Can gait kinetic data predict femoral bone mineral density in elderly men and women aged 50 years and older?

Influence of low bone mineral density on risk of falls and gait in post-menopausal women and elderly: A systematic review

BackgroundLow bone mineral density (LBMD) significantly contributes to loss of independence, gait impairment, and increased fall risk. Instrumental gait analysis provides an accurate evaluation of walking ability, that represent the first step for a personalized rehabilitation.ObjectiveTo collect and describe the available literature on the effect of LBMD on walking characteristics and the use of motion analysis systems in patients with LBMD.MethodsWe performed a literature search of the last ten years on PubMed, Web of Science and Scopus of papers on older people and patients with LBMD in terms of gait parameters, balance, and fall risk. The review protocol was registered on PROSPERO (CRD42024590090).ResultsThe database search identified totally 756 records; after duplicates deletion, 13 were considered eligible. The results reported that subjects with LBMD had kinematic alterations of the walk, alterations of posture, speed of walking and the strength generated in the gait. Patients with osteoporosis show a reduction of gait speed and trunk asymmetry; moreover, there is a a decrease in body rotation and lower hip and ankle moments in post-menopausal women.ConclusionsPatients with LBMD showed gait alterations that can higher the risk of falls. In this context, gait analysis can be useful in detecting variations in pattern, symmetry, gait speed and posture in elderly patients, that can represent an essential step for a personalized rehabilitation program.

Dynamic Foot Pressure During Walking: A Potential Indicator of Bone Mineral Density

BACKGROUND

Physical skeletal loading can affect the bone mineral density (BMD). This study investigated the association between BMD and dynamic foot pressure during gait.

METHODS

A total of 104 patients (mean age, 62.6 ± 12.4 years; 23 male and 81 female) who underwent dual x-ray absorptiometry and pedobarography were included. BMD values of the lumbar spine, femoral neck, and total femur were assessed. The mean and maximum pressures were measured at the hallux, lesser toes, 1st metatarsal head, 2nd and 3rd metatarsal heads, 4th and 5th metatarsal heads, midfoot, medial heel, and lateral heel. Multivariable regression analysis was performed to identify factors significantly associated with BMD.

RESULTS

The lumbar spine BMD was significantly associated with the mean pressure at the 4th and 5th metatarsal heads (p = 0.041, adjusted R 2 of model = 0.081). The femoral neck BMD was significantly associated with the maximum pressure at the 2nd and 3rd metatarsal heads (p = 0.002, adjusted R 2 = 0.213). The total femoral BMD also showed a significant association with the maximum pressure at the 2nd and 3rd metatarsal heads (p = 0.003, adjusted R 2 = 0.360).

CONCLUSIONS

Foot plantar pressure during gait was significantly associated with BMD, and could potentially be used to predict the presence of osteoporosis.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Perspectives from research and practice: A survey on external load monitoring and bone in sport

Introduction

There is limited information regarding the association between external load and estimated bone load in sport, which may be important due to the influence exercise can have on bone accrual and injury risk. The aim of this study was to identify external load measuring tools used by support staff to estimate bone load and assess if these methodologies were supported in research.

Methods

A survey was comprised of 19 multiple choice questions and the option to elaborate on if/how they monitor external load and if/how they used them to estimate bone load. A narrative review was performed to assess how external load is associated to bone in research.

Results

Participants were required to be working as support staff in applied sport. Support staff (n = 71) were recruited worldwide with the majority (85%) working with professional elite athletes. 92% of support staff monitored external load in their organisation, but only 28% used it to estimate bone load.

Discussion

GPS is the most commonly used method to estimate bone load, but there is a lack of research assessing GPS metrics with bone load. Accelerometry and force plates were among the most prevalent methods used to assess external load, but a lack of bone specific measurements were reported by support staff. Further research exploring how external load relates to bone is needed as there is no consensus on which method of external load is best to estimate bone load in an applied setting.

Explainable Artificial Intelligence and Wearable Sensor-Based Gait Analysis to Identify Patients with Osteopenia and Sarcopenia in Daily Life

Osteopenia and sarcopenia can cause various senile diseases and are key factors related to the quality of life in old age. There is need for portable tools and methods that can analyze osteopenia and sarcopenia risks during daily life, rather than requiring a specialized hospital setting. Gait is a suitable indicator of musculoskeletal diseases; therefore, we analyzed the gait signal obtained from an inertial-sensor-based wearable gait device as a tool to manage bone loss and muscle loss in daily life. To analyze the inertial-sensor-based gait, the inertial signal was classified into seven gait phases, and descriptive statistical parameters were obtained for each gait phase. Subsequently, explainable artificial intelligence was utilized to analyze the contribution and importance of descriptive statistical parameters on osteopenia and sarcopenia. It was found that XGBoost yielded a high accuracy of 88.69% for osteopenia, whereas the random forest approach showed a high accuracy of 93.75% for sarcopenia. Transfer learning with a ResNet backbone exhibited appropriate performance but showed lower accuracy than the descriptive statistical parameter-based identification result. The proposed gait analysis method confirmed high classification accuracy and the statistical significance of gait factors that can be used for osteopenia and sarcopenia management.