Aim: When elderly people return to their daily lives after inpatient treatment, they may be offered a chance to change the residence to which they are accustomed. The present study clarified the changes in the residence of elderly patients through an Integrated Community Care Ward (ICCW).

Subjects and methods: Patients were admitted to and discharged from the ICCW (53 beds) of Hospital A, located in a city with a population of 30,000 and an aging rate of 37%, for 2 years from April 1, 2018, to March 31, 2020. Patients ≥65 years old were included in the study. We conducted a retrospective survey of information recorded in the electronic medical record system and collected information on activities of daily living, medical procedures at the time of discharge, residence before and after hospitalization, and intentions regarding discharge destination within seven days of hospitalization.

Results: Of the 735 patients ≥65 years old who were admitted to the ICCW, 608 were included, excluding 127 patients admitted for scheduled surgeries. The average age was 82.9 years old, with 52% being over 85 years and 26% being over 90 years old. Of the 465 people hospitalized from home, 64% were discharged, 23% changed to a facility or hospital, and the remaining 13% died. More than 80% of the 143 discharged from facilities or hospitals returned to facilities, but 36 (25%) were discharged to a different facility from before admission. Of the 404 patients who were admitted from home and discharged alive, independence in eating, independence in movement, and having family members living with them were independently related factors for achieving discharge home. Regarding the intended discharge destination within 7 days after hospitalization, of the 246 hospitalized patients who wished to be discharged home, 56 said they wanted to be discharged to a facility or hospital, showing a discrepancy of 23%.

Conclusions: Many elderly people changed their residences after admission to the ICCW. While coordinating disagreements within families as well as navigating medical and nursing care constraints, dialogue across multiple professions should be continued to help elderly patients live their own lives.

Purpose: We aimed to develop a simulation program for physicians and nurses involved in virtual reality (VR) and augmented reality (AR) treatment and care from the perspective of these professionals and older adults with dementia who developed delirium, and to test the effectiveness of the program.

Methods: effectiveness of the program was analyzed through free-response statements from 67 nurses (84.8%) and 12 doctors (15.2%) who participated in the program between February 16 and April 18, 2023.

Results: Regarding the experience of delirium from the perspective of older adults with dementia (personal experience), the following statements were extracted "1. I do not understand where I am, the situation, and the treatment/care that is about to be given"; "2. I want the situation to be explained to me so that I can understand the reasons for my hospitalization and the treatment/care I am receiving"; "3. The eerie environment of the hospital and the high pressure of the staff made me feel anxious and fearful"; "4. Please respect my existence as I endure pain, anxiety, and loneliness"; "5. I feel relieved when doctors and nurses deal with me from my point of view"; and "6. I feel relieved when there is a familiar presence, such as a family member or the name I am calling on a daily basis".

Conclusion: Specific categories of self-oriented empathy were extracted from the experience of physical restraint at night using VR and the experience of delirium using AR. This suggests the possibility of objective effects on treatment and care in future practice.

Objective: To evaluate the frequency of malnutrition and sarcopenic obesity in elderly patients with diabetes according to the Global Leadership Initiative on Malnutrition (GLIM) phenotypes.

Methods: The subjects were outpatients with diabetes who were ≥65 years of age and were managed at Ise Red Cross Hospital. Undernutrition was assessed and categorized into the following GLIM criteria phenotypes: (1) no undernutrition, (2) undernutrition (weight loss or low body mass index [BMI]/no low appendicular skeletal muscle mass index [ASMI]), (3) undernutrition (no weight loss or no low BMI/low ASMI), and (4) undernutrition (weight loss or low BMI/low ASMI). Sarcopenia was diagnosed according to the definition of the Asian Working Group for Sarcopenia 2019, and obesity was diagnosed based on the body fat percentage.

Results: In total, 490 patients were included in the analysis. The frequency of undernutrition was 29.0%, and the frequency of undernutrition according to the GLIM criteria phenotypes was as follows: weight loss or low BMI/no low ASMI group, 10.6%; no weight loss and no low BMI/low ASMI group, 9.8%; and weight loss or low BMI/low ASMI group, 8.6%. The frequency of sarcopenic obesity was 7.3%, with the majority of cases found in the no weight loss or no low BMI/low ASMI groups.

