Role of Health-promoting Behaviours in Minimising Mental Health Vulnerability of Middle-aged Women: Insights from Cluster Analysis

Abstract

Background

A woman’s primary concern during her middle adulthood is menopausal transition—a key aspect of her reproductive health. Although extensive research has been conducted on menopause and associated physiological and psychological changes, there remains a paucity of research on the role of health-promoting behaviours of middle-aged women in addressing their transitory state of mental health vulnerability.

Purpose

The purpose of this study was threefold: (a) to identify profiles of middle-aged women based on their health-promoting behaviours using k-means cluster analysis, (b) to explore the role of health-promoting behaviours in minimising their mental health vulnerability and (c) to examine the major confounding variables in understanding their health-promoting behaviours.

Methods

This study used a simple retrospective design with one group involving 440 Indian women selected through purposive sampling based on a degree of homogeneity of their developmental stage—middle adulthood. Two measures, the Health-Promoting Lifestyle Profile and Depression Anxiety and Stress Scale-21, were used along with a case history form.

Results

The k-means cluster analysis yielded three clusters based on six domains of health-promoting behaviours. The discriminant analysis confirmed that the k-means clustering results were reliable with high classification accuracy. The findings posited that health-promoting behaviours played a significant role in minimising the mental health vulnerability of middle-aged women. Further analyses showed that the three clusters of middle-aged women were confounded by their age, menstrual stage and occupation.

Conclusion

The findings shed new light on the significant association between health-promoting behaviours and mental health vulnerability of middle-aged women during menopausal transition, emphasising the need for future research in designing easily accessible tailor-made biopsychosocial interventions.

Keywords

Health promotion menopause middle-aged woman mental health

Introduction

A woman’s primary concern during her middle age (35–65 years) is menopausal transition—a key aspect of her reproductive health. Menopause—the natural cessation of menstruation—is one of the significant irreversible physiological changes in the transitional phase between middle and late adulthood. Though menopause itself is the year-long absence of menstruation, the changes associated with it vary and begin much earlier in a woman’s middle adulthood, termed perimenopause—the period around menopause.¹ During the attainment phase of menopause, the main challenge many middle-aged women face is physiological changes, such as irregular uterine bleeding, vaginal dryness, hot flashes or night sweats, weight fluctuations, sleep disturbances and fatigue.² These physiological changes are often likely to make the woman vulnerable to experiencing the associated mental health problems—stress, anxiety, depression and mood swings. Generally, at no other time in her life is the woman so concerned about undergoing a state of mental health vulnerability than during such a developmental trajectory. Mental health vulnerability is conceptualised as an increased state of inclination to develop and experience mental health problems, predominantly comprising stress, anxiety and depression.

In India, about 29.8% of women are 35–64 years old, which approximates about 190 million women.³ Most of the research and policies on women revolve around pregnancy and pregnancy-related aspects, ignoring the women who can no longer contribute reproductively to society.⁴ The lack of health awareness and policies is causing substantial socioeconomic impact and psychological distress for such a large demography. Though women in middle age perceive their bodies and associate their femininity with the menstrual cycle, the loss of which during this transitional phase is seen as a precursor to taking a passive role in society.⁵

Nonetheless, the number of such middle-aged women as a group has substantially increased in the past few decades, and they are now seen for much more than their reproductive capacity. Women themselves are at the forefront of bringing this change, leading the world to create a sustainable and holistic life view. Women with a better sense of their health and lifestyle actively engage in productive endeavours, which act as protective factors from morbidities associated with the midlife transition.⁶ In this context, we look at the health-promoting behaviours operationalised as day-to-day actions of women that positively affect their health and well-being and act as preventive measures to minimise vulnerabilities. Further, research shows that stress, anxiety and depression affect the mental and physical health of women, especially during the middle-aged menstrual transition.⁷

