Relative and Absolute Intensity
Albert Einstein taught us that “everything is relative”—what we see, hear, and feel are relative to our own physical, psychological, or spiritual context. Our experience of physical activity (PA) is no different, with “relativism” particularly pertinent to the perception of intensity. The intensity of PA can be expressed in either absolute or relative terms. Absolute intensity refers to a one-size-fits-all approach with each individual evaluated against the same cut point (eg, 3–6 metabolic equivalents of task [METs] for moderate-intensity PA), whereas relative intensity is assessed relative to the individual’s own capacity or capability.1
Intensity plays a crucial part within the World Health Organization 2020 PA guidelines, which recommend adults participate in a minimum of 150 minutes per week moderate-intensity or 75 minutes per week vigorous-intensity PA.2 Self-reported activity reflects perceived intensity of effort,3 thus likely aligning with relative intensity, while accelerometer-assessed activity usually aligns with absolute intensity. Hence, whether accelerometer or self-report measures of PA are used to evaluate adherence to the guidelines may have an impact on estimates of prevalence of meeting guidelines. For example, as people age, they have higher levels of self-reported PA (likely relative intensity) but lower accelerometer-derived activity (absolute intensity), reflecting changes in PA perceptions with age.4
Accelerometer Measures of Intensity
Accelerometer-assessed PA is now commonplace in large epidemiological cohorts,3,5,6–10 and advancements in accelerometry data processing enable direct measures of movement (acceleration, mg) to be extracted as a marker of PA intensity comparable across devices and populations.11 This assessment of intensity usually remains focused on absolute intensity, typically classified as light and moderate or vigorous, using cut points based on group calibration studies.7,12 However, population-specific cut points are available that enable expression of intensity relative to the lower capacity of people who are older13 or have chronic disease.14 For example, an individual with a reduced exercise capacity will be working at a higher relative intensity for a given movement than someone with preserved exercise capacity performing the same activity.
Accounting for individual characteristics is the basis of personalized medicine, including individualized exercise training regimes, such as those seen within exercise-based rehabilitation programs where relative intensity is a core principle (eg,15). Despite this, the measurement and evaluation of free-living PA by accelerometry have remained predominantly based on summary cut points of intensity developed in healthy populations. Consequently, the same criteria have been applied to individuals with varying health statuses, chronic conditions, and physical capabilities (eg,16). This one-size-fits-all approach to PA in chronic disease populations has been debated among the scientific community,17,18 but there is consensus that appropriate interpretation of accelerometry data is pivotal to developing appropriate interventions and finding meaningful associations with health outcomes.
Contention comes as intensity cut points are inherently specific to the calibration protocol and participants, in which the cut points were generated. This introduces error into the resultant PA estimates.19,20 It is possible to account for group-level differences in exercise capacity by generating population-specific cut points in a calibration substudy21; or to account for individual differences in movement economy by expressing activity in terms of the amount of time spent at an intensity equivalent to an individual’s reference activity, for example, ArteACC22; or to account for both exercise capacity and individual differences in movement economy by developing cut points at the individual level through individual calibration.23,24 These approaches can increase time and resource burden and do not address the inherent protocol-specific nature of cut points,22 but the trade-off is that estimates may be more representative of the population or individual’s actual (relative) PA intensity and/or movement economy. Furthermore, simply determining relative intensity cut points incur the same limitations as absolute intensity cut points. For example, outcomes are restricted to predetermined relative intensities (eg, moderate and vigorous) and substantial proportions of the data are ignored.19,25
Individually calibrating accelerometry-derived PA metrics with existing tests of maximal exercise capacity may be useful for examining how physically active an individual is. By examining the intensity of a range of a person’s most active time periods across the day (ie, MX metrics),25,26 relative to their maximum intensity (acceleration), the need for cut points can be avoided, and the intensity distribution of the whole activity profile can be described. In this paper, we explore the potential for expressing the intensity of PA in both absolute terms and relative to movement intensity captured during a maximal field-based test of exercise capacity. This builds on previous approaches by (1) considering the intensity of the whole PA profile, rather than focusing only on predetermined intensities, (2) accounting for differences in both exercise capacity and movement economy, and (3) enabling complimentary description of the absolute and relative intensities of PA. Although some limitations associated with accelerometry persist,27 the use of individual-level data from the incremental shuttle walking test (ISWT) to determine relative intensity mitigates the impact of differences in movement economy.
