INTRODUCTION
Lower urinary tract symptoms (LUTS) collectively refer to a range of conditions including storage symptoms such as daytime frequency, nocturia, urgency, and urinary incontinence; voiding symptoms such as slow stream, splitting or spraying, intermittent stream, and hesitancy; and postmicturition symptoms such as the feeling of incomplete emptying and postmic turition dribble [1].
Individuals with LUTS often experience significant psychological distress, which can lead to mental health issues. Clinical observations indicate that patients with chronic LUTS frequently exhibit concomitant depression, anxiety, and personality traits of neurosis [2]. Moreover, among patients with overactive bladder (OAB), 37.6% of patients report severe disruptions to daily life. When depression coexists with OAB, it further impacts sleep quality [3,4]. Emotional factors can influence bladder function in healthy individuals as well, causing temporary changes in urinary urgency and frequency [5]. However, patients with mental illnesses, such as depression and anxiety, tend to experience LUTS more frequently with the severity of depressive symptoms significantly correlating with the intensity of LUTS [6].
To explain the mutually reinforcing relationship between urological conditions and mental illness, the term ‘uropsychiatry’ was proposed. However, this field remains undeveloped due to insufficient evidence confirming a causal relationship between the 2 conditions [7,8]. Nonetheless, numerous studies suggest a bidirectional association between LUTS and mental illness. Examples include survey-based research conducted in the United States, the United Kingdom, and Sweden [9], a study from Taiwan analyzing the risk of mental disorders in OAB patients [10], research quantifying the bidirectional association between LUTS and depression in males [11], and a meta-analysis of 814 published studies exploring the relationship between anxiety and LUTS [12].
In addition to the studies on the connection between LUTS and mental illness, clinical case research highlights that severe LUTS often worsens depression, while LUTS in patients with depression may be misdiagnosed as psychiatric symptoms, potentially leading to exacerbated LUTS [13,14]. Given the high comorbidity between LUTS and mental illness and their potential to exacerbate each other or interfere with treatment, a proactive urological diagnosis and management is essential for patients experiencing both conditions. Effective treatment of LUTS, therefore, requires an understanding of the relationship between LUTS and mental illness. It emphasizes an integrated approach that incorporates psychiatric elements to avoid incomplete solutions that address only one side of the problem [15].
Individuals with mental health conditions often avoid discussing symptoms due to fear of stigma and social rejection [16]. Similarly, urological conditions are often perceived as socially taboo, shameful, or uncomfortable to address openly [17,18]. Both conditions present challenges to fully understanding patients’ experiences and fostering meaningful engagement.
Disease-related online communities offer anonymity, enabling individuals to gather information, connect with others in similar situations, and share experiences without fear of stigma. These platforms overcome mobility limitations and facilitate emotional exchanges that might be difficult in face-to-face settings [19,20]. Posts from these interactions provide valuable insights into public perceptions of sensitive issues that are otherwise difficult to discuss openly.
Applying text-mining techniques to community posts allows for the effective collection and analysis of informal, spontaneous language from users unaware of the research objectives. These methods yield deeper insights into research topics through visualization techniques that meaningfully represent findings [21]. Latent Dirichlet allocation (LDA) topic modeling is especially useful for identifying latent themes within posts by patients and their families, offering nuanced insights into their authentic opinions and experiences.
This study analyzes online discussions among individuals experiencing both mental illness and LUTS to better understand the complex challenges they encounter and to inform more integrated treatment strategies.
MATERIALS AND METHODS
Text Mining and Topic Modeling
Text mining processes large-scale unstructured textual data to extract underlying meanings, patterns, and key topics using natural language processing techniques. Methods such as sentiment analysis and topic modeling are part of this approach. Topic modeling calculates the probability of words belonging to specific topics, identifying latent themes within the text [22]. This method effectively extracts meaningful topics from large datasets.
Data Collection and Preprocessing
Titles, content, and category names were collected from posts containing the keywords “pee” or “urine” within a major South Korean online mental health community established in July 2005. As of October 21, 2024, the community had 113,060 members and 368,352 posts. A total of 986 posts matching the keyword criteria were initially gathered. Python, Selenium WebDriver, and BeautifulSoup were used for data collection, with Pandas employed to organize and save the data as a CSV (comma-separated values) file.
