Association between wrist-worn free-living accelerometry and hand grip strength in middle-aged and older adults.

INTRODUCTION: Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices. METHODS: The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults. RESULTS: The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females). CONCLUSIONS: The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload.

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject's pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

Light- and drug-induced pupillary dynamics in eyes with a retropupillary iris-claw intraocular lens.

PURPOSE: We evaluated the pupillary characteristics and response to light and drugs in eyes with posterior chamber (PC) placement of iris-claw intraocular lens (IC-IOL). METHODS: In this cross-sectional, comparative study, we included adults with an IC-IOL implanted in the PC of a single eye. We excluded patients with ocular trauma, postoperative IC-IOL displacement or complications, and extended iris atrophy. We used anterior segment optical coherence tomography to perform light-controlled pupillography, measure the pupil diameter (PD), and estimated the pupil circularity under mesopic conditions. PD was also assessed under photopic, scotopic, pharmacological mydriasis, and miosis conditions. The results were compared to those of the fellow eye, phakic, or regular pseudophakic. RESULTS: The IC-IOL and control groups included 30 eyes each. The most frequent reasons for IC-IOL implantation were complicated cataract (37%) and dislocated/luxated prior IOL (33%). Compared to the control group, the IC-IOL group had lower visual acuity, a smaller PD under scotopic conditions (p = 0.0010) and after pharmacological mydriasis (p < 0.0001), and a larger PD after pharmacological miosis (p < 0.0001). Mesopic pupil circularity was comparable between the groups. We also considered ongoing extraocular treatments with possible effects on iris motility. CONCLUSIONS: The pupillary size and profile were similar between the groups in mesopic light. Reduced mydriasis was noted in response to light and drugs, while the degree of miosis was reduced in response to inducing drugs in the IC-IOL compared to the control group. This study complements previous results concerning the PC placement of IC-IOLs by adding original observations on drug-induced pupil motility.

12-year follow-up of the first endothelial keratoplasty without Descemet stripping in a 3-month newborn with Congenital Hereditary Endothelial Dystrophy (CHED).

BACKGROUND: Endothelial Keratoplasty (EK) is now considered as the standard treatment for Congenital Hereditary Endothelial Dystrophy (CHED) by many surgeons. We present the 12-year clinical outcome of the youngest operated patient with CHED in which we successfully performed a bilateral EK procedure without removing the recipient endothelium-Descemet complex. CASE PRESENTATION: In November 2010 we performed EK without Descemet Stripping in a 3-month female newborn, thinking that the lower manipulation obtained by leaving the recipient endothelium-Descemet complex could be the key factor for the success of our surgery. Such a particular technique was new in newborns. The surgery was a success, but the long-term visual result was not predictable at that time. We followed the patient at 4 months, and then yearly. At the latest visit in October 2022 the visual, cognitive, and motorial developments were normal, with Best-corrected Distance Visual Acuity of 0.4 LogMAR with - 0.75 D sf + 2.75 D cyl @ 105° in the right eye (RE) and 0.4 LogMAR with + 1.50 D sf + 2.50 D cyl @ 60° in the left eye (LE). The endothelial microscope showed an unexpected healthy endothelium, with a cell count of 2383 cells/mm2 in the RE and of 2547 cells/mm2 in the LE from a starting donor count of 2900 cells/mm2. No secondary procedures were performed during the 12-year follow-up. CONCLUSION: EK without Descemet stripping has proved to be a successful procedure over time in our newborn. The unexpected healthy endothelium suggests a role of the Descemet membrane in CHED.

Posterior capsule dynamics during femtosecond laser lens fragmentation.