Moreover, Pender’s health promotion model helps to detect unhealthy behaviours and bring change, subserving healthy actions and promoting well-being.⁸ The model focuses on interpersonal and situational influences on health, perceived benefits and barriers, behavioural emotions and self-efficacy through domains of physical activity, health responsibility, nutrition, spirituality, stress management and interpersonal relationships. Though most middle-aged women live a sedentary lifestyle incongruent with health promotion, certain behaviours elevate their lifestyle to healthy and positive living.⁹

Although extensive research has been carried out on menopause and associated physiological changes, there remains a paucity of research on the role of health-promoting behaviours of middle-aged women in addressing their transitory state of mental health vulnerability. Therefore, considering mental health vulnerability as a public mental health concern, this quantitative study was undertaken to address the paucity of research on health-promoting behaviours and mental health vulnerability of middle-aged women. Thus, the purpose of this study was threefold: (a) to identify profiles of middle-aged women based on their health-promoting behaviours using k-means cluster analysis, (b) to explore the role of middle-aged women’s health-promoting behaviours in minimising their mental health vulnerability and (c) to examine whether the sub-groups of middle-aged women based on health-promoting behaviours were confounded by any specific demographic variables.

Methods

Participants

This study used a simple retrospective design with one group involving 440 Indian women belonging to middle adulthood. The participants were drawn from rural and urban settings across India through purposive sampling on the basis of a degree of homogeneity of their developmental stage, that is, middle adulthood. Thus, middle-aged women (35–65 years) going through any of the three menstrual stages—regular menstruation, perimenopause and menopause—were included in this study. However, pregnant women or women who had been pregnant in the past year before this study were excluded. Further, women diagnosed with any pre-existing psychopathological condition were also excluded from this study. Based on these inclusion and exclusion criteria, 465 participants were contacted at the beginning of this study. Twenty-five of the 465 participants were excluded from this study because of the withdrawal. Thus, the final sample consisted of 440 participants belonging to two geographical settings (rural = 137, urban = 303) from various states of India. All participants included in this study were aged between 39 and 62 years (mean [M] = 46.31, standard deviation [SD] = 5.45). All three menstrual stages were represented among the participants (regular menstruation = 140, perimenopause = 140 and menopause = 160). The participants were from four different levels of education (secondary = 16, intermediate = 132, graduate = 247 and post-graduate = 45). Of the total participants, 33.6% were employed, 25.5% were self-employed and 40.9% were homemakers.

Research Tools

Health-promoting Lifestyle Profile

The Health-Promoting Lifestyle Profile (HPLP-II) measures health-promoting behaviours using 52 items.¹⁰ Each item was rated on a 4-point Likert scale (1 = never, 4 = routinely). The HPLP-II has six subscales—health responsibility, physical activity, nutrition, spiritual growth, interpersonal relations and stress management. Higher scores indicated higher health-promoting behaviour in the particular domain and also in the overall scale. For this study, the total score of the scale was calculated from the mean score of participants on all 52 items. Likewise, the subscale scores were obtained by calculating the mean on subscale items. Hence, the metric response of one to four was retained, which allowed easier comparison across subscales. The scale reports a Cronbach’s α value of 0.94, whereas the Cronbach’s α values range from 0.79 to 0.87 for the six subscales, indicating adequate reliability and validity of the measure.

Depression Anxiety and Stress Scale-21

The Depression Anxiety and Stress Scale-21 (DASS-21) is designed to measure the levels of depression, anxiety and stress in individuals.¹¹ The DASS-21—a shorter version of the original 42-item scale—comprises 21 items with three domains, such as depression, anxiety and stress. Each item was rated using a 4-point Likert scale (0 = did not apply to me at all, 3 = applied to me very much or most of the time). The scores for each domain are calculated by adding the scores for the appropriate items. The developers suggested cut-off scores for conventional severity markers as normal, moderate and severe. The higher scores indicated a higher level of severity. The reliability for the subscales of depression, anxiety and stress was adequate (Cronbach’s α = 0.81, 0.89 and 0.78, respectively).

In addition to these research tools, sociodemographic data and case histories, such as name, age, levels of education, place of residence, occupation and menstrual stage, were collected using a case history form.

