1. Looking beyond the ‘meaty motorists’ Using travel and dietary behaviours to identify and describe healthy, low-carbon lifestyles in the UK
Michaela A. Smith, Jan R. Böhnke, Hilary Graham,
Piran C. L. White, Stephanie L. Prady
20 June 2017
Hi everyone
Today I’m going to be presenting a piece of research related to my PhD, where we looked at associations between active travel and dietary behaviours in the National Diet and Nutrition survey.

2. Background
Climate change + chronic disease -> growing attention on promotion of lifestyles that are healthy and low-carbon (HLC)
e.g. reducing car travel, red and processed meat (RPM) consumption
But current understanding of HLC lifestyles is very limited, most research has focused on single behaviours in isolation
e.g. active travel OR low-carbon diets
To truly understand people’s lifestyles + their impacts, need to understand how different behaviours may interact or intersect
People may engage in HLC behaviour in one domain but not in others
Hypotheses
Largest class will be meaty motorists
Healthy, low carbon class will include cyclists
Class typologies will differ by gender
More typologies in NDNS because it includes more age groups

3. For example…
Some people may engage in healthy, low carbon behaviours in one domain but not in others
cycling itself is obviously low in carbon, estimating the full impact of a ‘cycling lifestyle’ needs to consider what is ultimately fuelling the cyclist: to use an extreme example, a cyclist fuelled by calories from cheeseburgers has the same emissions per mile as two people driving a fuel efficient car (Berners-Lee, 2010).
A cyclist fuelled by cheeseburgers has the same carbon emissions per mile as 2 people driving a fuel efficient car (Berners-Lee, 2010)

4. Research Aim
Understand how travel and dietary behaviours are patterned together across the UK population
Describe the prevalence and patterning of HLC lifestyles by identifying clusters of travel and dietary behaviours with health and carbon relevance
RQ: how travel and dietary behaviours are patterned together across the UK population ?

5. Methods: Data Source National Diet and Nutrition Survey (NDNS)
Nationally representative sample
Dietary data collected via 4-day food diary
Travel behaviour data collected via Recent Physical Activity Questionnaire (RPAQ), age 16+
Years 2, 3, 4 of rolling programme ( ), n=1609

6. Measures Diet Travel Mode use FV: average portions/day
< 3
3 – <5
5+ (meets guideline)
RPM: average grams/day
None
>0 – 70 g
>70 g (exceeds guideline)
Never consumes RPM
Never consumes any meat, poultry, fish (vegetarian)
Mode use
Car/private transport
Public transport
Walking
Cycling
Non-work travel
Main mode only (1 variable)
Commuting travel
Frequency of each mode:
always, usually, occasionally, never/rarely (4 variables)

7. Analysis Latent Class Analysis (LCA) Type of cluster analysis
Individuals classified into subgroups (typologies / patterns of behaviour) based on responses to observed variables
Group membership assigned by statistical probabilities
Best-fitting models selected using information criteria (AIC, BIC), bivariate residuals, interpretability
Models calculated separately by gender

8. Healthy, low-carbon rating
After identification in LCA model, each subgroup was characterised as higher-carbon, lower-carbon, mixed
Based on distribution of travel and dietary behaviour compared to gender-specific average
Dietary behaviour
Travel behaviour
Rating
Colour
> avg RPM
> avg car use
Higher-carbon
≤ avg RPM, < avg FV
predominant car use, but < avg
Mixed
< avg RPM, > avg FV
predominant non-car use
Healthy,
low-carbon
After identifying each subgroup, each cluster / typology characterised as higher-carbon or lower-carbon based on distribution of travel and dietary indicators compared to gender-specific average

9. Results: LCA models Females (n=904), 9 classes
Key points:
Females: 9 classes, males: 8 classes, rating scale for each group at the bottom
Right off the bat: largest classes are red (high carbon), patterns are different by gender
Don’t have time to go through each group in detail but I will try to go over some of the highlights
Class # 1 2 3 4 5 6 7 8 9
Travel behaviour
Dietary behaviour
Class # 1 2 3 4 5 6 7 8
Travel behaviour
Dietary behaviour

10. Females (n=904) Lower-carbon groups (2) Cyclists (1%)
Low RPM, high FV
Public transport users (7%)
Low RPM, low FV
Lower-carbon groups were cyclists, and PT commuters, both of which had lower RPM consumption than average
Class # 1 2 3 4 5 6 7 8 9
Travel behaviour
Dietary behaviour

11. Females (n=904) Mixed groups (5) Usual car commuters (7%)
Low RPM, high FV
Mostly walkers (7%)
Highest RPM, low FV
No RPM car commuters (4%)
No RPM non-commuters (2%)
Mixed travel non-commuters (25%)
High RPM, lowest FV
Mixed groups – travel and dietary behaviour went in the opposite direction. These were: usual car commuters, mostly walkers, two groups that didn’t consume RPM but travelled predominantly by car, and the largest group, Mixed travel non-commuters
Class # 1 2 3 4 5 6 7 8 9
Travel behaviour
Dietary behaviour

