Impact of playful objects on wellbeing, emotions and engagement in patients with dementia and post-stroke cognitive impairment

Abdul Seckam,Cathy Treadaway, Benjamin James Jelley

British Journal of Neuroscience Nursing(2024)

引用 0|浏览0
暂无评分
摘要
Stroke is a major public health concern, and finding ways to support the wellbeing and emotional regulation of stroke patients with post-stroke cognitive decline or dementia is vitally important. HUG™ was developed to be used as a psychosocial intervention to reduce anxiety and provide comfort and pleasure to those with cognitive impairment. The aim of the ludic artefacts using gesture and haptics evaluation and making playful objects for wellbeing, emotional regulation and engagement for dementia post-stroke cognitive impairment (LAUGH EMPOWERED PSCI) study, a 2-year collaborative project between the NHS and Sunrise Senior Living, is to improve the lives of older people living with advanced stages of dementia and those living with post-stroke cognitive impairment (PSCI). Post-intervention qualitative data were gathered from NHS health professionals (n=20) via a series of interviews. A qualitative interpretive grounded practical theory methodology underpinned by a ‘compassionate design’ approach was used to analyse qualitative interviews. Two key themes were identified: evidence of improvement in patient care, and difficulties and negative responses to using HUG™ (patient and staff), with respective sub-themes also identified. Findings from this study reveal that HUG™ can be used successfully in a hospital context to improve patient wellbeing and reduce anxiety. Although not every patient who received a HUG™ responded positively, the device did have a significant positive impact on the quality of life and care of those who did. It is clear from this research that, although HUG™ is not the right solution for every patient, it is a useful alternative to prescribed medication for some patients who are anxious and distressed.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要