Multidimensional physical activity: an opportunity, not a problem

Abstract

Our research shows that no single metric will reflect an individual's physical activity adequately because multiple biologically important dimensions are independent and unrelated. We propose that there is an opportunity to exploit this multidimensional characteristic of physical activity to improve personalized feedback and offer physical activity options and choices that are tailored to an individual's needs and preferences.

Figure 1

The proportion of middle-aged men in this sample who either met or failed to meet each of the 12 recommendations included in this analysis. A full description of these recommendations has been provided previously (27). Briefly, we included recommendations and various versions of recommendations from the American College of Sports Medicine (ACSM), Center for Disease Control (CDC), American Heart Association (AHA), UK Department of Health (DoH), Institute of Medicine (IOM) and US Department of Health and Human Services (USDHHS).

Figure 2

One example of the discrepancy at the individual level between different physical activity recommendations based upon different physical activity characteristics (27). Ranked individual data for physical activity energy expenditure is expressed as Physical Activity Level or PAL (Total Energy expenditure/Basal Metabolic Rate). The horizontal dashed line indicates a PAL-specific threshold of 1.6 (i.e., from the Institute of Medicine) whereas the shaded columns indicate where this specific participant also met the time/intensity recommendation from ACSM/AHA (i.e., either 5 days of moderate intensity activity or 3 days of vigorous activity per week).

Figure 3

Physical activity energy expenditure analysed and dissected according to a few selected potentially important physical activity characteristics and dimensions. In this example, two individuals have similar scores for overall physical activity energy expenditure but they have accumulated physical activity in very different ways. A, Physical Activity Level (PAL); B, time engaged in physical activity > 3 metabolic equivalents (METs) accumulated in bouts of at least 10 min; C, time engaged in physical activity > 6 METs; D, time spent below 1.5 METs (sedentary time). As demonstrated in the summary, using one descriptor alone and in isolation will lead to a very different picture regarding physical activity status.

Figure 4

Heterogeneity in physical activity across various physical activity dimensions (26). A, PAL versus daily time engaged in physical activity > 3 METs accumulated in bouts of at least 10 min; B, PAL versus daily time engaged in physical activity > 7.2 METs accumulated in bouts of at least 10 min; C, PAL versus daily time engaged in sedentary activities as a proportion of the waking day (i.e., below 1.5 METs accumulated on a minute-to-minute basis). Pearson correlations with 95% confidence intervals are reported. D-E shows the same relationships but with quadrants superimposed and highlighted (see text for details).

Figure 5

A simple representation for physical activity profiles across selected physiologically-important dimensions. As described previously (26), each profile captures five different physical activity dimensions for five participants and demonstrates how a multidimensional profile is more revealing than a unidimensional score. For example, participants 2 and 8 have similar physical activity energy expenditure (PAL) but differ for other dimensions which could be important for health. Participants 28 and 75 are similar for sedentary time but differ for many of the other dimensions (including PAL). In this simple iteration, we have used green/red to indicate the clear achievement/failure to achieve each threshold; with amber indicating that values were within 20% of the target value.

Figure 6

A schematic illustrating some of the advantages and opportunities from multidimensional physical activity profiling. This theoretical depiction includes three individuals with distinct physical activity patterns coupled to a simple iterative process to build a basic profile across four physical activity dimensions. Even this simple approach produces opportunities – and more sophisticated profiles will be able to include other considerations such as magnitude based scores and/or performance in other physical activity dimensions.