Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
UNKNOWN
Total Enrollment
300 participants
Study Classification
OBSERVATIONAL
Study Start Date
2019-04-23
Study Completion Date
2021-12-31
Brief Summary
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Gait impairments of patients with Parkinson's disease (PD) limit the independence in the daily activities and sensibly increase the risk of falls. New gait analysis methods, based on wearable inertial sensors, have been proposed to track the gait features during treatment and in real-life conditions. Gait training based on auditory cues as Rhythmical Auditory Stimulation (RAS) have preliminarily shown positive effects improving gait velocity, stride length, step cadence of walking in PD. In the current project, the research group will aim to develop a smartphone application (Parkinson App Smartphone Aimed: P.A.St.A.) integrated with sensors and RAS. In a second time, investigators will analyze the spatio-temporal gait parameters obtained by the wearable sensors and the sociodemographic and clinical data, thus generating a big data set, to improve the knowledge about current pharmacological therapies and rehabilitation.
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Detailed Description
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Parkinson's disease (PD) is a chronic and progressive movement disorder, mainly due to an altered motor control. Gait impairments are the primary symptoms in patients with PD, with a decreased step length and walking speed, abnormal gait phases distribution, inconstant pace, gait asymmetry and reduced joint coordination. The postural instability and the deterioration of the gait features sensibly increase the risk of falls in these patients, resulting in loss of independence and a worsening of long-term prognosis. It has been clearly demonstrated that motor impairments are connected to a dysfunction of basal ganglia, a brain structure that works as a "pacemaker" for the activation (and deactivation) of each sub-movement within a repetitive movement sequence. Disruption of internal rhythmic cues in PD may explain the poor smoothness of the movement execution and the difficulties in regulating stride length, resulting in a cadence increase. Levodopa therapy is the most effective treatment to improve the symptoms in PD, but a long-term administration reduces its efficiency over time and it is responsible for collateral effects (dyskinesia). Hence, researches have investigated alternative non-pharmacological approaches, based on auditory cues as the Rhythmical Auditory Stimulation (RAS). A large number of studies have reported positive effects both after a single session of treatment and after longer training programs, with improved gait velocity, cadence, and stride length as well as in the symmetry of muscle activation for upper and lower extremities. In such studies, RAS frequencies are pre-set as percentages of the patient's preferred walking frequency. Because the duration of all the therapies may change over time, it is necessary a continuous adjustment of the dosage and type of treatment, based both on patients' monitoring of the symptoms and on objective evaluations. Gait analysis (GA) methods have been proposed and validated for the study of physiologic gait and in several diseases. The traditional systems of GA are based on optoelectronic systems, but they are expensive, not portable and requiring skilled operators. The assessment is always performed in laboratories with experimental set-ups, likely different from real-life walking (noise environments, presence of objects and people or unlevelled pavement, different colors).
Consequently, traditional GA is not reliable for the daily control of the treatments or for the assessment of the real-life situations. Recently, researchers have developed wearable systems, based on Inertial Measurement Units (IMUs). Even if a considerable number of studies have explored the validity of IMUs analysis, very few have been involved in PD motion analyses. During the last few years, a set of wearable devices embedding inertial sensors, are spread on the market as low-cost solutions for monitoring human motion activities. While, they still have to be fully validated in accuracy, these systems can provide the user information to track his/her own activity. This persuasive technology has a terrific potential to enhance physical activity and motivation of the patients, with the advantage of prolonged and continuous recording.
The use and the analyses of a large amount of objective data (big data set) is the new frontier for an efficient use of scientific time and resources, engaging the care coordination program, saving economic resources, and providing a higher quality of care. In addition, these systems, integrated with a web-based application, telemedicine and mobile smartphones, could help clinicians to address more properly the treatments through a real-time monitoring in an everyday life. Our research group has been involved in the clinical evaluation of different aspects of gait quality in many neurological conditions, PD included. Investigators have already shown as wearable accelerometers could be useful in the quantitative assessment of the dynamic gait stability, in correlation with clinical scores. Researchers have also found that accelerations could be altered in a pathology-specific manner (intellectual disabilities performing different tasks simultaneously). A similar condition can be observed also in PD, showing that only the intrinsic gait harmony significantly correlate with severity of gait impairments. In the recent years, a lot of wearable tools have become available to quantify the daily activity. This is particularly important for possible therapeutic use of the RAS, that is addressed to restore a harmonic pace in patients with PD.
