The relationships between exercise and affective states: a naturalistic, longitudinal study of recreational runners

Introduction

It is generally acknowledged that physical activity or exercise produces an affective or feeling response, typically involving more positive affective valence, and higher levels of arousal, in the aftermath of exercise than when no exercise has been completed. Much of the early literature was based on experimentally assigned exercise sessions or programmes (see Byrne & Byrne, 1993; Reed & Buck, 2009 for reviews). However, concerns have been raised about the extent to which experimental findings translate into everyday life (Gauvin, Rejeski & Norris, 1996; Giacobbi, Hausenblas & Frye, 2005; Liao, Shonkoff & Dunton, 2015). Experimental treatments may produce demand characteristics in participant responses. Moreover, experimental participants do not exert spontaneous choice about type, timing, duration or intensity of their activity. Thus, interest has been growing in the examining the within-person relationships between physical activity and affective state in the course of spontaneous voluntary behaviour.

Two recent systematic reviews confirm that in studies of spontaneous voluntary behaviour, physical activity is associated with more positive and more energetic affective states (Kanning, Ebner-Priemer & Schlicht, 2013; Liao, Shonkoff & Dunton, 2015). Kanning and colleagues (2013) stress three key methodological prerequisites for informative work on this topic. First, the measurement of physical activity should be objective, since self-report has proven unreliable (Prince et al., 2008). Second, the measurements of affective states should be instantaneous rather than, for example, based on retrospective recall at the end of the day or week. Third, the method of collecting the affective reports needs to objectively record the time at which the response was actually made, to avoid, for example, participants “back-filling” paper-and-pencil responses just before the end of the study.

Only seven of 22 papers reviewed by Kanning, Ebner-Priemer & Schlicht (2013) met all three of these criteria. Likewise, only six of the 14 papers included in the more recent review by Liao, Shonkoff & Dunton (2015) both assessed physical activity objectively and captured momentary affective reports electronically with a precise time-stamp. Moreover, in those six studies, the monitoring period was often very short (as little as one day). Thus, the ability of these studies to examine variability in, and moderators of, the affective response to exercise, is limited.

Many of the previous studies that have met Kanning, Ebner-Priemer & Schlicht’s (2013) criteria have done so by providing participants with accelerometers for measuring physical activity, and pagers or other devices for providing momentary affective assessments when prompted. This is expensive for researchers. However, many people already possess smartphones that can be used to prompt them to provide momentary affective reports. Moreover, their smartphones, or alternatively, GPS-enabled running watches, can already capture the data required to objectively characterise certain kinds of physical activity. It is popular amongst recreational runners and cyclists to record data on their activity (time, location, distance and speed), and to upload it to online platforms such as Strava (http://www.strava.com), which describes itself as ‘the social network for athletes’. Many available devices can be set to upload data to such platforms automatically. Thus, in the present study, we explored the potential for using participants’ own technology for objective recording of their exercise bouts, and momentary electronic assessment of affective states, in a longitudinal study of recreational runners.

As well as establishing the feasibility of using participants’ own technology to capture data, we wished to explore possible moderators of the affective response to exercise. The response may or may not be moderated by gender (Roth, 1989; Rocheleau et al., 2004), and may or may not be moderated by the individual’s level of physical fitness (Steptoe & Bolton, 1988; Ekkekakis & Petruzzello, 1999; Blanchard et al., 2001). All possible forms of moderation of the affective response by exercise dose (that is, exercise intensity and duration) are represented in the literature: higher doses produce more positive effects (Rocheleau et al., 2004); lower doses produce more positive effects (Ekkekakis & Petruzzello, 1999); or intermediate doses produce the most positive effects (Berger & Motl, 2000). This may be partly an issue to do with elapsed time. The affective response to exercise attenuates over the scale of several hours (Wichers et al., 2012), but the attenuation rate may be different for different doses (Bixby, Spalding & Hatfield, 2001). To address the issue of the relationship between exercise dose, time, and affect, it is necessary to have quite long time series from each participant, to provide within-subject variation in exercise dose, and also in the time delay between exercise bout and affective report.

