A prospective examination of the impact of high levels of exercise training on sedentary behaviour

Abstract

The aim of this study is to determine changes in sedentary behaviour in response to extensive aerobic exercise training. Participants included adults who self-selected to run a marathon. Sedentary behaviour, total activity counts and physical activity (PA) intensity were assessed (Actigraph GT3X) for seven consecutive days during seven assessment periods (−3, −2, and —1 month prior to the marathon, within 2 weeks of the marathon, and +1, +2, and +3 months after the marathon). Models were fitted with multiple imputation data using the STATA mi module. Random intercept generalized least squares (GLS) regression models were used to determine change in sedentary behaviour with seven waves of repeated measures.

Results:

Twenty-three individuals (mean ± S_x: 34.4 ± 2.1y, 23.0 ± 1.9% fat, 15 women, 8 men) completed the study. Marathon finishing times ranged from 185 to 344 minutes (253.2 ± 9.6 minutes). Total counts in the vertical axis were 1,729,414 lower one month after the race, compared with two months prior to the race (peak training). Furthermore, counts per minute decreased by 252.7 counts·minute⁻¹ during that same time period. Daily sedentary behaviour did not change over the seven assessment periods, after accounting for age, gender, per cent body fat, wear time, marathon finishing time, and previous marathon experience. This prospective study supports the notion that PA and sedentary behaviours are distinct, showing that sedentary behaviour was not impacted by high levels of aerobic training.

Introduction

Recently, much attention has been given to the ill effects of too much sitting and prolonged periods of sitting. Epidemiological evidence has linked high levels of sitting with cardiovascular disease, type 2 diabetes, breast and colon cancer, and depression (Thorp, Owen, Neuhaus, & Dunstan, 2011). It remains unclear whether physical activity (PA), and what dose of PA, attenuates the ill effects of too much sitting. Epidemiological and experimental research has suggested that those who engaged in daily exercise, or were physically active (Bakrania et al., 2016; Ekelund et al., 2016) were immune to the ill effects of too much sitting. However, previous epidemiological evidence has suggested otherwise (Duvivier et al., 2013; Owen, Healy, Matthews, & Dunstan, 2010; Peddie et al., 2013).

PA and sedentary behaviours are distinct; individuals can be both highly sedentary and highly active. Research has shown that active people (those who meet the PA guidelines: >30 minutes moderate- to vigorous-intensity physical activity (MVPA) on at least 5 daysηweek⁻¹) sit as much as those who do not meet the PA guidelines (Craft et al., 2012; Garber et al., 2011). Craft et al. (2012; Garber et al., 2011) examined sitting, standing, and non-exercise stepping time in adult women and found that time spent in these activities did not differ between women who met the PA guidelines, and those that did not. Further, for active women, time spent sitting, standing, and lying did not differ between active and inactive days (Craft et al., 2012). While this study is informative, and suggests that PA and sedentary behaviours are distinct in those who meet or do not meet PA guidelines, little is known from a longitudinal perspective regarding the impact of high levels of exercise on sedentary behaviour. Exploring the effect of high levels of exercise training, such as training for a marathon, on sedentary behaviour can help to determine the extent to which these behaviours are distinct.

Scientists have used marathon runners as a model for high level exercise training to investigate the physiological benefits and ramifications of training for and completing a marathon (Neilan et al., 2006; Trappe et al., 2006; Wilhelm, Nuoffer, Schmid, Wilhelm, & Saner, 2012; Wilhelm, Roten, et al., 2012). However, these studies have not examined, or taken into account the change that marathon running may or may not have on objectively assessed non-exercise PA levels and sedentary behaviours. Whitfield, Pettee Gabriel, and Kohl (2013) recently explored the self-reported sitting habits of recreational runners training for a full or half marathon, specifically examining whether sitting is altered with training duration or running velocity. Results showed that recreational runners were both very active and very sedentary, spending about 10.75 hoursηwork day⁻¹ (median) and 8 hoursηweekend day⁻¹ (median) in sitting activities and 6 (half marathon) to 8 (marathon; median values) hoursηweek⁻¹ training. While the reason(s) for this level of sedentary behaviour in marathon runners is not clear, and may simply be due to the need for rest after extensive training sessions, this new information showing high levels of sitting activities in runners is valuable in better understanding sedentary and active behaviours. However, it remains unclear whether objectively assessed levels of sitting are altered while training for a marathon, and/or after completing a marathon. Therefore, the purpose of this study was to examine the impact of extensive aerobic exercise training on time spent in objectively measured sedentary behaviour, total activity counts, and PA intensities. To address this purpose, the specific aim of this study was to assess sedentary, count, and PA patterns during the time that an individual trains for a marathon, and after the marathon. It was hypothesised that levels of sedentary behaviour will not change with training or during the months after completion of the race.