Conclusion: The frequency of undernutrition and sarcopenic obesity in elderly patients with diabetes, according to the GLIM phenotypes, was revealed. It is important to pay attention not only to weight loss and low BMI, but also to undernutrition and sarcopenic obesity with reduced skeletal muscle mass when diagnosing undernutrition in elderly diabetic patients.

Aim: In community medicine, there are many opportunities in which senility is noted as the cause of death. However, there are no clear criteria for diagnosing senility, and this decision is often left to the judgment of individual doctors. This study investigated the clinical characteristics of patients diagnosed with senility at our hospital.

Methods: The subjects included 43 patients whose cause of death was listed as senility from among the death certificates of 282 patients prepared at our hospital. The survey items included age, sex, medical history, place of death, period from the day of explanation of the condition of senility to the date of death, BMI at the time of explanation, and blood sampling test.

Results: The mean age of patients who died due to senility was 92.2±6.5 years old. The male to female ratio was 15: 28. The most common medical history was dementia (76.7%), followed by hypertension and orthopedic disease (74.4%), respiratory disease (66.7%), and heart disease and gastrointestinal disease (60.5%). The places of death were nursing homes and private homes, and hospitals. The overall average time from presentation until death occurred was 110.2 days. There were also considerable differences depending on the case. The average BMI was 19.7±3.0, and the blood sampling results showed that total protein and serum albumin levels were lower than the reference values.

Conclusions: Although the diagnosis of senility is vague and unclear, it is important to explain such a diagnosis to the family at an appropriate time and to cooperate with multiple professionals in the treatment process.

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: 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.

Aim: The purpose of this study was to examine the relationship between health-related quality of life (QOL) and swallowing function among independent community-dwelling older Japanese adults aged 65 years or older.

Methods: A total of 500 participants (250 males and 250 females) were surveyed about BMI, dysphagia and eating disorders (Dysphagia Risk Assessment for the Community-dwelling Elderly [DRACE]), quality of life (QOL; SF-8 Physical and Mental Summary Score), sleep (Pittsburgh Sleep Questionnaire Japanese version [PSQI-J]), and depression (Geriatric Depression Scale [GDS]).

Results: Participants were divided into two groups based on risk of aspiration and data between the groups were compared. Logistic regression analysis revealed that the SF-8 physical component summary score (PCS) and mental component summary score (MCS) were associated with aspiration risk. In the multiple regression analysis, the SF-8 related to eating and swallowing function and PSQI-J were extracted.

Conclusions: The risk of aspiration among the older adults in this study was found to be associated with health-related QOL, sleep quality, revealing a wide-ranging impact on physical, mental, and social functioning. These associated factors may pose a risk for community-dwelling independent older adults, suggesting the need to focus on eating and swallowing function for frailty.

Objective: To evaluate the frequency of cachexia and its associated factors using the Asian Working Group for Cachexia (AWGC) criteria in elderly patients with diabetes and chronic diseases.

Methods: The subjects were diabetic outpatients of ≥65 years of age who were managed at Ise Red Cross Hospital. Patients with chronic disease (chronic heart failure, cancer, or chronic renal failure). Cachexia was evaluated based on the AWGC criteria and was defined as a body mass index (BMI) <21 kg/m² and one or more of the following: anorexia, elevated C-reactive protein, and decreased grip strength. A logistic regression analysis was used to identify cachexia-related factors, with cachexia as the dependent variable, and various variables (basic attributes, blood glucose-related parameters, diabetic complications, comorbidities, and treatment) as explanatory variables.

Results: Two hundred forty-two patients (male, n=164; female, n=78) were included in the study. Forty patients (16.5%) had cachexia. A logistic analysis revealed that age (odds ratio (OR), 1.16; P<0.001), type 1 diabetes (OR, 15.25; P=0.002), diabetic retinopathy (OR, 5.72; P=0.001), and physical frailty (OR, 7.06; P<0.001) were associated with cachexia.

Conclusion: Elderly diabetics with chronic diseases were more likely to have cachexia. According to the AWGC criteria, the frequency of cachexia was 16.5% in elderly patients with diabetes and chronic diseases. Additionally, type 1 diabetes, diabetic retinopathy, age, and physical frailty were identified as factors associated with cachexia. In elderly diabetes patients with chronic diseases, it is therefore important to raise awareness regarding cachexia when these related factors are diagnosed.