Multiple chronic conditions among older adults in China: differences in socio-demographic characteristics

Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study

BACKGROUND

China's older population is facing serious health challenges, including malnutrition and multiple chronic conditions. There is a critical need for tailored food recommendation systems. Knowledge graph-based food recommendations offer considerable promise in delivering personalized nutritional support. However, the integration of disease-based nutritional principles and preference-related requirements needs to be optimized in current recommendation processes.

OBJECTIVE

This study aims to develop a knowledge graph-based personalized meal recommendation system for community-dwelling older adults and to conduct preliminary effectiveness testing.

METHODS

We developed ElCombo, a personalized meal recommendation system driven by user profiles and food knowledge graphs. User profiles were established from a survey of 96 community-dwelling older adults. Food knowledge graphs were supported by data from websites of Chinese cuisine recipes and eating history, consisting of 5 entity classes: dishes, ingredients, category of ingredients, nutrients, and diseases, along with their attributes and interrelations. A personalized meal recommendation algorithm was then developed to synthesize this information to generate packaged meals as outputs, considering disease-related nutritional constraints and personal dietary preferences. Furthermore, a validation study using a real-world data set collected from 96 community-dwelling older adults was conducted to assess ElCombo's effectiveness in modifying their dietary habits over a 1-month intervention, using simulated data for impact analysis.

RESULTS

Our recommendation system, ElCombo, was evaluated by comparing the dietary diversity and diet quality of its recommended meals with those of the autonomous choices of 96 eligible community-dwelling older adults. Participants were grouped based on whether they had a recorded eating history, with 34 (35%) having and 62 (65%) lacking such data. Simulation experiments based on retrospective data over a 30-day evaluation revealed that ElCombo's meal recommendations consistently had significantly higher diet quality and dietary diversity compared to the older adults' own selections (P<.001). In addition, case studies of 2 older adults, 1 with and 1 without prior eating records, showcased ElCombo's ability to fulfill complex nutritional requirements associated with multiple morbidities, personalized to each individual's health profile and dietary requirements.

CONCLUSIONS

ElCombo has shown enhanced potential for improving dietary quality and diversity among community-dwelling older adults in simulation tests. The evaluation metrics suggest that the food choices supported by the personalized meal recommendation system surpass autonomous selections. Future research will focus on validating and refining ElCombo's performance in real-world settings, emphasizing the robust management of complex health data. The system's scalability and adaptability pinpoint its potential for making a meaningful impact on the nutritional health of older adults.

Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly hypertensive people in China: a longitudinal study

Background

Hypertension is a common chronic disease that can trigger symptoms such as anxiety and depression. Therefore, it is essential to predict their risk of depression. The aim of this study is to find the best prediction model and provide effective intervention strategies for health professionals.

Methods

The study subjects were 2733 middle-aged and older adults who participated in the China Health and Retirement Longitudinal Study (CHARLS) between 2018 and 2020. R software was used for Lasso regression analysis to screen the best predictor variables, and logistic regression, random forest and XGBoost models were constructed. Finally, the prediction efficiency of the three models was compared.

Results

In this study, 18 variables were included, and LASSO regression screened out 10 variables that were important for the establishment of the model. Among the three models, Logistic Regression model showed the best performance in various evaluation indicators.

Conclusion

The prediction model based on machine learning can accurately assess the likelihood of depression in middle-aged and elderly patients with hypertension in the next three years. And by combining Logistic regression and nomograms, we were able to provide a clear interpretation of personalized risk predictions.

Multiple geriatric syndromes in community-dwelling older adults in China

This study aims to assess the prevalence of geriatric syndromes and identify factors associated with multiple geriatric syndromes in community-dwelling older adults in China. We utilized a convenience sampling method to recruit older adults and from one rural and one urban community in Chengdu, China, from October 2022 to March 2023. A total of 706 older adults aged 60 years or older were included. Ten geriatric syndromes were investigated including two mental disorders: depressive symptoms, cognitive impairment; and eight somatic disorders: pain, falls, sleep disturbance, constipation, polypharmacy, multimorbidity, malnutrition and frailty. Multiple geriatric syndromes were defined as an individual having two or more geriatric syndromes. The data obtained were analysed using descriptive statistics. The independent risk factors for multiple geriatric syndromes were assessed using a logistic regression model. This study found that 90.5% of the participants had at least one geriatric syndrome, with 72.8% experiencing multiple geriatric syndromes. The top four geriatric syndromes in our study were polypharmacy (58.5%), malnutrition/at risk of malnutrition (43.1%), multimorbidity (42.1%), and frailty/prefrailty (34.3%). Of the older adults, 368(52.1%) had only somatic disorders, 18(2.5%) had only mental disorders and 253 (35.8%) had somatic-mental disorders. According to the logistic regression analysis, residence, age, marriage, BMI, and self-related health were significantly associated with multiple geriatric syndromes among older adults. This study highlights that multiple geriatric syndromes are prevalent among community-dwelling older adults in China, and underscores the significance of certain demographic factors in their occurrence. Future longitudinal studies are needed to establish the temporal relationship between multiple geriatric syndromes and these demographic factors, as well as to explore causal relationships and effective prevention strategies for geriatric syndrome.

Self-efficacy and well-being in the association between caregiver burden and sleep quality among caregivers of elderly patients having multiple chronic conditions in rural China: a serial multiple mediation analysis

BACKGROUND

Caregivers of elderly patients with multiple chronic conditions have heavy caregiver burden and poor sleep quality, which has an important impact on both caregivers and patients. This study aimed to examine among rural caregivers of elderly patients who have multiple chronic conditions in China, whether self-efficacy and well-being mediate the link between caregiver burden and sleep quality.

METHODS

The study recruited 325 caregivers of elderly patients having multiple chronic conditions in rural China. Several measures including the Caregiver Burden Inventory (CBI), Athens Insomnia Scale (AIS), General Self-Efficacy Scale (GSES) and Index of Well-Being (IWB) were utilized to collect data. Structural equation modeling was employed to study the relationships among caregiver burden, sleep quality, self-efficacy, as well as well-being.

RESULTS

Significant correlations were found between the measured variables (each p < 0.01). Self-efficacy and well-being acted as mediators in the link between caregiver burden and sleep quality, accounting for 10.94% and 15.63% of the total effect, respectively. In addition, self-efficacy and well-being had a serial multiple mediating effect in the association between caregiver burden and sleep quality, with this mediating pathway, explaining 9.93% of the total effect.

CONCLUSIONS

Caregivers of elderly patients having multiple chronic conditions in rural China experienced poor sleep quality due to the caregiver burden. Self-efficacy and well-being had serial mediating roles on the relationship between caregiver burden and sleep quality. Effective interventions should be developed to improve self-efficacy and well-being of caregivers, reduce their caregiver burden and, eventually, improve their sleep quality.