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Individuals with systemic lupus erythematosus (SLE) often suffer from sleep disturbance, which exhibits heterogeneity. Whether it could be grouped into different clusters remains unknown, posing challenges to the development of personalised interventions to address sleep disturbance.
To examine clusters of sleep disturbance and associated factors in people with SLE.
Cross-sectional design.
From November 2023 to January 2024, people diagnosed with SLE were recruited by a convenience sampling approach. Data were collected via an online platform Wenjuanxing. Sleep disturbance was evaluated by the Pittsburgh Sleep Quality Index (PSQI). Other information, such as disease activity, pain, fatigue, depression and anxiety was also collected using validated instruments. Latent profile analysis was performed to reveal the distinct clusters of sleep disturbance. Multiple logistic regression analysis was performed to investigate factors associated with the clusters.
A total of 538 participants were included, with a response rate of 85.1% (538/632). Only those with sleep disturbance (PSQI > 5) were included in the final analyses. Participant mean age was 32.9 (SD = 8.4) years and 402 (92.6%) were females. All had sleep disturbance (PSQI > 5) and their mean PSQI was 8.8 (SD = 2.9). Three distinct clusters were identified: mild sleep disturbance with poor sleep quality, adequate sleep duration and good daytime functioning (50.7%), mild sleep disturbance with poor sleep quality, adequate sleep duration and poor daytime functioning (30.9%) and moderate sleep disturbance with poor sleep quality, inadequate sleep duration and impaired daytime functioning (18.4%). There are both overlaps and unique aspects in terms of factors associated with each cluster of sleep disturbance, including age, body mass index, cardiovascular system damage, musculoskeletal system damage, depression and anxiety.
Sleep disturbance in patients with SLE showed three distinct clusters, with each cluster having slightly different predisposing factors.
In clinical practice, nurses are recommended to prioritise assessment and interventions for those at-risk subgroups. They could also use the above information to develop and provide personalised interventions to address the unique needs of each cluster of sleep disturbance.
Checklist for reporting of survey studies.
No patient or public contribution.
To classify the unmet integrated care needs of older adults with multimorbidity and to explore the factors associated with different categories of unmet integrated care needs among the target population.
A cross-sectional survey using the statistical method of latent profile analysis.
From July 2022 to March 2023, 397 older adults with multimorbidity, aged 60 years or older, were recruited from one primary healthcare setting and from four secondary and tertiary hospitals to participate in face-to-face questionnaire surveys. The questionnaire used in this study to assess unmet integrated care needs among older adults with multimorbidity was self-designed through a series of steps, including a scoping review, expert consultation and cognitive interviews. Latent profile analysis was applied to uncover distinct profiles of unmet integrated care needs, and multinomial logistic regression was employed to explore whether the profiles were further distinguished by participants' sociodemographic and health-related covariates. The data were analysed using IBM SPSS v.29.0 and Mplus v.8.0.
The optimal solution was a four-profile model, characterised by high unmet integration needs, high unmet system integration needs, low unmet system integration needs and low unmet integration needs, respectively. Multinomial logistic regression results indicated that profile differences were associated with place of residence, number of coresidents and the presence or absence of complex multimorbidity.
The integrated care needs of older adults with multimorbidity have not yet been fully met. Classifying and characterising unmet integrated care needs profiles is a crucial step in the rational allocation of integrated care resources.
This study was reported based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) for cross-sectional studies.
All participants were older adults with multimorbidity, and they were informed that they could withdraw from the study at any time.
To identify habitual dietary patterns in maintenance haemodialysis patients and analyse their association with malnutrition.
A multicentre cross-sectional dietary survey was conducted among 232 maintenance haemodialysis patients from three centres. A 3-day 24-h diet recall and demographic, clinical and therapeutic information were collected. Factor analysis was used to identify the major dietary patterns among haemodialysis patients. Logistic regression analysis was used to assess the relationship between dietary patterns and malnutrition risk.
Three dietary patterns were identified in this study and differed in age, gender and diabetes. An ‘animal foods and refined grains’ dietary pattern meets guideline requirements. A ‘fresh fruits and nuts’ dietary pattern had insufficient daily energy and protein intake with the lowest carbohydrates, lipids and minerals intake among haemodialysis patients. A ‘dairy products’ dietary pattern characterised by low calorie and moderate protein was found to be associated with malnutrition.
Habitual dietary patterns of maintenance haemodialysis patients were associated with personal characteristics, specifically age, gender and diabetes. Patients with habitual ‘dairy products’ dietary patterns may have poor nutritional status.
Educating haemodialysis patients about their daily diet pattern, rather than focusing on nutrients, is crucial and will help them to understand it better. Clinical staff can recognise patients at risk of malnutrition by a dietary pattern of lower intake of certain foods. They can recommend a balanced nutritional pattern that increases calories in the total diet and meets protein requirements.
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