Methods

Participants

Eligible participants included healthy adult runners that self-selected to complete a marathon during the first weekend in October. Both individuals who had completed a marathon previously (previous marathoners, n = 10) and those who had never completed a marathon (first-time marathoners, n = 13) were included in this study. Individuals were excluded if they were pregnant, or had been previously diagnosed with significant uncontrolled chronic pulmonary, cardiovascular, or metabolic diseases or were currently experiencing signs and symptoms of such diseases. Participants were recruited via posted and distributed flyers (paper and electronic) and convenience sampling from a large midwestern university and the surrounding metropolitan area.

Protocol

This prospective study was designed to monitor awake-time sedentary behaviour, counts, and PA for three months prior (July 2012-September 2012) to running a marathon (October 2012) and three months after completing the marathon (November 2012-January 2013). Since this was a prospective study, and not an interventional study, exercise programmes were not provided as part of this study, we followed and monitored eligible participants as they trained independently.

This study consisted of three laboratory visits (baseline, marathon, and post-marathon) and seven PA assessment periods (three, two, and one month prior to the marathon, within two weeks of the marathon, and one, two, and three months after completing the marathon). The laboratory visits occurred three months prior to the event, within two weeks of the event (pre or post), and three months after the event. During the baseline laboratory visit, participants read and signed an informed consent document that was approved by the University Institutional Review Board, completed health history questionnaire, and researchers measured body height and mass following standardised procedures with a stadiometer (Detecto 3PHTRODCM-WM, Webb City, MO) and a Physician’s Scale (Detecto 339, Webb City, MO) (Heyward & Wagner, 2004), respectively. Three compartment body composition and per cent fat in the abdominal region were assessed via dual energy X-ray absorptiometry (DXA; GE Lunar Prodigy, GE Health Care, Madison, WI). DXA has been shown to be a reliable and valid measure of per cent body fat (Bakrania et al., 2016; Fields, Goran, & McCrory, 2002).

Participants were then given instructions on how to wear the motion sensors and asked to wear the sensors and complete a wear time and exercise log once a month for a total of seven monitoring periods. Monitoring periods consisted of seven consecutive days during the same week of each of the months (e.g. first week of the month). Monitors were put on immediately upon waking, and were removed only while bathing, showering, or swimming, or at the end of the day, just before going to bed. Each month, research staff met the participant at the laboratory or his/her place of work, to issue and retrieve the monitor before and after each monitoring period, respectively.

Measures

Sedentary behaviour, total activity counts, and different intensities of PA (light, moderate, and vigorous) were assessed using an accelerometry-based motion sensor (Actigraph GT3X, Pensacola, FL) worn on the right hip during all waking hours. The Actigraph GT3X has been described in detail elsewhere (Dinesh & Freedson, 2012). The Actigraph accelerometer is a valid and reliable device to measure sedentary and PA intensity in both laboratory and field settings across age (O’Donovan et al., 2016).

The Actigraph data were analysed using ActiLife 6.10 software. Data were analysed in 60-second epochs with normal frequency extension. Sixty or more minutes of zero accelerometer counts were considered non-wear time, and were therefore excluded from analysis. Valid accelerometer wear was defined as 600 minutes of accelerometer wear time per day and a minimum of 4 days of data per week (Troiano et al., 2008). Data were averaged over the seven-day assessment periods. An amalgamation of Freedson (Freedson, Melanson, & Sirard, 1998) and Matthews (Matthews, 2005; Matthews et al., 2008) cut points were used to determine time spent sedentary and PA behaviours from the vertical axis counts. Sedentary behaviour was demarcated as <100 countsηminute⁻¹ (Matthews et al., 2008), light-intensity PA (LPA) was delineated as 100–1951 countsηminute⁻¹, moderate-intensity PA (MPA) was demarcated as 1952–5724 countsηminute⁻¹ and vigorous-intensity PA (VPA) included ≥5725 countsηminute⁻¹ (Freedson et al., 1998; Matthews, 2005; Matthews et al., 2008). Sedentary breaks and sedentary bouts lasting greater than 20, 30, and 60 minutes and MVPA bouts lasting longer than 30 minutes were identified through the ActiLife 6.10 software. A sedentary bout ended when counts increased ≥100 cpm. A period of continuous counts ≥100 was defined as a break from sedentary behaviour.

