 10 points increased the odds of frailty by almost 7% (SOF: OR 1.07, 95% CI 1.02–1.12), 6% (FRAIL: OR 1.06, 95% CI 1.02–1.11), or 6% (CHS: OR 1.06, 95% CI 1.01–1.11). In female participants, an increase in A4 by 0.1 conferred a significant 2.8-fold (SOF: OR 2.81, 95% CI 1.71–4.62), 2.3-fold (FRAIL: OR 2.31, 95% CI 1.45–3.68), or 2.8-fold (CHS: OR 2.82, 95% CI 1.76–4.51, CHS) increased odds of frailty.

Conclusions

Vocal biomarkers, especially spectral-domain voice parameters, might have potential for estimating frailty, as a non-invasive, instantaneous, objective, and cost-effective estimation tool, and demonstrating sex differences for individualised treatment of frailty.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03237-7.

Association between Domains of the Clinical-Functional Vulnerability Index and Falls History in Older Adults: A Cross-Sectional Study

Objectives: The study aimed to determine which domains, sets, and isolated or combined questions of the Clinical-Functional Vulnerability Index (CFVI-20) are associated with falls history in older adults. Methods: Instruments used were the CFVI-20 assessment and reported falls during the last year. The receiver operating characteristics (ROC) curves identified the performance of the CFVI-20 domains and questions in identifying older adults with and without falls history, while logistic regression identified relevant questions to identify fall history. Results: This study included 1725 individuals (71.9 ± 7.3 years). The area under the curve (AUC) between the CFVI-20 and fall history was 0.69. The mobility domain presented the largest AUC (0.71; p < 0.01), and most isolated domains showed low AUCs (0.51 to 0.58). Isolated questions were limited to identifying fallers. The regression analysis identified 7 questions of the CFVI-20 with falls. Conclusions: The CFVI-20 general score identified older adults with a fall history. When considered in isolation, most domains were limited to identifying falls, except for the mobility domain. Combining the CFVI-20 questions enabled identification of fallers.

Wearable-Based Assessment of Frailty Trajectories During Cardiac Rehabilitation After Open-Heart Surgery

Frailty in patients after open-heart surgery influences the type and intensity of a cardiac rehabilitation program. The response to tailored exercise training can be different, requiring convenient tools to assess the effectiveness of a training program routinely. The study aims to investigate whether kinematic measures extracted from the acceleration signals can provide information about frailty trajectories during rehabilitation. One hundred patients after open-heart surgery, assigned to the equal-sized intervention and control groups, participated in exercise training during inpatient rehabilitation. After rehabilitation, the intervention group continued exercise training at home, whereas the control group was asked to maintain the usual physical activity regimen. Stride time, cadence, movement vigor, gait asymmetry, Lissajous index, and postural sway were estimated during the clinical walk and stair-climbing tests before and after inpatient rehabilitation as well as after home-based exercise training. Frailty was assessed using the Edmonton frail scale. Most kinematic measures estimated during walking improved after rehabilitation along with the improvement in frailty status, i.e., stride time, cadence, postural sway, and movement vigor improved in 71%, 77%, 81%, and 83% of patients, respectively. Meanwhile, kinematic measures during stair-climbing improved to a lesser extent compared to walking. Home-based exercise training did not result in a notable change in kinematic measures which agrees well with only a negligible deterioration in frailty status. The study demonstrates the feasibility to follow frailty trajectories during inpatient rehabilitation after open-heart surgery based on kinematic measures extracted using a single wearable sensor.

The association between heart rate behavior and gait performance: The moderating effect of frailty

Introduction

Research suggests that frailty not only influence individual systems, but also it affects the interconnection between them. However, no study exists to show how the interplay between cardiovascular and motor performance is compromised with frailty.

Aim

To investigate the effect of frailty on the association between heart rate (HR) dynamics and gait performance.

