=Paper=
{{Paper
|id=Vol-3623/AIxAS_2023_paper_1
|storemode=property
|title=Acceptability and clinical usefulness of a telemonitoring and telerehabilitation system in people with Parkinson’s Disease in different disease stages: preliminary findings from the RAPIDO study
|pdfUrl=https://ceur-ws.org/Vol-3623/AIxAS_2023_paper_1.pdf
|volume=Vol-3623
|authors=Antonia Antoniello,Antonio Sabatelli,Simone Valenti,Lucia Pepa,Luca Spalazzi,Elisa Andrenelli,Silvia Vada,Marianna Capecci,Michele Tinazzi,Gianmatteo Farabolini,Marialuisa Gandolfi,Giulia Bonardi,Maria Gabriella Ceravolo,Nicolò Baldini
|dblpUrl=https://dblp.org/rec/conf/aixas/AntonielloSVPSA23
}}
==Acceptability and clinical usefulness of a telemonitoring and telerehabilitation system in people with Parkinson’s Disease in different disease stages: preliminary findings from the RAPIDO study==
https://ceur-ws.org/Vol-3623/AIxAS_2023_paper_1.pdf
Acceptability and clinical usefulness of a telemonitoring
and telerehabilitation system in people with Parkinson’s
Disease in different disease stages: preliminary findings
from the RAPIDO study.
Antonia Antoniello1, Antonio Sabatelli2, Simone Valenti2, Lucia Pepa2, Luca Spalazzi2,
Elisa Andrenelli1, Silvia Vada1, Nicolò Baldini1, Marianna Capecci1, Michele Tinazzi3,
Gianmatteo Farabolini1, Marialuisa Gandolfi3, Giulia Bonardi3 and Maria Gabriella
Ceravolo1
1 Department of Experimental and Clinical Medicine - Politecnica delle Marche University, Ancona, Italy.
2 Department of Information Engineering- Politecnica delle Marche University, Ancona, Italy.
3 Department of Neurosciences, Biomedicine and Movement Sciences, University Hospital, Verona, Italy.
Abstract
Telemonitoring and telerehabilitation techniques are significant approaches for people with
Parkinson’s disease (PD) to improve their clinical health status. In this work, we present a system to
monitor people with PD during their physical exercise sessions. Wearable devices are used to collect 24-
hour health parameters, that are successively stored on a remote server and then analyzed. A clinical
and technical analysis has been conducted on this data; the second one exploits techniques such as the
Tukey test, PCA technique, and the K-means clustering algorithm.
The main goal is to identify changes in patients’ health status over 3 months (monitoring period) and
assess the acceptability of the system.
Keywords
Data Analysis, Healthcare, Telerehabilitation, Telemonitoring, Parkinson’s Disease, Smartwatch.
1
1. Introduction
Parkinson’s disease (PD) is a neurodegenerative disorder that impacts a significant and
constantly growing number of people worldwide ([1]; approximately 1-2% of the population
over 65 years old and about 3% of individuals over 80 years old). The cardinal motor symptoms,
such as bradykinesia, muscular rigidity, resting tremor [2], postural and gait impairments, and
difficulties in speech and swallowing, are the most recognizable manifestations of PD. These
symptoms are considered clinical markers for assessing the progression and severity of the
disease. However, PD is also characterized by several non-motor symptoms (e.g., sleep disorders,
mood disorders, depression, alexithymia, etc.) with a strong impact on patients’ quality of life.
Hence, the clinical evaluation of PD focuses on studying its motor and non-motor manifestations.
This evaluation is typically carried out using standardized scales and questionnaires. Clinicians
assess symptoms through medical interviews and clinical examination, while patients provide
information about their habits and perceptions of the disease. However, most assessment
measures lack sensitivity to change, especially in the early stage of PD, and may not be fully
suitable for studying the disease’s progression in such phases [3].
