ABSTRACT
Background and Aims
Urinary incontinence (UI) is prevalent in older women, reducing quality of life and increasing healthcare burden. The purpose of this study was to investigate the association between UI and comprehensive body composition—viz, visceral fat, waist circumference, and muscle mass—in older Iranian women.
Methods
This case‐control study included 845 community‐dwelling women aged ≥ 60 years from the Amirkola Health Assessment Project (AHAP‐Phase 2). Participants were assigned to UI (n = 444) and non‐UI (n = 401) groups using the 3 Incontinence Questionnaire (3IQ). Data collection included demographic and clinical interviews, anthropometric measurements (BMI, waist, and hip circumference), and dual‐energy X‐ray absorptiometry (DXA), for body composition analysis (visceral adipose tissue, total fat mass, lean mass, and android/gynoid fat ratio). Multivariate logistic regression determined independent predictors of UI.
Results
The prevalence of UI was 52.5% (444/845). Women with UI were considerably older, had more chronic diseases, and more often used sedative‐hypnotic drugs. UI cases had higher rates of obesity, larger waist circumference (mean difference: 1.82 cm, 95% CI: 0.33–3.31), higher visceral fat (mean difference: 57.2 g, 95% CI: 11.6–102.8), and higher android/gynoid fat ratio (mean difference: 0.02, 95% CI: 0.002–0.038). Lean mass was not significantly different. No significant differences in body composition were found between UI subtypes. Multivariate logistic regression revealed that normal‐weight women (BMI < 25) had 48% lower odds of UI compared to obese women (OR = 0.52, 95% CI: 0.33–0.82, p = 0.005). Each additional chronic disease increased UI risk by 40%.
Conclusion
Central obesity and the burden of chronic diseases are strongly associated with UI in older women, while normal BMI has protective effects. It should be noted that a normal BMI does not fully account for UI risk factors. Interventions targeting the reduction of visceral fat and comorbidity control may improve urinary health.
Keywords: anthropometric indices, body composition, chronic diseases, elderly women, obesity, urinary incontinence, visceral fat
1. Introduction
Urinary incontinence (UI), defined as the involuntary leakage of urine, is a highly prevalent condition in women, and 15% to 55% of women are estimated to have this problem. The prevalence of UI, as reported in the literature, ranges greatly, from 5% to 70%, due to varying definitions, population heterogeneity, and sampling techniques, although most studies have reported a prevalence of 25%–45% [1]. Prevalence rates rise significantly with increasing age, occurring in more than 40% of women in their 70s, and are even more so among institutionalized elderly persons. Annual incidence rates have been reported to range between 1% and 9%, and remission rates have been estimated to range between 4% and 30%. In addition to its clinical impact, UI causes immense economic costs to healthcare systems and decreases quality of life, with many sufferers becoming socially isolated and depressed [2]. With the strong correlation of UI prevalence with increasing age—combined with increasing life expectancy—the burden of UI in women is expected to increase worldwide [3, 4, 5].
Stress urinary incontinence (SUI) is the most prevalent subtype, followed by mixed and urge incontinence. Women with UI consistently report reduced quality of life (QoL), which is related to symptom severity and subtype [6]. The condition negatively affects social, family, and sexual domains, in part due to the shared anatomical structures of the urinary and reproductive systems, which contribute to sexual dysfunction [7, 8]. UI, especially urge incontinence, is linked to anxiety, lowered self‐esteem, and social withdrawal, particularly in women younger than 50 who tend to hide their condition for fear of stigma [9].
The etiology of urinary incontinence (UI) is multifaceted, resulting from a complex interplay of sociodemographic factors, lifestyle choices, and chronic health conditions such as smoking, number of pregnancies, and diabetes [10, 11]. Known risk factors for UI are older age, obesity, history of pregnancy, smoking, and chronic illnesses such as diabetes, cognitive impairment, hypertension, and depression [12, 13]. Physiological changes accompanying aging, such as menopausal estrogen loss, cause vaginal atrophy, pelvic floor weakness, and decreased urethral support, predisposing women to UI [14, 15]. Obesity is especially a key modifiable risk factor, with excess weight augmenting intra‐abdominal pressure and straining pelvic structures [16].
