![Comparison of seasonally adjusted life satisfaction from the survey with the UK population [33]
Error bars show 95% confidence intervals.](https://www.researchgate.net/publication/384533918/figure/fig2/AS:11431281281347471@1727807210083/Comparison-of-seasonally-adjusted-life-satisfaction-from-the-survey-with-the-UK_Q320.jpg)
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RESEARCH ARTICLE
The impact of parkrun on life satisfaction and
its cost-effectiveness: A six-month study of
parkrunners in the United Kingdom
Steve HaakeID
1
*, Helen QuirkID
2☯
, Alice BullasID
1☯
1The Advanced Wellbeing Research Centre, Sheffield Hallam University, Sheffield, The United Kingdom,
2Sheffield Centre for Health and Related Research (SCHARR), School of Medicine and Population Health,
The University of Sheffield, Sheffield, The United Kingdom
☯These authors contributed equally to this work.
*s.j.haake@shu.ac.uk
Abstract
An intervention suggested by the World Health Organisation that might increase life satis-
faction is parkrun, a free, weekly, timed five kilometre run or walk. The issues with such inter-
ventions are (1) whether they impact on the life satisfaction of their participants, and (2)
whether they are cost-effective. A study of 548 newly registered parkrunners were asked
about their life satisfaction at baseline and six months later. A change of one life satisfaction
point per year per participant was defined as one WELLBY (wellbeing adjusted life year),
with a value of £13,000. Three approaches were used to estimate the additionality (added
value) of parkrun: (1) by comparing a participant’s number of parkruns to total activity; (2) by
accounting for the participant’s perceived impact of parkrun across 16 measures; and (3)
combining these two methods equally. After six months, weighted, seasonally adjusted life
satisfaction increased from a mean of 7.489 to 7.746, a change of 0.257. Both life satisfac-
tion improvement and additionality were greatest for the least active. Assuming only half a
year of benefit, the total value of the life satisfaction change for the 2019 parkrun population
of 400,167 participants was estimated as £667.4m, with the least active accounting for
almost half. Comparing to the cost of running parkrun in 2019 and using the activity, impact
and combined methods for additionality, benefit-cost analysis ratios were found to be 16.7,
98.5 and 59.3 to 1, respectively. These were between 2.8 to 16.7 times that of other popula-
tion-level physical activity interventions. Physical health was a mediator between activity
and life satisfaction; mental health was only found as a mediator when combined with physi-
cal activity. Successful features of parkrun that might guide other interventions include its
framing (role, time, place and cost) and ability to forge both strong and weak social ties.
Background
With a quarter of the world inactive and the large economic burden this represents [1], it is
important to explore how inactivity can be reduced cost-effectively at the population level.
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OPEN ACCESS
Citation: Haake S, Quirk H, Bullas A (2024) The
impact of parkrun on life satisfaction and its cost-
effectiveness: A six-month study of parkrunners in
the United Kingdom. PLOS Glob Public Health
4(10): e0003580. https://doi.org/10.1371/journal.
pgph.0003580
Editor: Hanna Nalecz, Akademia Wychowania
Fizycznego Jozefa Pilsudskego w Warszawie,
POLAND
Received: March 29, 2024
Accepted: August 12, 2024
Published: October 1, 2024
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pgph.0003580
Copyright: ©2024 Haake et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The dataset used in
this study is available from the University’s public
Mass participation events such as 10k races, half-marathons and marathons have been sug-
gested as one approach [2,3], although they do not always attract the least active and have lim-
ited potential to change behaviour of the population [2,4,5]. This may be because life
satisfaction increases may return to previous levels relatively quickly, reducing the effective-
ness of mass-events as interventions [6].
In its Global Action Plan on Physical Activity, the World Health Organization recom-
mended that countries “implement regular mass-participation initiatives in public spaces,
engaging whole communities,to provide free access to enjoyable and affordable,socially and cul-
turally appropriate experiences of physical activity”. It gave the parkrun initiative as an example
of how to do this [1]. Parkrun is a free, weekly, timed 5 km run or walk in which participants
can also volunteer. It has been operating for more than 20 years and now exists in 22 countries
worldwide; it has hundreds of thousands of participants each weekend [7].
A cross-sectional study of around 60,000 parkrun participants showed that those who were
previously inactive were more likely to report improvements to physical health, mental health,
lifestyle and a sense of personal achievement [8]. This was also true of those with mental health
conditions [9]. A scoping review on parkrun concluded that parkrun participants showed sus-
tained improvements to fitness, physical activity levels, and body mass index with a dose–
response effect for participation frequency [10]. These changes were found to last at least 12
months [11]. An Irish study on middle-aged men at parkrun found that both weak and strong
social connections were made after repeated participation, and that these led to improved sub-
jective mental wellbeing [12]. The physical and mental benefits of parkrun also extend to vol-
unteers who never run or walk but are considered as participants [13]. However, for parkrun
to be useful as a population-level physical activity intervention, it is important to understand if
it is a cost-effective approach.
There is increasing consensus amongst policy makers that when determining cost effective-
ness, we should pay more attention to the happiness and subjective wellbeing of citizens rather
than traditional measures of success such as growth in gross domestic product (GDP) [14]. The
World Happiness Report, for instance, has monitored happiness across the globe using an
index composed of three questions on life evaluation, positive emotions and negative emotions
[15]. The report has previously advocated the use of life satisfaction as a measure to quantify the
economic value of wellbeing to allow governments to justify budgets based on wellbeing [16].
