The relationship between climate change worry, premenstrual syndrome severity, and quality of life among women of reproductive age and related factors^*

Objective: this study aims to determine the relationship between climate change worry, premenstrual syndrome severity, and quality of life among women of reproductive age and related factors.

Method: this cross-sectional correlational study included women of reproductive age. The sample included 614 women. Data were collected using Descriptive Information Form, Climate Change Worry Scale, Premenstrual Syndrome Scale, SF-12 Quality of Life Scale.

Results: a positive correlation was found between the Climate Change Worry Scale and Premenstrual Syndrome Scale, whereas a negative correlation was discovered between the Climate Change Worry Scale and both the physical and mental sub-dimensions of SF-12 Quality of Life Scale (p<0.05). Climate Change Worry Scale, Premenstrual Syndrome Scale and SSF-12 Quality of Life Scale showed statistically significant effects on physical and mental dimension scores (p<0.05). Accordingly, a 1-unit increase in Climate Change Worry Scale score causes a 1.11-unit increase in Premenstrual Syndrome Scale score, a 0.08-unit decrease in physical component score of quality of life scale and a 0.13-unit decrease in mental component score.

Conclusion: as climate change worry among women intensifies, the severity of premenstrual syndrome symptoms increases, while their quality of life diminishes. Climate change anxiety, premenstrual syndrom severity, and quality of life were influenced by women’s identifying characteristics.

Descriptors:
Climate Change; Women; Premenstrual Syndrome; Quality of Life; Fertility; Anxiety

Highlights:

(1) Climate change anxiety, premenstrual symptoms, quality of life were at moderate level. (2) Climate change concerns, premenstrual syndrome severity, quality of life are related. (3) As climate change concerns increase, the severity of premenstrual syndrome increases. (4) As women’s climate change concerns increase, their quality of life decreases.

Objetivo: o objetivo deste estudo foi determinar a relação entre a ecoansiedade, a gravidade da síndrome pré-menstrual e a qualidade de vida entre mulheres em idade reprodutiva, bem como identificar os fatores relacionados.

Método: este estudo transversal e correlacional incluiu mulheres em idade reprodutiva. A amostra foi composta por 614 participantes. Os dados foram coletados por meio de um formulário com informações descritivas, da Escala de Ecoansiedade, da Escala de Síndrome Pré-Menstrual e da Escala de Qualidade de Vida SF-12.

Resultados: foram encontradas uma correlação positiva entre as medidas das Escalas de Ecoansiedade e de Síndrome Pré-Menstrual e uma correlação negativa entre as medidas da Escala de Ecoansiedade e das subdimensões, física e mental, da Escala de Qualidade de Vida SF-12 (p<0,05). A Escala de Ecoansiedade, a Escala de Síndrome Pré-Menstrual e a Escala de Qualidade de Vida SF-12 apresentaram efeitos estatisticamente significativos nos escores correspondentes aos componentes físico e mental (p<0,05). Especificamente, um aumento de um ponto na Escala de Ecoansiedade resultou em um aumento de 1,11 ponto na Escala de Síndrome Pré-Menstrual; uma diminuição de 0,08 ponto no componente físico e uma diminuição de 0,13 ponto no componente mental da Escala de Qualidade de Vida.

Conclusão: à medida que a ecoansiedade se intensifica entre as mulheres, a gravidade dos sintomas da síndrome pré-menstrual aumenta e a qualidade de vida diminui. A ecoansiedade, a gravidade da síndrome pré-menstrual e a qualidade de vida estão relacionadas às características individuais da mulher.

Descritores:
Mudanças Climáticas; Mulheres; Síndrome Pré-Menstrual; Qualidade de Vida; Fertilidade; Ansiedade

Destaques:

(1) A ecoansiedade, os sintomas pré-menstruais e a qualidade de vida apresentaram níveis moderados. (2) A ecoansiedade, a gravidade da síndrome pré-menstrual e a qualidade de vida estão relacionadas. (3) A gravidade da síndrome pré-menstrual aumenta à medida que a ecoansiedade se intensifica. (4) Com o aumento da ecoansiedade nas mulheres, a qualidade de vida diminui.