As a result, there is growing interest within the scientific community in the use of sophisticated
technologies for the clinical monitoring of people with PD [4]. Wearable devices have emerged as
AIxAS 2023: Fourth Italian Workshop on Artificial Intelligence for an Ageing Society, 6-9 November 2023, Rome, Italy
l.pepa@staff.univpm.it (L. Pepa)
0000-0003-1471-092X (L. Pepa)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
a promising alternative to current diagnostic tools, offering an efficient means of detecting PD in
its early stages [5]. For therapeutic purposes, pharmacological and non-pharmacological
approaches are available, to alleviate symptoms in PD patients [6]. These therapies encompass
drugs, such as Levodopa-based preparations and physical activity. It has been demonstrated that
rehabilitation through physical activity can attenuate motor symptoms, reduce disability
progression, and improve the overall quality of life for PD patients [6,7]. Therefore, incorporating
a physical activity plan into the treatment regimen is crucial for managing PD [8]. Furthermore,
telerehabilitation and telemonitoring techniques have emerged as innovative and effective
approaches for managing patients with PD [9,10], benefiting patients, healthcare providers, and
the healthcare system.
According to the mentioned guidelines, our work proposes a telerehabilitation and
telemonitoring system designed to reduce the costs that a standard rehabilitation therapy in
presence would have for the healthcare system and caregivers. The system is developed within
the project RAPIDO (teleRehabilitation for pAtient with ParkInson’s Disease at any mOment),
started in 2021 from the collaboration between Politecnica delle Marche University and Verona
University and supported by Fondazione Cariverona (Grant Agreement Call R&D 2020 Prot.
2020.0069 - ID 11656). The primary objective of this paper is to analyze the feasibility and
acceptability of the system as mentioned above, as well as to provide a methodological solution
to analyze data collected from wearable sensors that can detect changes in patients’ health status.
The proposed method first identifies the most significant variables, showing any changes
throughout the telerehabilitation period, and then presents an unsupervised clustering technique
to find an interesting structure of patients’ health data and if this structure has any consistent
relation with the advancement of the telerehabilitation program.
2. Material and methods
2.1 System Architecture
As described in [11], individuals with PD use a smartwatch (Garmin Vivosmart 4) to track their
health condition and a tablet (Samsung A7) to watch videos related to the exercises following the
rehabilitation program that clinicians prescribed to them. The information gathered from the
smartwatch is transmitted to a remote server and later examined to observe the users' health
condition progression. The structure of the system is illustrated in Fig. 1.
Figure 1: Architecture of the used telemonitoring system
2.2 Participants and Clinical Protocol
We enrolled people of either sex with a diagnosis of Parkinson’s disease who exhibited the
following eligibility criteria: age over 18 years; provision of written informed consent, ability to
perform the physical exercises and interact with the system, presence of family support, Hoehn
and Yahr (HY) disease stage < 4 (still able to walk also with physical help in at least few minutes
of the day, absence of moderate to severe depression, dementia or other neuropsychiatric
disorders.
Participants’ enrollment is still in progress. This work presents preliminary data from 31
subjects who completed the 3-month follow-up.
2.2.1 Procedures
Over 12-week, participants should follow a telerehabilitation program, accessing the web
platform to perform training sessions proposed by clinicians three times a week. Each training
session comprises several exercises which need about 45 minutes to be completed. The clinicians
designed 4 different rehabilitation protocols, each containing 36 training sessions. They matched
each participant with the most suitable rehabilitation protocol on enrollment, according to the
disease stage and health condition. The attending physicians provided the clinical and functional
assessment of patients at enrolment and 12 weeks later, applying a set of quantitative measures
routinely used to monitor the burden of motor symptoms (Unified Parkinson’s Disease Rating
scale-UPDRS), non-motor symptoms (Non-Motor Symptom Scale -NMSS), and health-related
quality of life (PDQ-8). Changes in disease-specific clinical measures, as the UPDRS II and III
scores and the NMSS score may signal PD progression or, conversely, indicate treatment efficacy.