Specific Aim 1: To analyze motor pattern in PD patients in real life setting, gait features will be obtained by wearable sensors in order to relate them with clinical and demographical data.
Specific Aim 2: To improve walking abilities and to reduce the risk of falls in patients with PD, researchers will test a real-time acoustic feed-back (RAS) and alerts from integrated sensors connected with a suitable and easy-to-use application for smartphones.
Specific Aim 3: To improve pharmacological and rehabilitative protocols, investigators will analyze the daily life gait and motility data and the clinical features to test the theoretical risk model of falls.
Experimental Design Aim 1: Patients with PD will be recruited at Fondazione Policlinico Universitario Gemelli and Fondazione Don Gnocchi ONLUS in Rome. All included patients will be affected by idiopathic PD. Clinical data will be acquired by a trained neurologist.
Three hundred patients will be remotely monitored through wearable sensors. Subjects will be instructed to wear the sensor for 14 consecutive days. Each patient will have more than 100 measurements every second, generating a large amount of data, which will be stored in a database and subsequently filtered for data of interest. Spatio-temporal gait parameterswill provide information to monitor the performance of patient's walking in order to get an accurate measure of the overall efficacy of the locomotor function. Then, they will be integrated with the electronic patient records. Clinical data, as physician's prescriptions, medical imaging and other administrative data will be recorded. Finally, a risk model for the prediction of falls will be constructed.
Experimental Design Aim 2: After the analysis of the obtained data, an application will be developed (Parkinson App Smartphone Aimed: P.A.St.A.), it will be characterized by reliability, easy-to-use, visual clarity, and affordability.
This application will be able to: 1) record the gait features sending them to a server in the cloud; 2) provide acoustic feedback adapted on patient's gait features (with higher or lower frequency on the basis of the patient's needs and on the predictive risks); 3) provide alert on the basis of risk indices (risk indices will be identified according to a risk model for the prediction of falls) and 4) be updated whenever needed (e.g. after a fall). Fifty patients will be provided with a smartphone, after a training on the use of the software application developed by the team project. They will use the application for a period of 14 consecutive days. Clinical assessments with scales will be performed, pre- and post-use of P.A.St.A., to analyze the motor function and the postural stability and, finally, the quality of life. In addition, the compliance to the adoption of the novel technology will be investigated.
Experimental Design Aim 3: The obtained computational data will be related with clinical features and gait patterns to identify clinical biomarkers for gait impairment in PD. The availability of a big data-sets, powered by the assessments of thousands of patients with PD, will allow the improvement of knowledge on motor pattern in real life setting and to improve the current therapeutic approach.
Metodologies and statistical analyses: All statistical analyses will be conducted using Stata software.
AIM 1 - Data will be summarized as frequencies and percentages for categorical variables. Continuous variables will be analyzed as means and standard deviations or medians and ranges. Investigators will look for normality by using normal plots or by significance tests (e.g. Shapiro-Wilk W test). The incidence of falls will be measured and the circumstances under which they occur and their consequences will be described, categorized as no injury, minor or major injuries. A multiple logistic regression analysis will be used to determine independent predictors of falls. Variables will be selected for entry into the logistic model based on the results of a univariate analyses. The Hosmer-Lemeshow goodnessof-fit test will be used to assess how well the model accounted for specific outcomes. The prediction model for falls will be developed from the results of the multivariate analysis. The predictive score will be calculated by odds ratio based scoring method, and the nearest integer scores will be assigned to each predictor. Model discriminative power will be evaluated by receiver-operating characteristic area under the curve analysis.
AIM 2 - Data recorded will be processed to extract gait and dynamic balance parameters. The incidence of falls, the use of RAS and its correlation to the risks indices within the 14 days of follow-up will be measured. The Wilcoxon signed-rank test will be performed to compare the number of fall(s) before and after the use of P.A.St.A.