Procedure

At first, ethical approval was taken from the Institutional Ethics Committee of the University of Hyderabad. The participants from rural and urban settings across India (Delhi, Haryana, Maharashtra, Uttar Pradesh and Uttarakhand) were contacted by the second author based on the inclusion and exclusion criteria. The second author also contacted the authorities of private clinics and hospitals (government and private) to interact with participants coming for doctor consultations. The informed consent was obtained from the participants, who expressed their willingness to take part in this study. Thus, the two standardised measures, along with the case history form, were administered to the participants individually. The investigator addressed any questions that the participants had when they answered the items of the research tools. It took an average of 40–50 minutes to administer the research tools to every participant. After the administration had completed, a debriefing was conducted with each participant. Thus, the investigator spent about one and a half hours with each participant during the assessment.

Results

The data were entered in IBM SPSS Statistics 20 and cleaned to eliminate data entry errors. After running descriptive statistics and testing assumptions, k-means cluster analysis was run to identify profiles of middle-aged women based on their health-promoting behaviours. Subsequently, Discriminant Analysis, one-way analysis of variance (ANOVA), Post-hoc tests and chi-squared tests were run to analyse the results.

The approach of k-means cluster analysis is adopted to classify individuals into exclusive natural clusters or groups based on their similarities to the selected criteria or classification characteristics. This is one of the most powerful statistical tools that helps researchers reveal latent groupings within the sample. In k-means cluster analysis, cluster centroids are calculated, and computation is done to measure the Euclidean distance between centroids and individual cases. Based on the distance, the cases shift from their initial clusters to the ones with minimal centroid distance. This process is repeated till no new reassignment takes place.¹²

In this study, we used k-means cluster analysis to examine how middle-aged women clustered together based on the six domains of health-promoting behaviours—physical activity, health responsibility, nutrition, interpersonal relationships, spiritual growth and stress management. In the initial stage of clustering, the exploratory phase was followed. Thereafter, different cluster solutions were run, including two-cluster, three-cluster and four-cluster solutions. The greatest amount of variance was explained by the three-cluster solution in the clustering variable of health-promoting behaviours. Therefore, the three-cluster solution was considered to be the best fit. Convergence criteria were fulfilled after fourteen iterations in the three-cluster solution, as no further changes in cluster centres were found. In addition to statistical vigour, the three-cluster solution implied a robust theoretical meaning to the clusters. The values of M and SD of six domains of health-promoting behaviours in the three emerging clusters are presented in Table 1.

Table 1.

Mean Values on Six Domains of Health-promoting Behaviours Defining Clusters, Total Sample and by Cluster.

Variable	Total	Cluster
Variable	Total	High	Medium	Low
Health responsibility	2.48 (0.36)	2.84 (0.36)	2.61 (0.25)	2.28 (0.25)
Physical activity	2.41 (0.35)	2.87 (0.36)	2.48 (0.25)	2.22 (0.28)
Nutrition	2.50 (0.32)	3.03 (0.26)	2.51 (0.22)	2.35 (0.28)
Spiritual growth	2.68 (0.40)	3.35 (0.32)	2.76 (0.24)	2.42 (0.24)
Interpersonal relationship	2.71 (0.41)	3.42 (0.25)	2.83 (0.27)	2.42 (0.21)
Stress management	2.60 (0.34)	3.13 (0.26)	2.70 (0.22)	2.37 (0.22)
N	440	57	176	207
% of the total sample	100	13	40	47

Note: Figures in parentheses are standard deviations.

Further, discriminant analysis was carried out to examine the accuracy of the classification of k-means clustering. Results from discriminant analysis (Table 2) confirmed that the k-means clustering results were reliable with high classification accuracy (>90%). The results revealed that 97.3% of originally grouped cases were correctly classified. The classification accuracy of the discriminant function was 98.6%, 96% and 96.5% for middle-aged women belonging to low, medium and high clusters respectively.

Table 2.

