What are the biopsychosocial risk factors associated with pain in postpartum runners? Development of a clinical decision tool

Shefali Mathur Christopher; Chad E Cook; Suzanne J Snodgrass

doi:10.1371/journal.pone.0255383

. 2021 Aug 12;16(8):e0255383. doi: 10.1371/journal.pone.0255383

What are the biopsychosocial risk factors associated with pain in postpartum runners? Development of a clinical decision tool

Shefali Mathur Christopher ^1,^2,^*, Chad E Cook ^3,^4,^5,^#, Suzanne J Snodgrass ^2,^#

Editor: Chris Harnish⁶

PMCID: PMC8360599 PMID: 34383792

Abstract

Background

In 2019, a majority of runners participating in running events were female and 49% were of childbearing age. Studies have reported that women are initiating or returning to running after childbirth with up to 35% reporting pain. There are no studies exploring running-related pain or risk factors for this pain after childbirth in runners. Postpartum runners have a variety of biomechanical, musculoskeletal, and physiologic impairments from which to recover from when returning to high impact sports like running, which could influence initiating or returning to running. Therefore, the purpose of this study was to identify risk factors associated with running-related pain in postpartum runners with and without pain. This study also aimed to understand the compounding effects of multiple associative risk factors by developing a clinical decision tool to identify postpartum runners at higher risk for pain.

Methods

Postpartum runners with at least one child ≤36 months who ran once a week and postpartum runners unable to run because of pain, but identified as runners, were surveyed. Running variables (mileage, time to first postpartum run), postpartum variables (delivery type, breastfeeding, incontinence, sleep, fatigue, depression), and demographic information were collected. Risk factors for running-related pain were analyzed in bivariate regression models. Variables meeting criteria (P<0.15) were entered into a multivariate logistic regression model to create a clinical decision tool. The tool identified compounding factors that increased the probability of having running-related pain after childbirth.

Results

Analyses included 538 postpartum runners; 176 (32.7%) reporting running-related pain. Eleven variables were included in the multivariate model with six retained in the clinical decision tool: runner type-novice (OR 3.51; 95% CI 1.65, 7.48), postpartum accumulated fatigue score of >19 (OR 2.48; 95% CI 1.44, 4.28), previous running injury (OR 1.95; 95% CI 1.31, 2.91), vaginal delivery (OR 1.63; 95% CI 1.06, 2.50), incontinence (OR 1.95; 95% CI 1.31, 2.84) and <6.8 hours of sleep on average per night (OR 1.89; 95% CI 1.28, 2.78). Having ≥ 4 risk factors increased the probability of having running-related pain to 61.2%.

Conclusion

The results of this study provide a deeper understanding of the risk factors for running-related pain in postpartum runners. With this information, clinicians can monitor and educate postpartum runners initiating or returning to running. Education could include details of risk factors, combinations of factors for pain and strategies to mitigate risks. Coaches can adapt running workload accounting for fatigue and sleep fluctuations to optimize recovery and performance. Future longitudinal studies that follow asymptomatic postpartum women returning to running after childbirth over time should be performed to validate these findings.

Introduction

Injuries are the most common reason for reductions or termination of recreational and fitive running [1–3]. Injury incidence, specifically overuse-related injury, has been reported to occur in as many as 92.4% of recreational runners [4]. Because of significant psychological and physiological health benefits, it is best to keep runners running. Running is associated with reductions in psychological distress, depression, anxiety, and improves one’s self-image, and mood [5], and is also linked with a decreased risk of cardiovascular disease, and assists with weight management [6,7].

Postpartum runners potentially make up a large portion of community runners as a majority of women participating in running events have been documented to be of childbearing age [8]. In one study of 406 postpartum runners, 45% returned to running within 4 weeks after childbirth, and 70% returned within 8 weeks [9]. Returning to running after childbirth is challenging for many women [10] as a postpartum woman experiences a plethora of perinatal related musculoskeletal and physiological changes. Early return to running may negatively influence the traditional healing and recovery processes associated with childbirth (e.g., pelvic floor damage, scarring, and strength loss) [11]. Receiving rehabilitative postpartum care is not common practice with respect to timing, intensity, and running [12]. Presently, there are no peer reviewed return to sport protocols guiding postpartum women [12], which may be one of the reasons that 35% of postpartum runners experience running-related pain [10].

A recently published Delphi study had experts report on common musculoskeletal deficiencies and risk factors that contribute to pain in postpartum runners [13]. The included risk factors and deficiencies consisted of strength impairments (abdominal, hip, and pelvic floor weakness, range of motion impairments (hip extension restriction, anterior pelvic tilt, and general hypermobility), flexibility impairments (abdominal wall, and tightness in hip flexors, lumbar extensors, iliotibial band, and hamstrings) and alignment impairments (Trendelenburg sign, dynamic knee valgus, lumbar lordosis, over-pronation, and thoracic kyphosis). The risk factors identified by the expert group for pain in postpartum runners were hip pain, decreased exercise tolerance, pain during pregnancy, trying to exercise “too much too soon”, life stressors, and pelvic floor trauma.

Experts, not postpartum runners, proposed the previously described risk factors and impairments. To our knowledge, an in-depth investigation identifying risk factors associated with running-related pain in postpartum runners has not been published. This study aims to identify potential risk factors associated with pain in postpartum runners, in a case control group of postpartum runners with and without a self-report of running-related pain. This study also aims to explore the compounding effects of multiple associative risk factors by developing a clinical decision tool to identify postpartum runners at higher risk for pain.

Methods

Design and reporting standards

An international cross-sectional survey was conducted between December 2019 and January 2021. This web-based, anonymous survey was available through social media and flyers posted in public spaces likely to be frequented by postpartum runners in Durham, Raleigh and Burlington, North Carolina, United States. The flyer was also emailed to physiotherapists colleagues, who treat runners or postpartum women, to share the survey flyer on their social media and post in their clinics all around the United States. This study was approved by university institutional review boards. Before consenting to take the survey, participants were provided with study details (S1 File). Methods and results are reported in accordance with the checklist of reporting results of internet e-surveys (CHERRIES) guidelines [14].

