Detection of baseline and near-fall postural stability

Clinical Usefulness of Retropulsion Tests in Persons with Mild to Moderate Parkinson's Disease

People with Parkinson's disease (PwPD) have an increased risk for falls and near falls. They have particular difficulties with maintaining balance against an external perturbation, and several retropulsion tests exist. The Unified PD Rating Scale item 30 (UPDRS30) is the most common, involving an expected shoulder pull. Others recommend using an unexpected shoulder pull, e.g., the Nutt Retropulsion Test (NRT). We aimed to evaluate the clinical usefulness of these tests for detecting future fallers. By using two different golden standards related to self-reported prospective falls and near falls over 6 months following two different time points with 3.5 years between, we estimated sensitivity/specificity, Youden index, predictive values, and likelihood ratios for each test. The different time points yielded a different prevalence of falls and near falls, as well as different predictive values. When comparing the performance of the NRT and UPDRS30 for detecting future fallers, we found that the NRT consistently performed better than UPDRS30. However, neither test exhibited optimal performance in terms of predictive values and associated likelihood ratios. Our findings speak against using either of these tests as a single assessment for this purpose and support previous recommendations of using a multifactorial approach when targeting balance problems in PwPD.

Falls are unintentional: Studying simulations is a waste of faking time

Researchers tend to agree that falls are, by definition, unintentional and that sensor algorithms (the processes that allows a computer program to identify a fall among data from sensors) perform poorly when attempting to detect falls 'in the wild' (a phrase some scientists use to mean 'in reality'). Algorithm development has been reliant on simulation, i.e. asking actors to throw themselves intentionally to the ground. This is unusual (no one studies faked coughs or headaches) and uninformative (no one can intend the unintentional). Researchers would increase their chances of detecting 'real' falls in 'the real world' by studying the behaviour of fallers, however, vulnerable, before, during and after the event: the literature on the circumstances of falling is more informative than any number of faked approximations. A complimentary knowledge base (in falls, sensors and/or signals) enables multidisciplinary teams of clinicians, engineers and computer scientists to tackle fall detection and aim for fall prevention. Throughout this paper, I discuss differences between falls, 'intentional falling' and simulations, and the balance between simulation and reality in falls research, finally suggesting ways in which researchers can access examples of falls without resorting to fakery.

External validation of a 3-step falls prediction model in mild Parkinson's disease

The 3-step falls prediction model (3-step model) that include history of falls, history of freezing of gait and comfortable gait speed <1.1 m/s was suggested as a clinical fall prediction tool in Parkinson’s disease (PD). We aimed to externally validate this model as well as to explore the value of additional predictors in 138 individuals with relatively mild PD. We found the discriminative ability of the 3-step model in identifying fallers to be comparable to previously studies [area under curve (AUC), 0.74; 95 % CI 0.65–0.84] and to be better than that of single predictors (AUC, 0.61–0.69). Extended analyses generated a new model for prediction of falls and near falls (AUC, 0.82; 95 % CI 0.75–0.89) including history of near falls, retropulsion according to the Nutt Retropulsion test (NRT) and tandem gait (TG). This study confirms the value of the 3-step model as a clinical falls prediction tool in relatively mild PD and illustrates that it outperforms the use of single predictors. However, to improve future outcomes, further studies are needed to firmly establish a scoring system and risk categories based on this model. The influence of methodological aspects of data collection also needs to be scrutinized. A new model for prediction of falls and near falls, including history of near falls, TG and retropulsion (NRT) may be considered as an alternative to the 3-step model, but needs to be tested in additional samples before being recommended. Taken together, our observations provide important additions to the evidence base for clinical fall prediction in PD.

Prediction of falls and/or near falls in people with mild Parkinson's disease

OBJECTIVE

To determine factors associated with future falls and/or near falls in people with mild PD.

METHODS

The study included 141 participants with PD. Mean (SD) age and PD-duration were 68 (9.7) and 4 years (3.9), respectively. Their median (q1-q3) UPDRS III score was 13 (8-18). Those >80 years of age, requiring support in standing or unable to understand instructions were excluded. Self-administered questionnaires targeted freezing of gait, turning hesitations, walking difficulties in daily life, fatigue, fear of falling, independence in activities of daily living, dyskinesia, demographics, falls/near falls history, balance problems while dual tasking and pain. Clinical assessments addressed functional balance performance, retropulsion, comfortable gait speed, motor symptoms and cognition. All falls and near falls were subsequently registered in a diary during a six-month period. Risk factors for prospective falls and/or near falls were determined using logistic regression.

RESULTS

Sixty-three participants (45%) experienced ≥ 1 fall and/or near fall. Three factors were independent predictors of falls and/or near falls: fear of falling (OR = 1.032, p<0.001) history of near falls (OR = 3.475, p = 0.009) and retropulsion (OR = 2.813, p = 0.035). The strongest contributing factor was fear of falling, followed by a history of near falls and retropulsion.

CONCLUSIONS

Fear of falling seems to be an important issue to address already in mild PD as well as asking about prior near falls.