Gait asymmetry, ankle spasticity, and depression as independent predictors of falls in ambulatory stroke patients

Abstract

BackgroundFalls are the leading cause of injury in stroke patients. However, the cause of a fall is complicated, and several types of risk factors are involved. Therefore, a comprehensive model to predict falls with high sensitivity and specificity is needed.MethodsThis study was a prospective study of 112 inpatients in a rehabilitation ward with follow-up interviews in patients’ homes. Evaluations were performed 1 month after stroke and included the following factors: (1) status of cognition, depression, fear of fall and limb spasticity; (2) functional assessments [walking velocity and the Functional Independence Measure (FIM)]; and (3) objective, computerized gait and balance analyses. The outcome variable was the number of accidental falls during the 6-month follow-up period after baseline measurements.ResultsThe non-faller group exhibited significantly better walking velocity and FIM scale compared to the faller group (P < .001). The faller group exhibited higher levels of spasticity in the affected limbs, asymmetry of gait parameters in single support (P < .001), double support (P = .027), and step time (P = .003), and lower stability of center of gravity in the medial-lateral direction (P = .008). Psychological assessments revealed that the faller group exhibited more severe depression and lower confidence without falling. A multivariate logistic regression model identified three independent predictors of falls with high sensitivity (82.6%) and specificity (86.5%): the asymmetry ratio of single support [adjusted odds ratio, aOR = 2.2, 95% CI (1.2–3.8)], the level of spasticity in the gastrocnemius [aOR = 3.2 (1.4–7.3)], and the degree of depression [aOR = 1.4 (1.2–1.8)].ConclusionsThis study revealed depression, in additional to gait asymmetry and spasticity, as another independent factor for predicting falls. These results suggest that appropriate gait training, reduction of ankle spasticity, and aggressive management of depression may be critical to prevent falls in stroke patients.

Effect of an ankle–foot orthosis on gait kinematics and kinetics: case study of post-stroke gait using a musculoskeletal model and an orthosis model

“…The gait speed is decreased in people with chronic hemiparesis with an asymmetrical step length [20]. Moreover, an asymmetrical gait pattern increases the fall risk [21]. Although the present subject was able to walk independently without an AFO, the results show that using an AFO immediately improved his walking ability.…”

A structural equation model of falls at home in individuals with chronic stroke, based on the international classification of function, disability, and health

“…A rate of falls was 8.7 [1] or 1.3-6.5 falls/person/year [2], otherwise, 3.57 [3], or about 1.77 times a year [4]. Falls are associated with several factors following stroke, for example, muscle strength [5], muscle tone [6,7], postural balance [4,5], activities of daily living [3,[8][9][10], going out into community for shopping [10], tripping hazards [11], confidence in mobility and balance [4], depression [3,5], number of medication [5]. With intrinsic or extrinsic factors, individuals with stroke are susceptible to falls.…”

A Novel Activity Recognition System for Alternative Control Strategies of a Lower Limb Rehabilitation Robot

“…Statistically, the probability of a stroke survivor falling within six months after surgery is 25%-37%, and subsequently, the fall probability is 23%-50% [5][6][7][8]. Accidental falls and fall-related injuries, such as hip fractures, often lead to severe disabilities and affect the patient's overall health [9]. Therefore, timely and effective fall prediction and protection during rehabilitation training are beneficial for stroke patients.…”

“…Threshold-based systems are simple to implement and computationally inexpensive, but they do not provide a good trade-off between false positives (if the threshold value is too low, the system could generate false alarms, specificity <100%) and false negatives (if the threshold value is too high, the system could miss real alarms, sensitivity <100%) because soft falls due to paralysis or fatigue of the gluteus medius are characterized by kinematic peaks similar to those of normal gaits, and small perturbations or "wiggly duck gait" are characterized by kinematic peaks similar to those of falls. In other words, an extrinsic fall [9,24] can be identified by a high threshold because extrinsic falls caused by trips and slips are often accompanied by sharp changes in acceleration. In contrast, intrinsic falls caused by dizziness, impaired balance, or gait [9,24] usually do not exhibit a sharp change in acceleration, and can easily go undetected.…”

“…In other words, an extrinsic fall [9,24] can be identified by a high threshold because extrinsic falls caused by trips and slips are often accompanied by sharp changes in acceleration. In contrast, intrinsic falls caused by dizziness, impaired balance, or gait [9,24] usually do not exhibit a sharp change in acceleration, and can easily go undetected. In addition, these methods cannot perform detailed classifications of falls or abnormal gaits.…”