Mobile Peer Support in Diabetes Taridzo CHOMUTARE a,b,c , Eirik RSAND - - PowerPoint PPT Presentation

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Jul 25, 2023 179 likes •258 views

Mobile Peer Support in Diabetes Taridzo CHOMUTARE a,b,c , Eirik RSAND a,b , Gunnar HARTVIGSEN a,b a Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway b Department of Computer Science, University of Troms,

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Mobile Peer Support in Diabetes

Oslo, Norway – 30 August 2011 Taridzo CHOMUTARE a,b,c , Eirik ÅRSAND a,b , Gunnar HARTVIGSEN a,b

a Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway b Department of Computer Science, University of Tromsø, Norway c Presenter

Oral Presentation

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n Main motivators

¨ mobile devices have become more user-friendly ¨ leverage social media success in healthcare ¨ counter Internet health info explosion by tailoring

Introduction

MIE2011, Taridzo Chomutare

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n Goals for a peer support framework

¨ find peers based on health status++ ¨ filter relevant user-generated content ¨ deploy on (ubiquitous) mobile devices ¨ measure effect of patient-to-patient

dialogue on:

¨ health outcomes ¨ self-efficacy

Research Goals

MIE2011, Taridzo Chomutare

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n Some challenges

¨ forming a good representation of the user [patient] ¨ persuading users to consent to data acquisition ¨ maintaining motivation to participate in a longitudinal trial ¨ sustaining relationships during a trial

Research Goals

MIE2011, Taridzo Chomutare

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n Patient data acquisition & modeling

¨ daily blood glucose values ¨ daily food habits ¨ daily physical activity and weight ¨ learning user data and modeling

health status

n Fostering mentor-protégé relationships

¨ extending Morris et al. (2009) ideas for fostering social engagement

Methods

MIE2011, Taridzo Chomutare

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ETSI extension

MIE2011, Taridzo Chomutare

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Discussion

Thats all folks!! Taridzo Chomutare taridzo.chomutare@telemed.no +47 47 68 00 32

n Early results confirm the feasibility of:

¨ adding health status to the user model to filter peers ¨ learning user data from (near) real-time health data ¨ adapting recommender systems to eHealth requirements ¨ managing relationships using mentor models

n Conclusion and future work

¨ personalizing health social media is feasible using personal health data ¨ Need for evaluating healthcare recommender algorithms (e.g., relevancy

and performance)

¨ Need to elaborate social/psychology theories - relationship dynamics

MIE2011, Taridzo Chomutare