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Food, Emotions and Behaviour Change
Behaviour change techniques are considered effective means for changing behaviour, and with an increase in their use, the interest in their exact working principles has also expanded. This information is required to make informed choices about when to apply which technique. Computational models that describe human behaviour can be helpful for this. In this paper, a few behaviour change techniques have been connected with a computational model of emotion and desire regulation. Simulations have been performed to illustrate the effect of the techniques. The results demonstrate the working mechanisms and feasibility of the techniques used in the model.
|1.||Information about health consequences [5.1]||Provide information (e.g., written, verbal, visual) about the health consequences of performing the behaviour|
|2.||Reduce negative emotions [11.2]||Advise on ways of reducing negative emotions to facilitate the performance of the behaviour|
|3.||Stress management [11.2]||Advise on ways of reducing stress|
|4.||Problem solving/coping planning [1.2]||Analyse, or prompt the person to analyse, factors influencing the behaviour and generate or select strategies that include overcoming barriers and/or increasing facilitators (includes ‘relapse prevention’ and ‘coping planning’)|
|Goal setting (outcome) [1.3]||Set or agree on a goal defined in terms of a positive outcome of wanted behaviour|
|Goal setting (behaviour) [1.1]||Set or agree on a goal defined in terms of the behaviour to be achieved|
2. The Interplay and Regulation of Emotions and Desires
3. Behaviour Change Interventions
4. Networking-Oriented Modelling Technique
Each connection carries some weight ωX,Y. from state X to state Y called casual impact;
Multiple incoming causal impacts ωX,YX(t) to state Y from some states X are aggregated using combination function cY(.);
There exists a notion of speed of change of each state to define how fast a state changes because of the incoming impact (speed factor ηY).
|States and connections||X, Y, X→Y||Describes the nodes and links of a network structure (e.g., in graphical or matrix form)|
|Connection weight||ωX,Y||The connection weight ωX,Y usually in [–1,1] represents the strength of the causal impact of state X on state Y through connection X→Y|
|Aggregating multiple impacts on a state||cY(.)||For each state Y a combination function cY(.) is chosen to combine the causal impacts of other states on state Y|
|Timing of the effect of causal impact||ηY||For each state Y a speed factor ηY ≥ 0 is used to represent how fast a state is changing upon causal impact|
|State values over time t||Y(t)||At each time point t, each state Y in the model has a real number value, usually in [0,1]|
|Single causal impact||impactX,Y(t) = ωX,Y X(t)||At t state X with a connection to state Y has an impact on Y, using connection weight ωX,Y|
|Aggregating multiple causal impacts||aggimpactY(t)
= cY(impactX1,Y(t),…, impactXk,Y(t))
= cY(ωX1,YX1(t), …, ωXk,YXk(t))
|The aggregated causal impact of multiple states Xi on Y at t, is determined using combination function cY(.)|
|Timing of the causal effect||Y(t+Δt) = Y(t) +
ηY [aggimpactY(t) − Y(t)] Δt
= Y(t) +
ηY [cY(ωX1,YX1(t), …, ωXk,YXk(t)) − Y(t)] Δt
|The causal impact on Y is exerted over time gradually, using speed factor ηY; here the Xi are all states with outgoing connections to state Y|
5. Computational Model
|ws(s, g.o, anx)||World state for stimulus ‘s’, goal setting (outcome) ‘g.o’, anxiety ‘anx’|
|ss(s, b−, b+, g.o, g.b, b.strs, b.anx)||Sensor state for stimulus ‘s’, negative body state ‘b−’, goal outcome ‘g.o’, goal behaviour ‘g.b’, body stress ‘b.strs’, body anxiety ‘b.anx’|
|srs(s, b−, b+, g.o, g.b, b.strs, b.anx)||Sensor representation state for stimulus ‘s’, negative body state ‘b−’, positive body state ‘b+’, goal outcome ‘g.o’, goal behaviour ‘g.b’, body stress ‘b.strs’, body anxiety ‘b.anx’|
|fs(b−, b+, g.b, b.strs, b.anx)||Feeling state for body state ‘b−’, goal behaviour ‘g.b’, stress ‘b.strs’, anxiety ‘b.anx’|
|ps(a, b−, b+, g.o, g.b, b.strs, b.anx)||Preparation state for action ‘a’, body state ‘b−’, body state ‘b+’, goal outcome ‘g.o’, goal behaviour ‘g.b’, body stress ‘b.strs’, body anxiety ‘b.anx’|
|es(a, b−, b+, g.o, g.b, b.strs, b.anx)||Execution state for action ‘a’, body state ‘b-’, body state ‘b+’, goal outcome ‘g.o’, goal behaviour ‘g.b’, body stress ‘b.strs’, body anxiety ‘b.anx’|
|dss||Desire state for stimulus ‘s’|
|bs(+, −, strs.−, anx.−)||Belief state for positive ‘+’, negative ‘−’, negative stress ‘strs.−’, and negative anxiety ‘anx.−’ beliefs|
|cs(reapp, e.reapp, d-s.m, e-p.solv)||Control state for (desires) reappraisal ‘reapp’, (emotion) reappraisal ‘e.reapp’, (desire) situation modification ‘d-s.m’, (emotion) problem solving ‘e-p.solv’|
|Ia,b,c(r.n.e, strs.mgt, inf.beh)||Intervention ‘a’ for regulation of negative emotions ‘r.n.e’, ‘b’ for stress management ‘strs.mgt’, ‘c’ for information about health consequences ‘inf.beh’|
6. Simulation Results
|1.||1||0||0||0||0.5||0||Reappraisal of food desire fails (lack of information)|
|1||Reappraise food desire|
|2.||1||1||0||0||1||0||Reappraisal fails: eat food ←→ feel stressed|
|1||Efficiently manages stress after eating|
|3.||1||1||0||0||0.5||0.5||Reappraise food desires only but also feel anxiety|
|1||Reappraises both food desire and anxiety|
|4.||1||1||1||0||1||1||Reappraisal fails: eat food←→feel stressed|
|1||Reappraisal fails: eat food←→feel stressed. Hence, stress management and situation modification (food) and problem solving (emotions) leads to stable situation|
The entry is from 10.3390/smartcities4020048
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