Methods

Eighty-five older adults (≥65 years and able to walk 9.14 meters) were recruited (October 2016—March 2018) and categorized into 26 non-frail (age = 78.65±7.46 years) and 59 pre-frail/frail individuals (age = 81.01±8.17) based on the Fried frailty phenotype. Participants performed gait tasks while equipped with a wearable electrocardiogram (ECG) sensor attached to the chest, as well as wearable gyroscopes for gait assessment. HR dynamic parameters were extracted, including time to peak HR and percentage increase in HR in response to walking. Using the gyroscope sensors gait parameters were recorded including stride length, stride velocity, mean swing velocity, and double support.

Results

Among the pre-frail/frail group, time to peak HR was significantly correlated with all gait parameters (p<0.0001, r = 0.51–0.59); however, for the non-frail group, none of the correlations between HR dynamics and gait performance parameters were significant (p>0.45, r = 0.03–0.15). The moderation analysis of time to peak HR, demonstrated a significant interaction effect of HR dynamics and frailty status on walking velocity (p<0.01), and the interaction effect was marginally non-significant for other gait parameters (p>0.10).

Conclusions

Current findings, for the first time, suggest that a compromised motor and cardiac autonomic interaction exist among pre-frail/frail older adults; an impaired HR performance (i.e., slower increase of HR in response to stressors) may lead to a slower walking performance. Assessing physical performance and its corresponding HR behavior should be studied as a tool for frailty screening and providing insights about the underlying cardiovascular-related mechanism leading to physical frailty.

Kinematic characteristics during gait in frail older women identified by principal component analysis

Frailty is associated with gait variability in several quantitative parameters, including high stride time variability. However, the associations between joint kinematics during walking and increased gait variability with frailty remain unclear. In the current study, principal component analysis was used to identify the key joint kinematics characteristics of gait related to frailty. We analyzed whole kinematic waveforms during the entire gait cycle obtained from the pelvis and lower limb joint angle in 30 older women (frail/prefrail: 15 participants; non-frail: 15 participants). Principal component analysis was conducted using a 60 × 1224 input matrix constructed from participants’ time-normalized pelvic and lower-limb-joint angles along three axes (each leg of 30 participants, 51 time points, four angles, three axes, and two variables). Statistical analyses revealed that only principal component vectors 6 and 9 were related to frailty. Recombining the joint kinematics corresponding to these principal component vectors revealed that frail older women tended to exhibit greater variability of knee- and ankle-joint angles in the sagittal plane while walking compared with non-frail older women. We concluded that greater variability of knee- and ankle-joint angles in the sagittal plane are joint kinematic characteristics of gait related to frailty.

Transductive Learning Models for Accurate Ambulatory Gait Analysis in Elderly Residents of Assisted Living Facilities

Instrumented footwear represents a promising technology for spatiotemporal gait analysis in out-of-the-lab conditions. However, moderate accuracy impacts this technology's ability to capture subtle, but clinically meaningful, changes in gait patterns that may indicate adverse outcomes or underlying neurological conditions. This limitation hampers the use of instrumented footwear to aid functional assessments and clinical decision making. This paper introduces new transductive-learning inference models that substantially reduce measurement errors relative to conventional data processing techniques, without requiring subject-specific labelled data. The proposed models use subject-optimized input features and hyperparameters to adjust the spatiotemporal gait metrics (i.e., stride time, length, and velocity, swing time, and double support time) obtained with conventional techniques, resulting in computationally simpler models compared to end-to-end machine learning approaches. Model validity and reliability were evaluated against a gold-standard electronic walkway during a clinical gait performance test (6-minute walk test) administered to N=95 senior residents of assisted living facilities with diverse levels of gait and balance impairments. Average reductions in absolute errors relative to conventional techniques were -42.0% and -33.5% for spatial and gait-phase parameters, respectively, indicating the potential of transductive learning models for improving the accuracy of instrumented footwear for ambulatory gait analysis.