Changes in PDQ-8, a patient-reported outcome, are used as proxies of perceived well-being.
Finally, the System Usability Scale (SUS) was administered at the end of the 3-month period to
assess the perception of ease of use of the telerehabilitation and telemonitoring system.
Table 1 synthesizes the content and scoring system of the quantitative clinical measures
applied.
2.3 Data Collection
The experimental protocol requires each participant to wear a smartwatch (Garmin
Vivosmart 4) throughout the day. This enables data collection regarding daily motor and sleep
activities for subsequent analysis. The smartwatch transfers the data in anonymous format to the
tablet using Bluetooth, which then forwards the gathered data to a remote server via the Garmin
Connect App. Once it arrives on the server, the data is saved as summary logs (in JSON format)
and can be retrieved for future analysis. Data is not pre-processed but used as provided by
Garmin. An example of the provided features can be found on table 2, while the complete list of
all the collected variables can be found in Garmin documentation and [12].
Only authorized personnel can access and manage the information and data stored in the
remote server. All patients’ data uploaded on the platform have been anonymized.
The features recorded by the smartwatch are used as indices of the amount of physical activity
performed by the patients during the study period and proxies of their general health status.
None of the parameters collected by the smartwatch is an index of disease.
2.4 Data Analysis
The primary objective of the data analysis was to see if the telerehabilitation system
influenced any health habits or physiological changes in patients over the 3 months of exercise.
Daily summaries from the smartwatches worn by the participants were used for this analysis,
extracting 35 features every day, the entire analysis was performed separately for each patient.
Table 1. Main features of the clinical outcome measures applied in the RAPIDO study
Outcome measure Acronym Score Meaning
Range
Hoehn & Yahr HY 1-5 Disease-related disability staging;
score > 3 = severe disability
Body Mass Index BMI kg/m2 (Normal range = 18.5 to
24.9)
Comorbidity Illness Rating scale CIRS 1-4 Comorbidity index: 1=no
comorbidity.
Montreal Cognitive Assessment MoCA Validated screening tool for
Cognitive functions: >26/30 =
normal function; 18-22 = mild
cognitive impairment; <22
cognitive impairment.
UPDRS Activities of Daily Living UPDRS II-ADL 0-52 UPDRS II part assessing disability.
UPDRS motor part UPDRS III 0-108 UPDRS III part assessing motor
symptoms
Non-Motor Symptom scale NMSS 0-360 international validated measure of
non-motor features: the higher the
score the greater the severity of
symptoms
Parkinson’s Disease Quality of life-8 PDQ 0-32 8-item rating scale assessing PD-
related quality of life: the higher
the score the worse quality of life
System Usability Scale SUS 0-100 10-item questionnaire designed to
evaluate a wide variety of products
and services, including hardware,
software, mobile devices, websites
and applications.
Figure 2 outlines the process for analyzing this data: Firstly, features were grouped by weeks,
and the Tukey Honestly Significant Difference (HSD) test was performed on each feature for
pairwise comparison between different weeks.
Only those features showing significant changes between weeks (with a p-value less than 0.05)
were further analyzed. Then, Principal Component Analysis (PCA) was performed on these
features, which helped simplify complex data into main components for easier interpretation. The
number of PC to use for subsequent analysis was chosen to reach at least 90% of the explained
variance. Due to his/her clinical history and medical condition, every subject has a different
number of PC.
The new PCA data was used as input for an unsupervised learning method called K-means
clustering. This technique was chosen since the collected data are not labeled in advance, and
clinicians did not define a list of possible classes that could be assigned to each observation in the
dataset. They were only interested in searching for any significant changes in the patient's
condition. K=3 was chosen to separate data into different clusters. An interesting analysis was to
compare the cluster identity of each point with the belonging of the same point to the first, second,
or third month of observation (it will be called month identity). The match between cluster
identity and month identity can be measured using a confusion matrix, where the month
identities are regarded as “true values”, and cluster identities as “predicted values”. Accuracy is
then calculated from the confusion matrix.
