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| Name | Class |
|---|---|
| Michael J. Fox Foundation for Parkinson's Research | OTHER |
| Philips Electronics Nederland B.V. acting through Philips CTO organization | INDUSTRY |
| Intel Corporation | INDUSTRY |
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Background: Long-term management of Parkinson's disease (PD) does not reach its full potential due to lack of knowledge about disease progression. The Real-PD study aim to evaluate the feasibility and compliance of usage of wearable sensors in PD patients in real life. Moreover, an explorative analysis concerning activity level, medication intake and mood will be done.
Methods: Overall, 1000 PD patients and 250 physiotherapist will be enrolled in this observational study. Dutch PD patients will be recruited across the country and an assessment will be performed using a short version of the Parkinson's Progression Markers Initiative (PPMI) protocol. Moreover, participants will wear a set of medical devices (Pebble Smartwatch, fall detector) and they will use a smartphone with The Fox Insight App (Android app), 24/7, during 13 weeks. Primary measures of interest are: 1) physical activity, falls and tremor, measured by the axial accelerometers embedded in the Pebble watch and fall detector; and 2) medication intake and mood reports measured by patients' self-report in the Android app. To measure motor impact, an assessment will be performed by physiotherapists who are all certified to perform the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).
Discussion: Management of PD patients is complex and appears to be a challenging task for health care professionals. The main reason is the lack of knowledge in the disease pattern. This issue could be solved by a long term follow-up of patients' during their everyday life, and wearable medical devices can act as a way to collect data about every day life activities. Therefore, the Real-PD study will be a first contribution in increasing the lack of knowledge in disease progression, developing a new medical decision system and improving PD patients' care.
Rationale: Today's management of patients with a chronic disorder like Parkinson's disease (PD) is imperfect. The understanding of clinical profiles is based on observations in small, selective populations with brief follow-up. Moreover, treatment decisions are based on averaged population results that may not apply to a specific individual context. These drawbacks will be addressed with a "big data" approach. Ambulatory sensors will be used as an objective measure of patients' performance under everyday circumstances, for longer periods of time. The researchers aim to explore the potential of using longitudinal ambulatory data to enrich a standardized clinical dataset, which reflects current clinical practice for the assessment of disease status.
Objective: The study will include a total of 250 physiotherapists and 1000 patients. The aims of this study are: (1) to perform "big data" analyses on the raw sensor data, in relation to concurrently acquired clinical data in these patients (limited version of the PPMI (Parkinson's Progression Markers Initiative) protocol) to develop patient profiles; and (2) to correlate the ambulatory sensor data to simple self-assessments made during follow-up.
Study design: Observational descriptive study.
Study population: Dutch Parkinson patients, male or female, age 30 years or older, with PD diagnosis given by a physician, and own a suitable smartphone.
lntervention: 250 ParkinsonNet physiotherapists and 1000 eligible patients will be included in this study. Patients and physiotherapists will be recruited in 5 consecutive cohorts based on geographic region. Patients will be asked to wear a smartwatch and a pendant movement sensor, both with triaxial accelerometers, during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. During the 13 week follow-up, trained physiotherapists will perform a standardized clinical assessment, based on the PPMI protocol (www.ppmi-info.org) for every included patient. This assessment will last for 60 minutes. The smartphone is used to transmit data from the watch to a cloud-based data platform. lntel developed this dedicated data analysis platform for ambulatory data. lntel will receive coded data only.
Main study parameters/endpoints: Study endpoints include parameters registered with the smartwatch, the pendant movement sensor, the self-monitoring app and collected with the PPMI assessment. The smartwatch data provides, after data processing, a measure for the level of physical activity during the day. Falls will be registered with the pendant movement sensor. Medication intake and mood are registered using the smartphone. Finally, PPMI assessment includes assessment of motor symptoms, cognition, depression, sleep and daily activity. Correlations will be determined between the above mentioned parameters.
