Efficiency assessment of follow-up methodology of patients with knee replacement to predict post-surgical functionality: a protocol for randomised control PROKnee trial.

INTRODUCTION: Even when total knee arthroplasty (TKA) is an extended treatment, most patients experience a suboptimal evolution after TKA. The objectives of this study are the following: (1) to determine the effectiveness of two different prosthesis stabilisation systems on the functionality in activities of daily life, and (2) to determine prognostic biomarkers of knee prosthesis function based on radiological information, quantification of cytokines, intra-articular markers and biomechanical functional evaluation to predict successful evolution. METHODS AND ANALYSIS: The PROKnee trial was designed as a randomised controlled patient-blinded trial with two parallel groups that are currently ongoing. The initial recruitment will be 99 patients scheduled for their first TKA, without previous prosthesis interventions in lower limbs, who will be randomly divided into two groups that differed in the stabilisation methodology incorporated in the knee prosthesis: the MEDIAL-pivot group and the CENTRAL-pivot group. The maximum walking speed will be reported as the primary outcome, and the secondary results will be patient-reported questionnaires related to physical status, cognitive and mental state, radiological test, laboratory analysis and biomechanical instrumented functional performance, such as the 6-minute walking test, timed up-and-go test, gait, sit-to-stand, step-over, and ability to step up and down stairs. All the results will be measured 1 week before TKA and at 1.5, 3, 6 and 12 months after surgery. ETHICS AND DISSEMINATION: All procedures were approved by the Ethical Committee for Research with Medicines of the University Clinical Hospital of Valencia on 8 October 2020 (order no. 2020/181). Participants are required to provide informed consent for the study and for the surgical procedure. All the data collected will be treated confidentially since they will be blinded and encrypted. The results from the trial will be published in international peer-reviewed scientific journals, regardless of whether these results are negative or inconclusive. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT04850300).

The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment.

Falls in older people are a major health concern as the leading cause of disability and the second most common cause of accidental death. We developed a rapid fall risk assessment based on a combination of physical performance measurements made with an inertial sensor embedded in a smartphone. This study aimed to evaluate and validate the reliability and accuracy of an easy-to-use smartphone fall risk assessment by comparing it with the Physiological Profile Assessment (PPA) results. Sixty-five participants older than 55 performed a variation of the Timed Up and Go test using smartphone sensors. Balance and gait parameters were calculated, and their reliability was assessed by the (ICC) and compared with the PPAs. Since the PPA allows classification into six levels of fall risk, the data obtained from the smartphone assessment were categorised into six equivalent levels using different parametric and nonparametric classifier models with neural networks. The F1 score and geometric mean of each model were also calculated. All selected parameters showed ICCs around 0.9. The best classifier, in terms of accuracy, was the nonparametric mixed input data model with a 100% success rate in the classification category. In conclusion, fall risk can be reliably assessed using a simple, fast smartphone protocol that allows accurate fall risk classification among older people and can be a useful screening tool in clinical settings.

Classification of Parkinson's disease stages with a two-stage deep neural network.

Introduction: Parkinson's disease is one of the most prevalent neurodegenerative diseases. In the most advanced stages, PD produces motor dysfunction that impairs basic activities of daily living such as balance, gait, sitting, or standing. Early identification allows healthcare personnel to intervene more effectively in rehabilitation. Understanding the altered aspects and impact on the progression of the disease is important for improving the quality of life. This study proposes a two-stage neural network model for the classifying the initial stages of PD using data recorded with smartphone sensors during a modified Timed Up & Go test. Methods: The proposed model consists on two stages: in the first stage, a semantic segmentation of the raw sensor signals classifies the activities included in the test and obtains biomechanical variables that are considered clinically relevant parameters for functional assessment. The second stage is a neural network with three input branches: one with the biomechanical variables, one with the spectrogram image of the sensor signals, and the third with the raw sensor signals. Results: This stage employs convolutional layers and long short-term memory. The results show a mean accuracy of 99.64% for the stratified k-fold training/validation process and 100% success rate of participants in the test phase. Discussion: The proposed model is capable of identifying the three initial stages of Parkinson's disease using a 2-min functional test. The test easy instrumentation requirements and short duration make it feasible for use feasible in the clinical context.

Assessment of Functional Activities in Individuals with Parkinson's Disease Using a Simple and Reliable Smartphone-Based Procedure.

Parkinson's disease (PD) is a progressive neurodegenerative disorder leading to functional impairment. In order to monitor the progression of the disease and to implement individualized therapeutic approaches, functional assessments are paramount. The aim of this study was to determine the impact of PD on balance, gait, turn-to-sit and sit-to-stand by means of a single short-duration reliable test using a single inertial measurement unit embedded in a smartphone device. Study participants included 29 individuals with mild-to moderate PD (PG) and 31 age-matched healthy counterparts (CG). Functional assessment with FallSkip® included postural control (i.e., Medial-Lateral (ML) and Anterior-Posterior (AP) displacements), gait (Vertical (V) and Medial-Lateral (ML) ranges), turn-to-sit (time) and sit-to-stand (power) tests, total time and gait reaction time. Our results disclosed a reliable procedure (intra-class correlation coefficient (ICC) = 0.58-0.92). PG displayed significantly larger ML and AP displacements during the postural test, a decrease in ML range while walking and a longer time needed to perform the turn-to-sit task than CG (p < 0.05). No differences between groups were found for V range, sit-to-stand test, total time and reaction time (p > 0.05). In conclusion, people with mild-to-moderate PD exhibit impaired postural control, altered gait strategy and slower turn-to-sit performance than age-matched healthy people.

Mobility assessment in people with Alzheimer disease using smartphone sensors.

BACKGROUND: Understanding the functional status of people with Alzheimer Disease (AD), both in a single (ST) and cognitive dual task (DT) activities is essential for identifying signs of early-stage neurodegeneration. This study aims to compare the performance quality of several tasks using sensors embedded in an Android device, among people at different stages of Alzheimer and people without dementia. The secondary aim is to analyze the effect of cognitive task performance on mobility tasks. METHODS: This is a cross-sectional study including 22 participants in the control group (CG), 18 in the group with mild AD and 22 in the group with moderate AD. They performed two mobility tests, under ST and DT conditions, which were registered using an Android device. Postural control was measured by medial-lateral and anterior-posterior displacements of the COM (MLDisp and APDisp, respectively) and gait, with the vertical and medial-lateral range of the COM (Vrange and MLrange). Further, the sit-to-stand (PStand) and turning and sit power (PTurnSit), the total time required to complete the test and the reaction time were measured. RESULTS: There were no differences between the two AD stages either for ST or DT in any of the variables (p > 0.05). Nevertheless, people at both stages showed significantly lower values of PStand and PTurnSit and larger Total time and Reaction time compared to CG (p < 0.05). Further, Vrange is also lower in CDR1G than in CG (p < 0.05). The DT had a significant deleterious effect on MLDisp in all groups (p < 0.05) and on APDisp only in moderate AD for DT. CONCLUSIONS: Our findings indicate that AD patients present impairments in some key functional abilities, such as gait, turning and sitting, sit to stand, and reaction time, both in mild and moderate AD. Nevertheless, an exclusively cognitive task only influences the postural control in people with AD.