Table 2
The main features provided by Garmin Server in the daily summary
Property Type Description
summaryId string Unique identifier for the summary
calendarDate string The calendar date this summary would be
displayed on in Garmin Connect. The date
format is ‘yyyy-mm-dd’
startTimeInSeconds integer Start time of the activity in seconds since
January 1, 1970, 00:00:00 UTC (Unix
timestamp).
startTimeOffsetInSeconds integer Offset in seconds to add to
startTimeInSeconds to derive the “local”
time of the device that captured the data.
activityType string This field is included in daily summaries for
backwards compatibility purposes. It can be
ignored and will always default to WALKING.
durationInSeconds integer Length of the monitoring period in seconds.
86400 once a full day is complete, but less if a
user syncs mid-day.
steps integer Count of steps recorded during the
monitoring period.
distanceInMeters float Distance traveled in meters.
activeTimeInSeconds integer Portion of the monitoring period (in seconds)
in which the device wearer was considered
Active. This relies on heuristics internal to
each device.
activeKilocaries integer Active kilocalories (dietary calories) burned
during the monitoring period. This includes
only the calories burned by the activity and
not calories burned as part of the basal
metabolic rate (BMR).
bmrKilocalories integer BMR Kilocalories burned by existing Basal
Metabolic Rate (calculated based on user
height/weight/age/other demographic data).
consumedCalories integer The number of calories that have been
consumed by the user through food for that
day (value subtracted from calorie goal). This
value is received from MyFitnessPal and is not
entered within Connect.
moderateIntensityDurationInSeconds integer Cumulative duration of activities of moderate
intensity. Moderate intensity is defined as
activity with MET value range 3-6.
The main idea is that a high match between clusters and months (high value of accuracy)
indicates that health data across months are separated and distinct, similarly to clusters, possibly
suggesting that the participant's health changed over the 3 months. From a clinical standpoint, if
data from the same month falls in the same cluster, it means that there is a steady health trend.
Otherwise, scattered data suggests fluctuating health without clear progression. Based on the
values of accuracy obtained for each patient and on the clinical assessments after the 3 months
telerehabilitation period, that established if clinical condition is stable, worsen or improved, it
was investigated if a threshold can be set on the accuracy in order to classify a certain participant
as stable or not stable (worsen or improved).
Lastly, the system acceptability was evaluated through the adherence of participants to the
rehabilitation protocol and smartwatch usage. Specifically, adherence was measured as the
percentage of completed training sessions over the total number of training sessions, while
smartwatch usage was measured as the percentage of correctly monitored days over the 3
months period. A day was considered correctly monitored if the smartwatch sent the daily report
to the server (if the user does not wear the smartwatch, no report is generated). The study was
performed according to the Declaration of Helsinki and approved by the Local Institutional
Committee on April 21st, 2022 (protocol number: CERM-2022-27). All participants signed
informed consent forms before participating in the study.
Figure 2: Flowchart of the data analysis process
3. Results
The clinical and technical results are presented separately in the following subsections.
3.1 Clinical Results
Patients’ demographic and clinical features at enrolment are detailed in Table 3. Data is
presented as mean and standard deviation (SD) for the total sample and for subgroups HY stage
1-2 and HY stage 3-4. Inter-group comparisons are also provided with respective p-values. Out of
the 31 enrolled subjects, 22 were male. Twenty-three patients were in the early, milder disease
stage, i.e., in the Hoehn & Yahr-HY stages 1-2, whereas 8 in the advanced disease stage (HY stage
3-4). As expected, the subgroup in the advanced disease stage showed a longer disease duration,
higher dependence on ADL (UPDRS-II ADL score), and poorer quality of life (higher PDQ-8 score),
than people with a mild condition (HY 1-2).