Participants also completed seven-day accelerometer wear time and exercise logs simultaneously while wearing the accelerometer. On the logs, participants recorded the time they put on and took off the accelerometer, and a description of any exercise they performed (type, duration, and intensity).

Statistical analysis

Descriptive statistics for average wear time, time spent in sedentary behaviour, and various PA intensities were estimated at seven different time points (i.e. from three-month pre-race to three-month post-race for seven days each month) as shown in Table II. Over the seven waves of observation time, there were 30% and 21% total missing values in log-reported wear time and objectively assessed sedentary behaviours, respectively. In order to properly handle missing data and to make valid statistical inference with minimal bias, we employed multiple imputation methods (Rubin, 1996). As recommended by Bodner (2008) and White, Royston, and Wood (2011), we produced 30 multiply imputed data, similar to the percentage of incomplete observations. We used the STATA PMM module (predictive mean matching) to generate multiply imputed data (Little, 1988). The PMM module preserves the distribution of the observed values in the missing part of the data, which is preferred when the normality of the underlying model is uncertain. The imputer model for wear time, and time spent in sedentary behaviour and physical activities used respondent’s age, gender, status of marathon experience (i.e. veteran vs. first-time runner), and multiple measures of body composition as predictors. All statistical analyses were conducted based on these multiply imputed data with the STATA mi estimate modules. To correctly specify our panel data which consisted of repeated measurements with time-varying covariates (i.e. wear time), we used a STATA random-effects GLS regression model procedure (mi estimate: xtreg) for estimating the time effects on sedentary behaviour and physical activities controlling for the background characteristics measured at baseline.

Table II.

Random effect regression for accelerometer derived PA data

	Sedentary behaviour (minutes day−1)		LPA (minutes day−1)		MPA (minutes day−1)		VPA (minutes day−1)		Total counts (vertical axis)		Counts per minute (vertical axis)
	b	Sx	b	Sx	b	Sx	b	Sx	b	Sx	b	Sx
Fixed effect
Age	0.0	1.4	0.45	1.00	−0.081	0.367	−0.408	0.226	−12,903.9	16,917.1	−3.6	3.0
Gender (male)	21.2	31.9	−55.79	23.18**	−2.736	8.625	−2.412	5.226	−239,786.1	393,635.7	−7.0	69.1
Body fat	2.3	1.7	−1.40	1.26	−0.502	0.463	−0.774	0.285**	−46,066.0	20,961.4*	−9.5	3.7*
Wear time	20.4	4.8***	15.46	4.60**	1.724	1.645	1.108	1.163	62,024.8	84,961.2	−3.2	14.9
Time	2.2	2.1	−0.51	1.62	−1.048	0.667	−1.746	0.536**	−287,381.8	44,003.0***	−38.1	7.7***
Previous marathon experience	−10.3	26.7	−4.54	19.64	−9.800	7.426	−2.085	4.462	−451,945.0	332,312.6	−65.3	58.3
Intercept	519.9	14.1***	241.82	9.21***	40.607	3.476***	18.061	2.121***	2,311,233.0	179,014.6***	492.0	31.4***
Random effect
Sigma_u	52.9		40.50		14.50		8.33		591,558.2		103.6
Sigma_e	38.4		33.63		13.73		12.12		824,557.3		143.2

Notes: Age, gender, body fat, wear time, and previous marathon experience were all mean-centred. Sigma_u: Standard deviation of residuals within individuals; Sigma_e: standard deviation of overall error term; Sedentary behaviour: <100 counts-minute⁻¹; LPA, light-intensity PA: 100–1951 countsηminute⁻¹; MPA, moderate-intensity PA: 1951–5724 countsηminute⁻¹; VPA, vigorous-intensity PA: >5725 countsηminute⁻¹.

^*p < .05.

^**p < .01.

^***p < .001.