AIM 3 - Spatio-temporal gait parameters, and demographic and clinical characteristics of patients before therapies, will be compared using the Chi squared test, Fisher's exact test, and independent t-tests where appropriate.
Consequently, traditional GA is not reliable for the daily control of the treatments or for the assessment of the real-life situations. Recently, researchers have developed wearable systems, based on Inertial Measurement Units (IMUs). Even if a considerable number of studies have explored the validity of IMUs analysis, very few have been involved in PD motion analyses. During the last few years, a set of wearable devices embedding inertial sensors, are spread on the market as low-cost solutions for monitoring human motion activities. While, they still have to be fully validated in accuracy, these systems can provide the user information to track his/her own activity. This persuasive technology has a terrific potential to enhance physical activity and motivation of the patients, with the advantage of prolonged and continuous recording.
The use and the analyses of a large amount of objective data (big data set) is the new frontier for an efficient use of scientific time and resources, engaging the care coordination program, saving economic resources, and providing a higher quality of care. In addition, these systems, integrated with a web-based application, telemedicine and mobile smartphones, could help clinicians to address more properly the treatments through a real-time monitoring in an everyday life. Our research group has been involved in the clinical evaluation of different aspects of gait quality in many neurological conditions, PD included. Investigators have already shown as wearable accelerometers could be useful in the quantitative assessment of the dynamic gait stability, in correlation with clinical scores. Researchers have also found that accelerations could be altered in a pathology-specific manner (intellectual disabilities performing different tasks simultaneously). A similar condition can be observed also in PD, showing that only the intrinsic gait harmony significantly correlate with severity of gait impairments. In the recent years, a lot of wearable tools have become available to quantify the daily activity. This is particularly important for possible therapeutic use of the RAS, that is addressed to restore a harmonic pace in patients with PD.
Specific Aim 1: To analyze motor pattern in PD patients in real life setting, gait features will be obtained by wearable sensors in order to relate them with clinical and demographical data.
Specific Aim 2: To improve walking abilities and to reduce the risk of falls in patients with PD, researchers will test a real-time acoustic feed-back (RAS) and alerts from integrated sensors connected with a suitable and easy-to-use application for smartphones.
Specific Aim 3: To improve pharmacological and rehabilitative protocols, investigators will analyze the daily life gait and motility data and the clinical features to test the theoretical risk model of falls.
Experimental Design Aim 1: Patients with PD will be recruited at Fondazione Policlinico Universitario Gemelli and Fondazione Don Gnocchi ONLUS in Rome. All included patients will be affected by idiopathic PD. Clinical data will be acquired by a trained neurologist.
Three hundred patients will be remotely monitored through wearable sensors. Subjects will be instructed to wear the sensor for 14 consecutive days. Each patient will have more than 100 measurements every second, generating a large amount of data, which will be stored in a database and subsequently filtered for data of interest. Spatio-temporal gait parameterswill provide information to monitor the performance of patient's walking in order to get an accurate measure of the overall efficacy of the locomotor function. Then, they will be integrated with the electronic patient records. Clinical data, as physician's prescriptions, medical imaging and other administrative data will be recorded. Finally, a risk model for the prediction of falls will be constructed.
Experimental Design Aim 2: After the analysis of the obtained data, an application will be developed (Parkinson App Smartphone Aimed: P.A.St.A.), it will be characterized by reliability, easy-to-use, visual clarity, and affordability.
This application will be able to: 1) record the gait features sending them to a server in the cloud; 2) provide acoustic feedback adapted on patient's gait features (with higher or lower frequency on the basis of the patient's needs and on the predictive risks); 3) provide alert on the basis of risk indices (risk indices will be identified according to a risk model for the prediction of falls) and 4) be updated whenever needed (e.g. after a fall). Fifty patients will be provided with a smartphone, after a training on the use of the software application developed by the team project. They will use the application for a period of 14 consecutive days. Clinical assessments with scales will be performed, pre- and post-use of P.A.St.A., to analyze the motor function and the postural stability and, finally, the quality of life. In addition, the compliance to the adoption of the novel technology will be investigated.