Digital Biomarkers of Cognitive Frailty: The Value of Detailed Gait Assessment Beyond Gait Speed

BACKGROUND

Cognitive frailty (CF), defined as the simultaneous presence of cognitive impairment and physical frailty, is a clinical symptom in early-stage dementia with promise in assessing the risk of dementia. The purpose of this study was to use wearables to determine the most sensitive digital gait biomarkers to identify CF.

METHODS

Of 121 older adults (age = 78.9 ± 8.2 years, body mass index = 26.6 ± 5.5 kg/m2) who were evaluated with a comprehensive neurological exam and the Fried frailty criteria, 41 participants (34%) were identified with CF and 80 participants (66%) were identified without CF. Gait performance of participants was assessed under single task (walking without cognitive distraction) and dual task (walking while counting backward from a random number) using a validated wearable platform. Participants walked at habitual speed over a distance of 10 m. A validated algorithm was used to determine steady-state walking. Gait parameters of interest include steady-state gait speed, stride length, gait cycle time, double support, and gait unsteadiness. In addition, speed and stride length were normalized by height.

RESULTS

Our results suggest that compared to the group without CF, the CF group had deteriorated gait performances in both single-task and dual-task walking (Cohen's effect size d = 0.42-0.97, p < 0.050). The largest effect size was observed in normalized dual-task gait speed (d = 0.97, p < 0.001). The use of dual-task gait speed improved the area under the curve (AUC) to distinguish CF cases to 0.76 from 0.73 observed for the single-task gait speed. Adding both single-task and dual-task gait speeds did not noticeably change AUC. However, when additional gait parameters such as gait unsteadiness, stride length, and double support were included in the model, AUC was improved to 0.87.

CONCLUSIONS

This study suggests that gait performances measured by wearable sensors are potential digital biomarkers of CF among older adults. Dual-task gait and other detailed gait metrics provide value for identifying CF above gait speed alone. Future studies need to examine the potential benefits of gait performances for early diagnosis of CF and/or tracking its severity over time.

Application of Machine Vision in Classifying Gait Frailty Among Older Adults

Background: Frail older adults have an increased risk of adverse health outcomes and premature death. They also exhibit altered gait characteristics in comparison with healthy individuals. Methods: In this study, we created a Fried's frailty phenotype (FFP) labelled casual walking video set of older adults based on the West China Health and Aging Trend study. A series of hyperparameters in machine vision models were evaluated for body key point extraction (AlphaPose), silhouette segmentation (Pose2Seg, DPose2Seg, and Mask R-CNN), gait feature extraction (Gaitset, LGaitset, and DGaitset), and feature classification (AlexNet and VGG16), and were highly optimised during analysis of gait sequences of the current dataset. Results: The area under the curve (AUC) of the receiver operating characteristic (ROC) at the physical frailty state identification task for AlexNet was 0.851 (0.827-0.8747) and 0.901 (0.878-0.920) in macro and micro, respectively, and was 0.855 (0.834-0.877) and 0.905 (0.886-0.925) for VGG16 in macro and micro, respectively. Furthermore, this study presents the machine vision method equipped with better predictive performance globally than age and grip strength, as well as than 4-m-walking-time in healthy and pre-frailty classifying. Conclusion: The gait analysis method in this article is unreported and provides promising original tool for frailty and pre-frailty screening with the characteristics of convenience, objectivity, rapidity, and non-contact. These methods can be extended to any gait-related disease identification processes, as well as in-home health monitoring.

Gait-based Frailty Assessment using Image Representation of IMU Signals and Deep CNN

Frailty is a common and critical condition in elderly adults, which may lead to further deterioration of health. However, difficulties and complexities exist in traditional frailty assessments based on activity-related questionnaires. These can be overcome by monitoring the effects of frailty on the gait. In this paper, it is shown that by encoding gait signals as images, deep learning-based models can be utilized for the classification of gait type. Two deep learning models (a) SS-CNN, based on single stride input images, and (b) MS-CNN, based on 3 consecutive strides were proposed. It was shown that MS-CNN performs best with an accuracy of 85.1%, while SS-CNN achieved an accuracy of 77.3%. This is because MS-CNN can observe more features corresponding to stride-to-stride variations which is one of the key symptoms of frailty. Gait signals were encoded as images using STFT, CWT, and GAF. While the MS-CNN model using GAF images achieved the best overall accuracy and precision, CWT has a slightly better recall. This study demonstrates how image encoded gait data can be used to exploit the full potential of deep learning CNN models for the assessment of frailty.

