eGuardian Angel Socialising health burden through different network - PowerPoint PPT Presentation

eGuardian Angel Socialising health burden through different network topologies: A simulation study Presented by Associate Professor Simon Poon Contributors: Adrian Peacock, Anthony Cheung, Associate Professor Peter Kim, Associate Professor Simon Poon The University of Sydney Page 1

Introduction: Guardian Angel The University of Sydney Page 2

Social Innovation: eGuardian Angel – Community Building: Socialising Health Burden a h b c X g d f e The University of Sydney Page 3 3

Literature and Related Work – Social Networks & Healthcare [Lazakidou, et al. 2016] Segregation model – Provide borderless support networks – Reduce stigma of disease – Social Contagion [Christakis and Fowler, 2013] – Ability for one individual to influence the health behaviours of one or many others in a social network – Indirect influence – Homophily [Mcpherson, 2001] – Social networks develop between individuals with similar diseases or traits – Homophilous ties are durable and resilient – Guardian Angel System [Szolovits, et al. 1994] – Patient centred to include them in their own decision making and reduce data fragmentation – Contact with other patients with similar diseases, cultures, economic backgrounds, symptoms, etc. The University of Sydney Page 4

Aim – Gain understanding into how different social network topologies can affect the distribution of benefit from a social messaging health intervention for specific chronic disease – Agent-based model simulation used to identify the network that minimises disparity between agents in the network The University of Sydney Page 5

eGuardian Angel (agents) – Social innovation for individuals with chronic disease, who’s social connections assist them in keeping motivated to reach health goals – Guardian – Provide motivation, support, advice to ‘child’ – Help achieving diet, exercise, physical activity, and health related goals – Child – Provides feedback to guardian if they are positively affected by their message positivity positivity positivity motivation motivation motivation User 1 User 3 User 2 The University of Sydney Page 6

Parameters – Motivation – From others • Complete exercises, maintain weight, medication adherence • Based on theories of social contagion, dynamic network theory and goal pursuit [4,8,9] – From external sources (environment) • Family, other health interventions, financial incentive – Positivity – Reflects mood, attitudes, and emotion toward a situation – Theoretical measure of how much influence a guardian has on their child – Goal: Improve group level motivation and positivity and display the lowest variation between individual nodes – Provide greatest benefit to all The University of Sydney Page 7

Simulation Design (Diffusion Model) Motivation: natural decay Guardian Positivity Motivation Guardian Environment: Normal distribution 𝒪(𝑛 𝑢 − 1 , 5) Positivity Motivation Child Positivity Motivation Child 𝑁𝑓𝑡𝑡𝑏𝑕𝑓 𝐺𝑏𝑑𝑢𝑝𝑠 𝑞 𝑕 (𝑢−1) Transfer: 𝑞 𝑢 = 𝑞 𝑢 − 1 ∙ ( 𝑈𝑠𝑏𝑜𝑡𝑔𝑓𝑠 𝐺𝑏𝑑𝑢𝑝𝑠 −𝑞 𝑢−1 𝑞 𝑑 𝑢 −𝑞 𝑑 (𝑢−1) Motivating Message : 𝑛 𝑢 = 𝑛 𝑢 − 1 + ( 𝑜𝑣𝑛𝑐𝑓𝑠 𝑝𝑔 𝑑ℎ𝑗𝑚𝑒𝑠𝑓𝑜 ) + 1) Fe Feedb dback k : 𝑛 𝑢 = 𝑛 𝑢 − 1 + ( 𝑜𝑣𝑛𝑐𝑓𝑠 𝑝𝑔 𝑕𝑣𝑏𝑠𝑒𝑗𝑏𝑜𝑡 ∙ 𝐺𝑓𝑓𝑒𝑐𝑏𝑑𝑙 𝐺𝑏𝑑𝑢𝑝𝑠) 𝑞(𝑢−1) The University of Sydney Page 8 8

Simulation Tool Simulation – NetLogo [7] – Agent-based programming language and modelling environment – Agents interact depending on defined formulas and variables • Functions based on the theoretical concepts of social contagion, homophily, and social network dynamics. – Cost effective – Test before full scale implementation Identify emergent properties ( expected or not) – The University of Sydney Page 9

Network Topologies Simulated Networks (N-K Landscape) – Random: User connected to at least one other at random – Paired: Two users mutually connected to each other – Ring: Users connected in series in a closed loop – Small World: n of connections rewired from ring topology – Example N = 8, K=2 influence Matrix Random Paired (Blocked) Ring Small-World The University of Sydney Page 10

Results: Social Dynamics (20 agents) 1:1 Guardian Angel network Random network Hub and Spoke network The University of Sydney Page 11 1

Results: Variability – Lowest standard deviation: Ring network – Paired, Random, and Small World networks had higher standard deviation – Less interconnectivity, unequal distribution of edges Standard deviation of motivation for each network topology over time. BUD: Paired network, RING: Ring network, RAND: Random network, Small-World: Small world network The University of Sydney Page 12

Discussion – Ring network most successfully socialises the burden of disease – Indirect connections with all other nodes chain effect of influence – Nodes with inherently higher motivation were able to help others to benefit – Increasing disorder from the ring network increases the disparity between individuals in the network The University of Sydney Page 13

Limitations & Conclusion – Transfer functions based on theory & limited knowledge – Limited empirical evidence due to difficulties quantifying social contagion principles – The model can’t be empirically validated - networks can only be used in comparison with each other • useful for displaying trends and emerging patterns based on theories of social influence and mood contagion – Future research – Clinical trial for Guardian Angel intervention – Managing Network sustainability – Network evolution – Conclusion – Add to current literature to correctly implement alternative and effective healthcare solutions for the future – Network topology must be considered when implementing a social network based intervention – Social “role” can be considered as part of the intervention in social networks The University of Sydney Page 14

References – [1] AIHW. How many medical practitioners are there? [Internet]. Canberra; 2015. Available from: http://www.webcitation.org/6qywQCvPn. – [2] K. Henry, The Economic Impact of Australia’s Aging Population, SAIS Review 24 (2004), 81– 92. – [3] P. Szolovits, J. Doyle, W. J. Long, I. Kohane, and S. G. Pauker, Guardian Angel : Health Information Systems, (1994). – [4] N. A. Christakis and J. H. Fowler, Social contagion theory: Examining dynamic social networks and human behavior, Statistics in Medicine 32 (2013), 556 – 577. – [5] M. Mcpherson, L. Smith-Lovin, and J. M. Cook, Birds of a Feather : Homophily in Social Networks, Annual Review of Sociology 27 (2001), 415 – 444. – [6] A. A. Lazakidou, S. Zimeras, and D. Iliopoulou, mHealth Ecosystems and Social Networks in Healthcare , Springer International, Switzerland, 2016. – [7] U. Wilensky, NetLogo, 1999. Available: http://www.webcitation.org/6r3m7wIYW. – [8] J. D. Westaby, D. L. Pfaff, and N. Redding, Psychology and social networks: A dynamic network theory perspective., American Psychologist 69 (2014), 269 – 284. – [9] J. D. Westaby, Dynamic goal pursuit: Network motivation, emotions, conflict, and power., in Dynamic network theory: How social networks influence goal pursuit. , American Psychological Association, Washingtion, DC, 2012, 33 – 95, , 33 – 95. The University of Sydney Page 15