Experimental Design Aim 3: The obtained computational data will be related with clinical features and gait patterns to identify clinical biomarkers for gait impairment in PD. The availability of a big data-sets, powered by the assessments of thousands of patients with PD, will allow the improvement of knowledge on motor pattern in real life setting and to improve the current therapeutic approach.
Metodologies and statistical analyses: All statistical analyses will be conducted using Stata software.
AIM 1 - Data will be summarized as frequencies and percentages for categorical variables. Continuous variables will be analyzed as means and standard deviations or medians and ranges. Investigators will look for normality by using normal plots or by significance tests (e.g. Shapiro-Wilk W test). The incidence of falls will be measured and the circumstances under which they occur and their consequences will be described, categorized as no injury, minor or major injuries. A multiple logistic regression analysis will be used to determine independent predictors of falls. Variables will be selected for entry into the logistic model based on the results of a univariate analyses. The Hosmer-Lemeshow goodnessof-fit test will be used to assess how well the model accounted for specific outcomes. The prediction model for falls will be developed from the results of the multivariate analysis. The predictive score will be calculated by odds ratio based scoring method, and the nearest integer scores will be assigned to each predictor. Model discriminative power will be evaluated by receiver-operating characteristic area under the curve analysis.
AIM 2 - Data recorded will be processed to extract gait and dynamic balance parameters. The incidence of falls, the use of RAS and its correlation to the risks indices within the 14 days of follow-up will be measured. The Wilcoxon signed-rank test will be performed to compare the number of fall(s) before and after the use of P.A.St.A.
AIM 3 - Spatio-temporal gait parameters, and demographic and clinical characteristics of patients before therapies, will be compared using the Chi squared test, Fisher's exact test, and independent t-tests where appropriate.
Conditions
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Parkinson Disease
Study Design
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Observational Model Type
COHORT
Study Time Perspective
PROSPECTIVE
Study Groups
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Parkinson Disease Patients
Patients with PD will be recruited at Fondazione Policlinico Universitario Gemelli and Fondazione Don Gnocchi ONLUS in Rome. All included patients will be affected by idiopathic PD. Clinical data will be acquired by a trained neurologist.
We will remotely monitor 300 patients through wearable sensors. Subjects will be instructed to wear the sensor for 14 consecutive days.
Werable sensors
Intervention Type
DEVICE
Parkinson Disease Patients monitored by werable sensors
Interventions
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Werable sensors
Parkinson Disease Patients monitored by werable sensors
Intervention Type
DEVICE
Eligibility Criteria
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Inclusion Criteria
* Idiopathic Parkinson's disease diagnosed by a neurologist
* Ability to sign informed consent
* Ability to sign informed consent
Exclusion Criteria
* Inability to sign informed consent
* Inability to fill out questionnaires (i.e. due to poor vision, inability to read, etc)
* Presence of psychiatric diseases (episode or any form of psychosis, diagnosed by a psychiatrist in the last year).
* Age under 18 yrs old
* Inability to fill out questionnaires (i.e. due to poor vision, inability to read, etc)
* Presence of psychiatric diseases (episode or any form of psychosis, diagnosed by a psychiatrist in the last year).
* Age under 18 yrs old
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
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Fondazione Policlinico Universitario Agostino Gemelli IRCCS
OTHER
Sponsor Role
collaborator
Catholic University of the Sacred Heart
OTHER
Sponsor Role
collaborator
Fondazione Don Carlo Gnocchi Onlus
OTHER
Sponsor Role
lead
Responsible Party
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Augusto Fusco
Principal Investigator; M.D.; Ph.D.
Responsibility Role
PRINCIPAL_INVESTIGATOR
Principal Investigators
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Augusto Fusco, M.D.; Ph.D.
Role: PRINCIPAL_INVESTIGATOR
IRCCS Fondazione Don Carlo Gnocchi
Locations
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Fondazione Don Carlo Gnocchi Onlus
Roma, , Italy
Site Status
RECRUITING
Countries
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Italy
Central Contacts
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Facility Contacts
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References
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Rubinstein TC, Giladi N, Hausdorff JM. The power of cueing to circumvent dopamine deficits: a review of physical therapy treatment of gait disturbances in Parkinson's disease. Mov Disord. 2002 Nov;17(6):1148-60. doi: 10.1002/mds.10259.