Detecting Fall Risk and Frailty in Elders with Inertial Motion Sensors: A Survey of Significant Gait Parameters

In the elderly, geriatric problems such as the risk of fall or frailty are a challenge for society. Patients with frailty present difficulties in walking and higher fall risk. The use of sensors for gait analysis allows the detection of objective parameters related to these pathologies and to make an early diagnosis. Inertial Measurement Units (IMUs) are wearables that, due to their accuracy, portability, and low price, are an excellent option to analyze human gait parameters in health-monitoring applications. Many relevant gait parameters (e.g., step time, walking speed) are used to assess motor, or even cognitive, health problems in the elderly, but we perceived that there is not a full consensus on which parameters are the most significant to estimate the risk of fall and the frailty state. In this work, we analyzed the different IMU-based gait parameters proposed in the literature to assess frailty state (robust, prefrail, or frail) or fall risk. The aim was to collect the most significant gait parameters, measured from inertial sensors, able to discriminate between patient groups and to highlight those parameters that are not relevant or for which there is controversy among the examined works. For this purpose, a literature review of the studies published in recent years was carried out; apart from 10 previous relevant reviews using inertial and other sensing technologies, a total of 22 specific studies giving statistical significance values were analyzed. The results showed that the most significant parameters are double-support time, gait speed, stride time, step time, and the number of steps/day or walking percentage/day, for frailty diagnosis. In the case of fall risk detection, parameters related to trunk stability or movements are the most relevant. Although these results are important, the total number of works found was limited and most of them performed the significance statistics on subsets of all possible gait parameters; this fact highlights the need for new frailty studies using a more complete set of gait parameters.

Wearable inertial sensors for human movement analysis: a five-year update

INTRODUCTION

The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings.

AREAS COVERED

Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings.

EXPERT OPINION

IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.

Spatiotemporal gait characteristics and risk of mortality in community-dwelling older adults

Gait is one of the best measures of physical function in older adults. The study examined the association between spatiotemporal gait variables and mortality among older adults. The participants were 4,298 older adults in the National Center for Geriatrics and Gerontology - Study of Geriatric Syndromes. At baseline we measured the following spatiotemporal gait variables: gait speed, stride length, cadence, and stride length variability. Demographic variables, medical conditions, cognitive function, and physical inactivity were also assessed at baseline. We obtained gait measurements over five trials using an electronic gait-measuring device mounted at the middle 2.4 m section of a 6.4 m straight and flat pathway, with 2 m allowed for acceleration and deceleration. Participants' usual gait speed was measured. Subsequent incident death was confirmed using administrative data. During follow-up (mean duration: 1,571 days), there were 185 incident deaths among participants. Low function on all gait variables increased risk of mortality (adjusted hazard ratio [95% confidence interval], gait speed: 1.83 [1.31-2.56], stride length: 1.85 [1.31-2.62], cadence: 1.60 [1.17-2.18], stride length variability: 1.50 [1.09-2.06]). In addition, mortality risk increased with the number of variables showing low gait function compared with normal gait function (p < .05). Slower gait speed, shorter stride length, lower cadence, and higher stride length variability were associated with increased mortality. Multifaceted gait analysis could be useful for evaluating mortality risk.

Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning

Remote monitoring of physical frailty is important to personalize care for slowing down the frailty process and/or for the healthy recovery of older adults following acute or chronic stressors. Taking the Fried frailty criteria as a reference to determine physical frailty and frailty phenotypes (slowness, weakness, exhaustion, inactivity), this study aimed to explore the benefit of machine learning to determine the least number of digital biomarkers of physical frailty measurable from a pendant sensor during activities of daily living. Two hundred and fifty-nine older adults were classified into robust or pre-frail/frail groups based on the physical frailty assessments by the Fried frailty criteria. All participants wore a pendant sensor at the sternum level for 48 h. Of seventeen sensor-derived features extracted from a pendant sensor, fourteen significant features were used for machine learning based on logistic regression modeling and a recursive feature elimination technique incorporating bootstrapping. The combination of percentage time standing, percentage time walking, walking cadence, and longest walking bout were identified as optimal digital biomarkers of physical frailty and frailty phenotypes. These findings suggest that a combination of sensor-measured exhaustion, inactivity, and speed have potential to screen and monitor people for physical frailty and frailty phenotypes.

Towards automated assessment of frailty status using a wrist-worn device

Wearable sensors potentially enable monitoring the users physical activity in daily life. Therefore, they are particularly appealing for the evaluation of older subjects in their environment, to capture early signs of frailty and mobility-related problems. This study explores the use of body-worn accelerometers for automated assessment of frailty during walking activity. Experiments involved 34 volunteers aged 70+, who were initially screened by geriatricians for the presence of frailty according to Frieds criteria. After screening, the volunteers were asked to walk 60 m at preferred speed, while wearing two accelerometers, one positioned on the lower back and the other on the wrist. Sensor-derived signals were analyzed independently to compare the ability of the two signals (wrist vs. lower back) in frailty status assessment. A gait detection technique was applied to identify segments made of four gait cycles. These segments were then used as input to compute 25 features in time and time-frequency domains, the latter by means of the Wavelet Transform. Finally, five machine learning models were trained and evaluated to classify subjects as robust or non-robust (i.e., pre-frail or frail). Gaussian naive Bayes applied to the features derived from the wrist sensor signal identified non-robust subjects with 91% sensitivity and 82% specificity, compared to 87% sensitivity and 64% specificity achieved with the lower back sensor. Results demonstrate that a wrist-worn accelerometer provides valuable information for the recognition of frailty in older adults, and could represent an effective tool to enable automated and unobtrusive assessment of frailty.

How wearable sensors have been utilised to evaluate frailty in older adults: a systematic review

BACKGROUND

Globally the population of older adults is increasing. It is estimated that by 2050 the number of adults over the age of 60 will represent over 21% of the world's population. Frailty is a clinical condition associated with ageing resulting in an increase in adverse outcomes. It is considered the greatest challenge facing an ageing population affecting an estimated 16% of community-dwelling populations worldwide.

AIM

The aim of this systematic review is to explore how wearable sensors have been used to assess frailty in older adults.

METHOD

Electronic databases Medline, Science Direct, Scopus, and CINAHL were systematically searched March 2020 and November 2020. A search constraint of articles published in English, between January 2010 and November 2020 was applied. Papers included were primary observational studies involving; older adults aged > 60 years, used a wearable sensor to provide quantitative measurements of physical activity (PA) or mobility and a measure of frailty. Studies were excluded if they used non-wearable sensors for outcome measurement or outlined an algorithm or application development exclusively. The methodological quality of the selected studies was assessed using the Appraisal Tool for Cross-sectional Studies (AXIS).

RESULTS

Twenty-nine studies examining the use of wearable sensors to assess and discriminate between stages of frailty in older adults were included. Thirteen different body-worn sensors were used in eight different body-locations. Participants were community-dwelling older adults. Studies were performed in home, laboratory or hospital settings. Postural transitions, number of steps, percentage of time in PA and intensity of PA together were the most frequently measured parameters followed closely by gait speed. All but one study demonstrated an association between PA and level of frailty. All reports of gait speed indicate correlation with frailty.

CONCLUSIONS

Wearable sensors have been successfully used to evaluate frailty in older adults. Further research is needed to identify a feasible, user-friendly device and body-location that can be used to identify signs of pre-frailty in community-dwelling older adults. This would facilitate early identification and targeted intervention to reduce the burden of frailty in an ageing population.

