Medical follow up assessments of iPrognosis application users for early Parkinson's disease detection

Klingelhoefer, Lisa; Bostanjopoulou, Sevasti; Trivedi, Dhaval; Hadjidimitriou, Stelios; Hausbrand, D.; Katsarou, Zoe; Charisis, Vasileios; Stadtschnitzer, Michael; Dias, Sofia B.; Ntakakis, George; Grammalidis, Nikos; Kyritsis, Konstantinos; Jaeger, Hagen; Iakovakis, Dimitrios; Παπαδόπουλος, Αλέξανδρος; Ioakimidis, Ioannis; Karayiannis, Fotis; Diniz, Jonas Silva; Delopoulos, Anastasios; Hadjileontiadis, Leontios; Reichmann, Heinz; Chaudhuri, K. Ray

doi:10.1016/j.parkreldis.2020.06.155

Cited by 2 publications

(7 citation statements)

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“…At least 1 study was conducted in each of the following countries: Canada (n=2) [73,74], the Netherlands (n=2) [75,76], Portugal (n=2) [77,78], Sweden (n=1) [79,80], Taiwan (n=2) [81,82], Australia (n=1) [83], Brazil (n=1) [84], Demark (n=1) [85], France (n=1) [86], Israel (n=1) [87], Greece (n=1) [88,89], Lithuania (n=1) [90], Norway (n=1) [91], and United Arab Emirates (n=1) [92]. Of the remaining reporting studies, 6 were multinational [93][94][95][96][97][98]. Sample size ranged from 8 [33] to 1465 [94] (median 40.5 participants).…”

Section: Study Characteristics and Data Collectionmentioning

confidence: 99%

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Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review

et al. 2022

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Background With the advent of smart sensing technology, mobile and wearable devices can provide continuous and objective monitoring and assessment of motor function outcomes. Objective We aimed to describe the existing scientific literature on wearable and mobile technologies that are being used or tested for assessing motor functions in mobility-impaired and healthy adults and to evaluate the degree to which these devices provide clinically valid measures of motor function in these populations. Methods A systematic literature review was conducted by searching Embase, MEDLINE, CENTRAL (January 1, 2015, to June 24, 2020), the United States and European Union clinical trial registries, and the United States Food and Drug Administration website using predefined study selection criteria. Study selection, data extraction, and quality assessment were performed by 2 independent reviewers. Results A total of 91 publications representing 87 unique studies were included. The most represented clinical conditions were Parkinson disease (n=51 studies), followed by stroke (n=5), Huntington disease (n=5), and multiple sclerosis (n=2). A total of 42 motion-detecting devices were identified, and the majority (n=27, 64%) were created for the purpose of health care–related data collection, although approximately 25% were personal electronic devices (eg, smartphones and watches) and 11% were entertainment consoles (eg, Microsoft Kinect or Xbox and Nintendo Wii). The primary motion outcomes were related to gait (n=30), gross motor movements (n=25), and fine motor movements (n=23). As a group, sensor-derived motion data showed a mean sensitivity of 0.83 (SD 7.27), a mean specificity of 0.84 (SD 15.40), a mean accuracy of 0.90 (SD 5.87) in discriminating between diseased individuals and healthy controls, and a mean Pearson r validity coefficient of 0.52 (SD 0.22) relative to clinical measures. We did not find significant differences in the degree of validity between in-laboratory and at-home sensor-based assessments nor between device class (ie, health care–related device, personal electronic devices, and entertainment consoles). Conclusions Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of the recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than one-quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change.

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Section: Study Characteristics and Data Collectionmentioning

confidence: 99%

“…Of the remaining reporting studies, 6 were multinational [93][94][95][96][97][98]. Sample size ranged from 8 [33] to 1465 [94] (median 40.5 participants). A total of 7995 participants were enrolled in the included studies.…”

Section: Study Characteristics and Data Collectionmentioning

confidence: 99%

Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review

et al. 2022

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“…We have developed an app for the early detection of PD, which was supported by a European Horizon 2020 grant given to centers in Greece, Portugal, the U.K., Germany, Sweden, and Belgium. We called the app “iPrognosis” (Iakovakis et al 2019 , 2020 , 2022 ; Hadjidimitriou, et al 2016 ; Klingelhoefer, et al 2017 ; Klingelhoefer, et al 2019 ; Klingelhoefer, et al 2019 ; Laganas et al 2021 ), and it can be downloaded from the google play store on any android smartphone for free by healthy volunteers or patients aged 40–90 years of age with early PD. The iPrognosis app is only active after the written, informed consent for study participation within the app and the provision of a few essential items of personal information such as age, gender, personal health status (whether healthy or diagnosed with PD), and family history (whether PD is known in the family).…”

Section: Introductionmentioning

confidence: 99%

“…We investigated whether the iPrognosis app can recognize patterns corresponding to the motor and non-motor symptoms of PD. For this purpose, we investigated the following parameters: speech, movement, dynamics of keyboard use, facial expression in selfies, and emotional content in text messages (Klingelhoefer, et al 2017 , 2019 , 2019 ). If the patient made a phone call via the smartphone, the app did not capture the content, but the app analyzed the patient’s voice to detect dysarthrophonia and speech festination.…”

Section: Introductionmentioning

confidence: 99%

“…At the end of the study, 4486 people had downloaded the iPrognosis app, and 2624 people consented to study participation with a mean age of 49 ± 16 years, 63% being male, 84% declared to be healthy, and 16% diagnosed with PD (Klingelhoefer et al 2019 ). From the 1529 study participants who agreed to be contacted, 112 were investigated for motor and non-motor symptoms of PD by a movement disorders specialist and by instrumental diagnostics such as olfaction test, substantia nigra sonography, etc.…”

Section: Introductionmentioning

confidence: 99%

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The use of wearables for the diagnosis and treatment of Parkinson’s disease

Reichmann¹,

Klingelhoefer²,

Bendig³

2023

J Neural Transm

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Parkinson’s disease (PD) is the second most common neurodegenerative disorder, with increasing numbers of affected patients. Many patients lack adequate care due to insufficient specialist neurologists/geriatricians, and older patients experience difficulties traveling far distances to reach their treating physicians. A new option for these obstacles would be telemedicine and wearables. During the last decade, the development of wearable sensors has allowed for the continuous monitoring of bradykinesia and dyskinesia. Meanwhile, other systems can also detect tremors, freezing of gait, and gait problems. The most recently developed systems cover both sides of the body and include smartphone apps where the patients have to register their medication intake and well-being. In turn, the physicians receive advice on changing the patient’s medication and recommendations for additional supportive therapies such as physiotherapy. The use of smartphone apps may also be adapted to detect PD symptoms such as bradykinesia, tremor, voice abnormalities, or changes in facial expression. Such tools can be used for the general population to detect PD early or for known PD patients to detect deterioration. It is noteworthy that most PD patients can use these digital tools. In modern times, wearable sensors and telemedicine open a new window of opportunity for patients with PD that are easy to use and accessible to most of the population.

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Medical follow up assessments of iPrognosis application users for early Parkinson's disease detection

Cited by 2 publications

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Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review

Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review

The use of wearables for the diagnosis and treatment of Parkinson’s disease

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