Although obesity and high body mass index (BMI) are established risk factors for UI, growing evidence indicates that body composition—more specifically, fat distribution, muscle mass, and visceral adiposity—contributes even more substantially to the pathophysiology of UI. Excess abdominal fat, for example, raises intra‐abdominal pressure, putting strain on pelvic floor structures, whereas sarcopenia (age‐related loss of muscle mass) can compound pelvic floor weakness [17]. Anthropometric measures, including waist‐to‐hip ratio (WHR) and waist circumference (WC), have been mooted as predictors of UI severity, but results are as yet inconsistent [18]. Body composition—the proportion of fat mass to lean mass (muscle, bone, and organs)—is an important determinant of health. Although body weight and BMI are widely used measures, they do not distinguish between fat and muscle mass, potentially obscuring important changes in body composition. Sedentary individuals, for example, can accrue fat and lose muscle without appreciable weight change [19, 20]. Growing evidence indicates that visceral fat and abdominal adiposity, rather than general obesity, are more closely linked with UI. Research using bioelectrical impedance analysis (BIA) has shown that women with UI have greater visceral fat and waist circumference, with a 1.13‐fold higher risk of UI per unit increase in visceral fat [21, 22].
Despite growing interest in the link between obesity and UI, there is still little agreement regarding which body composition variables are most closely associated with the risk of incontinence. Additionally, the nature of the relationship between functional limitations, medication, and self‐perceived health status among elderly adults requires further investigation. In Iran, where the population of elderly people has doubled in the last 40 years and is expected to rise to 20% in the next two decades, a pressing need exists to address UI in aging women to prevent healthcare expenses and improve QoL. Nonetheless, no research has explicitly investigated the relationship between obesity, body composition, and UI in Iranian elderly women.
The purpose of this study was to examine the correlation between urinary incontinence and body composition—specifically, visceral fat, waist circumference, and muscle mass—in Iranian elderly women. By clarifying these correlations, the results can guide targeted interventions to enhance pelvic health and quality of life for this expanding population.
2. Methods and Materials
A case‐control study design was employed to assess the association between urinary incontinence and anthropometric and body composition variables in elderly women. The study included 845 community‐dwelling women aged 60 years and older, recruited from Amirkola, Iran, in Phase 2 Amirkola Cohort [23]. Amirkola is a small town located in the north of Iran near the Caspian Sea. This study aimed to determine the prevalence of urinary incontinence. Moreover, determining the influence of anthropometric and body composition on urinary incontinence.
2.1. Sampling Method
This study was a component of the extensive protocol aimed at examining the health conditions of elderly individuals in Amirkola (AHAP: Amirkola Health and Aging Project) during the second phase of the AHAP. The total number of elderly women recruited in the second phase of AHAP was 1018 individuals. All women in the cohort who reported UI based on the 3IQ questionnaire were included as cases. Controls were randomly selected from the remaining non‐UI participants in the same cohort, matched on age (± 5 years) to minimize selection bias. The ‘3 Incontinence Questionnaire’ (3IQ) is a brief, self‐administered questionnaire designed to differentiate between stress, urge, and mixed incontinence. It consists of 3 questions and takes approximately 30 s to fill out.
The sample size was determined based on the total number of eligible women from the Amirkola Elderly Health Assessment Project (AHAP‐Phase 2) who met the inclusion criteria. With 1018 women initially recruited, all those with complete data and valid responses to the 3IQ questionnaire were included, resulting in 845 participants (444 UI, 401 non‐UI), providing adequate power (> 80%) to detect meaningful differences in body composition measures.
Inclusion criteria include: Presence of urinary incontinence, absence of known psychological disorders.
Exclusion criteria include: Individuals with incomplete records, failure to participate in completing the questionnaires, neurological diseases such as dementia, history of CVA, or spinal cord trauma.