Objetivo: el propósito de este estudio es determinar la relación entre ecoansiedad, gravedad del síndrome premenstrual y calidad de vida entre mujeres en edad reproductiva y detectar factores relacionados.

Método: este estudio transversal y correlacional incluyó mujeres en edad reproductiva. La muestra estuvo compuesta por 614 participantes. Los datos se recolectaron por medio de un Formulario de información descriptiva, de la Escala de Ecoansiedad, de la Escala de Síndrome Premenstrual y de la Escala SF-12 de Calidad de Vida.

Resultados: se verificó una correlación positiva entre la Escala de Ecoansiedad y la Escala de Síndrome Premenstrual; en contrapartida, se detectó una correlación negativa entre la Escala de Ecoansiedad y las subdimensiones física y mental de la Escala SF-12 de Calidad de vida (p<0,05). Las escalas de Ecoansiedad, Síndrome Premenstrual y SF-12 de Calidad de Vida presentaron efectos con significancia estadística sobre las puntuaciones correspondientes a las dimensiones física y mental (p<0,05). Específicamente, un aumento de 1 unidad en la puntuación de la Escala de Ecoansiedad causa un incremento de 1,11 unidades en la de la Escala de Síndrome Premenstrual, una reducción de 0,08 unidades en la correspondiente a la dimensión física de la Escala de Calidad de vida y una disminución de 0,13 unidades en la puntuación de la dimensión mental.

Conclusión: a medida que se intensifica la ecoansiedad entre las mujeres, la gravedad de los síntomas del síndrome premenstrual aumenta y la calidad de vida se reduce. La ecoansiedad, la gravedad del síndrome premenstrual y la calidad de vida se vieron influenciadas por las características específicas de cada mujer.

Descriptores:
Cambio Climático; Mujeres; Síndrome Premenstrual; Calidad de Vida; Fertilidad; Ansiedad

Destacados:

(1) La ecoansiedad, los síntomas premenstruales y la calidad de vida presentaron niveles moderados. (2) La ecoansiedad, la gravedad del síndrome premenstrual y la calidad de vida están relacionadas. (3) La gravedad del síndrome premenstrual aumenta a medida que se intensifica la ecoansiedad. (4) A medida que aumenta la ecoansiedad de las mujeres, su calidad de vida se reduce.

Introduction

Climate change represents a critical threat to human health(¹). The key drivers of climate change encompass solar energy fluctuations, alterations of Earth’s orbital and rotational patterns, greenhouse gas elevations, volcanic activity, and anthropogenic fossil fuel utilization(²). With escalating air pollution levels and surface temperatures, alongside more frequent extreme meteorological phenomena, climate change presents a mounting risk to global population health(¹). In this framework, climate change exerts deleterious effects on human health through water, food, and health-related issues(²).

Climate-related environmental factors, including water and food availability, societal unrest, severe weather patterns, and disease spread, manifest numerous adverse effects on vulnerable populations, particularly women. The American College of Obstetricians and Gynecologists contends that the adverse effects of climate change render women more susceptible to diseases, malnutrition, sexual violence, lack of reproductive control, adverse obstetric outcomes, and mortality(¹). Moreover, women’s perceptions of climate change may be accompanied by mental symptoms such as trauma, fear, worry, and depression, thus diminishing their quality of life(³). The effects of climate change constitute a relatively nascent area of inquiry. Therefore, it is imperative to conduct research across physical, economic, health, and social domains and to elucidate the literature to identify the effects of climate change and to develop preventive measures(¹-³).