Evidence suggests that low scores of life satisfaction are related to poor health, unemploy-
ment, lack of social contact and separation from partners [17]. Some have suggested that
improving life satisfaction over the long-term may not be possible since it may return to a ‘set-
point’ related to an individual’s underlying traits [18–20]. A study of 3,608 German nationals
over a 17-year period found that most did indeed have relatively unchanging life satisfaction
but, for almost one in ten, life satisfaction changed by at least three points on a 10-point scale
[21]. Not only was change over the long-term possible, variability was greater for those with
the lowest life satisfaction, suggesting they might be more receptive to external influences.
Levels of physical activity have been shown to influence life satisfaction [22] and two studies
in the UK and the USA found that life satisfaction correlated positively with activity [23,24].
The link between life satisfaction and physical activity is possibly mediated through mental
and physical health [17,22] with the ultimate outcome that risk factors for many non-commu-
nicable diseases are reduced [25].
The cost-effectiveness of sport and physical activity has predominantly been calculated
using detailed analysis of downstream economic benefits to give an overall social return on
investment [26]. This allows comparison with traditional economic analyses using benefit-cost
ratios. A systematic review showed that for interventions aimed at a general population, the
benefit-cost ratio was 5.9 to 1 [26]. If interventions were targeted to specific health conditions
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repository (SHURDA) at the following: http://doi.
org/10.17032/shu-180037.
Funding: This study was funded by Sheffield
Hallam University (SJH and AB) and by The
University of Sheffield (HQ). The funders had no
role in study design, data collection and analysis,
decision to publish, or preparation of the
manuscript.
Competing interests: I have read the journal’s
policy and the authors of this manuscript have the
following competing interests: all authors are
parkrunners and members of the parkrun Research
Board (SH is Chair; AB and HQ are Deputy-Chairs).
or populations (e.g. those with physical or mental health problems from deprived back-
grounds), the ratio rose to 44 to 1 [26]. While the social return on investment approach is both
detailed and thorough, it is resource intensive. An alternative is the wellbeing-adjusted life
year or WELLBY, used by the UK government and the World Happiness Report [16,27]. One
WELLBY is equivalent to a one-point change in life satisfaction per person per year. Each
WELLBY is estimated to be worth £13,000, with lower and upper limits of £10,000 and
£16,000 [27]. The life satisfaction question is useful in two ways: (1) it gives a subjective mea-
sure of wellbeing; and (2) it enables cost-effectiveness calculations to be carried out.
In 2018, a longitudinal survey that was sent to newly registered UK parkrunners included
the life satisfaction question required to calculate cost-effectiveness using the WELLBY
approach [28]. This survey gives an opportunity to ask the following two questions:
1. To what extent does parkrun participation impact on the life satisfaction of participants?
2. What is the cost-effectiveness of parkrun?
If engagement with parkrun as a runner or walker does improve life satisfaction, a second-
ary question is: what characteristics of parkrun contribute to this improvement?
Materials and methods
Ethics statement
Approval for the research was given by the Research Ethics Committee of Sheffield Hallam
University on 24
th
July 2018 (ER7034346). Participants gave written informed consent before
completion, and all procedures were in accordance with the UK Data Protection Act of 2018
and the EU General Data Protection Regulation.
The survey
An online (Qualtrics) survey, published in Quirk et al. [23], was designed to assess the health
and wellbeing of parkrun registrants (the full details of which can be found in S1 Text); meth-
ods adhere to The Checklist for Reporting Results of Internet E-Surveys (CHERRIES) [29].
Respondents could answer up to 47 questions at each time point (with the majority being
optional) and were able to review and change answers until submitting the completed survey.
This study uses responses to the following questions:
1. Life satisfaction: “Overall, how satisfied are you with your life nowadays? where 0 is ‘not at
all satisfied’ and 10 is ‘completely satisfied’.” Respondents were provided with a visual-ana-
logue scale [30].
2. Activity level (parkrun 4-week registration question): This question is asked routinely at
parkrun registration and was repeated in the survey. “Over the last 4 weeks, how often have
you done at least 30 minutes of moderate exercise (enough to raise your breathing rate)?”
(survey emphasis). Allowed responses were: less than once per week; about once per week;
about twice per week; about three times per week; four or more times per week; rather not
say/don’t know.
3. Activity level (survey 7-day question): To allow a scale response for activity, this question
asked. “In the past week, on how many days have you done a total of 30 minutes or more of
physical activity, which was enough to raise your breathing rate. This may include sport,
exercise, and brisk walking or cycling for recreation or to get to and from places, but should
not include housework or physical activity that may be part of your job.” Possible answers
were from 0 to 7 days.
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4. Mental health status: this was measured using the Short Warwick Edinburgh Mental Well-
being Scale (SWEMWBS): “Below are some statements about feelings and thoughts. Please
tick the box that describes your experience of each over the last 2 weeks” (survey empha-
sis). Respondents were given 7 statements with the following possible responses: none of
the time, rarely, some of the time, often, all of the time [31].
5. General health status: the EQ-5D visual-analogue-scale: “We would like to know how
good or bad your health is TODAY. This scale is numbered from 0 to 100 [32].