With these objectives in mind, we completed a six-week study of an opportunity sample of recreational runners who used the Strava platform. We polled affective states via a daily smartphone message, on a predetermined schedule unrelated to the schedule of exercise. Affective states were characterized based on the circumplex model of affect (Rusell, 1980; Posner, Russell & Peterson, 2015), which places momentary states on the two independent continua of valence (negative to positive), and arousal (deactivated to activated or energetic). Data on exercise bouts were separately harvested from the participants’ profile on http://www.strava.com. This gave use precise and objective information on exercise bouts without placing any additional demands on participants.

Our study questions were the following. First, what is the relationship between current affect (valence and arousal) and the time since the last run? Given previous findings about the affective response to acute exercise, we would expect valence and arousal to be highest on average in the immediate aftermath of a run, and to reduce progressively as time passes. Second, do dose of running, level of physical fitness, or gender moderate the relationships between affective state and time? For example, the affective response to a high-dose run may have a different time course than the response to a low-dose run, and the magnitude and timing of affective responses to running may be different in fitter versus less fit individuals.

Materials & Methods

Results

Descriptive statistics

Descriptive statistics for the main study variables are provided in Table 1. Runs were recorded on 545 of 1,331 participant-days with valence data. Individual participants recorded 2–45 runs during the study period (mean 16.75, s.d. 9.03). Examining correlations at the between-subject level, there was a significant correlation between participants’ running fitness and their average dose (r = 0.52, p < 0.01), and the correlation between running fitness and the total number of runs in the study period was marginally non-significant (r = 0.31, p = 0.06). Total number of runs was not significantly correlated with average dose (r = 0.13, p = 0.45).

Table 1:

Descriptive statistics for the main study variables.

Means, standard deviations and ranges, or numbers of participants, are shown. Note that standard deviations for valence, arousal and dose reflect the variation between reports, not between participants.

Variable	Descriptive statistics
Gender	Male 20, Female 18
Age groups	18–24 years: 26; 25–34 years: 3; 35–49 years: 9
Best 5k time	64.07% (s.d. 8.05%, range 45.18–84.33)
Valence	2.22 (s.d. 2.17, range −5–5)
Arousal	3.28 (s.d. 1.33, range 1–6)
Dose	1.73 (s.d. 1.08, range 0.18–9.3)
Day type	Baseline 831, Next day 457, Run day 315

DOI: 10.7717/peerj.4257/table-1

Valence and arousal were not strongly related to one another. There was evidence of an inverted-V shaped relationship between the two on average, as has been observed elsewhere (Kuppens et al., 2013). That is, average arousal was highest at the negative and positive extremes of valence, and lowest at neutral valence (Fig. 1). However, the association between valence and arousal was fairly weak, with the absolute value of valence accountingfor only 12% of the variation in arousal. For this reason, we henceforth treat valence and arousal as two independent outcomes, as is conventional in the affect literature (Barrett & Russell, 1999).

Average affect did not differ substantially with time of day. Defining morning as before noon, afternoon as noon to 6 pm, and evening from 6 pm, valence was not significantly different across the morning (mean 2.06, s.d. 2.25), afternoon (mean 2.20, s.d. 2.07) and evening (mean 2.36, s.d. 2.25; linear mixed model with morning as the reference category: t_afternoon = 0.94, p = 0.35; t_evening = 1.89, p = 0.06). Likewise, arousal was not significantly different across the morning (mean 3.38, s.d. 1.39), afternoon (mean 3.31, s.d. 1.28) and evening (mean 3.18, s.d. 1.36; linear mixed model with morning as the reference category: t_afternoon =  − 0.88, p = 0.38; t_evening =  − 1.80, p = 0.07). Thus, we felt justified in not including time of day of report in subsequent analyses.

Valence in relation to running

Our first model predicted valence by day type. Mean valence was significantly higher on run days than next days, and also significantly higher on next days than baseline days (Table 2, model 1; PRV due to time since run 6.9%). This is illustrated in Fig. 2A. The higher valence of run days could reflect either direction of causation: running increases valence, or more positive valence makes going for a run more likely. To distinguish these two possibilities, we compared valence on the day when a run occurred and the reportwas before the run, and when the report was after the run (Table 2, model 2). Valence was significantly higher after than before the run (after: mean 3.19, s.d. 1.77; before: mean 2.08, s.d. 2.04; PRV 10.1%). This supports running being causal in increased valence.