One of the prevalent public health issues affecting more than half of women of reproductive age worldwide is Premenstrual Syndrome (PMS)(⁴). PMS manifests as a constellation of physical, behavioral, and emotional symptoms that arise approximately one week before the onset of menstruation, coinciding with the end of the luteal phase, and generally subside upon the commencement of menstrual bleeding. The frequency and severity of PMS symptoms vary between individuals. PMS encompasses physical symptoms such as increased appetite, breast swelling and tenderness, palpitations and headaches, as well as psychological symptoms including fatigue, irritability, aggression, depression, suicidal ideation, difficulty concentrating, social isolation, and a depressed mood(⁵). While the physical and mental symptoms of PMS subside within a few days, they can become challenging to manage. Accordingly, PMS adversely affects women’s daily activities, academic performance, quality of life, and productivity(⁴-⁵). The epidemiology, etiology, and pathophysiology of PMS remain insufficiently elucidated, and environmental factors are suggested to play a role in its manifestation(⁴). The Sustainable Development Goals 2030 prioritize safe and dignified menstrual management. The Healthy and Quality Life Goal (Goal 3) emphasizes universal access to sexual and reproductive health services, including education on menstrual management, including PMS. PMS is also central to achieving the Quality Education Goal (Goal 4), as it negatively impacts women’s attendance at school and work and their academic performance. In addition, effective management of PMS is closely aligned with the Gender Equality Goal (Goal 5), which aims to achieve gender equality and empowerment by addressing health-related barriers to women’s full participation in society. Furthermore, Climate Action (Goal 13) calls for urgent action to combat the impacts of climate change. However, the literature on the impacts of climate change on PMS and women’s health is insufficient(⁶). However, there is a paucity of research regarding the effects of climate change on PMS and women’s health in the literature. The main question of this research is “Is there a relationship between climate change worry, PMS severity, and quality of life among women of reproductive age?”. In this context, “What are the levels of climate change worry experienced by women of reproductive age?”, “What is the severity of PMS among women of reproductive age?”, “What are the levels of quality of life among women of reproductive age? and”Do women’s identifying characteristics affect the climate change worry experienced, Premenstrual Syndrome severity, and levels of quality of life?“. Answers to these questions are also sought. In line with these objectives, this study aims to determine the relationship between climate change worry, premenstrual syndrome severity, and quality of life among women of reproductive age.

Method

Study design

Cross-sectional studies are studies that involve observing the state of a sample at a specific time, based on observations that illuminate a specific point. This research also has a cross-sectional design, allowing data to be collected from many different individuals at a given time(⁷). This study also has a correlational design because the relationship between variables is examined(⁸).

Study population and sample

The study population consisted of women of reproductive age residing in Turkey. According to the 2021 data from the Turkish Statistical Institute, there are 22 million women aged 18-49 in Turkey(⁹). The sample size was calculated as 385 using a known sampling method, with a 5% margin of error and a 95% confidence interval, assuming p = q = 0.5. The study was completed with 614 women who met the inclusion criteria. The inclusion criteria for the study are as follows: (1) being female, (2) being of reproductive age (18-49 years), (3) being literate in Turkish, (4) owning an Android smartphone, (5) having internet access, (6) experiencing a menstrual cycle, (7) not being pregnant, (8) not having a diagnosis of menopause, (9) having no physical or mental disabilities.

Study variables

Dependent Variables: Premenstrual Syndrome Severity and Quality of Life Level.

Independent Variables: Climate Change Worry Level.

Data collection tools

Data were collected using the Descriptive Information Form, the Climate Change Worry Scale, the Premenstrual Syndrome Scale, and the SF-12 Quality of Life Scale.

Descriptive Information Form

The form, which was created by the researchers in line with the relevant literature, consists of 21 questions that include demographic characteristics of the participants such as age, education level, marital status, occupation, income level, region of residence, etc.(¹⁰-¹⁴).

The Climate Change Worry Scale (CCWS)

Developed by Stewart(³), CCWS was adapted into Turkish by Gezer and İlhan(¹⁵) by testing it on women of reproductive age. The CCWS, is a ten-item scale utilizing a five-point Likert format (ranging from Never (1) to Always (5)). The CCWS was adapted to Turkish culture to consist of two sub-dimensions: worry and helplessness. There are no reverse-scored items in the scale. The minimum and maximum obtainable scores are 10 and 50, respectively. Higher scores indicate greater levels of climate change worry. The Cronbach’s alpha coefficient for the original scale was reported as 0.91(¹⁵), while in this study, it was calculated to be 0.94.