Data collection comprised standardized physical measurements and structured face‐to‐face interviews by trained health professionals. Demographic and health‐related variables included age, education, marital status, medication use, and self‐rated health status. Anthropometric data comprised body mass index (BMI), waist, and hip circumference. Advanced body composition analysis was performed using bioelectrical impedance technology, providing measures of visceral fat, total fat mass, lean mass, and the android/gynoid fat ratio.
In order to calculate the body mass index (BMI), the participants' weight was measured using a Falcon scale with an accuracy of 0.5 kilograms, while their height was taken using a tape measure attached to a wall, with an accuracy of 0.1 centimeters. BMI was calculated by taking the weight (in kilograms) divided by the height (in meters) squared. The participants were then sorted into three different groups depending on their BMI: normal (BMI ≥ 18.5 and < 25), overweight (BMI ≥ 25 and < 30), and obese (BMI ≥ 30). Measurements were conducted with great care for accuracy, and frequent checks were in place during the process.
Estimates of body composition, including lean mass (bone‐free) in grams (g), visceral adipose tissue (VAT) mass in grams (g) and volume in cubic centimeters (cm3), and total body fat mass in grams (g), were obtained using a whole‐body DXA scan on the same Hologic densitometer. The software estimates VAT by subtracting subcutaneous adipose tissue (SAT) from the total adipose tissue in the android region. The android and gynoid regions are automatically determined by the software provided by the manufacturer. The android region is the part of the abdomen between the line joining the two superior iliac crests, extending cranially to 20% of the distance from this line to the base of the skull. In contrast, the gynoid region is described as the part of the legs beginning from the femoral greater trochanter, extending caudally to a height twice that of the android region.
During the scanning process, the patient lay in a supine position on the examination table with arms placed alongside the body. Before starting each scan, the device was calibrated as per the manufacturer's specifications. The data obtained were then analyzed by Hologic Apex software version 5.6.0.2. Dual‐energy X‐ray absorptiometry (DXA) is a valid tool for measuring body composition that relies on the principle of resistance to the transmission of X‐rays. This method enables the division of body weight into parts such as fat mass, lean mass, bone mass, and mineral content. In this protocol, fat mass was quantified in grams and consists of visceral adipose tissue (VAT), localized adipose deposits, and total body fat. The software estimates visceral fat by deducting subcutaneous adipose tissue (SAT) from the total fat in the android region. The android and gynoid regions are automatically delineated using software provided by the manufacturer. Furthermore, all densitometry measurements were conducted by a qualified professional with extensive experience in the field of radiology and densitometry.
2.2. Ethical Approval
The study design was approved by the Ethics Committee of Babol University of Medical Sciences, Babol, Iran (IR. MUBABOL. REC.1403.057). All procedures were conducted in compliance with applicable guidelines and regulations.
2.3. Statistical Analysis
Statistical analyses were conducted using SPSS v.26 (IBM Corp., Armonk, NY, USA). Normality of continuous variables was assessed using the Shapiro–Wilk test. Depending on distribution, comparisons between groups utilized chi‐square tests for categorical variables, and independent t‐tests or Mann–Whitney U tests for continuous variables. For multiple group comparisons, ANOVA or Kruskal–Wallis tests were applied. All tests were two‐sided with a significance level of p < 0.05. To identify independent predictors of urinary incontinence, multivariate logistic regression analysis was performed using the backward likelihood ratio (LR) method. Results are presented as odds ratios (OR) with 95% confidence intervals (CI). Statistical reporting followed the guidelines by Assel et al. for clinical research in urology.