Reference Type
BACKGROUND
Rogers MW. Disorders of posture, balance, and gait in Parkinson's disease. Clin Geriatr Med. 1996 Nov;12(4):825-45.
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BACKGROUND
Yogev G, Plotnik M, Peretz C, Giladi N, Hausdorff JM. Gait asymmetry in patients with Parkinson's disease and elderly fallers: when does the bilateral coordination of gait require attention? Exp Brain Res. 2007 Mar;177(3):336-46. doi: 10.1007/s00221-006-0676-3.
Reference Type
BACKGROUND
Heiderscheit BC. Movement Variability as a Clinical Measure for Locomotion. Journal of Applied Biomechanics 16(4): 419-427, 2000.
Reference Type
BACKGROUND
van der Heeden JF, Marinus J, Martinez-Martin P, Rodriguez-Blazquez C, Geraedts VJ, van Hilten JJ. Postural instability and gait are associated with severity and prognosis of Parkinson disease. Neurology. 2016 Jun 14;86(24):2243-50. doi: 10.1212/WNL.0000000000002768. Epub 2016 May 13.
Reference Type
BACKGROUND
Cunnington R, Iansek R, Bradshaw JL, Phillips JG. Movement-related potentials in Parkinson's disease. Presence and predictability of temporal and spatial cues. Brain. 1995 Aug;118 ( Pt 4):935-50. doi: 10.1093/brain/118.4.935.
Reference Type
BACKGROUND
Morris ME, Iansek R, Matyas TA, Summers JJ. Stride length regulation in Parkinson's disease. Normalization strategies and underlying mechanisms. Brain. 1996 Apr;119 ( Pt 2):551-68. doi: 10.1093/brain/119.2.551.
Reference Type
BACKGROUND
Kestenbaum M, Fahn S. Safety of IPX066 , an extended release carbidopa-levodopa formulation, for the treatment of Parkinson's disease. Expert Opin Drug Saf. 2015 May;14(5):761-7. doi: 10.1517/14740338.2015.1015986. Epub 2015 Feb 19.
Reference Type
BACKGROUND
Oung QW, Muthusamy H, Lee HL, Basah SN, Yaacob S, Sarillee M, Lee CH. Technologies for Assessment of Motor Disorders in Parkinson's Disease: A Review. Sensors (Basel). 2015 Aug 31;15(9):21710-45. doi: 10.3390/s150921710.
Reference Type
BACKGROUND
Rochester L, Hetherington V, Jones D, Nieuwboer A, Willems AM, Kwakkel G, Van Wegen E. The effect of external rhythmic cues (auditory and visual) on walking during a functional task in homes of people with Parkinson's disease. Arch Phys Med Rehabil. 2005 May;86(5):999-1006. doi: 10.1016/j.apmr.2004.10.040.
Reference Type
BACKGROUND
del Olmo MF, Arias P, Furio MC, Pozo MA, Cudeiro J. Evaluation of the effect of training using auditory stimulation on rhythmic movement in Parkinsonian patients--a combined motor and [18F]-FDG PET study. Parkinsonism Relat Disord. 2006 Apr;12(3):155-64. doi: 10.1016/j.parkreldis.2005.11.002. Epub 2006 Feb 3.
Reference Type
BACKGROUND
Ford MP, Malone LA, Nyikos I, Yelisetty R, Bickel CS. Gait training with progressive external auditory cueing in persons with Parkinson's disease. Arch Phys Med Rehabil. 2010 Aug;91(8):1255-61. doi: 10.1016/j.apmr.2010.04.012.
Reference Type
BACKGROUND
Baker K, Rochester L, Nieuwboer A. The effect of cues on gait variability--reducing the attentional cost of walking in people with Parkinson's disease. Parkinsonism Relat Disord. 2008;14(4):314-20. doi: 10.1016/j.parkreldis.2007.09.008. Epub 2007 Nov 7.
Reference Type
BACKGROUND
Taborri J, Palermo E, Rossi S, Cappa P. Gait Partitioning Methods: A Systematic Review. Sensors (Basel). 2016 Jan 6;16(1):66. doi: 10.3390/s16010066.