Premenstrual Syndrome Scale (PMSS)

The PMSS, developed by Gençdoğan(¹⁶) after testing it on women of reproductive age in order to measure premenstrual symptoms and determine their severity, consists of 44 items and is a five-point Likert type (Never (1), Continuous (5)). The PMSS consists of nine sub-dimensions: depressive affect, anxiety, fatigue, irritability, depressive thoughts, pain, appetite changes, sleep changes, and bloating. The total PMSS score is obtained by summing the scores from these sub-dimensions. The minimum and maximum scores obtainable from the scale are 44 and 220, respectively, with higher scores indicating greater severity of premenstrual symptoms. When interpreting the PMSS results, the presence or absence of PMS is determined by whether the total or subscale scores exceed 50% of the maximum possible score. The Cronbach’s alpha coefficient of the PMSS was reported as 0.75(¹⁶), whereas it was calculated to be 0.97 in this study.

SF-12 Quality of Life Scale (SF-12 QoLS)

The SF-12 QoLS, developed by Ware, et al. and adapted to Turkish by Soylu, et al.(¹⁷) for women of childbearing age, contains a total of 12 items: physical functionality, physical role, body pain, general health, energy, social functionality, emotional role and mental health. Items related to physical and emotional roles are answered with Yes or No, while the remaining items are rated using Likert scales ranging from three to six points. The SF-12 QoLS is evaluated using two component scores: the Physical Component (SF-12 QoLS PC) and the Mental Component (SF-12 QoLS MC). The SF-12 QoLS PC score is derived from items related to general health, physical functioning, physical role, and bodily pain, whereas the SF-12 QoLS MC score is obtained from items assessing social functioning, emotional role, mental health, and vitality. The calculation of the SF-12 QoLS PC and MC scores is similar to that used for the SF-36 Quality of Life Scale - Short Form. The total score ranges from 0 to 100, with higher scores indicating better quality of life. The Cronbach’s alpha coefficients for the SF-12 QoLS PC and MC scores were reported as 0.73 and 0.72, respectively(¹⁷). This study calculated the Cronbach’s alpha values as 0.94 for the SF-12 QoLS PC and 0.92 for the SF-12 QoLS MC.

Data collection

Data were collected from July 2023 to March 2024 via an online survey platform (Google Forms) through self-reported responses from women residing in Turkey, using a snowball sampling method. Initially, participants from the researchers’ networks were approached, who then extended the survey to their contacts. The survey form was regularly disseminated three times a week on alternating days through social media platforms (WhatsApp, Instagram, Facebook). Women completed the self-reported survey online, with an average response time of 10 minutes. Participants were restricted from completing the form more than once to prevent duplicate responses. Upon survey completion, participants automatically received a thank-you message from the system.

Statistical analysis

The data were processed using IBM SPSS Statistics version 26.0. Descriptive statistics were calculated, including frequency distributions (count and percentage) for categorical variables and means, standard deviations, and minimum and maximum values for numerical variables. Independent samples t-test was employed to assess differences between two groups, while one-way analysis of variance (one-way ANOVA) was used to evaluate differences among more than two groups. Following one-way ANOVA, Levene’s test was used to assess the homogeneity of variance, and multiple comparison tests (Bonferroni or Tamhane’s T2) were employed to determine the specific groups responsible for significant differences. Bonferroni’s test was used for variables with homogeneous variances, whereas Tamhane’s T2 was applied when variances were heterogeneous. Pearson correlation analysis was used to examine relationships between numerical measures. Correlation coefficients obtained from correlation analyses indicate the strength and direction of the relationship between variables. Correlations between 0 and 0.30 generally indicate a weak relationship; correlations between 0.30 and 0.70 indicate a moderate relationship; and correlations greater than 0.70 indicate a high relationship(¹⁸). Linear regression analysis was conducted to evaluate factors affecting CCWS scores. The reliability of the scales was assessed using Cronbach’s alpha coefficient. Statistical significance was set at p<0.05.

Ethical considerations

Ethical approval for the study was received from Bartın University Social and Human Sciences Ethics Committee (Protocol Number: 2023-SBB-0410, Approval Date: 12.07.2023). Women participating in the survey were provided with informed information regarding the study, and their written consent was obtained via the online survey platform. The study was conducted following the principles outlined in the Declaration of Helsinki.