Nature and extent of the burden and risks associated with participation, benefit and group relatedness: First, participants are asked to wear the devices 24/7 and data will be recorded continuously, for a total duration of 13 weeks. Second, data will be transmitted to a data platform developed and managed by lntel, on behalf of the Michael J. Fox Foundation for Parkinson's Research. To access these data, researchers can grant permission for research purposes, provided by Michael J. Fox Foundation. Patients will be asked for permission to share the raw coded data for dissemination to the research community, analysis and use in future publications. Participation in the study warrants that patients provide written permission for this.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Cohort 1 | Dutch Parkinson's patients resident in the region of Noord-Holland whose fulfill the eligibility criteria. Interventions/Exposures to be administered:
|
| |
| Cohort 2 | Dutch Parkinson's patients resident in the region of Zuid-Holland whose fulfill the eligibility criteria. Interventions/Exposures to be administered:
|
| |
| Cohort 3 | Dutch Parkinson's patients resident in the region of Gelderland and Utrecht whose fulfill the eligibility criteria. Interventions/Exposures to be administered:
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Clinical assessment | Other | During the 13 week follow-up, trained physiotherapists will perform a standardized clinical assessment, based on the PPMI protocol (www.ppmi-info.org) for every included patient. This assessment will last for 60 minutes, and it will be done once. |
| Measure | Description | Time Frame |
|---|---|---|
| Parkinson's Disease Symptoms | The MDS-UPDRS is a revision of the Unified Parkinson's Disease Rating Scale (UPDRS). It was developed to evaluate various aspects of Parkinson's disease including non-motor and motor experiences of daily living, as well as motor complications. The MDS-UPDRS characterizes the extent and burden of disease across various populations. Here, we used data from one-point assessment (baseline). The total score used here was calculated by a sum of all scores from the 4 sub-scales (i.e. part I, up to part IV) composing the MDS-UPDRS. Total score ranges from 0 to 272. A higher score indicates higher disease severity and burden, being thus a worse outcome. | Baseline |
| Depression Scores as a Measure of Depression Rates | The scores obtained with the Geriatric Depression Scale were analysed in order to create a percentage of probably depressed participants. The total score goes from 0 to 15. A score higher than 6 indicates higher probability of suffer from a depression. | Baseline |
| Cognitive Impairment. | Total sum score obtained with the Montreal Cognitive Assessment. We analyzed the full score to investigate percentage of participants with a possible cognitive impairment. The Montreal Cognitive sum scores ranges from 0 to 30, in which a score lower or equal to 26 is considered as possible cognitive decline. | Baseline |
| Independency Level | The total sum score obtained with the Schwab and England activities of daily living scale were analyzed to describe the functional level of the sample. The total sum scores varies from 0 to 100, in which lower scores are associated with more dependency of others to perform daily life activities. | Baseline |
| Measure | Description | Time Frame |
|---|---|---|
| Number of Falls Per Patient Registered by the Falls Detector. | The fall event is recognized by the falls detector. Every time that the patient falls, the algorithm embedded at the falls detector recognize as a fall and record the fall event. At the end of the follow-up time, a sum of the falls event for each patient will be done. | Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. |
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Inclusion Criteria:
Exclusion Criteria:
None exclusion criteria will be used.
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Dutch Parkinson patients.
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| Name | Affiliation | Role |
|---|---|---|
| Bastiaan R Bloem, Prof. Dr. | Radboud University Medical Center | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Cohort 1 | Multiple Locations | North Holland | Netherlands |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 16445257 | Background | Parkka J, Ermes M, Korpipaa P, Mantyjarvi J, Peltola J, Korhonen I. Activity classification using realistic data from wearable sensors. IEEE Trans Inf Technol Biomed. 2006 Jan;10(1):119-28. doi: 10.1109/titb.2005.856863. | |
| 24689772 | Background | Hobert MA, Maetzler W, Aminian K, Chiari L. Technical and clinical view on ambulatory assessment in Parkinson's disease. Acta Neurol Scand. 2014 Sep;130(3):139-47. doi: 10.1111/ane.12248. Epub 2014 Apr 1. |
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After changing the inclusion process for one cohort only, a total of 347 eligible PD patients were invited to participate. Among those invited, 43 refused to participate. The main refusal reasons were "Study protocol seems too burdensome" (44%, n = 19), followed by "Personal circumstances" (33%, n = 14). A total of 304 patients (enrolment rate = 88%) were enrolled.
