Rationale
Accurate measurement of active commuting and associated physical activity in free-living conditions is important for describing patterns in these behaviours and studying their determinants in populations. It is also essential if we are to have confidence in the effect sizes from intervention studies and for the attribution of subsequent health impacts. A validated, short, self-reported measure of time spent walking or cycling for transport was not available when data collection for our study began in 2009. Many studies have used comparatively simple measures of travel behaviour. These have often focused on either the main mode of transport, that is the mode used for the longest part of a trip (if more than one mode is used), or on the usual mode of transport, that is the mode used most frequently over a specified period of time. Neither approach is entirely satisfactory for estimating the physical activity associated with commuting because each entails simplifying the detail and complexity of travel behaviour. Focusing on the main mode of transport means that active travel may be underascertained, because actual commuting journeys,
especially those involving public transport, often involve more than one mode of transport (‘multimodal’
trips) and may involve walking or cycling at either end of the trip. Focusing on the usual mode of
transport, on the other hand, may fail to capture day-to-day variation in the mode, distance or duration of trips. Although some validation of the modes and frequency of trips recorded in detailed day-by-day travel diaries has been reported, it remains important to validate measures of time spent in active commuting
because this information can be used to quantify the health impacts of commuting.70
The selection of a‘gold standard’ method for validating self-reported measures in this field is challenging.
Direct observation of participants, as well as other methods such as wearable cameras, have been used in other, smaller studies, but it is unlikely that these would have been acceptable to participants or practical
to implement in a study of our size.71Likewise, the identification of walking and cycling from the data
captured by physical activity monitors is not sufficiently advanced to enable the time spent in active travel
to be confidently determined.72A similar problem exists with the use of GPS data alone. These have been
used to infer mode of transport from either the maximum or the average speed recorded in a trip, whereas in our study many participants commuted into city centre locations at low average speeds, often using a
We therefore developed a new method for obtaining objective estimates of the time spent using active and motorised modes of transport, by combining reported data on mode of transport with georeferenced combined heart rate and movement sensor data in a subsample of our study cohort. We then used these objective estimates to assess the convergent validity of self-reported estimates of time spent in active
commuting derived from our core questionnaire data.75
Methods
In order to generate objective estimates of time spent using active and motorised modes of travel, and total commuting travel time, it was first necessary to process the GPS data to identify commuting trips and stages within trips. Because the processing and cleaning of trip-level GPS data is a labour-intensive task, we selected a quasi-random sample of commuters from the larger subsample who provided combined heart rate and movement sensor and GPS data, as well as information about their commuting trips in the past week in both the travel diary and the core questionnaire, in either 2010 or 2011. We aimed to include at least 50 commuting trips for each of six combinations of mode of transport found in the cohort study: (1) walking, (2) cycling, (3) car or motorcycle only, (4) car in combination with walking, (5) car in combination with cycling and (6) bus with or without walking or cycling (see Chapter 4, Patterns of active commuting). Because relatively few participants had recorded walking all the way to or from work, reflecting our recruitment strategy and local commuting patterns, we achieved only 42 such trips for analysis.
We used the GIS to project the GPS data onto mapping information and to locate the home and workplace of each participant. We then inspected the GPS tracks to obtain an objective estimate of the total duration of each commuting trip by calculating the difference between the time at which participants left home or entered the building outline of their workplace. If the trip involved only one mode of
transport (a‘unimodal’ trip), it was added to the data set for analysis at this stage.
If a trip involved a combination of active and motorised travel, we needed to obtain objective estimates of the duration of each stage of the trip. For trips that combined walking or cycling with car travel, we were able to identify the time at which an active stage of the trip began from the combined heart rate and movement sensor data alone, but this proved impossible for trips that involved walking or cycling in combination with bus travel. We therefore cross-referenced the GPS data with the locations of bus stops, which enabled us to more clearly identify the start and end times of the bus stages of trips. Waiting times at bus stops before or after boarding were identified by GPS points with non-regular speeds in close geographic proximity. These waiting periods were excluded from analysis, along with any periods of longer than 5 minutes spent at a location en route (such as a school or shop) if no change in mode of transport was reported.
Once the different stages of commuting trips had been identified, we calculated our objective estimates of time spent using active and motorised modes of travel, as well as the total duration of the commuting trip. We then carried out three comparisons to assess the convergent validity of our self-reported measures of active commuting:
1. We compared estimates of the duration of commuting trips, and stages of trips, spent using active and motorised modes of transport, as well as total trip duration, all derived from the detailed travel diary, with the corresponding objective estimates.
2. We compared estimates of usual time spent using active modes of transport derived from the core questionnaire with the corresponding objective estimates.
3. We compared the two self-reported estimates, namely the estimates of usual time spent using active modes of transport derived from the core questionnaire with those derived from the detailed travel diary.
If data were of insufficient quality to allow modal transitions and destinations en route to be confidently identified, trips were included only in the analysis of total trip duration and excluded from the analyses of time spent using active or motorised modes.
