Impact of anxiety and depression on treatment adherence and nursing workload in hospitalized patients with gastrointestinal diseases

Abstract

BACKGROUND

Anxiety and depression are associated with high comorbidity rates in patients with gastrointestinal diseases. Previous studies have confirmed a significant correlation between severe gastrointestinal symptoms and decreased quality of life. However, the specific impact of anxiety and depression on patients’ medical behaviors (such as treatment compliance) and healthcare staff workload (especially nursing workload) in an inpatient environment has not been fully described.

AIM

To explore the anxiety and depression effects on treatment compliance and nursing workload of hospitalized patients with gastrointestinal diseases.

METHODS

We retrospectively analyzed the data of 160 patients with gastrointestinal diseases admitted to the First People’s Hospital of Yongkang City between January 2021 and December 2025. According to the Hospital Anxiety and Depression Scale score, patients were divided into moderate/severe anxiety and depression and no/mild anxiety and depression groups. Baseline features, Medication Adherence Reporting Scale (MARS) scores, Mean Daily Activity Score, Length of Stay, total hospitalization costs, unplanned 30-day readmission rate, Gastrointestinal Symptom Rating Scale (GSRS) scores, and Gastrointestinal Quality of Life Index (GIQLI) scores were compared.

RESULTS

Compared with the no/mild group, patients in the moderate/severe anxiety and depression group were younger, had a higher proportion of functional gastrointestinal disorders, had higher disease severity, and had higher levels of C-reactive protein upon admission. The moderate/severe anxiety and depression groups had significantly lower MARS scores and higher average daily Nursing Activity Rating Scale scores. In addition, this group showed higher total GSRS scores and lower GIQLI total scores. Regarding the utilization of medical resources, the service level of the moderate/severe group was longer, the total hospitalization cost was higher, and the unplanned 30-day readmission rate was significantly higher. After adjusting for confounding factors, the multivariate linear regression analysis identified moderate/severe anxiety and depression as independent risk factors for decreased treatment compliance and increased nursing workload. Logistic regression analysis confirmed that this was an independent risk factor for unplanned readmission in the short term. A dose-response relationship was observed between the severity of anxiety/depression symptoms and primary outcome measures. In the subgroup analysis, the association between anxiety and depression status and nursing workload remained significant in both disease categories, with a stronger numerical impact on patients, which should be interpreted as exploratory.

CONCLUSION

Anxiety and depression were independently associated with decreased treatment compliance, increased nursing workload, prolonged hospital stay, and higher short-term readmission risk in hospitalized patients with gastrointestinal diseases. Routine screening and considering the psychological status of patients should be included in the management protocol. A tiered support strategy should be considered, providing basic psychological support for patients with mild symptoms and referring severe cases to specialists.

Key Words: Gastrointestinal diseases; Anxiety; Depression; Treatment compliance; Nursing workload

Core Tip: Anxiety and depression are common but often underestimated comorbidities in hospitalized patients with gastrointestinal diseases. This retrospective observational study showed that moderate-to-severe anxiety and depression were independently associated with poorer treatment adherence, greater nursing workload, longer hospital stay, higher hospitalization costs, and increased short-term readmission risk. A dose-response relationship was observed, with worsening psychological symptoms associated with lower adherence and higher nursing burden. These findings support the importance of routine psychological assessment and tiered psychosomatic management in the inpatient care of gastrointestinal diseases.