Results

The demographic characteristics of the women are presented in Table 1. No significant differences were observed between the demographic characteristics of the women and CCWS scores. A statistically significant difference was found between PMS scores and marital status, educational level, employment status, monthly income, and dysmenorrhea (p<0.05). Specifically, PMS scores were higher among single women, those with postgraduate education, unemployed women, women with an income lower than their expenses, and those experiencing dysmenorrhea. Statistically significant differences were also observed between SF-12 QoLS PC scores and marital status, spouse’s employment status, dysmenorrhea, presence of chronic or psychiatric conditions, and medication use (p<0.05). Higher physical component scores were found among single women, those without chronic or psychiatric conditions, and those not using medication. There were significant differences between SF-12 QoLS MC scores and marital status, educational level, employment status, and psychiatric conditions (p<0.05). Notably, higher mental component scores were noted among married women, those with a high school education or lower, employed women, and those without psychiatric conditions (Table 1).

Table 1-
Examining the differences between the descriptive characteristics of women and the CCWS*, PMSS^† and SF-12 QoLS^‡ (n^§ = 614). Turkey, 2023-2024

Values		n § (%)	CCWS *	PMSS †	SF-12 QoLS ‡
					Physical Component Dimension	Mental Component Dimension
			X̄ || ±SD ¶	X̄ || ±SD ¶	X̄ || ±SD ¶	X̄ || ±SD ¶
Marital status
Single		485 (79)	28.14±9.29	140.50±36.16	46.04±7.72	39.09±9.06
Married		129 (21)	28.85±10.17	114.12±34.93	44.30±7.97	43.65±8.73
	t ** /p ††		-0.755/0.451	7.416/<0.001	2.253/0.025	-5.123/<0.001
Education status
High school and below		121 (19.7)	27.85±10.06	123.93±41.85	45.45±8.19	43.17±9.63
University and above		493 (80.3)	28.39±9.33	137.66±35.82	45.72±7.70	39.27±8.90
	t ** /p ††		-.562/.575	7.116/0.001	-.339/.735	4.239/<0.001
Working status
Not working		460 (74.9)	28.00±9.28	138.85±36.32	45.90±7.87	39.31±8.95
Working		154 (25.1)	29.18±10.01	123.36±38.49	45.01±7.55	42.23±9.53
	t ** /p ††		-1.336/0.182	4.512/<0.001	1.226/0.221	-3.441/0.001
Education status of spouse
High school and below		57 (40.7)	24.52±10.99	108.77±36.94	44.47±9.33	44.01±8.91
University and above		83 (59.3)	28.78±9.51	117.59±32.86	44.59±7.08	43.16±8.61
	t ** /p ††		1.806/0.149	-1.483/.140	-.085/.932	.213/.573
Spouse’s employment status
Not working		12 (8.6)	26.91±10.31	112.50±43.23	49.33±8.27	44.82±9.50
Working		128 (91.4)	29.85±10.09	114.14±34.02	44.10±7.91	43.39±8.67
	t ** /p ††		-1.680/.095	-0.156/0.876	2.182/0.031	0.542/0.588
Monthly income status
Income is less than expenses		192 (31.3)	28.51±9.80	139.83±35.84 §§	44.68±8.13	39.17±9.00
Income is equal to expenses		364 (59.3)	28.44±9.14	133.98±38.02	45.97±7.61	40.53±9.31
Income is more than expenses		58 (9.4)	26.67±10.41	125.00±37.29 ||||	47.06±7.56	39.89±8.88
	F ‡‡ /p ††		0.941/0.391	3.837/0.022	2.746/0.065	1.390/0.250
Residential area
Northern Region		278 (45.3)	27.65±9.81	131.64±40.37	45.85±7.61	40.29±9.28
Western and Southern Region		178 (29.0)	29.45±9.28	137.57±35.93	45.48±8.15	39.84±9.84
Inner and Eastern Regions		158 (25.7)	28.10±9.47	137.86±33.29	45.57±7.73	39.82±8.20
	F ‡‡ /p ††		2.00/0.136	2.00/0.135	0.137/0.872	0.191/0.826
Regular menstruation
Yes		519 (84.5)	28.45±9.46	134.54±36.76	45.85±7.71	40.17±9.07
No		95 (15.5)	27.45±9.54	137.24±41.15	44.68±8.21	39.34±9.75
	t ** /p ††		0.940/0.347	-0.645/0.519	1.346/0.179	0.819/0.413
Dysmenorrhea
Yes		437 (71.2)	28.53±9.36	138.98±36.48	44.71±7.64	39.73±9.21
No		177 (28.8)	27.72±9.75	125.05±38.08	48.05±7.71	40.82±9.08
	t ** /p ††		0.958/0.338	4.232/<0.001	-4.892/<0.001	-1.334/0.183
Chronic disease
Yes		76 (12.4)	28.33±9.25	133.92±37.12	43.55±8.23	39.06±8.42
No		538 (87.6)	28.29±9.51	135.11±37.53	45.97±7.70	40.18±9.28
	t ** /p ††		0.035/0.972	-0.258/0.796	-2.547/0.001	-0.997/0.319
Psychiatric illness
Yes		40 (6.5)	28.18±10.60	143.03±34.78	42.74±9.75	36.52±6.29
No		574 (93.5)	28.30±9.40	134.40±37.59	45.88±7.61	40.29±9.30
	t ** /p ††		0.082/0.935	1.410/0.159	-2.467/0.014	-3.527/0.001
Drug use status
Yes		107 (17.4)	28.62±9.42	133.58±38.40	43.81±8.70	38.72±8.93
No		507 (82.6)	28.22±9.49	135.25±37.28	46.07±7.54	40.32±9.21
	t ** /p ††		0.389/0.698	-0.420/0.675	-2.491/0.014	-1.647/0.100
Smoking status
Yes		135 (22)	28.82±9.86	139.28±39.16	45.01±8.19	38.88±9.91
No		479 (78)	28.14±9.37	133.74±36.91	45.86±7.68	40.37±8.94
	t ** /p ††		0.734/0.463	1.519/0.129	-1.112/0.267	-1.677/0.09