Table 2:

Summary of statistical models with valence as the outcome variable.

All models contain a random intercept for participant. Model 2 is restricted to days where one run occurred, and model 4 to days where a report was received after a run.

Model	Predictor	B	95% CI	t	p-value
1	Day type: Baseline	Reference category
	Day type: Next day	0.26	0.01–0.51	2.00	0.046*
	Day type: Run day	1.38	1.10–1.66	9.65	<0.01*
2	Before run	Reference category
	After run	1.21	0.89–1.52	7.49	<0.01*
3	Day type: Baseline	Reference category
	Day type: Next day	0.26	−0.23–0.76	1.03	0.305
	Day type: Run day	1.16	0.80–1.53	6.21	<0.001*
	Dose	0.01	−0.35–0.38	0.07	0.940
	Running fitness	0.65	0.22–1.07	3.05	0.004*
	Next day*Dose	−0.50	−1.10–0.11	−1.59	0.111
	Run day*Dose	0.21	−0.19–0.62	1.02	0.309
	Next day*Fitness	−0.77	−1.37–−0.18	−2.52	0.012*
	Run day*Fitness	−0.62	−1.02–−0.22	−3.03	0.003*
4	Time since run (minutes)	−0.001	−0.002–−0.001	−2.98	0.003*

DOI: 10.7717/peerj.4257/table-2

Notes:

*p < 0.05.

Next, we examined possible moderators of the relationship between valence and day type using the stepwise model selection procedure described in Methods. Gender and its interactions were eliminated in the model selection, and so the final model contained day type, running fitness, dose, and the interactions of best 5 k time and dose with day type (Table 2, model 3). The magnitudes of the parameter estimates for the differences between run days, next days and baseline was similar to those in model 1. Although the interaction of dose and day type was retained by the model selection procedure, neither of the parameter estimates describing this interaction differed significantly from zero (Table 2, model 3). There was a significant main effect of running fitness, with higher running fitness time associated with higher average valence (Table 2, model 3). There was also significant interaction between day type and running fitness (Table 2, model 3, PRV 1.8%): the magnitude of the difference between participants high and low running fitness was larger on baseline days than on other days (Fig. 2B).

For run days only, we modelled valence in relation to time in minutes since the completion of the run. Time since run was a significant predictor (Table 2, model 4; PRV 2.7%). The parameter estimate corresponds to a reduction in valence of 0.07 units on the scale (or 0.04 standard deviations) with each hour that passes after the run (Fig. 3). This estimate was not substantially altered by additional controlling for time of day.

Arousal in relation to running

Our first model predicted arousal from time since run alone. Arousal was significantly higher on run days than baseline, but baseline days and next days did not differ significantly (Table 3, model 1; Fig. 4A). This is consistent with running producing a short-term increase in arousal, though the effect size (PRV 0.6%) was much smaller than for valence. We once again tested the direction of causation by comparing arousal on run-days where the report came after the run as opposed to before (Table 3, model 2). There was not a clear significant difference (after: mean 3.54, s.d. 1.39; before: mean 3.48, s.d. 1.26; PRV 0.2%). Thus, it is possible that participants were motivated to go for a run when already feeling aroused, or became more aroused in anticipation of a planned run, rather than, or in addition to, running increasing arousal.

Table 3:

Summary of statistical models with arousal as the outcome variable.

All models contain a random intercept for participant. Model 2 is restricted to days where one run occurred, and model 4 to days where a report was received after a run.

