Can period-related symptoms predict menstrual manipulation among Australian female cyclists?

Figures & data

Table 1. Demographic of Australian Female Cyclists.

Demographic	Elite		Sub-elite		Recreational		Total
	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Age (years)*^	29.1	8.4	30.9	10.3	38.3	8.7	33.5	10.2
Height (m)	1.68	0.84	1.67	0.61	1.67	0.62	1.67	0.65
Body mass (kg)*^	61.0	13.6	60.6	6.6	67.2	11.2	63.2	10.2
Years cycling at current level	3.9	2.1	5.0	3.8	5.0	5.8	4.8	4.5
No. of cycling sessions per week*^~	8.0	2.6	6.4	1.9	4.1	2.0	5.7	2.5
Total weekly cycling time (hours)*^~	16.4	5.4	13.4	5.0	7.1	4.4	11.4	5.9
Menstrual manipulation (%)	39		48		42		44
	n = 28		n = 98		n = 79		n = 205

Elite refers to a cyclist who participates in international competitions including world cups, world championships and world tour cycling events. Sub-elite refers to a cyclist who participates in domestic competitions such as state championships, national championships and national series cycling events. The n value indicates the number of respondents per categorical variable.

p<.05 recreational vs. elite.

p<.05 recreational vs. sub-elite.

~p<0.05 sub-elite vs. elite.

Table 2. Menstrual characteristics of Australian female cyclists.

		n=205
Age at menarche	<11	29
	12 to 14	141
	>15	32
	do not know	1
	(missing)	2
		MC	OC	AC
		n=114	n=51	n=40
Regular periods	yes	84	35	17
(menstruation/withdrawal bleed)	no	29	7	21
	do not know	0	7	1
	(missing)	1	2	1
Heavy period flow	yes	37	11	13
	no	45	24	4
	(missing/NA)	3/29	9/7	2/21
Period length	1 to 2 days	1	2	0
	3 to 4 days	42	18	9
	5 to 6 days	31	13	6
	7 to 8 days	7	2	0
	>9	1	0	2
	(missing/NA)	3/29	9/7	2/21
No of periods in the last year	12 or more	35	9	7
	9 to 11	34	16	8
	6 to 8	12	7	1
	3 to 5	0	3	0
	0 to 2	1	0	1
	(missing/NA)	3/29	9/7	2/21
Period change with exercise	Yes	44	18	23
	No	67	31	16
	(missing)	3	2	1

Abbreviations. MC, menstrual cycle; OC, oral contraceptive; AC, alternative contraceptive (i.e., those using non-OC forms of contraceptives).

The n value indicates the number of respondents per categorical variable.

Table 3. Menstrual manipulation per sports performance level.

Elite (n = 28)
No (MC)				Yes (OC)				Yes (AC)
n = 17 (61%)				n = 5 (18%)				n = 6 (21%)
Previous OC Use	Yes	65%	11	Extended OC regime	Yes	60%	3	Previous OC Use	Yes	83%	5
	No	35%	6		No	40%	2		No	17%	1
Sub-Elite
No (MC)				Yes (OC)				Yes (AC)
n = 51 (52%)				n = 31 (32%)				n = 16 (16%)
Previous OC Use	Yes	73%	37	Extended OC regime	Yes	40%	12	Previous OC Use	Yes	88%	14
	No	27%	14		No	60%	18		No	12%	2
Recreationally Active
No				Yes (OC)				Yes (AC)
n = 46 (58%)				n = 15 (19%)				n = 18 (23%)
Previous OC Use	Yes	76%	35	Extended OC regime	Yes	60%	9	Previous OC Use	Yes	94%	17
	No	24%	11		No	40%	6		No	6%	1
Total
No (MC)				Yes (OC)				Yes (AC)
n = 114 (56%)				n = 51 (25%)				n = 40 (20%)
Previous OC Use	Yes	73%	83	Extended OC regime	Yes	47%	24	Previous OC Use	Yes	90%	36
	No	27%	31		No	53%	27		No	10%	4

Abbreviations: OC, oral contraceptive; AC, alternative contraceptive (i.e., those using non-OC forms of contraceptives); MC, menstrual cycle (i.e., not using menstrual manipulation).

