Non-pharmacological interventions for cognitive impairment in older adults with heart failure: A systematic review

Zhao, Qing; Liu, Xianliang; Wan, Xiaoxia; Yu, Xin; Cao, Xiaodong; Wang, Fang; Cai, Yong

doi:10.1016/j.gerinurse.2023.04.008

Cited by 5 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…42 self-care, and electrical muscle stimulation may be effective as a non-pharmacological intervention for older adults with HF. 43 The strength of this study is its large sample size which helps gain insights of symptom cluster profiles among community-residing older adults. Instead of directly assigning people to specific profiles, this study used probabilistic class assignments, allowing for a better evaluation of differences between profiles and the impact of predictive factors on profile assignments.…”

Section: Discussionmentioning

confidence: 99%

Symptom Cluster Profiles among Community-residing Older Adults with Heart Failure: Findings from the U.S. Health and Retirement Study

Wang,

JEON,

TOCCHI

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: The incidence of heart failure (HF) rises significantly as people age due to the accumulated influence of risk factors in cardiovascular structure and function. Among older adults with HF, symptoms are manifested in clustered symptoms. Few studies have addressed symptoms specifically in older adults with HF and most have been conducted with small samples. The aims of this study were to (1) describe symptom cluster profiles in older adults with HF in a nationally representative sample of community-dwelling older adults; and (2) determine the associations between demographic and clinical characteristics and membership in symptom cluster profiles. Methods: A secondary analysis was conducted using data from the Health and Retirement Study. Fatigue, shortness of breath, pain, swelling, depressive symptoms, and dizziness were measured. Latent class analysis was used to identify symptom cluster profiles. Bivariate associations and multinomial logistic regression were used to determine the associations between symptom cluster profiles and demographic and clinical characteristics. Results: The sample included 690 participants. Three symptom cluster profiles were identified [high-burden, low-burden, and cardiopulmonary-depressive]. Age, gender, BMI, marital status, alcohol consumption, diabetes, lung disease, and arthritis were significantly different across the three profiles. People in the high-burden and cardiopulmonary-depressive profiles, compared to those in low-burden, had higher odds of having lung disease and arthritis, yet lower odds of higher alcohol consumption. Conclusions: Older adults with HF residing in the community experienced distinct symptom cluster profiles. Research is needed to identify and test targeted interventions for specific symptom cluster profiles.

show abstract

Section: Discussionmentioning

confidence: 99%