ICH

nline tailoring of physical exercise to enhance self-management of fibromyalgia

S . Rubine lli, L. Came rini, M. Giacobaz z i, M. Bone schi, P.J. S chulz

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Fibromyalgia

Fibromyalgia Syndrome (FMS) is a condition characterized by chronic widespread pain and tenderness in 11 or more of 18 specific tender point sites

Three mayor symptoms: pain, sleep disorder and fatigue

Lack of precise diagnostic criteria

Pharmacological and non-pharmacological interventions

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The project Oneself

• Goal: to enhance self-management of chronic conditions in patients in the perspective of improving their health status and quality of their life

• From 2003 to 2008: interactive website for patients suffering from low-back pain

• From June 2008: new area on fibromyalgia

• From November 2008: tailored gymnasium

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Tailoring Health Messages

• Tailoring: a process for creating individualized communication by gathering and assessing personal data related to a given outcome, in order to determine the most appropriate information or strategies to meet a person’s unique needs (Rimer & Kreuter 2006)

• “Classical” aim: to persuade an intended audience to change or reinforce behavior. Usually designed on a set of behavioral theories

• Our aim: to maximize the appropriateness of the treatment exercises to users’ specific situation

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To match with high-quality criteria

•To be translated into algorithms

•To refine the tailoring rules

•To boost the testing phase

But … guidelines for non-pharmacological treatment of fibromyalgia are scant

The importance of guidelines

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Goal:• To find relevant variables (determinants) that can make an

exercise fit for a specific fibromyalgic user

Methodology:• Knowledge acquisition technique: semi-structured interviews

with 5 FMS experts and 5 FMS patients• Interviews were repeated during the development of the tool

Defining guidelines

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Results:8 determinants were included in the final algorithm

Available timePainTime of the dayAvailable toolsLocalizationLevel of difficultyExperienceUser Judgement

Included Determinants

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Variables not identified as determinants: SexAgeAdditional FMS information

Excluded Determinants

Possible determinants that would require further analysis:

Training activity• Position• Vertigo

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Tailored application: 2 main modulesAssessment component Feedback component

Tailored gymnasium starting elements:Corpus of 39 video recorded exercisesList of 8 determinantsSet of rules to combine them

General tailoring framework

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sers are asked to answer a series of questions leading to the evaluation of the 8 determinants

he system receives:a self-reported set of data obtained from the online questionnaire (some questions are optional)data about previous uses of the tool, retrieved from the application database

ach time that a new session is started, the assessment should be repeated

Assessment module

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The feedback module shows:

5 standard (non-tailored) warming up exercises

Personalized exercises, one at a time (video, textual description) – exercises can be rated and users can leave comments

Recapitulation of performed exercises

Feedback module

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Feedback module

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he algorithm starts from a given set of exercises, coded with the appropriate metadata:

the position in which they should be executed the level of difficulty the parts of the body involved the tools needed for execution

he corpus of video exercises is organized in functional categories:

Relaxation Mobilization Stretching Stabilization Massage

Matching algorithm

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Matching algorithm - Boolean Exclusion

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rom the elaboration of variables and metadata 6 new “ranking variables” are created:

The adequacy of the exercise’s difficulty to user’s pain levelThe adequacy of the exercise’s difficulty to user’s preferred difficultyThe adequacy of the exercise to the request of obtaining a new or already seen exerciseUser’s eventual previous votesOther users’ eventual previous votesThe adequacy of the exercise to the part of the body the user wants to train

Matching algorithm – Ranking

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weighted mean of the ranking variables is used to obtain a final adequacy score for each exercise

xercises are ordered in a single list according to this score

Matching algorithm – Ranking (2)

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he exercises are then divided into different tables according to their category

he last step in the selection of videos is the extraction from these tables

Matching algorithm – Extraction

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new set of rules is used for this extraction:•

ifferent percentages are assigned to each category, according to the level of pain (low, medium or high) and the moment of the day (morning, afternoon, evening)

•ne exercise at a time is selected through a weighted random extraction of a category

•he extraction process is repeated until all eligible exercises have been added to the final list

•The exercises are finally delivered to the user in order of extraction

Matching algorithm – Extraction

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Matching algorithm – Extraction (2)

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Output and use of the application

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The project ONESELFConclusion

Main results:• Definition of relevant variables• Implementation of an algorithm

Limitations:

• The knowledge acquisition approach does not guarantee exhaustiveness in the identification of the determinants

• Lack of evaluation of the tool (currently ongoing)

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The project ONESELF

Many thanks for your attention

michele.giacobazzi@usi.ch