INTRODUCTION

Anxiety and depression, as two highly prevalent psychological disorders, often accompany gastrointestinal organic or functional diseases in the form of comorbidities. Previous studies have revealed a possible causal chain between negative emotions and upper gastrointestinal disorders, and a Mendelian randomization analysis has provided preliminary confirmatory evidence for this[1]. In patients with non-eosinophilic gastrointestinal diseases, anxiety is closely linked to a significant decline in the quality of life, which highlights the important role of psychological variables in the field of gastroenterology. Functional gastrointestinal diseases, including irritable bowel syndrome and disorders related to brain-gut interaction, have a high probability of co-occurrence with anxiety and depression, often accompanied by heavier symptom burden and reduced quality of life[2-4]. Comprehensive clinical intervention is no longer optional but necessary. The theoretical framework of the brain-gut axis explains how psychological states regulate gastrointestinal function through multiple pathways, such as neurotransmitters, endocrine hormones, and immune inflammation, revealing from a mechanistic perspective how psychological intervention measures have become an indispensable part of holistic treatment. Mendelian randomization further links severe depression with an increased risk of developing gastric ulcers and irritable bowel syndrome, confirming the role of emotional disorders as drivers of the pathological and physiological evolution of the gastrointestinal tract[5]. In addition, anxiety and depression in gastroenterology outpatients have been reported to be more strongly associated with upper gastrointestinal symptoms and symptom clusters than with lower gastrointestinal symptoms[6]. Research in the field of oncology suggests that anxiety and depression are often associated with poor coping strategies and disease outcomes in patients, suggesting that psychological factors may exert some degree of regulatory effect on the treatment outcomes of various disease entities[7]. In chronic digestive system diseases, the symbiotic relationship between digestive discomfort and psychological distress continues to worsen a patient’s quality of life, outlining a subtle and complex interaction between the mind and body[8]. The gut microbiota also plays a specific role in this. Previous studies have shown that fecal microbiota transplantation technology can alleviate clinical symptoms in patients with functional constipation complicated with depression and anxiety, which may indicate a promising emerging treatment pathway[9]. In addition, using Mendelian randomization methods, scholars have linked emotional instability to an increased risk of various digestive diseases, further consolidating the argument that psychological states have a wide range of impacts[10]. Based on the existing literature, the core idea is becoming increasingly clear: Integrating psychological assessment and intervention methods into the care framework for gastrointestinal diseases is crucial for achieving optimal patient outcomes.

Despite ample evidence indicating an association between anxiety, depression, and gastrointestinal diseases, existing research designs are generally constrained by several key shortcomings. Many studies use cross-sectional surveys, which cannot derive causal relationships and are highly susceptible to confounding biases[11]. The limitation of sample size is not uncommon, and insufficient statistical power makes it difficult to detect more precise effect quantities, which undermines the robustness of the research conclusions[12]. Research subjects often focus on outpatient populations or are limited to special subgroups such as women and specific diseases, which poses significant obstacles when extrapolating research conclusions to a wider and more complex group of hospitalized patients[13]. There is a lack of specialized exploration of hospitalization scenarios, especially in terms of the impact of psychological factors on treatment compliance and nursing manpower investment, and there are still significant gaps in the knowledge map[14,15]. The design attribute of retrospective research is associated with the risk of introducing recall bias or questioning the integrity of data records, all of which may pose a threat to the accuracy of the conclusions. In addition, although tools such as the Hospital Anxiety and Depression Scale (HADS) have been widely used, their measurement equivalence in cross-cultural contexts needs to be rigorously examined and verified, and their applicability to different populations may not be unlimited. All these limitations point to a clear need; by relying only on more rigorous research designs and larger sample sizes, we can fully clarify the specific role of psychological factors in hospitalized patients with gastrointestinal disease.

This study aimed to fill these cognitive gaps. We conducted a single-center, retrospective observational study to investigate the specific effects of anxiety and depression on treatment compliance and nursing workload in hospitalized patients with gastrointestinal disease. By calling on a relatively large sample library and applying multivariate adjustments to correct for potential confounding factors, this study aimed to overcome the limitations of previous researchers and contribute a more solid evidence chain to the academic community. The findings of this study are expected to provide practical insights for clinical practice, promote the integration of psychological care elements into digestive disease management systems, and ultimately serve the grand goal of optimizing patient outcomes and the rational allocation of medical resources.

MATERIALS AND METHODS

General information

This single-center, retrospective, observational study was conducted at the First People’s Hospital of Yongkang City, Zhejiang Province, China. The electronic medical record system continuously identifies hospitalized patients. The research plan was reviewed and approved by our institutional review committee and conducted in accordance with the Declaration of Helsinki. Given that the retrospective design only involves anonymous analysis of historical medical record data without direct patient intervention and that all data analysis was conducted on the identified information, the ethics committee granted an exemption from obtaining informed consent.