Digital Health Interventions among People Living with Frailty: A Scoping Review

OBJECTIVES

Digital health interventions (DHIs) are interesting resources to improve various health conditions. However, their use in the older and frail population is still sparse. We aimed to give an overview of DHI used in the frail older population.

DESIGN

Scoping review with PRISMA guidelines based on Population, Concept, and Context.

SETTING AND PARTICIPANTS

We included original studies in English with DHI (concept) on people described as frail (population) in the clinical or community setting (context) and no limitation on date of publication. We searched 3 online databases (PubMed, Scopus, and Web of Science).

MEASURES

We described DHI in terms of purpose, delivering, content and assessment. We also described frailty assessment and study design.

RESULTS

We included 105 studies that fulfilled our eligibility criteria. The most frequently reported DHIs were with the purpose of monitoring (45; 43%), with a delivery method of sensor-based technologies (59; 56%), with a content of feedback to users (34; 32%), and for assessment of feasibility (57; 54%). Efficacy was reported in 31 (30%) studies and usability/feasibility in 57 (55%) studies. The most common study design was descriptive exploratory for new methodology or technology (24; 23%). There were 14 (13%) randomized controlled trials, with only 4 of 14 studies (29%) showing a low or moderate risk of bias. Frailty assessment using validated scales was reported in only 47 (45%) studies.

CONCLUSIONS AND IMPLICATIONS

There was much heterogeneity among frailty assessments, study designs, and evaluations of DHIs. There is now a strong need for more standardized approaches to assess frailty, well-structured randomized controlled trials, and proper evaluation and report. This work will contribute to the development of better DHIs in this vulnerable population.

Turning assessment for discrimination of frailty syndrome among community-dwelling older adults

BACKGROUND

Frailty is a common geriatric syndrome and is characterized by decreased physiological reserve and increased vulnerability towards adverse health outcomes including falls. Turning is a challenging task and is reported to be one of the daily activities that leads to falling in older populations.

RESEARCH QUESTION

Does 180° walking turns and 360° turning on the spot differ among frail, pre-frail, and non-frail older adults? Can 180° walking turns and 360° turning on the spot cutoffs discriminate older adults with frailty from those without?

METHODS

A cross-sectional study was conducted on community-dwelling older adults aged over 65 years. Frailty was assessed using Fried's phenotype method, and turning tasks were measured by inertial sensors. The turn duration (s) and angular velocity (°/s) were recorded for analysis.

RESULTS

In total, 109 participants were enrolled including 50 pre-frail and 12 frail individuals. Frail older adults took significantly longer and had slower angular velocities to complete the 180° and 360° turning than did either pre-frail (p = 0.002 and p < 0.001, respectively) or non-frail (p = 0.03 and p < 0.001, respectively) older adults. Cutoff times of 2.45 and 3.46 s were found to best discriminate frail people from those without frailty in both the 180° (sensitivity 83.3 %, specificity 71.1 %, area under the receiver operating characteristic curve (AUC) 0.796) and 360° (sensitivity 91.7 %, specificity 74.2 %, AUC 0.857) turn tasks.

SIGNIFICANCE

Older individuals with frailty syndrome had difficulty turning as evidenced by a longer turning duration and a slower angular velocity. The turn duration could be a potential biomarker of frailty in older populations. Assessing the turning performance can facilitate early detection of the onset of frailty and inform early prevention and rehabilitation interventions in clinical practice.