Survey

As no standardized questionnaires were identified to investigate pain in postpartum runners, this survey was designed utilizing previously published works on postpartum runners [9,10,12,13,15,16]. The survey draft was reviewed by five experts and content was edited. The survey was then piloted by six postpartum runners to test usability and functionality before launching. Pilot data were not included in the statistical analysis.

Participants were women 18-years and older who had given birth to at least one child in the past three years, were running at least one time per week or trying to run but were unable to due to pain, and were not currently pregnant [17,18]. The first three questions of the survey confirmed eligibility and the survey terminated if inclusion criteria were not met (S1 File). Those who were not running at least once a week were provided an additional question to identify the reason for their limitation (pain, time or other), and were included if pain was the limiting factor. The youngest child’s date of birth was provided to confirm eligibility.

Study variables

Descriptive/Independent variables

For demographics, age (years), parity (primiparous or multiparous), race (Caucasian or other), education (high school or greater) and relationship status (married yes/no) were collected. For postpartum variables, diastasis recti diagnosis (yes/no), breastfeeding status (yes/no), incontinence (any), delivery type of youngest child (vaginal, cesarean, or other), fatigue (yes/no), postpartum accumulated fatigue scale score (PAFS), Edinburgh postpartum depression score (EDPS), average hours of sleep (hours) and average sleep interruptions (1–5 or more) were collected. To collect running-related variables participants reported on average weekly running amount (miles), time to first postpartum run (weeks), type of runner (e.g., novice, recreational, competitive/elite), and previous running-related injury (RRI). Since the survey spanned the COVID-19 pandemic, in April 2020 a question regarding the pandemic’s effect on running mileage was added to the survey.

When possible, validated surveys were used to measure postpartum related variables. Postpartum fatigue, a common postpartum symptom, was measured by asking participants if they experienced fatigue, and those that said yes answered the PAFS. Those that said no, were coded as 0 for the PAFS score. The PAFS includes questions covering three areas of physical, emotional, and cognitive fatigue and has good validity and internal consistency [19,20]. To measure depression, we used the EPDS, a self-report questionnaire designed to screen new mothers [21] and their emotional experience over the previous seven days [21,22]. It is a widely used screening instrument used for assessing depression and anxiety in the perinatal population [22,23].

Outcome/Dependent variables

In this survey, a report of pain associated with running was the outcome variable. Participants were asked if they had current pain when running, which was scored as “yes” or “no”. Although pain descriptors and alleviating/aggravating factors were collected, they were not included in this study.

Missing values

The raw survey data were evaluated for missing values and there were 12 (0.10%), reflected in a total of 6 (1.11%) cases. Little’s test for missingness showed the data were missing completely at random. We performed multiple regression-based imputation to replace missing values and pooled the results of five iterations. Upon completion, all analyses were performed on the pooled imputed dataset.

Statistical analysis for data modeling

All analyses were performed using SPSS version 26.0.0.2 (IBM corp. Armonk, NY, USA). Descriptive statistics representing raw data for the categories were calculated, including means and standard deviations or proportions and percentages. When appropriate, frequencies and distributions were also calculated. Independent samples t-tests or Chi square tests were performed to understand differences between postpartum runners with and without pain. Before the bivariate analysis, continuous variables were converted to binomial variables using the midpoint of the ROC (Receiver Operating Characteristic) curve generated discrimination threshold.

Bivariate logistic regression modeling

For the initial step of clinical decision tool modeling, we evaluated dedicated cumulative combinations of factors that were related to postpartum running-related pain [24,25]. Bivariate relationships (one predictor to a single outcome) were analyzed with 19 unique logistic regression analyses for the outcome variable (do you currently have pain with running?). When there were multiple variables that measured the same construct (e.g., fatigue and PAFS score or sleep average hours and number of sleep interruptions) the authors identified the single item or scale that most accurately reflected the latent construct. For example, PAFS total score was used for fatigue and for sleep average hours was used for sleep.

Multivariate logistic regression modeling

Variables that achieved a P value of <0.15 in their bivariate logistic regression were retained in the multivariate regression analysis [24]. To ensure appropriate modeling, multicollinearity was assessed for each of the retained variables using Phi and Cramer’s V to reflect the data type (nominal). Variables with multicollinearity R values of less than 0.6 were used in the multivariate analysis. For the multivariate analysis, a backward conditional stepwise logistic regression was used [24]. Variables that had 95% confidence intervals that did not cross 1.0 were considered statistically significant.

Creation of conditions for the clinical decision tool

The retained variables in the multivariate model were used to understand the effect of the cumulative combinations of variables on the presence of pain in the sample, a feature typical to clinical decision rules modeling [24]. The retained regression variables from the aforementioned stepwise regression findings were entered into 2x2 contingency tables such that the combination of variables 1 of X, 2 of X and 3 of X and so on generated sensitivity, specificity, positive and negative likelihood ratios and 95% CIs [24]. For each combination (e.g., 1 of X), the odds ratio and 95% confidence intervals, P value and Nagelkerke R² were captured [24,26]. The Nagelkerke R² is a goodness of fit measure that helps explain the strength of the independent variable with the model [24]. We also include a post-test probability of a negative and positive finding using a post-test prevalence calculator (Diagnostic post-test probability disease calculator) [27].

Results

Participants

The survey was initiated by 826 participants; 538 who met the inclusion criteria and completed the survey were included in the final analyses (Fig 1). The majority of incomplete surveys involved women who did not meet the eligibility criteria (n = 262), which triggered the survey to terminate early. Twenty-six eligible respondents did not finish the survey. Eligible non-completers had similar average weekly running mileage and proportions of each type of runner. The percentage of non-completers with pain (11/26, 42%) was slightly higher than in the completers (176/538, 33%), suggesting the prevalence of running-related pain in postpartum women might be slightly higher than our survey suggests. Information about the demographics of non-completers could not be analyzed as most did not complete that section of the survey.

