The BRadykinesia Akinesia INcoordination (BRAIN) Tap Test: Capturing the Sequence Effect

Hasan, Hasan; Burrows, Maggie; Hellman, Bruce; James, Ben; Warner, Thomas T.; Foltynie, Tom; Giovannoni, Gavin; Lees, Andrew J.; Noyce, Alastair J.

doi:10.1002/mdc3.12798

Cited by 30 publications

(43 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When quantifying bradykinesia, examples of technologies used vary from more rudimentary to increasingly sophisticated methods. For instance, arcade buttons [ 11 ], midi-keyboards [ 12 ], Inertial Measurement Units [ 13 – 19 ], and touchscreen devices [ 20 – 28 ] have all been used in previous studies. Touchscreen-based tapping tasks have been shown to not only differentiate reliably between PD patients and healthy controls (HCs) [ 12 , 20 – 29 ] but also to detect medication effects [ 14 , 20 , 23 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Touchscreen-based finger tapping: Repeatability and configuration effects on tapping performance

et al. 2021

View full text Add to dashboard Cite

Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects almost 2% of the population above the age of 65. To better quantify the effects of new medications, fast and objective methods are needed. Touchscreen-based tapping tasks are simple yet effective tools for quantifying drug effects on PD-related motor symptoms, especially bradykinesia. However, there is no consensus on the optimal task set-up. The present study compares four tapping tasks in 14 healthy participants. In alternate finger tapping (AFT), tapping occurred with the index and middle finger with 2.5 cm between targets, whereas in alternate side tapping (AST) the index finger with 20 cm between targets was used. Both configurations were tested with or without the presence of a visual cue. Moreover, for each tapping task, within- and between-day repeatability and (potential) sensitivity of the calculated parameters were assessed. Visual cueing reduced tapping speed and rhythm, and improved accuracy. This effect was most pronounced for AST. On average, AST had a lower tapping speed with impaired accuracy and improved rhythm compared to AFT. Of all parameters, the total number of taps and mean spatial error had the highest repeatability and sensitivity. The findings suggest against the use of visual cueing because it is crucial that parameters can vary freely to accurately capture medication effects. The choice for AFT or AST depends on the research question, as these tasks assess different aspects of movement. These results encourage further validation of non-cued AFT and AST in PD patients.

show abstract

Section: Introductionmentioning

confidence: 99%

Touchscreen-based finger tapping: Repeatability and configuration effects on tapping performance

et al. 2021

View full text Add to dashboard Cite

show abstract

“…PD is heterogeneous-the motor signs of tremor and bradykinesia typically vary within a patient across body parts and over time 12 . Most current digital assessments that focus on only 5-7 pre-selected subtests [13][14][15][16][17] are likely to have reduced sensitivity as compared to the 33-item MDS-UPDRS III. We rely on 16 independent smartphone subtests to evaluate individual heterogeneity across body parts and report results for individual tests in addition to a combined score (see Figs.…”

Section: Discussionmentioning

confidence: 99%

“…Bradykinesia tests also showed variable performance with pronation/supination measures standing out as having good sampling variance across the population and good subject-level correspondence between clinical and digital measures (mean LOSO-CV accuracy 73-74.6%). This is an important consideration for current smartphone assessments that typically focus on finger-tapping only [13][14][15][16] . Future analyses should determine whether a combination of subtests can be used to predict non-tested items (such as limb rigidity) or approximate total MDS-UPDRS III score but this is beyond the scope of the pre-specified analysis plan that we report here.…”

Section: Discussionmentioning

confidence: 99%

The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters

Jha

Menozzi

Oyekan

et al. 2020

npj Parkinsons Dis.

