Using online interventions to change
behaviour
Linda Little PhD, Cpsychol, AFBPsS
PaCT Lab
Department of Psychology
Colleagues
Overview
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Behaviour change
Self-affirmation
Implementation intentions
Summary and conclusions
Behaviour change
What we wanted to know…
 Can we deliver behaviour change techniques online
to:
(i) increase people’s fruit and vegetable intake;
(ii) reduce teenagers energy consumption?
 Health and sustainability – two different areas but
similar issues i.e. changing behaviour
Why online?
 Systematic review by Griffith et al (2006) discussed
the advantages of delivering health interventions
online including:
 Cost
 Convenience
 User groups
Internet Use
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Once a month or less
At least once a week
(but not every day)
Everyday or almost
everyday
16-24
25-44
45-54
55-64
65+
Frequency of Internet use by age group 2010 (OfNS, 2010)
Behaviour change and health
Deprivation and Obesity
Proportion of overweight and obese (combined) three year-old children in Swansea, Neath and Port Talbot from the ‘least’
and ‘most’ deprived areas, linear regression, 1995–2005. Brunt, Lester, Davies, and Williams (2008)
Prevalence of Obesity by Social Class
Messages!
Closing the Intention Behaviour Gap
Individual differences exist in how people respond to a
health threat or prevention measure.
Many health behaviour models fail to explain how
intention translates into behaviour.
When developing behaviour change interventions need to
consider:
Health locus of control
Self-efficacy & Response-efficacy
Optimism & Defensiveness
Self Efficacy and Response Efficacy
Self efficacy – belief about own ability (Bandura, 1977,
1986)
Response efficacy – belief action with avoid threat
Optimism and defensiveness
Both linked to personality.
We consistently underestimate our own risk in comparison
to others (Pitts, 2001).
My nana smoked forty
cigarettes a day since
she was 14 and she’s
still going strong at 80!
You keep on hearing
about people
dropping down dead
when exercising-it’s
best not to bother!
Threat Appraisal and Health
Messages
Messages often focus on identifying a threat (e.g. the
negative consequences of not eating 5 a day) and
increasing the individuals awareness of their
susceptibility to the possible consequences.
Intervention studies often make the assumption that
participants will digest the message in a carefully
considered manner.
Assumption flawed!
Closing the Intention Behaviour Gap
Messages that incorporate the understandings and
capabilities of the target population are more effective
than those that are formed top down (Hesketh et al,
2005).
Barriers to adopting healthier lifestyles? (Fielden, 2011):
Money; Environment (safety, space); Lack of support;
Lack of skills; Knowledge isn’t the problem!
Health messages or education programmes must consider
target group specific components!
Defensive Processing of Health
Information
We also know people:
Have a desire to hold beliefs that fit with social demands (similar
to Social Norms or Impression Motivation)
Are motivated to protect their self-definitional beliefs (Defence
Motivation).
Defensive Processing of Health
Information
When the information presented engenders defense
motivation and cognitive resources are available
defensive systematic processing is most probable.
The threat from the health message leads the
individual to doubt the validity/credibility of the
information and thus unlikely lead to action.
Self Affirmation Theory (Steele, 1988)
In terms of self-defence people are primarily concerned
with their global sense of self worth.
When a threat relevant to one domain is met
defensiveness to it can be reduced by affirming an aspect
of the individuals identity in another domain.
Through reflecting on one’s cherished values, actions and
attributes, self affirming reinforces an individuals’ sense of
who they are.
Applied successfully in several health related areas e.g.
HIV
Study 1- Self affirmation and health
hypotheses
 Fruit and vegetable consumption, would be higher amongst the
participants whom had self affirmed as opposed to those in the
control group.
 The effects of self-affirmation on behavior will be greater in
participants with lower baseline fruit and vegetable
consumption than those whose consumption is higher at
baseline.
 Participants who self-affirmed would score significantly higher
on measures of intent, change their behaviours and on measures
of self-efficacy and response efficacy than non-affirmed
participants. The mediating effects of these variables will also be
investigated
 When considering the target group of low SES mothers fruit and
vegetable consumption would be higher amongst the
participants whom had self affirmed as opposed to those in the
control group.