• *CCWS0 = The Climate Change Worry Scale; ^†PMSS = Premenstrual Syndrome Scale; ^‡SF-12 QoLS = SF-12 Quality of Life Scale; ^§n = Number of participants; ^||X̄ = Mean; ^¶SD = Standard Deviation; **t = Independent sample t test; ^††p-value; ^‡‡F = One-way ANOVA test; ^§§The difference between the groups and the highest value; ^||||The difference between the groups and the lowest value

The mean age of the participants was 24.41±8.08 years, number of pregnancies was 1.87±1.29, and the mean body mass index (BMI) was 22.71±4.29. The mean CCWS score was 28.29±9.47, PMSS score was 134.96±37.45, SF-12 QoLS PC score was 45.67±7.80, and SF-12 QoLS MC score was 40.04±9.18. Table 2 presents the relationships between CCWS, PMSS, SF-12 QoLS, and participants’ demographic characteristics. A positive correlation was identified between CCWS and PMSS scores. Negative correlations were found between age, BMI, SF-12 QoLS PC and MC scores, and PMSS scores. Negative correlations were also observed between SF-12 QoLS PC scores and pregnancy, number of births, and PMSS scores, whereas positive correlations were identified between SF-12 QoLS MC scores and age, number of pregnancies, number of births, and number of children. Statistically significant negative correlations were found between the mental component and CCWS and PMSS scores (p<0.05) (Table 2).

Table 2-
Examining the relationship between CCWS*, PMSS^†and SF-12 QoLS^§and women’s descriptive characteristics (n^||= 614). Turkey, 2023-2024