Discriminant Analysis of the Three Values Clusters Created by k-means Clustering.

		Predicted Group of Discriminant Analysis
		Low	Medium	High	Total
Groups of k-means clustering	Low	204 (98.6%)	3 (1.4%)	0 (0%)	207
	Medium	6 (3.4%)	169 (96%)	1 (0.6%)	176
	High	0 (0%)	2 (3.5%)	55 (96.5%)	57

Note: 97.3% of originally grouped cases were correctly classified.

In the three-cluster solution, the major cluster (47%) was characterised by low levels of all six domains of health-promoting behaviour. Therefore, the cluster was labelled as ‘low health-promoting behaviour’ sub-group (n = 207). The maximum values on all six domains of health-promoting behaviour characterised the smallest cluster (13%). For this, the cluster was labelled as the ‘high health-promoting behaviour’ sub-group (n = 57). The remaining cluster (40%) labelled as ‘medium health-promoting behaviour’ sub-group (n = 176) was categorised by medium values on all six domains of health-promoting behaviour. Figure 1 depicts the profile of health-promoting behaviour of middle-aged women using the means of all six domains of health-promoting behaviours across the three emerged clusters.

Figure 1.

Mean Values of the Six Domains of Health-promoting Behaviours Across the Three Sub-groups of Middle-aged Women.

Further, we explored the role of middle-aged women’s health-promoting behaviours in minimising their mental health vulnerability. For this, we used one-way ANOVA. The results of one-way ANOVA and the descriptive statistics (M and SD) are presented in Table 3.

Table 3.

Comparison of Mental Health Vulnerability Across the Three Sub-groups (Clusters) of Health-promoting Behaviours.

Variables	Sub-groups (Clusters)			F (2,437)	P Value	Partial η²
	High	Medium	Low
	M (SD)	M (SD)	M (SD)
Stress	11.23 (9.04)	17.48 (9.00)	23.32 (7.70)	53.96	<.001	.20
Anxiety	10.21 (7.92)	16.80 (9.13)	21.57 (7.31)	47.63	<.001	.18
Depression	6.18 (6.47)	15.33 (9.18)	20.83 (7.57)	76.93	<.001	.26

As evident, the stress scores of middle-aged women were significantly different across the three sub-groups, F (2,437) = 53.96, P < .001, with a large effect size (Partial η² = .20), suggesting that a 20% significant proportion of variance in stress scores was accounted for by the differences due to the sub-groups of the middle-aged women. The multiple comparisons using Tukey’s Honestly Significant Difference (HSD) showed that women belonging to the low health-promoting behaviour sub-groups (M = 23.32, SD = 7.70) had significantly higher stress scores than those belonging to the medium (M = 17.48, SD = 9.0) and high (M = 11.23, SD = 9.04) health-promoting behaviour sub-groups. Similarly, there was a significant difference across the three sub-groups of middle-aged women, F (2,437) = 47.63, P < .001. The effect size was found to be large (Partial η² = .18), suggesting that about 18% significant proportion of the variance of anxiety scores could be explained by the differences among middle-aged women due to sub-groups. Tukey’s HSD revealed that anxiety scores of women in the low health-promoting behaviour sub-group (M = 21.57, SD = 7.31) were significantly higher than the scores of those belonging to the medium (M = 16.80, SD = 9.13) and high (M = 10.21, SD = 7.92) health-promoting behaviour sub-groups.

Moreover, there was a significant difference observed across three sub-groups of middle-aged women in terms of their experiencing depression, F (2,437) = 76.93, P < .001. Further, the effect size was found to be large (Partial η² = .26), indicating that about 26% significant proportion of the variance of depression could be accounted for by the differences due to the sub-groups of middle-aged women. The multiple comparisons using Tukey’s HSD showed that the scores of depression scores of middle-aged women in the low health-promoting behaviour sub-group (M = 20.83, SD = 7.57) were significantly more than that of women in the medium (M = 15.33, SD = 9.18) and high (M = 6.18, SD = 6.47) health-promoting behaviour sub-group. The depression scores of middle-aged women in the high health-promoting behaviour sub-group were significantly lower than those of women in the medium and low health-promoting behaviour sub-groups.