3. Results
Of the total sample, 52.5% (n = 444) reported experiencing urinary incontinence. Women in the UI group had a significantly higher mean age (p = 0.046) and demonstrated a greater number of chronic diseases (p < 0.001). The use of sedative hypnotics was also more frequent among participants with UI (30.0%, 133/444) compared to controls (23.4%, 94/401, p = 0.03). Additionally, a greater proportion of women with UI rated their health as poor (p = 0.001). Anthropometric comparisons revealed that the prevalence of obesity (BMI ≥ 30) was higher among women with UI (54.2.3%, 225/444) compared to controls (45.8%, 180/401), with a statistically significant difference in BMI distribution (p = 0.01). Moreover, UI was associated with elevated waist circumference (mean difference: 1.82 cm, 95% CI: 0.33–3.31; p = 0.02), increased visceral fat levels (mean difference: 57.2 g, 95% CI: 11.6–102.8; p = 0.01), and an elevated android/gynoid fat ratio (mean difference: 0.02, 95% CI: 0.002–0.038; p = 0.03). Lean mass did not differ significantly between groups (mean difference: −48 g, 95% CI: −702 to 606; p = 0.92) (Tables 1, 2 and 2).
Table 1.
Demographic and clinical characteristics of elderly women with and without urinary incontinence.
| Variable | Without incontinence (n = 401) | With incontinence (n = 444) | p | |
|---|---|---|---|---|
| BMI | < 24.99 | 84 (59.6%) | 57 (40.4%) | |
| N (%) | 25–29.99 | 134 (45.3%) | 162 (54.7%) | |
| ≥ 30 | 180 (45.8%) | 225 (54.2%) | 0.01 | |
| Marital status | Married | 295 (46.7) | 325 (52.4) | |
| N (%) | Single | 106 (47.1) | 119 (52.9) | 0.18 |
| Education | Illiterate | 259 (45.4) | 312 (54.6) | |
| N (%) | Primary | 91 (51.7) | 85 (48.3) | |
| High school | 45 (53.6) | 39 (46.4) | ||
| College/University | 6 (42.9) | 8 (57.1) | 0.3 | |
| Income satisfaction | High/very high | 6 (33.3) | 12 (66.7) | |
| N (%) | Moderate | 116 (47.5) | 128 (52.5) | |
| Low | 167 (49.1) | 173 (50.9) | ||
| Very low | 112 (46.1) | 131 (53.9) | 0.57 | |
| Sedative hypnotic taking | No | 307 (49.7) | 311 (50.3) | |
| N (%) | Yes | 94 (41.4) | 133 (58.6) | 0.03 |
| Diabetes | No | 262 (47.4) | 291 (52.6) | |
| N (%) | Yes | 139 (47.6) | 153 (52.4) | 0.95 |
| Self‐rated health | Worst | 23 (34.3) | 44 (65.7) | |
| N (%) | Moderate | 53 (39.6) | 81 (60.4) | |
| Good | 153 (45.4) | 184 (54.6) | ||
| Very good | 123 (54.2) | 104 (45.8) | ||
| Excellent | 49 (62) | 30 (38) | 0.001 | |
| Age | 68.78 ± 7.03 | 69.75 ± 7.20 | 0.046 | |
| mean ± SD | ||||
| Number of children | 5 (3–6) | 5 (4–6) | 0.08 | |
| Median (IQR) | ||||
| Number of chronic diseases | 4 (2–5) | 5 (3–7) | < 0.001** | |
| Median (IQR) |
Mann‐Whitney.
Table 2.
Association of Anthropometrics and body composition with urinary incontinence.
| Variables | Without incontinence (n = 401) | With incontinence (n = 444) | Mean Difference (95% CI) | p |
|---|---|---|---|---|
| BMI (kg/m2) | 29.55 ± 5.51 | 30.07 ± 4.77 | 0.52 (−0.18 to 1.22) | 0.15 |
| Waist circumference (cm) | 92.14 ± 11.42 | 93.96 ± 10.54 | 1.82 (0.33 to 3.31) | 0.02* |
| Hip circumference (cm) | 102.64 ± 10.89 | 103.79 ± 9.78 | 1.15 (−0.26 to 2.56) | 0.11 |
| AGR (Android/gynecoid) | 1.03 ± 0.12 | 1.05 ± 0.11 | 0.02 (0.002 to 0.038) | 0.03 |
| EVM (Visceral fat) | 888.9 ± 346.8 | 946.1 ± 328.1 | 57.2 (11.6 to 102.8) | 0.01 |
| Fat mass | 30875 ± 8884 | 32011 ± 7629 | 1136 (1.8 to 2270.2) | 0.05 |
| Lean mass | 35761 ± 5057 | 35713 ± 4813 | −48 (−702 to 606) | 0.92 |
Independent t test.