The sample size was 160. This number was determined based on a pilot analysis and an estimation of the effect size. In January 2021, 30 medical records were randomly selected from the hospitalized patients for preliminary analysis. Preliminary data suggest that patients with clinically significant anxiety or depression (based on HADS scores) have an average treatment compliance score that is approximately 1.8 points lower (standard deviation of approximately 3.5 points) than the no/mild group, with a corresponding effect size (Cohen’s d) of approximately 0.51. Based on this, with a significance level (α) of 0.05 (bilateral) and a statistical power of 80%, calculations using G power 3.1 software showed that each group had at least 62 patients. According to pilot studies, the detection rate of clinically significant anxiety or depressive symptoms in hospitalized patients with gastrointestinal diseases is estimated to be between 30% and 45%. We expected the proportion of the moderate/severe anxiety and depression groups to be approximately 40%; therefore, the total sample size was set at 160 to ensure sufficient statistical power for preliminary analysis and to leave space for exploratory subgroup analysis stratified by disease type (such as functional and organic gastrointestinal diseases). The expected group distribution was approximately 67 cases in the moderate/severe anxiety and depression group (group B) and approximately 93 cases in the no/mild anxiety and depression group A (group A). This sample size estimation was used primarily to justify the adequacy of statistical power for the main analyses, rather than to prospectively determine enrollment (Figure 1).

Figure 1 The patient screening and inclusion process. HADS: Hospital Anxiety and Depression Scale.

Inclusion and exclusion criteria

Inclusion criteria: (1) Age between 18 and 75 years; (2) The primary discharge diagnosis for the index hospitalization fell within the spectrum of gastrointestinal diseases (according to International Classification of Diseases-10 codes K00-K93), including but not limited to peptic ulcer disease, inflammatory bowel disease (ulcerative colitis, Crohn’s disease), irritable bowel syndrome, functional dyspepsia, and acute pancreatitis; (3) Hospitalization duration exceeding 24 hours to ensure sufficient nursing and treatment records for evaluation; and (4) The hospital medical records contained a complete HADS assessment result or a standardized consultation note from a psychiatrist containing a clear assessment and documentation of anxiety and depressive status.

Exclusion criteria: (1) The primary admission diagnosis was a psychiatric disorder (e.g., depressive disorder, anxiety disorder, or schizophrenia); (2) Medical records indicating a confirmed diagnosis of dementia, moderate to severe intellectual disability, or other significant cognitive impairments assessed as potentially compromising the reliability of self-rated psychological scales or understanding treatment decisions; (3) Medical records with more than 30% missing data for key study variables (including HADS scores, primary treatment regimens, and nursing records); and (4) Female patients who were pregnant or lactating women.

Definition of anxiety and depression status and grouping

Core exposure variables: Determination of anxiety and depression states: The HADS is used as an assessment tool. This self-report scale consists of 14 items divided into anxiety (HADS-A) and depression (HADS-D) subscales, each with seven items. Each project was rated from 0 to 3 based on symptom frequency. The total score for each subscale ranges from 0 to 21. Based on the consensus on the application of this scale in the Chinese clinical population and the standards of numerous previous studies, and considering that the Chinese version of HADS has been widely validated and has acceptable reliability and validity, we have adopted the following critical values: 0-7 points for “asymptomatic”, 8-10 points for “mild”, 11-14 points for “moderate”, and 15-21 points for “severe”. For preliminary analysis, patients were divided into two groups: Group A (no/mild anxiety and depression group): HADS-A and HADS-D scores were both ≤ 10; group B (moderate/severe anxiety and depression group): HADS-A or HADS-D score ≥ 11. In addition, the raw continuous scores of the HADS-A and HADS-D were used as continuous exposure variables to explore dose-response relationships.

Data collection and covariates

Data extraction and quality control: A detailed standardized data extraction manual was developed. Two research assistants who received unified training were blinded to the research hypotheses and independently extracted data from the hospital’s electronic medical record system and paper archives in the medical record department. Data extraction covered multiple modules, including admission, course, nursing, and medication records; assessment scales; and discharge summaries. After extraction, use EpiData 3.1 software to independently input data and configure logic check rules (such as date sequence, value range). After reviewing the original medical records, a third senior researcher (the first co-author of this study) ruled out all differences in the data input.

Collection and definition of covariates: In order to control for potential confounding factors in the analysis, the following covariates were systematically collected: (1) Demographic characteristics: Age, gender, education level (junior high school and below/high school/university and above); (2) Clinical characteristics: Specific disease diagnosis (classified as “functional gastrointestinal disease” such as irritable bowel syndrome, functional dyspepsia or “organic gastrointestinal disease”), disease severity at admission (assessed using department specific scores, such as the modified Mayo score for ulcerative colitis, or graded according to the description of “disease severity” in the record), and whether there are chronic complications outside the gastrointestinal tract (such as hypertension, diabetes); and (3) Laboratory indicators: The level of C-reactive protein (CRP) (mg/L) detected for the first time after admission serves as an objective marker of systemic inflammation.

