Exploring the Complex Phenotypes of Impaired Finger Dexterity in Mild-to-moderate Stage Parkinson’s Disease: A Time-Series Analysis

Abstract

Background: Impaired dexterity is an early motor symptom in Parkinson’s disease (PD) that significantly impacts the daily activity of patients; however, what constitutes complex dexterous movements remains controversial. Objective: To explore the characteristics of finger dexterity in mild-to-moderate stage PD. Methods: We quantitatively assessed finger dexterity in 48 mild-to-moderate stage PD patients and 49 age-matched controls using a simple alternating two-finger typing test for 15 seconds. Time-series analyses of various kinematic parameters with machine learning were compared between sides and groups. Results: Both the more and less affected hands of patients with PD had significantly lower typing frequency and slower typing velocity than the non-dominant and the dominant hands of controls (p = 0.04, p = 0.03, p < 0.001, p < 0.001). The slope of the typing velocity decreased with time, indicating a sequence effect in the PD group. A typing duration of 6 seconds was determined sufficient to discriminate PD patients from controls. Typing error, repetition, and repetition rate were significantly higher in the more affected hands of patients with PD than in the non-dominant hand of controls (p = 0.03, p = 0.01, p < 0.001). The error rate was constant, whereas the repetition rate was steep during the initiation of typing. A predictive model of the more affected hand demonstrated an accuracy of 70% in differentiating PD patients from controls. Conclusion: Our study demonstrated complex components of impaired finger dexterity in mild-to-moderate stage PD, namely bradykinesia with sequence effects, error, and repetition at the initiation of movement, suggesting that multiple neural networks may be involved in dexterity deficits in PD.

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“…Impaired dexterity has been documented as one of the earliest motor signs in PD and can be demonstrated about 6 years prior to the diagnosis of PD ( 46 ). Abnormal dexterous movements can be detected by alternating two fingers tapping on the computer keyboard or on the smartphone ( 96 , 136 , 137 ). Studies have shown that the machine learning approach using the various parameters of finger tapping tests can discriminate PD from controls with good accuracy, suggesting that the alternating finger tapping test would be a promising digital marker to quantify subtle motor symptoms in early PD ( 96 ).…”

“…The research team at ChulaPD has extensive experience in the field of digital technologies, with a track record of publication on device/sensor development and applications (96,(100)(101)(102)(103)(104). To date, the devices and technologies that have been developed have been focused on strategies for patients who have already been diagnosed with PD.…”

“…Testing activities for PD diagnosis using smartphone applications have been used successfully and with good accuracy for analysis of gait ( 89 , 90 ), spoken voice ( 91 , 92 ) and tremor ( 93 , 94 ). Finger movement while typing has also shown promising results with high accuracy for the early detection of PD ( 95 , 96 ). Data gathered from these activities by the smartphone’s sensors are processed for extraction of diagnostic features, which can include general statistical analyses (mean, standard deviation, percentiles), but also measurements of time and frequency.…”

User-centred design, validation and clinical testing of an anti-choking mug for people with Parkinson’s disease

“…Sip volume is another factor to consider, where the recommendation for cup sipping is about 25 ml per sip for males and 20 ml for females 30 . Impaired hand function that encompasses poor manual dexterity, reduced grip strength, and low self-perceived hand functional ability can result in delays in hand opening and pauses between the reach-grasp and take-to-lip parts of the drinking action 31 – 33 . Changes in hand grip anthropometry in PD is another consideration, as decreased hand grip strength is associated with decreased muscle mass and predicts functional limitations in the individuals 34 .…”

Disentangling Bradykinesia and Rigidity in Parkinson's Disease: Evidence from Short‐ and Long‐Term Subthalamic Nucleus Deep Brain Stimulation

“…44 Bradykinesia in PD is currently thought to reflect a corticosubcortical network disorder implying abnormal functional connections among the fronto-basal-gangliathalamocortical motor loop and the cerebellum. [7][8][9][10]45 By contrast, parkinsonian rigidity likely arises from abnormal inputs from GPi and substantia nigra pars reticulata to the pontobulbar reticular formation, leading to hyperactive descending projections (dorsal and medial reticulospinal tracts) directed to propriospinal as well as spinal Ia and Ib interneurons. [11][12][13] This in turn results in hyperexcitability of gamma and alpha motoneurons responsible for increased muscle tone.…”