Within the UI subgroups, those with mixed UI had the highest mean age and the number of chronic diseases. However, no significant differences in body composition indices were observed between the different types of UI (stress, urge, or mixed) (Tables 3 and 4).
Table 3.
Association of demographic characteristics with type of urinary incontinence.
| Variables | Stress incontinence N (%) | Urgency incontinence N (%) | Mixed incontinence N (%) | p | |
|---|---|---|---|---|---|
| Marital status | Married | 66 (20.6) | 173 (53.9) | 82 (25.5) | 0.05 |
| Single | 14 (11.9) | 63 (53.4) | 41 (34.7) | ||
| Education | Illiterate | 41 (13.4) | 170 (55.4) | 96 (31.3) | 0.005 |
| Primary | 25 (29.4) | 42 (49.4) | 18 (21.2) | ||
| High school | 11 (28.2) | 21 (53.8) | 7 (17.9) | ||
| College/University | 3 (37.5) | 3 (37.5) | 2 (25) | ||
| Income satisfaction | High/very high | 2 (16.7) | 8 (66.7) | 2 (16.7) | 0.007 |
| Moderate | 33 (26.4) | 68 (54.4) | 24 (19.2) | ||
| Low | 29 (16.8) | 97 (56.1) | 47 (27.2) | ||
| Very low | 16 (12.4) | 63 (48.8) | 50 (38.8) | ||
| Tranquilizer taking | No | 54 (17.4) | 169 (54.5) | 87 (28.1) | 0.78 |
| Yes | 26 (20.2) | 67 (51.9) | 36 (27.9) | ||
| Diabetes | No | 59 (20.5) | 149 (51.7) | 80 (27.8) | 0.22 |
| Yes | 21 (13.9) | 87 (57.6) | 43 (28.5) | ||
| Self‐rated health | Worst | 7 (16.3) | 22 (51.2) | 14 (32.6) | 0.04 |
| Moderate | 13 (16.3) | 34 (42.5) | 33 (41.3) | ||
| Good/very good | 60 (19) | 180 (57.1) | 75 (23.8) | ||
| Age mean ± sd | 67.18 ± 6.08 | 69.79 ± 6.92 | 71.22 ± 8.02 | < 0.001 | |
| Number of children median (IQR) | 4 (3–5) | 5 (4–6) | 5 (4–6) | 0.001 | |
| Number of chronic diseases median (IQR) | 4 (3–6) | 5 (4–6) | 6 (4–8) | < 0.001 | |
Table 4.
Association of anthropometric and body composition with the type of urinary incontinence.
