A Decision Support System for Self-management of Low Back Pain - PILOTSTUDY
NCT ID: NCT03697759
Last Updated: 2019-02-28
Study Results
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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COMPLETED
NA
51 participants
INTERVENTIONAL
2018-08-20
2019-02-17
Brief Summary
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In this pilot study all participants are allocated to the intervention group.
The intervention consists of a digital decision support system delivering a weekly plan of suggested activities that the participant can use to self-manage their low back pain. The plan is presented to the participant in the selfBACK app.
Detailed Description
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The SELFBACK system constitutes a data-driven predictive decision support system that uses Case-Based Reasoning methodology to capture and reuse participant cases in order to suggest the most suitable self-management plan for participants. The selfBACK system is an intelligent system that will adjust the suggested self-management plan to the individual participants by using the information available on the participant (baseline questionnaires), weekly self-reported of changes in health status through the app, and data on physical activity via the step-detecting wristband.
The weekly plan includes three categories of content; 1) information/education, 2) physical activity monitoring through wearing a step-detecting wristband, and 3) strength and flexibility exercises.
Conditions
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Keywords
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Study Design
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NA
SINGLE_GROUP
TREATMENT
NONE
Study Groups
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Usual care + selfBACK
Participants will use the selfBACK system and app
Usual care + selfBACK
The selfBACK intervention is a digital Decision Support System (DSS) for self-management of LBP provided to the participant via a smartphone app (selfBACK app). In addition, the participant is provided with a step-detecting wristband (Xiaomi Mi Band 2) that interacts with the selfBACK app.
The DSS provides individually tailored self-management plans including content from three categories; 1) information/education, 2) physical activity monitoring through wearing the step-detecting wristband, and 3) strength and flexibility exercises. The weekly plans are individually tailored to the specific patient, using the data available from the patient, and each week the patients report back their progress on physical activity (step count) and exercise (volume completed). These data are matched with the patients follow-up data to create a self-management plan that is up to date and adaptable to variation in health-status of the individual patient.
Interventions
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Usual care + selfBACK
The selfBACK intervention is a digital Decision Support System (DSS) for self-management of LBP provided to the participant via a smartphone app (selfBACK app). In addition, the participant is provided with a step-detecting wristband (Xiaomi Mi Band 2) that interacts with the selfBACK app.
The DSS provides individually tailored self-management plans including content from three categories; 1) information/education, 2) physical activity monitoring through wearing the step-detecting wristband, and 3) strength and flexibility exercises. The weekly plans are individually tailored to the specific patient, using the data available from the patient, and each week the patients report back their progress on physical activity (step count) and exercise (volume completed). These data are matched with the patients follow-up data to create a self-management plan that is up to date and adaptable to variation in health-status of the individual patient.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
* LBP of any duration
* Mild-to severe pain-related disability rated as 16 or below on the PROMIS-PF4 function scale.
* Age: ≥18 years
* Own and regularly use a smart phone (with at least Android 7.0 or iOS11.0) with internet access (Wi-Fi and/or mobile data)
* Have a working email address and have access to a computer with internet access to complete questionnaires in a web browser.
Exclusion Criteria
* Unable to speak, read or write in the national language (Danish/ Norwegian)
* Cognitive impairment or learning disabilities
* Pathology, such as fracture, cancer, inflammatory diseases, and signs of radiculopathy (severe leg pain, loss of leg strength, or loss of or altered sensation in a myotomal or dermatomal distribution)
* Serious mental illness, such as major depression, schizophrenia, and psychosis
* Terminal illness
* Unable to take part in exercise/physical activity (such as non-ambulatory patients, use of walking aids/assistance, unable to get down and up from the floor independently)
* Fibromyalgia (diagnosed by a Health Care Professional)
* Pregnancy
* Previous back surgery
* Ongoing participation in other research trials for LBP management
18 Years
ALL
No
Sponsors
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Norwegian University of Science and Technology
OTHER
National Research Centre for the Working Environment, Denmark
OTHER_GOV
University of Glasgow
OTHER
Robert Gordon University
OTHER
University of Southern Denmark
OTHER
Responsible Party
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Principal Investigators
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Karen Søgaard, PhD
Role: PRINCIPAL_INVESTIGATOR
University of Southern Denmark
Locations
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Physical Activity and Health at Work, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark
Odense, , Denmark
Norwegian University of Science and Techonology
Trondheim, , Norway
Countries
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References
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Bach K, Szczepanski T, Aamodt A, Gundersen OE, Mork. PJ. Case Representation and Similarity Assessment in the selfBACK Decision Support System ICCBR 2016: Case-Based Reasoning Research and Development pp 32-46
Sandal LF, Overas CK, Nordstoga AL, Wood K, Bach K, Hartvigsen J, Sogaard K, Mork PJ. A digital decision support system (selfBACK) for improved self-management of low back pain: a pilot study with 6-week follow-up. Pilot Feasibility Stud. 2020 May 23;6:72. doi: 10.1186/s40814-020-00604-2. eCollection 2020.
Related Links
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project webpage
Other Identifiers
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2018/791
Identifier Type: OTHER
Identifier Source: secondary_id
S-20182000-24
Identifier Type: -
Identifier Source: org_study_id