Furthermore, we used the one-way ANOVA and chi-squared tests to explore the presence of confounding variables. As the age of the middle-aged women was measured in an interval scale, one-way ANOVA was run to examine whether the three sub-groups of middle-aged women that emerged from health-promoting behaviours were confounded by their age. The results revealed that there was a significant difference in age across the three sub-groups of middle-aged women, F (2, 437) = 11.14, P < .001. The effect size was medium (Partial η² = .05), signifying that about 5% significant proportion of variance in age was accounted for by the differences in the sub-groups of middle-aged women. Thus, the age of the middle-aged women played a role of a confounding variable.

We computed chi-squared tests to examine whether the three sub-groups of middle-aged women were confounded by their other important demographic variables which were categorical in nature (e.g., menstrual stage, occupation and geographical setting). Table 4 contains the frequencies and percentages of middle-aged women and the results of the chi-squared tests.

Table 4.

Association Between Three Sub-groups (Clusters) and the Important Demographic Characteristics of Middle-aged Women.

Demographic Characteristics	Sub-groups (Clusters)				χ²
	Low (n = 207)	Medium (n = 176)	High (n = 57)	Total (n = 440)
	f (%)	f (%)	f (%)	f (%)
Menstrual stage					37.47, P < .001
Regular	61 (43.6)	57 (40.7)	22 (15.7)	140 (100)
Perimenopause	93 (66.4)	41 (29.3)	6 (4.3)	140 (100)
Menopause	53 (33.1)	78 (48.8)	29 (18.1)	160 (100)
Occupation					14.07, P = .007
Homemaker	84 (46.7)	73 (40.5)	23 (12.8)	180 (100)
Self-employed	67 (59.82)	37 (33.04)	8 (7.14)	112 (100)
Employed	56 (37.8)	66 (44.6)	26 (17.6)	148 (100)
Geographical setting					1.35, P = .509
Rural	70 (51.1)	50 (36.5)	17 (12.4)	137 (100)
Urban	137 (45.2)	126 (41.6)	40 (13.2)	303 (100)

The results of Cluster (3) X menstrual stage (3) chi-squared test of association revealed that the menstrual stages of middle-aged women were significantly different across the three sub-groups, χ² = 37.47, P < .001. The proportions of middle-aged women belonging to regular (43.6%) and perimenopause (66.4%) stages were significantly greater in the low health-promoting behaviour sub-group than in the other two sub-groups. However, in comparison to the other two sub-groups, the medium health-promoting behaviour sub-group had a significantly higher percentage of middle-aged women (48.8%) who were in their menopause. Thus, the menstrual stage was observed to be a confounding variable in understanding the health-promoting behaviours of middle-aged women. The percentages of middle-aged women belonging to three menstrual stages across three clusters are presented in Figure 2.

Figure 2.

Percentages of Middle-aged Women Belonging to Three Menstrual Stages Across Three Clusters.

Further, to determine whether the three sub-groups of middle-aged women were confounded by their occupation, a cluster (3) X occupation (3) chi-squared test of association was computed. The results showed that the occupation of middle-aged women was significantly different across the three sub-groups, χ² = 14.07, P = .007. The results further indicated that compared to medium and high health-promoting behaviour sub-groups, the low health-promoting behaviour sub-group had a significantly higher number of middle-aged women who were homemakers (46.7%) and self-employed (59.82%). Moreover, the medium health-promoting behaviour sub-group had a significantly higher percentage (44.6%) of employed middle-aged women compared to the other two sub-groups. Thus, it was ascertained that the three sub-groups of middle-aged women were confounded by their occupations. The percentages of middle-aged women belonging to three occupations across three clusters are presented in Figure 3.

Figure 3.

Percentages of Middle-aged Women Belonging to Three Occupations Across Three Clusters.

