In this study we aimed to see if the SenseCam digital camera could be used in a travel research setting for public health purposes. The results from our convenience sample of volunteers show they are happy to wear the camera and generally remember to wear it (only forgotten in 3 out of 105 journeys). In this study participants remembered to wear the device more often than they remembered to enter a journey in the diary. From the participant interviews, protocols for preparing and instructing SenseCam wearers can be refined to further reduce 'lost' journeys and participant burden. The images give an objective measure of travel mode, suggesting that SenseCam has the potential to be a criterion measure for assessing journey mode.
We also aimed to compare self-reported journey duration and SenseCam recorded journey duration and the correlation between methods was strong (r = 0.92, p < 0.001). In physical activity measurement a value above 0.80 is said to demonstrate acceptable validity [20]. However, this study has indicated there is substantial disagreement between the measures and that journey duration is generally over-reported. This finding is in agreement with recent studies that found self-reported physical activity to be over-reported in comparison to accelerometer measured physical activity [33, 34]. The limits of agreement analysis (see Figure 3) suggests that error on reporting is only very weakly correlated to journey length but apparently may vary with mode, with bike journeys over-reported by a greater magnitude than driving or walking (see Figure 4). The wide interval for the limits of agreement and the large standard error of the estimate indicate a lack of precision and suggests that the SenseCam and self-report methods should not be used interchangeably to assess duration of individual journeys. The substantial fixed bias also reveals that the two methods do not agree well on average for a sample of journeys.
There are a number of possible reasons for the over-reporting of travel time in this study. Journey durations in the diaries were often rounded to the nearest 5 or 10 minutes, and it may be that there is a tendency to round up rather than down. Furthermore, the retrospective interviews revealed that participants are likely to report the 'door-to-door' journey duration, including some activities at either end of the journey, rather than the more specific information the researcher is seeking to gather: the physical activity researcher is interested in the time spent in motion (walking or cycling) and similarly, the environmental researcher is interested in time spent driving with the motor engine running. It is clear that there may be some disconnect between the question being asked and the information desired.
The following two case studies illustrate this point and show how the digital images can be used to stimulate discussion about the journey with participants and reveal where over-reporting may originate from. This is a feature of using SenseCam in this way that would not be possible using conventional methods;
Case study 1 - one participant reported a 25 minute car journey, however the SenseCam images revealed that the journey was in fact only 12 minutes 35 seconds. By reviewing the images with the participant we were able to determine that they had reported the time from exiting their door to arriving at school. However, between exiting the door and starting to drive they had spent almost 12 minutes getting their 2 children into the car, collecting coats and retrieving a forgotten lunch box. This resulted in over-reporting the journey duration by almost 99%.
Case study 2 - another participant reported a 20 minute cycle on their normal commute to work. However the images revealed the cycling only lasted 12 minutes 48 seconds. In interview it became clear that the extra time was spent looking for space to lock their bike up as the usual cycle-rack was full. The additional information about this cycle journey may have been undetected by other methods.
These examples suggest that there may be some systematic bias in the travel diary encouraging the over-reporting of journey time. However, there is also likely to be random error at an individual participant level due to differences in accuracy of diary completion. This is because the precision and accuracy with which journey time is remembered and then reported will vary from person to person, from day to day and from journey to journey [35]. That some participants are likely to report more accurately than others was illustrated by the fact that 11 participants used the accompanying pocket diary reminder which may have improved accuracy while 9 did not. On 4 occasions the researchers observed the participants completing the travel diary when they arrived to collect it. It is possible that these journeys would be recalled less accurately than those recorded on the day of travel as per the protocol.
Implications
The average over-report for all mode journey duration in this study was 154 seconds (95% CI = 89 to 218 s). In this study there was an average of three active travel (walking or cycling) journeys per person per day. This means that 462 seconds or 7 minutes 42 seconds of physical activity per person per day was reported but was not happening. This translates to almost 54 minutes per week, or 36% of the 150 minutes of moderate intensity activity recommended in current international and national guidelines [7–9, 13]. The over-report on active transport journeys was slightly higher (results not presented) so this could be considered a conservative estimate.
Robust calculations of the measurement error for self-reported journey duration from future studies with larger samples and sufficient precision of estimation may allow for statistical adjustment (calibration) of existing data sets using appropriate regression methods. Using the images to determine the sources of error may allow for improved diary design and protocol.
Strengths and Limitations
Our analysis of the data is limited by having just 88 journeys from 20 participants and therefore indications of over-reporting will need to be tested in a larger sample. This pilot study used willing volunteers and whether members of a larger population representative study sample will wear the device and with the same high response rate is an important question for future studies. However, the same applies to their motivation to complete the diary. Furthermore, the protocol required participants to wear the device for just one day. The feasibility of using the device for multiple days (the normal protocol for the National Travel Survey) with the associated burden of charging the device each evening needs to be assessed. However, pilot studies in the computing domain have shown promise in terms of elderly populations independently using SenseCam over 2 week periods [36].
The device has certain limitations; there are particular settings where participants are not comfortable to wear it and in certain situations such as very low light the images do not always show the journey clearly. Images can also be lost when the lens is obscured by clothing or when participants forget to put it on. The 10 second epoch between image capture introduces a small error on our calculation of journey duration. This can be reduced to 5 seconds, though this compromises battery life.
The strength of the device is that we can verify journey mode with the image, rather than having to infer from another measure. The images also give us a detailed visual record of the journeys so that interesting or unexplained findings can be followed up.
Previous research has used GPS devices to investigate the error on self-reported travel behaviour [37–41]. We feel that the potential advantage of using SenseCam images is; (1) they are an objective measure of journey mode as discussed rather than inferred from GPS traces; (2) they can provide a more accurate measure of journey duration through the time-stamped images at 15 second intervals due to 'cold start' and lost signal on GPS [41, 42]; and (3) GPS creates large data sets which are difficult to clean, process and manage [41] whereas SenseCam images can be analysed with annotation software in a relatively short time. With practice a standard day (c. 2000 images and 4-5 journeys) takes 30 minutes to classify. In terms of larger sample sizes, we are developing machine learning algorithms that will semi-automate the annotation process and greatly reduce analysis time. We feel there is a need to investigate the synergistic value of using both devices in independent and integrated platforms.
Future Study
Having demonstrated the feasibility of this device in travel research, the next step is to calculate the size of potential over-reporting in a larger population representative study sample, with enough participants and journeys to have acceptable confidence on the calculations.
It may also be possible to use the images to sub-classify the different domains of each travel mode. For example walking could be classified by: (1) green-space, suburban or urban; (2) high, medium or low pedestrian levels; (3) well-lit or poorly lit; (4) obstructed (traffic works, etc) or clear. Cycling could be classified by: (1) high, medium or low traffic levels; (2) in or out of cycle lane; (3) well-lit or poorly lit. Vehicle travel could be classified by: (1) driver or passenger; (2) car, taxi, bus or motorcycle; (3) other e.g. train, tram, ferry. The implications of this information to travel planners or intervention workers should be assessed.
It may be that the greatest potential lies in using SenseCam in combination with other tools such as GPS, accelerometer or heart rate monitors to add visual contextual information of the behaviour to currently available activity, location and intensity data.