6. Perceived impact of running or walking at parkrun: At six months, the following question
was asked: “Thinking about the impact of parkrun on your health and wellbeing, to what
extent has running or walking at parkrun changed:” Respondents were presented with a
randomised list of 16 impacts and were allowed the following responses: ‘much worse’,
‘worse’, ‘no impact’, ‘better’ and ‘much better’ and were coded as -1, -0.5, 0, 0.5 and 1
respectively.
The longitudinal survey was emailed to new parkrun registrants during the first quarter of
2019 between 14
th
January and 11
th
February 2019, and again at follow up six months later in
the third quarter between 16
th
July and 10
th
August 2019.
Matching data from parkrun and comparison with the full parkrun
population
To enable the survey data to be weighted to the UK parkrun population, parkrun provided
summative demographic data of all new registrants in the 2019 calendar year who subse-
quently completed at least one parkrun. This data included the following: age (16+); gender
(male or female); index of multiple deprivation ranking derived from postcode at registration
(converted to quartiles where the first quartile indicates the most deprived neighbourhoods);
activity level at registration (using the question above) and total number of parkruns com-
pleted. Survey respondents provided their unique parkrun ID number (from their barcode),
their date of birth and name of home parkrun to allow them to be individually matched to this
data and to the number of parkruns completed.
Seasonal effect
Since the baseline survey took place in the UK in quarter one of 2019, and the follow up survey
in quarter three, a seasonal effect on life satisfaction was accounted for using weighting factors
created by the ONS [33]. The quarter one and quarter three weighting factors were 1.00265
and 0.99846 (see S1 Data). These had the effect of increasing values in the winter when life sat-
isfaction was depressed and decreasing them in the summer months when life satisfaction was
enhanced.
Estimation of life satisfaction change and additionality due to parkrun
Life satisfaction change for each participant was calculated by subtracting the value at baseline
from the value at 6 months. The formal definition of a WELLBY is a one-point change in life
satisfaction per person per year and thus the WELLBYs for participant iwas calculated as half
the life satisfaction using:
WELLBYi¼1
2LSi6months LSibaseline
� ð1Þ
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where LS
i6months
and LS
ibaseline
represent values of seasonally adjusted life satisfaction values at
six months and baseline. The ½accounts for the change over half a year.
Three methods were used to estimate the additionality due to parkrun, i.e. the proportion
of the life satisfaction change in Eq 1 that might be attributed to parkrun participation.
Method 1 assumed that any additionality due to parkrun was caused solely by participation
as a runner or walker and was equal to an individual’s number of parkruns as a proportion of
their total activity. For participant icompleting N
i
parkruns after six months and being active
A
i
days per week at six months (using the single-item activity question), the activity additional-
ity was:
Activity additionality ¼Ni=ð26 AiÞ ð2Þ
The minimum value of this ratio was one parkrun in six months (or 182.5 days) giving an
additionality factor of 0.0055. A maximum value of 1 was possible if the participant did one
parkrun every week for 26 weeks and selected ‘one day per week’ for A
i
. WELLBYs for each
participant were calculated using the following:
WELLBY ðactivityÞi¼Activity additionalityi1
2LSi6months LSibaseline
� ð3Þ
Method 2. Survey question 6 above assessed the perceived impact of participation in park-
run as a runner or walker. The mean score of all 16 responses was used to calculate the relative
impact for each individual and was assumed to be their unique impact additionality of park-
run. For example, a participant responding ‘better’, or 0.5, to all 16 questions would have a
mean impact of 0.5 so that only half their life satisfaction change was attributed to parkrun.
WELLBYs were calculated for each participant iusing the following:
WELLBY ðimpactÞi¼Impact additionalityi1
2LSi6months LSibaseline
� ð4Þ
Method 3 assumed that additionality was caused by an equal combination of activity addi-
tionality and impact additionality. WELLBYs for each participant iwere calculated using the
following:
WELLBY ðcombinedÞi¼ðActivity additionality þimpact additionalityÞi
21
2LSi6months LSibaseline
� ð5Þ
Total WELLBYs and benefit-cost ratio
The weighted WELLBY for each individual was calculated using Eqs 1to 5, allowing a mean
value for each method to be determined. Total WELLBYs for 2019 were calculated by multi-
plying the mean for each method by the number of newly registered participants in 2019
(400,167). Benefit-cost ratios were calculated using the following:
Benefit cost ratio ¼ðTotal WELLBYs Cost of parkrunÞ
Cost of parkrun ð6Þ
The 2019 cost for operating parkrun was £4,549,499 (£2,274,749 for six months) [31].
Statistics
Preliminary analysis. Data was validated using Microsoft Excel for Mac (v16.82) using
standard descriptors such as counts, mean, median, quartiles, minimum, maximum, skewness
and kurtosis. This resulted in 548 respondents with data at baseline and six months and
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approximately 75% matched to registration data. Survey questions were optional giving vary-
ing frequencies: all counts are shown in the analysis.
Propensity weighting was used to weight the longitudinal sample to the 2019 full parkrun
population using age, parkruns completed, activity at registration, index of multiple depriva-
tion and gender (in this order). Three weighting iterations were performed (available with full
dataset).
Primary analysis. All statistical analysis was carried out using IBM SPSS Statistics for
Mac (v26). Participant characteristics for the survey were compared to the 2019 parkrun popu-
lation using Chi-square tests (X
2
), as were categorical data between baseline and follow-up.