Values		r¶/p **	CCWS *	PMSS †	SF-12 QoLS §
					Physical Component	Mental Component
Age		r ¶	0.074	-0.322	-0.055	0.200
		p **	0.065	<0.001	0.171	<0.001
Number of pregnancies		r ¶	0.002	-0.031	-0.191	0.202
		p **	0.981	0.709	0.019	0.013
Number of births		r ¶	0.032	-0.006	-0.165	0.207
		p **	0.698	0.945	0.044	0.011
Number of children		r ¶	0.035	-0.001	-0.165	0.184
		p **	0.666	0.991	0.044	0.024
Number of miscarriages		r ¶	-0.054	-0.025	-0.093	0.135
		p **	0.508	0.765	0.257	0.100
Age of first menstrual period		r ¶	0.056	-0.001	-0.059	0.042
		p **	0.166	0.984	0.145	0.298
BMI ††		r ¶	0.054	-0.113	-0.066	0.074
		p **	0.180	0.005	0.100	0.067
CCWS *		r ¶	-	.281	-.105	-.136
		p **	-	<0.001	.009	0.001
PMSS †		r ¶	.281	-	-.197	-.470
		p **	<0.001	-	<0.001	<0.001
SF-12 QoLS §	Physical Component	r ¶	-.105	-.197	-	-.029
		p **	0.009	<0.001	-	0.474
	Mental Component	r ¶	-.136	-.470	-.029	-
		p **	0.001	<0.001	0.474	-

• *CCWS = The Climate Change Worry Scale; ^†PMSS = Premenstrual Syndrome Scale; ^§SF-12 QoLS = SF-12 Quality of Life Scale; ^||n = Number of participants; ^¶r = Pearson correlation coefficient; **p-value; ^††BMI = Body Mass Index

The effect of CCWS scores on PMSS and SF-12 QoLS PC and MC scores is shown in Table 3. CCWS scores were found to have a statistically significant effect on PMSS scores as well as on SF-12 QoLS PC and SF-12 QoLS MC scores (p<0.05). Specifically, a one-unit increase in the CCWS score resulted in a 1.11-unit increase in the PMSS score, a 0.08-unit decrease in the SF-12 QoLS PC score, and a 0.13-unit decrease in the SF-12 QoLS MC score.

Table 3-
Examining the effect of CCWS* score on PMSS^†, and the Physical and Mental components SF-12 QoLS^‡. Turkey, 2023-2024

	Unstandardized coefficient		Standardized coefficient	t §	p ||	95.0% Confidence interval
	Beta	Standard error	Beta			Lower limit	Upper limit
PMSS †
(Fixed value)	103.560	4.576		22.632	<0.001	94.574	112.546
CCWS*	1.110	0.153	0.281	7.236	<0.001	0.809	1.411
(F ¶: 52.366, p<0.001, R 2 **:0.079)
SF-12 QoLS ‡
Physical component
(Fixed value)	48.127	0.987		48.758	<0.001	46.188	50.065
CCWS *	-0.087	0.033	-0.105	-2.621	<0.001	-0.152	-0.022
(F ¶: 6.871, p:0.009, R 2 **:0.011)
Mental component
(Fixed value)	43.770	1.157		37.815	<0.001	41.496	46.043
CCWS *	-0.132	0.039	-0.136	-3.394	<0.001	-0.208	-0.055
(F ¶: 11.518, p:0.001, R 2 **:0.018)

• *CCWS = The Climate Change Worry Scale; ^†PMSS = Premenstrual Syndrome Scale; ^‡SF-12 QoLS = SF-12 Quality of Life Scale, ^§t = t-statistics for the significance of regression coefficients; ^||p-value; ^¶F = Regression value; **R² = Regression coefficient of determination

Discussion

This study aimed to investigate the relationship and related factors between climate change worry among women of reproductive age and the severity of premenstrual syndrome (PMS) as well as their quality of life. Existing literature highlights that PMS is influenced by various demographic, physiological, physical, psychological, social, and cultural factor(¹⁹-²¹). In this study, PMS was found to be higher among single women, those with education beyond university level, non-working individuals, those with lower income than expenditure, and those experiencing dysmenorrhea. Consistent with these findings, other studies have reported associations between PMS severity and marital status, education, employment status, and dysmenorrhea(¹³,²¹-²⁴). Accordingly, comprehensive, woman-centric interventions are deemed more effective in reducing PMS severity.

