1. Physical Activity through Sustainable Transport Approaches (PASTA)
Conceptual framework and study design of a  prospective European multi-center study on active transport and physical activity behaviour.
Thomas Götschi, PhD
13/11/2018

2. Overview - General background on active transport projects at ISPMZ Physical Activity and Health Unit - From call text to proposal to study protocol - Details on developing the conceptual framework - Details on issues related to study design - Discussion

3. Active transport research framework PANH
Primary Outcomes
Secondary Outcomes (Impacts)
Output & Results
Health
Transportation
Social
Environmental
Economic Benefits
Convenience Access, destinations, land use
Safety Perceived and actual
Hurdles & Incentives Cost, culture
Infrastructure Facilities, built environment
Programs Marketing, education
Levels of Active Travel
Input
Policy
Politics
Advocacy
Research
Leadership
Resources
External Factors
Benefits and Risks of Active Travel
Policy analysis (HEPA Europe)
Literature reviews, AT and PA, infrastructure, measures, etc. (OBSAN)
Conceptual reports on evaluation and monitoring (MPV Zürich, SchweizMobil)
Empirical data collection infrastructure, survey, counts (VIVUS, GemeindeBewegt)
Analysis of travel survey data (MZ Verkehr, SGB)
Development and application of impact assessment tools (HEAT WHO, Transportrechnung)
13/11/2018

4. FP7 Call Text: HEALTH : Social innovation for health promotion. FP7-HEALTH-2013-INNOVATION-1. EU research should aim to identify, develop and better understand innovative approaches to reduce sedentary behaviour and enhance the level of physical activity in the population. Research should include the evaluation of innovative on-going initiatives that reduce sedentary behaviour, enhance the level of physical activity combined with dietary or other interventions. In this context, research should include the identification of "good practices", as well as the analysis of their economic and social benefits and impact. Correlates will have to be detected (such as cultural, environmental, economic, psychological and others) that inhibit or promote the individuals capacity to increase physical activity, reduce sedentary behaviour and self-regulate their dietary or other relevant behaviour. Research may cover various areas affecting lifestyle (e.g. sports, health, education, transport, urban planning, working environment, leisure) as well as different intervention levels (local, national, European). As a social innovation it should address the role of diverse public and private entities, such as business, including social enterprises, civil society organisations and public authorities, as well as their interaction. The views of potential end-users should be integrated in the design of the project as well as the methodology for assessing impact and outcomes throughout the project. The project should have a strong communication strategy.

5. FP7 call keywords in the context of PANH projects
Primary Outcomes
Secondary Outcomes (Impacts)
Output & Results
Health
Transportation
Social
Environmental
Economic Benefits
Convenience Access, destinations, land use
Safety Perceived and actual
Hurdles & Incentives Cost, culture
Infrastructure Facilities, built environment
Programs Marketing, education
Levels of Active Travel
Input
Policy
Politics
Advocacy
Research
Leadership
Resources
External Factors
Benefits and Risks of Active Travel
reduce sedentary behaviour
physical activity
on-going initiatives
"good practices",
economic and social benefits and impact
Correlates
Health
transport, urban planning
intervention levels
the role of diverse public and private entities
end-users
methodology for assessing impact
13/11/2018

6. FP7 call title: Social innovation for health promotion
What could possibly be innovative about walking and biking? Judging from footprints discovered on a former shore in Kenya, it is thought possible that ancestors of modern humans were walking in ways very similar to the present activity as many as 1.5 million years ago. Bicycles were introduced in the 19th century in Europe.
13/11/2018

7. First proposal idea (by others)
Building on WHO‘s HEAT tool, provide decision makers with better impact assessment tool to make the case for active transport
Decision makers
Health impact assessment
Input
Secondary Outcomes (Impacts)
Leadership
Health
Policy
Transportation
Politics
Benefits and Risks of Active Travel
Advocacy
Environmental
Research
Social
Resources
Economic Benefits
External Factors
13/11/2018

8. Need for innovation in active transport research
Improve HIA:
Safety of AT
Substitution of PA between transport and other domains
Effectiveness of measures to promote AT
Understanding AT behavior, determinants, etc.
Interdisciplinary research between transport, planning, health (and others)
Scientifically robust, data driven research
User-friendly tools for implementation
13/11/2018

