The impact of adverse childhood experiences on sensory thresholds in adults living with multimorbidity and chronic pain: an observational feasibility study
Dhaneesha N.S. Senaratne , Blair H. Smith , Tim G. Hales , Louise Marryat , Lesley A. Colvin
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引用次数: 0
Abstract
Background
Epidemiological studies have linked adverse childhood experiences (ACEs) to multimorbidity and chronic pain. One possible mechanism may be altered sensory processing, which could influence symptom development and persistence, and can be assessed by psychophysical methods such as quantitative sensory testing (QST). In this single-site feasibility study, we evaluated a study protocol to examine these relationships. Our primary aim was to assess the feasibility and acceptability of the study procedures. Our secondary aim was to generate preliminary data exploring relationships between ACE exposure and QST parameters.
Methods
This was a single-site feasibility study with a cross-sectional design. Adult participants completed questionnaires (including a 20-item ACE questionnaire), static QST based on the German Research Network on Neuropathic Pain protocol, and dynamic QST (conditioned pain modulation) using pressure and heat test stimuli.
Results
Of 101 people directly approached, 60 were recruited (recruitment rate, 59.4%; 73.3% female [n=44]; mean [range] age, 48.8 [19–87] yr). Study completion rate was 100%, and all participants reported that the protocol was either ‘completely acceptable’ (93.3%, n=56) or ‘acceptable’ (6.7%, n=4). In exploratory analyses, higher ACE exposure was associated with higher odds of chronic pain, multimorbidity, greater medication use, and higher pain severity and interference scores. Higher ACE count was also linked to some mechanical static QST parameters, but there was no relationship with dynamic QST parameters.
Conclusions
This study demonstrated that assessing sensory processing in relation to ACEs among adults with chronic pain and multimorbidity was both feasible and acceptable. Feasibility metrics and preliminary effect estimates will inform protocol refinement, outcome prioritisation, sample size calculations, and recruitment timelines for a future appropriately powered definitive study.