Observation indicators

Main outcome measure 1: Treatment compliance. Evaluation using the Medication Adherence Reporting Scale (MARS). This 5-point scale was used to measure patients’ attitudes towards medication and their actual medication behavior (e.g., “Have you ever forgotten to take your medication?”). Answer ‘no’ earns 1 point, answer ‘yes’ earns 0 points. The total score ranged from 0 to 5, with higher scores indicating better compliance. In this retrospective study, we employed a proxy assessment method adapted from a five-item MARS to assess medication adherence. Considering that this study was a retrospective analysis, medical records did not routinely retain direct questionnaires completed by the patients themselves. Therefore, compliance assessment is based on a structured review of nursing records, medication management records, doctor-patient communication documents, and records of medication behavior during hospitalization. Two independent researchers performed their own compliance evaluations based on predetermined criteria and reached a consensus through discussions to minimize misclassification bias. The higher the score, the better was the treatment cooperation. This compliance assessment project is an adaptation of the original MARS tool for noncommercial academic research purposes.

Main outcome measure 2: Nursing workload. Quantified using the Nursing Activity Rating Scale (NAS). This scale contains 23 items that cover basic nursing, monitoring, and treatment support activities. Based on the patients’ daily electronic nursing records, all nursing activities were determined within 24 hours, and the daily NAS total score was calculated according to the standard NAS weights. This score represents the percentage of theoretical working hours per shift (24 hours) that a registered nurse must pay to care for the patient. This study calculated and compared the differences in the daily NAS scores between the two groups during hospitalization.

Secondary outcome measure one: Length of hospital stay. The total length of hospitalization was calculated based on the actual calendar days from the day the patient completed the admission procedure to the day the discharge order was issued. This indicator is objective and clear, and is one of the core parameters for measuring the degree of medical resource utilization.

Secondary outcome measures: Total hospitalization expenses. All expenses incurred during hospitalization (in Chinese yuan) were extracted directly from the financial information column on the first page of the medical records. This value intuitively reflects the direct economic burden of diagnosis and treatment.

Secondary outcome measure three: Short term readmission rate. This specifically refers to unplanned readmission of patients within 30 days of discharge. We tracked and recorded whether the patients were readmitted because of the same or related gastrointestinal complaints during the thirty days window after discharge. Relevant information was obtained from the hospital’s internal readmission registration system and verified using routine follow-up records.

Secondary outcome measures: Burden of gastrointestinal symptoms. The Gastrointestinal Symptom Rating Scale (GSRS) was used as an evaluation tool. This scale covers five dimensions: Abdominal pain, reflux, diarrhea, indigestion, and constipation, with a total of 15 symptom items. Each symptom is classified into levels one to seven based on its severity and level of interference with daily activities (1 point represents “completely no discomfort” and 7 points represents “extremely severe discomfort”). When conducting the statistical analysis, the total score of the scale (ranging from 15 points to 105 points) and the individual scores for each dimension were calculated. The higher the score, the greater is the symptom burden borne by the patient. This score was compiled based on self-reported records completed by patients upon admission or with the assistance of nursing staff during their hospital stay.

Secondary outcome measure: Health-related quality of life. The Gastrointestinal Quality of Life Index (GIQLI) was used for evaluation. This assessment tool includes 36 items divided into five evaluation areas: Core gastrointestinal symptoms, physical functional status, emotional and psychological status, social activity participation, and specific distress caused by the disease. Each item was evaluated on a Likert five points scale from 0 to 4, based on the degree of functional limitation or frequency of symptom occurrence. The theoretical range for the total score of the scale ranges from 0 to 144 points; the higher the score, the better the quality of life of the evaluated individual. If the patients completed the questionnaire during hospitalization (usually during the admission evaluation phase or near discharge), their total scores were extracted and included in the analysis.

Exploratory indicators: Independent contributions of anxiety and depressive symptoms. We analyzed the independent correlations between the continuous scores of the HADS-A anxiety and depression subscales and the aforementioned outcome indicators, particularly treatment compliance, nursing workload, and GSRS score, to investigate whether anxiety and depression symptoms exhibited different patterns of influence. In previous studies on inpatient compliance, this retrospective proxy scoring method was used when there were no direct self-reported data and was conducted by two independent researchers to minimize misclassification bias.