9. Active transport research today
Simplistic claim (working hypothesis): Active transport is dealt with by two fields which work rather separated, but could benefit tremendously from each other. Innovation: Integrated, interdisciplinary approach to active transport
Transport and urban planning
Health
Primary Outcomes
Secondary Outcomes (Impacts)
Output & Results
Health
Transportation
Social
Environmental
Economic Benefits
Convenience Access, destinations, land use
Safety Perceived and actual
Hurdles & Incentives Cost, culture
Infrastructure Facilities, built environment
Programs Marketing, education
Levels of Active Travel
Input
Policy
Politics
Advocacy
Research
Leadership
Resources
External Factors
Benefits and Risks of Active Travel
13/11/2018

10. The SHAPES study
Prospective tracking of Belgian cyclists‘ exposure and accidents to derive accident risks per km.
13/11/2018

11. Proposed PASTA framework
Primary Outcomes
Secondary Outcomes (Impacts)
Output & Results
Health
Transportation
Social
Environmental
Economic Benefits
Convenience Access, destinations, land use
Safety Perceived and actual
Hurdles & Incentives Cost, culture
Infrastructure Facilities, built environment
Programs Marketing, education
Levels of Active Travel
Input
Policy
Politics
Advocacy
Research
Leadership
Resources
External Factors
Benefits and Risks of Active Travel
WP1: Review of AM state-of-the-Art
WP2: Assessment of AM in 7 case study cities
WP3: Longitudinal study on correlates of AM and evaluation of top measures
G1: Identification of effective policies to promote AM
G2: Understanding correlates and quantification of effects on AM, overall PA, and injury risk, etc.
WP4: Health impact assessment model and tool
G3: Quantification of health impacts and publication of a practice-ready HIA tool
WP5: Good practices and recommendations
WP6: Communication and dissemination
G4: Increased uptake of AM as an innovative approach to increase PA, by policy makers and practitioners based on scientific evidence
13/11/2018

12. Implementing the proposed project

13. PASTA WP3 longitudinal study
Main research questions:
Relationship between active transport and overall physical activity
Relationship between active transport and injury risk
Relationships between various factors (determinants, correlates) and AT
Inputs for HIA
A few givens:
7 cities
Opportunistic sample
Online survey
Longitudinal design
Evaluation of top measures
Add-on modules
13/11/2018

14. PASTA WP3 longitudinal study
7 cities
General population, some modes oversampled
Continuous recruitment campaign (Media, Social-media, Events etc.)
Top-measure target populations, i.e. car-sharing, e-bikes, etc.
Rolling online registration
Crash location audit
Control location audit
Crash Q
Baseline Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Add-on registration
Accelerometry
GPS
App
Air Pollution
Health effects
Time
13/11/2018

15. PASTA WP3 longitudinal study
Two main challenges to start with:
Define questionnaire contents
Define follow-up design
Develop conceptual framework to identify factors of interest
Balance contents and follow-ups to suit research needs without over- burdening participants
Baseline Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
13/11/2018

16. Developing a conceptual framework for PASTA

17. Original framework from Ogilvie, 2011: iConnect Study
„..\Google Drive\PASTA\Literature\WP3\Iconnect\Evaluating Infrastructure for c&w_Iconnect Ogilvie AJPH 2011.pdf"

