Prevalence of symptoms
About two thirds of participants (N = 603, 65.6%) stated that they suffered from at least one of the following symptoms: facial or eyelid swelling, swelling of toes/feet or fingers/hands, swelling of extremities (arms or legs), reduced or increased urine output, concentrated or less concentrated urine, bubbly or foamy urine. Four-hundred-and-ninety-eight (N = 498, 54.2%) participants stated at least one swelling symptom, and 524 (57.0%) stated at least one urine-related symptom. On average, swelling symptoms and urine-related symptoms onset after 3.14 (± 1.56) days and 2.22 (± 1.42) days of the bicycle ride, respectively. Details on the prevalence of specific symptoms are outlined in Tables 2, 3, and 4.
The question about weight changes pre to post bike ride was answered by 644 participants (70.1%). Out of these, 37 participants who weighed themselves had gained weight and another 25 participants who did not weigh themselves felt they gained weight (total N = 62, 6.8%). Most participants lost weight or felt they lost weight (N = 582, 63.3%).
Predictors of edema (1): female sex correlates with edema-like symptoms
Overall, women suffered from swelling symptoms more frequently than men (Fig. 2 A). These differences between men and women were significant in terms of the following symptoms: facial swelling (T(1, 599) = 6.05, p < .001), eyelid swelling (T(1, 599) = 5.24, p < .001), swelling of fingers/hands (T(1, 599) = 3.15, p = .002) and swelling of extremities (T(1, 599) = 3.54, p < .001; Fig. 2 A). Other symptoms did not show significant differences between female and male participants (Tables 2, 3 and 4).
Linear regression models (Table 6) confirmed the results of the raw correlations (Table 5). Importantly, there were no sex differences in drinking strategies (ambient: T(914) = − 1.19, p = .234; thirst: T(914) = .962, p = .336; as much as possible: T(914) = −.632, p = .527). Men and women did not differ in estimated daily fluid intake (Mwomen = 5.67, Mmen = 6.02, t(121) = − 1.04, p = .296), but differed in terms of BMI (t(125) = − 5.12, p < .001). Women had a slightly lower BMI (Mwomen = 22.39) than men (Mmen = 23.78). However, as BMI was no significant predictor for any symptom (Tables 5 and 6), these differences were not considered further.
With regard to body mass-normalized daily fluid intake, men and women differed significantly. Women drank an estimated amount of 0.092 (± 0.057) liters per kg body weight per day, while men drank 0.078 (± 0.036) liters per kg (t(111) = 2.41, p = .018). However, ingested fluid amount per kg body weight neither predicted swelling symptoms in the linear regression model (t(590) = −.34, p = .734), nor urine-related symptoms (t(590) = 1.15, p = .253). Meanwhile, other relations, especially the influence of sex, remained stable in these analyses. Therefore, we did not further investigate fluid intake per body weight as a relevant covariate.
Predictors of edema (2): drinking strategies are related to edema-like symptoms
In total, 63% (N = 579) of participants affirmed that they adapted their liquid intake to ambient temperature and intensity of sweating. However, 43.2% (N = 397) stated that they drank as much as possible. Another 22.7% (N = 209) affirmed they only drank when they were thirsty. Only 0.3% (N = 3) of participants affirmed that they drank “as little as possible to reduce weight” (Fig. 1 C).
Drinking adapted to ambient temperature and sweating negatively correlated with swelling of fingers and hands and concentrated/darker urine. Further, drinking as much as possible positively correlated with overall swelling symptoms, the swelling of fingers and hands as well as toes and feet. However, drinking behavior correlated with BMI. The correlation of BMI and “drinking as much as possible” was r = .09 ([.03, .15], p = .009), while the correlation of BMI and drink “adapted to ambient” was r = −.09 ([−.16, −.03], p = .005). Additionally, there was a marginal significant correlation of BMI and “drink only when thirsty”, r = .06 ([−.01, .12], p = .089). This is why we included an interaction effect of BMI and drinking strategies into linear regression models, whenever BMI was a significant predictor.
Drinking as much as possible positively predicted overall swelling symptoms for men, and marginally, the swelling of fingers and hands. Only drinking when thirsty negatively predicted the following symptoms: eyelid swelling (marginally), swelling of fingers and hands in men, and swelling of extremities (arms/legs). Additionally, only drinking due to thirst was negatively related to increased urine output and less concentrated/lighter urine. Drinking adapted to ambient temperature negatively predicted concentrated/darker urine. The estimated liquid intake per day did not have any effects on dependent variables (Tables 5 and 6).
