Measures of perceived mobility ability in community-dwelling older adults: a systematic review of psychometric properties

OBJECTIVES

The objective of this systematic review was to synthesise the psychometric properties of measures of perceived mobility ability and related frameworks used to define and operationalise mobility in community-dwelling older adults.

METHODS

We registered the review protocol with PROSPERO (CRD42022306689) and included studies that examined the psychometric properties of perceived mobility measures in community-dwelling older adults. Five databases were searched to identify potentially relevant primary studies. We qualitatively summarised psychometric property estimates and related operational frameworks. We conducted risk of bias and overall quality assessments, and meta-analyses when at least three studies were included for a particular outcome. The synthesised results were compared against the Consensus-based Standards for the Selection of Health Measurement Instruments criteria for good measurement properties.

RESULTS

A total of 36 studies and 17 measures were included in the review. The Late-Life Function and Disability Index: function component (LLFDI-FC), lower extremity functional scale (LEFS), Mobility Assessment Tool (MAT)-short form (MAT-SF) or MAT-Walking, and Perceived Driving Abilities (PDA) Scale were identified with three or more eligible studies. Most measures showed sufficient test-retest reliability (moderate or high), while the PDA scale showed insufficient reliability (low). Most measures had sufficient or inconsistent convergent validity (low or moderate) or known-groups validity (low or very low), but their predictive validity and responsiveness were insufficient or inconsistent (low or very low). Few studies used a conceptual model.

CONCLUSION

The LLFDI-FC, LEFS, PDA and MAT-SF/Walking can be used in community-dwelling older adults by considering the summarised psychometric properties. No available comprehensive mobility measure was identified that covered all mobility domains.

Egocentric vision-based detection of surfaces: towards context-aware free-living digital biomarkers for gait and fall risk assessment

Background

Falls in older adults are a critical public health problem. As a means to assess fall risks, free-living digital biomarkers (FLDBs), including spatiotemporal gait measures, drawn from wearable inertial measurement unit (IMU) data have been investigated to identify those at high risk. Although gait-related FLDBs can be impacted by intrinsic (e.g., gait impairment) and/or environmental (e.g., walking surfaces) factors, their respective impacts have not been differentiated by the majority of free-living fall risk assessment methods. This may lead to the ambiguous interpretation of the subsequent FLDBs, and therefore, less precise intervention strategies to prevent falls.

Methods

With the aim of improving the interpretability of gait-related FLDBs and investigating the impact of environment on older adults’ gait, a vision-based framework was proposed to automatically detect the most common level walking surfaces. Using a belt-mounted camera and IMUs worn by fallers and non-fallers (mean age 73.6 yrs), a unique dataset (i.e., Multimodal Ambulatory Gait and Fall Risk Assessment in the Wild (MAGFRA-W)) was acquired. The frames and image patches attributed to nine participants’ gait were annotated: (a) outdoor terrains: pavement (asphalt, cement, outdoor bricks/tiles), gravel, grass/foliage, soil, snow/slush; and (b) indoor terrains: high-friction materials (e.g., carpet, laminated floor), wood, and tiles. A series of ConvNets were developed: EgoPlaceNet categorizes frames into indoor and outdoor; and EgoTerrainNet (with outdoor and indoor versions) detects the enclosed terrain type in patches. To improve the framework’s generalizability, an independent training dataset with 9,424 samples was curated from different databases including GTOS and MINC-2500, and used for pretrained models’ (e.g., MobileNetV2) fine-tuning.

Results

EgoPlaceNet detected outdoor and indoor scenes in MAGFRA-W with 97.36$$\%$$ % and 95.59$$\%$$ % (leave-one-subject-out) accuracies, respectively. EgoTerrainNet-Indoor and -Outdoor achieved high detection accuracies for pavement (87.63$$\%$$ % ), foliage (91.24$$\%$$ % ), gravel (95.12$$\%$$ % ), and high-friction materials (95.02$$\%$$ % ), which indicate the models’ high generalizabiliy.

Conclusions

Encouraging results suggest that the integration of wearable cameras and deep learning approaches can provide objective contextual information in an automated manner, towards context-aware FLDBs for gait and fall risk assessment in the wild.

