The Impact of Job Conditions on Health-Related Quality of Life among Working Japanese Older Adults: A Five-Year Longitudinal Study Using J-MICC Okazaki Study Data

ObjectivesThe number of older adults who continue working after retirement is increasing in Japan. Little is known about how job conditions affect older adults’ health. We examined the association between job conditions and health-related quality of life (HRQOL) during a five-year follow-up study.MethodsThis study included participants aged 65 years or older from the Japan Multi-Institutional Collaborative Cohort Study in the Okazaki area recruited at baseline between 2007 and 2011 and followed up five years later. Participants completed a self-reported questionnaire on the physical and mental health aspects of HRQOL (SF-8™), employment status, and job conditions (job satisfaction, skill use, and job suitability).ResultsData of 1,146 men and 522 women were analyzed (mean age: 69.1 and 68.6 years, respectively). Generalized mixed linear regression analysis revealed that, compared to the not-working group, skill use was positively associated with mental health aspects among men (skill use × time: β = 0.16, SE = 0.08, p < 0.05), while poor job satisfaction and job suitability were negatively associated with mental health aspects among women (job satisfaction, not satisfied × time: β = -0.93, SE = 0.47, p < 0.05; job suitability, not suitable × time: β = -1.06, SE = 0.50, p < 0.05).ConclusionsRegarding job conditions among older adults, skill use in men was marginally associated with mental health, and poor job satisfaction and suitability in women were negatively associated with mental health. Considering the job conditions of older workers is necessary to protect their mental health.     

Decline in skeletal muscle mass is associated with cognitive decline in type 2 diabetes mellitus

AimsTo examine the longitudinal association between skeletal muscle mass (SMM) loss and cognitive decline over time in type 2 diabetes mellitus (T2DM).MethodsWe conducted a prospective cohort study of 453 patients from SMART2D cohort with follow-up intervals of 1.6 to 6.4 years. Baseline and follow-up measurements included bio-impedance analysis (BIA) measure of skeletal muscle mass index (SMI) and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) measure of cognitive function. We examined the association between annual rate of SMI and RBANS scores using linear regression, adjusting for demographics, education, depression, clinical co-variables and presence of apolipoprotein E4 (APOE) _?4 allele.ResultsThe mean age of participants was 60.3 ± 7.4 years. Compared to patients with Tertile 1 SMI change, the group with greater SMI decline (Tertile 3 SMI change) experienced 0.30 decline in RBANS total score (95%CI ?0.57 to ?0.03; p = 0.030) in the adjusted analysis. RBANS scores for subdomains in immediate memory and visuo-spatial/construction were lower in Tertile 3 SMI change group with corresponding coefficients ?0.54 (95%CI ?1.01 to ?0.06; p = 0.026), and ?0.71 (95%CI ?1.30 to ?0.12; p = 0.019) respectively.ConclusionIn patients with T2DM, BIA measure of muscle mass loss over time was independently associated with cognitive decline globally and in the domains of memory and visuo-spatial/construction.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.