Paired continuous data between baseline and follow-up were compared using the Wilcoxon
Rank Test.
Effect sizes were calculated for the X
2
statistic using ϕ
c
=p(χ
2
/n(k-1) where χ
2
was the test
statistic, nwas the number of respondents and k-1 was the number of rows or columns (which-
ever was the smaller). For the Wilcoxon Rank Tests, effect size was calculated using r=Z/pn,
where Zwas the standardised test statistic and nthe number of ranked respondents [34,35].
Effect sizes <0.1 were considered small, between 0.1 and <0.2 small to moderate, and 0.2
moderate. The strength of correlations between life satisfaction, activity and mediating vari-
ables were categorised as weak r<0.1, moderate 0.1r<0.5 and strong 0.5 [34].
Mediation analysis was carried out by using partial correlations (IBM SPSS Statistics for
Mac, v26) and controlling for mediation variables. Physical health and mental health were
tested as mediating variables between activity level and life satisfaction, in a similar fashion to
previous research [36].
Results
Characteristics of the survey sample compared to the 2019 parkrun
population
Fig 1 shows summary data for the unweighted sample compared to the full 2019 parkrun pop-
ulation with data and statistical information given in Table 1. These show the following:
1. The gender distribution of the sample and the 2019 population were similar (p= 0.122)
with just under half of the sample female (44.6% vs 48.1%; Fig 1A).
2. The age distribution of the sample was skewed towards older participants (median 44 to 49
years) compared to younger participants for the 2019 population (35 to 39 years) (Fig 1B);
the two were statistically different at p<0.001 with a small effect size.
3. The distributions of the Index of Multiple Deprivation for the sample and the 2019 popula-
tion were statistically similar (p= 0.652) and showed that proportions in each IMD quartile
increased from around 10–12% in quartile one to 36–37% in quartile four (Fig 1C).
4. The distributions of activity at registration for the sample and the 2019 population were sta-
tistically similar (p= 0.917) and showed that proportions increased from around 9% for
those doing <1 day per week to around 30% for those doing around 3 days per week (Fig
1D).
5. More than 60% of the 2019 population did 1 or 2 parkruns, with proportions decreasing in
an exponential manner as the number of parkruns increased. In comparison, only 18% of
the sample did 1 or 2 parkruns with proportions decreasing in a more linear fashion as the
number of parkruns increased (Fig 1E). The distributions were significantly different
(p<0.001).
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Changes between baseline and six months
Table 2 shows that participants had run or walked a median of eight parkruns after six months;
when weighted this reduced to two parkruns. Activity level for both the weighted and
unweighted samples did not change between baseline and six months with means of 3.2 to 3.4
days per week. EQ-5D VAS mean score increased significantly from baseline to six months for
the unweighted sample from 78.1 to 81.6 (p<0.001) and from 79.0 to 81.5 for the weighted
sample (p<0.001); the effect sizes were moderate. The SWEMWBS mean score increased
between baseline and six months from 24.8 to 25.4 (p<0.001; small effect size) but showed no
change when weighted to the full population.
The mean value of life satisfaction increased significantly between baseline and six months
by 0.26 when unweighted and 0.29 when weighted (p<0.001); effect sizes were moderate.
Fig 1. Unweighted demographics of the sample compared to the 2019 parkrun population: (a) gender; (b) age; (c) index of multiple deprivation quartile of
home postcode; (d) activity at registration in days per week over the last four weeks; and (e) number of parkruns completed by 6 months.
https://doi.org/10.1371/journal.pgph.0003580.g001
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Table 1. Characteristics of the sample compared to new registrants who participated at least once in 2019. Comparisons use the chi-square test, with effect size given
by Cramer’s V (Fv).
Gender 2019 population Sample Test statistic pFv
Male 192,379 48.1% 218 44.6% 2.388 0.122 0.00
Female 207,788 51.9% 271 55.4%
Total 400,167 100.0% 489 100.0%
Age 16+ 2019 population Sample Test statistic pFv
<25 53,717 15.9% 21 4.4% 511.4 <0.001 0.04
25 to 29 49,941 14.8% 30 6.2%
30 to 34 44,322 13.1% 31 6.4%
35 to 39 42,161 12.5% 28 5.8%
40 to 44 40,188 11.9% 50 10.4%
45 to 49 38,287 11.3% 60 12.5%
50 to 54 30,245 9.0% 66 13.7%
55 to 59 20,370 6.0% 87 18.1%
60 to 64 10,304 3.1% 45 9.4%
65 to 69 5,016 1.5% 34 7.1%
70 3,350 1.0% 29 6.0%
Total 337,901 100.0% 481 100.0%
IMD Quartile 2019 population Sample Test statistic pFv
1 42,611 11.8% 49 10.1% 1.634 0.652 0.00
2 79,022 21.9% 110 22.7%
3 107,598 29.8% 151 31.1%
4 131,745 36.5% 175 36.1%
Total 360,976 100.0% 485 100.0%
Activity level
(days per week)
2019 population Sample Test statistic pFv
<1 33,206 8.6% 45 9.3% 0.950 0.917 0.00
1 56,756 14.7% 65 13.5%
2 95,287 24.7% 117 24.2%
3 114,336 29.6% 147 30.4%
4 86,566 22.4% 109 22.6%
Rather not say / don’t know 6,369
Unset -
Total 386,634 100.0% 483 100.0%
Number of parkruns completed in 6 months 2019 population*Sample Test statistic pFv
2 603,031 60.8% 87 18.1% 601.7 <0.001 0.02
3 to 4 138,502 14.0% 73 15.2%
5 to 6 70,267 7.1% 49 10.2%
7 to 8 44,462 4.5% 51 10.6%
9 to 10 32,383 3.3% 38 7.9%
11 to 12 25,242 2.5% 31 6.4%
13 to 14 20,858 2.1% 39 8.1%
15 to 16 17,165 1.7% 33 6.9%
17 to 18 14,101 1.4% 26 5.4%
19 to 20 10,830 1.1% 27 5.6%
>20 15,421 1.6% 27 5.6%
Total 992,262 100.0% 481 100.0%
*This column represents all those who participated during 2019 regardless of when they had registered.