Development of eye-hand coordination in typically developing children and adolescents assessed using a reach-to-grasp sequencing task

Eye-hand coordination is required to accurately perform daily activities that involve reaching, grasping and manipulating objects. Studies using aiming, grasping or sequencing tasks have shown a stereotypical temporal coupling pattern where the eyes are directed to the object in advance of the hand movement, which may facilitate the planning and execution required for reaching. While the temporal coordination between the ocular and manual systems has been extensively investigated in adults, relatively little is known about the typical development of eye-hand coordination. Therefore, the current study addressed an important knowledge gap by characterizing the profile of eye-hand coupling in typically developing school-age children (n = 57) and in a cohort of adults (n = 30). Eye and hand movements were recorded concurrently during the performance of a bead threading task which consists of four distinct movements: reach to bead, grasp, reach to needle, and thread. Results showed a moderate to high correlation between eye and hand latencies in children and adults, supporting that both movements were planned in parallel. Eye and reach latencies, latency differences, and dwell time during grasping and threading, showed significant age-related differences, suggesting eye-hand coupling becomes more efficient in adolescence. Furthermore, visual acuity, stereoacuity and accommodative facility were also found to be associated with the efficiency of eye-hand coordination in children. Results from this study can serve as reference values when examining eye and hand movement during the performance of fine motor skills in children with neurodevelopmental disorders.

IMU, sEMG, or their cross-correlation and temporal similarities: Which signal features detect lateral compensatory balance reactions more accurately?

BACKGROUND AND OBJECTIVE

Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide. While laboratory evidence supports the view that impaired ability to execute compensatory balance responses (CBRs) is linked to an increased risk of falling, existing unsupervised fall risk assessment methods are mainly focused on detecting changes in spatio-temporal gait parameters over time rather than naturally-occurring CBR events. To address the gap in available methods, this paper compares the capability of machine learning-based models trained on the kinematic data from inertial measurement units (IMU) and surface electromyography (sEMG) features to detect lateral CBRs, to ultimately address detection of CBRs in free-living conditions. Moreover, we propose a novel "Hybrid" feature set, which considers cross-correlation and temporal similarities between the normalized kinematic and sEMG signals.

METHODS

Focusing on frontal plane perturbations, a classifier to automatically: 1) detect lateral CBRs during normal gait, and 2) identify type (i.e., crossover, sidestep) using data from three wearable IMUs and 4 sEMG signals from the thigh (i.e., biceps femoris, rectus femoris) and lower leg muscles (i.e., gastrocnemious, tibialis anterior) was developed. In total, 600 trials (including 358 lateral CBRs) from 7 young, healthy adults were analyzed. The effects of feature type (IMU, sEMG, Hybrid) and sensor placement on the random forest-based classifier performance were investigated.

RESULTS

CBR detection (i.e., CBR vs normal gait) accuracies (leave-one-subject-out cross validation) were 83.95% and 99.21% using sEMG-based and IMU-based features, respectively, which dropped to 72.17% and 84.83% for the multiclass identification (i.e., side-step vs cross-over vs normal gait) problem. Findings yielded shank as the best overall location for the multiclass problem, and chest as the most accurate for CBR detection. In general, adding sEMG and Hybrid features to IMUs yielded incremental improvements in CBR detection and type identification (87.03% leave-one-subject-out cross-validation for type identification).

CONCLUSION

The findings of this study demonstrate that IMU-based features are favourable over sEMG and Hybrid features for the task of CBR detection, with incremental value for type identification. Evidence presented suggests that Hybrid features may increase performance for other wearable sensor applications (e.g. activity recognition systems).

Automated Detection of Multidirectional Compensatory Balance Reactions: A Step Towards Tracking Naturally Occurring Near Falls

Falls are the leading cause of fatal and non-fatal injuries among seniors with serious and costly consequences. Laboratory evidence supports the view that impaired ability to execute compensatory balance reactions (CBRs) or near-falls is linked to an increased risk of falling. Therefore, as an alternative to the commonly used fall risk assessment methods examining spatial-temporal parameters of gait, this study focuses on the development of machine learning-based models to detect multidirectional CBRs using wearable inertial measurement units (IMUs). Random forest models were developed based upon the data captured by five wearable IMUs to 1) detect CBRs during normal gait, and 2) identify the type of CBR (eight different classes). A perturbation treadmill (PT) was employed to systematically elicit CBRs (i.e. PT-CBRs) during walking in different directions (e.g slip-like, trip-like, and medio-lateral) and amplitudes (e.g., low-, high-amplitude). We hypothesized that these PT-CBRs could simulate naturally-occurring CBRs (N-CBRs). Proof-of-concept testing in 9 young, healthy adults demonstrated accuracies of 96.60% and 80.64% for the PT-CBR detection and type identification models, respectively. Performance of the detection model was tested against a published dataset (IMUFD) simulating N-CBRs, including the most common types observed in older adults in long-term care facilities, which achieved sensitivity of 100%, but poor specificity. Adding normal gait data from IMUFD for training improved specificity, indicating treadmill walking alone is insufficient exemplar data. Perturbation treadmill combined with overground walking data is a suitable paradigm to collect training datasets of involuntary CBR events. These findings suggest that accurate detection of naturally-occurring CBRs is feasible, and supports further investigation of implementing a wearable sensor system to track naturally-occurring CBRs as a novel means of fall risk assessment.