Method
 Students (N=59) and mothers low in SES (N=26) recruited
 Fruit and vegetable consumption measured at baseline then
randomly allocated to self-affirmation or control condition
 Exposed to website containing health message
 Manipulation check in experimental condition to check
participants had self-affirmed
Self-affirmation manipulation (Reed
and Aspinwall, 1998)
Experimental
 SA group e.g. asked to
think of a time they had
forgiven someone after
being hurt and write
about this
Control
 Control group e.g. what
flavour of ice cream do
you like best? Describe
why
Materials
 Online questionnaire specifically designed for each
target group
 24hr recall baseline measure for fruit ad vegetable
consumption; Stage of readiness to change; I-PANASSF-groups matched on the basis of these measures
 Self-efficacy, response efficacy and intention to
change measured immediately after exposure to
health message
 Followed by a 7 day online food diary
Procedure
 Participants received unique user code sent via email
 Attended 30-minute testing session at either the
University or Sunderland Children’s Centre
 Completed baseline measures, SA manipulation or
control task
 Directed to health website and read each page prior
to returning to the questionnaire
 Completed post manipulation measures and a 7-day
food diary
Results
Participants
Self-affirmed (SA)
Non-affirmed (NA)
Low SES mothers
11
13
Students
22
26
Total
33
39
Table 1.1 Distribution of the two groups of participants across the testing conditions
Baseline differences
 No significant difference found between the
experimental and control groups for:
State of change, F (1, 70) = 0.713, p = .401
F&V consumption, , F (1, 70) = 2.604, p = .111
 Significant difference between groups on
manipulation scores, , F (1, 70) = 85.969, p <0.001
Effects of self-affirmation on
behaviour
Figure 1 Reported fruit and vegetable consumption in the 7 days post manipulation by
condition F (1, 69) = 49.466, p < .001
Effects of self-affirmation on the
other dependent measures
 Significant differences between groups were found
with the SA group reporting higher scores for:
Intent, F (1, 70) = 141.562, p < .001
 Self-efficacy, F (1, 70) = 5.799, p = .019
 Response efficacy, F (1, 70) = 3.936, p = .051
(approaching)
Figure 2 Simple slopes for the interaction between condition and baseline fruit and
vegetable consumption on fruit and vegetable consumption at 1 week follow-up
Summary
 SA consumed F & V over 7 days in comparison to non-SA
 First study to show SA online can promote acceptance of
health message and lead to behaviour change
 Those with lowest baseline in SA group benefitted more
 Targeting hard to change group using online intervention
successfully has important implications for potential lowcost high impact interventions.
 Supports previous research in that SA promotes intention
to change – essential step in terms of behaviour change,
self-efficacy and response efficacy (tentatively)
 Evidence personally relevant information can increase SA
Taking on the Teenagers
Background
 Energy consumption
increasing
 Negative environmental
and financial ramifications
 Teenagers high
consumers of electrical
energy
Implementation Intentions –IMPs
(Gollwitzer, 1993)
 IMPs based on Theory of Planned Behaviour
 Willing and planning – key concepts
 Deliberate about something and then form goal
intention and plan
 Intentions more salient with environmental cues
 Goal intentions based on IMPs more likely to reached
 Planning creates strong memory traces that are
highly accessible
 IMPs based on ‘if’ ‘then’ plan
 Linking ‘if’ and ‘then’ increases cue accessibility
‘IF I finish my assignment before 5pm THEN I will go
out tonight’
 People who form IMPs more likely to carry out
behaviour
Hypotheses
 H1. In comparison to baseline levels of energy-saving
behaviour (Time 1), teenagers who receive the intervention
will engage in more energy-saving behaviour at Time 2 and
Time 3, than teenagers in the control condition.
 H2. Teenagers in the preparation stage of change, who
receive the intervention, will report a larger increase in
energy-saving behaviour at Time 2 and Time 3, relative to
their baseline levels of energy-saving behaviour.
Method
 Investigated whether an online intervention based on
IMPs would increase teenagers intentions to save
energy
 240 teenagers aged 13-15 participated in the research182 completed data sets
 Randomly allocated to experimental (IMPs) or control
condition
 96 participants in the experimental and 86
participants in the control
 All participants completed an online energy saving
diary for 5 consecutive days and then again at a 6
week follow up
 Baseline measures: readiness to change, energy
saving behaviour, energy saving intention =
completed again at 5 days and 6 weeks
Intervention
 4 types of energy saving behaviour were identified
(Toth et al 2013)
 Online intervention developed based in IMPs
 Participants invited to plan the energy saving
behaviour they intended to do e.g. ‘When I leave the
room I will turn off the light’
 Intention also measured ‘I intend to use less electrical
energy at home in the next 7 days’
Procedure
 Day 1: participants logged on and completed all
baseline measures and asked to keep a record of their
energy saving behaviour over the next 5 days
 Experimental condition then completed IMPs before
completing record of energy saving intentions
 All logged on and completed online diary for 5 days
 Day 5 and after 6 weeks: completed all baseline
measures
Results
 No difference between groups for readiness to change: X2
(4) = 0.53, p > .05
 Participants exposed to the intervention reported greater
energy-saving behavioural intentions than those in the
control group, and that these differences remained
consistent across time.
 A 2x3 mixed model ANOVA revealed a significant main
effect of condition (F (1, 167) = 5.02, p < .05), in that
participants who received the intervention reported
significantly stronger overall behavioural intentions to save
energy and were consistent across time-points
Figure 1: Intention to save energy between the 3 time intervals
Findings
 Participants who received the intervention reported stronger
behavioural intentions and engaged in more energy-saving
behaviour at a five day and six week follow-up than those who did
not.
 An unequal distribution of teenagers across the stages of change
meant comparison could only be made between the precontemplation and preparation group only.
 Participants in the preparation stage of change, who reported
occasionally engaging in energy-saving behaviour, reported an
increase in energy-saving behavioural intentions and behaviours
across time, as a result of the intervention.
 However, the energy saving intentions and behaviours of teenagers
in the pre-contemplative stage of change who do not currently save
energy and are not thinking about doing so shows the intervention
is only effective for some groups.
Summary & Conclusions
 Implementation intentions can be an effective
strategy for increasing teenagers’ energy-saving
intentions and behaviour, but only for those
teenagers who are ready to start saving electrical
energy and may even do so already
 The research has contributed to three emergent
research areas: (i) online delivery; (ii) environmental
and health behaviours ; (iii) teenage and low SES
samples
References
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Questions?