PURPOSE: The dynamics of the posterior capsule during femtosecond laser lens fragmentation has received little attention in the literature. We analysed the movements of the posterior capsule to identify the rupture risk factors, if any, and to suggest possible modification of the laser spot energy pattern during fragmentation. MATERIALS AND METHODS: Posterior capsule ruptures during fragmentation were identified over a 10-year period of femtosecond laser use. In addition, the dynamics of the posterior capsule were identified through the real-time swept-source OCT lateral view available during the surgeries. RESULTS: Out of the 1465 laser cataract procedures performed, we recorded 1 case of posterior capsule rupture during lens fragmentation, which was caused by eye movement that was detected but ignored by the surgeon. Three types of posterior capsule dynamics were identified, all related to a gas bubble formation during the first part of the lens fragmentation. In eyes with a hard nucleus, the concussion of the posterior capsule was evident, however, with no capsule rupture. DISCUSSION: Maintaining good docking throughout the whole procedure seems important in avoiding a posterior capsule cut by the femtosecond laser. In addition, a Gaussian pattern of spot energy is suggested when fragmenting hard cataracts.

One-dioptre toric IOL versus spherical IOL in eyes with low preoperative corneal astigmatism.

PURPOSE: To investigate the advantages/disadvantages of a 1.0 D toric IOL vs spherical IOL after regular phacoemulsification in eyes with preoperative astigmatism ≤ 1 D. METHODS: Retrospective comparative series involving pseudophakic eyes with preoperative topographic astigmatism ≤ 1.0 D implanted either with monofocal 1.0 D Toric IOL (T-group), or with spherical IOL (S-group). The postoperative refractive astigmatism (PRA, i.e. surgically induced + corneal) was the main outcome; also considered in the analyses were the uncorrected and best-corrected distance visual acuity (VA). The data were referred to the last postoperative follow-up visit, 2 to 4 months after surgery. RESULTS: A total of 60 eyes were included: 30 in the T-group and 30 in the S-group, matched for patient's age, laterality, and axial length. Before surgery, the mean corneal astigmatism was 0.62 ± 0.39 D in the T-group and 0.54 ± 0.33 D in the S-group (p = 0.4). In the S-group, PRA was 0.73 ± 0.37 D, higher than the corresponding preoperative corneal astigmatism (p = 0.040). In the T-group, PRA was 0.58 ± 0.31 D; the variation was not statistically significant. Uncorrected VA was significantly better in the T-group vs the S-group (p = 0.007), and the best-corrected VA was comparable in the two groups. CONCLUSION: The present study indicated that in eyes with very low preoperative astigmatism, 1.0 D toric IOLs were able to limit the increase of the PRA instead of those observed with the spherical IOLs. This could support the better uncorrected VA recorded in the T-group.

Tilt and decentration of posterior and anterior iris-claw intraocular lenses: a pilot study using anterior segment optical coherence tomography.

BACKGROUND: Information on the centration and tilt of iris-claw intraocular lenses (IC-IOLs) is limited. In this study, we tested the capacity of an anterior segment optical coherence tomography (AS-OCT) instrument to measure decentration and tilt of anterior and posterior IC-IOLs through an integrated software. METHODS: The present observational, cross-sectional study was conducted at University Eye Clinic of Parma (Parma, Italy). The CASIA2 swept-source AS-OCT (Tomey Corp.) was used to measure the tilt and decentration of posterior and anterior IC-IOLs in patients implanted at least 6 months in advance. After failure with full-automation, semi-manual IOL tracing was applied. In-the-bag (IB) contralateral IOLs, when present, were measured automatically. The Bland-Altman method was used to evaluate the agreement between repeated measurements (2 images for each study eye). The amount and direction of tilt and decentration were recorded and plotted into polar charts for evaluation. RESULTS: A total of 21 patients were included: 14 with posterior and 7 with anterior IC-IOL fixation. In 17 eyes (81%), the AS-OCT provided a repeatable measurement of tilt and decentration. All contralateral eyes with IB IOL were automatically measured. The median decentration was 0.67 mm, 0.24 mm, and 0.24 mm in posterior IC-IOLs, anterior IC-IOLs, and IB IOLs group, respectively. The median tilt was 5.0°, 5.6°, and 5.6° for posterior IC-IOLs, anterior IC-IOLs, and IB IOLs, respectively. Tilt direction was mainly temporal, while decentration was inferior-temporal with posterior IC-IOLs and scattered with anterior IC-IOLs and IB IOLs. CONCLUSIONS: The semi-manual tracing function of the CASIA2 AS-OCT provides repeatable and affordable measurements of the decentration and tilt of IC-IOLs in both the anterior and posterior chamber. Data from the former group were similar to the IB group.

