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This proof-of-principle study describes the methodology and explores and demonstrates the applicability of a system, existing of miniature inertial sensors on the hand and a separate force sensor, to objectively quantify hand motor symptoms in patients with Parkinson’s disease (PD) in a clinical setting (off- and on-medication condition). Four PD patients were measured in off- and on- dopaminergic medication condition. Finger tapping, rapid hand opening/closing, hand pro/supination, tremor during rest, mental task and kinetic task, and wrist rigidity movements were measured with the system (called the PowerGlove). To demonstrate applicability, various outcome parameters of measured hand motor symptoms of the patients in off- vs. on-medication condition are presented. The methodology described and results presented show applicability of the PowerGlove in a clinical research setting, to objectively quantify hand bradykinesia, tremor and rigidity in PD patients, using a single system. The PowerGlove measured a difference in off- vs. on-medication condition in all tasks in the presented patients with most of its outcome parameters. Further study into the validity and reliability of the outcome parameters is required in a larger cohort of patients, to arrive at an optimal set of parameters that can assist in clinical evaluation and decision-making.

Deep brain stimulation (DBS) treatment has proven effective in suppressing symptoms of rigidity, bradykinesia, and tremor in Parkinson’s disease. Still, patients may suffer from disabling fluctuations in motor and non-motor symptom severity during the day. Conventional DBS treatment consists of continuous stimulation but can potentially be further optimised by adapting stimulation settings to the presence or absence of symptoms through closed-loop control. This critically relies on the use of ‘physiomarkers’ extracted from (neuro)physiological signals. Ideal physiomarkers for adaptive DBS (aDBS) are indicative of symptom severity, detectable in every patient, and technically suitable for implementation. In the last decades, much effort has been put into the detection of local field potential (LFP) physiomarkers and in their use in clinical practice. We conducted a research synthesis of the correlations that have been reported between LFP signal features and one or more specific PD motor symptoms. Features based on the spectral beta band (~ 13 to 30 Hz) explained ~ 17% of individual variability in bradykinesia and rigidity symptom severity. Limitations of beta band oscillations as physiomarker are discussed, and strategies for further improvement of aDBS are explored.

Essential tremor (ET) is characterized by upper limbs action tremor, sometimes extending to other body parts. However, ET can present with additional neurological features known as “soft signs.” These signs of uncertain clinical significance are not sufficient to suggest an alternative neurological diagnosis, and include, among others, questionable dystonia and subtle voluntary movement alterations, i.e., bradykinesia and related features. This study aimed to explore the prevalence and relationship between questionable dystonia and subtle bradykinesia features in ET. Forty ET patients were video-recorded during clinical examination. Five movement disorder experts reviewed the videos to identify soft motor signs, i.e., dystonia and movement alterations during finger-tapping namely, (i) bradykinesia (reduced velocity), (ii) dysrhythmia, and (iii) sequence effect. Inter-rater agreement was quantified using the Fleiss’ Kappa index. Data analysis was performed using nonparametric tests. We found a fair inter-rater agreement for upper limb dystonia (Fleiss’ K = 0.27). Inter-rater agreement was higher (moderate) for head dystonia (Fleiss’ K = 0.49) and finger-tapping assessment (Fleiss’ K = 0.45). Upper limb dystonia was identified in 70% of patients, head dystonia in 35%, and finger-tapping alterations (in variable combinations) were observed in 95% of individuals ( P  < 0.001 by Fisher’s exact test), including subtle bradykinesia and related features. No significant concordance or correlation was found between the soft signs. Subtle bradykinesia and related features are the most easily identifiable and frequent soft signs in ET, appearing in a higher percentage of patients than subtle dystonia. These findings provide insights into the clinical and pathophysiological understanding of ET.