To demonstrate the utility of this approach, we apply this approach to a chronic disease population and present case studies depicting patient profiles according to their exercise capacity (“can or cannot do”) and free-living PA (“does or does not do”).
We focus on a chronic respiratory disease (CRD) population to illustrate our concept for a new methodological and analytical approach synchronizing accelerometry with field-based tests of exercise capacity, building on previous work.28–30 In a recent systematic review of studies in chronic obstructive pulmonary disease, the need for a standardized approach to examine free-living relative PA intensity to optimize interventions and ensure patient safety was acknowledged.31 A reduced exercise capacity and low levels of PA are common features of individuals living with CRDs,32–34 not only compared with healthy adults35 but also other chronic disease populations.36 Furthermore, routine measures of exercise capacity are embedded within their clinical care, such as during pulmonary rehabilitation15; this offers an opportunity to implement a protocol for expressing intensity relative to individual maximum and/or developing relative intensity cut points.
Deriving Relative Intensity From Accelerometry During Walking Tests
The ISWT,37 a multistage maximal walking test (12 levels, 12 min, and maximum distance 1020 m), is routinely conducted within many pulmonary rehabilitation programs (see Supplementary Material [available online]). Wearing an accelerometer during the ISWT enables an individual’s personalized acceleration levels to be determined for each of the incremental walking speeds (level 1: 1.80 km/h to level 12: 8.53 km/h).29 Importantly, the maximal acceleration a person can sustain for the duration of a complete level (1 min) in the ISWT can be used to express their free-living PA intensity relative to their personal maximum sustained acceleration. Within a healthy population, a similar approach could be taken using the “multistage fitness test.”38
To illustrate how to derive relative intensity, data from 17 individuals (60% male, mean [SD] age: 46.2 [13.4] y, height: 1.68 [0.10] m, weight: 59.4 [14.7] kg, body mass index: 21.2 [6.0] kg/m2) living with posttuberculosis lung disease (pTBLD) in Uganda who wore ActiGraph wGT3X-BT accelerometers (ActiGraph) at the waist during an ISWT and 1 week of free-living are utilized.39 Ethical approvals from the Mulago Hospital Research and Ethics Committee (MHREC1478), Kampala, Uganda; the Uganda National Council for Science and Technology (SS5105); and the University of Leicester (22349) were obtained. All participants provided written informed consent.
Herein we demonstrate what can be gained from assessing both the absolute and relative intensities of free-living PA. The acceleration associated with each ISWT level, and the maximum 1-minute rolling average of acceleration sustained during the test was recorded for each patient. From the free-living data, the acceleration value above which the person’s most active continuous 1- to 30-minute bout (MXCONT, where X = time period) and the person’s most active accumulated 1 minute to 12 hours (MX) at any point across the 24-hour day were determined.25 The MXCONT and MX during free-living PA were expressed in absolute terms (acceleration, mg) and (percentage maximum sustained acceleration; see Supplementary Material [available online]).