https://doi.org/10.1371/journal.pgph.0003580.t001
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When life satisfaction was seasonally adjusted, the mean unweighted and weighted changes
were smaller at 0.22 and 0.26 respectively and were no longer statistically significant. For con-
text, Fig 2 compares the weighted, seasonally adjusted life satisfaction data at baseline and six
months to the full UK population [32]. Life satisfaction for the sample is lower than the UK
population at baseline, and higher than it after six months.
Influence of activity level at registration
Table 2 also shows mean life satisfaction segmented by activity level at registration. The least
active (<1) group had the lowest life satisfaction at baseline of 6.626 (weighted, seasonally
adjusted) and the most active (4) the greatest at 7.656. Life satisfaction increased for each
group at six months with the least active group increasing the most by 0.768 to 7.389. Fig 3A
shows the life satisfaction changes in the form of box plots and suggests lower improvement in
life satisfaction with increasing activity level to 0.056 and 0.174 for the two most active (3
and 4 groups).
Fig 3B shows the total weighted number of parkruns completed; this was higher for the
least active (mean of 4.60 and 5.35 parkruns for <1 and 1 groups) and lower for the most
active (mean of 3.11 parkruns for 4 group). Fig 3C shows total activity in days per week; 2.01
days per week for the least active (<1) group rising to 4.25 days per week for the most active
(4) group.
Table 2. Mean, median and standard deviations of variables at baseline and six months for both weighted and unweighted sample data. Standard deviations shown
in brackets.
Unweighted
Mean
Median
(Standard Deviation)
Wilcoxon Rank Test for paired
comparisons
Weighted
Mean
Median
(Standard Deviation)
Wilcoxon Rank Test for paired
comparisons
Survey item nBaseline Six
months
Change Standard-ised
test statistic
pEffect
size
nBaseline Six
months
Change Standard-ised
test statistic
pEffect
size
Number of parkruns
completed at six
months
489 9.05
8
(6.48)
468 4.07
2
(4.18)
Activity level using
single item question
(days per week)
548 3.383
3
(1.64)
3.318
3
(1.78)
-0.066
0
(1.76)
-1.040 0.298 -0.04 465 3.245
3
(1.56)
3.222
3
(1.82)
-0.023
0 (1.95)
-0.156 0.876 -0.01
EQ-5D VAS 544 78.10
80
(13.13)
81.62
85
(12.93)
3.52
5
(11.83)
7.437 <0.001 0.32 463 79.00
80
(12.33)
81.48
85
(12.90)
2.478
0
(11.61)
4.518 <0.001 0.21
SWEMWBS 506 24.82
24.6
(4.41)
25.44
25.0
(4.37)
0.625
0
(3.74)
3.775 <0.001 0.17 429 24.74
25.0
(4.36)
24.94
24.1
(4.42)
0.208
0 (3.37)
0.249 0.804 0.01
Life satisfaction 548 7.578
8
(1.55)
7.834
8
(1.49)
0.258
0
(1.32)
4.702 <0.001 0.20 465 7.469
8
(1.62)
7.758
8
(1.52)
0.288
0 (1.32)
4.904 <0.001 0.23
Life satisfaction
seasonally adjusted
548 7.599
8.0
(1.55)
7.822
8.0
(1.48)
0.224
0.0
(1.32)
-0.793 0.428 -0.03 465 7.489
8.0
(1.62)
7.746
8.0
(1.52)
0.257
0.0
(1.31)
-0.859 0.390 -0.04
Means by activity level
<1 43 7.112 7.622 0.528 40 6.626 7.389 0.768
1 65 7.497 7.888 0.391 69 7.507 7.931 0.423
2 113 7.720 7.937 0.180 114 7.422 7.731 0.297
3 145 7.821 7.901 0.093 130 7.657 7.709 0.056
4 109 7.653 8.001 0.348 105 7.656 7.830 0.174
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Self-reported impact of parkrun
Table 3 shows values from the 16 self-reported responses on the impact of running or walking
at parkrun. Responses ranged from a mean of 0.455 for ‘your sense of personal achievement’
in which most participants selected at least ‘better’, to 0.096 for ‘the amount of time you spend
with family’ in which most participants indicated ‘no impact’. The mean for all 16 questions
was 0.271, close to the median of 0.254. Health measures such as ‘your fitness’, ‘your physical
health’, and ‘your mental health’ had values above the overall mean (0.441, 0.419 and 0.291
respectively). Measures related to social connections such as ‘the amount of time you spend
with family’, ‘the amount of time you spend with friends’, ‘the number of new people you
meet’, and ‘how much you feel part of a community’ had values below the overall mean (0.096,
0.149, 0.198 and 0.247 respectively).