Statistical analysis

Because of the obvious right-skewed distribution of CRP, a logarithmic transformation was applied before inclusion in the regression model. All statistical analyses were conducted using R software (version 4.2.1). First, a normality test (Shapiro-Wilk test) was performed on the continuous variables. Variables that follow a normal distribution are described as mean ± SD and compared between groups using independent sample t-test. Variables that did not conform to a normal distribution were described as median (interquartile range) and compared between groups using the Mann-Whitney U test. Categorical variables are described as n (%) and compared between groups using the χ² test or Fisher’s exact test.

For preliminary analysis, a multivariate linear regression model was used to investigate the relationship between the anxiety and depression groups (A/B group) and continuous outcome variables (MARS score, daily NAS score, Length of Stay, impact of hospitalization expenses, GSRS total score, and GIQLI total score). The model construction process is as follows. First, the grouping variables are forcibly introduced into the model. We then used a strategy that combined clinical knowledge and statistical screening to include covariates. In univariate analysis, all variables that may be related to the outcome variable (P < 0.10), as well as variables considered clinically important (such as age, sex, and disease category), were considered for inclusion in the initial complete model. Stepwise regression (forward and reverse: Alpha input = 0.05, alpha removal = 0.10) was used to select the final model while ensuring that the core exposure variables (groups) were retained in the model. The final model reported the adjusted regression coefficient (β), its 95% confidence interval, and P value. The variance inflation factor (VIF) was used to diagnose multicollinearity in the model. If VIF > 5, the relevant variables were handled.

For the binary outcome variable (unplanned readmission within 30 days), binary logistic regression analysis was used to report the adjusted odds ratio and 95% confidence interval. To explore the dose-response relationships, HADS-A and HADS-D were entered as continuous independent variables into the adjusted multivariable linear or logistic regression models described above.

Furthermore, we planned two pre-specified subgroup analyses: Stratification by disease category (functional vs organic), repeating the primary regression analyses within each stratum, and performing stratified analyses by disease category (functional vs organic gastrointestinal disorders) to explore the potential heterogeneity of associations. All P values were two-tailed, and statistical significance was set at P < 0.05.

RESULTS

Comparison of baseline patient characteristics

Table 1 compares the baseline clinical characteristics of group A and group B. Inter-group comparisons revealed that patients in group B were significantly younger and had a higher proportion of functional disorders, higher disease severity scores, higher admission CRP levels, and higher HADS-A and HADS-D scores than those in group A (all P < 0.05). No statistically significant differences were observed between the two groups regarding sex, education level, marital status, number of extra-gastrointestinal comorbidities, surgery acceptance rate, or admission albumin level (all P > 0.05) (See Table 1 and Figure 2).

Figure 2 Comparison of baseline patient characteristics. HADS-A: Hospital Anxiety and Depression Scale-anxiety subscale; MARS: Medication Adherence Reporting Scale; NAS: Nursing Activity Rating Scale.

Table 1 Comparison of baseline patient characteristics, mean ± SD/n (%)/median (P25, P75).

Characteristic	Group A (n = 93)	Group B (n = 67)	Statistic	P value
Demographics
Age (years)	50.89 ± 13.41	44.62 ± 13.18	t = 2.939	0.004
Sex (male)	47 (50.54)	31 (46.27)	χ2 = 0.284	0.594
Education, college and above	57 (61.29)	39 (58.21)	χ2 = 0.154	0.695
Marital status, married	80 (86.02)	56 (83.58)	χ2 = 0.182	0.670
Clinical features
Disease type, functional	27 (29.03)	40 (59.70)	χ2 = 15.050	< 0.001
Disease severity score	3 (2, 5)	5 (4, 7)	U = 3562.500	< 0.001
Number of extra-gastrointestinal comorbidities	1 (0, 2)	1 (0, 2)	U = 5235.500	0.664
Underwent surgery, yes	12 (12.90)	13 (19.40)	χ2 = 1.248	0.264
Laboratory indicators
Admission CRP (mg/L)	3.90 (1.80, 9.40)	8.15 (3.20, 18.33)	U = 3820.000	0.003
Admission albumin (g/L)	38.92 ± 5.01	37.71 ± 5.33	t = 1.467	0.144
Psychological scores
HADS-A total score	4.89 ± 2.41	11.23 ± 4.52	t = -11.462	< 0.001
HADS-D total score	4.61 ± 2.35	10.31 ± 4.38	t = -10.615	< 0.001

CRP: C-reactive protein; HADS-A: Hospital Anxiety and Depression Scale-anxiety subscale; HADS-D: Hospital Anxiety and Depression Scale-depression subscale.