Model	Predictor	B	95% CI	t	p-value
1	Time: Baseline	Reference category
	Time: Next day	0.01	−0.15–0.16	0.06	0.949
	Time: Run day	0.24	0.07–0.42	2.74	0.006*
2	Before run	Reference category
	After run	0.09	−0.12–0.30	0.88	0.380
3	Time: Baseline	Reference category
	Time: Next day	−0.03	−0.35–0.30	−0.17	0.864
	Time: Run day	0.12	−0.13–0.36	0.94	0.347
	Dose	−0.15	−0.39–0.09	−1.24	0.217
	Next day*Dose	−0.14	−0.54–0.26	−0.69	0.499
	Run day* Dose	0.26	−0.01–0.52	1.88	0.061
4	Time since run	−0.001	−0.001–−0.000	−2.28	0.023*

DOI: 10.7717/peerj.4257/table-3

Notes:

*p < 0.05.

We next performed the model selection procedure on possible models including dose, fitness, gender and their interactions with day type. Gender, running fitness and their interactions were excluded in the model selection, so the final model contained day type, dose, and their interaction (Table 3, model 3; PRV 1.6%; although the day type by dose interaction was retained by the model selection, the parameter estimate was marginally non-significantly different from zero, p = 0.06). The interaction between day type and dose appeared to be driven by the pattern of lower arousal on next and baseline days than on run days only being present for high-dose runs. For low-dose runs, next day and subsequent arousal appears to be no lower than on the day of the run (Fig. 4B).

For run days only, we again modelled arousal in relation to time in minutes since the completion of the run. Time since run was a significant predictor (Table 3, model 4; PRV 1.7%). The parameter estimate corresponds to a reduction in arousal of 0.04 units on the scale (or 0.03 standard deviations) with each hour that passes after the run (Fig. 5). This estimate was not substantially altered by additional controlling for time of day.

Discussion

In a six-week study of how valence and arousal fluctuate in relation to running, we found clear evidence that average affective states are related to the occurrence of runs. Average valence was highest on days when the participant had been for a run, and this appeared to be due to running increasing valence rather than high valence initiating a run. The valence premium declined with each hour that passed as the day went on. Taking the parameter estimates from model 4 of valence, it would take approximately four hours for valence to decline by an amount equivalent to the difference between the average run-day valence and the average next-day valence. Thus, our findings are consistent with evidence that having done exercise has a positive acute effect on valence, an effect that attenuates over the course of hours (Reed & Ones, 2006; Wichers et al., 2012; Liao, Shonkoff & Dunton, 2015). We did however find that valence was still slightly higher the day after a run compared to baseline. This suggests either that the valence effects of running are not completely dissipated in 24 h, or else that, for these habitual runners, not running for more than a day or two tends to be caused by other factors that could cause low valence, such as illness or work demands.

Higher-fitness participants (as measured by their best performance for age and gender in a 5 km timed run), had higher valence overall than low-fitness participants. One simple explanation for the positive effects of long-term physical exercise participation on affect is just the repeated reinstatement of acute effects by exercise bouts (the “maintenance” explanation; Reed & Buck, 2009). This explanation would predict that fitter runners feel more positive at any given time simply because they are more likely to have run recently. However, this cannot be the complete explanation in our data. Fitness was associated with more positive valence even after controlling for day type. Moreover, the biggest valence difference between fitter and less-fit individuals was on baseline days; that is, when they hadn’t run recently. This suggests that there may be long-term affective adaptation that comes with physical fitness, as a result of training or associated lifestyle factors. For example, fitter individuals have reduced chronic pain, reduced body weight and improved immune function (Nielens & Plaghki, 2001; Warburton, Nicol & Bredin, 2006), all of which could contribute to more positive valence at baseline.

Arousal was also higher in the immediate post-run period than at any other time. However, the association between arousal and time since run was weaker than that for valence, with time since run explaining less than 1% of report-to-report variation in arousal. Moreover, arousal was not clearly higher after compared to before the run on days when a run occurred. This suggests that the association between arousal and the timing of runs could be at least partly due to people choosing to run when their arousal is high, or their arousal rising in anticipation of a forthcoming run, rather than exclusively due to the run itself increasing arousal. Arousal did however reduce with increasing time since the run on run days, at a rate suggesting it would take approximately 6 h for the arousal premium of a run day to dissipate. We also found that running dose moderated the relationship between arousal and time since run. Specifically, a higher dose of running (compared to a lower dose) was associated with similar arousal on the day of the run, but lower arousal the next day and on subsequent days. Unfortunately it is impossible to distinguish with our data whether this is a general effect of high-dose exercise, or something specific about the particular high doses that these runners completed (for example, that the highest doses in the dataset represented races to which the runners had been building up, after which there was a drop in arousal, rather than regular training runs).