Results

All participants (Figure 1) were associated with the university as either an employee (n = 17) or a student (n = 6), and therefore, 96% of participants had a college or graduate degree. Thirteen participants were first-time marathon runners, while 10 were previous marathon runners. Participants were mostly women (65%), predominantly white (83%), with an average age of 34 years (S_x = 2 years) (Table I) and normal BMI and body fat levels at baseline (BMI = 23.4 kg m⁻², S_x = .5 kg m⁻²; % fat = 23.0%, S_x = 1.9%). On average, participants wore the accelerometer for 14 hoursηday⁻¹ at each measurement period (Table III). There were no significant differences in any of the characteristics measured at baseline between previous marathon runners and first-time marathon runners (Table I), or students and employees. Further, there were no changes in body composition over the length of the study.

Figure 1.

CONSORT diagram.

Table I.

Descriptive characteristics of participants three months pre-race

	All participants (N = 23)		First-time marathon runners (n = 13)		Previous marathon runners (n = 10)		|t|
Baseline measures	Mean	Sx	Mean	Sx	Mean	Sx
Age (years)	34.4	2.1	32.4	2.9	37.0	2.9	1.12
Body mass index (kg m2)	23.4	0.5	23.1	0.6	23.7	0.9	0.59
Waist-to-hip ratio	0.8	0.0	0.8	0.0	0.8	0.0	1.32
Body fat (%)	23.0	1.9	24.6	2.7	21.0	2.6	0.97
Android region fat (%)	27.2	2.4	28.0	3.7	26.2	3.0	0.38
Gynoid region fat (%)	30.7	2.0	32.6	2.7	28.2	3.0	1.11
Marathon finishing time (minutes)	253.2	9.6	254.9	9.9	251.1	18.3	0.20

Table III.

Average wear time, sedentary behaviour and PA before and after completing a marathon (N = 23)

	Wear time (minutes day−1)		Sedentary behaviour (minutes day−1)		LPA (minutes day−1)		MPA (minutes day−1)		VPA (minutes day−1)		Total counts (vertical axis)		Counts per minute (vertical axis)
	Mean	Sx	Mean	Sx	Mean	Sx	Mean	Sx	Mean	Sx	Mean	Sx	Mean	Sx
Three-month pre-race	897.9	22.6	561.0	24.3	246.28	13.05	40.54	4.84	19.54	3.89	3,113,662	404,950.2	546.2	53.1
Two-month pre-race	850.2	17.1	530.6	14.4	238.70	12.32	47.38	4.78	26.70	4.00	3,506,479	253,249.9	648.9	50.3
One-month pre-race	836.0	17.8	522.7	18.4	246.29	14.96	42.95	3.84	21.76	3.58	2,711,513	238,518.9	565.7	41.8
Race month	828.4	17.3	534.0	17.8	240.65	11.45	39.73	4.66	16.17	3.94	2,320,094	255,428.5	474.5	50.3
One-month post-race	841.6	29.7	546.3	24.3	248.08	15.26	35.63	5.08	13.86	3.21	1,777,065	230,235.6	396.2	39.3
Two-month post-race	823.9	16.4	542.0	18.2	236.86	12.79	38.22	4.42	14.48	3.31	1,819,637	230,428.5	430.4	41.0
Three-month post-race	823.9	18.8	541.6	15.3	239.18	13.82	38.95	4.13	13.87	2.90	1,956,875	176,333.3	420.2	23.4

Notes: Sedentary behaviour: <100 countsηminute⁻¹; LPA, light-intensity PA: 100–1951 countsηminute⁻¹; MPA, moderate-intensity PA: 1951–5724 countsηminute^η1; VPA, vigorous-intensity PA: ≥5725 countsηminute⁻¹.

