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To quantify how specific patient-level characteristics influence the actual amount of mobilisation received during ICU care, thereby identifying key predictors to support individualised mobilisation strategies.
A prospective observational study was conducted in four tertiary hospitals among a convenience sample of 141 critically ill patients from July to November 2023. Data on mobilisation and patient characteristics were collected using standardised data collection tools, including a mobilisation log and a demographic information sheet. Data were analysed using non-parametric tests, Spearman correlation analysis, and multivariate regression to examine associations between early mobilisation and patient-related factors.
Males and surgical patients engaged in more activity (p < 0.001). Muscle strength (r = 0.568, p < 0.001) and haemoglobin levels (r = 0.207, p = 0.014) were positively associated with mobilisation, while higher disease severity (r = −0.321, p < 0.001) and greater pain (r = −0.284, p < 0.001) were linked to reduced activity. Muscle strength, disease severity, surgical status, and sex were independent predictors, explaining 32.5% of the variance.
Early mobilisation in the ICU is influenced by various patient-related factors. Protocols should be tailored to individual patient profiles to enhance outcomes.
This study provides guidance for ICU clinicians to develop targeted mobilisation strategies that consider patients' specific clinical profiles. Tailored approaches may help optimise early mobilisation practices and patient outcomes.
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.
Optimal glycemic control is known to be challenging for people with type 2 diabetes (T2D) due to the maintenance of long-term self-management behavior. Incorporating the transtheoretical model (TTM) components into motivational interviewing (MI) has been applied to promote self-management behaviors such as physical activity in T2D patients. However, the effectiveness of the TTM-based MI intervention in improving glycemic control, self-management, and self-efficacy in adults with T2D remains unclear.
This systematic review and meta-analysis of randomized controlled trials aimed to estimate the effect of a TTM-based MI intervention on glycemic control, self-management, and self-efficacy in adults with T2D patients.
We searched five electronic databases up to September 13, 2023. Two researchers independently screened records, extracted data, and assessed study quality using the Cochrane Risk of Bias Tool 2.0. Pooled effects were estimated in standardized mean differences (SMDs) or mean differences (MDs) using fixed- and random-effects models. Sensitivity analysis and meta-regression explored the reasons for heterogeneity.
Thirty trials with 4214 participants were identified. The TTM-based MI intervention significantly reduced HbA1c (MD = −0.92, 95% CI [−1.08, −0.75], p < 0.001, I 2 = 65%), FPG (SMD = −1.06, 95% CI [−1.38, −0.73], p < 0.001, I 2 = 93%), and 2hPG (MD = −1.42 mmol/L, 95% CI [−1.83, −1.00], p < 0.001, I 2 = 89%), with high, moderate, and low certainty of evidence, respectively. The intervention also improved self-management (SMD = 1.47, 95% CI [1.16, 1.78], p < 0.001, I 2 = 80%) and self-efficacy (SMD = 1.53, 95% CI [1.04, 2.02], p < 0.001, I 2 = 92%). Meta-analysis revealed that MI treatment dose and initial glycemic status contributed to the high heterogeneity.
The TTM-based MI intervention can be a promising intervention for understanding patients' stage of change with tailored strategies and MI techniques to facilitate behavior change, resulting in improved glycemic control, self-management, and self-efficacy in T2D patients. Nevertheless, given the moderate to high risk of bias in the included studies, further rigorous randomized controlled trials should be conducted to examine the effectiveness of TTM-based MI interventions. Short and multiple sessions that comply with the fidelity of MI in the intervention plans are suggested in daily nursing routine for diabetes self-management education.
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.
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