Among the 538 eligible respondents who completed the survey, 176 (32.7%) reported current pain with running. Postpartum runners in pain had significantly higher prevalence of any incontinence, vaginal deliveries, fatigue, sleep interruptions, novice runners in the group, previous RRI and PAFS and EPDS scores. They also had lower education level, less average weekly hours of sleep, lower total weekly running mileage, when compared to postpartum runners without pain (Table 1).

Table 1. Descriptive statistics for demographics, postpartum, running and COVID-19 related factors between postpartum runners with (N = 176) and without self-reported running-related pain (n = 362).

Variables		Total (N = 538)	Postpartum pain (N = 176)	Postpartum no pain (N = 362)	P value
Demographics	Mean age (SD)	33.62(4.04)	33.38 (3.81)	33.74 (3.75)	0.300
	Parity (1 child)	237 (44.1)	84 (47.7)	153 (42.3)	0.231
	Race (Caucasian)	503 (93.5)	163 (92.6)	340 (93.91)	0.564
	Education level (≥High school)	503 (93.5)	158 (89.8)	345 (95.3)	0.015^*
	Relationship status (Married)	526 (97.7)	170 (96.6)	356 (98.3)	0.200
Postpartum
	Diastasis Recti diagnosis	115 (21.4)	46 (26.1)	69 (19.1)	0.060
	Breastfeeding/pumping	252 (46.8)	85 (48.3)	167 (46.1)	0.637
	Incontinence	230 (42.8)	95 (54.0)	135 (37.3)	<0.01^*
	Delivery type
	Vaginal	367 (68.2)	130 (73.9)	237 (65.5)	0.050^*
	Cesarean	132 (24.5)	37 (21.0)	95 (26.2)	0.187
	VBAC/Vaginal assisted	39 (7.3)	9 (5.1)	30 (8.3)	0.183
	Fatigue (yes)	461 (85.7)	164 (93.2)	297 (82.0)	0.001^*
	Mean Postpartum accumulated fatigue scale (PAFS) score (SD)	10.54 (8.07)	13.59 (8.67)	9.05 (7.33)	<0.001^*
	Mean Edinburgh Postpartum Depression score (SD)	6.70 (4.84)	7.38 (5.07)	6.37 (4.69)	0.024^*
	Mean hours of sleep per night (SD)	6.67 (1.17)	6.40 (1.21)	6.80 (1.12)	<0.001^*
	Missing values	1 (0.2)	1 (0.6)	0 (0.0)
	Number of sleep interruptions per night^‡
	0	46(8.6)	11 (6.3)	35 (9.7)	<0.001^*
	1	156 (29.0)	41 (23.30)	115 (31.77)
	2	168 (31.2)	59 (33.52)	109 (30.11)
	3	94 (17.5)	29 (16.48)	65 (17.96)
	4	37 (6.9)	17 (9.66)	20 (5.52)
	5	15 (2.8)	3 (1.70)	12 (3.31)
	>5	22 (4.1)	16 (9.09)	6 (1.66)
Running
	Mean total weekly mileage (SD)	13.07 (12.37)	11.80 (12.99)	13.68 (12.02)	0.100^*
	Missing values	1 (0.2)	1(0.6)	0 (0.0)
	Mean weeks to first run postpartum(SD)	12.72(14.31)	14.53 (16.80)	11.85 (12.87)	0.066
	Missing values	4 (0.7)	3 (1.7)	1 (0.3)
	Runner type
	Missing values	2 (0.4)	2 (1.1)	0 (0.0)
	Runner type- Novice	36 (6.69)	20 (11.5)	14 (3.9)	0.001^*
	Runner type- Recreational	401 (74.54)	126 (72.4)	275 (76)	0.380
	Runner type- Elite	101 (18.77)	28 (16.1)	73 (20.2)	0.260
	Currently running with stroller	292 (54.2)	94 (53.4)	197 (54.4)	0.826
	Previous running injury	292 (54.3)	108 (61.4)	184 (50.8)	0.021^*
COVID-19	COVID related running changes^‡	164 (30.3%)
	Mileage increased	64 (11.9)	20 (35.71)	44 (40.74)	0.359
	Mileage decreased	31 (5.8)	14 (25%)	17 (15.74)
	No change in mileage	69 (12.8)	22 (39.30)	47 (43.52)

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*Significant P <0.05; VBAC-vaginal birth after cesarean

‡- Chi square test; Variables represent number (%) or t-test analysis unless otherwise noted.

Bivariate modeling of associative factors and pain during running

Of the nineteen variables analyzed in the bivariate models, eleven were retained in the multivariate model (Table 2). There were no variables removed for multicollinearity (r values were well below 0.6). When the eleven variables were entered into the multivariate analysis, six variables were retained and were associated with pain with running (Table 3).

Table 2. Bivariate relationship between risk factors and having current pain with running.

Variable (Binomial distinction)		Odds ratio (95% CI)	P value	Nagelkerke R²
Demographics	Parity (only 1 child)	1.23 (0.87, 1.79)	0.232	0.004
	Race (Caucasian)	0.81 (0.40, 1.65)	0.564	0.001
	Education (≥High school)	0.43 (0.22, 0.86)	0.017^*	0.014
	Relationship status (Married)	0.48 (0.15, 1.50)	0.206	0.004
	Diastasis recti diagnosis (yes/no)	1.50 (0.98, 2.30)	0.061^*	0.009
Postpartum	Breastfeeding/pumping (yes/no)	1.09 (0.76, 1.56)	0.637	0.001
	Incontinence: Urine, feces and/or gas (yes/no)	1.97 (1.37, 2.84)	<0.001^*	0.034
	Delivery type- Vaginal (yes/no)	1.49 (0.10, 2.22)	0.050^*	0.010
	Delivery type- C-section (yes/no)	0.75 (0.49, 1.15)	0.188	0.005
	Delivery type- Other (yes/no)	0.40 (0.28, 1.29)	0.187	0.005
	PAFS score (≥19)	2.84 (1.70, 4.73)	<0.001^*	0.041
	EPDS (≥12.5)	1.84 (1.13, 3.01)	0.014^*	0.015
	Sleep (≤6.83 hours)	2.09 (1.45, 3.01)	<0.001^*	0.040
Running	Total weekly mileage (≥15.25 miles)	0.73 (0.49, 1.10)	0.134^*	0.006
	First run postpartum (≥24.5 weeks)	1.61 (0.90, 2.89)	0.108^*	0.008
	Runner type- Novice (yes/no)	3.29 (1.62, 6.72)	0.001^*	0.027
	Runner type- Recreational (yes/no)	0.77 (0.48, 1.26)	0.302	0.002
	Runner type- Elite (yes/no)	0.81 (0.54, 1.21)	0.301	0.004
	Previous running injury (yes/no)	1.54 (1.07, 2.22)	0.022^*	0.014