On average, the self-reported longest runs completed by participants ranged from 5.0 to 18.3 miles over the seven months (three-month pre-race = 8.1 miles S_x = 3.1; two-month pre-race = 18.3 miles S_x = 22.1; one-month pre-race = 15.5 miles S_x = 6.5; race month = 7.0 miles S_x = 11.0; one-month post-race = 5.1 miles S_x = 3.4; two-month post-race = 5.0 miles S_x=3.4; and three-month post-race = 5.6 miles S_x = 4.1). Further, participants self-reported exercising an average of 5 daysηweek⁻¹ (S_x = 1.3), 5.5 daysηweek⁻¹ (S_x = 1.0), 4.5 daysηweek⁻¹ (S_x = 1.4), 3.3 daysηweek⁻¹ (S_x = 2.0), 3.5 daysηweek⁻¹ (S_x = 2.3), 3.4 daysηweek⁻¹ (S_x = 2.2), and 3.2 daysηweek⁻¹ (S_x = 1.9) at three-, two-, one-month pre-race, race month, one-, two-, and three-month post-race, respectively, and engaging in rest days 2 daysηweek⁻¹ (S_x = 1.3), 1.8 daysηweek⁻¹ (S_x = 1.5),2.6 daysηweek⁻¹ (S_x = 1.4), 3.4 daysηweek⁻¹ (S_x = 2.1), 3.1 daysηweek⁻¹ (S_x = 2.3), 3.3 daysηweek⁻¹ (S_x = 2.2), and 3.8 daysηweek⁻¹ (S_x = 1.9) at three-, two-, one-month pre-race, race month, one-, two-, and three-month post-race, respectively. Race results showed that participants completed the marathon in 253.2 minutes (S_x = 9.6).

The random effect regression model showed that sedentary behaviour, LPA, and MPA, measured by the motion sensor did not change significantly over the seven-month observation period (Table II). However, the model showed that VPA minutes, total counts, and countsηminute⁻¹ changed significantly over time. The model also confirmed that some of the individual characteristics measured at baseline were associated with time spent in sedentary behaviour and/or various PA intensities. For example, sedentary behaviour and LPA were associated with wear time. Male participants were also less likely to engage in LPA. Finally, body fat was negatively associated with VPA, total counts and countsηminute^η1. Body mass index, android region fat and gynoid region fat were all considered as control variables; however, due to high level of multi-collinearity with body fat, they were not included in the model.

The measured sedentary behaviour ranged from 523 to 561 minutesηday⁻¹ without any noticeable pattern, or significant difference over time (Table III). LPA and MPA as assessed by the accelerometer did not change over the course of the months before and after the marathon. A significant change in VPA was seen over the course of the study (Tables II and III), with the highest VPA seen two months prior to the race (26.7 minutesηday⁻¹), and the lowest MVPA seen one month after the race (13.9 minutesηday⁻¹). To further explore the activity of these individuals, total activity counts and countsηminute⁻¹ were evaluated, both showed a significant change over the seven-month period (Tables II and III). Total counts, providing an indication of total volume of activity, in the vertical axis were highest three and two months prior to the race, and lowest during one, two, and three months after the race. Countsηminute⁻¹, providing an indication of average intensity of activity, were highest during the three months prior to the race and lowest during the three months after the race. The lowest level of total counts and countsηminute⁻¹ occurred during the month after the race, while the highest level of total counts and countsηminute⁻¹ occurred two months prior to the race.

First-time marathoners were found to spend 16.7 and 13.6 more minutes in LPA than those with previous marathon experience two-month pre-race (p < .05) and one-month pre-race (p < .05), respectively. First-time marathon participants also spent more time per day in MPA than first-time marathoners before and after the race. Specifically, firsttime marathoners spent 8.1, 8.8, 15.9, 9.8, 6.2, 10.4 more minutes in MPA than those with marathon experience three-, two-, and one-month pre-race and one-, two-, and three-month post-race, respectively (all p < .05). Finally, first-time marathon runners were also found to spend significantly more time per day in VPA at three-month pre-race (5.53 more minutes, p < .05) and one-month pre-race (3.91 more minutes, p < .05) than those with previous marathon experience. However, first-time participants spent less time in VPA during the month of marathon (6.8 fewer minutes, p < .05). No difference was found between the first-time and previous marathoners in sedentary behaviour, total counts, or countsηminute⁻¹.

In addition to total activity, the number of bouts of sedentary behaviour and MVPA were examined. Participants averaged 15.6 sedentary breaksηday⁻¹ over the seven-month period (S_x = 0.25; range 15.01–6.1). Average number of daily bouts over the seven-month period that were 20 minutes or longer, 30 minutes or longer, and 60 minutes or longer were 9.8 (S_x = 0.18; range 9.4–9.9), 5.4 (S_x =0 .14; range 5.1–5.7), and 1 (S_x = 0.05; range 1.0–1.1), respectively. Additionally, the average number of MVPA boutsηday⁻¹ over the seven-month period that were 30 minutes or longer were .5 (S_x = 01; range 0.4–0.6). None of the measured sedentary or MVPA boutsηday⁻¹ changed significantly over the seven-month period, and there was no significant difference between first-time and previous marathoners in these measures.