Univariate analysis of observation indicators between groups

Table 2 presents the results of univariate analysis comparing the observation indicators between the two groups. Statistically significant differences were observed for all indicators (P < 0.05). Specifically, compared with group A patients in group B had lower MARS scores, higher average daily NAS scores, longer total hospitalization time, higher total hospitalization costs, higher 30 days unplanned readmission rates, and higher GSRS total scores. In contrast, GIQLI total scores were lower (Table 2).

Table 2 Univariate analysis of observation indicators between groups, mean ± SD/n (%)/median (P25, P75).

Observation indicator	Group A (n = 93)	Group B (n = 67)	Statistic	P value
Primary outcomes
MARS score	4 (3, 5)	3 (2, 4)	U = 3280.000	< 0.001
Mean daily NAS score (%)	52.34 ± 18.27	66.89 ± 21.45	t = -4.618	< 0.001
Secondary outcomes
Total length of stay (days)	7 (5, 10)	9 (7, 13)	U = 3547.000	< 0.001
Total hospitalization cost (10000 CNY)	2.15 (1.42, 3.58)	2.84 (1.78, 4.65)	U = 3695.500	0.001
30-day unplanned readmission, yes	8 (8.60)	14 (20.90)	χ2 = 4.963	0.026
GSRS total score	38.72 ± 12.46	55.41 ± 14.83	t = -7.715	< 0.001
GIQLI total score	98.56 ± 23.18	76.34 ± 25.67	t = 5.718	< 0.001

MARS: Medication Adherence Report Scale; NAS: Nursing Activities Score; GSRS: Gastrointestinal Symptom Rating Scale; GIQLI: Gastrointestinal Quality of Life Index; CNY: Chinese yuan.

Multivariate linear regression analysis of anxiety/depression status on primary outcomes

Table 3 shows the multiple linear regression analysis with the MARS and average daily NAS scores as dependent variables, examining the impact of anxiety/depression status on the primary outcome. The results showed that belonging to group B and having a higher disease severity score were independent negative factors affecting the MARS score (P < 0.05). For the average daily NAS score in group B, functional disease type, higher disease severity score, and higher admission CRP level were independent positive influencing factors (P < 0.05). Age showed no significant correlation in either model (P > 0.05). The inter group difference in average daily NAS was approximately 14.5 points, indicating a clinically significant increase in nursing time and monitoring needs (Table 3 and Figure 3).

Figure 3 Multivariate linear regression analysis of the impact of anxiety/depression status on primary outcome indicators. MARS: Medication Adherence Reporting Scale; NAS: Nursing Activity Rating Scale; CRP: C-reactive protein; NS: Not significant.

Table 3 Multivariate linear regression analysis of the impact of anxiety/depression status on primary outcome indicators.

Dependent variable	Independent variable	β (95%CI)	t value	P value
MARS score	Group B (vs group A)	-1.36 (-1.82, -0.90)	-4.962	< 0.001
	Age	0.01 (-0.01, 0.03)	1.267	0.267
	Disease type (functional vs organic)	-0.12 (-0.58, 0.34)	-0.617	0.583
	Disease severity score	-0.15 (-0.23, -0.07)	-3.582	< 0.001
	Admission CRP (log-transformed)	-0.18 (-0.45, 0.09)	-1.253	0.184
Mean daily NAS score	Group B (vs group A)	8.42 (5.21, 11.63)	5.072	< 0.001
	Age	-0.16 (-0.35, 0.03)	-1.634	0.089
	Disease type (functional vs organic)	4.12 (0.78, 7.46)	2.317	0.012
	Disease severity score	1.85 (1.08, 2.62)	4.621	< 0.001
	Admission CRP (log-transformed)	2.56 (0.67, 4.45)	2.523	0.006

CI: Confidence interval; CRP: C-reactive protein; MARS: Medication Adherence Report Scale; NAS: Nursing Activities Score.