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*Met criteria (p <0.15) for inclusion in multivariate model; Abbreviations; PAFS = Postpartum accumulated fatigue scale; EPDS = Edinburgh Postpartum Depression Score.

Table 3. Results of final multivariate model (backwards stepwise) demonstrating variables that are associated with current pain with running postpartum (R² = 0.161).

Variable	Odds Ratio (95% CI)	P value
Delivery type- Vaginal (yes/no)	1.63 (1.06, 2.50)	0.027^*
Incontinence (Urine, feces and/or gas)	1.93 (1.31, 2.84)	0.001^*
PAFS score (≥19)	2.48 (1.44, 4.28)	0.001^*
Amount of sleep (≤6.84)	1.89 (1.28, 2.78)	0.001^*
Runner type- Novice (yes/no)	3.51 (1.65, 7.48)	0.001^*
Previous running injury (yes/no)	1.95 (1.31, 2.91)	0.001^*

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*Significant P<0.05; Abbreviations; CI = Confidence intervals; PAFS = Postpartum accumulated fatigue scale.

Multivariate modeling of associative factors and pain during running

The sensitivity, specificity, positive and negative likelihood ratios, and probabilities of having pain with running or not (clinical decision tool) in the presence of one or more of the six identified risk factors are outlined in Table 4. The probability of having running-related pain increased with each cumulative risk factor. The use of 4 of 6 to “rule in” the risk for postpartum running pain is recommended for the clinical decision tool, as the confidence intervals of the positive likelihood at this level were narrow and it represented a moderate percentage of the sample (N = 23) with postpartum running-related pain (Table 4).

Table 4. Clinical prediction tool for postpartum running-related pain based on having different numbers of risk factors and current pain with running.

Number (of 6^*) risk factors present	Sensitivity (95% CI)	Specificity (95% CI)	Positive Likelihood Ratio (95% CI)	Negative Likelihood Ratio (95% CI)	Post-test probability when finding is positive (%)^**	Post-test probability when finding is negative (%)
1 or more	98.9 (96.3, 99.8)	5.0 (3.70, 5.40)	1.04 (1.0, 1.06)	0.23 (0.04, 1.00)	33.6	10.1
2 or more	87.5 (82.4, 91.6)	30.10 (27.60, 32.10)	1.25 (1.14, 1.35)	0.42 (0.26, 0.64)	37.8	16.9
3 or more	63.60 (57.4, 69.5)	67.4 (64.4, 70.2)	1.95 (1.61, 2.34)	0.54 (0.43,0.66)	48.7	20.8
4 or more	23.30 (18.70, 27.40)	92.80 (90.60, 94.80)	3.24 (2.01, 5.29)	0.83 (0.77, 0.90)	61.2	28.7
5 or more	5.10 (3.10, 5.70)	99.7 (98.7, 100.00)	18.51 (2.46, 390.14)	0.95 (0.94, 0.98)	90.0	31.6
6 of 6	0.60 (0.00, 0.60)	100.0 (99.70, 100.00)	Inf (0.12, Inf)	0.99 (0.99, 1.00)	~100	32.5

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*Six significant variables: Runner type-novice, postpartum accumulated fatigue scale score (>19), previous running injury, most recent delivery-vaginal, incontinence and amount of sleep (<6.8 hours).

**Pre-test probability was 32.7% before statistical analysis was performed to evaluate cumulative effects of associated variables. Abbreviations; CI = Confidence intervals.

Discussion

Women are running after childbirth and up to 35% are reporting pain; however, studies investigating pain in postpartum runners are sparse [10,13]. This study is the first large survey to investigate risk factors for running-related pain in the postpartum population. By creating a clinical decision tool, this study also provides information on the compounding effect of multiple associative variables and pain in postpartum runners. In this sample of runners, the six significant variables associated with having pain were runner type-novice, postpartum accumulated fatigue scale score (>19), previous running injury, most recent delivery-vaginal, incontinence, and average amount of sleep per night (<6.8 hours). When four or more risk factors were present, our model suggested the probability of having pain increased from 32.7% (pre-test prevalence) to 61% (post-test). While this model needs validation in a longitudinal cohort of pain free postpartum runners to determine its predictive capacity, the results provide a deeper understanding of the risk factors for running-related pain in postpartum runners. This may assist health care providers educate postpartum runners and develop interventions that assist postpartum women stay injury free as they initiate or return to running.

Risk factors associated with postpartum running-related pain

One of the factors that had the highest odds for pain in postpartum runners was self-identifying as a novice runner, a finding that is consistent with previous work [28]. Novice runners are more likely to be older, have higher BMI, have had a previous injury, and have no previous experience with running [29]. Novice runners who participate in a self-devised training program are also more likely to be injured compared to those using a structured “couch to 5K” program [27] suggesting the need for a structured program that addresses the potential confounding variables that may increase the risk of injury in a novice runner.