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| ID | Title | Description |
|---|---|---|
| FG000 | Cohort 1 | Dutch Parkinson's patients who fulfill the eligibility criteria. Interventions/Exposures to be administered:
|
| Title | Milestones | Reasons Not Completed | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
|
Inclusion was changed from 5 cohorts (total of 100 participants) to 1 cohort only.
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| ID | Title | Description |
|---|---|---|
| BG000 | Cohort 1 | Dutch Parkinson's patients who fulfill the eligibility criteria. Interventions/Exposures to be administered:
|
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Parkinson's Disease Symptoms | The MDS-UPDRS is a revision of the Unified Parkinson's Disease Rating Scale (UPDRS). It was developed to evaluate various aspects of Parkinson's disease including non-motor and motor experiences of daily living, as well as motor complications. The MDS-UPDRS characterizes the extent and burden of disease across various populations. Here, we used data from one-point assessment (baseline). The total score used here was calculated by a sum of all scores from the 4 sub-scales (i.e. part I, up to part IV) composing the MDS-UPDRS. Total score ranges from 0 to 272. A higher score indicates higher disease severity and burden, being thus a worse outcome. | We have analysed data from all participants who completed the study. | Posted | Median | Full Range | units on a scale | Baseline |
|
13 weeks
Serious Adverse Events: Include adverse events that result in any of the following outcomes: death, a life-threatening adverse event, inpatient hospitalization or prolongation of existing hospitalization, a persistent or significant incapacity or substantial disruption of the ability to conduct normal functions, or a congenital anomaly/birth defect.
Other (Not Including Serious) Adverse Events: Adverse events that are not Serious Adverse Events.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Cohort 1 (The Study Only Included 1 Cohort). | Dutch Parkinson's patients who fulfill the eligibility criteria. Interventions/Exposures to be administered:
|
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Marjan Meinders | Radboudumc | +00000000 | Marjan.Meinders@radboudumc.nl |
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| ID | Term |
|---|---|
| D010300 | Parkinson Disease |
| D018450 | Disease Progression |
| ID | Term |
|---|---|
| D020734 | Parkinsonian Disorders |
| D001480 | Basal Ganglia Diseases |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
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Not provided
| ID | Term |
|---|---|
| D002985 | Clinical Protocols |
| ID | Term |
|---|---|
| D013812 | Therapeutics |
| D016020 | Epidemiologic Study Characteristics |
| D017531 | Health Care Evaluation Mechanisms |
| D011787 | Quality of Health Care |
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|
| Cohort 4 | Dutch Parkinson's patients resident in the region of Groningen, Friesland, Drenthe and Overijssel whose fulfill the eligibility criteria. Interventions/Exposures to be administered:
|
|
| Cohort 5 | Dutch Parkinson's patients resident in the region of Zeeland, Noord-Brabant and Limburg whose fulfill the eligibility criteria. Interventions/Exposures to be administered:
|
|
|
| Fox Insight self-monitoring android app and falls detector | Device | Patients will be asked to wear a smartwatch and a pendant movement sensor, both with triaxial accelerometers, during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. |
|
|
| Number of Mood Reports for Each Patient Measured With a Four Point Scale | The number of mood reports will be collected through the smartphone application. A four point scale (very good, good, poor and fair) will be available, and by pressing the button which correspond to how the patient feels at that moment the report can be performed. At the end of the follow-up time a sum of all the reports will be done in order to measure the number of mood reports over the follow-up time. | Patients will be assessed during the follow-up time (up to 13 weeks after the enrollment date). It is expected that the assessment (self-report) will be performed as many times as the patient wants to report how they feel or at least once a day. |
| Number of Medication Intake Annotations Made by Each Participant Via the Self-report App. | The number of medication intake annotations made by the patients will be collected through the smartphone application. Every time that the patient take medication they must press the button reporting that they took the medication. At the end of the follow-up time a sum of all the reports will be done in order to measure the number of medication intake over the follow-up time. | Baseline |
