Background: The concept of hope is an important theme in chronic illness and palliative care and has been associated with increased psycho-spiritual well-being and quality of life. Psycho-spiritual interventions have been described in this population, but no systematic review of hope-enhancing interventions or hopelessness-reducing interventions has been conducted for persons with palliative care diseases. Aim: To describe and assess the effectiveness of interventions in palliative care that measure hope and/or hopelessness as an outcome. Design: This systematic review and meta-analysis was pre-registered (Prospero ID: CRD42019119956). Data sources: Electronic databases, journals, and references were searched. We used the Cochrane criteria to assess the risk of bias within studies. Results: Thirty-five studies (24 randomized controlled trials, 5 quasi-experimental, 6 pre-post studies) involving a total of 3296 palliative care patients were included. Compared with usual/standard cancer care alone, interventions significantly increased hope levels at a medium effect size ( g = 0.61, 95% confidence interval (CI) = 0.28–0.93) but did not significantly reduce hopelessness ( g = −0.08, 95% CI = −0.18 to 0.02). It was found that interventions significantly increase spirituality ( g = 0.70, 95% CI = 0.02–1.37) and decrease depression ( g = −0.29, 95% CI = −0.51 to −0.07), but had no significant effect over anxiety, quality of life, and symptom burden. Overall, quality of evidence across the included studies was rated as low. Conclusions: Evidence suggests that interventions can be effective in increasing hope in palliative care patients.

Single-case experimental designs (SCEDs) are used to study the effects of interventions on the behavior of individual cases, by making comparisons between repeated measurements of an outcome under different conditions. In research areas where SCEDs are prevalent, there is a need for methods to synthesize results across multiple studies. One approach to synthesis uses a multilevel meta-analysis (MLMA) model to describe the distribution of effect sizes across studies and across cases within studies. However, MLMA relies on having accurate sampling variances of effect size estimates for each case, which may not be possible due to auto-correlation in the raw data series. One possible solution is to combine MLMA with robust variance estimation (RVE), which provides valid assessments of uncertainty even if the sampling variances of effect size estimates are inaccurate. Another possible solution is to forgo MLMA and use simpler, ordinary least squares (OLS) methods with RVE. This study evaluates the performance of effect size estimators and methods of synthesizing SCEDs in the presence of auto-correlation, for several different effect size metrics, via a Monte Carlo simulation designed to emulate the features of real data series. Results demonstrate that the MLMA model with RVE performs properly in terms of bias, accuracy, and confidence interval coverage for estimating overall average log response ratios. The OLS estimator corrected with RVE performs the best in estimating overall average Tau effect sizes. None of the available methods perform adequately for meta-analysis of within-case standardized mean differences.