Searching for predictors of sense of quality of health: A study using neural networks on a sample of perimenopausal women

• Published in: PloS one 2019-01, Vol.14 (1), p.e0200129-e0200129
• Main Authors: Wlodarczyk, Malgorzata, Dolinska-Zygmunt, Grazyna
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial neural networks; Attitudes; Biology and Life Sciences; Cognitive ability; Cognitive behavioral therapy; Computer and Information Sciences; Emotions; Female; Genetic algorithms; Genetic analysis; Health; Health aspects; Hormone replacement therapy; Humans; Hypotheses; Mathematical models; Medicine and Health Sciences; Menopause; Middle Aged; Model testing; Multivariate analysis; Neural networks; Neuroendocrine tumors; Optimization; Perimenopause; Personality; Personality traits; Prediction intervals; Psychology; Quality of Life; Sampling methods; Self Concept; Self esteem; Self image; Social Sciences; Surveys and Questionnaires; Women's Health; Womens health
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0200129

We assumed that perimenopausal women's sense of quality of health (SQH) is a subjective evaluation of their psycho-physical health, and comprises three dimensions: sense of quality of life, menopausal symptoms, and the level of positive and negative affect. The aim of the study was to: 1) test a model about SQH, and 2) explore the role of personality traits, self-esteem, body self, and self-stereotype as predictors of SQH. The sample included 201 women aged between 45 and 55 (50.11±3.07). Participants filled out the Rosenberg Self-Esteem Scale, the Personality Inventory based on the Big Five Factor Model, the Body Self Questionnaire, and a survey querying perimenopausal women's self-stereotype. To determine the individual SQH dimensions we used the Sense of Quality of Life Questionnaire, the Menopause Symptom List, and the Positive and Negative Affect Schedule. To verify the assumptions of the SQH model and look for SQH predictors we conducted a neural networks analysis with structure optimization via genetic algorithms (a multivariate analysis). The SQH model was verified in the course of several neural networks analyses with structure optimization via genetic algorithms (R = 0.849, R2 = 0.723, F = 133,232, p < 0.01). Moreover, we confirmed that SQH comprised three dimensions: quality of life, menopausal symptoms, and affect. SQH and menopausal symptoms were correlated. Similarly, positive and negative affect modified the women's global sense of quality of life. SQH predictors included: personality traits, self-esteem, the body-self, and menopausal woman's self-stereotype. In practical terms, our findings may help raise awareness among women and medical practitioners, calling for a holistic approach to the health of menopausal women. Our findings may also facilitate the creation of both prevention and therapeutic programs for women transitioning through menopause, for example, cognitive-behavioral therapy.