At 3 months, the clinical and functional assessment did not reveal any statistically significant
change in any outcome measure. No subject complained of side effects or adverse events
correlated to the training experience.
The compliance with the study protocol was quite high, with 68% of the total sample
completing more than 70% of the assigned sessions. This value ranged from 74% in subjects in
HY stages 1-2 to 50% in those with HY stages 3-4.
The perception of system usability at the end of the study period was overall high with all
people, both in the early and advanced phase, reporting SUS scores higher than 70.
Table 3
Demographic and clinical features of the enrolled sample. Data refer to the total sample of 31
subjects, and to the subgroups of people in different disease stages. Legenda: HY= Hoehn & Yahr;
BMI= Body Mass Index; CIRS= Comorbidity Illness Rating scale; MoCA= Montreal Cognitive
Assessment; UPDRS= Unified Parkinson’s Disease Rating scale; ADL= activities of Daily Living;
NMSS= Non-Motor Symptom scale; PDQ= Parkinson’s Disease Quality of life; SD= Standard
Deviation
Parameter TOTAL (N. 31) HY stage 1-2 HY stage 3-4 Inter-group
[mean (SD)] (n. 23) (N.9) comparison
[mean (SD)] [mean (SD)] p-value
Age(years) 68,4 (8,7) 66,6 (8,3) 73,5 (8,5) n.s.
Education(years) 13,1 (4,1)
BMI 25,7 (4,0) 25,8 (4,3) 25,3 (2,9) n.s.
Disease 7,1 (4,3) 6,0 (3,0) 10,2 (5,9) <0,01
duration(years)
CIRS score 1,1 (0,7) 1,0 (0,5) 1,5 (1,1) n.s.
MoCA score 25,6 (3,1) 26,3 (2,7) 23,9 (3,9) n.s.
UPDRS II-ADL 9,7 (5,6) 7,7, (3,4) 15,5, (3,8) <0,01
UPDRS III 19,9 (8,3) 18,0 (11,0) 25,1 (15,1) n.s.
NMSS 47,8 (34,2) 43,2 (33,1) 62,7 (36,1) n.s.
PDQ-8 6,9 (4,7). 5,4 (3,7) 11,4 (4,6) <0,001
SUS 74,0 (13,5) 70,9 (6,6) 79,0 (20,5) n.s.
Based on data recorded through the smartwatch, the analysis of motor behavior exhibited
daily over the 3-month observation period, produced different results in the two subgroups with
different disease severity.
People in HY stages 1-2 showed a trend towards increasing the number of steps (Figures 3-4)
and the distance (meters) covered each day (Figures 5-6). The increase started after the first
week to reach a maximum in the second week and remained overall stable up to the 10th week
to decrease afterward slightly. Energy expenditure showed a similar trend (Figures 7-8). People
in HY stages 3-4 did not show any recognizable trend. However, this subgroup comprised only 8
people who provided very different performances, so this inhomogeneity likely prevented the
definition of standard behavior.
Figure 3: Number of steps/day (average values recorded daily in the subgroups of people
with mild (HY stage 1-2) or advanced disease (HY stage 3-4)
Figure 4: Trend in the number of steps/day (values are averaged over 2-week periods in the
subgroups of people with mild (HY stage 1-2) or advanced disease (HY stage 3-4)
Figure 5: Distance (meters) covered /day (average values recorded daily in the subgroups of
people with mild (HY stage 1-2) or advanced disease (HY stage 3-4)
Figure 6: Trend in distance (meters) covered daily (values are averaged over 2-week
periods in the subgroups of people with mild (HY stage 1-2) or advanced disease (HY stage 3-4)
Figure 7: Active energy expenditure (kcal) /day (average values recorded daily in the
subgroups of people with mild (HY stage 1-2) or advanced disease (HY stage 3-4)
Figure 8: Trend in active energy expenditure (kcal)/day (values are averaged over 2-week
periods in the subgroups of people with mild (HY stage 1-2) or advanced disease (HY stage 3-4)
3.2 Technical Results
3.2.1. Health Status Monitoring
Among the enrolled participants, 60% were considered clinically stable after the
telerehabilitation period, while the other 40% manifested changes (improved or worsen). Mean
values of accuracy measure between cluster and month identities were 47% and 65% for the
stable and not stable conditions respectively. By setting a threshold of 60% on these accuracy
values, the capability of the proposed analysis to classify a patient as stable and not stable can be
measured through accuracy, precision, recall, and F1 score, as shown in Table 4.