Parkinson’s disease patients face numerous motor symptoms that eventually make their life different from those of normal healthy controls. Out of these motor symptoms, tremor and bradykinesia, are relatively prevalent in all stages of this disease. The assessment of these symptoms is usually performed by traditional methods where the accuracy of results is still an open question. This research proposed a solution for an objective assessment of tremor and bradykinesia in subjects with PD (10 older adults aged greater than 60 years with tremor and 10 older adults aged greater than 60 years with bradykinesia) and 20 healthy older adults aged greater than 60 years. Physical movements were recorded by means of an AWEAR bracelet developed using inertial sensors, i.e., 3D accelerometer and gyroscope. Participants performed upper extremities motor activities as adopted by neurologists during the clinical assessment based on Unified Parkinson’s Disease Rating Scale (UPDRS). For discriminating the patients from healthy controls, temporal and spectral features were extracted, out of which non-linear temporal and spectral features show greater difference. Both supervised and unsupervised machine learning classifiers provide good results. Out of 40 individuals, neural net clustering discriminated 34 individuals in correct classes, while the KNN approach discriminated 91.7% accurately. In a clinical environment, the doctor can use the device to comprehend the tremor and bradykinesia of patients quickly and with higher accuracy.

Introduction Bradykinesia affects handwriting and smartphone usage in patients with Parkinson’s disease (pwPD). Objective To assess handwriting, hand dexterity, smartphone usage, and bradykinesia in pwPD and identify features that best describe upper-limb alterations in pwPD. Methods Forty pwPD and 30 age/sex-matched healthy controls were included. We used standard handwriting/dexterity tests: Manual-Ability-Measure-36, Purdue-Pegboard-Test (PPT) and copy of a text on paper. Spatiotemporal handwriting parameters were assessed using tests on a tablet: copy of text and pre-writing tasks. To obtain objective data on movement speed and amplitude on the smartphone, we developed tests involving the most commonly used gestures (tap, swipe, and slide). Bradykinesia during a finger-tapping task was evaluated using electromagnetic sensors. Sequential feature selection models were used to identify the parameters best distinguishing pwPD and healthy controls. Results PwPD relative to healthy controls showed reduced manual ability and dexterity. They showed reduced movement amplitude and speed in smartphone tests and signs of micrographia during handwriting tests. Moreover, kinematic parameters correlated with both PPT and Movement Disorder Society-Unified Parkinson’s Disease Rating Scale III. Each feature selection model demonstrated a good accuracy, particularly when including standard handwriting/dexterity tests ( R 2  = 0.90), tests on smartphone ( R 2  = 0.94) and all the features together ( R 2  = 0.97). The best features were self-reported manual abilities, PPT, tap and swipe speed/amplitude on smartphone. Conclusions This study showed that technological assessments can be added to standard evaluations to provide quantitative measures of handwriting, dexterity, and bradykinesia that will be useful to assess PD progression and the effects of interventions in pwPD.

Despite the clinical impact of motor symptoms such as agitation or retardation on the course of depression, these symptoms are poorly understood. Novel developments in the field of instrumentation and mobile devices allow for dimensional and continuous recording of motor behavior in various settings, particularly outside the laboratory. Likewise, the use of novel assessments enables to combine multimodal neuroimaging with behavioral measures in order to investigate the neural correlates of motor dysfunction in depression. The research domain criteria (RDoC) framework will soon include a motor domain that will provide a framework for studying motor dysfunction in mood disorders. In addition, new studies within this framework will allow investigators to study motor symptoms across different stages of depression as well as other psychiatric diagnoses. Finally, the introduction of the RDoC motor domain will help test how motor symptoms integrate with the original five RDoC domains (negative valence, positive valence, cognitive, social processes, and arousal/regulation).

