Real-life experiences of patients with Parkinson's disease

BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson’s disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson’s Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

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A Real-Life Search for the Optimal Set of Conversion Factors to Levodopa-Equivalent-Dose in Parkinson’s Disease Patients on Polytherapy

Journal of Parkinson s Disease ◽

10.3233/jpd-191769 ◽

2020 ◽

Vol 10 (1) ◽

pp. 173-178

Author(s):

Muneer Abu Snineh ◽

Amal Hajyahya ◽

Eduard Linetsky ◽

Renana Eitan ◽

Hagai Bergman ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Real Life ◽

Equivalent Dose ◽

Conversion Factors ◽

Optimal Set

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Assistive HCI-Serious Games Co-design Insights: The Case Study of i-PROGNOSIS Personalized Game Suite for Parkinson’s Disease

Frontiers in Psychology ◽

10.3389/fpsyg.2020.612835 ◽

2021 ◽

Vol 11 ◽

Author(s):

Sofia Balula Dias ◽

José Alves Diniz ◽

Evdokimos Konstantinidis ◽

Theodore Savvidis ◽

Vicky Zilidou ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Health Status ◽

Game Design ◽

Serious Games ◽

Linear Regression Analysis ◽

Real Life ◽

Design Parameters ◽

Online Questionnaire ◽

Design Elements

Human-Computer Interaction (HCI) and games set a new domain in understanding people’s motivations in gaming, behavioral implications of game play, game adaptation to player preferences and needs for increased engaging experiences in the context of HCI serious games (HCI-SGs). When the latter relate with people’s health status, they can become a part of their daily life as assistive health status monitoring/enhancement systems. Co-designing HCI-SGs can be seen as a combination of art and science that involves a meticulous collaborative process. The design elements in assistive HCI-SGs for Parkinson’s Disease (PD) patients, in particular, are explored in the present work. Within this context, the Game-Based Learning (GBL) design framework is adopted here and its main game-design parameters are explored for the Exergames, Dietarygames, Emotional games, Handwriting games, and Voice games design, drawn from the PD-related i-PROGNOSIS Personalized Game Suite (PGS) (www.i-prognosis.eu) holistic approach. Two main data sources were involved in the study. In particular, the first one includes qualitative data from semi-structured interviews, involving 10 PD patients and four clinicians in the co-creation process of the game design, whereas the second one relates with data from an online questionnaire addressed by 104 participants spanning the whole related spectrum, i.e., PD patients, physicians, software/game developers. Linear regression analysis was employed to identify an adapted GBL framework with the most significant game-design parameters, which efficiently predict the transferability of the PGS beneficial effect to real-life, addressing functional PD symptoms. The findings of this work can assist HCI-SG designers for designing PD-related HCI-SGs, as the most significant game-design factors were identified, in terms of adding value to the role of HCI-SGs in increasing PD patients’ quality of life, optimizing the interaction with personalized HCI-SGs and, hence, fostering a collaborative human-computer symbiosis.

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Virtual Exam for Parkinson's Disease Enables Frequent and Reliable Measurements of Motor Function

10.1101/2021.12.22.21268260 ◽

2021 ◽

Author(s):

Maximilien Burq ◽

Erin Rainaldi ◽

King Chung Ho ◽

Chen Chen ◽

Bastiaan R Bloem ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Early Stage ◽

Real Life ◽

Dropout Rate ◽

Therapeutic Interventions ◽

Retest Reliability ◽

Time Sensitivity ◽

Remote Measurements ◽

Test Retest Reliability

Sensor-based remote monitoring could help us better track Parkinson's disease (PD) progression, and measure patients' response to putative disease-modifying therapeutic interventions. To be useful, the remotely-collected measurements should be valid, reliable and sensitive to change, and people with PD must engage with the technology. We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. 388 study participants with early-stage PD (Personalized Parkinson Project, 64% men, average age 63 years) wore a smartwatch for a median of 390 days, allowing for continuous passive monitoring. Participants performed unsupervised motor tasks both in the clinic (once) and remotely (twice weekly for one year). Dropout rate was 2% at the end of follow-up. Median wear-time was 21.1 hours/day, and 59% of per-protocol remote assessments were completed. In-clinic performance of the virtual exam verified that most participants correctly followed watch-based instructions. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (R=0.70), bradykinesia (R=-0.62), and gait (R=-0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC: 0.75 - 0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen's d: 0.19 - 0.54). Of note, in-clinic assessments often did not reflect the patients' typical status at home. This demonstrates the feasibility of using smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements can create a more complete picture of patient functioning by providing a real-life distribution of disease severity, as it fluctuates over time. Sensitivity to medication-induced change, together with the improvement in test-retest reliability from temporal aggregation implies that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic intervention or disease progression.

