Leisure-Related Social Work Interventions for Patients with Cognitive Impairment: A Systematic Review and Meta-Analysis.
Hui YangZhezhen LvYuyue XuHong-Lin ChenPublished in: International journal of environmental research and public health (2023)
The social work profession has been exploring nonpharmacological interventions for patients with cognitive impairment, but there are few evidence-based research outputs. Systematically evaluating the effectiveness of social work interventions for people with cognitive impairment can shed light on the matter to further improve similar interventions. Randomized controlled trials of nonpharmacological interventions for patients with cognitive impairment were selected from key literature databases in both English and Chinese from 2010 to 2021. A systematic review and meta-analysis with Revman 5.4 were performed. Seven trials were included, involving 851 patients with cognitive impairment. The meta-analysis showed that, in terms of overall cognitive function, the Montreal Cognitive Assessment score (MD = 1.64, 95% CI [0.97, 2.30], p < 0.001) of the intervention group was superior to the control group, but there was no significant difference in the Mini-Mental State Examination score between the two groups (MD = 0.33, 95% CI [-0.16, 0.82], p = 0.18). Compared with the control group, nonpharmacological intervention can effectively improve the neuropsychiatric condition of patients (SMD = -0.42, 95% CI [-0.64, -0.20], p = 0.0002). In summary, the current evidence shows that nonpharmacological social work interventions had a positive effect on the cognitive function and neuropsychiatric status of patients with cognitive impairment. Suggestions for future nonpharmacological intervention practice are discussed.
Keyphrases
- cognitive impairment
- randomized controlled trial
- physical activity
- systematic review
- healthcare
- mental health
- end stage renal disease
- meta analyses
- chronic kidney disease
- newly diagnosed
- molecular dynamics
- clinical trial
- study protocol
- prognostic factors
- machine learning
- artificial intelligence
- quality improvement
- patient reported
- double blind
- patient reported outcomes