| Variables | Stress incontinence | Urgency incontinence | Mixed incontinence | p |
|---|---|---|---|---|
| BMI (kg/m2) | 29.77 ± 5.06 | 29.97 ± 4.69 | 30.49 ± 4.81 | 0.52 |
| Waist circumference (cm) | 93.79 ± 10.77 | 93.35 ± 10.16 | 95.07 ± 10.95 | 0.36 |
| Hip circumference (cm) | 104.03 ± 9.18 | 103.45 ± 9.78 | 104.15 ± 10.21 | 0.79 |
| AGR (Android/gynecoid) mean ± SD (median) | 1.04 ± 0.1 (1.05) | 1.05 ± 0.11 (1.05) | 1.06 ± 0.09 (1.07) | 0.59 |
| EVM (Visceral fat) mean ± SD (median) | 925.49 ± 0.271.23 (956.50) | 940.15 ± 325.12 (939.5) | 971.74 ± 363.31 (901.00) | 0.89 |
| Fat Mass (g) mean ± SD (median) | 31961.78 ± 6862.48 (31799.55) | 31751.23 ± 7826.11 (31620.35) | 32538.64 ± 7730.25 (32677.20) | 0.69 |
| Lean Mass (g) mean ± SD (median) | 35791.07 ± 4126.03 (35338.50) | 35575.81 ± 5105.68 (35229.8) | 35925.40 ± 4712.19 (35516.40) | 0.92 |
Multivariate logistic regression identified two key predictors of UI: women classified as overweight BMI < 24.99 exhibited a significantly lower odds of incontinence compared to normal‐weight peers (OR = 0.52; 95% CI: 0.33–0.82; p = 0.005), suggesting a potential protective effect. In contrast, each one‐point increase in the number of chronic diseases was associated with a 40% increase in the odds of experiencing UI (OR = 1.40; 95% CI: 1.29–1.52; p < 0.001) (Table 5).
Table 5.
Multivariate logistic regression predicting urinary incontinence.
| Variable | Category | OR (95% CI) | p |
|---|---|---|---|
| BMI | < 24.99 | 0.52 (0.33–0.82) | 0.005 |
| 25–29.99 | 1.13 (0.79–1.60) | 0.51 | |
| ≥ 30 | Ref | ||
| Number of chronic diseases | (per 1‐unit increase) | 1.40 (1.29–1.52) | < 0.001 |
Note: Model adjusted for age, marital status, education, and sedative hypnotic use.
4. Discussion
This research examined the demographic, clinical, anthropometric, and body composition characteristics related to urinary incontinence (UI) in older women, in those with and without UI.
BMI, sedative hypnotic use, self‐rated health, age, and number of chronic conditions were all significantly associated with UI prevalence. In accordance with previous studies [24, 25, 26, 27, 28, 29], UI was more common in women with a BMI ≥ 25 than in those with a BMI < 24.99. Yet our analysis indicates the association is complex: notably, normal weight status was linked with decreased risk of UI when compared to obese peers (OR = 0.52, 95% CI: 0.33–0.82), with a possible protective effect worthy of further exploration. This could be due to the effect of muscle mass [27], fat distribution [30], or metabolic resilience in this group.
Sedative hypnotic use was positively associated with UI (58.6% vs. 50.3% among non‐users), a relationship that is probably explained by their muscle relaxant effects on urethral sphincter tone. In as much as insomnia is very prevalent among older persons [31], use of nighttime sedatives may also contribute to nocturnal enuresis through an impairment of arousal for voiding [32].
Self‐assessed health was strongly inversely related to UI: 65.7% prevalence in those who reported “worst” health compared with 38.0% in those who reported “excellent” health (p = 0.001). This indicates that UI is not only a reflection but also probably a cause of health status.
The relationship between increasing age and UI is consistent with recognized physiological alterations such as reduced muscle tone, hormonal changes (e.g., decline in estrogen), and connective tissue breakdown [33]. In addition, the presence of increasing numbers of chronic diseases was strongly associated with increased UI risk (OR = 1.40 per disease, 95% CI: 1.29–1.52; p < 0.001). Oxidative stress, a shared pathogenic mechanism in aging and chronic disease [34, 35, 36, 37, 38] could mediate this relationship by inducing functional or structural alterations in the lower urinary tract, which manifest as lower urinary tract symptoms (LUTS).
The prevalence of UI types differed by demographics: urgency incontinence was the most prevalent (53.9%), followed by mixed (25.5%) and stress incontinence (20.6%). Education level, marital status, income satisfaction, self‐assessed health, age, number of children, and number of chronic diseases were significantly related to UI type.
College education and marriage were linked with greater percentages of stress UI. This may be due to variations in physical activity, healthcare‐seeking patterns, reporting habits, parity, or sexual activity.