| Time That Each Patient Was Active During the Day | The time that the patient was active during the day is calculated automatically through the app at the smartphone. The calculation is performed by using an algorithm, which analyze the patterns of walk. This algorithm is able to predict when the patient was active in a zone above his/her usual threshold (e.g. when the patient was performing one activity that makes him/her more active than during a quiet time). At the end of the follow-up time a sum of all active hours will be done in order to measure the amount of time that the patient was active over the follow-up time. | Patients will be automatically assessed continuously during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. The analyses was limited to walking activities. |
| Level of Activity for Each Patient During the Day | The level of activity for each patient is calculated automatically through the app at the smartphone. The calculation is performed by using the data collect with the accelerometers embedded in the smartwatch. An algorithm installed in the phone, which analyze the data collected with the smartwatch, can calculate the level of activity for each patient throughout the day. | Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. |
| Scores in Autonomic Dysfunctions Measure With the Autonomic Dysfunctions Scale | The scores for autonomic dysfunctions will be obtained with the Assessment of autonomic dysfunction in Parkinson's disease (SCOPA-AUT). The total sum scores ranges from 0 to 100, in which high scores are correlated with more burden of autonomic dysfunctions in Parkinson's patients. | Baseline |
| Sleepiness Rates in the Epworth Sleepiness Scale as a Measure of Sleep Quantity. | The Epworth sleepiness scale was used to rate the level of sleepiness during the day. The scale's scores are related to the usual duration of sleep at night and increase with relative sleep deprivation. Here, we used data from one-point assessment (baseline). Then, we can suggest that if the sample has high scores they will have a low sleep quantity. Total sum score ranges from 0 (no sleepiness at all) to 24 (excessive sleepiness). | Baseline |
| 24030855 | Background | Maetzler W, Domingos J, Srulijes K, Ferreira JJ, Bloem BR. Quantitative wearable sensors for objective assessment of Parkinson's disease. Mov Disord. 2013 Oct;28(12):1628-37. doi: 10.1002/mds.25628. Epub 2013 Sep 12. |
| 19846382 | Background | Patel S, Lorincz K, Hughes R, Huggins N, Growdon J, Standaert D, Akay M, Dy J, Welsh M, Bonato P. Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors. IEEE Trans Inf Technol Biomed. 2009 Nov;13(6):864-73. doi: 10.1109/TITB.2009.2033471. Epub 2009 Oct 20. |
| 22254617 | Background | Patel S, Chen BR, Mancinelli C, Paganoni S, Shih L, Welsh M, Dy J, Bonato P. Longitudinal monitoring of patients with Parkinson's disease via wearable sensor technology in the home setting. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1552-5. doi: 10.1109/IEMBS.2011.6090452. |
| Background | Tsanas A, Little MA, McSharry PE, Ramig L. Using the cellular mobile telephone network to remotely monitor parkinsons disease symptom severity. IEEE Transactions on Biomedical Engineering. 2012. |
| Background | Arora S, Venkataraman V, Donohue S, Biglan KM, Dorsey ER, Little MA, editors. High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones. Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on; 2014: IEEE. |
| Background | Pastorino M, Arredondo M, Cancela J, Guillen S, editors. Wearable sensor network for health monitoring: the case of Parkinson disease. Journal of Physics: Conference Series; 2013: IOP Publishing. |
| Background | Sharma V, Mankodiya K, De La Torre F, Zhang A, Ryan N, Ton TG, et al. SPARK: Personalized Parkinson Disease Interventions through Synergy between a Smartphone and a Smartwatch. Design, User Experience, and Usability User Experience Design for Everyday Life Applications and Services: Springer; 2014. p. 103-14. |
| 25393786 | Background | Tzallas AT, Tsipouras MG, Rigas G, Tsalikakis DG, Karvounis EC, Chondrogiorgi M, Psomadellis F, Cancela J, Pastorino M, Waldmeyer MT, Konitsiotis S, Fotiadis DI. PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease. Sensors (Basel). 2014 Nov 11;14(11):21329-57. doi: 10.3390/s141121329. |
| 25257518 | Background | Lakshminarayana R, Wang D, Burn D, Chaudhuri KR, Cummins G, Galtrey C, Hellman B, Pal S, Stamford J, Steiger M, Williams A; SMART-PD Investigators. Smartphone- and internet-assisted self-management and adherence tools to manage Parkinson's disease (SMART-PD): study protocol for a randomised controlled trial (v7; 15 August 2014). Trials. 2014 Sep 25;15:374. doi: 10.1186/1745-6215-15-374. |
| 25141850 | Background | Gschwind YJ, Eichberg S, Marston HR, Ejupi A, Rosario Hd, Kroll M, Drobics M, Annegarn J, Wieching R, Lord SR, Aal K, Delbaere K. ICT-based system to predict and prevent falls (iStoppFalls): study protocol for an international multicenter randomized controlled trial. BMC Geriatr. 2014 Aug 20;14:91. doi: 10.1186/1471-2318-14-91. |
