Background: While tremor in Parkinson’s Disease (PD) can be characterised in the consulting room, its relationship to treatment and fluctuations can be clinically helpful. Objective: To develop an ambulatory assessment of tremor of PD. Methods: Accelerometry data was collected using the Parkinson’s KinetiGraph System (PKG, Global Kinetics). An algorithm was developed, which could successfully distinguish been subjects with a resting or postural tremor that involved the wrist whose frequency was greater than 3 Hz. Percent of time that tremor was present (PTT) between 09 : 00 and 18 : 00 was calculated. Results: This algorithm was applied to 85 people with PD who had been assessed clinically for the presence and nature of tremor. The Sensitivity and Selectivity of a PTT ≥0.8% was 92.5% and 92.9% in identifying tremor, providing that the tremor was not a fine kinetic and postural tremor or was not in the upper limb. A PTT >1% provide high likely hood of the presence of clinical meaningful tremor. These cut-offs were retested on a second cohort ( n = 87) with a similar outcome. The Sensitivity and Selectivity of the combined group was 88.7% and 89.5% respectively. Using the PTT, 50% of 22 newly diagnosed patients had a PTT >1.0%. The PKG’s simultaneous bradykinesia scores was used to find a threshold for the emergence of tremor. Tremor produced artefactual increase in the PKG’s dyskinesia score in 1% of this sample. Conclusions: We propose this as a means of assessing the presence of tremor and its relationship to bradykinesia.

Device-assisted therapies (DAT) benefit people with Parkinsons Disease (PwP) but many referrals for DAT are unsuitable or too late, and a screening tool to aid in identifying candidates would be helpful. This study aimed to produce such a screening tool by building a classifier that models specialist identification of suitable DAT candidates. To our knowledge, this is the first objective decision tool for managing DAT referral. Subjects were randomly assigned to either a construction set (n = 112, to train, develop, cross validate, and then evaluate the classifier’s performance) or to a test set (n = 60 to test the fully specified classifier), resulting in a sensitivity and specificity of 89% and 86.6%, respectively. The classifier’s performance was then assessed in PwP who underwent deep brain stimulation (n = 31), were managed in a non-specialist clinic (n = 81) or in PwP in the first five years from diagnosis (n = 22). The classifier identified 87%, 92%, and 100% of the candidates referred for DAT in each of the above clinical settings, respectively. Furthermore, the classifier score changed appropriately when therapeutic intervention resolved troublesome fluctuations or dyskinesia that would otherwise have required DAT. This study suggests that information from objective measurement could improve timely referral for DAT.

Introduction: While objective measurement is routine in clinical care of most conditions, this has not been the case for Parkinson’s Disease. Recent innovations make objective measurement in Parkinson’s Disease possible and its utility and how this should be assessed is discussed here. Areas covered: Whilst therapies are effective in Parkinson’s Disease, symptoms fluctuate in relation to treatment over the course of the day. Objective measurement makes it possible to assess symptom control, whether treatment is required and whether it achieved control. Objective measurement makes it possible to consider targets for therapeutic control and to begin an assessment of the value of improved control. Evidence for the effect of improved measurement on outcomes is only beginning to emerge. As symptom severity relates to quality of life and costs, reducing clinical scores and fluctuations through objective measurement is in the interest of both the patient and the health system. Expert commentary: In broad terms objective measurement should be used to identify patients whose symptoms lie outside the target range and then to assess whether therapy was effective in bringing them into control. While this is relevant to all stages of Parkinson’s Disease specific clinical situations where this had greatest impact are discussed.

Axial Bradykinesia is an important feature of advanced Parkinson’s disease (PD). The purpose of this study is to quantify axial bradykinesia using wearable sensors with the long-term aim of quantifying these movements, while the subject performs routine domestic activities. We measured back movements during common daily activities such as pouring, pointing, walking straight and walking around a chair with a test system engaging a minimal number of Inertial Measurement (IM) based wearable sensors. Participants included controls and PD patients whose rotation and flexion of the back was captured by the time delay between motion signals from sensors attached to the upper and lower back. PD subjects could be distinguished from controls using only two sensors. These findings suggest that a small number of sensors and similar analyses could distinguish between variations in bradykinesia in subjects with measurements performed outside of the laboratory. The subjects could engage in routine activities leading to progressive assessments of therapeutic outcomes.