There was a definite age‐related trend: stress UI was most prevalent in the youngest subjects (mean age 67.18 years), followed by urgency UI (69.79 years), and mixed UI (71.22 years). This pattern implies that stress UI occurs earlier and that urgency and mixed components increase with increasing age [39], in line with neurological and detrusor muscle alterations with aging.
Mixed UI was significantly inversely related to income satisfaction, with the highest occurrence (38.8%) in those with “very low” satisfaction. This suggests obstacles to healthcare access, prevention, or treatment [40, 41].
Strong correlations between UI and greater visceral fat, waist circumference, and android/gynoid fat ratio are consistent with the hypothesis that central adiposity causes elevated intra‐abdominal pressure and pelvic floor dysfunction [18, 42, 43]. Such factors could also facilitate systemic inflammation and metabolic disruption, further impairing urinary control [44]. No differences in body composition were observed between UI subtypes (stress, urgency, mixed); however, this implies that neurological function, parity, or hormonal status might be more important in subtype discrimination.
Our ultimate multivariate model validated normal weight (BMI < 24.99) as protective for UI (OR 0.52, 95% CI 0.33–0.82) relative to obesity (BMI ≥ 30). Each incremental chronic disease raised the odds of UI by 40% (OR 1.40, 95% CI 1.29–1.52). This highlights the importance of including both weight control and comprehensive chronic disease management in the prevention and control of UI.
These findings are consistent with and build on current evidence. They reinforce the concept that traditional anthropometric measures, such as BMI, can be limited in ascertaining obesity‐related risk of UI, whereas indicators of fat distribution (e.g., waist circumference, estimates of visceral fat) have more robust associations [45, 46], although controversies [47, 48] do exist.
Future research should include longitudinal designs to establish causal relationships between visceral fat, chronic diseases, and UI. Additionally, investigations into the protective role of muscle mass, adipokine profiles, and metabolic health in overweight older women are warranted. Validation of these findings in diverse ethnic and geographic populations will enhance generalizability and inform targeted prevention strategies.
4.1. Limitations
Although the 3IQ questionnaire is a validated tool for UI classification, reliance on self‐report may introduce misclassification bias, particularly in an elderly population with potential variability in literacy and comprehension. Future studies may benefit from supplementing self‐report with clinical assessment to enhance diagnostic accuracy.
5. Conclusion
The results highlight the multifactorial etiology of UI in older women. The results exhibit that the relationship between BMI and UI is not simply linear. Management must be holistic, including the promotion of physical fitness, body composition profiling (especially central adiposity), weight management, and coordinated management of chronic diseases. Strategies aimed at enhancing strength, decreasing visceral fat, and improving mobility have great potential in the prevention and management of UI in this group.
Author Contributions
Shiva Khoshroo: software, writing – review and editing, investigation. Shabnam Omidvar: conceptualization, methodology, writing – original draft, supervision, validation. Reza Ghadimi: writing – original draft, supervision, project administration. Seyed Reza Hoseini: writing – review and editing. Ali Bijani: software, formal analysis, writing – review and editing. Hajar Pasha: writing – review and editing. All authors have read and approved the final version of the manuscript. Dr. Shabnam Omidvar had full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.
Funding
The authors have nothing to report.
Ethics Statement
Ethics approval and consent to participate: The study design was approved by the Ethics Committee of the Babol University of Medical Sciences (IR. MUBABOL. REC.1403.057). Written informed consent was obtained from all the participants. The methods involved in our research are carried out in accordance with relevant guidelines and regulations (Declaration of Helsinki).
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Transparency Statement
The lead author, Shabnam Omidvar, affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
Acknowledgments
The authors would like to thank all the participants. We also appreciate the Deputy of Research and Technology of Babol University of Medical Sciences. This article is derived from the doctoral dissertation of Dr. Shiva Khoshroo, in partial fulfillment of the requirements for the degree of Doctor of Medicine (GP).
Data Availability Statement
The datasets used and/or analyzed during the current study are available from Seyed Reza Hosseini (person in charge of AHAP) upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from Seyed Reza Hosseini (person in charge of AHAP) upon reasonable request.