Table 4
Confusion matrix of the clustering analysis
Class Precision Recall F1 Score
NON-STABLE 83,33% 71,43% 76,92%
STABLE 85,71% 92,31% 88,89%
change detection accuracy 85%
3.2.2. System Acceptability
Results reported in Table 5 show how adherence percentage differs between participants in
the early disease stages (H&Y stage 1-2) and in the advanced disease stage (H&Y stage 3-4). As
shown, PD patients belonging to the first category participated more actively to the
telerehabilitation program.
Otherwise, the percentage of correct smartwatch wearing, and usage is almost the same.
Table 5
Adherence percentage to the rehabilitation program and smartwatch usage
Adherence Smartwatch usage
H&Y (1-2) 87% 84,15%
H&Y (3-4) 50,75% 85%
4. Discussion
The main aim of the current research is to analyze the acceptability of a telerehabilitation
program, as well as to explore potential related changes in participants’ health status over the 3-
month intervention. Regarding the system's acceptability, we obtained encouraging results.
Indeed, all participants complied with the study protocol, accepting to wear the monitoring
device continuously during the day. As shown in Table 5, smartwatch usage percentage is near
85% for both patients with PD in the early stages (H&Y stage 1-2) and for patients in the more
advanced stages (H&Y stage 3-4), demonstrating that most of the participants showed no
difficulties in accepting the proposed system.
Looking at the intervention adherence, we found that PD patients in the early stages showed
higher involvement and commitment than patients in the more advanced stages (Table 5 displays
the percentage results obtained for participant adherence). Our results are in line with the
literature [9], where feasibility projects related to telerehabilitation in PD patients with H&Y
stage <3 [13] or <2 [14,15] showed acceptable adherence to the telerehabilitation program.
This evidence pushed us to consider the current program rich in potential benefits for people
who are not called to fight against the advanced symptoms of the disease.
The intervention compliance was higher than 70% in almost two out of three patients, that is
the expected value. The percentage was higher if considering people in the early stages of the
disease. Considering motor outcome, our results highlighted that the number of steps increased
in patients in the early stages of the disease but not in patients with H&Y stage 3-4.
Both adherence and compliance’s evidence underlined the system is feasible and acceptable
by patients with PD in the H&Y stage 1-2, suggesting that the current program might be further
tested on this population, and further enhancements might be required for patients in the
advanced stages. The current telerehabilitation program might be implemented in rural and
remote geographical areas [15], and it should be promoted also among patients with mid-low
socio-economic status [16].
Regarding the technical analysis, we propose a technique to identify possible changes in the
health status based on PCA and K-means clustering.
Values reported in Table 4 show that the proposed method may be used to classify a patient
as clinically stable or not.
40% percent of patients changed their health status after the telerehabilitation period and the
average accuracy between clusters and month identities was 65%. This possibly indicates that
data points are displaced in different clusters on the PC space during 3 months.
The remaining 60% percent of patients presented a clinically stable condition, reporting a
mean accuracy of 47% between clusters and month identities. In this case, the more casual
displacement of data points across months may be interpreted as the absence of significant
change.