Test-Retest Reliability of Acoustic Emission Sensing of the Knee during Physical Tasks.

Acoustic emission (AE) sensing is an increasingly researched topic in the context of orthopedics and has a potentially high diagnostic value in the non-invasive assessment of joint disorders, such as osteoarthritis and implant loosening. However, a high level of reliability associated with the technology is necessary to make it appropriate for use as a clinical tool. This paper presents a test-retest and intrasession reliability evaluation of AE measurements of the knee during physical tasks: cycling, knee lifts and single-leg squats. Three sessions, each involving eight healthy volunteers were conducted. For the cycling activity, ICCs ranged from 0.538 to 0.901, while the knee lifts and single-leg squats showed poor reliability (ICC < 0.5). Intrasession ICCs ranged from 0.903 to 0.984 for cycling and from 0.600 to 0.901 for the other tasks. The results of this study show that movement consistency across multiple recordings and minimizing the influence of motion artifacts are essential for higher test reliability. It was shown that motion artifact resistant sensor mounting and the use of baseline movements to assess sensor attachment can improve the sensing reliability of AE techniques. Moreover, constrained movements, specifically cycling, show better inter- and intrasession reliability than unconstrained exercises.

Parsplana vitrectomy alone versus parsplana vitrectomy combined with phacoemulsification for the treatment of rhegmatogenous retinal detachment: a randomized study.

BACKGROUND: To compare parsplana vitrectomy (PPV) with and without phacoemulsification to treat rhegmatogenous retinal detachment (RRD). METHODS: Subjects aged 48-65 years with RRD in a phakic eye due to superior retinal tears with an overall extension of retinal breaks < 90° underwent to PPV alone (group A); or PPV plus phacoemulsification (phacovitrectomy, PCV, group B). Post-operative follow-up visits occurred at 1 week, 1 month (m1), 3 months (m3), and 6 months (m6) after surgery. The main outcome was the rate of retinal reattachment. Secondary outcomes included best-corrected visual acuity (BCVA), intraocular pressure (IOP), central macular thickness (CMT), and cataract progression (in the lens-sparing [PPV-alone] group). RESULTS: In this initial phase of the study a total of 59 patients (mean age: 55 years, 59 eyes) were enrolled: 29 eyes in group A and 30 eyes in group B. Both groups had similar gas tamponade. During the follow-up there were three cases of RRD recurrence in group A and one in group B. The relative risk of recurrence in group A was 3.22 times higher but the difference was not significant (p = 0.3). The two groups were also similar in terms of BCVA and IOP variation. At m3, CMT was significantly higher in group B (p = 0.014). In group A, cataract progression was significant at m6 (p = 0.003). CONCLUSIONS: In a cohort of RRD patients selected according to their preoperative clinical characteristics, PPV was comparable to PCV in terms of the rate of retinal reattachment after 6 months. TRIAL REGISTRATION: ISRCTN15940019 . Date registered: 15/01/2021 (retrospectively registered).

Feasibility of Sensor Technology for Balance Assessment in Home Rehabilitation Settings.

The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings.

Daily step count and incident diabetes in community-dwelling 70-year-olds: a prospective cohort study.