After removing duplicate and restricted-access posts, the final dataset included 945 entries. Morphological analysis and stopword removal were conducted using the Okt tokenizer from the KoNLPy library. During processing, segmentation errors occurred with drug names, which were not initially recognized as proper nouns. To address this, drug names were collected from the Korean Ministry of Food and Drug Safety’s website and refined to produce a list of 17,948 entries. Regular expressions were then applied to ensure these names remained intact during tokenization.
LDA Topic Modeling, Frequency Analysis, and Visualization
Data visualization involved 3 main approaches. First, tokenized titles and content were used to create a word cloud, highlighting frequently occurring keywords. Second, LDA topic modeling was performed on the dataset, with results visualized using py-LDAvis. To ensure consistency and appropriateness, multiple experiments were conducted by adjusting parameters, including topics (3 to 8), words per topic (10 to 30), passes (10 to 30), and iterations (100 to 200). The final model, configured with 4 topics, 30 words per topic, 10 passes, and 100 iterations, achieved a Coherence Score of 0.432, which was deemed acceptable as a measure of interpretability. Lastly, category frequencies were analyzed to identify categories most associated with the keywords.
Qualitative Analysis
To complement the topic modeling results, a qualitative analysis was conducted on the top 5 categories identified through frequency analysis. This analysis examined the characteristics of posts in each category, offering deeper insights into the experiences, psychological conditions, and social contexts of patients and their families. It revealed nuanced themes that were not fully captured through topic modeling.
The entire research process is summarized in Fig. 1.
RESULTS
Word Cloud Visualization
As shown in Fig. 2, the word cloud revealed that the most prominent words were ‘go’ and ‘medicine.’ While ‘go’ can appear in various contexts, ‘medicine’ indicates frequent discussions about medications when addressing urinary symptoms. Though less frequent, ‘side effect’ likely appeared in the context of medication-related issues.
Other common words included ‘work,’ ‘time,’ ‘day,’ and ‘sleep,’ suggesting that urinary problems disrupted patients’ routines or added lifestyle stress. Users may have also shared urinary-related episodes from daily experiences.
The word ‘know’ was mainly used when seeking advice or opinions from other participants. Additionally, words such as ‘treatment,’ ‘mind,’ ‘patient,’ and ‘hospital’ reflect discussions about management and treatment strategies. Terms like ‘increase,’ ‘depression,’ and ‘many’ were likely used to describe symptoms or conditions associated with urinary problems.
LDA Topic Modeling and pyLDAvis Visualization
Based on the collected post titles and content data, the pyLDAvis visualization of the LDA topic modeling, configured to achieve the most balanced Intertopic Distance Map and stable Coherence Score, is presented below in Fig. 3.
The words identified in the analysis, listed in order, are ‘child,’ ‘medicine,’ ‘patient,’ ‘mother,’ ‘administration,’ ‘husband,’ ‘abnormality,’ ‘dosage,’ ‘self,’ ‘medication,’ ‘home,’ ‘test,’ ‘side effect,’ ‘depression,’ ‘hospital,’ ‘case,’ ‘appreciation,’ ‘bupropion,’ ‘person,’ ‘treatment,’ ‘substance,’ ‘puppy,’ ‘effect,’ ‘serotonin,’ ‘or,’ ‘increase,’ ‘response,’ ‘intake,’ ‘antidepressant,’ and ‘urine.’ Similar to the word cloud, medication-related terms are the most prominent. In addition to ‘medicine’ and ‘medication,’ terms associated with drug administration, effects, and responses, such as ‘administration,’ ‘dosage,’ ‘effect,’ ‘response,’ ‘intake,’ and ‘side effect,’ appear frequently, along with the names of specific medications like ‘bupropion’ and ‘antidepressant.’ Furthermore, terms related to care and treatment, such as ‘treatment,’ ‘hospital,’ and ‘test,’ are also observed.
A key difference from the word cloud is the prominence of family-related terms like ‘mother’ and ‘husband,’ suggesting that discussions of urinary issues and medications go beyond personal experiences to impact family relationships and daily life.
In topic modeling visualization, the λ (lambda) value balances word uniqueness and frequency. A λ closer to 0 emphasizes distinctive words within a topic, while values near 1 highlight frequent words [23]. In this study, λ was set to 0.5 to balance uniqueness and frequency. The outcomes are summarized in Table 1.