Figure 1 plots the intensity of the most active continuous bout of PA lasting 1 to 30 minutes (MXCONT; blue polygon) on a radar plot25 in (1) absolute terms and (2) relative terms for the same individuals living with pTBLD. The gray dashed lines on plot A show the mean acceleration output for ISWT levels 1 to 8, with the innermost ring being level 1 (1.8 km/h) and the outermost ring being level 8 (6.1 km/h; see Supplementary Material [available online]). In absolute terms, on average, individuals living with pTBLD did not sustain even a 1-minute bout per day of moderate-intensity (3 METs) activity (Figure 1A) and only 2 minutes at a pace equivalent to ISWT level 3 (3.0 km/h). Indeed, the maximum sustained acceleration of some of the individuals was below this moderate cut point highlighting the futile nature of using it to investigate PA levels in such populations. In relative terms (Figure 1B), on average, individuals living with pTBLD spent 1 to 10 continuous minutes per day at or above 40% of their maximum sustained acceleration (∼moderate intensity). The same pattern is evident when considering PA accumulated across the day (MX; Supplementary Figure S2 [available online]), with only ∼15 minutes per day accumulated across the day at an absolute moderate intensity, but ∼45 minutes approximating a relative moderate intensity.
—Intensity of the most active continuous 1 (M1CONT) to 30 (M30CONT) minute bouts of free-living activity in absolute (mg, A) and relative (percentage maximum, B) terms. The gray dashed lines on plot A (indicated by the arrows) show the mean acceleration output for ISWT levels 1 to 8, with the innermost ring being level 1 (1.8 km/h) and the outermost ring being level 8 (6.1 km/h). The red dashed circles (labeled moderate and vigorous) show intensities indicative of specified absolute (A) and relative (B) intensities. The absolute moderate (3 METs) and vigorous (6 METs) intensities are taken from calibration studies as previously described.45,47,48 Relative intensity is based on percentage of maximum sustained acceleration in the ISWT and should primarily be interpreted in this way, for example, percentage of maximal walking speed. Additionally, in this sample, percentage maximum acceleration approximated percentage predicted VO2 reserve (VO2 minus resting; Supplementary Figure S1 [available online]). In relative terms, 40% of maximum VO2 reserve is indicative of moderate intensity and 60% of maximum VO2 reserve is indicative of vigorous intensity.1 The intensity of the M1CONT bout during free-living physical activity relative to the intensity of the 1-minute ISWT maximum indicates the extent to which the maximum sustained acceleration the individual can sustain in test conditions is experienced by them in everyday life. For bouted physical activity (eg,9), for the most active continuous bout (MXCONT), brief breaks were allowed with 75% of the specified period having to be above the acceleration value. Note. ISWT indicates incremental shuttle walking test; MET, metabolic equivalent.
Citation: Journal of Physical Activity and Health 20, 4; 10.1123/jpah.2022-0590
Relative Intensity Case Studies: “Can/Cannot Do, Does/Does Not Do”
Before the measurement of PA became commonplace within CRD research, there was an assumption that improving exercise capacity, such as through pulmonary rehabilitation, would translate into increased free-living PA. It has become clear that this is not necessarily the case, and service users have a differential response between exercise capacity and PA following pulmonary rehabilitation.40,41 For example, the likelihood of improving PA after pulmonary rehabilitation has been shown to increase with greater exercise capacity at baseline.42 Stratifying patients by exercise capacity and PA may have value when tailoring interventions.
Studies stratifying individuals have typically done so by dichotomizing exercise capacity (what an individual can or cannot do) and PA (what an individual does or does not do) to form quadrant categories.43 Someone categorized as “can do, does not do” (preserved exercise capacity and low PA) may require behavioral support to be more physically active (ie, to utilize their capacity in daily life). An individual classified as “cannot do, does not do” (reduced exercise capacity and low PA) may need an additional intervention like pulmonary rehabilitation (ie, to increase their capacity to be more physically active).44 However, similar to the absolute intensity cut points applied to accelerometer-derived free-living PA, stratifications of what it means to be classified as “can do” or “cannot do” and “does do” or “does not do” have taken a largely one-size-fits-all approach.