BACKGROUND: Older adults with diabetes take fewer steps per day than those without diabetes. The purpose of the present study was to investigate the association of daily step count with incident diabetes in community-dwelling 70-year-olds. METHODS: This prospective cohort study included N = 3055 community-dwelling 70-year-olds (52% women) who participated in a health examination in Umeå, Sweden during 2012-2017, and who were free from diabetes at baseline. Daily step count was measured for 1 week using Actigraph GT3X+ accelerometers. Cases of diabetes were collected from the Swedish National Patient Register. The dose-response association was evaluated graphically using a flexible parametric model, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated using Cox regressions. RESULTS: During a mean follow-up of 2.6 years, diabetes was diagnosed in 81 participants. There was an inverse nonlinear dose-response association between daily step count and incident diabetes, with a steep decline in risk of diabetes from a higher daily step count until around 6000 steps/day. From there, the risk decreased at a slower rate until it leveled off at around 8000 steps/day. A threshold of 4500 steps/day was found to best distinguish participants with the lowest risk of diabetes, where those taking ≥ 4500 steps/day, had 59% lower risk of diabetes, compared to those taking fewer steps (HR, 0.41, 95% CI, 0.25-0.66). Adjusting for visceral adipose tissue (VAT) attenuated the association (HR, 0.64, 95% CI, 0.38-1.06), which was marginally altered after further adjusting for sedentary time, education and other cardiometabolic risk factors and diseases (HR, 0.58, 95% CI, 0.32-1.05). CONCLUSIONS: A higher daily step count is associated with lower risk of incident diabetes in community-dwelling 70-year-olds. The greatest benefits occur at the lower end of the activity range, and much earlier than 10,000 steps/day. With the limitation of being an observational study, these findings suggest that promoting even a modest increase in daily step count may help to reduce the risk of diabetes in older adults. Because VAT appears to partly mediate the association, lifestyle interventions targeting diabetes should apart from promoting physical activity also aim to prevent and reduce central obesity.

Motion Capture Technology in Industrial Applications: A Systematic Review.

The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.

Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition.

Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task that a machine learning algorithm is trained for requires separating multiple activities such as walking, running, sitting, and falling from each other. Despite a large body of work on multi-class HAR, and the well-known fact that the performance on a multi-class problem can be significantly affected by how it is decomposed into a set of binary problems, there has been little research into how the choice of multi-class decomposition method affects the performance of HAR systems. This paper presents the first empirical comparison of multi-class decomposition methods in a HAR context by estimating the performance of five machine learning algorithms when used in their multi-class formulation, with four popular multi-class decomposition methods, five expert hierarchies-nested dichotomies constructed from domain knowledge-or an ensemble of expert hierarchies on a 17-class HAR data-set which consists of features extracted from tri-axial accelerometer and gyroscope signals. We further compare performance on two binary classification problems, each based on the topmost dichotomy of an expert hierarchy. The results show that expert hierarchies can indeed compete with one-vs-all, both on the original multi-class problem and on a more general binary classification problem, such as that induced by an expert hierarchy's topmost dichotomy. Finally, we show that an ensemble of expert hierarchies performs better than one-vs-all and comparably to one-vs-one, despite being of lower time and space complexity, on the multi-class problem, and outperforms all other multi-class decomposition methods on the two dichotomous problems.

Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance.

The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many users achieves better subject-independent performance than one trained with data from a single person, and whether one trained with data from a single specific target user performs better for that user than one trained with data from many. To those ends, we compare four popular machine learning algorithms' subject-dependent and subject-independent performances across eight datasets using three different personalisation-generalisation approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). We further consider three different ways to construct such an ensemble: unweighted, κ -weighted, and baseline-feature-weighted. Our analysis shows that PSMs outperform PIMs by 43.5% in terms of their subject-dependent performances, whereas PIMs outperform PSMs by 55.9% and κ -weighted EPSMs-the best-performing EPSM type-by 16.4% in terms of the subject-independent performance.

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players.

Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5-10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred.

Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review.

In the last few years, estimating ground reaction forces by means of wearable sensors has come to be a challenging research topic paving the way to kinetic analysis and sport performance testing outside of labs. One possible approach involves estimating the ground reaction forces from kinematic data obtained by inertial measurement units (IMUs) worn by the subject. As estimating kinetic quantities from kinematic data is not an easy task, several models and protocols have been developed over the years. Non-wearable sensors, such as optoelectronic systems along with force platforms, remain the most accurate systems to record motion. In this review, we identified, selected and categorized the methodologies for estimating the ground reaction forces from IMUs as proposed across the years. Scopus, Google Scholar, IEEE Xplore, and PubMed databases were interrogated on the topic of Ground Reaction Forces estimation based on kinematic data obtained by IMUs. The identified papers were classified according to the methodology proposed: (i) methods based on direct modelling; (ii) methods based on machine learning. The methods based on direct modelling were further classified according to the task studied (walking, running, jumping, etc.). Finally, we comparatively examined the methods in order to identify the most reliable approaches for the implementation of a ground reaction force estimator based on IMU data.