According to the presented discussion, a low accuracy may suggest a clinically stable
condition, while a higher accuracy indicates a possible change in the patient’ health status. From
the collected data, a threshold of 60% proved to be suitable to quantitatively define “low” and
“high” accuracy and hence detect a change in health status, yielding a quite high precision for both
stable and not stable conditions (> 83%) and an 85% accuracy (Table 4).
The study was not designed to detect the impact of the telerehabilitation device on disease
progression, as a 3-month period is too short to capture significant changes in the
neurodegeneration process. That very period, however, is enough to detect behavioral changes,
thus allowing us to reply to the question whether or not getting feedback on own daily physical
activity and being involved in a structured telerehabilitation protocol impacts patients’ behavior,
by urging them to increase their daily physical activity. Preliminary results presented in this
paper are encouraging.
5. Conclusions
This work presented the preliminary results of the feasibility and acceptability of a
telerehabilitation program system for PD patients equipped with a telemonitoring device, used
to measure autonomic functions and motor behavior to detect changes in the subjects’ health
status. We found encouraging results in PD patients in the early stage of the disease in terms of
system acceptability and technical accuracy. Further enhancements are recommended for PD
patients in the advanced stages who showed lower results in the intervention’s compliance.
Despite these promising findings, they may be conditioned by the limited sample size and need
to be validated on a larger sample.
Furthermore, while the current analysis provides a quantitative assessment, it does not
specify the direction of changes, be it negative or positive. The next step for future research would
be to find a method that differentiates between patients who experience changes due to
worsening versus those who show improvements. Hopefully, once our data collection is closed,
our results might shed light on the overall usefulness of the system for people with PD at any
stage.
Acknowledgements
The authors gratefully acknowledge support from Revolt Srl and Garmin Ltd. This research is
partially supported by Fondazione Cariverona, inside the project RAPIDO (teleRehabilitation for
pAtient with ParkInson’s Disease at any mOment) (Grant Agreement Call R&D 2020 Prot.
2020.0069 - ID 11656).
Moreover, both L.P and A.A. received research grants by PON “Ricerca e Innovazione” 2014-
2020 Azione IV.6 “Contratti di ricerca su tematiche green” and Azione IV.4 “Dottorati e contratti
di ricerca su tematiche dell'innovazione”.
References
[1] GBD 2016 Parkinson's Disease Collaborators. Global, regional, and national burden of
Parkinson's disease, 1990-2016: a systematic analysis for the Global Burden of Disease Study
2016. Lancet Neurol; 17(11):939-953, 2018. doi: 10.1016/S1474-4422(18)30295-3.
[2] E.R. Dorsey, B.R. Bloem, The Parkinson Pandemic-A Call to Action. JAMA Neurol.; 75(1):9-10,
2018. doi: 10.1001/jamaneurol.2017.3299.
[3] A. Regnault, B. Boroojerdi, J. Meunier, M. Bani, T. Morel, S. Cano, Does the MDS-UPDRS provide
the precision to assess progression in early Parkinson's disease? Learnings from the Parkinson's
progression marker initiative cohort. J Neurol; 266(8):1927-1936, 2019. doi: 10.1007/s00415-
019-09348-3.
[4] H. Zhang, C. Li, W. Liu, J. Wang, J. Zhou, S. Wang, A Multi-Sensor Wearable System for the
Quantitative Assessment of Parkinson's Disease. Sensors (Basel); 20(21):6146, 2020. doi:
10.3390/s20216146.
[5] K. Yang, W.X. Xiong, F.T. Liu, Y.M. Sun, S. Luo, Z.T. Ding, J.J. Wu, J. Wang, Objective and
quantitative assessment of motor function in Parkinson's disease-from the perspective of
practical applications. Ann Transl Med; 4(5):90, 2016. doi: 10.21037/atm.2016.03.09.