However, no significant association was found between three sub-groups and two geographical settings by means of a cluster (3) X geographical settings (2) chi-squared test, χ² = 1.35, P = .509. Therefore, the three sub-groups of middle-aged women were not confounded by their geographical settings.

Discussion

This study set out with the aim of identifying the profiles of middle-aged women based on their health-promoting behaviours. The most interesting finding is that middle-aged women, initially a homogenous group, are clustered into three meaningful natural sub-groups based on their health-promoting behaviours comprising six domains—physical activity, nutrition, health responsibility, interpersonal relationships, spiritual growth and stress management. The three data-driven sub-groups are labelled as high, medium and low health-promoting behaviour sub-groups. The classification signifies the heterogeneity in the degree of health-promoting behaviours in the homogenous group of middle-aged women. This classification system of a homogenous natural group of middle-aged women into heterogeneous sub-groups is more scientific since it is based on statistical models—cluster analysis and discriminant analysis. Thus, this study provides a different angle to look at the importance of individual differences among middle-aged women in terms of their health-promoting behaviours.

Further, we explored the role of health-promoting behaviours in minimising the mental health vulnerability of middle-aged women. The most important finding is that stress, anxiety and depression are significantly lower among middle-aged women in the high health-promoting behaviour sub-group compared to women belonging to the medium and low health-promoting behaviour sub-groups. Thus, the findings suggest that middle-aged women who possess higher levels of health-promoting behaviours are less susceptible to mental health vulnerability. Our results corroborate the findings of previous studies, which suggest that health-promoting behaviours act as protective factors against psychological distress among middle-aged women.¹³ In accordance with the present results, previous research proposes that a network of social support, engagement in physical activity, nutritional awareness and a sense of self-achievement encourage women in their middle age to manage the psycho-physical symptoms of menopause transition.¹⁴

Research suggests that women who focus on self-care and actively support their health during middle age are more successful in managing menopausal symptoms and experience lower levels of psychological distress.¹⁵ Engaging in physical activity and maintaining nutrition is associated with a reduction in both psychological distress and menopausal symptoms.¹⁶ Further, studies show that social relations,¹⁷ spirituality¹⁸ and effective stress management techniques along with a healthy lifestyle¹⁹ significantly ease distress among middle-aged women. Nevertheless, engaging in health-promoting behaviours and maintaining a healthy lifestyle has a direct mediating effect on the relationship between psychological distress and menopausal symptoms.²⁰

Moreover, this study also examined whether the three sub-groups of middle-aged women based on health-promoting behaviours are confounded by any specific demographic variables. The findings suggest that the three heterogeneous sub-groups of middle-aged women are confounded by their age, menstrual stage and occupation. These results reflect those of researchers who also found that sociodemographic factors affect the health-promoting behaviours of Indian women.²¹

Age is observed as a confounding variable, indicating that middle-aged women’s health-promoting behaviour is dependent on their age. This study shows that the age of women can affect their menopausal transition and influence their lifestyle.²² Further, the results are of particular interest for menstrual stages, where the highest frequency of women in the perimenopause stage is in the low health-promoting behaviour sub-group. This is in line with past literature on the subject that women in menopausal transition stages lead a sedentary lifestyle, which has a cyclical negative effect on their health and symptom management.²³ However, after menopause, women may engage more in health-promoting behaviours, connoting menopause as a liberating experience.²⁴ This is evident in our result, where a higher number of women in the menopause stage belong to the medium health-promoting behaviour sub-group. It is also pertinent to mention that it is not the menopausal symptoms that affect women’s healthy lifestyle but rather the stress and anxiety that emanate from it.²⁵

In addition to age and menstrual stage, the occupation of middle-aged women acts as an important confounding variable influencing the health-promoting behaviour of middle-aged women. Research suggests that employed middle-aged women show better symptoms and health management than self-employed and unemployed women.²⁶ Thus, it is important to remember that the demographic attributes (menstrual stage and occupation) could influence some aspects of the results. However, it is somewhat surprising that in this study, the three heterogeneous sub-groups of middle-aged women are not confounded by their geographical settings (rural and urban). In accordance with this result, previous research has demonstrated that demographic factors may not always influence health-promoting lifestyles.²⁷