Logistic regression analysis of anxiety/depression status on 30-day unplanned readmission

Table 4 presents the logistic regression analysis of the impact of anxiety/depression status on 30-day unplanned readmissions, with readmission events as the dependent variable. In the univariate analysis, group B had a higher disease severity score, and a longer total length of stay was associated with an increased risk (P < 0.05). After multivariate adjustment, group B and longer total length of stay remained significant independent risk factors (P < 0.05), whereas age, disease type, and disease severity score showed no independent influence (P > 0.05).

Table 4 Logistic regression analysis of the impact of anxiety/depression status on 30-day unplanned readmission.

Variable	Univariate analysis OR (95%CI)	P value	Multivariate analysis aOR (95%CI)	P value
Group B (vs group A)	3.07 (1.38, 5.92)	0.004	2.63 (1.09, 5.92)	0.026
Age (per 10-year increase)	0.78 (0.49, 1.08)	0.240	0.81 (0.52, 1.11)	0.187
Disease type (functional vs organic)	1.49 (0.73, 3.34)	0.275	1.19 (0.56, 2.82)	0.652
Disease severity score	1.26 (1.08, 1.52)	0.010	1.21 (0.98, 1.43)	0.062
Total length of stay	1.10 (1.08, 1.14)	0.008	1.03 (1.01, 1.06)	0.048

The multivariate model adjusted for all variables listed in the table. OR: Odds ratio; aOR: Adjusted odds ratio; CI: Confidence interval.

Dose-response relationship analysis of anxiety (HADS-A) and depression (HADS-D) symptom scores on primary outcomes

Table 5 shows the dose-response relationship between anxiety and depression symptom scores and primary outcomes. In the multivariate linear regression models, after adjusting for covariates, both HADS-A and HADS-D total scores showed significant negative correlations with the MARS score (P < 0.05) and significant positive correlations with the mean daily NAS score (P < 0.05) (Table 5 and Figure 4).

Figure 4 Dose-response relationship analysis of anxiety (Hospital Anxiety and Depression Scale-anxiety subscale) and depression (Hospital Anxiety and Depression Scale-depression subscale) symptom scores on primary outcomes. HADS-A: Hospital Anxiety and Depression Scale-anxiety subscale; HADS-D: Hospital Anxiety and Depression Scale-depression subscale; MARS: Medication Adherence Reporting Scale.

Table 5 Dose-response relationship analysis of anxiety (Hospital Anxiety and Depression Scale-anxiety subscale) and depression (Hospital Anxiety and Depression Scale-depression subscale) symptom scores on primary outcomes.

Independent variable	Dependent: MARS score β (95%CI)	P value	Dependent: Mean daily NAS score β (95%CI)	P value
HADS-A total score	-0.10 (-0.16, -0.08)	< 0.001	0.62 (0.40, 0.85)	< 0.001
HADS-D total score	-0.11 (-0.13, -0.07)	< 0.001	0.72 (0.49, 0.93)	< 0.001

Both models were adjusted for age, disease type, disease severity score, and admission C-reactive protein level. HADS-A: Hospital Anxiety and Depression Scale-anxiety subscale; HADS-D: Hospital Anxiety and Depression Scale-depression subscale; MARS: Medication Adherence Report Scale; NAS: Nursing Activities Score; CI: Confidence interval.

Subgroup analysis by disease category

Stratified analyses according to disease category (functional vs organic gastrointestinal disorders) showed that the association between anxiety and depression status and nursing workload remained significant in both subgroups, with a numerically stronger effect observed in patients with functional gastrointestinal disorders.