Additionality due to parkrun
Additionality using each of the three methods previously described are shown in Fig 4, seg-
mented by activity level at registration. Mean weighted values for the activity, impact and com-
bined additionality were 0.064, 0.271 and 0.172 respectively. Activity additionality in Fig 4A is
highest for the least active (<1) group (mean = 0.124) and lowest for the most active (4)
group (mean = 0.036).
Fig 4B shows impact additionality which are highest for the least active (<1) group (0.315)
and lowest for the most active (4) group (0.250), the difference is less marked when com-
pared to activity additionality. Fig 4C shows the combined additionality; this falls linearly from
0.225 for the least active (<1) group to 0.144 for the most active (4) group.
Fig 2. Comparison of seasonally adjusted life satisfaction from the survey with the UK population [33]. Error bars show 95%
confidence intervals.
https://doi.org/10.1371/journal.pgph.0003580.g002
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Fig 3. Weighted variables following six months of participation in parkrun as a runner or walker: (a) life satisfaction change; (b) total parkruns completed; and
(c) total activity in days per week. Circles indicate means while boxes indicate interquartile ranges and medians.
https://doi.org/10.1371/journal.pgph.0003580.g003
Table 3. Mean, standard deviation, median, minimum and maximum for impact questions relating to the impact of parkrun as a runner or walker. The responses
‘much worse’, ‘worse’, ‘no impact’, ‘better’ and ‘much better’ were coded as -1, -0.5, 0, 0.5 and 1 respectively.
“Thinking about the impact of parkrun on your health and wellbeing, to what extent has
running or walking at parkrun changed:”
N Mean Standard
deviation
Median Minimum Maximum
Your sense of personal achievement 458 0.455 0.348 0.452 -0.5 1
Your fitness 459 0.441 0.332 0.431 -0.5 1
Your physical health 459 0.419 0.312 0.409 -0.5 1
The amount of time you spend outdoors 451 0.317 0.319 0.299 0 1
Your happiness 458 0.314 0.305 0.302 -0.5 1
Your ability to be active in a safe environment 462 0.314 0.324 0.295 0 1
Your enjoyment of competing 449 0.294 0.331 0.277 -1 1
Your mental health 458 0.291 0.340 0.268 -0.5 1
Your confidence 457 0.288 0.320 0.275 -0.5 1
How much you feel part of a community 466 0.247 0.314 0.230 -0.5 1
Your ability to control your weight 457 0.244 0.327 0.220 -0.5 1
Your overall lifestyle choices (e.g. diet & smoking) 458 0.227 0.307 0.210 -0.5 1
The number of new people you meet 458 0.198 0.302 0.178 0.0 1
The amount of time you spend with friends 455 0.149 0.265 0.139 -0.5 1
The amount of time you spend with family 458 0.096 0.285 0.083 -0.5 1
To what extent has running or walking at parkrun changed your ability to manage your health
condition, disability or illness
458 0.117 0.271 0.104 -0.5 1
Mean impact 468 0.271 0.201 0.254 -0.312 1
https://doi.org/10.1371/journal.pgph.0003580.t003
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Calculation of WELLBYs
Table 4 shows mean WELLBYs for each method described by Eqs 1to 5and the total number
of WELLBYs and its value for the full parkrun population of new registrants in 2019
(N = 400,167)
Fig 4. Weighted variables used to estimate the additionality of parkrun after six months of participation as a runner or walker: (a) parkrun activity
additionality; (b) parkrun impact additionality; and (c) combined additionality (average of parkrun activity and impact additionality). Circles indicate means
while boxes indicate interquartile ranges and medians.
https://doi.org/10.1371/journal.pgph.0003580.g004
Table 4. Mean WELLBYs segmented by activity level at registration using four methods to represent additionality of participation in parkrun.
Mean WELLBYs
Activity level at registration Life satisfaction
change
Method 1 Life satisfaction change
and activity additionality
Method 2 Life satisfaction change
and impact additionality
Method 3 Life satisfaction change
and combined additionality
<1
1
2
3
4
0.769 0.029 0.232 0.159
0.423 0.035 0.129 0.082
0.297 0.011 0.082 0.042
0.056 0.002 0.039 0.020
0.174 0.021 0.074 0.048
Mean 0.257 0.016 0.087 0.053
Total WELLBYs for 2019
population N = 400,167
51,341 3,101 17,407 10,544
Value of 2019 population £667.4m £40.3m £226.3m £137.1m
Benefit-cost ratio n/a 16.7:1 98.5:1 59.3:1
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The direct conversion of life satisfaction change to WELLBYs gave a mean value of 0.257
WELLBYs (per person per year), equating to a total of 51,341 WELLBYs for six months. At
£13,000 per WELLBY this represented the total change, equivalent to £667.4m.
Using the three additionality methods, the benefit for six months change ranged from a
minimum of 3,101 WELLBYs using activity additionality to 17,407 WELLBYs using impact
additionality; the combined additionality benefit gave a value of 10,544 WELLBYs. This
equated to values of £40.3 to £226.3m using activity and impact, and £137.1m using the com-
bined approach.
Fig 5 shows the proportion of total WELLBYs in each activity level at registration; blue seg-
ments indicate the two least active groups (<1 and 1). These groups represent between 45
and 50% of the WELLBY value of parkrun, compared to Fig 1D which shows that they repre-
sent 23.3% of the parkrun population of new participants.
