Participating in habitual physical activity (HPA) may slow onset of dependency and disability for people with Parkinson's disease (PwP). While cognitive and physical determinants of HPA are well understood, psychosocial influences are not. This pilot study aimed to identify psychosocial factors associated with HPA to guide future intervention development. Sixty-four PwP participated in this study; forty had carer informants. PwP participants wore a tri-axial accelerometer on the lower back continuously for seven days at two timepoints (18 months apart), measuring volume, pattern and variability of HPA. Linear mixed effects analysis identified relationships between demographic, clinical and psychosocial data and HPA from baseline to 18 months. Key results in PwP with carers indicated that carer anxiety and depression were associated with increased HPA volume ( < 0.01), while poorer carer self-care was associated with reduced volume of HPA over 18 months ( < 0.01). Greater carer strain was associated with taking longer walking bouts after 18 months ( < 0.01). Greater carer depression was associated with lower variability of HPA cross-sectionally ( = 0.009). This pilot study provides preliminary novel evidence that psychosocial outcomes from PwP's carers may impact HPA in Parkinson's disease. Interventions to improve HPA could target both PwP and carers and consider approaches that also support psychosocial wellbeing.
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http://dx.doi.org/10.3390/s22030871 | DOI Listing |
Phys Eng Sci Med
January 2025
Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bangalore, India.
Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment of the disease progression and reflects these complex patterns. This study focuses on the non-linear analysis of resting state EEG signals in PD, with a gender-specific, brain region-specific, and EEG band-specific approach, utilizing recurrence plots (RPs) and machine learning (ML) algorithms for classification.
View Article and Find Full Text PDFMov Disord
January 2025
Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
Alzheimers Dement
January 2025
Center for Motor Neuron Biology and Disease, Columbia University Medical Center, New York, New York, USA.
This proceedings article summarizes the inaugural "T Cells in the Brain" symposium held at Columbia University. Experts gathered to explore the role of T cells in neurodegenerative diseases. Key topics included characterization of antigen-specific immune responses, T cell receptor (TCR) repertoire, microbial etiology in Alzheimer's disease (AD), and microglia-T cell crosstalk, with a focus on how T cells affect neuroinflammation and AD biomarkers like amyloid beta and tau.
View Article and Find Full Text PDFAlzheimers Dement
January 2025
Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada.
Introduction: We aimed to compare gait between individuals with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and cognitively unimpaired (CU) individuals and to evaluate the association between gait and regional amyloid beta (Aβ) burden in AD and DLB.
Methods: We included 420 participants (70 AD, 70 DLB, 280 CU) in the Mayo Clinic Study of Aging (MCSA). Gait was assessed using a pressure-sensor walkway.
Front Aging Neurosci
January 2025
School of Nursing, Nanjing Medical University, Nanjing, China.
Aims: In light of the escalating global incidence of Parkinson's disease and the dearth of therapeutic interventions that can alter the disease's course, there exists an urgent necessity to comprehensively elucidate and quantify the disease's global burden.
Methods: This study analyzed the incidence, prevalence, and disability-adjusted life years (DALYs) of Parkinson's disease at global, regional, and national levels based on the Global Burden of Disease Study 2021. Bayesian age-period cohort (BAPC) analysis was used to predict the burden in Parkinson's disease from 2022 to 2035.
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