Extended OC regime refers to taking active pills for two or more cycles, skipping the sugar pill within each 28-day cycle. The n value indicates the number of respondents per categorical variable and categorical variables are summarised as percentage (count).

Table 4. Perceived Effects of Period on Training and Performance.

Impact of period pain on training	MC		OC/AC		Total
	n	%	n	%	n	%
No effect	28	32	14	20	42	27
Skipping training sessions	18	21	22	32	40	26
Causes fatigue	15	17	11	16	26	17
Causes discomfort	20	23	6	9	26	17
Causes pain	11	13	10	14	21	13
Requires modifications to training	9	10	12	17	20	13
Ignored effect	15	17	2	3	17	11
Demotivated for training	4	5	11	16	15	10
Reduced quality of training	9	10	4	6	13	8
Takes medication	7	8	6	9	13	8
Causes decreases in performance	6	7	3	4	9	6
Increases sense of well-being	1	1	0	0	4	3
Differs per period	2	2	3	4	5	3
Decreases sense of well-being but increases performance	2	2	0	0	2	1
Causes increases in performance	2	2	0	0	2	1
N	87		69		156
Changes in tolerance to workout
High-intensity interval training	46	42	27	35	73	39
No effect	21	19	19	24	40	21
Temperature regulation	17	16	13	17	30	16
Endurance	16	15	12	15	28	15
Fatigue	14	13	6	8	20	11
Energy	10	9	3	4	13	7
Power	9	8	4	5	13	7
Gym	4	4	7	9	11	6
Better tolerance	8	7	3	4	11	6
Motivation	5	5	6	8	11	6
Recovery	6	6	3	4	9	5
Sleep	3	3	1	1	4	2
Others	2	2	1	1	3	2
Altitude	2	2	1	1	3	2
N	109		78		187
Changes in mood states
Irritable	73	68	33	42	106	57
Emotional	24	22	22	28	46	25
Tired	18	17	15	19	33	18
Depressed	21	20	12	15	33	18
No change	10	9	10	13	20	11
Unmotivated	3	3	8	10	11	6
Sensitive	3	3	7	9	10	5
Anxious	3	3	6	8	9	5
Irrational	1	1	1	1	2	1
Asocial	1	1	1	1	2	1
N	107		78		185
Changes in energy levels and food cravings
Low energy	42	40	19	24	61	33
Craving sugar	38	36	21	27	59	32
No change	19	18	24	31	43	23
Hunger/cravings (unspecified)	21	20	14	18	35	19
Craving carbs	17	16	6	8	23	13
Craving salt	9	8	1	1	10	5
Craving junk food	3	3	2	3	5	3
Variable energy	0	0	3	4	3	2
Craving spicy	0	0	1	1	1	1
Craving fat	1	1	0	0	1	1
N	106		78		184

Abbreviations. MC, menstrual cycle (i.e., not using menstrual manipulation); OC, oral contraceptive; AC, alternative contraceptive (i.e., those using non-OC forms of contraceptives).

The n value indicates the number of respondents per categorical variable. The N value indicates the total number of respondents per categorical variable.

Table 5. The observed and the predicted frequencies for menstrual manipulation by logistic regression with the cut-off of 0.50.

	Predicted
Observed	Yes	No	% Correct
Yes	80	26	75.5
No	47	31	39.7
Overall % Correct			60.3

Sensitivity = 75.5%. Specificity = 39.7%. False positive = 47/(47+80) = 37%. False negative = 26/(26+31) = 46%.

Table 6. Predictor coefficients for the model predicting menstrual manipulation.

					95% CI for Odds Ratio
Predictor	$\mathit{\beta }$ (S.E.)	Wald’s x^2	df	Sig., p	Lower	Odds Ratio	Upper
Tolerance	−0.458 (0.201)	5.185	1	0.023	0.426	0.632	0.938
Mood	0.113 (0.205)	0.305	1	0.581	0.750	1.120	1.673
Energy	−0.070 (0.167)	0.174	1	0.676	0.672	0.933	1.294
Pain	−0.165 (0.171)	0.937	1	0.333	0.607	0.848	1.185
Constant	1.052 (0.521)	4.084	1	0.043		2.865

R² = .070 (Hosmer & Lemeshow), .064 (Cox & Snell), .086 (Nagelkerke). Model x²(1) = 12.11, p = 0.017.