By synchronizing the measurement of exercise capacity (walking test) with PA (accelerometer worn during the walking test), it becomes possible to take this a step further, classifying which “can/cannot do, does/does not do” category is most appropriate at an individual level. To illustrate this, we provide individual case studies (Figure 2), from the same trial,39 depicting and describing how performance of the ISWT and ISWT-derived PA intensity cut points can be applied to the “can/cannot do, does/does not do” paradigm.
—Demonstration of utility of the ISWT to facilitate determination of the relative intensity of accelerometer-assessed free-living physical activity. The intensity (mg) of the most active continuous 1 (M1CONT) to 30 (M30CONT) minute bouts of free-living activity for 4 individuals living with pTBLD: A “can do, does not do”; B “cannot do, does do”; C “can do, does do”; and D “cannot do does not do.” The dashed red circles show the accelerations associated with each level of the ISWT. The innermost dashed red circle is level 1 (1.8 km/h), with each increasing circle representing the incremental levels completed during the ISWT. The outermost circle is the final completed level of the ISWT. The blue polygon shows the intensity of the most active free-living continuous bouts of physical activity lasting 1 to 30 minutes. Thus, the intensity of the individual’s free-living activity can be interpreted with respect to their personalized accelerations achieved during the ISWT. Note. ISWT indicates incremental shuttle walking test; pTBLD, posttuberculosis lung disease.
Citation: Journal of Physical Activity and Health 20, 4; 10.1123/jpah.2022-0590
Case study A (Figure 2A) has a high maximum acceleration during the ISWT, 290 mg (outermost red dashed circle), elicited while walking briskly at ∼6 km/h (level 8 of the ISWT). However, the intensity of their most active free-living continuous 1 minute is very low (46 mg), both in absolute (lower than the intensity of the second level of the ISWT: 2.4 km/h, and the moderate-intensity cut point of 100 mg39) and relative terms (<20% of their ISWT maximum). This individual would be categorized as “can do, does not do.” In contrast to case study A, case study B (Figure 2B) has a low maximum acceleration (97 mg), below the moderate-intensity cut point of 100 mg.45 In further contrast to case study A, case study B uses their capacity in daily life with their most active continuous 5 minutes (74 mg) at > 75% of their ISWT maximum. This individual would be categorized as “cannot do, does do.” Case study C (Figure 2C) has a capacity (outermost red dashed circle; ∼160 mg) that enables moderate absolute intensity activity while utilizing this capacity in daily life with 5 continuous minutes spent at ∼80% (130 mg) of their ISWT maximum. This individual would be categorized as “can do, does do” but at a greater absolute intensity than case study B. Finally, while case study D has a similarly low maximum ISWT acceleration to case study B (97 mg), the intensity of their most active free-living continuous 5 minutes (31 mg) is low in relative (∼32% maximum) as well as absolute terms (lower than the intensity of the first level of the ISWT: 1.8 km/h, 59 mg). This person would be categorized as “cannot do, does not do.” The same patterns are seen for activity accumulated across the day (Supplementary Figure S3 [available online]).
Translation Into Practice: Stratifying to Appropriate Interventions
The plots we have used (MX plots; Figure 2) are helpful for researchers to identify the continuous (MXCONT, Figures 1A and 2) and accumulated (Supplementary Figures S2 and S3 [available online]) time spent at each walking speed of the ISWT as they relate to free-living PA (at absolute and relative intensities). A more user-friendly format may better facilitate practical relevance. We propose an initial concept using a “glass half-full” analogy (Figure 3). Here, the capacity of the individual is represented by the size of the glass, and the extent to which the glass is filled with water represents how much PA an individual does. The optimal scenario is a tall glass (high exercise capacity) full of water (the individual is physically active relative to their capacity). We have transformed the MX plots in Figure 2 into the “glass half-full” concept (Figure 3) to illustrate this.
—Example translation of an individual’s absolute and relative physical activity intensity and actual physical capacity in terms of the 4 categories: A “can do, does not do”; B “cannot do, does do”; C “can do, does do”; and D “cannot do, does not do.”