18. Conceptual framework for PASTA (inspired by Ogilvie, 2011)
Factors (determinants, correlates, mediators)(WP1-3)
Individual Decision Process (mode choice, trip choice, PA choices)(WP3)
Observed behaviour
(over time)(WP3)
Imputed impacts
(WP4)
Individual factors
Physical activity behaviour
Socio-demographics
Domain
Trans
Rec.
Work
Home
Time tT tR tW tH
Intensity
Mod
Vig 
Preferences, attitudes, knowledge, beliefs
Intention 
Transport options
Habit strength
Health benefits from PA
Evaluation of avail. choices:
Utility-maximization,
disutility minimization
Decision
Household,
workplace
Travel behaviour
Plus routes, origin-destination
Environmental impacts AP, CO2
Social environment
Mode W B T D
Time tW tB tT tD
Dist. dW dB dT dD
Purpose
Trans
Rec. 
Culture, trends, general attitudes, peer support
Health risks from AP
Physical enviroment
Conceptual framework for the study of active travel and physical activity behavior in PASTA WP1-4
Factors include determinants (aka causal factors) and correlates of a decision process, which decides over mode choice, whether or not to travel, and whether or not to engage in physical activities. (Roughly, WP1 will focus on identifying the relevant factors, WP2 will provide data for factors „beyond the individual level“, and WP3 will collect data on factors „on the individual level“).
Most of these factors can also function as mediators, such as they can affect how other factors affect the „decision“. (Need example for Macro contextual factor)
Each decision is the result of intention, habit strength and an assessment of utilities and dis-utilities that come along with a certain choice. (There are several models being used for how to think about this. Which one we display in the graph may be defined by which (intermediary) factors we decide to collect in the WP3 survey, if any. Ogilvie cites the following models as popular: social cognitive theory, theory of planned behavior, transtheoretical model. These, however, do not readily consider environmental factors. A behavioural economics (?) model maximizing utility may be a useful complement)
The behaviors of interest (measured) determined by the decision are physical activities (PA) and travel behavior.
PA is assessed by (certain/4?) domains, as duration (time), and by intensity (moderate, vigorous)
Travel behavior is assessed by mode (walk, bike, transit, drive), as duration and distance, and by trip purpose (transport, recreation, other?). In addition, route information is collected.
Safety relevant events are tracked over time (crashes, close-calls, injuries). (only from travel??)
In WP4, a number of impacts are imputed, based on WP3 data or equivalent data from other sources. Also more generally such computations are developed to be used with custom data (HEAT tool).
Once we agree on the core conceptual framework, this can be used for several additional purposes:
Illustration of main research questions (see separate slide)
Illustration of evaluation of „top measures“ (see separate slide).
There are a large number of (possible) secondary research questions, which should be illustrated in a separate version of this diagram
There are several add-on modules which focus on specific elements of this diagram. These should also be illustrated in a separate diagram.
The general factors displayed here consist of numerous more specific factors. The specific factors need to be defined to be able to design questionnaires and collect data from other sources. For this purpose, the crude structure for factors should be exploded. This is best done outside of power point in mind mapping software (i.e. concept maps or similar )
Feedback loops, or more generally, specific mechanisms of interest (research questions!?) should be illustrated in detail in separate diagrams.
Various intermediate concepts may diserve more detailed (exploded) illustrations, i.e. decision process, safety, etc.
COLLECTION OF FEEDBACK
I like the general approach. It’s hard to tell where arrows start and end, and it seems some are missing regarding for example BE and social context will have impacts on the individual factors you have listed. I can’t tell what the light blue lines mean in the last diagram, especially the vertical one.
- Excellent start.  A few comments so far.
For the figure, individual, social and physical factors there is generally an association and perhaps that needs to be reflected with arrows.  Currently it could be perceived they are independent an influence decisions.
Make the arrows you have straight (not diagonally), it makes it less messy.
Built enviroment, i.e.
Transport supply cycling/walking infrastructure, land use
Injury risks from travel
Safety events
Natural enviroment, i.e.
Topography, weather, green space
Safety-events
Crash
Injury
Close call
Net health benefits from active travel
Safety related factors
Macro context

19. Identifying Behavior theory frameworks of relevance for PASTA

20. PASTA Theoretical Behaviour Framework of Active Travel (WIP)
Society
Policies, Systems, Instituions
Community
Social environment
Physical environment
Inter-Individual
Individual
Needs, Experiences, Perceptions, Emotions
Extended Micro-economic Framework
Subjective access and barriers
Socio-demographics
Household
Social Norm
Extended Theory of Planned Behavior
Attitudes
Utility
Peers
Subjective Norm
Behavioral Intention
Decision
Behavioral control
Habit
Actual Behaviour

21. Physical activity behaviour Community
Conceptual framework for PASTA, revised behavioural framework ( )
Ecological framework
Observed behaviour
Imputed impacts
Individual
Policies
Socio-demographics
Policies
Home location,
Work location
Employment status
Physical activity behaviour
Community
Social Environment
Behavioral framework
Social norm
Attitudes, subjective norm, behavioral control
Health benefits
from PA
Social support
Needs, Experience,
Perception, Emotions
Physical enviroment
Travel behaviour
Environmental impacts AP, CO2
Transport options
Behavioral Intention
Built enviroment, infrastructure,
land use
PASTA Project framework
Utility
Health risks from AP
Natural enviroment,
topography, weather, green space
Decision
Subjective access
Safety events
Injury risks from travel
Habit strength
Net health benefits from active travel

22. Identifying questionnaire contents based on conceptual framework for PASTA
… and several other criteria:
Top research needs vs. other interesting stuff
Exact phrasing of questions and choices
Reuse existing items/questionnaires vs. inventing better ones
Visual presentation, difficulty of questions
Overall volume, level of detail
Distribution across follow-ups
13/11/2018

23. Developing a follow-up design for PASTA

24. Original follow-up design SHAPES study
Cyclists only
Baseline Q
Weekly follow-up Q (very short)
- In case of accident, more detailed Q
- 1 year, 1200 participants, 64 injuries
13/11/2018

