The effect of mindfulness-based interventions on mental health outcomes and wellbeing of informal caregivers of people with mental illness: A systematic review and meta-analysis

Effectiveness of mindfulness-based interventions on psychosocial well-being and occupational-related outcomes among nurses in the intensive care unit: A systematic review and meta-analysis

BACKGROUND

Mindfulness-based interventions may contribute to the mental health status and well-being of nurses. Current results are inconsistent, and there are no systematic review and meta-analyses for intensive care unit (ICU) nurses.

AIM

The aim of this study was to assess the effectiveness of mindfulness-based interventions on psychosocial well-being and occupational-related outcomes among nurses in the ICU.

METHODS

We conducted a comprehensive literature search across 12 electronic databases, including PubMed, Web of Science, Embase, CINAHL, PsycINFO, Cochrane Library, CNKI, Wanfang, VIP, SinoMed, ClinicalTrials.gov, and preprint repositories (bioRxiv and medRxiv). Additionally, we screened the reference lists of included studies to identify other potentially relevant research. The search covered records from the inception of each database up to July 2024. Two researchers independently screened studies and extracted data. ReviewManager (version 5.3 was used to conduct the meta-analysis.

RESULTS

A total of 29 studies were included in the meta-analysis to evaluate the effects of mindfulness-based interventions across different timeframes: immediate-term effects (within 10 days post intervention), short-term effects (10 days-3 months), medium-term effects (3-6 months), and long-term effects (beyond 6 months). The results showed that mindfulness-based interventions significantly alleviated anxiety (mean difference [MD]: -10.80, 95% confidence interval [CI]: [-16.76, -4.83], I2 = 78%, P < 0.001) and depression (MD: -12.02, 95% CI: [-12.43, -11.61], I2 = 0%, P < 0.001) in the medium term for ICU nurses. Significant immediate-term effects were observed on well-being (standardised mean difference [SMD]: 0.58, 95% CI: [0.40, 0.76], I2 = 0%, P < 0.001), resilience (MD: 14.41, 95% CI: [9.71, 19.11], I2 = 91%, P < 0.001), and death anxiety (MD: -2.35, 95% CI: [-4.39, -0.31], I2 = 30%, P = 0.02). Mindfulness-based interventions also showed significant short-term effects on well-being (SMD: 0.54, 95% CI: [0.10, 0.99], I2 = 43%, P = 0.02), sleep quality (MD: -1.19, 95% CI: [-2.32, -0.05], I2 = 50%, P = 0.04), and stress (SMD: -0.75, 95% CI: [-1.34, -0.17], I2 = 79%, P = 0.01). Significant effects were observed across immediate-term, short-term, and medium-term timeframes for stress reduction (medium-term effects: MD: -9.69, 95% CI: [-10.18, -9.21], I2 = 0%, P < 0.001) and mindfulness improvement (medium-term effects: MD: 9.28, 95% CI: [7.20, 11.37], I2 = 0%, P < 0.001). Additionally, mindfulness-based interventions significantly reduced burnout in the immediate term (SMD: -1.28, 95% CI: [-2.31, -0.25], I2 = 92%, P = 0.01).

CONCLUSIONS

Mindfulness-based interventions have a positive effect in improving psychosocial well-being and occupational-related outcomes for ICU nurses. Nursing managers may consider integrating mindfulness-based interventions into ICU nurses' practice to enhance their well-being.

REGISTRATION

The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under the registration number CRD42024572633 (available at https://www.crd.york.ac.uk/PROSPERO/view/CRD42024572633.).

Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data

BACKGROUND

Shame and stigma often prevent individuals with social anxiety disorder (SAD) from seeking and attending costly and time-intensive psychotherapies, highlighting the importance of brief, low-cost, and scalable treatments. Creating prescriptive outcome prediction models is thus crucial for identifying which clients with SAD might gain the most from a unique scalable treatment option. Nevertheless, widely used classical regression methods might not optimally capture complex nonlinear associations and interactions.

OBJECTIVE

Precision medicine approaches were thus harnessed to examine prescriptive predictors of optimization to a 14-day fully self-guided mindfulness ecological momentary intervention (MEMI) over a self-monitoring app (SM).

METHODS

This study involved 191 participants who had probable SAD. Participants were randomly assigned to MEMI (n=96) or SM (n=95). They completed self-reports of symptoms, risk factors, treatment, and sociodemographics at baseline, posttreatment, and 1-month follow-up (1MFU). Machine learning (ML) models with 17 predictors of optimization to MEMI over SM, defined as a higher probability of SAD remission from MEMI at posttreatment and 1MFU, were evaluated. The Social Phobia Diagnostic Questionnaire, structurally equivalent to the Diagnostic and Statistical Manual SAD criteria, was used to define remission. These ML models included random forest and support vector machines (radial basis function kernel) and 10-fold nested cross-validation that separated model training, minimal tuning in inner folds, and model testing in outer folds.

RESULTS

ML models outperformed logistic regression. The multivariable ML models using the 10 most important predictors achieved good performance, with the area under the receiver operating characteristic curve (AU-ROC) values ranging from .71 to .72 at posttreatment and 1MFU. These prerandomization and early-stage prescriptive predictors consistently identified which participants had the highest probability of optimization of MEMI over SM after 14 days and 6 weeks from baseline. Significant predictors included 4 strengths (higher trait mindfulness, lower SAD severity, presence of university education, no current psychotropic medication use), 2 weaknesses (higher generalized anxiety severity and clinician-diagnosed depression or anxiety disorder), and 1 sociodemographic variable (Chinese ethnicity). Emotion dysregulation and current psychotherapy predicted remission with inconsistent signs across time points.

CONCLUSIONS

The AU-ROC values indicated moderately meaningful effect sizes in identifying prescriptive predictors within multivariable models for clients with SAD. Focusing on the identified notable client strengths, weaknesses, and Chinese ethnicity may enhance our ability to predict future responses to scalable treatments. Estimating the likelihood of SAD remission with a "prescriptive predictor calculator" for each client may help clinicians and policymakers allocate scarce treatment resources effectively. Clients with high remission probability may benefit from receiving the MEMI as a vigilant waitlist strategy before intensive therapist-led psychotherapy. These efforts may aid in creating actionable treatment selection tools to optimize care for clients with SAD in routine health care settings that use stratified care principles.

TRIAL REGISTRATION

OSF Registries 10.17605/OSF.IO/M3KXZ; https://osf.io/m3kxz.