Falls are a major concern in people with Parkinson's disease (PwPD). Although perturbation-based balance training could reduce fall risk, it remains unclear whether PwPD can tolerate and adapt to repeated large-scale stance perturbations. This study examined the feasibility of applying large-scale standing-slips to PwPD and their capacity for rapid adaptation to recurrent slips. Ten PwPD experienced five unexpected standing-slips induced by a standardized-magnitude acceleration-deceleration treadmill perturbation on an ActiveStep treadmill, while wearing a safety harness. Dynamic gait stability and limb support were calculated at slip onset (ON) and at recovery step liftoff (LO) and touchdown (TD), serving as the primary outcome measures. Secondary outcomes included recovery-step latency, duration, length, speed, slip distance, and faller rate. Adaptive changes across trials were assessed using repeated-measures ANOVA or Friedman tests, followed by paired t-tests or Wilcoxon tests, respectively. No participants reported injuries or discomfort related to the slips. They demonstrated significant improvements in dynamic gait stability at ON, LO, and TD (p = 0.005 - 0.017) and limb support (p = 0.010 at LO and 0.020 at TD) across repeated slips. Recovery step latency was shorter in later trials (p = 0.013), accompanied by a reduction in slip distance at LO (p = 0.018). The faller rate declined from 70% on the first slip to 40% on the last (p = 0.187). PwPD tolerated high-intensity standing-slips and rapidly adapted to repeated slips by refining dynamic gait stability, limb support, and recovery step performance during slips. The findings can inform future trials of applying standing-slips to PwPD to reduce their fall risk.

Adiposity-related mechanisms likely impair performance and dynamic balance, leading to fall. This study aims to assess the role of sarcopenia and physical performance tests in predicting falls among individuals with increased body mass index (BMI) (≥ 25 kg/m[2]) compared to those with normal BMI (< 25 kg/m[2]). This cross-sectional study recruited 604 community-dwelling adults (≥ 60 years). Body composition, fall history, sarcopenia, timed-up and go (TUG), handgrip strength (HGS), and gait speed (GS) were assessed. Participants were grouped by BMI and fall history. Logistic regression model was adjusted for potential confounders. Individuals with increased BMI had higher odds of sarcopenia than non-fallers with normal BMI. Among BMI < 25 kg/m[2] fallers, low GS was higher (OR (95% CI) 1.64 (1.04-2.58)), but was attenuated by comorbidities. Low TUG was significant in both BMI groups, but only remained significant for normal BMI fallers (OR (95% CI) 3.74 (1.31-10.72)), potentially due to anthropometric measures. Reduced HGS was significant within BMI ≥ 25 kg/m[2], but was influenced by fat percentage. HGS demonstrated better discriminatory ability for falls in older adults with increased BMI. The higher odds of sarcopenia among fallers with high BMI highlight the need for intervention studies addressing sarcopenic obesity as a growing risk factor for falls.

Individuals with Parkinson's disease (PD) often experience a misalignment between their perceived and actual balance ability, known as balance discordance, which has been associated with falls. We examined whether high-intensity balance and gait training (HiBalance) alters balance discordance in individuals with PD, and whether baseline sedentary behavior influences these changes. A secondary analysis examining pre- to post-intervention discordance changes of two HiBalance clinical trials (N = 97) using linear regression with interactions between sedentary behavior and pre-intervention discordance. The sample included two cohorts: one clinical and one research based. The model including sedentary behavior and its interaction with pre-intervention discordance explained 49% of variance. Significant predictors of post-intervention discordance were pre-intervention discordance (β = 8.78, p < 0.001) and cohort (β = 8.82, p = 0.006), while the interaction between pre-intervention discordance and sedentary time did not reach significance (β = -2.64, p = 0.05). Sensitivity analyses revealed that the clinical-based cohort model explained 24.7% of the variance in post-intervention discordance, with the interaction between pre-intervention discordance and sedentary time (β = -7.23, p = 0.004) as a significant predictor. HiBalance training did not significantly alter balance discordance. However, pre-intervention sedentary behavior may influence how much individuals with PD recalibrate the relationship between perceived and actual balance following physical rehabilitation.