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Real-Life Use of Levodopa/Carbidopa Intestinal Gel in Parkinson’s Disease According to Analysis of Pump Data

Journal of Parkinson s Disease ◽

10.3233/jpd-202114 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1529-1534

Author(s):

Dag Nyholm ◽

Malak Adnan ◽

Marina Senek

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Daily Life ◽

Real Life ◽

Median Number ◽

Motor Fluctuations ◽

Adherence To Treatment ◽

Therapy Adherence ◽

Median Size ◽

Efficacious Treatment

Background: Levodopa/carbidopa intestinal gel (LCIG) infusion is an efficacious treatment of motor and non-motor fluctuations in people with Parkinson’s disease (PD). Real-life use of the treatment is not previously studied. Objective: The aims of the study were to explore the use of LCIG and to determine how extra doses of LCIG are used in daily life. Methods: Twenty-five PD patients with ongoing LCIG therapy were consecutively included. Pump data was retrieved from 30 days on average, by means of software, extracting the most recent pump events. Results: The daily duration of infusion was 15 hours on average, in 18 patients, whereas the remaining 7 patients used 24-hour infusion. Morning doses ranged from 38–190 mg levodopa, for patients who utilized this function. Median number of daily extra doses was 2.5 (range: 0–10.6) and median size of the extra dose was 24 mg (0–80 mg) levodopa. Median total daily levodopa intake with LCIG was 1201 mg (range: 417–2322 mg). Conclusion: Retrieving pump data is possible and may be important for evaluating the at-home use of LCIG, to optimize the therapy. Adherence to treatment should be monitored, which is not technically difficult, at least in device-aided treatments for PD.

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P.015 Long-term progression and prognosis in different subtypes of Parkinson’s disease: validation of a new multi-domain subtyping method

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2019.116 ◽

2019 ◽

Vol 46 (s1) ◽

pp. S17

Author(s):

S Fereshtehnejad ◽

Y Zeighami ◽

A Dagher ◽

RB Postuma

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

De Novo ◽

Early Stage ◽

Real Life ◽

Clinical Manifestations ◽

Personalized Care ◽

Autonomic Disturbance ◽

Progression Markers

Background: Parkinson’s disease (PD) varies in clinical manifestations and course of progression from person to person. Identification of distinct PD subtypes is of great priority to develop personalized care approaches. We aimed to compare long-term progression and prognosis between different PD subtypes. Methods: Data on 421 individuals with de novo early-onset PD was retrieved from Parkinson’s Progression Markers Initiative (PPMI). Using a newly developed multi-domain subtyping method (based on motor phenotype, RBD, autonomic disturbance, early cognitive deficit), we divided PD population into three subtypes at baseline: “mild motor-predominant”, “Diffuse malignant” and “Intermediate”. Rate of global progression (mixed motor and non-motor features) and developing dementia were compared between the subtypes. Results: Patients with “diffuse malignant” PD experienced 0.5 z-score further worsening of global composite outcome (p=0.017) and 2.2 further decline in MOCA score (p=0.001) after 6-years of follow-up. Hazard for MCI/dementia was significantly higher in “diffuse malignant” (HR=3.2, p<0.001) and “intermediate” (HR=1.8, p<0.001) subtypes. Individuals with “diffuse malignant” PD had the lowest level of CSF amyloid-beta (p=0.006) and SPECT striatal binding ratio (p=0.001). Conclusions: This multi-domain subtyping is a valid method to predict subgroups of PD with distinct patterns of long-term progression at drug-naïve early-stage with potential application in real-life clinical practice.

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Feasibility of a Mobile-Based System for Unsupervised Monitoring in Parkinson’s Disease

Sensors ◽

10.3390/s21154972 ◽

2021 ◽

Vol 21 (15) ◽

pp. 4972

Author(s):

Raquel Bouça-Machado ◽

Filipa Pona-Ferreira ◽

Mariana Leitão ◽

Ana Clemente ◽

Diogo Vila-Viçosa ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Real Life ◽

Functional Mobility ◽

Clinical Assessments ◽

Ecological Information ◽

Promising Tool ◽

System A ◽

System Usability ◽

High Level

Mobile health (mHealth) has emerged as a potential solution to providing valuable ecological information about the severity and burden of Parkinson’s disease (PD) symptoms in real-life conditions. Objective: The objective of our study was to explore the feasibility and usability of an mHealth system for continuous and objective real-life measures of patients’ health and functional mobility, in unsupervised settings. Methods: Patients with a clinical diagnosis of PD, who were able to walk unassisted, and had an Android smartphone were included. Patients were asked to answer a daily survey, to perform three weekly active tests, and to perform a monthly in-person clinical assessment. Feasibility and usability were explored as primary and secondary outcomes. An exploratory analysis was performed to investigate the correlation between data from the mKinetikos app and clinical assessments. Results: Seventeen participants (85%) completed the study. Sixteen participants (94.1%) showed a medium-to-high level of compliance with the mKinetikos system. A 6-point drop in the total score of the Post-Study System Usability Questionnaire was observed. Conclusions: Our results support the feasibility of the mKinetikos system for continuous and objective real-life measures of a patient’s health and functional mobility. The observed correlations of mKinetikos metrics with clinical data seem to suggest that this mHealth solution is a promising tool to support clinical decisions.

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