Average time spent in all sedentary bouts ranged from 323 to 342 minutes (Supplemental Table 1). Additionally, the daily average time spent in sedentary and MVPA bouts were estimated: 335 minutes·day⁻¹ (S_x = 7.1) in sedentary bouts 20 minutes or longer; 249 minutesηday⁻¹ (S_x = 6.7) in 30-minute or longer sedentary bouts; 77 minutesηday⁻¹ (S_x = 3.7) in 60-minute or longer sedentary bouts; and 31 minutesηday⁻¹ (S_x = 1.8) in MVPA bouts of 30 minutes or longer. There were no significant changes over the observation period in any of the estimated sedentary and MVPA categories. No difference between first-time and previous marathoners was found in any of these measures (data not shown).

Discussion

Results of this study demonstrate that extensive exercise training does not alter time spent in sedentary behaviour in previous and first-time marathoners, despite significant changes in VPA, total counts, and countsηminute⁻¹. For the three months prior to completing the marathon, sedentary behaviour did not significantly change, whether expressed in absolute minutes or as per cent of daily wear time (561 minutes or 62.6% of waking time three-month pre to 523 minutes or 62.7% of waking time one-month pre), and during the three months after completing the marathon, sedentary behaviour only varied five minutes, or less than 1% of wear time. These data reinforce the habitual nature of sedentary behaviour and the fact that significant alterations in activity do not impact sedentary behaviour.

The results of this study support previous research indicating the independent nature of sedentary and PA behaviours. Results of our study are in agreement with previously published papers examining relationships between sitting time and running distance or anticipated race velocity in marathon and half-marathon runners (Whitfield et al., 2013), and observational data showing muscular inactivity is not different between days when exercise occurs and days where no exercise occurs (Finni, Haakana, Pesola, & Pullinen, 2014). Whitfield et al. (2013) examined a group of individuals who signed up to run a half or full marathon. Self-reported time spent sitting in the study by Whitfield was similar to the results of this study, with Whitfield et al. reporting average sitting durations ranging from 645 minutes on work days and 480 minutes on non-work days, compared with a range of 523–561 minutesηday⁻¹ in this study, which objectively assessed seven days of activity including working and non-working days and training and non-training days. Further, Whitfield et al. showed self-reported sitting behaviour was not associated with training duration or training velocity, demonstrating that despite how much time per week was spent training, or how fast the participant’s anticipated running velocity, sedentary behaviour did not change. Our study extends these findings by examining objectively measured sedentary and PA behaviours prospectively over training and post-marathon periods. Similarly, results of this study did not reveal much variation, and no significant change in sedentary behaviour of the participants, with a maximum non-significant difference of 38.3 minutes across all time point observations.

There were no significant differences in sedentary time between previous marathon runners and those who were running a marathon for the first time. Significant differences in MPA (pre- and post-marathon) and VPA (pre-marathon and race month) time were seen between the groups. The previous marathoners engaged in less MPA (−15.9 minutes) and VPA (−5.5 minutes) during the month prior to the marathon, suggesting that previous marathoners engaged in a larger taper, did not increase their mileage as much, or ran at a faster pace than the first-time marathoners in the month leading up to the race. However, there was no significant difference in marathon finishing times between the two groups.

Significant changes in VPA were observed over the course of the study, with average VPA ranging from a high of 26.7 minutesηday⁻¹ (two-month pre-marathon) to a low of 13.9 minutesηday⁻¹ (one-month post-marathon). MPA did not significantly change, but average daily MPA did range from a high of 47.4 minutesηday⁻¹ (two-month pre-marathon) to a low of 35.6 minutesηday⁻¹ (one-month post-marathon). Research suggests (Finni et al., 2014; Hamilton, Hamilton, & Zderic, 2007) that regular planned exercise increases time spent in moderate- to vigorous-intensity PA (MVPA) as a proportion of the entire day, but only slightly, and likely not significantly because it is such a small proportion of the day (e.g. 30–60 minutes). Further, averaging PA data over seven days results in an averaging of both rest and training days which will in turn in result in lower MPA and VPA than expected. In this study, participants engaged in rest days ranging from 1.8 daysηweek⁻¹ pre-marathon to 3.8 daysηweek⁻¹ post-marathon, or 25-over 50% of the week.

