The activities of daily living partially mediate the relationship between rapid eye movement sleep behavior disorder and depressive symptoms in Parkinson's disease.

Objective: A longitudinal study was conducted to investigate whether rapid eye movement sleep behavior disorder affect depression in patients with Parkinson's disease through activities of daily living.

Methods: A total of 387 Parkinson's disease patients' six-year follow-up data (one follow-up per year) were obtained from the Parkinson's Progression Markers Initiative. To allow causal effects to manifest, this study increased the lag period and divided the data from the six follow-ups into two groups: wave 1 (wave refers to time points), wave 3, and wave 5 as one group, and wave 2, wave 4, and wave6 as the other group.

Sex differences in the association between Body Mass Index and cognitive function in Parkinson disease: a cross-sectional study.

Objective: This study utilized a binary logistic regression model to explore the relationship between Body Mass Index (BMI) and cognitive function in Parkinson's disease (PD) patients.

Methods: In this cross-sectional study, data were obtained from 1,005 Parkinson's patients enrolled in the Parkinson's Progression Markers Initiative (PPMI) from 2010 to 2023, including 378 females and 627 males. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) scale, and the correlation between BMI and cognitive function was determined using binary logistic regression.

Motor dysfunction in Parkinson's patients: depression differences in a latent growth model.

Objective: This study aims to utilize latent growth model (LGM) to explore the developmental trajectory of motor dysfunction in Parkinson's disease (PD) patients and investigate the relationship between depression and motor dysfunction.

Methods: Four-year follow-up data from 389 PD patients were collected through the Parkinson's Progression Marker Initiative (PPMI). Firstly, a univariate LGM was employed to examine the developmental trajectory of motor dysfunction in PD patients.

[The application of artificial neural network in studying landless farmer's mental health problems].

To introduce a method of classification with high precision--the artificial neural network (ANN), and to compare the results using logistic regression method. Using data from 1070 landless peasants' mental health survey, the artificial neural network models and logistic regression model were built and compared on their advantages and disadvantages of the two models. The prediction accuracy for artificial neural network was 94.