Analysis of the trajectory and influencing factors of kinesiophobia in elderly patients during the rehabilitation phase of limb fractures.

OBJECTIVES

To explore the trajectory and influencing factors of kinetophobia in elderly patients with limb fracture during the rehabilitation period.

METHODS

In this retrospective study, we retrieved the follow-up records of 150 elderly patients with limb fractures from our hospital's electronic medical record system. We collected the demographic data and Tampa Scale for Kinesiophobia (TSK) scores of patients at postoperative day 1 (T0), 1 week (T1), 3 weeks (T2), 6 weeks (T3), and 12 weeks (T4) to track changes in kinesiophobia over time. We used Mplus 8.3 software to fit the development trajectory types of kinesiophobia based on TSK scores at time points T0 to T4 using a Latent Class Growth Model (LCGM). After selecting the best fitting model, logistic regression analysis was performed to identify the risk factors for kinesiophobia in different types. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to compare the predictive value of relevant influencing factors for kinetophobia in elderly patients recovering from limb fracture.

RESULTS

The TSK scores decreased steadily from T0 to T4 [(46.03±7.88) at T0, (41.14±8.89) at T1, (34.61±5.64) at T2, (29.95±6.79) at T3, and (26.71±5.03) at T4], [ (4, 745) = 193.1, < 0.001]. We identified the trajectory of changes in kinesiophobia symptoms through LCGM, gradually establishing models with 1 to 5 categories. By integrating the results of relevant fit indices, we ultimately selected the best fitting model with 2 categories. Among them, 119 patients in Class 1 (79.3%) showed a slow and continuous decline in kinesiophobia symptoms from T0 to T4, while 31 patients in Class 2 (20.7%) exhibited rapid decline followed by rebound in kinesiophobia symptoms. Logistic regression showed that older the age ( = 1.219), per capita monthly income < 3000 yuan ( = 12.657), numeric rating scale (NRS), patients with higher NRS ( = 2.401) and higher self-efficacy ( = 1.212) were more likely to be in Class 1. The ROC curve results show that the combined above indicators have a higher predictive value for the changes in fear of movement in elderly patients with lower limb fractures during the rehabilitation period (AUC = 0.934), compared to age (AUC = 0.694), per capita monthly income (AUC = 0.654), NRS score (AUC = 0.812), and self-efficacy (AUC = 0.811) as individual indicators.

CONCLUSION

As the recovery time progresses for elderly patients with limb fractures, the overall trend of kinesiophobia scores decreases. Kinesiophobia presents with two different trajectories, with age, average monthly income, NRS score, and self-efficacy being important factors influencing the trajectory categories of kinesiophobia changes.