Limitations

A few important limitations need to be noted regarding this study to increase the rigor of future research. Though we included middle-aged women from various states of India to make the sample nationally representative, we underexplored the experiences of middle-aged women having no formal education. Future studies should include adequate numbers of middle-aged women chosen from cultural and sociodemographic diversity. Future studies should also focus on the stigma surrounding menopause and mental health that might have skewed the results of this study. There may be a possibility of new stressors, like new-age digital dependency, that might have contributed to the mental health vulnerability of the participants. Furthermore, k-means cluster analysis—a non-model-based cluster analytic method—primarily works with linear relationships and cannot handle complex non-linear relationships between data points. Therefore, based on the statistical assumptions, researchers should choose appropriate pre-processing steps or consider alternative clustering methods suitable for their data. By filling these gaps, future research would provide a more comprehensive picture of the role of health-promoting behaviours in minimising middle-aged women’s mental health vulnerability.

Conclusion

Notwithstanding these limitations, the findings reported here shed new light on the significant association between health-promoting behaviours and the mental health vulnerability of middle-aged women in the presence of important confounding variables during their menopausal transition. This study suggests that middle-aged women should practice health-promoting behaviours to minimise the risks of experiencing mental health problems during this transition period. Thus, the findings lay the groundwork for future research by opening up avenues for designing easily accessible tailor-made biopsychosocial interventions for vulnerable middle-aged women undergoing menopausal transition. However, further research is required to establish the therapeutic efficiency of such interventions.

Footnotes

Abbreviations

ANOVA: Analysis of variance

DASS-21: Depression Anxiety and Stress Scale-21

HPLP-II: Health-Promoting Lifestyle Profile

M: Mean

SD: Standard deviation

HSD: Honestly significant difference

Acknowledgements

We would like to express our gratitude to all the participants for their active participation. We are thankful to the University Grants Commission for awarding JRF and SRF to the second author for conducting doctoral research.

Authors’ Contribution

NS was involved in conceptualisation, methodology, writing—original draft, writing—review and editing, supervision. AN contributed to investigation, review of literature, data curation and writing—original draft. SR helped in statistical analysis, visualisation, data interpretation, writing—original draft and writing—review and editing.

Statement of Ethics

This study was approved by the Institutional Ethics Committee of the University of Hyderabad (IEC No. UH/IEC/2021/177, dated 21/12/2021). This research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki.

Declaration of Conflicting Interests

The authors declare no potential conflicts of interest for this research, authorship and/or publication.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Informed Consent

Informed consent was obtained from the participants before the study was conducted. The informed consent form primarily consisted of the purpose of the study, the participant’s voluntary participation and withdrawal, the absence of any potential risks and confidentiality. There were no potential risks involved for the participants.

ORCID iDs

Naga Seema

Suvashisa Rana

References

Bacon

JL.

The menopausal transition. Obstet Gynecol Clin North Am 2017; 44(2): 285–296.

Menopause [Internet]. [cited 2024 Oct 22]. Available from: https://www.who.int/news-room/fact-sheets/detail/menopause.

Women and Men in India, Ministry of Statistics and Programme Implementation, Government of India. 2018 [Internet]. [cited 2024 Sep 14]. Available from: https://www.mospi.gov.in/sites/default/files/publication_reports/Women%20and%20Men%20%20in%20India%202018.pdf.

Mann

and Stephenson

Reproductive health and wellbeing—Addressing unmet needs . London: British Medical Association, 2018, pp.1–10.

Kaulagekar

Menopause and femininity: Qualitative enquiry into menopause of urban women from Pune, Maharashtra. Anthropologist 2010; 12(1): 23–26

Guérin

, Goldfield

and Prud’homme

Trajectories of mood and stress and relationships with protective factors during the transition to menopause: Results using latent class growth modeling in a Canadian cohort. Arch Womens Mental Health 2017; 20(6): 733–745.

