Clinical trial NCT03061240: Smart Pain Assesment Tool Based on Internet of Things
Official title: Smart Pain Assesment Tool Based on Internet of Things
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Pain
Keywords: Internet of Things
Lead sponsor: University of Turku
Sponsor class: OTHER
Collaborators: name: Academy of Finland
name: Turku University Hospital
First posted: 2017-02-23
Enrollment: 58
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief summary: 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: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Facial muscle activity
timeFrame: 2 hours after surgery when the subject has woken up
Secondary outcomes:
measure: Skin conductance
timeFrame: 2 hours after surgery when the subject has woken up
measure: Pain assesment by VAS
timeFrame: 2 hours after surgery when the subject has woken up
measure: Heart rate
timeFrame: 2 hours after surgery when the subject has woken up
measure: Respiratory rate
timeFrame: 2 hours after surgery when the subject has woken up
measure: Oxygen saturation
timeFrame: 2 hours after surgery when the subject has woken up
Other outcomes:
Locations:
city: Turku
country: Finland
facility: University of Turku
Study officials:
affiliation: University of Turku
name: Sanna Salanterä, Prof
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: EPoSS. 2008. Internet of Things in 2020: a Roadmap for the Future. European Technology Platform on Smart Systems Integration. European Commission Information Society.