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

The University of Sydney Page 1

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 2

Introduction: Guardian Angel

The University of Sydney Page 3

Social Innovation: eGuardian Angel

– Community Building: Socialising Health Burden

3

X h a b g e d c f

The University of Sydney Page 4

Literature and Related Work

– Social Networks & Healthcare [Lazakidou, et al. 2016]

– 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

• thers 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.

Segregation model

The University of Sydney Page 5

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 6

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 motivation motivation motivation positivity positivity User 1 User 3 User 2

The University of Sydney Page 7

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 8

Simulation Design (Diffusion Model)

8

Guardian Positivity Motivation Child Positivity Motivation

Environment: Normal distribution 𝒪(𝑛 𝑢 − 1 , 5)

Transfer: 𝑞 𝑢 = 𝑞 𝑢 − 1 ∙ (𝑈𝑠𝑏𝑜𝑡𝑔𝑓𝑠 𝐺𝑏𝑑𝑢𝑝𝑠 −𝑞 𝑢−1

𝑞(𝑢−1)

+ 1) Motivation: natural decay Fe Feedb dback k : 𝑛 𝑢 = 𝑛 𝑢 − 1 + (

𝑞𝑑 𝑢 −𝑞𝑑(𝑢−1) 𝑜𝑣𝑛𝑐𝑓𝑠 𝑝𝑔 𝑕𝑣𝑏𝑠𝑒𝑗𝑏𝑜𝑡 ∙ 𝐺𝑓𝑓𝑒𝑐𝑏𝑑𝑙 𝐺𝑏𝑑𝑢𝑝𝑠)

Motivating Message : 𝑛 𝑢 = 𝑛 𝑢 − 1 + (

𝑁𝑓𝑡𝑡𝑏𝑕𝑓 𝐺𝑏𝑑𝑢𝑝𝑠 𝑞𝑕(𝑢−1) 𝑜𝑣𝑛𝑐𝑓𝑠 𝑝𝑔 𝑑ℎ𝑗𝑚𝑒𝑠𝑓𝑜 )

Child Positivity Motivation Guardian

The University of Sydney Page 9

Simulation Tool

– 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)

Simulation

The University of Sydney Page 10

Network Topologies

– 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

Simulated Networks (N-K Landscape)

Random Paired (Blocked) Ring Small-World

The University of Sydney Page 11

Results: Social Dynamics (20 agents)

1

Random network Hub and Spoke network 1:1 Guardian Angel network

The University of Sydney Page 12

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 13

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 14

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 15

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.