View full text Add to dashboard Cite

Digital assessments of motor severity could improve the sensitivity of clinical trials and personalise treatment in Parkinson’s disease (PD) but have yet to be widely adopted. Their ability to capture individual change across the heterogeneous motor presentations typical of PD remains inadequately tested against current clinical reference standards. We conducted a prospective, dual-site, crossover-randomised study to determine the ability of a 16-item smartphone-based assessment (the index test) to predict subitems from the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS III) as assessed by three blinded clinical raters (the reference-standard). We analysed data from 60 subjects (990 smartphone tests, 2628 blinded video MDS-UPDRS III subitem ratings). Subject-level predictive performance was quantified as the leave-one-subject-out cross-validation (LOSO-CV) accuracy. A pre-specified analysis classified 70.3% (SEM 5.9%) of subjects into a similar category to any of three blinded clinical raters and was better than random (36.7%; SEM 4.3%) classification. Post hoc optimisation of classifier and feature selection improved performance further (78.7%, SEM 5.1%), although individual subtests were variable (range 53.2–97.0%). Smartphone-based measures of motor severity have predictive value at the subject level. Future studies should similarly mitigate against subjective and feature selection biases and assess performance across a range of motor features as part of a broader strategy to avoid overly optimistic performance estimates.

show abstract

“…Data from cases with PD were derived from several sources: the assessment of UPSIT and RBDSQ scores, and anxiety and depression were taken from baseline data of those aged between 60 and 80 years in the Tracking Parkinson's study (young-onset PD cases were excluded), a multicentre prospective longitudinal epidemiological and biomarker study of PD (n = 1046, mean age 69 years, 47% female; RBDSQ scores available on n = 983; anxiety and depression data available on n = 872) 33 . For the BRAIN test, 59 PD cases came from previously published data 34 , supplemented with unpublished data for a total of n = 87 PD cases. BRAIN tests were performed 'off' treatment in PD cases.…”

Section: Data Sourcesmentioning

confidence: 99%

Optimising classification of Parkinson’s disease based on motor, olfactory, neuropsychiatric and sleep features

Bestwick

Auger

Schrag

et al. 2021

npj Parkinsons Dis.

Self Cite

View full text Add to dashboard Cite

Olfactory loss, motor impairment, anxiety/depression, and REM-sleep behaviour disorder (RBD) are prodromal Parkinson’s disease (PD) features. PD risk prediction models typically dichotomize test results and apply likelihood ratios (LRs) to scores above and below cut-offs. We investigate whether LRs for specific test values could enhance classification between PD and controls. PD patient data on smell (UPSIT), possible RBD (RBD Screening Questionnaire), and anxiety/depression (LADS) were taken from the Tracking Parkinson’s study (n = 1046). For motor impairment (BRAIN test) in PD cases, published data were supplemented (n = 87). Control data (HADS for anxiety/depression) were taken from the PREDICT-PD pilot study (n = 1314). UPSIT, RBDSQ, and anxiety/depression data were analysed using logistic regression to determine which items were associated with PD. Gaussian distributions were fitted to BRAIN test scores. LRs were calculated from logistic regression models or score distributions. False-positive rates (FPRs) for specified detection rates (DRs) were calculated. Sixteen odours were associated with PD; LRs for this set ranged from 0.005 to 5511. Six RBDSQ and seven anxiety/depression questions were associated with PD; LRs ranged from 0.35 to 69 and from 0.002 to 402, respectively. BRAIN test LRs ranged from 0.16 to 1311. For a 70% DR, the FPR was 2.4% for the 16 odours, 4.6% for anxiety/depression, 16.0% for the BRAIN test, and 20.0% for the RBDSQ. Specific selections of (prodromal) PD marker features rather than dichotomized marker test results optimize PD classification. Such optimized classification models could improve the ability of algorithms to detect prodromal PD; however, prospective studies are needed to investigate their value for PD-prediction models.

show abstract

The BRadykinesia Akinesia INcoordination (BRAIN) Tap Test: Capturing the Sequence Effect

Cited by 30 publications

References 24 publications

Touchscreen-based finger tapping: Repeatability and configuration effects on tapping performance

Touchscreen-based finger tapping: Repeatability and configuration effects on tapping performance

The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters

Optimising classification of Parkinson’s disease based on motor, olfactory, neuropsychiatric and sleep features

Contact Info

Product

Resources

About