Results
We carried out these comparisons separately for commuting trips that involved different combinations of modes of transport. In the first comparison these showed that in the travel diary, participants overestimated the time they spent walking or cycling to work by 1.13 minutes per trip on average
[95% limit of agreement (LOA)−7.67 to 9.95; p = 0.001]. The median duration of these trips was
38 minutes. The mean overestimation of active travel time was slightly larger in the subset of trips made by
walking or cycling alone (2.39 minutes per trip, 95% LOA−6.78 to 11.55), even though these trips
tended to be shorter, with a median duration of 17 minutes. However, the difference between objective
and self-reported estimates in this subset of trips was not significant (p= 0.25). For the subset of trips
involving active travel in combination with any motorised mode of transport, the magnitude of the
difference was smaller (mean+0.40 minutes per trip) but statistically significant (p = 0.001) (Table 5).
As we discuss in more detail below (see Interpretation), this suggests that participants are able to provide acceptably accurate estimates of their active travel time for trips that also involve other modes of transport
in a travel diary. In contrast, we found that participants’ estimates of time spent using motorised modes of
travel were subject to a substantially larger overestimation than were their estimates of active travel time,
with an average overestimation of 10.48 minutes per trip (95% LOA−13.62 to 34.58). Trips involving
motorised modes in combination with active modes appear to be responsible for this finding (mean
difference 14.49 minutes per trip, 95% LOA−8.86 to 37.84; median total trip duration 45 minutes).
TABLE 5 Validating self-reported measures of time spent commuting by different modes of transport, derived from a day-to-day travel diary
Quantity estimated
Number of trips
Mean
difference (SD) 95% LOA p-value
Time spent in active travel for trip or stagea
All active trips 136 1.13 (4.49) –7.67 to 9.95 **
Walking or cycling only 50 2.39 (4.68) –6.78 to 11.55 NS
Walking or cycling in combination with other modes 86 0.40 (4.23) –7.90 to 8.69 ** Time spent in motorised travel for trip or stagea
All motorised trips 139 10.48 (12.30) –13.62 to 34.58 ***
Car only 40 0.56 (6.04) –11.27 to 12.40 ***
Car or bus in combination with other modes 99 14.49 (11.91) –8.86 to 37.84 * Total trip duration
All trips 204 1.85 (8.75) –15.31 to 19.01 ***
Single mode used 90 1.40 (6.05) –10.46 to 13.26 ***
More than one mode used 114 2.21 (10.42) –18.21 to 22.62 ***
*, p< 0.05; **, p < 0.01; ***, p < 0.001; NS, not significant.
a Where only one mode was used, this represents the duration of the trip; where a combination of modes was used, this represents the duration of the active or motorised stage(s) of the trip. Distances and durations given were derived from objective estimates. The number of trips used does not equate to the sum of trips made by‘car only’ and ‘walking or cycling in combination with other modes’.
Agreement between travel diary and objective estimates of time spent using active and motorised modes and total trip duration. Statistically significant differences are in bold.
When we compared estimates of usual active commuting time derived from the core questionnaire with the corresponding objective estimates, we found differences of a similar magnitude to those observed in the travel diary comparisons. The difference was smaller for cycling, with an average underestimation of
1.12 minutes per trip compared with a median reported usual time of 17 minutes (95% LOA–8.67 to
6.44, n= 11 participants), than for walking, with an average overestimation of 2.37 minutes per trip
compared with a median reported usual time of 13 minutes (95% LOA–10.91 to 15.64, n = 16
participants); neither difference was statistically significant (p> 0.35). The questionnaire and objective measures showed stronger agreement for time spent cycling (with a concordance coefficient of 0.96) than for walking (with a concordance coefficient of 0.84).
In our third comparison, we found that the questionnaire estimates of usual active travel time were not significantly different from the mean duration derived from the trips reported in the travel diary (p> 0.1), with an average difference of less than 1 minute per trip for both cycling and walking.
Interpretation
When we compared estimates of active and total travel time from day-by-day travel diaries and from retrospective questionnaires to objective estimates, both self-reported measures performed acceptably well for our purposes. The mean differences between self-reported and objective estimates were relatively small, both for the detailed travel diary (an overestimation of approximately 1 minute per trip) and for the
retrospective travel record. These findings are similar to those of previous validation studies.71The results
of the travel diary comparison further suggest that where trips involved a combination of active and motorised modes of transport, participants tended to report the duration of the active stages more accurately than the duration of the motorised stages. The small mean overestimation in the self-reported measures indicates that such methods can provide a reasonably accurate estimate of aggregate active commuting time in a population. Nevertheless, the wide LOAs indicate a risk of substantial over- or underestimation at an individual level, which means that self-reported measures should be used with caution to infer aggregate weekly quantities of physical activity as part of commuting. For example, a 2-minute overestimation per trip corresponds to a 20-minute difference over a 5-day working week. The effect sizes of the most promising interventions to promote walking for transport are of the order of
15–30 minutes per week, which is directly comparable to the size of the measurement error suggested by
our analysis.27This suggests that evaluations of interventions that are anticipated to have relatively modest
effect sizes may have limited power to detect effects on time spent in active commuting if they rely on self-reported measures alone, because the potentially large measurement error may mask small but important real changes in behaviour.
For further details, see Panter et al.75