Background Parkinson’s disease (PD) encompasses motor (e.g., bradykinesia) and non-motor (e.g., apathy) symptoms. Objective We aimed to use reflexive and voluntary saccades as a proxy for bradykinesia and apathy. Methods Seventeen PD patients and thirteen controls (matched for age and educational level) were recruited. We assessed apathy using the Dimensional Apathy Scale (DAS) and bradykinesia using MDS-UPDRS III. Subjects were asked to fixate successively two green points (cues, 40° apart) alternating at 1 Hz. After 20 s, all stimuli disappeared, and participants were required to continue fixating on the previous locations of the cues at the same frequency for another 20 s. We measured the Maximal Amplitude (MA) (saccade amplitude from side to side) and its period. Linear mixed models assessed the effect of the group (patient/control), cue, DAS, and bradykinesia score. Results Overall, the DAS was similarly correlated to the period (p = 0.0157) and the MA (p = 0.0002) in the absence of a cue. However, this correlation was significant only in the patient subgroup for the MA (p = 0.0005). In the absence of cue, bradykinesia was similarly correlated to the period (p = .0001) and the MA (p = 0.0004). However, the period was better correlated to bradykinesia than the DAS. Conclusions While the saccade period best correlates with bradykinesia, maximal amplitude in the absence of cue better reflects the severity of apathy. Our paradigm may be a promising objective biomarker for assessing bradykinesia and apathy in PD.

Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

Most studies of smartphone-based assessments of motor symptoms in Parkinson's disease (PD) focused on gait, tremor or speech. Studies evaluating bradykinesia using wearable sensors are limited by a small cohort size and study design. We developed an application named smartphone tapper (SmT) to determine its applicability for clinical purposes and compared SmT parameters to current standard methods in a larger cohort. A total of 57 PD patients and 87 controls examined with motor UPDRS underwent timed tapping tests (TT) using SmT and mechanical tappers (MeT) according to CAPSIT-PD. Subjects were asked to alternately tap each side of two rectangles with an index finger at maximum speed for ten seconds. Kinematic measurements were compared between the two groups. The mean number of correct tapping (MCoT), mean total distance of finger movement (T-Dist), mean inter-tap distance, and mean inter-tap dwelling time (IT-DwT) were significantly different between PD patients and controls. MCoT, as assessed using SmT, significantly correlated with motor UPDRS scores, bradykinesia subscores and MCoT using MeT. Multivariate analysis using the SmT parameters, such as T-Dist or IT-DwT, as predictive variables and age and gender as covariates demonstrated that PD patients were discriminated from controls. ROC curve analysis of a regression model demonstrated that the AUC for T-Dist was 0.92 (95% CI 0.88-0.96). Our results suggest that a smartphone tapping application is comparable to conventional methods for the assessment of motor dysfunction in PD and may be useful in clinical practice.

Background Subtle parkinsonian signs, i.e., rest tremor and bradykinesia, are considered soft signs for defining essential tremor (ET) plus. Objectives Our study aimed to further characterize subtle parkinsonian signs in a relatively large sample of ET patients from a clinical and neurophysiological perspective. Methods We employed clinical scales and kinematic techniques to assess a sample of 82 ET patients. Eighty healthy controls matched for gender and age were also included. The primary focus of our study was to conduct a comparative analysis of ET patients (without any soft signs) and ET-plus patients with rest tremor and/or bradykinesia. Additionally, we investigated the asymmetry and side concordance of these soft signs. Results In ET-plus patients with parkinsonian soft signs (56.10% of the sample), rest tremor was clinically observed in 41.30% of cases, bradykinesia in 30.43%, and rest tremor plus bradykinesia in 28.26%. Patients with rest tremor had more severe and widespread action tremor than other patients. Furthermore, we observed a positive correlation between the amplitude of action and rest tremor. Most ET-plus patients had an asymmetry of rest tremor and bradykinesia. There was no side concordance between these soft signs, as confirmed through both clinical examination and kinematic evaluation. Conclusions Rest tremor and bradykinesia are frequently observed in ET and are often asymmetric but not concordant. Our findings provide a better insight into the phenomenology of ET and suggest that the parkinsonian soft signs (rest tremor and bradykinesia) in ET-plus may originate from distinct pathophysiological mechanisms.