Predictors of edema (3): intake of analgesics correlates with edema-like symptoms
Two-hundred-and-sixty (N = 260; 28.3%) participants took analgesics due to pain during the specific long distance bike ride. In addition, sixty-nine (N = 69, 7.5%) participants stated that they took analgesics preventively (before pain occurred). The remaining participants (N = 610, 66.4%) stated that they took no analgesics at all. The most frequently used analgesics were non-steroidal anti-inflammatory drugs (NSAIDs, N = 218), paracetamol (N = 55), and opioids (N = 9) (Fig. 1 D). While the use of analgesics was unrelated to urine-related symptoms, it moderately correlated with swelling symptoms. In particular, use of analgesics correlated with facial swelling, eyelid swelling, swelling of toes and feet, fingers and hands, and of extremities (Table 5, Fig. 2 B). Linear multiple regression analyses showed that use of analgesics was positively related to all swelling symptoms, but not to urine-related symptoms (Table 6).
Predictors of edema (4): electrolyte intake does not correlate with edema-like symptoms
In regression models, electrolyte intake was positively related to increased urine output, darker and lighter urine, but unrelated to swelling symptoms (Table 6). Comparison of participants who took electrolytes and participants who did not, as well as correlational analyses (Tables 5 and 6), did not identify any relations (swelling symptoms, overall: T(1, 601) = .631, p = .528; facial swelling: T(1, 601) = .603, p = .547; eyelid swelling: T(1, 601) = −.539, p = .590; swelling of toes/feet: T(1, 601) = −.061, p = .951; swelling of fingers/hands: T(1, 601) = 1.41, p = .159; swelling of extremities: T(1, 601) = .524, p = .601; reduced urine output: T(1, 601) = − 1.06, p = .289; increased urine output: T(1, 601) = − 1.48, p = .140; concentrated/darker urine: T(1, 601) = − 1.43, p = .152; less concentrated/lighter urine: T(1, 601) = .496, p = .620 and bubbly/foamy urine: T(1, 601) = − 1.28, p = .201).
Predictors of edema (5): intake of contraceptives does not correlate with edema-like symptoms
Twenty-six (2.8% of the total sample; twenty-five female, one diverse) took hormonal contraceptives at the time of the specific long bicycle ride. Twenty-four of those participants using hormonal contraceptives claimed any kind of symptom. Although groups of participants taking hormonal contraceptives and participants not doing so were naturally unequally sized, additional analyses were performed to gain further insights in the role of hormonal contraceptives for developing swelling symptoms. First, the group taking contraceptives and the group not taking contraceptives were compared using a t-test for independent samples. Results showed a significant difference for facial swelling (t(596) = − 2.05, p = .041) and eyelid swelling (t(596) = − 2.27, p = .024) only, with participants taking contraceptives claiming higher prevalence of swelling symptoms than the others (facial: M = 1.71 (SD = .15) vs. M = 1.34 (SD = .04); eyelid: M = 1.75, (SD = .17) vs. M = 1.34 (SD = .04). For all other symptoms, there were no significant differences.
However, when further investigating the role of contraceptives in the interplay with other variables, these differences were not maintained in multiple linear regression analyses. Predicting facial swelling, the intake of contraceptives was no longer influential (β = .00, t(585) = .03, p = .973), when simultaneously taking daily distance, duration of the bike ride, BMI, electrolyte intake, sex, drinking strategies and analgesic intake into account (model statistics: F(585) = 3.01, p < .001). Similarly, use of hormonal contraceptives was not influential for predicting eyelid swelling in linear multiple regression analysis (β = .00, t(585) = .50, p = .618; model statistics: F(585) = 2.88, p < .001). All results of these multiple regression models are summarized in Table 7.
Predictors of edema (6): menopause does not correlate with edema-like symptoms
Menopause usually starts around the age of 50 [17]. To investigate the effects of menopause, we compared women younger (N = 86) and older (N = 16) than the age of 50. The following results need to be considered in awareness of the rather small sample size of menopaused women (Fig. 1). In a t-test for independent samples, no significant differences were found; women over the age of 50 did not differ from other female participants in terms of any potential kidney-related symptom.
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