Based on the Analysis Presented in Table 1, the Following Meanings Were Identified
Topic 1: Mental health treatment and personal experiences
Words related to hospitals (‘hospital,’ ‘test,’ ‘hospitalization,’ ‘psychiatry,’ ‘doctor’) and medication (‘medicine,’ ‘side effects,’ ‘taking,’ ‘lithium’) are prominent, along with terms expressing challenging, negative everyday experiences (‘too much,’ ‘none,’ ‘can't,’ ‘hard,’ ‘work,’ ‘sick,’ ‘symptoms’).
These words suggest that patients may be articulating the difficulties associated with hospitalization or frequent visits to hospitals for mental health treatment. Furthermore, the co-occurrence of the world ‘urine’ alongside terms related to hospitals, medication, and daily routines indicates that patients may have experienced or perceived urinary problems as a side effect of their treatment or medication.
Topic 2: Family and urinary problems
This topic includes words related to family members and the household (‘child,’ ‘mom,’ ‘husband,’ ‘home,’ ‘puppy,’ ‘sister,’ ‘mother-in-law [respectfully],’ ‘raise’) as well as household chores (‘clean,’ ‘clear’). Negative words such as ‘unable,’ ‘do not’ also appear in this topic. These words suggest that negative experiences or conflicts within the home environment may be associated with urinary issues.
Topic 3: Challenges in social relationships and emotional exchange
This topic contains words related to social interactions and emotional exchanges (‘person,’ ‘love,’ ‘bullying,’ ‘talk,’ ‘society,’ ‘friend’) alongside several negative expressions (‘none,’ ‘do not,’ ‘bullying,’ ‘be subject to,’ ‘not,’ ‘unable,’ ‘obsession’). The co-occurrence of ‘pee’ or ‘urine’ with these keywords suggests that urinary conditions or symptoms may have negatively impacted social relationships and activities, or conversely, that negative social experiences may have affected urinary function.
Topic 4: Impact of medication use on urinary function
This topic primarily consists of medication-related terms (‘administration [of medication],’ ‘abnormal,’ ‘dosage,’ ‘bupropion,’ ‘substance,’ ‘action,’ ‘increase,’ ‘drug,’ ‘reaction,’ ‘antidepressant,’ ‘treatment,’ ‘absorption’). Additionally, words related to neurotransmitters and metabolism (‘serotonin,’ ‘dopamine,’ ‘metabolism,’ ‘vitamin,’ ‘calcium’) are also present. These words suggest that patients are concerned about the effects of medication on urinary function and that neurotransmitters and metabolic processes play a significant role in urinary regulation.
Frequency Analysis of Categories
To understand where urinary problems appeared, the data's category column was sorted by frequency, as visualized in Fig. 4.
While frequency analysis provides an overview, it lacks sufficient context for many urination-related posts, such as those in Free Board. Therefore, qualitative analysis of the top 5 categories with the most posts was conducted for more detailed insights. In Free Board, posts were more specific about diseases and symptoms than in categories named after mental illnesses. Patients or families may have chosen this board for its less stigmatized environment or because it was better suited for discussing urinary symptoms. Common topics included side effects like residual urine, urinary retention, and frequent urination following medication changes. Posts often detailed symptoms, sought advice, or shared personal experiences. Other issues included incontinence, difficulty using public restrooms due to intestinal obstructions or obsessive-compulsive disorder, emotionally triggered incontinence, and concerns about pets’ urination problems.
In major depressive disorder, the most discussed topic was worsening urinary symptoms due to medication. Many users questioned if new medications caused dysfunction. Posts also reported abnormal urine frequency or volume, speculated about long-term issues related to depression, or expressed concern about accidents during social activities.
In bipolar disorder, posts frequently mentioned urinary retention, frequent urination, and incontinence linked to medication. Users sought advice on managing dosages and shared concerns about side effects despite doctors’ reassurances, posting detailed reviews of medications. Only one post in this category mentioned visiting a urologist for severe LUTS, highlighting a focus on self-management over formal consultation.
In schizophrenia spectrum, 15 out of 55 posts came from family members or acquaintances reporting patients’ LUTS symptoms along with schizophrenia, seeking advice or guidance. Many detailed involuntary urinary accidents. Some raised concerns about employment-related urine tests, fearing antipsychotic medication would be revealed.
In the help forum, posts focused on urination issues from medication side effects and concerns about urine tests for employment or certification.
Across categories, users saw urination problems as a natural side effect of mental health medications. Despite significant discomfort in daily life, few users intended to visit a urologist or treat their LUTS actively. Many instead tried discontinuing medication, adjusting dosages, or managing side effects independently. Common themes included seeking medication advice, empathizing with others, and considering asking doctors for medication changes. Known side effects were widely shared, with urinary dysfunction often accepted as inevitable. Severe dysuria sometimes led users to question if their symptoms were related to depression or schizophrenia. Routine voiding issues were discussed casually, but incidents in public or during sleep triggered intense self-blame and emotional distress, especially in socially challenging contexts like work or school.