DISCUSSION

This study aimed to investigate the impact of anxiety and depression on treatment adherence and nursing workload among hospitalized patients with gastrointestinal disorders. The results demonstrated that patients with moderate-to-severe anxiety and depressive symptoms had significantly reduced treatment adherence and required substantially more direct nursing time. These patients had longer hospital stays, a higher risk of short-term readmission, a heavier burden of gastrointestinal symptoms, and poorer quality of life. Overall, these findings elucidate the detrimental role of psychological distress in the inpatient management of gastrointestinal diseases, which not only affects the rehabilitation trajectory of individual patients but also increases the nursing burden on the healthcare system[16,17]. An analysis of baseline characteristics showed that patients in the anxiety and depression groups were younger, had a higher proportion of diagnosed functional gastrointestinal diseases, and exhibited more significant levels of systemic inflammation[18]. This profile is consistent with previous descriptions of the population with gut-brain interaction disorders, indicating that young patients may be more susceptible to life changes and psychological impacts of the disease, and that functional gastrointestinal diseases itself is closely related to abnormalities in the central emotional processing network[19]. The higher CRP levels observed in group B may reflect a heavier acute disease burden, which is closely related to the coexistence of psychological distress and gastrointestinal symptoms, rather than any other explanation[20]. After adjusting for multiple variables, anxiety and depression remained independent predictors of decreased treatment compliance and increased workload. Poor compliance, a state of despair caused by increased symptom burden, a lack of motivation, and executive dysfunction are associated with depression[21]. The extension of nursing time directly reflects the additional requirements of patients for symptom management, emotional comfort, and treatment supervision, which is consistent with complex clinical observations[22]. Although previous studies have typically focused on symptom reports from outpatients, this study quantified their impact on specific medical behaviors of hospitalized patients, providing new evidence[23]. The association between anxiety and depression, prolonged hospitalization, increased medical expenses, and increased risk of readmission has significant clinical and economic implications. Consistent with this, the Rome IV definition of irritable bowel syndrome is associated with a severe disease impact and a poorer disease-specific quality of life, particularly in terms of more severe symptoms[24]. Long-term hospitalization may be a direct consequence of delayed efficacy owing to poor compliance and the need for complex symptom management. The increased risk of readmission indicates that unresolved mental comorbidities may be an important factor leading to disease recurrence and poor prognosis, which is consistent with research results on heart failure, diabetes, and other chronic diseases[25]. This study further confirms the close association between anxiety/depression and more severe gastrointestinal symptoms as well as decreased quality of life[26]. This reinforces the importance of the biopsychosocial model in the field of gastroenterology. Amplified perceptions of symptoms and emotional distress nourish each other, forming an inseparable vicious cycle that ultimately erodes the overall health of patients[27]. Therefore, treatment of the local gastrointestinal tract alone is often insufficient to achieve satisfactory therapeutic effects. However, given that the research design was retrospective and the sample size was limited, caution should be exercised when interpreting the observations of the above subgroups. In the formal interaction test, no statistically significant interaction effects were detected; therefore, the presence or absence of true effect modifications remains uncertain. Further large-scale prospective cohort studies are required to determine whether different disease categories have a substantial corrective effect on the association between psychological distress and inpatient care processes[28-30].

This study has several limitations. First, the design of retrospective observation queues fundamentally eliminated explicit causal inferences. Second, the single-center data source may have limited the extrapolation of the research conclusions. Third, for the retrospective evaluation of treatment compliance, the standardized patient self-report questionnaire was replaced with a proxy evaluation from the medical records. Although this is supplemented by objective documentation, it may still introduce measurement bias and compromise the accuracy of the compliance assessment. Fourth, even though we corrected for many potential confounding variables through statistical models, unquantifiable factors, such as the density of social support networks and individuals’ inherent coping styles, may still have some residual impact on the observed strength of the associations. Looking ahead, it is necessary for future research to adopt a prospective cohort design and attempt to integrate objective biomarker detection, such as heart rate variability analysis, inflammatory cytokine lineage determination, and even neuroimaging techniques, in order to more thoroughly elucidate the neurobiological mechanisms underlying the phenomenon. In addition, developing and validating a structured, comprehensive psychological intervention program for hospitalized patients with gastrointestinal disease and systematically evaluating its practical effectiveness in improving compliance, reducing nursing burden, and optimizing long-term prognosis are valuable exploration directions in clinical research. However, in current clinical practice, a layered management model that emphasizes effectiveness may be considered: Providing basic psychological comfort and disease knowledge education to those with mild symptoms; for individuals with moderate to severe psychological distress, the consultation process of the psychiatric department should be initiated promptly. Finally, it should be emphasized that even after adjusting for socioeconomic status, social support network architecture, history of mental illness, and a range of underlying comorbidities that may influence emotional states, unobserved confounding factors may confuse the associations observed in this study to some extent.

CONCLUSION

The results of this study suggest that there is an independent statistical association between anxiety and depression and a decline in treatment compliance, an increase in nursing workload, a prolonged hospitalization period, and an increased risk of readmission among patients hospitalized for gastrointestinal diseases. These findings repeatedly emphasize the viewpoint that systematic screening and subsequent management of anxiety and depression symptoms should not be seen as optional extras in the routine care process for hospitalized digestive patients but should become a necessary component.