12. Females (n=904) Higher-carbon groups (2) Exclusive car commuters (26%)
High RPM, low FV
Mostly car non-commuters (21%)
High RPM, highest FV
Finally: 2 high carbon groups: exclusive car commuters and mostly car non-commuters, both of which had higher than average RPM consumption
These are the meaty motorists…
Class # 1 2 3 4 5 6 7 8 9
Travel behaviour
Dietary behaviour

13. Males (n=705) Similar groups: Exclusive car commuters (38%)
Public transport users (9%)
Mostly walkers (6%)
Point out key highlights, distinctions, summaries
Class # 1 2 3 4 5 6 7 8
Travel behaviour
Dietary behaviour

14. Males (n=705) Similar groups: Differences
Exclusive car commuters (38%)
Public transport users (9%)
Mostly walkers (6%)
Differences
Cyclists – highest RPM consumption – 79% exceeded guideline
‘No RPM’ groups less car dependent
Mostly car non-commuters did not exist
Point out key highlights, distinctions, summaries
Class # 1 2 3 4 5 6 7 8
Travel behaviour
Dietary behaviour

15. Summary Lower-carbon lifestyles are rare (8% females, 12% males)
only 2% completely HLC (1% females, 1% males)
Most groups have mixed lifestyles (45% females, 45% males) or higher-carbon lifestyles (47% females, 43% males)
Clustering between travel and dietary behaviours differs by gender (only 3 groups patterned the same)
Exclusive car commuters, PT commuters, Mostly walkers
Class # 1 2 3 4 5 6 7 8 9
Travel behaviour
Dietary behaviour
Class # 1 2 3 4 5 6 7 8
Travel behaviour
Dietary behaviour

16. Conclusions
Examining travel and diet together can give greater insights into the full impacts of different lifestyles
Most people who engage in HLC travel do not follow a HLC diet (and vice versa)
Many people leading ‘mis-matched’ lifestyles (mix of high- and low-carbon behaviours)
Future research: examine potential for behaviour change in intermediary ‘mixed’ groups in addition to the higher-carbon ‘meaty motorists’

17. Thanks!

19. Distributions by gender
Females (n=904)
Males (n=705)
% exceeding RPM guideline
Point out key highlights, distinctions, summaries
% using travelling by car for non-work journeys

20. Female classes – highlights
Exclusive car commuters,
high RPM, low FV
90% non-work car travel
100% car commuting
31% exceed RPM guideline
27% meet 5 a day
Point out key highlights, distinctions, summaries
Mixed travel
non-commuters,
high RPM, lowest FV
43% non-work car travel
33% exceed RPM guideline
5% meet 5 a day
Mostly car
non-commuters,
high RPM, highest FV
75% non-work car travel
31% exceed RPM guideline
59% meet 5 a day
Class # 1 2 3 4 5 6 7 8 9
Travel behaviour
Dietary behaviour

21. No RPM non-commuters No RPM car commuters Mostly walkers,
66% non-work car travel
65% always car commuting
92% no RPM, 54% vegetarian
46% meet 5 a day
No RPM non-commuters
64% non-work car travel
80% no RPM, 33% vegetarian
36% meet 5 a day
Mostly walkers,
high RPM, low FV
78% walking travel
74% always commute on foot
36% exceed RPM guideline
24% meet 5 a day
Usual car commuters,
low RPM, high FV
72% non-work car travel
66% usual car commuting
26% exceed RPM guideline
33% meet 5 a day
Point out key highlights, distinctions, summaries
Class # 1 2 3 4 5 6 7 8 9
Travel behaviour
Dietary behaviour

22. Cyclists, low RPM, high FV PT travellers, low RPM, low FV
99% cycling travel
75% always commute by bike
11% exceed RPM guideline
57% meet 5 a day
PT travellers,
low RPM, low FV
60% PT travel
83% always commute by PT
24% exceed RPM guideline
27% meet 5 a day
Point out key highlights, distinctions, summaries
Class # 1 2 3 4 5 6 7 8 9
Travel behaviour
Dietary behaviour

23. Females – Social Profile
  age group
+ Non-White
+ No qualifications
+ Not employed
+ London
+ Degree
+ Employed
+ Average deprivation
+ East Midlands
+ White
+ Degree
+ Yorkshire
+ South East
  age group
+ Non-White
+ No partner
+ FT education
+ London
- Wales
age group
- Non-white
+ 3 person hholds
+ Children and partner
+ Degree
+ Higher ed <degree
- Not employed
+ Higher man / prof occupations
+ Highest incomes
- London
age group
+ FT education
+ Employed
- Least deprived
- South East
- 70+ age group
+ FT education
+ Employed
Point out key highlights, distinctions, summaries
age group
+ GCSEs
+ No qualifications
+ Not employed
+ Routine occupations, never worked
+ Lowest incomes
+ Highest deprivation
+ Scotland
age group
+ 1 person hholds
+ No children
+ Foreign qualifications
+ Not employed
+ Lower man / prof occupations, small employers
+ Lowest deprivation
Class # 1 2 3 4 5 6 7 8 9
Travel behaviour
Dietary behaviour