Perceptual-motor styles

Even for a stereotyped task, sensorimotor behavior is generally variable due to noise, redundancy, adaptability, learning or plasticity. The sources and significance of different kinds of behavioral variability have attracted considerable attention in recent years. However, the idea that part of this variability depends on unique individual strategies has been explored to a lesser extent. In particular, the notion of style recurs infrequently in the literature on sensorimotor behavior. In general use, style refers to a distinctive manner or custom of behaving oneself or of doing something, especially one that is typical of a person, group of people, place, context, or period. The application of the term to the domain of perceptual and motor phenomenology opens new perspectives on the nature of behavioral variability, perspectives that are complementary to those typically considered in the studies of sensorimotor variability. In particular, the concept of style may help toward the development of personalised physiology and medicine by providing markers of individual behaviour and response to different stimuli or treatments. Here, we cover some potential applications of the concept of perceptual-motor style to different areas of neuroscience, both in the healthy and the diseased. We prefer to be as general as possible in the types of applications we consider, even at the expense of running the risk of encompassing loosely related studies, given the relative novelty of the introduction of the term perceptual-motor style in neurosciences.

How gait influences frailty models and health-related outcomes in clinical-based and population-based studies: a systematic review

Aging is often associated with a decline in physical function that eventually leads to loss of autonomy in activities of daily living (ADL). Walking is a very common ADL, important for main determinants of quality of life in older age, and it requires the integration of many physiological systems. Gait speed has been described as the 'sixth vital sign' because it is a core indicator of health and function in aging and disease. We reviewed original studies up to June 2020 that assessed frailty in both longitudinal and cross-sectional observational studies, paying particular attention to how gait is measured in older population and how the gait parameter adopted may influence the estimated frailty models and the health-related outcomes of the various studies (i.e. clinical, cognitive, physical, and nutritional outcomes). Eighty-five studies met the search strategy and were included in the present systematic review. According to the frailty tools, more than 60% of the studies used the physical phenotype model proposed by Fried and colleagues, while one-third referred to multi-domain indexes or models and only 5% referred to other single-domain frailty models (social or cognitive). The great heterogeneity observed in gait measurements and protocols limited the possibility to directly compare the results of the studies and it could represent an important issue causing variability in the different outcome measures in both clinical-and population-based settings. Gait appeared to be an indicator of health and function also in frail older adults, and different gait parameters appeared to predict adverse health-related outcomes in clinical, cognitive, and physical domains and, to a lesser extent, in nutritional domain. Gait has the potential to elucidate the common basic mechanisms of cognitive and motor decline. Advances in technology may extend the validity of gait in different clinical settings also in frail older adults, and technology-based assessment should be encouraged. Combining various gait parameters may enhance frailty prediction and classification of different frailty phenotypes.

Validation of a Laser Ranged Scanner-Based Detection of Spatio-Temporal Gait Parameters Using the aTUG Chair

This article covers the suitability to measure gait-parameters via a Laser Range Scanner (LRS) that was placed below a chair during the walking phase of the Timed Up&Go Test in a cohort of 92 older adults (mean age 73.5). The results of our study demonstrated a high concordance of gait measurements using a LRS in comparison to the reference GAITRite walkway. Most of aTUG's gait parameters demonstrate a strong correlation coefficient with the GAITRite, indicating high measurement accuracy for the spatial gait parameters. Measurements of velocity had a correlation coefficient of 99%, which can be interpreted as an excellent measurement accuracy. Cadence showed a slightly lower correlation coefficient of 96%, which is still an exceptionally good result, while step length demonstrated a correlation coefficient of 98% per leg and stride length with an accuracy of 99% per leg. In addition to confirming the technical validation of the aTUG regarding its ability to measure gait parameters, we compared results from the GAITRite and the aTUG for several parameters (cadence, velocity, and step length) with results from the Berg Balance Scale (BBS) and the Activities-Specific Balance Confidence-(ABC)-Scale assessments. With confidence coefficients for BBS and velocity, cadence and step length ranging from 0.595 to 0.798 and for ABC ranging from 0.395 to 0.541, both scales demonstrated only a medium-sized correlation. Thus, we found an association of better walking ability (represented by the measured gait parameters) with better balance (BBC) and balance confidence (ABC) overall scores via linear regression. This results from the fact that the BBS incorporates both static and dynamic balance measures and thus, only partly reflects functional requirements for walking. For the ABC score, this effect was even more pronounced. As this is to our best knowledge the first evaluation of the association between gait parameters and these balance scores, we will further investigate this phenomenon and aim to integrate further measures into the aTUG to achieve an increased sensitivity for balance ability.