Soares

and Shea

AK.

The midlife transition, depression, and its clinical management. Obstet Gynecol Clin North Am . 2021; 48(1): 215–229.

Pender

, Murdaugh

and Parsons

MA.

Health promotion in nursing practice . 7th ed.

US:

Prentice Hall; 2015, pp. 34–40.

Singhania

, Kalhan

, Choudhary

, . Association of menopausal symptoms with overweight and obesity among rural middle aged women in North India: A population based study. J Midlife Health 2020; 11(3): 137–143.

10.

Walker

, Sechrist

and Pender

NJ.

The health-promoting lifestyle profile: Development and psychometric characteristics. Nurs Res 1987; 36(2): 76–81.

11.

Lovibond

and Lovibond

SH.

The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther 1995; 33(3): 335–343.

12.

Rana

, Kumar

, Seema

, . Profile of diabetes patients’ chronic illness care in India and its role in their adherence. Diabetes Metab Syndr 2021; 15(1): 303–308.

13.

Cybulska

, Szkup

, Schneider-Matyka

, . Depressive symptoms among middle-aged women—Understanding the cause. Brain Sci 2021; 11(1): 26.

14.

Agarwal

, Kiron

, Gupta

, . A study of assessment menopausal symptoms and coping strategies among middle age women of North Central India. Int J Community Med Public Health 2018; 5(10): 4470–4477.

15.

, Yu

, Chou

, . Empowering self-care ability–A follow-up study of clinical-based perimenopausal women personal health counselling. J Clin Nurs 2016; 25(19–20): 2979–2988.

16.

Marques

, Bordado

, Peralta

, . Cross-sectional and prospective relationship between physical activity and depression symptoms. Sci Rep 2020; 10(1): 16114.

17.

Chang

and Sohn

JN.

Stress, stress coping, social support, generativity and subjective well-being in the middle-aged people. Indian J Public Health Res Dev 2020 Apr 1; 11(4): 770–775.

18.

Roh

, Lee

, Hsieh

, . Protective factors against depressive symptoms in female American Indian cancer survivors: The role of physical and spiritual well-being and social support. Asian Pac J Cancer Prev 2021; 22(8): 2515.

19.

Iioka

and Komatsu

Effectiveness of a stress management program to enhance perimenopausal women’s ability to cope with stress. Jpn J Nurs Sci 2015; 12(1): 1–7.

20.

Park

Mediating effect of a health-promoting lifestyle in the relationship between menopausal symptoms, resilience, and depression in middle-aged women. Health Care Women Int 2020; 41(9): 967–983.

21.

Malik

, Sheoran

and Siddiqui

Health-promoting behaviors and menopausal symptoms: An interventional study in rural India. J Mid-life Health . 2018; 9(4): 200–206.

22.

Pimenta

, Leal

, Maroco

, . Menopause symptoms’ predictors: The influence of lifestyle, health-and menopause-related, and sociodemographic characteristics. J Women Aging 2012; 24(2): 140–151.

23.

Kózka

, Prażmowska

, Dziedzic

, . Women’s lifestyle during menopause transition years (perimenopause)–Introductory research. Med Rev 2013; (1): 61–74.

24.

Minkin

MJ.

Menopause: Hormones, lifestyle, and optimizing aging. Obstet Gynecol Clin 2019; 46(3): 501–514.

25.

Thomas

, Mitchell

and Woods

NF.

The challenges of midlife women: Themes from the Seattle midlife women’s health study. Womens Midlife Health . 2018; 4: 1–10.

26.

Jack

, Riach

, Bariola

, . Menopause in the workplace: What employers should be doing. Maturitas . 2016; 85: 88–95.

27.

Amirabadizadeh

, Sharifzadeh

and Moodi

Middle-aged women’s quality of life and health-promoting lifestyle. Mod Care J 2016 Jul 31; 13(3): e11597. https://doi.org/10.5812/modernc.11597.