Concurrent maturation of visuomotor skills and motion perception in typically-developing children and adolescents

Perceptual and visuomotor skills undergo considerable development from early childhood into adolescence; however, the concurrent maturation of these skills has not yet been examined. This study assessed visuomotor function and motion perception in a cross-section of 226 typically-developing children between 4 and 16 years of age. Participants were tested on three tasks hypothesized to engage the dorsal visual stream: threading a bead on a needle, marking dots using a pen, and discriminating form defined by motion contrast. Mature performance was reached between 8 and 12 years, with youngest maturation for kinematic measures for a reach-to-grasp task, and oldest maturation for a precision tapping task. Performance on the motion perception task shared no association with motor skills after controlling for age.

Effect of Visual Information on Dominant and Non-dominant Hands During Bimanual Drawing with a Robotic Platform

In a stable bimanual trajectory tracing task with interlimb spatial and temporal synchrony, blocking the visual information from one hand may alter the performance of either hand. In this paper, we investigate the effect of visual information on motor behaviour of dominant and non-dominant hands during a bimanual task, with a focus on motor lateralization theory's anticipation for a more pronounced distortion on one hand due to visual information withdrawal. To address this question, four bimanual circle tracing experiments were designed with two rehabilitation robotic arms with real time visual feedback. Two experiments were conducted under the free-visual condition whereas the visual feedback from one hand was blocked for the other two. The in-depth analysis of the metrics extracted from 685 circles, drawn by 6 participants, revealed that non-dominant hand, when visible, generally performs worse than the dominant hand, for instance it exhibits less circularity. In their invisible modes, the performance of the dominant and non-dominant hands displayed inconsistent difference across the participants. Moreover, both hands showed a higher pace when partial visual information was available. Our findings using this robotic framework as a systematic tool on developing new paradigms are discussed.

Inertial wearables as pragmatic tools in dementia

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.

Evaluation of the Leap Motion Controller during the performance of visually-guided upper limb movements

Kinematic analysis of upper limb reaching provides insight into the central nervous system control of movements. Until recently, kinematic examination of motor control has been limited to studies conducted in traditional research laboratories because motion capture equipment used for data collection is not easily portable and expensive. A recently developed markerless system, the Leap Motion Controller (LMC), is a portable and inexpensive tracking device that allows recording of 3D hand and finger position. The main goal of this study was to assess the concurrent reliability and validity of the LMC as compared to the Optotrak, a criterion-standard motion capture system, for measures of temporal accuracy and peak velocity during the performance of upper limb, visually-guided movements. In experiment 1, 14 participants executed aiming movements to visual targets presented on a computer monitor. Bland-Altman analysis was conducted to assess the validity and limits of agreement for measures of temporal accuracy (movement time, duration of deceleration interval), peak velocity, and spatial accuracy (endpoint accuracy). In addition, a one-sample t-test was used to test the hypothesis that the error difference between measures obtained from Optotrak and LMC is zero. In experiment 2, 15 participants performed a Fitts’ type aiming task in order to assess whether the LMC is capable of assessing a well-known speed-accuracy trade-off relationship. Experiment 3 assessed the temporal coordination pattern during the performance of a sequence consisting of a reaching, grasping, and placement task in 15 participants. Results from the t-test showed that the error difference in temporal measures was significantly different from zero. Based on the results from the 3 experiments, the average temporal error in movement time was 40±44 ms, and the error in peak velocity was 0.024±0.103 m/s. The limits of agreement between the LMC and Optotrak for spatial accuracy measures ranged between 2–5 cm. Although the LMC system is a low-cost, highly portable system, which could facilitate collection of kinematic data outside of the traditional laboratory settings, the temporal and spatial errors may limit the use of the device in some settings.

Detection of compensatory balance responses using wearable electromyography sensors for fall-risk assessment

Loss of balance is prevalent in older adults and populations with gait and balance impairments. The present paper aims to develop a method to automatically distinguish compensatory balance responses (CBRs) from normal gait, based on activity patterns of muscles involved in maintaining balance. In this study, subjects were perturbed by lateral pushes while walking and surface electromyography (sEMG) signals were recorded from four muscles in their right leg. To extract sEMG time domain features, several filtering characteristics and segmentation approaches are examined. The performance of three classification methods, i.e., k-nearest neighbor, support vector machines, and random forests, were investigated for accurate detection of CBRs. Our results show that features extracted in the 50-200Hz band, segmented using peak sEMG amplitudes, and a random forest classifier detected CBRs with an accuracy of 92.35%. Moreover, our results support the important role of biceps femoris and rectus femoris muscles in stabilization and consequently discerning CBRs. This study contributes towards the development of wearable sensor systems to accurately and reliably monitor gait and balance control behavior in at-home settings (unsupervised conditions), over long periods of time, towards personalized fall risk assessment tools.