A higher postpartum accumulated fatigue scale (PAFS) score had higher odds for pain in postpartum runners. Fatigue in new parents is well studied, with up to 64% reporting fatigue during the postpartum period [30,31]. In athlete populations, fatigue is often studied to understand underperformance and injury [32]. Insufficient recovery time, the lack of optimal training load, and training intensity have all been associated with fatigue [32,33]. Although fatigue has been well documented in postpartum and athlete populations separately, it has not been studied in postpartum athlete populations. The finding that postpartum runners with greater accumulated fatigue had higher odds of pain than those with less fatigue may highlight the need to monitor this risk factor in the postpartum running population. Coaches and clinicians may have to adjust typical running workload (frequency, intensity, duration) when working with fatigued postpartum runners. It is important to note however that fatigue is often associated with pain and thus this fatigue-related pain may not be related to running [34]. Therefore, postpartum runners initiating or returning to running should be screened not only for pain but also for fatigue which may also contribute to symptoms [34].

Previous running-related injury (RRI) was associated with higher odds for postpartum running-related pain in the current study. To our knowledge, only one previous study reported an association between pain during pregnancy and pain in postpartum runners; however, it was not clear if this previous pain was running-related [10]. A history of a previous injury has been well established as a strong risk factor for future RRI injury in numerous prospective studies investigating non-postpartum populations [4,35–39]. Whereas previous injury may be a non-modifiable risk factor, improved rehabilitation programs may assist reducing any subsequent injuries [38]. Although the relationship between previous and subsequent RRI has not been studied in any population, clinicians may want to screen postpartum runners with previous RRI to identify running-related risk factors as well as increase education on training related risk factors, to decrease pain and future injury in this population [35].

Women with incontinence were found to have increased odds of postpartum running-related pain. Nevertheless, from the results of the present study, it is unknown if the pain caused the incontinence, or the incontinence caused the pain. Pain, specifically low back pain, has been associated with urinary incontinence in large epidemiological studies [40]. Thirty percent of postpartum mothers experience urinary incontinence and 10% experience anal incontinence [41] as pregnancy and parity are well known causes of pelvic floor dysfunction [42]. Participating in a sport, specifically one with high impact such as running, is also a risk factor for incontinence [42]. This finding of an association between incontinence and pain suggests that screening for incontinence in postpartum runners should be routinely performed and that appropriate referrals to pelvic health physical therapists are recommended.

A lack of sleep was also associated with postpartum running-related pain in this study. Sleep deprivation can increase the prevalence of clinical pain and change pain processing [43,44]. Chronic insufficiency of sleep can lead to sensitization and habituation [45,46]. Postpartum women experience significant sleep disruptions after childbirth due to infant sleep and feeding patterns [47,48]. To our knowledge, no studies have investigated the effects of postpartum sleep deprivation and its relationship with performance, recovery, or injury. Nonetheless, adults need between 7 and 9 hours of sleep per night for optimal health, with athletes requiring 9–10 hours per night for optimal performance [49–51]. Sleep deprivation affects pain facilitating agents and the immune system and can hinder muscle recovery and repair of damage when exercising at high intensity [52–54]. It is important for sleep to be screened [55] and postpartum runners should be educated on worsening sleep patterns and strategies to prevent sleep related problems [55].

Mode of delivery, specifically vaginal delivery, increased the odds of pain in postpartum runners. In other studies, 74.9% of the postpartum runners with pain reported a vaginal delivery [10]. To our knowledge, no other studies have investigated delivery type and running-related pain in the postpartum population. Chronic pain intensity has been observed to be higher after vaginal delivery than caesarean delivery, severely affecting mood and quality of life [56–58]. Although we did not query participants about vaginal tearing in this survey, it may be that the participants with vaginal delivery also had significant perineal trauma, which has been linked with persistent postpartum pain [59]. The findings of this study highlight that when evaluating a postpartum runner with running-related pain, questions about delivery should be routinely asked due to the potential contribution to pain intensity and potential recovery.

Limitations

The study design is cross sectional, and correlational, consequently only non-causal associations can be inferred from the findings. Survey results are subjective to recall bias and to address this concern, analysis was restricted to three years postpartum, and runners were asked about their symptoms currently or in the past week. It is possible that this study is not a representative sample of the full postpartum running population (e.g., our sample was predominantly white, with a higher level of education). Survey methodology is limited in that it cannot collect data on possible biomechanical, musculoskeletal, and physiologic impairments that might be measured in a clinical or laboratory environment, however it is a first step towards identifying possible risk factors for running-related pain in postpartum women. There are limitations with the methodology we used to develop the clinical decision tool, methods that are traditionally used to develop clinical prediction rules [60,61]. However, these methods best reflected our purpose of combining parsimonious factors related to pain. A factor analysis or a cluster analysis could have been used, but these models do not reflect the variables associated with pain, they only reflect variables that have similar constructs (independent of pain). Further, as clinical prediction rules are generally developed from longitudinal modeling, our population sample would need to be followed over time to establish evidence for these factors as predictors of pain and clinicians should use caution when considering application of this evidence in practice. Finally, we did not measure intensity of pain in this study but merely if runners had pain or not.

Conclusion

This study created a clinical decision tool that identified the cumulative effect of six risk factors (runner type-novice, postpartum accumulated fatigue scale score (>19), previous running injury, most recent delivery-vaginal, incontinence and amount of sleep (<6.8 hours) that were associated with pain when running. With this information, clinicians can monitor and educate postpartum runners initiating or returning to running. Coaches can adapt running workload accounting for fatigue and sleep fluctuations to optimize recovery and performance. Future longitudinal studies that follow asymptomatic postpartum women returning to running after childbirth over time should be performed to validate these findings.

Supporting information

S1 File. Qualtrics survey seen by participants.

(DOCX)

Click here for additional data file.^{(125.4KB, docx)}

S2 File. Anonymized data set.

(XLSX)

Click here for additional data file.^{(731.8KB, xlsx)}

Acknowledgments

The authors would like to thank experts in the postpartum running rehabilitation field, who provided edits during survey creation and the pilot participants who provided feedback on the survey. Also, thank you to Rita Deering PT, PhD for providing expert feedback on early drafts of the manuscript.