A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry.

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future.

Utilising dynamic motor control index to identify age-related differences in neuromuscular control.

PURPOSE: Considering the relationship between aging and neuromuscular control decline, early detection of age-related changes can ensure that timely interventions are implemented to attenuate or restore neuromuscular deficits. The dynamic motor control index (DMCI), a measure based on variance accounted for (VAF) by one muscle synergy (MS), is a metric used to assess age-related changes in neuromuscular control. The aim of the study was to investigate the use of one-synergy VAF, and consecutively DMCI, in assessing age-related changes in neuromuscular control over a range of exercises with varying difficulty. METHODS: Thirty-one subjects walked on a flat and inclined treadmill, as well as performed forward and lateral stepping up tasks. Motion and muscular activity were recorded, and muscle synergy analysis was conducted using one-synergy VAF, DMCI, and number of synergies. RESULTS: Difference between older and younger group was observed for one-synergy VAF, DMCI for forward stepping up task (one-synergy VAF difference of 2.45 (0.22, 4.68) and DMCI of 9.21 (0.81, 17.61), p = 0.033), but not for lateral stepping up or walking. CONCLUSION: The use of VAF based metrics and specifically DMCI, rather than number of MS, in combination with stepping forward exercise can provide a low-cost and easy to implement approach for assessing neuromuscular control in clinical settings.

A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables.

Wearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods, such as machine learning algorithms, to determine fatigue during shoulder joint movements, which have complex biomechanics. The presented dataset aims to provide data collected via wearable sensors during a fatigue protocol involving dynamic shoulder internal rotation (IR) and external rotation (ER) movements. Thirty-four healthy subjects performed shoulder IR and ER movements with different percentages of maximal voluntary isometric contraction (MVIC) force until they reached the maximal exertion. The dataset includes demographic information, anthropometric measurements, MVIC force measurements, and digital data captured via surface electromyography, inertial measurement unit, and photoplethysmography, as well as self-reported assessments using the Borg rating scale of perceived exertion and the Karolinska sleepiness scale. This comprehensive dataset provides valuable insights into physical fatigue assessment, allowing the development of fatigue detection/prediction algorithms and the study of human biomechanical characteristics during shoulder movements within a fatigue protocol.

Implementation of a Frailty Care Bundle (FCB) Targeting Mobilisation, Nutrition and Cognitive Engagement to Reduce Hospital Associated Decline in Older Orthopaedic Trauma Patients: Pretest-Posttest Intervention Study.

Objective: To implement and evaluate a Frailty Care Bundle (FCB) targeting mobilisation, nutrition, and cognition in older trauma patients to reduce hospital associated decline. Methods: We used a two group, pretest-posttest design. The FCB intervention was delivered on two orthopaedic wards and two rehabilitation wards, guided by behaviour change theory (COM-B) to implement changes in ward routines (patient mobility goals, nurse assisted mobilisation, mealtimes, communication). Primary outcomes were patient participants' return to pre-trauma functional capability (modified Barthel Index - mBI) at 6-8 weeks post-hospital discharge and average hospital daily step-count. Statistical analysis compared pre versus post FCB group differences using ordinal regression and log-linear models. Results: We recruited 120 patients (pre n=60 and post n=60), and 74 (pre n=43, post n=36) were retained at follow-up. Median age was 78 years and 83% were female. There was a non-significant trend for higher mBI scores (improved function) in the post compared to pre FCB group (OR 2.29, 95% CI 0.98-5.36), associated with an average 11% increase in step-count. Conclusion: It was feasible, during the Covid-19 pandemic, for multidisciplinary teams to implement elements of the FCB. Clinical facilitation supported teams to prioritise fundamental care above competing demands, but sustainability requires ongoing attention. ISRCTN registry: ISRCTN15145850 (https://doi.org/10.1186/ISRCTN15145850).