Reference Type
BACKGROUND
Trojaniello D, Ravaschio A, Hausdorff JM, Cereatti A. Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson's disease and Huntington's disease subjects. Gait Posture. 2015 Sep;42(3):310-6. doi: 10.1016/j.gaitpost.2015.06.008. Epub 2015 Jun 25.
Reference Type
BACKGROUND
Ozminkowski RJ, Wells TS, Hawkins K, Bhattarai GR, Martel CW, Yeh CS. Big Data, Little Data, and Care Coordination for Medicare Beneficiaries with Medigap Coverage. Big Data. 2015 Jun;3(2):114-25. doi: 10.1089/big.2014.0034.
Reference Type
BACKGROUND
Iosa M, Fusco A, Morone G, Pratesi L, Coiro P, Venturiero V, De Angelis D, Bragoni M, Paolucci S. Assessment of upper-body dynamic stability during walking in patients with subacute stroke. J Rehabil Res Dev. 2012;49(3):439-50. doi: 10.1682/jrrd.2011.03.0057.
Reference Type
BACKGROUND
Iosa M, Fusco A, Morone G, Paolucci S. Development and decline of upright gait stability. Front Aging Neurosci. 2014 Feb 5;6:14. doi: 10.3389/fnagi.2014.00014. eCollection 2014.
Reference Type
BACKGROUND
Iosa M, Morelli D, Marro T, Paolucci S, Fusco A. Ability and stability of running and walking in children with cerebral palsy. Neuropediatrics. 2013 Jun;44(3):147-54. doi: 10.1055/s-0033-1336016. Epub 2013 Mar 13.
Reference Type
BACKGROUND
Iosa M, Morone G, Fusco A, Marchetti F, Caltagirone C, Paolucci S, Peppe A. Loss of fractal gait harmony in Parkinson's Disease. Clin Neurophysiol. 2016 Feb;127(2):1540-1546. doi: 10.1016/j.clinph.2015.11.016. Epub 2015 Dec 2.
Reference Type
BACKGROUND
Iosa M, Morelli D, Nisi E, Sorbara C, Negrini S, Gentili P, Paolucci S, Fusco A. Assessment of upper body accelerations in young adults with intellectual disabilities while walking, running, and dual-task running. Hum Mov Sci. 2014 Apr;34:187-95. doi: 10.1016/j.humov.2014.02.005. Epub 2014 Mar 11.
Reference Type
BACKGROUND
Manns PJ, Baldwin E. Ambulatory activity of stroke survivors: measurement options for dose, intensity, and variability of activity. Stroke. 2009 Mar;40(3):864-7. doi: 10.1161/STROKEAHA.108.531590. Epub 2009 Jan 15.
Reference Type
BACKGROUND
Skidmore FM, Mackman CA, Pav B, Shulman LM, Garvan C, Macko RF, Heilman KM. Daily ambulatory activity levels in idiopathic Parkinson disease. J Rehabil Res Dev. 2008;45(9):1343-8.
Reference Type
BACKGROUND
Padua L, Pazzaglia C, Pareyson D, Schenone A, Aiello A, Fabrizi GM, Cavallaro T, Santoro L, Manganelli F, Gemignani F, Vitetta F, Quattrone A, Mazzeo A, Russo M, Vita G; CMT-TRIAAL Group. Novel outcome measures for Charcot-Marie-Tooth disease: validation and reliability of the 6-min walk test and StepWatch() Activity Monitor and identification of the walking features related to higher quality of life. Eur J Neurol. 2016 Aug;23(8):1343-50. doi: 10.1111/ene.13033. Epub 2016 May 10.
Reference Type
BACKGROUND
Mileti I, Germanotta M, Di Sipio E, Imbimbo I, Pacilli A, Erra C, Petracca M, Rossi S, Del Prete Z, Bentivoglio AR, Padua L, Palermo E. Measuring Gait Quality in Parkinson's Disease through Real-Time Gait Phase Recognition. Sensors (Basel). 2018 Mar 20;18(3):919. doi: 10.3390/s18030919.
Reference Type
BACKGROUND
Other Identifiers
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GR-2016-02362879
Identifier Type: -
Identifier Source: org_study_id
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