Data Availability

Data attached in supplemental S2 File.

Funding Statement

The authors received no specific funding for this work. Participant incentives were sponsored by The University of Newcastle Research Training Program funds.

References

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PLoS One. doi: 10.1371/journal.pone.0255383.r001

Decision Letter 0

Chris Harnish

21 Jun 2021

PONE-D-21-18398

What are the biopsychosocial risk factors associated with pain in postpartum runners? Development of a clinical decision tool

PLOS ONE

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Reviewer #2: Yes

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Reviewer #1: This manuscript is well written and clearly aims to fill a void within the running/postpartum literature.

Please consider adding more information to the CONSORT diagram to include a breakdown of those with/without postpartum pain (both included and excluded in the final analysis); number of participants with missing data that were included in the analysis.

Please identify which test was used for each comparison in Table #1 (t-test or Chi-square).

Please identify if the researchers were blinded to the presence of pain among the participants during development of the tool.

I would like to first point out that I am not an expert in clinical prediction rule methodologies; however, the methods seem to utilize classical techniques. Furthermore, I suspect the target audiences will likely mirror my experience in this area. Methodological citations are largely missing. These are necessary to increase clarity and to ensure reproducibility. Please add these to justify the data cleaning/conversion methods, the bivariate and multivariate regression methods, as well as the internal validation methods employed in this manuscript.

In addition to adding citations in the "Statistical Analysis for Data Modeling" section, I would suggest a brief statement(s) speculating the impact of using other CPR methods on the final model (particularly if different factor reduction techniques were utilized).

Please clarify the "Bivariate Logistic Regression Modeling" subsection. It seems as if both 19 individual tests were conducted along with a mystery number of construct tests were conducted to assist with factor reduction. Did the results from construct tests reduce the total number of individual tests? Was only 1 variable from a construct included in the multivariate test?

Please consider varying more of the diction and syntax used in the "Statistical Analysis for Data Modeling" section as it draws heavily from citation #23.

Biomechanical, musculoskeletal, and physiologic impairments are directly identified in the "Introduction" and "Abstract" as contributors to postpartum pain with running. The authors even detail a recent Delphi study (citation #13) that considered such factors. However, the survey created and administered for this manuscript seems to only graze the surface of the potential impact that biomechanical, musculoskeletal, and physiologic impairments may have. The lack of direct biomechanical, musculoskeletal, and/or physiologic measurements included in the initial model should be directly addressed.

Reviewer #2: Overall, this is a clear, concise, and well-written manuscript. The introduction is relevant and theory based. Information about the previous study findings is presented for readers to follow the present study rationale and procedures. This is a topic with little exploration and/or previous research into identifying risk factors associated with pain in postpartum runners. The methods are generally appropriate, although clarification of a few details should be provided. At the start of the methods section, it states that the survey was administered between December 2019 and January 2021 (line 103-104), later on in the paragraph regarding study variables, it states survey was modified due to COVID in April 2021 (line 137). Please verify and adjust the dates of study collection.

Eligible participants could have had a child within three years, was the data looked at regarding when they started to have pain; did they have complications with delivery (not just tears) or recovery after childbirth, did the pain come closer to due delivery or pain when more miles were accrued, did they have pain just this time engaging in physical activity or after second or third child? I know from experience that starting to run a few weeks to months after childbirth vs. three years later is a big difference ( from both a fatigue, physically and emotionally). Moreover, where they cleared to engage in high intensity physical activity by OB/GYN.

In addition, it might have been helpful to ask if they were now running most of the miles pushing a stroller or without. Many mothers will run with a stroller and this can alter their body mechanics which can lead to injuries as well.

Using PAFS and EDPS was a great capture of data in the surveys. Statistical analysis and handle of missing data was appropriate and explained well.

Line 193- cite the calculator website in the reference section instead of in the body of the manuscript.

Overall, the results are clear and compelling. In Table 1 Columns three and four, believe it would be better to have n (%) only. No Mean +/- SD are represented in these columns. Make sure that the titles are fulling seen in rows (it could just be the way it printed out for this reviewer) but the words are cut off onto another row and hard to read.

Interesting to see the COVID- 19 data added to this manuscript. Would have been helpful to see if childcare (lack of daycare and schools) or jobs impacted as well.

In results and in the discussion, it was noted that most recent delivery – vaginal was a factor. This should be explored more. As stated in discussion, did not ask about tears or other complications. Would have been interesting to see what prior injuries the runners had previous.

Interesting sentence at end regarding pain. Pain is subjective. Many runners/ athletes may not state they have pain because they use to higher levels of discomfort.

The authors makes a contribution to the research literature in this area of investigation. Overall, this is a high quality manuscript that has implications for the theoretical basis, development, and important clinical decisions.

I believe this article should be accepted after the minor revisions are noted above. This reviewer is looking forward to reading more about this topic in the future.

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Reviewer #2: No

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PLoS One. 2021 Aug 12;16(8):e0255383. doi: 10.1371/journal.pone.0255383.r002

Author response to Decision Letter 0

29 Jun 2021

PONE-D-21-18398

What are the biopsychosocial risk factors associated with pain in postpartum runners? Development of a clinical decision tool

PLOS ONE

(Author comments: We’d like to thank the review team for such a quick, thorough, and relevant review. We understand that finding good reviewers these days is very challenging for journals and we appreciate the time you’ve dedicated to our paper. Please note that line numbers and page numbers correspond to revised manuscript with track changes.)

Review Comments to the Author: Reviewer 1

Reviewer #1: This manuscript is well written and clearly aims to fill a void within the running/postpartum literature.

(Author comments: Thank you for your time to review this manuscript and provide valuable feedback!)

• Please consider adding more information to the CONSORT diagram to include a breakdown of those with/without postpartum pain (both included and excluded in the final analysis); number of participants with missing data that were included in the analysis.

