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Smart Pain Assesment Tool Based on Internet of Things

NCT ID: NCT03061240

Last Updated: 2018-12-24

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

COMPLETED

Clinical Phase

NA

Total Enrollment

58 participants

Study Classification

INTERVENTIONAL

Study Start Date

2017-11-20

Study Completion Date

2018-06-11

Brief Summary

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This study is the second phase of a research project called "Smart Pain Assessment Tool based on Internet-of-Things". During the course of this project, a smart pain assessment tool (SPA) to detect and assess pain employing behavioural and physiologic indicators will be developed. We aim to assess pain based on changes in electromyographic (EMG) activity in facial muscles, i.e. changes in facial expressions and simultaneously use physiologic signs such as heart rate, respiratory rate and galvanic skin response as adjuvant measures to develop an algorithm for pain assessment in critically ill patients.

Detailed Description

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The aim of this "smart pain assessment tool based on internet of things" SPA-research project is to develop an automatic and versatile pain assessment tool algorithm for detection and assessment of pain in a reliable and objective way in non-communicative patients. The final objective of the research project is to develop a smart pain assessment tool to detect and assess pain employing behavioural and physiologic indicators for a wide range of users/patients from infants to elderly people who are unable to communicate normally. The research project consists of three clinical phases (European Commision. Meddev 2.7/4/2010). The clinical phase I of the research project focused on developing pain assessment techniques involuntary working-age healthy study subjects. This current Clinical phase II includes the further development and research of the smart pain assessment tool in elective (non emergency) postoperative surgical patients during their stay after surgery in a recovery room.

Conditions

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Pain

Keywords

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Internet of Things

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Postoperative patients

We recruit postoperative patients who undergo open surgery and are not receiving local anesthesia. We install a smart pain assessment tool on patient's skin to capture different type of data.

Group Type EXPERIMENTAL

Smart pain assessment tool

Intervention Type DEVICE

We record and analyze multiple bio-signals from post-operative patients in the attempt to evaluate their experienced pain.

Interventions

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Smart pain assessment tool

We record and analyze multiple bio-signals from post-operative patients in the attempt to evaluate their experienced pain.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Subject is scheduled for major surgery that requires general anesthesia and is likely to cause moderate to severe pain postoperatively which needs to be treated with systemic analgesics
* Ability to communicate
* Written informed consent
* Healthy facial skin

Exclusion Criteria

* Subject treated with local anesthesia during surgery
* Any diagnosed condition affecting cognitive functions
* Surgery affecting hands where pulse oximetry and galvanic skin reaction are recorded or areas where facial muscle activity is measured
* Any diagnosed condition affecting central nervous system, facial nerves or muscles.
* Significant facial hair growth in the area where the sensors will be attached
* Tattoos in the area where the sensors will be attached
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Academy of Finland

OTHER

Sponsor Role collaborator

Turku University Hospital

OTHER_GOV

Sponsor Role collaborator

University of Turku

OTHER

Sponsor Role lead

Responsible Party

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Sanna Salanterä

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Sanna Salanterä, Prof

Role: PRINCIPAL_INVESTIGATOR

University of Turku

Locations

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University of Turku

Turku, , Finland

Site Status

Countries

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Finland

References

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Jeitziner MM, Schwendimann R, Hamers JP, Rohrer O, Hantikainen V, Jakob SM. Assessment of pain in sedated and mechanically ventilated patients: an observational study. Acta Anaesthesiol Scand. 2012 May;56(5):645-54. doi: 10.1111/j.1399-6576.2012.02660.x. Epub 2012 Mar 7.

Reference Type BACKGROUND
PMID: 22404146 (View on PubMed)

Viertio-Oja H, Maja V, Sarkela M, Talja P, Tenkanen N, Tolvanen-Laakso H, Paloheimo M, Vakkuri A, Yli-Hankala A, Merilainen P. Description of the Entropy algorithm as applied in the Datex-Ohmeda S/5 Entropy Module. Acta Anaesthesiol Scand. 2004 Feb;48(2):154-61. doi: 10.1111/j.0001-5172.2004.00322.x. No abstract available.

Reference Type BACKGROUND
PMID: 14995936 (View on PubMed)

Prkachin KM. Assessing pain by facial expression: facial expression as nexus. Pain Res Manag. 2009 Jan-Feb;14(1):53-8. doi: 10.1155/2009/542964.

Reference Type BACKGROUND
PMID: 19262917 (View on PubMed)

Ledowski T, Ang B, Schmarbeck T, Rhodes J. Monitoring of sympathetic tone to assess postoperative pain: skin conductance vs surgical stress index. Anaesthesia. 2009 Jul;64(7):727-31. doi: 10.1111/j.1365-2044.2008.05834.x. Epub 2009 Jan 28.

Reference Type BACKGROUND
PMID: 19183409 (View on PubMed)

EPoSS. 2008. Internet of Things in 2020: a Roadmap for the Future. European Technology Platform on Smart Systems Integration. European Commission Information Society.

Reference Type BACKGROUND

Other Identifiers

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29092016

Identifier Type: -

Identifier Source: org_study_id