[6] M. Capecci, M.G. Ceravolo, Thirty Years of Parkinson’s Disease Management: From a Symptom-
Based Approach to a Holistic Perspective. In: The First Outstanding 50 Years of “Università
Politecnica delle Marche”; 107-121, 2020. doi: 10.1007/978-3-030-33832-9_9.
[7] C. McDonald, G. Gordon, A. Hand, R.W. Walker, J.M. Fisher, 200 Years of Parkinson's disease:
what have we learnt from James Parkinson? Age Ageing; 47(2):209-214, 2018. doi:
10.1093/ageing/afx196.
[8] L. Hou, W. Chen, X. Liu, D. Qiao, F.M. Zhou, Exercise-Induced Neuroprotection of the
Nigrostriatal Dopamine System in Parkinson's Disease. Front Aging Neurosci; 9:358, 2017. doi:
10.3389/fnagi.2017.00358.
[9] C. Vellata, S. Belli, F. Balsamo, A. Giordano, R. Colombo, G. Maggioni, Effectiveness of
Telerehabilitation on Motor Impairments, Non-motor Symptoms and Compliance in Patients
With Parkinson's Disease: A Systematic Review. Front Neurol; 12:627999, 2021. doi:
10.3389/fneur.2021.627999.
[10] E. Tardelli, A. Moreira-Neto, E. Okamoto, F. Rogatto, M. Vergari-Filho, E.R. Barbosa, C. Silva-
Batista, Telerehabilitation during social distancing for people with Parkinson's disease: a
retrospective study. Acta Neurol Belg; 123(4):1267-1277, 2023. doi: 10.1007/s13760-022-
02160-3.
[11] A. Antoniello, A. Sabatelli, S. Valenti, M. Di Tillo, L. Pepa, et al. A low-cost telerehabilitation
and telemonitoring system for people with Parkinson’s disease: the architecture. IEEE 12th
International Conference on Consumer Electronics (ICCE-Berlin); 1-2, 2022. doi: 10.1109/ICCE-
Berlin56473.2022.9937125.
[12] A. Sabatelli, S. Valenti, A. Antoniello, M. Di Tillo, L. Pepa, et al. Parkinson’s disease
telemonitoring and telerehabilitation based on commercial wearable sensor data analysis: a pilot
study. IEEE 12th International Conference on Consumer Electronics (ICCE-Berlin); 1-2, 2022. doi:
10.1109/ICCE-Berlin56473.2022.9937140.
[13] E. Bianchini, C. Onelli, C. Morabito, M. Alborghetti, D. Rinaldi, P. Anibaldi, A. Marcolongo, M.
Salvetti, F.E. Pontieri, Feasibility, Safety, and Effectiveness of Telerehabilitation in Mild-to-
Moderate Parkinson's Disease. Front Neurol; 13:909197, 2022. doi: 10.3389/fneur.2022.909197.
[14] M.G. Maggio, A. Luca, C. D'Agate, M. Italia, R.S. Calabrò, A. Nicoletti, Feasibility and usability
of a non-immersive virtual reality tele-cognitive app in cognitive rehabilitation of patients
affected by Parkinson's disease. Psychogeriatrics; 22(6):775-779, 2022. doi:
10.1111/psyg.12880.
[15] L.F. Pastana Ramos, T.C.S. Vilacorta-Pereira, J.D.S. Duarte, E.S. Yamada, B.L. Santos-Lobato,
Feasibility and effectiveness of a remote individual rehabilitation program for people with
Parkinson's disease living in the Brazilian Amazon: a randomized clinical trial. Front Neurol;
14:1244661, 2023. doi: 10.3389/fneur.2023.1244661.
[16] E. Nizeyimana, C. Joseph, N. Plastow, G. Dawood, Q.A. Louw, A scoping review of feasibility,
cost, access to rehabilitation services and implementation of telerehabilitation: Implications for
low- and middle-income countries. Digit Health; 8:20552076221131670, 2022. doi:
10.1177/20552076221131670.