Citation: Journal of Physical Activity and Health 20, 4; 10.1123/jpah.2022-0590
Case study A, categorized as “can do, does not do,” can be visualized as a tall, fairly empty glass of water. This individual is capable of continuous brisk walking (∼5.6 km/h) but does not engage in continuous walking even at a slow-to-moderate pace (∼3 km/h) in their daily life. For this person, the priority intervention may focus on increasing PA to prevent a potential decline in exercise capacity.
Case study B, categorized as “cannot do, does do,” can be visualized as a short glass of water, fairly full. This individual is only capable of continuous slow-to-moderate walking (approximately 3–3.6 km/h) but is physically active within their capacity. For this person, the priority intervention may focus on increasing exercise capacity to enable maintenance of activities necessary for daily living.
Case study C, categorized as “can do, does do” with a capacity somewhere between case study A and case study B, can be visualized as a medium-sized glass fairly full of water. This individual is capable of continuous slow-to-moderate (but not brisk) walking and is physically active relative to their exercise capacity. For this person, the priority intervention may focus on increasing exercise capacity to facilitate maintenance or further increases to their PA.
Case study D, categorized as “cannot do, does not do,” can be visualized as a short glass fairly empty of water. This individual is only just capable of continuous slow-to-moderate walking (∼3 km/h) but does not engage in this in their daily life. For this person, an intervention, or interventions, should aim to increase both exercise capacity and PA.
Strengths and Limitations
The key strength of this approach is the concurrent description of the absolute and relative intensities of the PA profile. Importantly, this approach considers the distribution of intensity across the PA profile rather than focusing only on predetermined intensities that may not capture PA in all individuals. Furthermore, the method also accounts for interindividual differences in accelerometer output at a given walking speed. Some of the limitations associated with accelerometry are not addressed by our proposed approach, for example, not capturing cycling, walking uphill, or carrying a load.27 However, the use of individual-level data from the ISWT to determine relative intensity mitigates the impact of differences in movement economy.
Summary
Markers of absolute and relative intensities of PA have different but complimentary utilities, with absolute intensity considered best for PA guideline adherence and relative intensity for personalized exercise prescription.46 Our proposed method of synchronizing accelerometry with the ISWT (or similar maximal exercise tests) using the MX approach allows both absolute and personalized relative intensities of free-living PA to be evaluated simultaneously. We have shown that existing absolute (one size fits all) intensity thresholds may not capture the PA performed by CRD populations. Our approach is able to generate “can do” or “cannot do” and “does do” or “does not do” classifications, facilitating the selection of appropriate individually tailored interventions.
We believe the next steps are as follows: (1) to determine the feasibility and effectiveness of using relative and absolute intensities in combination to personalize the approach for improving PA, physical function, and/or health; (2) to compare the sensitivity to change following interventions using this personalized approach to standard care; and (3) to explore use of this personalized approach to standard approaches in other long-term conditions and with other maximal tests, for example, the multistage fitness test in healthier populations.
Acknowledgments
The authors thank the participants for their involvement in the study. Data were processed using R package GGIR version 2.7.1. The processing code used to generate the free-living MX and MXCONT metrics is in Supplementary Material (available online). Radar plots were generated in R using open-source code available at: www.github.com/Maylor8/RadarPlotGenerator. This research was funded by the National Institute for Health Research (NIHR; 17/63/20) using UK aid from the UK government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK government. Professor Singh is an NIHR senior investigator. The views expressed in this article are those of the author(s) and not necessarily those of the NIHR, or the Department of Health and Social Care. The research was supported by the NIHR Leicester Biomedical Research Center (which is a partnership between the University Hospitals of Leicester National Health Service (NHS) Trust, Loughborough University, and University of Leicester) and the NIHR Applied Research Collaboration—East Midlands (NIHR ARC-EM). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. Trial registration number: ISRCTN18256843, September 12, 2019 (prospectively registered).
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