The minimum benefit-cost ratio of 16.7 to 1 was found using activity additionality; this
increased to 98.5 to 1 using impact additionality with a ratio of 59.3 to 1 when activity and
impact additionality were combined.
Mediation model for parkrun
Fig 6 shows a possible mediation model between physical activity and life satisfaction following
participation in parkrun for 6 months, as suggested by Maher et al. [36].
In the test of physical health as a mediator, activity was moderately correlated with physical
health (r= 0.227, p<0.001), and physical health was also strongly correlated with life
Fig 5. Proportion of WELLBYs for each activity level at registration for the four methods used to estimate parkrun additionality: (a) WELLBYS from all
sources; (b) Method 1, WELLBYs using parkrun activity additionality; (c) Method 2, WELLBYs using parkrun impact additionality; and (d) Method 3,
WELLBYs using a combined approach of parkrun activity and impact additionality. Blue segments indicate the two least active groups (<1 and 1).
https://doi.org/10.1371/journal.pgph.0003580.g005
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satisfaction (r= 0.541, p<0.001). There was no correlation between activity and life satisfaction
when physical health was controlled for, offering evidence of mediation.
In the test of mental health as a mediator, activity was not correlated with mental health,
although mental health was strongly correlated with life satisfaction (r= 0.609, p<0.001). A
weak correlation remained between activity and life satisfaction when mental health was con-
trolled for (r= 0.118, p= 0.013). This did not show evidence of mediation.
Finally, in the test of both mental and physical health as combined mediators there was no
correlation between activity and life satisfaction when they were both controlled for (r= 0.024,
p= 0.611. This showed evidence of mediation when mental and physical health were combined.
Discussion
Change in life satisfaction due to participation in parkrun
The first research question posed in this study was whether participation in parkrun as a run-
ner or walker impacted life satisfaction. The weighted survey results showed that after a
median of two parkruns over a period of six months, newly registered parkrunners increased
seasonally adjusted life satisfaction by a mean of 0.26. Life satisfaction went from below the
UK average to above it with a change equivalent to about two-thirds of the drop experienced
by the UK population during the COVID-19 pandemic, indicating that the change after park-
run participation is relatively large [33]. The least active were found to have the greatest
increase in life satisfaction and may reflect previous findings showing that those with the least
life satisfaction had the greatest capacity for change [21]. Despite this, the life satisfaction of
the least active group was still the lowest and remained lower than the UK average, a feature
found in other research in the UK [24].
A key question for a credible cost-effectiveness calculation is how much of the life satisfac-
tion change can be attributed to parkrun. Considering only activity additionality to represent
parkrun’s impact, the attribution (converted to percentage) is estimated to be around 6.3%. At
12.4% for the least active, this is more than three times the 3.6% for the most active. A possible
hypothesis for this is that parkrun may be new physical activity for the least active but might
mostly replace other activities for the most active.
When considering the perceived impact of parkrun, this study suggests that parkrun might
account for 27.1% of any life satisfaction change, with a gradual decrease from the least active
at 31.5% to the most active at 25.0%, a difference of only 1.3 times. The smaller differences
compared to activity additionality may be explained by a previous cross-sectional analysis of
parkrun that found proportions reporting improvements to physical health, fitness and a sense
of personal achievement, for instance, were quite similar regardless of demographic or activity
level [8]. Taking a combination of both additionality approaches suggests an overall addition-
ality of 17.2%, ranging from 22.5% for the least active to 14.4% to the most active.
Fig 6. Mediation model between physical activity and life satisfaction adapted from Maher et al. [36].
https://doi.org/10.1371/journal.pgph.0003580.g006
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The cost-effectiveness of parkrun
The second research question addressed the cost-effectiveness of parkrun. Comparing to the
cost of running parkrun for a 2019, this gave benefit-cost analysis ratios of 16.7 to 1, 98.5 to 1
and 59.3 to 1 using the activity, impact and combined methods respectively. This is between
2.8 and 16.7 times the 5.9 to 1 found for population-level interventions found previously [26].
Using a different method to calculate cost-effectiveness, a non-reviewed commission by park-
run suggested benefit-cost ratios of 120 to 1, much higher than those here [37]. The authors
included benefits from volunteering, included children below 16 and arbitrarily assumed addi-
tionality of 25%, all leading to greater assumed benefits than here.
For transparency, it is prudent to consider the minimum possible cost-effectiveness using
the data described in this study. Using the smallest benefit-cost ratio from activity additionality
of 16.7 to 1 and the lower WELLBY confidence interval of £10,000 rather than £13,000, then a
revised lowest benefit-cost ratio would be 12.8 to 1. This is still 2.2 times the value for popula-
tion-level interventions [26].
The conclusion is that parkrun is at least twice as cost effective as other population-level
physical activity interventions and potentially 16.7 times as high. In absolute terms, this makes
parkrun a suitable population intervention with potential economic benefits for the 2019 park-
run population in the UK of up to £266.3m for a half year of participation. Previous research
found that wellbeing benefits such as increased happiness and reduced stress could last at least
12 months [11] so it is possible that the potential economic benefits for 2019 would be double
the values described here (although the benefit-cost ratios would stay the same). Future
research should investigate whether any increase in life satisfaction is sustained or whether it
follows the ‘set-point’ hypothesis and reduces over time. If parkrun only represents a propor-
tion of the full increase in life satisfaction of parkrun participants, this prompts the question of
which other factors influence life satisfaction: using the combined additionality approach, only
21.7% is attributed to parkrun so that 78.3% is still unaccounted for.