Parkinson's disease (PD) is a progressive neurological disorder that often leads to gait impairments and an increased risk of falls, negatively impacting quality of life. Inertial-based wearables have extended gait evaluation beyond clinical settings, but their technical integration and general accessibility hinder widespread adoption. Smartphones, equipped with inertial sensors, present a scalable approach for gait assessment and retraining/rehabilitation. Auditory cueing, specifically music, has shown promise to retrain gait, yet its use and uptake is often limited by a lack of personalisation. This paper outlines an exploratory and experimental protocol to evaluate CuePD, a smartphone application/app that delivers near real-time gait assessment and personalised auditory cues (including music) for gait retraining in people with PD (PwPD). The protocol characterises beneficial and non-beneficial responses to different cueing strategies and presents separate analysis for the technical validation and gait response outcomes to reduce interpretive ambiguity between measurement performance and behavioural effects. Specifically, the protocol presents: 1) validation of CuePD's spatio-temporal gait characteristics in a controlled laboratory setting; 2) process to understand the usefulness of various auditory cues (metronome beats, instrumental music, and vocal music) to improve gait stability (i.e., increase gait speed and stride length while reducing gait variability); 3) evaluation of acceptance and preference of PwPD for personalised music cues; and 4) relationship between PwPD's musicality and their ability to adhere to auditory cues. Findings from the study will inform the suitability of gait retraining via smartphone-based app technology, personalised to the individual. Personalisation (gait characteristics and music) may potentially encourage long-term engagement for gait retraining which could lead to reduce falls and improved quality of life for PwPD. By explicitly accounting for individual variability, this protocol aims to clarify for whom and under what conditions auditory cueing is appropriate. Future work should systematically evaluate this approach as responses to auditory and/or music-based cueing in PwPD are heterogeneous with possible neutral or adverse effects, e.g., cognitive overload or exacerbation of freezing. This trial is registered at ClinicalTrials.gov (NCT06941779).

Classification of fallers in Parkinson's disease (PD) is challenging due to the heterogenous motor and non-motor symptoms. We developed a machine learning model integrating clinical and gait data to identify key clinical markers of faller status in PD. Of 468 participants, 396 with complete data were analyzed, with 298 assigned to training from one center and 98 to external validation from the other. Clinical assessments and GAITRite-derived gait metrics were obtained. Fall history classified participants as PD fallers, PD non-fallers, or healthy controls. Features were selected through statistical and importance-based approaches, and seven machine learning algorithms were trained. The Extra Trees classifier utilizing statistics-based feature selection demonstrated the highest performance (accuracy 88% internal, 89% external). Three principal domains consistently emerged: fear of falling (FoF), balance/gait measures (stride length, velocity, 360° rotation), and autonomic dysfunction. These findings support the feasibility of externally validated, multidomain machine learning-based faller classification in PD.

The correlations between the serum 25(OH)D level with white matter hyperintensities (WMH) and gait disorders were determined in patients with cerebral small vessel disease. Patients with WMH (n = 217) were enrolled, while 52 healthy individuals were designated as the control group. The 217 patients populated the vitamin D deficiency, insufficiency, and sufficiency groups. The 217 patients with WMH were also divided into periventricular white matter hyperintensities (PWMH) and deep white matter hyperintensities (DWMH) groups. The serum 25(OH)D level was negatively correlated with the severity of WMH, especially PWMH (P < 0.05). A significant difference in gait existed among the groups with different 25(OH)D levels and the control group (all P < 0.001). All indicators had statistical significance between the 25(OH)D insufficiency and deficiency groups, between the 25(OH)D sufficiency and deficiency groups, and the control and 25(OH)D deficiency groups. The serum 25(OH)D level was positively correlated with the total SPPB score, the total Tinetti balance and gait analysis (TBGA) score (P < 0.05), but negatively correlated with TUG time (P < 0.05). The combination of WMH severity and the serum 25(OH)D level more effectively indicated the falling risk than a single indicator, with an AUC value of 0.806. Logistic regression analysis showed that 25(OH)D, age, whether a cerebral infarction had occurred, hypertension, and smoking were the significant influencing factors for WMH (P < 0.05); and gender, age, presence of coronary heart disease, total score of WMH, and 25(OH)D had significant impacts on falls (P < 0.05). The serum 25(OH)D level had a negative correlation with WMH, especially PWMH. The serum 25(OH)D level was negatively correlated with gait disorders. The combination of WMH severity and serum 25(OH)D level was more discriminative at identifying low-risk versus high-risk falling groups than relying on a single indicator. 25(OH)D and age were significant influencing factors for WMH, as well as for the falls.

Treatment adjustments in Parkinson's Disease (PD) are often based on clinical evaluations at single time points which are insufficient to adequately assess real-life motor fluctuations. Patient-written symptom diaries on the other hand are highly subjective and require well-educated and adherent patients to provide reliable results. Wearable accelerometry might provide a reliable, objective, and continuous diagnostic method to assess PD motor symptoms & fluctuations. However, large datasets of simultaneous sensor data and symptom diaries are needed for such method development and validation. We here provide a well-described, open-science dataset of simultaneous, bilateral, wrist-worn accelerometry and symptom diary data from 66 participants (41 male, 25 female) with PD. On average, participants provided data for 6.0 consecutive days resulting in a total of 393.8 days for the dataset as a whole. Symptom diaries include data on kinetic state, tremor, freezing of gait, falls, and PD-related medication intake. Further demographic information is also provided. This dataset will support the development and validation of accelerometry-based approaches to assessing motor symptoms and fluctuations in PD.