Japanese Journal of Geriatrics最新文献

Objective: To examine the relationship between the dissatisfaction of family caregivers with diabetes treatment and depressive symptoms among the elderly.

Methods: The subjects were diabetic patients of 65 years of age or older and their family caregivers, who were outpatients at Ise Red Cross Hospital. The Japanese version of the Patient Health Questionnaire 9, which consists of nine items, was used to measure depressive symptoms. The Japanese version of the Treatment Satisfaction Scale for Caregivers of Dependent Diabetic Patients (STCD₂-J) was used to measure the satisfaction of family caregivers with diabetes treatment. A logistic regression analysis was performed using depressive symptoms as dependent variable, satisfaction of the family caregiver with diabetes treatment as an explanatory variable, and adjustment variables.

Results: In total, 272 patients were included in the analysis. Taking the quintiles of STCD₂-J scores, the adjusted odds ratios for patient depressive symptoms in Q2 (27-29), Q3 (24-26), Q4 (22-23) and Q5 (14-21) based on Q1 (30-36) (the group with the highest STCD₂-J scores) were 2.44 (95% confidence interval (CI), 0.69-8.61; P=0.163), 3.08 (95% CI, 0.93-10.12; P=0.063), 2.69 (95% CI, 0.68-10.65; P=0.156), and 4.54 (95% CI, 1.44-14.32; P=0.010), respectively.

Conclusion: We found that family caregivers' decreased satisfaction with diabetes treatment was associated with depressive symptoms. It is important to alert primary care physicians about depressive symptoms when they see family caregivers who show decreased satisfaction with diabetes treatment.

Aim: To reveal the characteristics and the oral function of institutionalized frail older adults and the factors contributing to frailty.

Methods: This multicenter, cross-sectional study included 214 patients. A questionnaire was administered to registered dietitians from these institutions. Sex, age, height, weight, grip strength, calf circumference, level of care need, FRAIL-NH, MNA^® -SF, dysphagia, food form and water thickening, number of medications, major diseases, comorbidities, independence in daily living of older people with dementia, use of medication with dry mouth, nutritional care issues (malnutrition-related problems) by multiple occupations in Nutrition and Eating Swallowing Screening, Assessment and Monitoring, and nine oral-related items were evaluated.

Results: One hundred six patients (49.5%) were classified as frail, 75% of the patients were women, and the mean BMI was 19.7 kg/m². Older adults with frailty were characterized by high care needs, malnutrition, multiple comorbidities, multiple medications, eating and swallowing disorders, the requirement of feeding assistance, and the need to adjust the shape of meals and fluids. The multivariable OR (95%CI) for "choking and residue problems" was 1.81 (1.20-2.73), while that for "dietary concentration problems" was 4.28 (2.10-8.74).

Conclusion: Caregivers must maintain posture and provide meal assistance. Professionals in various occupations must adjust the proper food form and medication content. Meal times must be examined in consideration of the times at which drugs will be most effective. Oral care must be provided, and an environment must be created to help the subject concentrate. Focusing on problems of choking, residue, and concentration on meals is expected to improve frailty, aspiration pneumonia, and the prognosis of institutionalized older adults.

Objective: To examine the relationship between decreased appetite and the cognitive function in elderly diabetic patients.

Methods: The study subjects were outpatients with diabetes who were 60 years of age or older, and who were managed at Ise Red Cross Hospital. The cognitive function was assessed using a self-administered Dementia Checklist. The Japanese version of the Simplified Nutritional Appetite Questionnaire (SNAQ) was used to measure decreased appetite. A logistic regression analysis, in which the dependent variable was cognitive decline and the explanatory variables were appetite loss and adjustment variables, was used to calculate the odds ratio for cognitive decline according to the presence of appetite loss.

Results: Four hundred eighty patients were included in the analysis. Seventeen percent of the patients had decreased appetite and 21% had a decreased cognitive function. The unadjusted and adjusted odds ratios of cognitive decline for those with decreased appetite were 2.78 (95% confidence interval (CI), 1.66-4.65; P<0.001) and 2.26 (95% CI, 1.16-4.37; P=0.015), respectively, based on the absence of decreased appetite.

Conclusion: Decreased appetite in elderly patients with diabetes was associated with a decreased cognitive function.

Aim: An easy-to-use tool that can detect cognitive decline in mild cognitive impairment (MCI) is required. In this study, we aimed to construct a machine learning model that discriminates between MCI and cognitively normal (CN) individuals using spoken answers to questions and speech features.

Methods: Participants of ≥50 years of age were recruited from the Silver Human Resource Center. The Japanese Version of the Mini-Mental State Examination (MMSE-J) and Clinical Dementia Rating (CDR) were used to obtain clinical information. We developed a research application that presented neuropsychological tasks via automated voice guidance and collected the participants' spoken answers. The neuropsychological tasks included time orientation, sentence memory tasks (immediate and delayed recall), and digit span memory-updating tasks. Scores and speech features were obtained from spoken answers. Subsequently, a machine learning model was constructed to classify MCI and CN using various classifiers, combining the participants' age, gender, scores, and speech features.

Results: We obtained a model using Gaussian Naive Bayes, which classified typical MCI (CDR 0.5, MMSE ≤26) and typical CN (CDR 0 and MMSE ≥29) with an area under the curve (AUC) of 0.866 (accuracy 0.75, sensitivity 0.857, specificity 0.712).

Conclusions: We built a machine learning model that can classify MCI and CN using spoken answers to neuropsychological questions. Easy-to-use MCI detection tools could be developed by incorporating this model into smartphone applications and telephone services.

Aim: This study clarified the association between the amount of physical activity and apathy after one year among community-dwelling older adults.

Methods: Two hundred community-dwelling older adults (mean age: 74.3±5.0 years old, female: 52.5%) who participated in the 2018 and 2019 "Kasama Longevity Health Examination" were included. Apathy was assessed using the Apathy Scale (0-42 points; the higher the score, the lower the motivation), physical activity by Physical Activity Scale for the Elderly (PASE), and Geriatric Depression Scale-15 (GDS). The 2018 PASE data were grouped into tertiles. A multiple regression analysis was performed with the 2019 Apathy Scale as the dependent variable and the 2018 PASE as the independent variable, and the sex, age, years of education, economic situation, body mass index, chronic illness, smoking history, alcohol drinking habits, physical function, cognitive function, GDS, and the 2018 Apathy Scale as adjustment variables. The PASE subcategories (leisure-time, household, and work-related activities) were examined using a similar method.

Results: The mean Apathy Scale in 2019 was 14.0±6.2 for the low physical activity group, 12.8±6.0 for the medium physical activity group, and 10.1±5.9 for the high physical activity group. The high physical activity group showed a significant negative association with the Apathy Scale (B=-1.56, 95% confidence interval=-2.91 to -0.21, p=0.023). No association was found for any activity of the PASE sub-items.

Conclusions: A high level of physical activity may protect against apathy among community-dwelling older adults.