Quality of life is affected by both modifiable and non-modifiable risk factors, assessed across different dimensions of physical and mental well-being(²⁵-²⁶). In this study, physical component quality of life was found to be higher in single women, those without chronic or psychiatric illnesses, and non-users of medication, while mental component quality of life was higher among married individuals, those with a high school education or lower, employed individuals, and those without psychiatric illnesses. Various studies have indicated that demographic and sociocultural factors significantly impact the quality of life in women(²⁶-²⁷). Parallel to our findings, it was found that the presence of chronic and psychiatric diseases reduced the quality of life(²⁵). Enhancing women’s quality of life requires addressing all factors affecting health, identifying current conditions, and implementing beneficial interventions.

In this study, women exhibited moderate levels of climate change worry alongside moderate levels of PMS severity. Similar moderate levels of climate change worry have been observed in studies conducted in Turkey(²⁸-²⁹). While our findings on PMS severity align with previous studies(¹²-¹³), it is found in the study that the severity of PMS is milder(²²). Similarly, women’s quality of life in this study was also moderate. Consistent with the literature, other studies have indicated that women’s quality of life tends to be moderate and requires improvement(²⁶,³⁰).

The study found that as women’s climate change worries increased, so did the severity of PMS and the decrease in quality of life. In the study, women’s worries about climate change were directly proportional to the severity of PMS, while their quality of life was significantly more proportional. Parallel to the findings in this study, a study conducted with young adult women in Spain found that their quality of life decreased as the severity of PMS increased(⁵). Climate change impacts menstrual cycles through increased occurrences of hurricanes, tornadoes, avalanches, and other extreme weather events, as well as altering crop availability and consumption(¹⁰). High temperatures have also been reported to cause anomalies in menstrual cycles(¹⁴). Furthermore, climate change has been emphasized to increase stress levels among women and exacerbate PMS symptoms(³¹). Another study conducted in Turkey with women of childbearing age determined that women with PMS have higher ecological worries(²⁹). Changes in menstrual cycles due to climate change can adversely affect physical and mental quality of life, increasing the burden of disease(¹⁰). Consistent with our findings, a systematic review has indicated that climate change worry negatively impacts mental health(¹¹). Another study highlighted that climate change worry increases mental distress among Australian women and leads to a more pessimistic outlook on the future(³²). It has been stated that climate change worry increases mental problems as well as physical problems(³³). Therefore, reducing women’s climate change worries is expected to decrease PMS severity and improve their quality of life.

This study provides significant and current evidence from Turkey regarding the relationship between women’s climate change worry, PMS severity, and quality of life, contributing uniquely to the literature. However, there are several limitations to consider. Primarily, the study relies on self-reported data from women. Additionally, the limited number of studies investigating the impact of climate change on PMS may have introduced limitations to the discussion. Moreover collecting research data using the snowball sampling method may limit the generalizability of the research.

Conclusion

The study determined that women exhibit moderate levels of climate change worry, PMS severity, and both physical and mental quality of life. As women’s climate change worries increase, so does the severity of PMS and the decrease in quality of life. Women’s climate change anxiety, PMS severity and quality of life levels are affected by various introductory characteristics. Based on these findings, interventions aimed at reducing women’s climate change worries by public health nurses and PMS severity by women’s health nurses through education and awareness campaigns are crucial. Public service announcements and informative advertisements on platforms such as television and social media could prove beneficial in mitigating the effects of climate change. Notably, conducting various qualitative and quantitative studies to enrich the literature is recommended to minimize the adverse effects of current climate change conditions.

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• Data Availability Statement
  All data generated or analysed during this study are included in this published article.
• *
  Supported by Scientific and Technological Research Council of Türkiye 2209-A University Students Research Projects Support, Grant # 1919B012300090, Turkey.
• How to cite this article
  Ekrem EC, Akar A, Akar A. The relationship between climate change worry, premenstrual syndrome severity, and quality of life among women of reproductive age and related factors. Rev. Latino-Am. Enfermagem. [cited]. Available from: https://doi.org/10.1590/1518-8345.7793.4837

Edited by

• Associate Editor:
  Rosana Aparecida Spadoti Dantas

Data availability

All data generated or analysed during this study are included in this published article.

Publication Dates

• Publication in this collection
  10 Apr 2026
• Date of issue
  2026

History

• Received
  02 Feb 2025
• Accepted
  14 Oct 2025