Although the measurement of physical activity is an area of investigation in its own right and our study was not designed to compare different measures of physical activity, we explored the possible
consequences of using different measures of physical activity in two papers.
In the first paper, we investigated which of a range of sociodemographic, health and geographic
characteristics were associated with either self-reported (‘reported’) or accelerometer-measured (‘recorded’)
estimates of daily time spent in MVPA.76We did this because our knowledge about the extent to which
the correlates of physical activity vary according to the approach to measurement is limited.77If different
measures were to identify different correlates, this would in turn suggest different determinants of change in physical activity and the possibility that different intervention effects might be detected with different
measurement methods. In the second paper, we set out to understand the characteristics of those whose
physical activity level was ranked differently according to the approach to measurement.78This is important
because such mismatches may complicate the assessment of the effects of interventions. Methods
Both papers used baseline cohort data from 2009. In the first paper,‘recorded’ daily time in MVPA was
derived as previously described (see Chapter 2, Derivation of key variables).76In the second paper, we
processed the accelerometer data to obtain average daily accelerometer counts per minute for each
participant.78Participants were further assigned to one of three groups according to whether their average
level of physical activity was (1) ranked equally (i.e. categorised in the same quartile) by self-report and accelerometer, (2) ranked higher (i.e. categorised in a higher quartile) by self-report than by accelerometer, or (3) ranked higher by accelerometer than by self-report. For both papers, participants needed to provide accelerometer data on at least 3 days to be included in analysis, and the analysis was carried out separately for men and women because previous research suggests that the correlates of physical activity differ by gender. Multiple regression models of recorded daily time in MVPA were adjusted for accelerometer wear time, because participants who wore the accelerometer for longer periods tended to accumulate more minutes of physical activity.
Results
The first paper showed that different correlates were associated with‘reported’ and ‘recorded’ daily time
spent in MVPA, and few variables were associated with both measures in either men or women.76
In men, four individual characteristics were significantly associated with reported MVPA. Men who had a standing or manual occupation as opposed to a sedentary occupation, who did not have at least degree-level education, who had access to a bicycle and who reported a mental component summary score above the lowest quartile reported substantially more MVPA. Different variables were significantly
associated with men’s recorded MVPA. Men who lived with additional adults in the same household,
or in areas with higher proportions of greenspace, achieved lower levels of recorded MVPA.
In women, three variables were significantly associated with reported MVPA. Women with access to a bicycle and with degree-level education reported more time spent in MVPA per day, while women in
households with at least one child reported less. In contrast, one variable– having at least degree-level
education– was associated with higher recorded MVPA. Both having access to a car and being overweight
or obese were associated with lower recorded MVPA. Although the directions of associations tended to be consistent across both measures for variables such as age, access to a bicycle and weight status, especially for women, not all of these associations were statistically significant.
In the second paper, we found that approximately one-third of participants were ranked in the same quartile with both physical activity measures, while one-third were ranked lower, and one-third ranked
higher, on one measure than the other.78In adjusted analyses, the physical activity of overweight or obese
individuals was less likely to be ranked in a higher quartile by an accelerometer than that of normal-weight individuals [odds ratio (OR) 0.54, 95% confidence interval (CI) 0.32 to 0.92] and twice as likely as that of normal-weight individuals to be ranked in a higher quartile by self-report (OR 2.07, 95% CI 1.28 to 3.34). These overall results were replicated in the separate analysis for women, but not in that for men. This suggests that individual characteristics such as gender and weight status may be associated with mismatches in estimates of physical activity between different measures.
Both analyses included a smaller number of men than women, which may have further reduced the power to detect associations in this group. In addition, the estimates of reported and recorded MVPA used in
these analyses were neither synchronous nor of matching duration, in that‘recorded’ activity was
measured over 7 days, whereas‘reported’ activity was captured in terms of weekly frequency and average
duration over the past 4 weeks and transformed to daily MVPA. However, it is unlikely that levels of activity differed systematically between these two time periods.
Interpretation
The results from these two papers illustrate some common themes. The first paper indicated that few of the sociodemographic characteristics investigated were associated with both recorded and reported measures of
daily time spent in MVPA, and the second paper showed that it was common for people’s activity to be
ranked differently according to the measurement method. These findings probably reflect differences in
the way activity is ascertained by questionnaires and activity monitors. Activity of‘moderate to vigorous’
intensity covers a very wide range of behaviours, such as cycling, swimming and team sports, which are undertaken in a variety of settings and may be differentially related to sociodemographic, health and contextual characteristics. Questionnaires collect information on physical activity behaviours, while devices measure physical movement. Although our experience, as well as that of others, suggests that pedometers or accelerometers capture walking adequately for most purposes, other devices or, indeed, self-reported measures may be required to capture a wider range of activities. At the same time, the different specificities of measurement techniques need to be weighed against the practical implications of using them in different situations. In some cases, it may be helpful to use a combination of reported and recorded measures to establish a more accurate and comprehensive assessment of context-specific behaviours.
For further details, see Panter et al.76and Tully et al.78