DISCUSSION
The word cloud analysis revealed several key themes, such as the connection between medications and urinary problems, the stress and disruptions to daily life caused by urinary issues, community support through shared experiences and information.
LDA topic modeling identified 4 main conversation themes: (1) the challenges in the mental health treatment and its association with urinary dysfunction, (2) the impact of negative family experiences on urinary problems, (3) the negative interactions between urinary problems and social activities or interpersonal relationships, and (4) interest in the effects of medication administration and neurotransmitters on bladder control.
The qualitative analysis across categories revealed 4 major observations. First, urinary dysfunction was frequently seen as a medication side effect, particularly in the major depressive disorder and bipolar disorder categories, where urinary dysfunction was widely accepted as a side effect of psychiatric medications. Patients often expressed distress and discomfort due to these side effects but tended to accept them as inevitable. Secondly, patients commonly displayed a passive approach to urological care. Instead of seeking medical help, patients frequently attempted self-regulation or medication discontinuation, which was common across all categories. Thirdly, concerns about the relationship between mental illness and urinary dysfunction were observed. Although many posts were uploaded by patients themselves, in the schizophrenia spectrum category, it was common for family members or acquaintances to post inquiries on behalf of the patient.
Lastly, self-blame and despair resulting from urinary accidents in public settings or during sleep were found to pose a risk of exacerbating the patient’s emotional issues.
Across all analysis methods, the findings suggest 4 main implications. First, urinary dysfunction was widely recognized as psychiatric medication side effects, with patients generally accepting them as unavoidable. Second, self-adjustment of medication doses was preferred over medial intervention. Third, patients frequently sought information on whether their urinary problems were related to their mental illnesses. Finally, selfblame and distress due to public or sleep-related urination incidents intensified emotional struggles.
On the other hand, there were notable differences between analysis methods. Topic modeling emphasized the negative impact of urinary issues on social relationships and activities, while the category-specific analysis highlighted connections between negative family experiences and urinary problems. This indicates that urinary dysfunction can strain multiple aspects of life, from family dynamics to social interactions.
In conclusion, the study results showed that urinary dysfunction is widely recognized as a side effect of psychiatric medications among patients with mental illness, and patients often accept these difficulties as inevitable. However, many attempt to adjust or discontinue medications on their own rather than seeking urological or medical intervention-efforts which frequently prove unsuccessful. Self-management of LUTS, or resignation to symptoms, places patients a heightened risk of worsening psychiatric issues, as LUTS significantly impacts quality of life and elevates mental distress. This study’s findings align with this, as patients with LUTS often report challenges in social and family life and use extreme expressions of self-blame and despair regarding urinary incontinence in public settings or while sleeping.
Given this, it is critical to provide psychiatric patients with accurate, accessible information on LUTS to improve awareness and encourage timely urological interventions and alternative treatments aimed at enhancing quality of life. Additionally, this study also revealed that patients frequently seek to understand the connection between urinary dysfunction and mental illness, and they actively sought and shared relevant information through online communities to address these concerns. Thus, offering clear guidance on the urological effects of psychiatric conditions and medications may help reduce embarrassment and confusion for patients dealing with LUTS.
This study contributes to exploring and supporting the possibility that mental illness and urinary dysfunction can mutually exacerbate each other while also enhancing understanding of the complex effects these co-occurring conditions have on patients and their struggles. By basing the analysis on real patient and family experiences, this study captures candid insights often omitted in clinical settings. The findings also shed light on patients’ frequent attempts to self-manage medication and the resulting side effects.
However, this study has limitations since it relied on self-reported patient narratives, which may impact the accuracy of medical diagnoses and claims about medication side effects. Additionally, the sample may lack representativeness, as the experiences of patients who are not active in online communities are not included. Thus, future research can be done to incorporate reliable medical diagnoses and objective data to further clarify the relationship between urinary dysfunction and metal illness. Broadening data collection methods, such as in-person interviews or surveys, will help to capture a more comprehensive view.
It is hoped that this study will inform the development of psychological support and alternative interventions for patients with co-occurring urinary issues and emotional challenges and serve as a valuable resource for understanding their experiences and perceptions.
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