This study aimed to develop and internally validate a clinical prediction rule to identify people at risk of developing an unfavourable movement behaviour pattern within the first two years following a stroke.
A prospective cohort study was conducted using data from 200 participants with a first-ever stroke (mean age 67.8 ± 11.5 years; 64% male; median NIHSS= 3), who were discharged to their home environment. The primary outcome was the movement behaviour patterns of the participants, objectively assessed with an accelerometer (activPAL) at three weeks, six months, one year, and two years post-discharge. Movement behaviour patterns were based on the amount of light and moderate-to vigorous physical activity and prolonged sedentary bouts, classified into three categories: ‘sedentary exercisers’ (more favourable), ‘sedentary movers’ (unfavourable), and ‘sedentary prolongers’ (most unfavourable). Fourteen baseline characteristics, including demographic, stroke-related, care-related, health-related factors, and movement behaviour patterns, were measured within three weeks post-discharge. A two-step prediction model was developed using multinominal logistic regression. Significant variables were identified using Generalized Estimating Equations with backward selection. Internal validation was performed through bootstrap resampling.
Female sex (B = -0.92, p = 0.001), higher age (B = 0.04, p 0.001) and increased fatigue (CIS-Fatigue) (B = 0.03, p = 0.002) were significant predictors of an unfavourable movement behaviour pattern two years after discharge. Slower walking speed (5MWT)(B = 0.09, p = 0.006) and movement behaviour pattern directly after discharge (B = -3.30, p 0.001) predicted the most unfavourable pattern. The final model showed good fit (QICC = 739.60) and moderate discrimination (AUC = 0.730). Internal validation confirmed the model’s robustness, with a shrinkage factor of 0.893.
We developed and internally validated a clinical prediction rule to identify patients at risk of developing unfavourable movement behaviour post-stroke. Early identification of high-risk patients, characterized by female sex, older age and higher levels of fatigue, as well as those at highest risk, distinguished by slower walking speed and post-discharge movement behaviour, can facilitate stratification for tailored behaviour change interventions.
Integrating this clinical prediction rule into routine clinical practice can enhance decision-making for healthcare professionals, including physiotherapists, by facilitating the early identification of patients with a first-ever stroke who are at risk of developing unfavourable movement behaviour.
Stroke
Secondary Prevention