When an individual increases time spent performing PA, the time must be re-allocated from some other behaviour, such as sleeping, sedentary behaviour, or LPA. While sleep was not directly measured in this study, sleep time can be inferred from accelerometer non-wear time. Calculations show that sleep time was estimated to be 9.0, 9.8, 10.1, 10.2, 9.8, 10.3, and 10.3 hoursηday⁻¹ for three-, two-, one-month pre-race, race month, one- two-, and three-month post-race, respectively. When examining the data on a 24-hour clock or based on wear time, results of this study do not provide a clear indication of where substitution of behaviours occurred. When training was at its peak (two-month pre-race) MPA and VPA accounted for 3.3% and 1.9% of the 24-hour day, respectively, while sleep, sedentary behaviour, and LPA accounted for 41.0%, 36.8%, and 16.6% of the day, respectively. Conversely, when MPA and VPA were at their lowest levels (one-month post-race) MPA and VPA accounted for 2.5% and 1.0% of the day, while sleep, sedentary behaviour, and LPA accounted for 41.6%, 37.9%, and 17.2% of the day, respectively. The absence of a time reallocation pattern may be due to the relatively small portion of the day that MPA and VPA occupy. Therefore, these data suggest that there was no clear pattern of time reallocation from one behaviour to another while training for, or after completing a marathon.

The data clearly show significant changes in total activity countsηday⁻¹ and counts minute⁻¹. Activity counts were at their highest two months before the marathon (3,113,662 countsηweek⁻¹ and 546.2 countsηminute⁻¹), and at their lowest one month after the marathon (1,777,065 countsηday⁻¹ and 396.2 countsηminute⁻¹). This range of activity counts is substantial with the total countsηday⁻¹ and countsηminute⁻¹ being 43% and 27% lower, respectively, one month after the marathon compared to peak training (two-month pre-race). Previous research has examined normative count values for the population (Wolff-Hughes, Fitzhugh, Bassett, & Churilla, 2015) and for healthy individuals and those with chronic disease (Swartz, Cho, Welch, & Strath, 2016). Data from our study shows that two months before the marathon, total count levels would place these individuals at 90–95% for women aged 34 years, and at 75% for men aged 34 years, while total count levels one month after the marathon places these individuals just below 50% for women aged 34 years, and at 25% for men aged 34 years (Wolff-Hughes et al., 2015). Countηminute⁻¹ values were just below the level for healthy individuals (428 countsηminute⁻¹) one month after the marathon, and well above that value when at peak training (two months prior to marathon) (Swartz et al., 2016).

There are a number of limitations to consider when examining the results of this study. First, the sample size was small; however, it was powered sufficiently to detect changes over time in sedentary and PA behaviours. The use of the multiple imputation method enabled us to use all 23 cases with 7 repeated measures, which provided 80% power to detect effect size as small as 0.1 at α = .05. Further, the prospective monitoring is additive to the literature, providing new longitudinal information on sedentary behaviour in response to extensive exercise training. Second, the use of an accelerometer to assess sedentary and PA behaviours has limitations. While the accelerometer has been shown to be valid and reliable when assessing sedentary and PA behaviour (O’Donovan et al., 2016), it cannot differentiate different activities, postures, and countsηminute⁻¹ tend to plateau with higher speeds of running. Finally, sleep was not directly assessed in this study, but was inferred from self-report of accelerometer wear time.

Conclusion

Sedentary behaviour did not change significantly over the course of training for a marathon, or after completion of a marathon, despite changes in VPA, total activity counts and countsηminute⁻¹, suggesting that PA and sedentary behaviour are distinct behaviours and engaging in high levels of activity will not influence sedentary behaviour. Given recent publications highlighting the significant international economic cost of inactivity (Ding et al., 2016), along with recent findings highlighting the protective nature of MPA against the negative effects of sitting time, but not television viewing on mortality (Ekelund et al., 2016), results of this study highlight the need for behavioural health recommendations to include suggestions for both decreasing sedentary behaviour and increasing PA behaviour.