(Author comments: Thank you for your edits we have now added more information to a flow diagram -See below for track changes, a clean copy was submitted. As we imputed data for the analysis (Table 2 and 3) and accounted for it in Table 1, we did not include missing values in the revised figure. Of the missing data there were 12 (0.10%), reflected in a total of 6 (1.11%) cases and Little’s test for missingness showed this missing date were missing completely at random.)

• Please identify which test was used for each comparison in Table #1 (t-test or Chi-square)

(Author comments: We have added a symbol and a footnote for clarity to identify when a Chi-square test was used.)

• Please identify if the researchers were blinded to the presence of pain among the participants during development of the tool.

(Author comments: Yes, researchers were blinded to the presence of pain during survey development. The pilot participant data was not included in the analysis).

• I would like to first point out that I am not an expert in clinical prediction rule methodologies; however, the methods seem to utilize classical techniques. Furthermore, I suspect the target audiences will likely mirror my experience in this area. Methodological citations are largely missing. These are necessary to increase clarity and to ensure reproducibility. Please add these to justify the data cleaning/conversion methods, the bivariate and multivariate regression methods, as well as the internal validation methods employed in this manuscript.

(Author comments: We appreciate the comment. We have added a few more references that explain the “classic” methodology used in developing this tool. Our team has actually created a number of these methods previously and have endeavored to explain this process in greater detail.)

• In addition to adding citations in the "Statistical Analysis for Data Modeling" section, I would suggest a brief statement(s) speculating the impact of using other CPR methods on the final model (particularly if different factor reduction techniques were utilized).

(Author Comments: We added additional discussion about the pros in cons in the limitations and mentioned other models that could have been considered.

Line 355, page 21: There are limitations with the methodology we used to develop the clinical decision tool, methods that are traditionally used to develop clinical prediction rules [61,62]. However, these methods best reflected our purpose of combining parsimonious factors related to pain. A factor analysis or a cluster analysis could have been used, but these models do not reflect the variables associated with pain, they only reflect variables that have similar constructs (independent of pain). Further, as clinical prediction rules are generally developed from longitudinal modeling, our population sample would need to be followed over time to establish evidence for these factors as predictors of pain and clinicians should use caution when considering application of this evidence in practice.)

• Please clarify the "Bivariate Logistic Regression Modeling" subsection. It seems as if both 19 individual tests were conducted along with a mystery number of construct tests were conducted to assist with factor reduction. Did the results from construct tests reduce the total number of individual tests? Was only 1 variable from a construct included in the multivariate test?

(Author comments: There were only two constructs which required selection of single variables and those were fatigue and sleep. To make it more clear in the paper we have added text to explain the selection process for these two constructs.

Line 175 page 9: When there were multiple variables that measured the same construct (e.g., fatigue and PAFS score or sleep average hours and number of sleep interruptions) the authors identified the single item or scale that most accurately reflected the latent construct. For example, PAFS total score was used for fatigue and for sleep average hours was used for sleep.)

• Please consider varying more of the diction and syntax used in the "Statistical Analysis for Data Modeling" section as it draws heavily from citation #23.

(Author comments: The author who did the analysis for this manuscript is on citation 23 and used the standard procedures associated with all mentioned statistical analysis. This section has been modified to change syntax (page 9-10))

• Biomechanical, musculoskeletal, and physiologic impairments are directly identified in the "Introduction" and "Abstract" as contributors to postpartum pain with running. The authors even detail a recent Delphi study (citation #13) that considered such factors. However, the survey created and administered for this manuscript seems to only graze the surface of the potential impact that biomechanical, musculoskeletal, and physiologic impairments may have. The lack of direct biomechanical, musculoskeletal, and/or physiologic measurements included in the initial model should be directly addressed.

(Author Response: Prior to this survey biomechanical, musculoskeletal and physiologic impairments had not been measured in postpartum runners. Studies have mainly focused on general running populations, or postpartum participants who are non-runners to generate information for this unique population. Even the Delphi used expert clinicians working with this population to identify variables that are present in postpartum runners and did not query postpartum runners themselves. To identify which variables were present in postpartum runners with and without pain, this survey was a first attempt at collecting information in this population. Survey methodology is limited, and it cannot collect on all possible biomechanical, musculoskeletal and physiologic data. Our lab is working on biomechanical and physiologic data collections to supplement this body of literature in the next few years.

We have edited the manuscript to add a limitation related to this point:

Line 352 page 21: Survey methodology is limited in that it cannot collect data on possible biomechanical, musculoskeletal and physiologic impairments that might be measured in a clinical or laboratory environment, however it is a first step towards identifying possible risk factors for running-related pain in postpartum women.

(Author response: Thank you for your helpful feedback. We appreciate your time and insight.)

Review Comments to the Author: Reviewer 2

• At the start of the methods section, it states that the survey was administered between December 2019 and January 2021 (line 103-104), later on in the paragraph regarding study variables, it states survey was modified due to COVID in April 2021 (line 137). Please verify and adjust the dates of study collection.

(Author response: Thank you for catching this error. It should say April 2020 and has been corrected in the manuscript)

• Eligible participants could have had a child within three years, was the data looked at regarding when they started to have pain; did they have complications with delivery (not just tears) or recovery after childbirth, did the pain come closer to due delivery or pain when more miles were accrued, did they have pain just this time engaging in physical activity or after second or third child? I know from experience that starting to run a few weeks to months after childbirth vs. three years later is a big difference ( from both a fatigue, physically and emotionally). Moreover, where they cleared to engage in high intensity physical activity by OB/GYN.

(Author response: We did not collect information about when runners were cleared to resume activity because based on our experience this clearance is highly variable and dependent on the practitioner. This is directly because of the lack of information and medical guidelines for this population. However, we do report the number of weeks postpartum that participants began to run. It met the criteria for inclusion in the multivariate model but because it did not have a strong relationship with pain, was not present in the final model. This may be because a large number of factors may have influenced when they started to run and many of these would not be related to pain (e.g., when they found the time outside of mothering responsibilities and what supports they may have had for mothering). Because first time to postpartum run (in weeks) was not significant, it was not discussed or explored further. We did not ask about complicated delivery as the purpose of this survey was a first step in exploring which variables are related with postpartum pain. We do plan on further exploring this in future studies.)

