Using online interventions to change behaviour

Linda Little PhD, Cpsychol, AFBPsS

PaCT LabDepartment of Psychology

Colleagues

Behaviour change Self-affirmation Implementation intentions Summary and conclusions

Overview

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

Frequency of Internet use by age group 2010 (OfNS, 2010)

0%10%20%30%40%50%60%70%80%90%

100%

16-24 25-44 45-54 55-64 65+

Once a month or less

At least once a week(but not every day)

Everyday or almosteveryday

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 controlSelf-efficacy & Response-efficacyOptimism & 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

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.

Study 1- Self affirmation and health

hypotheses

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

Online questionnaire specifically designed for each target group

24hr recall baseline measure for fruit ad vegetable consumption; Stage of readiness to change; I-PANAS-SF-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

Materials

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

Procedure

Participants Self-affirmed (SA) Non-affirmed (NA)

Low SES mothers 11 13

Students 22 26

Total 33 39

Results

Table 1.1 Distribution of the two groups of participants across the testing conditions

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

Baseline differences

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 

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)

Effects of self-affirmation on the other dependent

measures

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

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 low-cost 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

Summary

Taking on the Teenagers

Background

Energy consumption increasing

Negative environmental and financial ramifications

Teenagers high consumers of electrical energy

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

Implementation Intentions –IMPs (Gollwitzer, 1993)

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

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.

Hypotheses

Investigated whether an online intervention based on IMPs would increase teenagers intentions to save energy

240 teenagers aged 13-15 participated in the research- 182 completed data sets

Randomly allocated to experimental (IMPs) or control condition

96 participants in the experimental and 86 participants in the control

Method

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

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’

Intervention

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

Procedure

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

Results

Figure 1: Intention to save energy between the 3 time intervals

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 pre-contemplation 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.

Findings

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

Summary & Conclusions

References

Armitage, C. J. (2006). Evidence that implementation intentions promote transitions between the stages of change. Journal of consulting and clinical psychology, 74(1), 141-151.

Armitage, C. J. (2008). A volitional help sheet to encourage smoking cessation: a randomized exploratory trial. Health psychology, 27(5), 557-566

Armitage, C. J. (2009). Effectiveness of experimenter-provided and self-generated implementation intentions to reduce alcohol consumption in a sample of the general population. Health psychology, 28(5), 545-553.

Department of Energy & Climate Change (2010). Energy consumption in the United Kingdom, Domestic Data Tables 2010 Update Retrieved from http://www.decc.gov.uk/en/content/cms/statistics/publications/ecuk/ecuk.aspx (Accessed 12 November 2010).

Griffiths, F., Lindenmeyer, A., Powell, J., Lowe, P., & Thorogood, M. (2006). Why are health care interventions delivered over the internet? A systematic review of the published literature. J Med Internet Res, 8(2), e10.

Sky News (2008). 'Greenagers' want climate change action. Retrieved from http://news.sky.com/skynews/Home/Sky-News-Archive/Article/20080641306701 ( Accessed 1 November, 2010).

Verplanken, B., & Orbell, S. (2003). Reflections on Past Behavior: A Self-Report Index of Habit Strength. Journal of applied social psychology, 33(6), 1313-1330.

Webb, T. L., Sheeran, P., & Luszczynska, A. (2009). Planning to break unwanted habits: habit strength moderates implementation intention effects on behaviour change. British journal of health psychology, 48, 507-523.

Questions?