25. Differences PASTA vs. SHAPES
All four travel modes
Broader interest, incl. physical activity, larger burden on participants
Technically more refined, lower burden on participants
Goal: 2000 participants per city, over 2 years
13/11/2018

26. Considerations about follow-up design
General population, some modes oversampled
Continuous recruitment campaign (Media, Social-media, Events etc.)
Top-measure target populations, i.e. car-sharing, e-bikes, etc.
Rolling online registration
Crash location audit
Control location audit
Crash Q
Baseline Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Time
Longitudinal research questions in PASTA:
Changes in AT and PA overtime
Exposure adjusted crash risk
Effects of top measures (interventions)
Frequency and distribution of FU?
Time spans covered by questions?
Contents? Time varying correlates?
Comparability to baseline?

27. Considerations about follow-up design
Baseline Q
Survey design
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Time windows
Childhood
Long term
Since baseline/last follow-up
Since baseline/last follow-up
Recently
Last week
Last week
Currently
Yesterday
Yesterday
1 trip
1 trip
Distribution patterns
Weekly
Monthly/Quarterly
7 random weekdays
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Follow-up Q
Mon
Wed
Sat
Tue
Sun
Alternative questions
Not exact wording
During your childhood, which of the following travel modes did you use regularly (and for what purposes)?
In the last year, on average, how much did you walk per week?
Since [data of last FU], how much did you walk on average per week?
In a typical week, how much do you walk on average?
Last week, how much did you walk in total?
Last week, how much did you walk in total?
Yesterday, how much did you walk in total?
Yesterday, what trips did you take? (travel diary)
Yesterday, what trips did you take? (travel diary)
For trip X, could you please provide us with the exact route that you took? (map tool)

28. A) Temporal variation
Baseline Q
Follow-up Q
Follow-up Q Ø
Follow-up Q
Follow-up Q
Follow-up Q
Research questions (Type A)  using aggregated long term estimates of AM, PA, incidents. The accuracy of such estimates depends on the temporal variation of the variable of interest.
AT usage by mode
Longterm PA (overall or by domain)
Injury risk by mode
Main sources:
Season
Weekday/weekend
Weather
13/11/2018

29. B Relationships and between multiple items and their changes over time
Follow-up PA
Follow-up PA
Baseline PA
Follow-up PA
Follow-up AM
Follow-up PA
Baseline AM
Follow-up AM
Follow-up PA
Follow-up AM
Follow-up AM
Follow-up AM
Research questions (Type B)  using non-aggregated follow-up data. The accuracy of such assessments depends on the variation of the measure of interest, which is derived at every follow-up from more than one questionnaire item.
E.g. substitution effect between AT and PA
What do we need to know to define an optimal follow-up pattern?
Temporal variation in behaviors of interest AM PA
What is the relevant statistic ?
13/11/2018

30. Considerations about questionnaire contents and how they relate to baseline and follow-up questionnaires and possible comparisons (analyses)
Baseline Questionnaire
Baseline a cross-sectional assessment, i.e. contents by themselves should fullfill all needs for cross-sectional analyses (independent of follow-up contents): What should be in there vs. what can fit in there?
PA and AM overlap. Consider whether/to what extent items from travel surveys (travel diaries, etc.) should replace/overlap/complement items from health surveys (GPAQ etc.)
What time period do items estimate (long term average/current) and what time period do they ask for (typical/current/week/day).
Consider which correlates (i.e. additional items) need to be collected to serve all planned analyses
Follow-up Questionnaire
Consider to what extent selected baseline items can/should be kept/comparable in follow-up questionnaires (orange arrows)
What should be in there vs. what/how can it fit?
How many follow-ups (and what patterns) do we need to estimate accurate cummulative/long term averages ? (blue and green lines) I.e. accurately capture temporal variation ? (blue and green arrows)
How many follow-ups do we need to analyze changes over time (blue and green arrows) as well as relationships between AM and PA, on average and over time (red arrows)
Follow-up PA
Follow-up PA
Follow-up PA
Baseline PA
Follow-up AM
Follow-up PA
Baseline AM
Follow-up AM
Follow-up PA
Follow-up AM
Follow-up AM
Follow-up AM
Baseline Correlates
Time

31. Concluding Remarks - Conceptual frameworks:
Concluding Remarks - Conceptual frameworks: - key to successful grant applications? - starter or on-the-go? - Regression to the mean: - will many players, ideas, thoughts and attempts lead to an optimally balanced study protocol? - (and is it going to be good enough)?

32. Questions?