When Will My Patient Fall? Sensor-Based In-Home Walking Speed Identifies Future Falls in Older Adults

BACKGROUND

Although there are known clinical measures that may be associated with risk of future falls in older adults, we are still unable to predict when the fall will happen. Our objective was to determine whether unobtrusive in-home assessment of walking speed can detect a future fall.

METHOD

In both ISAAC and ORCATECH Living Laboratory studies, a sensor-based monitoring system has been deployed in the homes of older adults. Longitudinal mixed-effects regression models were used to explore trajectories of sensor-based walking speed metrics in those destined to fall versus controls over time. Falls were captured during a 3-year period.

RESULTS

We observed no major differences between those destined to fall (n = 55) and controls (n = 70) at baseline in clinical functional tests. There was a longitudinal decline in median daily walking speed over the 3 months before a fall in those destined to fall when compared with controls, p < .01 (ie, mean walking speed declined 0.1 cm s-1 per week). We also found prefall differences in sensor-based walking speed metrics in individuals who experienced a fall: walking speed variability was lower the month and the week just before the fall compared with 3 months before the fall, both p < .01.

CONCLUSIONS

While basic clinical tests were not able to differentiate who will prospectively fall, we found that significant variations in walking speed metrics before a fall were measurable. These results provide evidence of a potential sensor-based risk biomarker of prospective falls in community living older adults.

Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

Background and objectives: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail). Materials and methods: 133 participants (≥60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters were assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19 s), stance phase time (0.68 s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity. Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.

Motor style at rest and during locomotion in human

Humans exhibit various motor styles that reflect their intra- and interindividual variability when implementing sensorimotor transformations. This opens important questions, such as, At what point should they be readjusted to maintain optimal motor control? Do changes in motor style reveal the onset of a pathological process and can these changes help rehabilitation and recovery? To further investigate the concept of motor style, tests were carried out to quantify posture at rest and motor control in 18 healthy subjects under four conditions: walking at three velocities (comfortable walking, walking at 4 km/h, and race walking) and running at maximum velocity. The results suggest that motor control can be conveniently decomposed into a static component (a stable configuration of the head and column with respect to the gravitational vertical) and dynamic components (head, trunk, and limb movements) in humans, as in quadrupeds, and both at rest and during locomotion. These skeletal configurations provide static markers to quantify the motor style of individuals because they exhibit large variability among subjects. Also, using four measurements (jerk, root mean square, sample entropy, and the two-thirds power law), it was shown that the dynamics were variable at both intra- and interindividual levels during locomotion. Variability increased following a head-to -toe gradient. These findings led us to select dynamic markers that could define, together with static markers, the motor style of a subject. Finally, our results support the view that postural and motor control are subserved by different neuronal networks in frontal, sagittal, and transversal planes.NEW & NOTEWORTHY During human locomotion, motor control can be conveniently decomposed into a static and dynamic components. Variable dynamics were observed at both the intra- and interindividual levels during locomotion. Variability increased following a head-to-toe gradient. Finally, our results support the view that postural and motor control are subserved by different neuronal networks in the frontal, sagittal, and transversal planes.

Social frailty: the importance of social and environmental factors in predicting frailty in older adults

Frailty can have multifaceted contributors, including physical, psychological, social and environmental elements. There is a lack of clarity surrounding frailty as it lacks a universal common definition, multiple factors are attributed to frailty and no definitive assessment tool is available. The evidence suggests that there is a lack of research to indicate causal relationships between the various determinants of frailty in ageing individuals. This review discusses the important role that social and environmental factors play in predicting the risk of frailty in older adults, as well as discussing the various signs of frailty. Health professionals in primary and community care settings are well placed to prevent and identify social frailty in the persons they care for.