Mediation effects by physical and mental health and implications for
practice
Previous research has shown that the impact of physical activity on life satisfaction is likely to
be mediated through physical and mental health [17,22,36]. The mediation analysis here also
showed that physical health was a strong mediator between levels of activity and life satisfac-
tion. Mental health alone was not found to be a mediator, although it had a notable effect
when combined with physical health. The link between parkrun participation and life satisfac-
tion, then likely stems from improvements to physical and possibly mental health.
Behaviour change to support this mediation at the population level requires careful design
and one of the features of parkrun that supports this might be its ‘framing’. Manuals for the
delivery of effective psychotherapy emphasise the need for framing of any intervention in
terms of its role, time, place and cost [38]. Parkrun addresses these as follows: its role is as a
“community event where you can walk,jog,run,volunteer or spectate” [7]; it is at the same time
every week (9am on a Saturday morning; 9.30am in Scotland and Northern Ireland); it is in
the same place (a local park); and it is free.
Parkrun has also embraced methods of behaviour change identified by Abrahams and
Michie in their taxonomy of behaviour change techniques [39,40]. These include self-moni-
toring behaviour (getting a 5 km time), providing instruction (online and at the start of every
event), encouragement (by marshals, onlookers and by email) and rewards (milestone t-shirts
for number of parkruns completed). Finally, the repeated nature and longevity of parkrun
might support participants to elevate physical and mental health over long periods of time
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Cost-effectiveness of parkrun
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through both strong and weak social connections [12], keeping life satisfaction elevated and
alleviating the issue of short-term change common for one-off mass events.
When total WELLBYs were calculated, almost half of the economic value came from the
two least active categories, or participants doing about one or less than one bout of activity per
week at registration. These are often from more deprived neighbourhoods and are traditionally
those targeted most by government interventions [41]. This study shows that trying to attract
those who are inactive is a logical approach since they experience the greatest increases in life
satisfaction and the greatest additionality due to parkrun. It should also be noted, though, that
those who are more active represent at least half the potential WELLBY benefit. Despite having
lower increases in life satisfaction and additionality, they are more numerous. This appears to
endorse the approach suggested in the Marmot review Fair Society,Healthy Lives, in which the
principle of proportionate universalism was proposed: “Actions should be universal,but with
an intensity and a scale that is proportional to the level of disadvantage” [42].
Methodological considerations
Life satisfaction and other variables used in this study will contain subjective biases. While par-
ticipants may reflect the parkrun population when weighted, the biases will still be present,
and answers may not reflect those of the population as a whole. In particular, the sample was
older and did more parkruns than the 2019 parkrun population; subjective assessments of
impact will reflect this. The additionality of parkrun has been hypothesised to be a combina-
tion of activity and health and wellbeing impact. These (and other) estimates could be checked
by comparing the WELLBY approach to a detailed social return on investment analysis [43].
Conclusions
After six months, weighted, seasonally adjusted life satisfaction increased from a mean of 7.489
to 7.746, a change of 0.257. This took participants from below the UK average to above it. Life
satisfaction was lowest for the least active and remained below the UK average after 6 months.
It was estimated that 17.2% of life satisfaction could be attributed to the additionality of park-
run and both life satisfaction change and additionality was greatest for the least active. A calcu-
lation of total WELLBYs showed that almost half came from those doing one or less bout of
activity per week at registration, and the overall benefit-cost ratio of parkrun was between 16.7
to 1 and 98.5 to 1. This was 2.8 to 16.7 times higher than other population-level interventions,
suggesting parkrun is cost-effective. Physical health was found to be a mediator between activ-
ity and life satisfaction with mental health a possible additional factor. Features of parkrun that
might be useful to other interventions were described: these include its framing (role, time,
place, cost) and ability to forge both strong and weak social ties.
Supporting information
S1 Data. ONS wellbeing data.
(XLSX)
S1 Text. Health and Wellbeing survey.
(PDF)
Acknowledgments
Many thanks to Chrissie Wellington and Mike Graney at parkrun for helping create the origi-
nal questionnaire and enabling the distribution of the survey. Thanks also to all the partici-
pants who filled out the surveys.
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Cost-effectiveness of parkrun
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Author Contributions
Conceptualization: Steve Haake.
Data curation: Steve Haake, Helen Quirk, Alice Bullas.
Formal analysis: Steve Haake.
Funding acquisition: Steve Haake, Helen Quirk.
Investigation: Steve Haake, Helen Quirk.
Methodology: Steve Haake.
Project administration: Helen Quirk, Alice Bullas.
Resources: Alice Bullas.
Supervision: Steve Haake, Alice Bullas.
Validation: Helen Quirk, Alice Bullas.
Visualization: Steve Haake.
Writing – original draft: Steve Haake.
Writing – review & editing: Steve Haake, Helen Quirk, Alice Bullas.
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PLOS GLOBAL PUBLIC HEALTH
Cost-effectiveness of parkrun
PLOS Global Public Health | https://doi.org/10.1371/journal.pgph.0003580 October 1, 2024 19 / 19