| 27565186 | Derived | Silva de Lima AL, Hahn T, de Vries NM, Cohen E, Bataille L, Little MA, Baldus H, Bloem BR, Faber MJ. Large-Scale Wearable Sensor Deployment in Parkinson's Patients: The Parkinson@Home Study Protocol. JMIR Res Protoc. 2016 Aug 26;5(3):e172. doi: 10.2196/resprot.5990. |
| Participants |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Ethnicity (NIH/OMB) | These data were not reported. | Count of Participants | Participants |
|
| Disease severity | Participants were assessed using the Hoehn and Yarhn scale. The scale is a grading of the degree of severity of Parkinson's Disease. The scale goes from 0 to 5, in which a higher score indicates higher disease severity. | Count of Participants | Participants |
|
| Depression | Depression was assessed using the Geriatric Depression Scale. The total score goes from 0 to 15. A score higher than 6 indicates higher probability of suffer from a depression. | Count of Participants | Participants |
|
| Cognitive impairment | Cognitive Impairment was measured using the Montreal Cognitive Assessment (MoCA). The MoCA total scores goes from 0 to 30. A score lower than 26 indicates possible presence of Mild Cognitive Impairment. | Count of Participants | Participants |
|
| Independency level | Independency Level was assessed using the Modified Schwab and England Activities of Daily Living Scale. The scale goes from 0 to 100. The higher the score, the more independent is the person. We opted for clustering the data into 4 categories because this provided a better overview of participants completely independent, independent, somewhat independent and dependent. | Number | participants |
|
| MDS-UPDRS | The Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) was developed to evaluate various aspects of Parkinson's disease, including non-motor and motor experiences of daily living, as well as motor complications. The total score used here was calculated by a sum of all scores from the 4 sub-scales (i.e. part I up to part IV) composing the MDS-UPDRS. Total score ranges from 0 to 272. A higher score indicates higher disease severity and burden, being thus a worse outcome. | Median | Full Range | units on a scale |
|
| Age at disease onset. | These data were not reported. | Age at disease onset was first included as an outcome. During data-collection, we have chosen not to collect it because other measures (such as MDS-UPDRS) better reflects the impact, burden and severity of the disease. | Mean | Standard Deviation | years |
|
| Time since Diagnose | These data were not reported. | Time since diagnose was first included as an outcome. During data-collection, we have chosen not to collect it because other measures (such as MDS-UPDRS) better reflects the impact, burden and severity of the disease. | Mean | Standard Deviation | years |
|
| Level of education | We asked participants to report their highest education finished. After, we categorized answers into: > Highschool - High; < Highschool AND > Middel education - middle; < middel educatoin - low | Count of Participants | Participants |
|
|
|
| Primary | Depression Scores as a Measure of Depression Rates | The scores obtained with the Geriatric Depression Scale were analysed in order to create a percentage of probably depressed participants. The total score goes from 0 to 15. A score higher than 6 indicates higher probability of suffer from a depression. | We have analyzed all participants who completed the study. | Posted | Count of Participants | Participants | Baseline |
|
|
|
| Primary | Cognitive Impairment. | Total sum score obtained with the Montreal Cognitive Assessment. We analyzed the full score to investigate percentage of participants with a possible cognitive impairment. The Montreal Cognitive sum scores ranges from 0 to 30, in which a score lower or equal to 26 is considered as possible cognitive decline. | We analyzed all participants who completed the study. | Posted | Count of Participants | Participants | Baseline |
|
|
|
| Primary | Independency Level | The total sum score obtained with the Schwab and England activities of daily living scale were analyzed to describe the functional level of the sample. The total sum scores varies from 0 to 100, in which lower scores are associated with more dependency of others to perform daily life activities. | We analyzed all participants who completed the study. | Posted | Count of Participants | Participants | Baseline |
|
|
|
| Secondary | Number of Falls Per Patient Registered by the Falls Detector. | The fall event is recognized by the falls detector. Every time that the patient falls, the algorithm embedded at the falls detector recognize as a fall and record the fall event. At the end of the follow-up time, a sum of the falls event for each patient will be done. | We invited 43 study participants to wear the fall detector, of whom 13 consented to wear it. Unfortunately the quality of the collected data did not allow for automated falls detection. Therefore we couldn't determine this outcome measure. | Posted | Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. |