• In addition, it might have been helpful to ask if they were now running most of the miles pushing a stroller or without. Many mothers will run with a stroller and this can alter their body mechanics, which can lead to injuries as well.

(Author response: This is a good point. We did collect this information; however due to the focus of this manuscript we chose not to present the analysis of this variable in the study. We have now added it to the manuscript in Table 1 (see data below).

Variables Total (N=538) Postpartum pain (N=176) Postpartum no pain (N=362) P value

Currently running with stroller 292 (54.2) 94 (53.4) 1 97 (54.4) 0.826

• Using PAFS and EDPS was a great capture of data in the surveys. Statistical analysis and handle of missing data was appropriate and explained well.

Line 193- cite the calculator website in the reference section instead of in the body of the manuscript.

(Author response: Thank you, we have cited the website now in the text (line 198, page 10) and reference section)

• Overall, the results are clear and compelling. In Table 1 Columns three and four, believe it would be better to have n (%) only. No Mean +/- SD are represented in these columns.

(Author response: Thank you for helping us improve the messaging of the table. We have now been more deliberate in representing the variables that had mean and standard deviations (page 12-13). It represents AMA formatting.)

• Make sure that the titles are fulling seen in rows (it could just be the way it printed out for this reviewer) but the words are cut off onto another row and hard to read.

(Author response: We have made sure to check this and will remember to check this in the proof as well. Thank you.)

• Interesting to see the COVID- 19 data added to this manuscript. Would have been helpful to see if childcare (lack of daycare and schools) or jobs impacted as well.

(Author response: Yes we agree. We added the question to account for change in mileage, sleep, depression etc. that would influence our understanding of pain and postpartum running. As this wasn’t a study focusing on changes in running due to COVID-19, we did not add further questions on this topic as it would have made the survey very long.)

• In results and in the discussion, it was noted that most recent delivery – vaginal was a factor. This should be explored more. As stated in discussion, did not ask about tears or other complications. Would have been interesting to see what prior injuries the runners had previous.

(Author response: Yes we agree. The mode of delivery is discussed in a paragraph on lines 335 page 21 of the paper. We agree this is an interesting topic for future research and we hope to investigate this in the future. Exploring all previous injury types was beyond the scope of this paper. As the main purpose of the paper was to identify variables associated with running-related pain, more data would have shifted the focus away from the main purpose of the current paper)

• Interesting sentence at end regarding pain. Pain is subjective. Many runners/ athletes may not state they have pain because they use to higher levels of discomfort.

(Author response: Yes we agree. And pain has multiple dimensions, which is important to capture when exploring this pain. These data would have distracted from main purpose of the current paper and were thus not included.)

• The authors makes a contribution to the research literature in this area of investigation. Overall, this is a high quality manuscript that has implications for the theoretical basis, development, and important clinical decisions.

I believe this article should be accepted after the minor revisions are noted above. This reviewer is looking forward to reading more about this topic in the future.

(Author response: Thank you for your helpful feedback. We appreciate your time and insight.)

Attachment

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PLoS One. doi: 10.1371/journal.pone.0255383.r003

Decision Letter 1

Chris Harnish

15 Jul 2021

What are the biopsychosocial risk factors associated with pain in postpartum runners? Development of a clinical decision tool

PONE-D-21-18398R1

Dear Dr. Christopher,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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PLOS ONE

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3. Has the statistical analysis been performed appropriately and rigorously?

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

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6. Review Comments to the Author

Reviewer #1: The authors have considered the questions/suggestions from both reviewers carefully and considerately; all changes made to the manuscript reflect this point.

Line 110 of the redlined copy seems uncited (I assume: Eysenbach G. Improving the Quality of Web Surveys: The Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J Med Internet Res. 2004; 6(3): e34.). The correct citation should be added. Otherwise, I believe that this manuscript is ready for publication and have reflected this point in my recommendation to "accept" rather than require "minor revision."

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PLoS One. doi: 10.1371/journal.pone.0255383.r004

Acceptance letter

Chris Harnish

3 Aug 2021

PONE-D-21-18398R1

What are the biopsychosocial risk factors associated with pain in postpartum runners? Development of a clinical decision tool

Dear Dr. Christopher:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Qualtrics survey seen by participants.

(DOCX)

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S2 File. Anonymized data set.

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Data Availability Statement

Data attached in supplemental S2 File.

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PERMALINK

What are the biopsychosocial risk factors associated with pain in postpartum runners? Development of a clinical decision tool

Shefali Mathur Christopher

Chad E Cook

Suzanne J Snodgrass

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Design and reporting standards

Survey

Study variables

Descriptive/Independent variables

Outcome/Dependent variables

Missing values

Statistical analysis for data modeling

Bivariate logistic regression modeling

Multivariate logistic regression modeling

Creation of conditions for the clinical decision tool

Results

Participants

Fig 1. Flow chart of survey study participants.

Table 1. Descriptive statistics for demographics, postpartum, running and COVID-19 related factors between postpartum runners with (N = 176) and without self-reported running-related pain (n = 362).

Bivariate modeling of associative factors and pain during running

Table 2. Bivariate relationship between risk factors and having current pain with running.

Table 3. Results of final multivariate model (backwards stepwise) demonstrating variables that are associated with current pain with running postpartum (R2 = 0.161).

Multivariate modeling of associative factors and pain during running

Table 4. Clinical prediction tool for postpartum running-related pain based on having different numbers of risk factors and current pain with running.

Discussion

Risk factors associated with postpartum running-related pain

Limitations

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Chris Harnish

Roles

Author response to Decision Letter 0

Decision Letter 1

Chris Harnish

Roles

Acceptance letter

Chris Harnish

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Cited by other articles

Links to NCBI Databases

Table 3. Results of final multivariate model (backwards stepwise) demonstrating variables that are associated with current pain with running postpartum (R² = 0.161).