Although our main results support the widespread consensus in the literature that physical exercise can lead to higher affective valence and increased arousal, our findings differ in some regards from previous findings in the literature. First, we found no evidence that—in this sample—higher doses of running are associated with a less positive (or even negative) effect on affect, compared to lower doses (Ekkekakis & Petruzzello, 1999; Reed & Ones, 2006). The only negative effects of dose for which we found any evidence were possibly in the days after the run, on arousal. This may reflect the difference between interventional studies that assign individuals to exercise, and more naturalistic studies such as ours where the participants self-selected their running dose on each occasion. It is also important to recall that all our reports were gathered after the exercise had finished. Measurements collected during exercise might have produced a different picture (Steptoe & Bolton, 1988). Thus, our findings are not inconsistent with the idea that if individuals were assigned very high doses, this might produce a negative affect response, or that they might feel bad during the effort; they just show that in the course of spontaneous self-directed running, valence and arousal are more positive on average after a run than at any other time, regardless of dose. Our data did not support the possibility that the affective response to running would be different for men and women (Rocheleau et al., 2004).

It is important to acknowledge both the strengths and limitations of our design. As our study was observational and only included habitual runners, it is not suitable for estimating the affective impact that would follow if current non-runners began to run. This is because the population sub-group that runs habitually is likely to be biased towards those who have a more positive affective response to running (Hoffman & Hoffman, 2008; Williams et al., 2008; Hallgren, Moss & Gastin, 2010). Whilst the non-experimental approach limits the causal inferences that can be made, it has high ecological validity. Experimental studies may assign doses of exercise that individuals would not choose at times they do not wish to do them, and there is always the risk of experimental demand characteristics. Our dataset thus allows more naturalistic insight into the role that running plays in fluctuations of affective state over time in the everyday lives of recreational runners.

The combination of gathering affect data via experience sampling, and using a social network for athletes to automatically record running using their own GPS devices, was extremely effective. Participant demand was light, since our affective measures were very quick to complete, and running data would have been uploaded to Strava anyway. Thus, participant compliance was good, and the running data had greater precision than would be feasible with manual reporting. It also made it unnecessary for the researchers to interfere in any way in participants’ self-selected running schedules. However, the methodology did produce some specific limitations. The schedule of reporting was completely independent of the schedule of running. Thus, we relied on reports happening to fall into various time windows after runs. This means that, unlike Gauvin, Rejeski & Norris (1996), we could not make explicit immediately-before/immediately-after affect comparisons. Participants were not prevented from engaging in other forms of exercise during the study; these would not have been captured, and nor would any run where the participant had for some reason not used their device. Thus, by failing to account for the fact that certain affective reports may have come in the aftermath of non-recorded bouts of physical activity, we may if anything have underestimated the affective response to activity.

Moreover, although the running data was precise, it was not very rich. Some runs may have been completed socially, but Strava data did not allow us to identify these, and hence test whether social running has different affective consequences than non-social running (Cruwys et al., 2013). Similarly, we made no attempt to distinguish runs taking place in different environments, such as on green space versus roads.

Conclusions

Our naturalistic study suggested that, in recreational runners, running provides a boost to both valence and arousal, a boost that is larger for valence than for arousal, and is of around the same magnitude for men and women. The boost dissipates over the course of hours, and high doses may produce a relative slump in arousal on the next day and subsequent days compared to low doses. We also found evidence that fitter individuals have more positive valence, especially when they have not run recently. This suggests that the effects of exercise may involve not just a series of immediate valence boosts, but adaptation in baseline valence as well. Our research demonstrates the feasibility of studying affect and exercise in a way that is economical, precise, easy for participants, and convenient for researchers, using technologies that participants already own and use.