|
|
| Secondary | Number of Mood Reports for Each Patient Measured With a Four Point Scale | The number of mood reports will be collected through the smartphone application. A four point scale (very good, good, poor and fair) will be available, and by pressing the button which correspond to how the patient feels at that moment the report can be performed. At the end of the follow-up time a sum of all the reports will be done in order to measure the number of mood reports over the follow-up time. | Though the smartphone application had to option to report "mood", participants hardly used this feature. Therefore there were no data collection that were suitable for analysis. | Posted | Patients will be assessed during the follow-up time (up to 13 weeks after the enrollment date). It is expected that the assessment (self-report) will be performed as many times as the patient wants to report how they feel or at least once a day. |
|
|
| Secondary | Number of Medication Intake Annotations Made by Each Participant Via the Self-report App. | The number of medication intake annotations made by the patients will be collected through the smartphone application. Every time that the patient take medication they must press the button reporting that they took the medication. At the end of the follow-up time a sum of all the reports will be done in order to measure the number of medication intake over the follow-up time. | 96% (n = 280) of data-contributors reported their medication through the app. | Posted | Mean | Standard Error | number of annotations | Baseline |
|
|
|
| Secondary | Time That Each Patient Was Active During the Day | The time that the patient was active during the day is calculated automatically through the app at the smartphone. The calculation is performed by using an algorithm, which analyze the patterns of walk. This algorithm is able to predict when the patient was active in a zone above his/her usual threshold (e.g. when the patient was performing one activity that makes him/her more active than during a quiet time). At the end of the follow-up time a sum of all active hours will be done in order to measure the amount of time that the patient was active over the follow-up time. | Posted | Mean | Standard Deviation | minutes per day | Patients will be automatically assessed continuously during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. The analyses was limited to walking activities. |
|
|
|
| Secondary | Level of Activity for Each Patient During the Day | The level of activity for each patient is calculated automatically through the app at the smartphone. The calculation is performed by using the data collect with the accelerometers embedded in the smartwatch. An algorithm installed in the phone, which analyze the data collected with the smartwatch, can calculate the level of activity for each patient throughout the day. | The collected accelerometer data turned out to be unsuitable to calculate the level of activity during the day. Algorithms produced unreliable information on activity levels and therefore no data on this outcome measure were available. | Posted | Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. |
|
|
| Secondary | Scores in Autonomic Dysfunctions Measure With the Autonomic Dysfunctions Scale | The scores for autonomic dysfunctions will be obtained with the Assessment of autonomic dysfunction in Parkinson's disease (SCOPA-AUT). The total sum scores ranges from 0 to 100, in which high scores are correlated with more burden of autonomic dysfunctions in Parkinson's patients. | Posted | Mean | Standard Deviation | score on a scale | Baseline |
|
|
|
| Secondary | Sleepiness Rates in the Epworth Sleepiness Scale as a Measure of Sleep Quantity. | The Epworth sleepiness scale was used to rate the level of sleepiness during the day. The scale's scores are related to the usual duration of sleep at night and increase with relative sleep deprivation. Here, we used data from one-point assessment (baseline). Then, we can suggest that if the sample has high scores they will have a low sleep quantity. Total sum score ranges from 0 (no sleepiness at all) to 24 (excessive sleepiness). | Posted | Mean | Standard Deviation | score on a scale | Baseline |
|
|
|
| 0 |
| 291 |
| 0 |
| 291 |
Not provided
Not provided
Not provided
| D009422 | Nervous System Diseases |
| D009069 | Movement Disorders |
| D000080874 | Synucleinopathies |
| D019636 | Neurodegenerative Diseases |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D017530 | Health Care Quality, Access, and Evaluation |
| higher than 90 |
|
| Missing |
|