Evidence map›Paper›PMID 30916665›Full record

ArticleJMIR research protocols2019

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study.

Milla Jauhiainen, Juha Puustinen, Saeed Mehrang, Jari Ruokolainen, Anu Holm, Antti Vehkaoja, Hannu Nieminen

Registry-linked trialAbstract read
In one paragraph

Article in JMIR research protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03366558 (Sensor-based Motion Characterization in Patients With Parkinson's Disease), which is not on this map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03366558 completednot on this map

Sensor-based Motion Characterization in Patients With Parkinson's Disease

Typeobservational_patient_registrySponsorSatakunta Central HospitalRan2018 to 2019Enrolled97ConditionsParkinson DiseaseArmsUPDRS questionnaires, 20-step walking test
3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Milla JauhiainenFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.ORCID http://orcid.org/0000-0001-5289-8105
Juha PuustinenUnit of Neurology, Satakunta Central Hospital, Satakunta Hospital District, Pori, Finland.ORCID http://orcid.org/0000-0002-4033-1539
Saeed MehrangFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.ORCID http://orcid.org/0000-0002-2092-2868
Jari RuokolainenFaculty of Management and Business, Tampere University, Tampere, Finland.ORCID http://orcid.org/0000-0001-8313-9507
Anu HolmFaculty of Medicine, University of Turku, Turku, Finland.ORCID http://orcid.org/0000-0003-4282-6479
Antti VehkaojaFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.ORCID http://orcid.org/0000-0003-3721-3467
Hannu NieminenFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.ORCID http://orcid.org/0000-0003-1614-2604

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments.

objectiveThe primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns.

methodsThis paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings.

resultsData collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD.

conclusionsThis study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders.

trial registrationClinicalTrials.gov NCT03366558; https://clinicaltrials.gov/ct2/show/NCT03366558. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12808.

Indexed as

gaithome monitoringmobile phonemovement analysisParkinson diseasesmartphonewearable sensors

Identifiers

PMID30916665
PMCPMC6456828

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.