Walking into aging: real-world mobility patterns and digital benchmarks from the InCHIANTI Study.

Mobility is a cornerstone of health and quality of life, particularly in older adults. Digital mobility outcomes (DMOs) from real-world walking data offer crucial insights into the functional status and early markers of mobility decline. This study provides reference values for walking activity, pace, rhythm, and gait bout-to-bout variability in community-dwelling older adults and evaluates the effects of age, sex, height, and weight on these parameters.

Evaluating the Accuracy and Reliability of Real-World Digital Mobility Outcomes in Older Adults After Hip Fracture: Cross-Sectional Observational Study.

Background: Algorithms estimating real-world digital mobility outcomes (DMOs) are increasingly validated in healthy adults and various disease cohorts. However, their accuracy and reliability in older adults after hip fracture, who often walk slowly for short durations, is underexplored.

Objective: This study examined DMO accuracy and reliability in a hip fracture cohort considering walking bout (WB) duration, physical function, days since surgery, and walking aid use.

Association of Cut-Point Free Metrics and Common Clinical Tests Among Older Adults After Proximal Femoral Fracture.

Wearable and lightweight devices facilitate real-world physical activity (PA) assessments. MX metrics, as a cut-point-free parameter, evaluate acceleration above which the most active X minutes are accumulated. It provides insights into the intensity of PA over specific durations.

Automated Multicohort Mobility Assessment With an Instrumented L-Test (iL-test).

The L-test is a performance-based measure to assess balance and mobility. Currently, the primary outcome from this test is the time required to finish it. In this study we present the instrumented L-test (iL-test), an L-test wherein mobility is evaluated by means of a wearable inertial sensor worn at the lower back.

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults.

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data.

Imbalance and Falls in Patients with Parkinson's Disease: Causes and Recent Developments in Training and Sensor-Based Assessment.

Imbalance and falls in patients with Parkinson's disease (PD) do not only reduce their quality of life but also their life expectancy. Aging-related symptoms as well as disease-specific motor and non-motor symptoms contribute to these conditions and should be treated when appropriate. In addition to an active lifestyle, advanced exercise training is useful and effective, especially for less medically responsive symptoms such as freezing of gait and postural instability at advanced stages.

Effects of multidisciplinary inpatient rehabilitation on everyday life physical activity and gait in patients with multiple sclerosis.

Background: Multiple sclerosis is a progressive neurological disease that affects the central nervous system, resulting in various symptoms. Among these, impaired mobility and fatigue stand out as the most prevalent. The progressive worsening of symptoms adversely alters quality of life, social interactions and participation in activities of daily living.

Effects of physiotherapy and home-based training in parkinsonian syndromes: protocol for a randomised controlled trial (MobilityAPP).

Introduction: Gait and mobility impairment are pivotal signs of parkinsonism, and they are particularly severe in atypical parkinsonian disorders including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). A pilot study demonstrated a significant improvement of gait in patients with MSA of parkinsonian type (MSA-P) after physiotherapy and matching home-based exercise, as reflected by sensor-based gait parameters. In this study, we aim to investigate whether a gait-focused physiotherapy (GPT) and matching home-based exercise lead to a greater improvement of gait performance compared with a standard physiotherapy/home-based exercise programme (standard physiotherapy, SPT).

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.

Background: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing.

Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist-Worn Accelerometer Data: Development and Validation of ElderNet.

Progressive gait impairment is common in aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data.

It needs more than a myocardial infarction to start exercising: the CoLaus|PsyCoLaus prospective study.

Background: Increased physical activity (PA) is recommended after an acute coronary event to prevent recurrences. Whether patients with acute coronary event actually increase their PA has not been assessed using objective methods such as accelerometer. We aimed to assess the subjectively and objectively measured physical activity (PA) levels of patients before and after an acute coronary event.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.

Methods: Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions.

Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts).

A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings.

Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e.

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization.

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored.

Design and validation of a multi-task, multi-context protocol for real-world gait simulation.

Background: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety.

Methods: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking.

Impact of adherence to a lifestyle-integrated programme on physical function and behavioural complexity in young older adults at risk of functional decline: a multicentre RCT secondary analysis.

Context: Long-term adherence to physical activity (PA) interventions is challenging. The Lifestyle-integrated Functional Exercise programmes were adapted Lifestyle-integrated Functional Exercise (aLiFE) to include more challenging activities and a behavioural change framework, and then enhanced Lifestyle-integrated Functional Exercise (eLiFE) to be delivered using smartphones and smartwatches.

Objectives: To (1) compare adherence measures, (2) identify determinants of adherence and (3) assess the impact on outcome measures of a lifestyle-integrated programme.

Dual-Task Treadmill Training for the Prevention of Falls in Parkinson's Disease: Rationale and Study Design.

Various factors, such as fear of falling, postural instability, and altered executive function, contribute to the high risk of falling in Parkinson's disease (PD). Dual-task training is an established method to reduce this risk. Motor-perceptual task combinations typically require a patient to walk while simultaneously engaging in a perceptual task.

Concurrent Evolution of Biomechanical and Physiological Parameters With Running-Induced Acute Fatigue.

Understanding the influence of running-induced acute fatigue on the homeostasis of the body is essential to mitigate the adverse effects and optimize positive adaptations to training. Fatigue is a multifactorial phenomenon, which influences biomechanical, physiological, and psychological facets. This work aimed to assess the evolution of these three facets with acute fatigue during a half-marathon.

Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors.

Long-term monitoring of real-life physical activity (PA) using wearable devices is increasingly used in clinical and epidemiological studies. The quality of the recorded data is an important issue, as unreliable data may negatively affect the outcome measures. A potential source of bias in PA assessment is the non-wearing of a device during the expected monitoring period.

Technical validation of real-world monitoring of gait: a multicentric observational study.

Introduction: Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations.