From cognition to action: Exploring factors associated with dietary management behaviors in patients with inflammatory bowel disease via path model construction

Abstract

This study examined the current status of dietary management behavior in patients with inflammatory bowel disease (IBD) and explored factors associated with these behaviors using Partial Least Squares Structural Equation Modeling, guided by the Temporal Self-regulation Theory. Validated scales were employed to assess dietary beliefs, consideration of future consequences, intention, habit, self-regulation capacity and dietary management behaviors in patients with IBD. The results indicated that intention was associated with dietary management behaviors and showed indirect associations between dietary beliefs and dietary management behaviors, as well as between consideration of future consequences and dietary management behaviors. The moderating effects of habit and self-regulation capacity on the association between intention and dietary management behaviors were not statistically significant. These findings suggest that dietary beliefs, consideration of future consequences, intention, habit, and self-regulation capacity are associated with dietary management behaviors among patients with IBD. These results provide implications for the development of supportive strategies for dietary self-management in patients with IBD.

Keywords

inflammatory bowel disease dietary management behavior the Temporal Self-regulation Theory path analysis Partial Least Squares Structural Equation Modeling

Introduction

Inflammatory bowel disease (IBD) primarily encompasses ulcerative colitis (UC) and Crohn’s disease (CD), representing a chronic, nonspecific, recurrent inflammatory condition of the intestines. In recent years, IBD has gradually become a global health concern, with rising incidence and prevalence worldwide (Wang et al., 2023a). IBD predominantly affects young adults and middle-aged individuals and typically presents with recurrent episodes of diarrhea, bloody mucus stools, abdominal pain, and varying systemic symptoms.

The etiology of IBD is complex, involving genetic, immune, and environmental factors (Xiong et al., 2024). As one of the important environmental factors, diet plays a crucial role in the prevention and alleviation of IBD (Wark et al., 2020). Diet is a key determinant of the gut microbiota structure and directly or indirectly participates in the regulation of intestinal inflammation through the gut microbiota. Scientific and reasonable dietary regimen may contribute to the regulation of immune responses and intestinal barrier function, potentially supporting disease control and remission (Chicco et al., 2021). However, current evidence indicates that dietary adherence among patients with IBD remains suboptimal. This poor adherence significantly undermines disease remission efforts and adversely affects patients’ quality of life (Marsh et al., 2022).

According to the Transtheoretical Model of Health Behavior Change, individuals typically progress through several stages when adopting new health behaviors, including precontemplation, contemplation and action. These stages reflect different levels of readiness to implement behavioral changes such as dietary modification (Prochaska and Velicer, 1997). Shi et al. (2022) investigated the stages of dietary management behavior in patients with IBD and found that 7.57% were in the pre-contemplation stage, 47.70% in the contemplation stage, and only 44.74% in the action stage. This indicates that the majority of patients with IBD remain at the intention-forming phase, with only a minority successfully translating their intention into actual dietary management behaviors. This result highlights a substantial intention-behavior gap in dietary management among patients with IBD, which is similar to the findings of Zhu et al on healthy diet (Zhu et al., 2025). However, most of current studies have only explored the possible factors influencing the dietary management behaviors from a single aspect such as disease activity, or patient belief, without delving deeply into the process and the underlying multiple associations between dietary management intention and behaviors in patients with IBD. For example, a previous study results show that individuals were more likely to change their diet during the active stage of the disease (Kamp et al., 2021). The study of Crooks et al. (2022) indicates that dietary beliefs and dietary-related knowledge can affect the implementation of dietary management behaviors in British South Asian patients with IBD. Apart from an individual’s knowledge and beliefs, dietary traditions rooted in culture, family’s eating habits, and social eating behaviors may also influence patients’ food choices, as well as their willingness or ability to change their eating habits (Monterrosa et al., 2020). These cultural factors indicate that dietary management behaviors may be shaped not only by individual psychological factors but also the broader social and cultural context. However, previous studies have not fully examined the integrated psychological and behavioral associations between intention and behaviors. Consequently, a deeper exploration of the complex associations within the intention-behavior transition may help to provide a more comprehensive understanding.

The Temporal Self-regulation Theory (TST) developed on the basis of the Theory of Planned Behavior, provides a framework for examining how intention may translate into behavior through the balancing of immediate decisions and future consequences (Hall and Fong, 2007). TST includes six constructs of connectedness beliefs (perceptions of present actions on future outcomes), temporal valuations (judgment values of behavioral outcomes), intention, behavioral pre-potency (likelihood of behavior engagement, influenced by habits and environmental cues), self-regulation capacity, and observed behaviors (Chen et al., 2023). TST proposes that in the motivation sphere, an individual’s positive connectedness beliefs and temporal valuations are beneficial to form intention to transform behavior, whereas in the ambient temporal contingencies sphere, behavioral pre-potency and self-regulation capacity directly affect behavior and can moderate the intention-behavior relationship. TST has been applied to explain behaviors such as sugar-sweetened beverage consumption (McAlpine and Mullan, 2022), medication adherence (Liddelow et al., 2021) and self-care (Dorina et al., 2023). In particular, a meta-analysis indicated that intention, behavioral pre-potency and self-regulation capacity are significantly associated with healthy eating behaviors including the intake of fruit, vegetables, and breakfast (Wu et al., 2025). Accordingly, using TST as a framework, this study aimed to explore the associations among factors related to dietary management behaviors in patients with IBD via partial least squares structural equation modeling. The results are expected to provide evidence-based insights for developing effective behavioral intervention measures that support dietary management among patients with IBD.

Materials and methods

Design, participants and procedure

This study used a cross-sectional design. From August 2024 to January 2025, patients with IBD who visited the gastroenterology outpatient department of two tertiary hospitals in Qingdao city were recruited by convenience sampling method. The inclusion criteria were (1) diagnosed as IBD based on the criterion of the Digestive Diseases Society of the Chinese Medical Association (Wu et al., 2018), (2) aged ⩾18 years old, and (3) voluntarily participated in the study and provided informed consent. The exclusion criteria were (1) with mental disorders (identified based on patients’ medical records or physician diagnoses documented in the hospital information system), and (2) with concurrent severe gastrointestinal or cardiovascular diseases requiring dietary restrictions. Sample size was calculated using G*Power 3.1.9 software, with 21 independent variables, an effect size of 0.15, a significance level of 0.05, and a test power of 0.90. Considering a 20% of non-response rate, the minimal sample size was determined to be 244 cases. A total of 290 questionnaires were distributed. After excluding invalid responses due to inconsistent answers, contradictions, or incomplete completion, 278 valid questionnaires were retained, generating a valid response rate of 95.86%. This study was approved by the Ethics Committee of Medical College of Qingdao University (Approval No.: QDU-HEC-2024404). All participants provided informed consent and voluntarily participated in the study.

Measures

Socio-demographic and disease information questionnaire

By drawing on literature (Godala et al., 2023), a social-demographic and disease information questionnaire was designed by the research group, including gender, age, education, marital status, residence, living arrangement, occupation, annual household income, medical payment method, type of IBD, current IBD disease status (i.e. remission or recurrence), duration of disease, number of recurrence, comorbidities (other diseases except for IBD and not caused by IBD, such as diabetes and coronary heart disease), IBD related complications and treatment with biological agents or not.

Measurement tools for the main variables of TST

According to TST, the definitions of key constructs are operationalized as follows: connectedness beliefs, temporal valuations, intention, behavioral pre-potency, self-regulation capacity and observed behaviors are reflected by dietary beliefs, consideration of future consequences, intention, habit, self-regulation capacity and dietary management behavior, respectively. To be noted, behavioral pre-potency in TST encompasses both the strength of habits and environmental cues. However, due to the data availability and the primary focus on individual-level determinants in the study, behavioral pre-potency was quantified solely through the indicator of habit. Therefore, this study should be regarded as a partial application of TST rather than a comprehensive examination of all theoretical components.

IBD Dietary Beliefs Questionnaire

The IBD Dietary Beliefs Questionnaire was adapted from the original English version by Godala et al. (2023) to assess connectedness beliefs among patients with IBD. The original questionnaire included six questions about dietary beliefs. Through a thorough review, the research team clarified its core framework and measurement objectives of the original questionnaire, then selected appropriate items and modified items based on the specific purposes of the present study. Specifically, two items were removed: “Do you believe that diet is more important in IBD than medicines?” and “Do you think that your diet may cause nutritional deficiencies?”. The first item reflected a value judgment rather than a belief about the correlation between diet and diseases, while the second focused on nutritional outcomes instead of beliefs about diet as a trigger or aggravator of diseases. Another item, “Which products, in your opinion, should be avoided?” was replaced with “Do you think certain foods may exacerbate your symptoms?” to better capture beliefs about symptom triggers. The original item aimed to assess dietary knowledge or preferences rather than general beliefs, which may reduce measurement consistency. The revised preliminary questionnaire was then independently translated by two bilingual professional researchers, who cross-checked their translations to form the first draft. Five relevant experts reviewed the draft for item adaptability, semantic accuracy, and cultural adaptability. Finally, a pre-test with 30 eligible participants was conducted, and the final Chinese version of the questionnaire was established based on feedback and preliminary reliability analysis. The questionnaire comprises four items, each with the response options “Yes” (scored as 1 point) and “No” (scored as 0 point). The total score ranges from 0 to 4 points, with a higher score indicating stronger dietary beliefs among patients with IBD. The Cronbach’s α coefficient for this questionnaire was 0.730.

Consideration of future consequences-food scale

The Consideration of Future Consequences-food Scale was adapted by van Beek et al. (2013) and translated into Chinese by Jiang (2023). In the present study, the Chinese version was employed to assess the temporal valuations of dietary management behaviors among patients with IBD. The scale comprises 10 items in two dimensions of “Consideration of Present” and “Consideration of Future.” The items were rated using a 5-point Likert scale from1 (strongly disagree) to 5 (strongly agree), with items in the “Consideration of Present” dimension being reversely scored. A higher total score indicates greater temporal valuation of health behaviors. The Cronbach’s α coefficient for this scale in the present study was 0.880.

Intention

A single item “Do you plan to manage your diet?” was used to assess patients’ dietary management intention. Responses were rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). A higher score indicates stronger intention to manage dietary behaviors. This single-item measure was employed based on the recommendations of a previous study (Fishman et al., 2020). Previous methodological research indicates that single-item measures may possess acceptable validity when assessing simple and clearly defined constructs. Moreover, recent health behavior studies have also employed single-item measures to assess behavioral intentions (e.g. physical activity, HIV self-testing), demonstrating that these measures possess good predictive validity (Dairo et al., 2024; Zeleke et al., 2024).

Self-Report Habit Index

The Self-Report Habit Index (SRHI) was employed to evaluate behavioral pre-potency in dietary management among patients with IBD (Verplanken and Orbell, 2003). The SRHI consists of a single stem (Behavior X) with 12 items. In the present study, Behavior X was defined as dietary management behavior. The SRHI was scored using a 5-point Likert scale from 1 (completely disagree) to 5 (completely agree). The total score ranges from 12 to 60 points, with a higher score indicating greater behavioral pre-potency. The Cronbach’s α coefficient for this questionnaire in this study was 0.965.

Multidimensional Self-Control Scale

The Chinese version of Multidimensional Self-control scale (Dong et al., 2024) originally developed by Nilsen et al. (2020) was utilized to assess patients’ self-regulation capacity. The scale comprises 29 items across six dimensions of impulse control, procrastination control, attention control, emotion control, goal orientation, and self-control strategies. It employs a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), yielding a total score between 29 and 145 points. A higher score suggests greater self-regulation capacity. The Cronbach’s α coefficient for this scale in the present study was 0.952.

Inflammatory Bowel Disease Patient Self-Management Behavior Scale

The Inflammatory Bowel Disease Patient Self-Management Behavior Scale developed by Shang et al. (2019) comprises seven dimensions of medication management, dietary management, disease monitoring, emotional management, exercise management, daily life management, and resource utilization. This study employed the dietary management dimension to measure dietary management behaviors among patients with IBD. This dimension comprises nine items using a 5-point Likert scale from 1 (completely disagree) to 5 (completely agree). The total score ranges from 9 to 45 points, with a higher score indicating better dietary management behaviors. The Cronbach’s α coefficient for this dimension in this study was 0.893.

Data collection and quality control methods

Data were collected through face-to-face distribution of paper questionnaires. Following approval from the gastroenterology outpatient department and inpatient ward, trained investigators recruited eligible patients on-site. Prior to the survey, investigators used standardized instructions to explain the study purpose and procedures to participants. After obtaining informed consent, paper questionnaires were distributed and collected on-site after patients completing the questionnaires independently.

Statistical analyses

SPSS 26.0 software was used to perform statistical description, univariate analysis of the general demographic characteristics and hierarchical regression analysis. The counting data were presented as frequencies and percentages, and the measurement data were represented by mean ± standard deviation. The variables showing statistically significant differences in the univariate analysis were selected as control variables. Furthermore, existing studies have demonstrated that patients’ current disease status, duration of disease, and therapeutic regimens can significantly influence their dietary management behaviors (Cucinotta et al., 2021; Limketkai et al., 2022; Zhu et al., 2021). Accordingly, the three variables (current IBD disease status, disease duration, and use of biological agents) were also included as control variables in the statistical model in the present study. All control variables were specified as predictors of the dependent variable (dietary management behavior).

Path analysis and model validation were conducted via Partial Least Squares Structural Equation Modeling (PLS-SEM) by using SmartPLS 3.0 software. PLS-SEM is a predictive modeling approach based on variance analysis. It differs significantly from traditional covariance-based structural equation modeling (CB-SEM) in both theoretical foundations and application scenarios, primarily manifested in three aspects. First, PLS-SEM has low sensitivity to sample size, providing stable estimates even with small samples (n < 100) and non-normally distributed data. In contrast, CB-SEM requires a sample size at least ten times the number of observed variables to ensure robust estimation. Second, regarding methodological orientation, PLS-SEM prioritizes prediction and is well-suited for exploratory research, whereas CB-SEM emphasizes theoretical model validation and model fit testing, making it appropriate for verifying established theories. Third, PLS-SEM directly constructs latent variable scores using component weighting, thereby avoiding the iterative convergence failures that can arise from model specification errors in CB-SEM (Dash and Paul, 2021; Sarstedt et al., 2016). Given these advantages, PLS-SEM was adopted as the statistical analysis method in this study. PLS-SEM involves two components in model construction and evaluation. The first component is testing measurement models for relationships between observed and latent variables, primarily used to assess the reliability and validity of measurement instruments. The second component is testing structural models for relationships between exogenous and endogenous latent variables. For the measurement model, Cronbach’s α, composite reliability (CR), average variance extracted (AVE), and the heterotrait–monotrait (HTMT) ratio were calculated to assess the reliability and validity of the constructs. Thresholds of 0.70 for both Cronbach’s α and CR were adopted as evidence of satisfactory internal consistency (Dijkstra and Henseler, 2015). Convergent validity was deemed acceptable when AVE exceeded 0.50, and discriminant validity was considered adequate if the HTMT ratio fell below 0.85 (Henseler et al., 2015). Regarding evaluation of structural models, R² was used to assess model explanatory power, and Q² for evaluating predictive relevance. According to Cohen’s criteria, an R² value > 0.260 indicates high explanatory power, and a Q² value > 0 signifies good predictive capability (Cohen et al., 2015). Additionally, the standardized root mean square residual (SRMR) was employed to evaluate model fit, with an SRMR value < 0.08 indicating good model fit. The mediation effect was tested via 5000 bootstrap resamples, wherein an effect was deemed significant if the 95% confidence interval did not contain zero (Hayes and Andrew, 2009). Moderation effects were assessed using the product indicator approach. Prior to constructing interaction terms, all variables involved in the interaction terms were mean-centered to reduce multicollinearity. Interaction terms were then created by multiplying the mean-centered indicators of the independent variable and moderator constructs. Hierarchical regression analysis was used to test the robustness of the model. The significance level α was set at 0.05.

Results

Demographic characteristics, disease-related characteristics and the differences in dietary management behavior score

A total of 278 patients with IBD, aged 18–82 years (mean ± SD: 46.54 ± 16.11), were included in the study. The majority (79.5%) were diagnosed with Ulcerative Colitis and were in the remission state (70.5%) at the time of the survey. The score for patients’ dietary management behavior was (33.36 ± 7.70). Significant differences in dietary management behavior scores were observed among participants of different ages, marital statuses, and occupations. Additional information is provided in Table 1.

Table 1.

General information of patients with inflammatory bowel disease and their differences in diet management behavior scores(N = 278).

Variables		n	(%)	x ¯ ±s	t/F	p
Gender	Male	153	55.0	33.02 ± 7.72	−0.814^a	0.416
Gender	Female	125	45.0	33.78 ± 7.69	−0.814^a	0.416
Age (years)	18~	132	47.5	31.40 ± 7.43	8.570^b	<0.001
	45~	87	31.3	35.10 ± 7.59
	>65	59	21.2	35.17 ± 7.53
Education	Junior high school or below	94	33.8	34.16 ± 7.75	2.497^b	0.060
	Senior high school	57	20.5	34.96 ± 7.67
	Associate degree	50	18.0	32.42 ± 9.44
	Bachelor’s degree or above	77	27.7	31.81 ± 6.02
Marital status	Unmarried	51	18.3	30.98 ± 7.64	3.228^b	0.023
	Married	217	78.1	33.82 ± 7.60
	Divorced	4	1.4	40.75 ± 3.95
	Widowed	6	2.2	32.17 ± 9.43
Residence	Urban area	201	72.3	32.89 ± 7.64	−1.646^a	0.101
Residence	Rural area	77	27.7	34.58 ± 7.77	−1.646^a	0.101
Living arrangement	Living alone	22	7.9	31.14 ± 9.39	1.720^b	0.181
	Living with family	250	89.9	33.64 ± 7.54
	Living with friends	6	2.2	29.83 ± 6.88
Occupation	Full time job	152	54.7	32.10 ± 7.63	4.323^b	0.005
	Part-time job	19	6.8	34.47 ± 7.12
	Unemployment	41	14.7	33.20 ± 8.52
	Retirement	66	23.7	36.05 ± 6.89
Annual household income(10,000 yuan)	<1	41	14.7	33.56 ± 7.87	2.036^b	0.090
	1~	44	15.8	33.41 ± 7.83
	3~	46	16.5	33.11 ± 7.91
	5~	78	28.1	35.05 ± 7.93
	>10	69	24.8	31.46 ± 6.84
Medical payment method	Self-payment	10	3.6	32.70 ± 8.04	−0.265^a	0.888
Medical payment method	Medical insurance	268	96.4	33.38 ± 7.70	−0.265^a	0.888
Type of IBD	Crohn’s disease	57	20.5	32.16 ± 7.96	−1.323^a	0.187
Type of IBD	Ulcerative Colitis	221	79.5	33.67 ± 7.62	−1.323^a	0.187
Current IBD disease status	Recurrence	82	29.5	33.62 ± 7.79	0.367^a	0.714
Current IBD disease status	Remission	196	70.5	33.25 ± 7.68	0.367^a	0.714
Duration of disease(years)	<1	50	18.0	34.26 ± 7.38	0.285^b	0.836
	1~	130	46.8	33.18 ± 7.50
	6~	54	19.4	33.02 ± 7.96
	>10	44	15.8	33.27 ± 8.50
Number of recurrence	<5	214	77	33.48 ± 7.48	0.969^b	0.381
	5~	28	10.1	31.54 ± 8.89
	>10	36	12.9	34.08 ± 8.03
Comorbidities	No	164	59	32.76 ± 7.49	−1.572^a	0.117
Comorbidities	Yes	114	41	34.23 ± 7.95	−1.572^a	0.117
Use of biological agents	No	109	39.2	34.06 ± 6.90	1.210^a	0.227
Use of biological agents	Yes	169	60.8	32.91 ± 8.17	1.210^a	0.227
IBD related complications	No	229	82.4	33.39 ± 7.61	0.156^a	0.876
IBD related complications	Yes	49	17.6	33.20 ± 8.19	0.156^a	0.876

Note. The bold p values were less than 0.05, signifying the statistically significant difference between groups.

t value.

H value.

Diagnosis of common method bias and multicollinearity

All data in this study were collected through self-reporting. Therefore, the Harman single-factor method was employed to examine potential common method bias. Analysis identified 11 factors with eigenvalues greater than 1, and the first factor accounted for 36.23% (below the threshold of 40%) of the total variance. Thus, the common-method bias did not pose a serious threat to the validity of the findings (Zhou and Long, 2004).

Multicollinearity among the predictors was diagnosed via variance inflation factors (VIF). The VIFs for all the predictors were between 1.068 and 3.841, below the established cutoff of 10, indicating no significant multicollinearity (Kutner et al., 2004). Detailed results are shown in Table 5.

Measurement model validation

The results indicate that the Cronbach’s α coefficients of the measurement tools in this study ranged from 0.730 to 0.965, and the CR values ranged from 0.830 to 0.969. Both exceeded the threshold of 0.70, suggesting that the measurement tools in this study had good reliability. The AVE values ranged from 0.541 to 0.721 (greater than the threshold of 0.50), indicating that the measurement tools had good convergent validity. Details are presented in Table 2. The HTMT values of the measurement tools ranged from 0.152 to 0.746, all below the threshold of 0.850. Details are shown in Table 3.

Table 2.

Results of PLS-SEM analysis on the reliability and convergent validity of the measurement tools.

Variable	Factor loadings	VIF	Cronbach’s α	CR	AVE
Dietary beliefs			0.730	0.830	0.571
DB1	0.902	2.795
DB2	0.765	1.501
DB3	0.754	1.222
DB4	0.872	1.737
Consideration of future consequences			0.880	0.901	0.541
CFC1	0.847	2.868
CFC2	0.805	2.320
CFC3	0.540	1.310
CFC4	0.698	1.782
CFC5	0.722	2.719
CFC6	0.652	2.289
CFC7	0.656	1.726
CFC8	0.765	2.870
CFC9	0.746	2.559
CFC10	0.508	1.253
Intention
Habit			0.965	0.969	0.721
Habit1	0.869	4.218
Habit2	0.877	4.460
Habit3	0.857	4.611
Habit4	0.817	3.251
Habit5	0.846	5.014
Habit6	0.825	3.867
Habit7	0.875	4.491
Habit8	0.851	3.269
Habit9	0.807	3.046
Habit10	0.865	4.989
Habit11	0.874	4.144
Habit12	0.821	3.450
Self-regulatory capacity			0.876	0.906	0.619
SRC1 (5 items)	0.693	1.656
SRC2 (5 items)	0.752	2.284
SRC3 (5 items)	0.854	2.965
SRC4 (5 items)	0.756	2.234
SRC5 (5 items)	0.800	2.169
SRC6 (5 items)	0.851	3.072
Dietary management behavior			0.893	0.914	0.544
DMB1	0.770	2.028
DMB2	0.802	2.709
DMB3	0.749	2.309
DMB4	0.833	2.595
DMB5	0.626	1.545
DMB6	0.795	2.290
DMB7	0.703	1.707
DMB8	0.684	1.714
DMB9	0.645	1.499

DB: dietary beliefs; CFC: consideration of future consequences; SRC: self-regulatory capacity; DMB: dietary management behavior.

Table 3.

Results of discriminant validity analysis of measurement tools by PLS-SEM (HTMT Values).

Variable	Dietary beliefs	Consideration of future consequences	Intention	Habit	Self-regulation capacity	Dietary management behavior
Dietary beliefs
Consideration of future consequences	0.299	-	-	-	-	-
Intention	0.266	0.643	-	-	-	-
Habit	0.241	0.739	0.681	-	-	-
Self-regulation capacity	0.152	0.606	0.483	0.615	-	-
Dietary management behavior	0.192	0.736	0.663	0.746	0.587	-

Structural model assessment

Dietary beliefs and consideration of future consequences jointly accounted for 40.1% of the variance in dietary-management intention (R²_intention = 0.401), whereas intention, habit, and self-regulation capacity together explained 56.0% of the variance in dietary management behavior (R²_behavior = 0.560). Both coefficients exceed Cohen’s threshold of 0.260 for large explanatory power. The Q² value of this study was 0.307 (greater than 0), indicating that the model has good predictive ability. Additionally, the SRMR was 0.059 for the saturated model and 0.063 for the estimated model, both below the 0.08 cut-off, signifying acceptable model fit.

Structural model path and mediation effect test

As shown in Figure 1, the dietary beliefs (β = 0.098, p < 0.05) and consideration of future consequences (β = 0.605, p < 0.001) were positively associated with intention among patients with IBD. Intention (β = 0.232, p < 0.001), habits (β = 0.410, p < 0.001) and self-regulation capacity (β = 0.184, p < 0.01) were positively associated with dietary management behaviors. However, the interaction of intention and habit (β = −0.051, p > 0.05), and the interaction terms of intention and self-regulation capacity (β = 0.034, p > 0.05) were not significantly associated with dietary management behaviors.

Figure 1.

Schematic diagram of model path test results.

Furthermore, this study explored the potential associations underlying the dietary management behaviors of patients with IBD through mediation effect tests. The results demonstrated that intention was significantly associated with both dietary beliefs and dietary management behaviors (β = 0.027, 95% CI [0.006, 0.057]). Intention was also significantly associated with both consideration of future consequences and dietary management behaviors (β = 0.163, 95% CI [0.090, 0.242]). These findings suggest a potential mediating pattern. Detailed results are presented in Figure 1 and Table 4.

Table 4.

Structural model results.

Paths/Construct	β	t	p	95% CI	f ²	R ²	Q ²
Structural paths
Dietary beliefs→Intention	0.098	2.162	0.031	[0.010,0.185]	0.161
Consideration of future consequences→Intention	0.605	14.738	<0.001	[0.508,0.669]	0.577
Intention→Dietary management behavior	0.232	4.270	<0.001	[0.142,0.380]	0.060
Habit→Dietary management behavior	0.410	7.204	<0.001	[0.319,0.549]	0.173
Self-regulation capacity→Dietary management behavior	0.184	2.962	0.003	[0.047,0.252]	0.050
Moderation effects
Intention × Habit→Dietary management behavior	−0.051	0.156	0.876	[−0.141,0.101]	0.001
Intention × Self-regulation capacity→Dietary management behavior	0.034	0.605	0.545	[−0.066,0.140]	0.004
Mediation effects
Dietary beliefs→Intention→Dietary management behavior	0.027	1.957	0.041	[0.006,0.057]	-
Consideration of future consequences→Intention→Dietary management behavior	0.163	4.032	<0.001	[0.090,0.242]	-
Endogenous constructs
Intention						0.401	0.394
Dietary management behavior						0.560	0.307

Note. The 95% CI marked in bold in the table does not include 0; f²: 0.02 = small, 0.15 = medium, 0.35 = large.

To further strengthen the robustness of the findings, hierarchical regression analysis was conducted as a complementary approach to PLS-SEM. The results were consistent with those obtained from PLS-SEM, with key structural paths remaining significant. The inclusion of interaction terms did not significantly improve the model’s explanatory power (ΔR² = 0.002, p = 0.563), indicating the absence of moderation effects. The convergence of results across different analytical techniques provides strong support for the robustness and reliability of the findings. Detailed results are presented in Table 5.

Table 5.

Hierarchical regression analysis predicting dietary management behavior.

Variable	Model 1 β	Model 2 β	Model 3 β	VIF(Model 3)
Age	0.061	0.059	0.059	3.841
Ref: Unmarried
Married	1.191	−0.327	−0.291	2.222
Divorced	9.362*	4.665	4.857	1.172
Widowed	−0.324	−2.483	−2.366	1.464
Ref: Full time job
Part-time job	1.754	2.574*	2.349	1.120
Unemployment	0.290	0.030	−0.092	1.369
Retirement	2.486	0.682	0.620	2.431
Ref: Recurrence
Remission	−0.540	−0.378	−0.400	1.100
Ref: <1 (years)
1~	−0.711	0.459	0.516	2.034
6~	−2.051	−0.987	−0.922	1.807
>10	−2.258	−0.418	−0.437	1.464
Ref: No biological agents use
Use of biological agents	−1.353	0.090	0.122	1.154
Dietary beliefs		0.346*	0.367*	1.068
Consideration of future consequences		0.216***	0.222***	1.068
Intention		1.561**	1.603**	2.172
Habit		0.201***	0.200***	2.352
Self-regulation capacity		0.065*	0.063*	1.623
Intention × Habit			−0.019	2.136
Intention × Self-regulation capacity			0.021	2.121
R ²	0.048	0.569	0.567
ΔR²	-	0.521***	0.002

p < 0.05. **p < 0.01. ***p < 0.001.

Discussion

Dietary management is a vital component of disease management for patients with IBD. Its implementation and effectiveness are associated with multiple factors. This study examined factors associated with dietary management behaviors in patients with IBD based on the TST framework, and provided insights into the intention-behavior gap.

Associations of intention with dietary management behaviors

The study demonstrated that intention was positively associated with dietary management behaviors in patients with IBD, which is similar to findings on sugar-sweetened beverage consumption (Moran and Mullan, 2021) and discretionary foods consumption (Dominguez Garcia et al., 2023). The Theory of Planned Behavior also points out that intention is the direct antecedent of behavior, with stronger intention being associated with a higher likelihood of an individual taking action (Ajzen, 1991). Thus, fostering positive intention may be a key strategy for promoting dietary self-management in this population.

The present study also reveals that dietary beliefs and consideration of future consequences are positively associated with healthy dietary intention. Mediation analysis showed significant indirect associations via intention, suggesting that intention may be a proximal correlate linking dietary beliefs and consideration of future consequences with dietary management behaviors. This result is consistent with the perspective of TST, while causal inferences cannot be drawn due to the cross-sectional design (Hall and Fong, 2007). In patients with IBD, dietary beliefs refer to their perceptions regarding the relationship between diet and health. These beliefs encompass several specific notions, such as the health benefits of a Mediterranean diet (rich in fresh vegetables and fruits, monounsaturated fatty acids, complex carbohydrates, and lean protein), the role of reduced intake of red and processed meats in lessening ulcerative colitis flares, and the efficacy of exclusive enteral nutrition in treating Crohn’s disease (Hashash et al., 2024). These beliefs may be associated with the formation of healthy dietary intention by enhancing individuals’ cognitive appraisal of healthy eating, self-efficacy, and social support, which in turn may be linked to dietary management behaviors.

In addition, dietary beliefs are also influenced to a certain extent by the dietary cultural background. In different cultural contexts, traditional dietary concepts, family eating habits, and the social dietary environment may all shape individuals’ understanding of the relationship between diet and diseases (Monterrosa et al., 2020). For example, in Chinese dietary culture, people often classify foods according to traditional attributes such as “cold” and “hot,” and generally believe that spicy or greasy foods may induce or exacerbate gastrointestinal discomfort. These dietary cognitions, formed over a long period in the socio-cultural environment, may further affect patients’ judgments on dietary risks and the value of a healthy diet, and thus may be associated with their intentions and behaviors related to dietary management.

As for consideration of future consequences, it refers to the degree to which an individual contemplates the potential future consequences of their present actions. It represents the patient’s weighing and selection between immediate benefits and long-term interests (Strathman et al., 1994). For patients with IBD, consideration of future consequences involves their expectations regarding future health status and the impact of dietary behaviors on these expectations. The present study found that consideration of future consequences was positively associated with dietary management intention among patients with IBD, which in turn was positively associated with dietary behaviors. This aligns with previous findings on healthy dietary decision-making among college students (Jiang, 2023). According to the TST, when patients hold positive expectations about future health outcomes, they are more likely to believe that healthy eating may be beneficial for symptom alleviation and related to a lower risk of disease recurrence. This belief may, in turn, strengthen dietary management intention among patients with IBD, thereby increasing the likelihood of adopting corresponding dietary behaviors. Therefore, when developing dietary management interventions, healthcare professionals may consider adopting a patient-centered approach by integrating diverse educational resources to strengthen patients’ beliefs in dietary management and enhance their perception of the benefits of healthy dietary practices for long-term disease prognosis (Shi et al., 2022). In addition, healthcare professionals may also employ motivational interviewing techniques to encourage patients to express their thoughts and feelings about dietary management. Simultaneously, healthcare professionals could educate patients on the principles and necessity of dietary management, helping them recognize the benefits of proper dietary management and the negative consequences of inappropriate eating habits, thus stimulating the motivation for behavioral change (Thongsunti et al., 2024). Furthermore, establishing a support group for patients with IBD, and inviting those who have successfully improved their symptoms through diet to share their stories can be highly effective. By highlighting these real-world successes, healthcare professionals could enhance patients’ recognition of the benefits, strengthen their confidence in dietary therapy, and ultimately increase their motivation to adopt these practices. These strategies may be associated with increased patients’ motivation and intention toward dietary management, and may contribute to improvements in dietary management behaviors among patients with IBD.

Habit was positively associated with dietary management behaviors

The study found that habit was positively associated with dietary management behaviors of patients with IBD, which is consistent with previous findings (Wang et al., 2023c). Habit constitutes a crucial aspect of behavioral pre-potency, representing an automatic process wherein individuals prioritize certain responses over others when triggered by environmental cues (Hall and Fong, 2015). While individuals can maintain healthy behaviors through reflection and self-regulation, these cognitive strategies require limited mental resources. When these resources become insufficient, individuals may struggle to sustain such healthy behaviors (Kwasnicka et al., 2016). In contrast, as unconscious automatic processes, habits are generally associated with lower cognitive demands. This may be particularly valuable in patients with IBD, for whom dietary management is complex and requires long-term adherence. In clinical practice, healthcare professionals typically advise patients to adopt disease-stabilizing dietary practices during remission to maintain disease control. Repeated engagement in such practices may be associated with the gradual development of more habitual patterns of behavior, which in turn may be associated with more consistent dietary management (Reznikov and Suskind, 2023). A previous study suggested that keeping a food diary may be an effective approach to cultivate dietary management habit (Yin et al., 2022) and thus could be leveraged by patients with IBD. In addition, peer support approaches, such as patient communities, may help patients maintain engagement with dietary management practices and facilitate the development of more stable behavioral patterns.

Self-regulation capacity was positively associated with dietary management behaviors

In this study, self-regulation capacity was positively associated with dietary management behaviors among patients with IBD, consistent with the findings of Xiao et al. (2023). Self-regulation capacity refers to an individual’s ability to adjust their behavior to achieve goals, primarily encompassing self-control and executive function (Chen et al., 2023). In disease management for chronic conditions, self-regulation is associated with patients’ adherence to treatment plans and may be related to reduced risk of disease recurrence (He et al., 2023). As a chronic, recurrent, and persistent disease, IBD requires patients to maintain long-term dietary management for effective symptom control and minimizing flare-ups. In a qualitative study examining dietary restrictions among patients with IBD, participants described long-term dietary limitations as an agonizing and unbearable experience, and overcoming cravings for delicious foods as particularly challenging (Zhu et al., 2024). In this context, effective self-regulation may be associated with managing internal desires, maintaining rational dietary practices, which are linked to better disease management. A study indicates that teaching specific self-regulation strategies (e.g. setting and following healthy eating goals and using distraction to shift attention away from unhealthy foods) is associated with patients’ ability to regulate their thoughts, emotions, and capabilities when facing adverse environments, supporting goal-oriented healthy behaviors (Wang et al., 2023b). Accordingly, healthcare professionals can collaborate with patients to analyze the factors associated with their dietary management behaviors based on the characteristics of IBD and the patient’s condition. This approach enables the development of tailored self-regulation strategies, which may be connected with improvements in self-regulation abilities related to dietary management and consequently, linked to higher overall dietary management levels.

Furthermore, the findings of this study indicate that habit and self-regulation capacity did not moderate the path from intention to dietary management behavior, a finding inconsistent with the TST perspective. This suggests that intention, habit, and self-regulation capacity are relatively independent factors associated with dietary management behavior among patients with IBD. In other words, each factor demonstrates independent association with dietary management behavior. This result aligns with the findings of Dominguez Garcia et al. (2023) on discretionary food consumption.

Conclusion

This study identified several factors associated with dietary management behaviors among patients with IBD. Dietary beliefs, consideration of future consequences, intention, habit, and self-regulation capacity were all found to be associated with dietary management behaviors, with patterns consistent with direct, indirect and moderating relationships. Given the cross-sectional design of this study, these findings should be interpreted as associative rather than causal. Nevertheless, these results still contribute to a deeper understanding of the psychological and behavioral factors involved in dietary management among patients with IBD. From a practical perspective, healthcare professionals may consider supporting patients in strengthening dietary beliefs and consideration of future consequences, as well as in fostering dietary management habits and self-regulation capacity. These factors may be relevant for supporting dietary management behaviors, while future longitudinal or experimental research is needed to further examine their potential roles in patient outcomes.

Limitations

This study has several limitations. First, the cross-sectional survey cannot establish causal relationships between variables. Therefore, the mediation pathways identified in this study represent statistical associations rather than causal processes. IBD is a chronic progressive disease characterized by alternating periods of activity and remission, making dietary management a lifelong challenge for patients. A longitudinal research design would be thus beneficial to explore the dynamic changes in dietary management behaviors, the influencing factors and potential causal mechanisms among patients with IBD. Second, all participants were recruited from tertiary hospitals in Qingdao, limiting the generalizability of the findings. Future studies should consider conducting multicenter, large-scale surveys to comprehensively analyze dietary management behaviors among patients with IBD and the underlying mechanisms. Third, although TST defines behavioral prepotency aspect as including both habits and situational cues, this study mainly focused on habits due to data availability. Therefore, the current model represents only a partial application of TST. Future research should incorporate the environmental opportunity structure to conduct a more comprehensive test of this theory. Finally, dietary management intentions were assessed using a single-item measure. While this approach reduces the response burden for participants, single-item measures may increase measurement error and reduce model stability, compared to multi-item scales. As a result, the strength of relationships observed in structural models may be underestimated. Future research should consider employing validated multi-item intention scales to enhance measurement precision and model stability.

Footnotes

Acknowledgements

The authors would like to sincerely thank the patients for their participation in this study. The authors also appreciate the support and assistance from chief physicians and nurses in the Department of Gastroenterology of the Hospital during the process of approaching patients.

ORCID iDs

Wenjing Liu

Ting Liu

Ethical considerations

The study complied with the Declaration of Helsinki and was approved by the Ethics Committee of Medical College of Qingdao University (approval No. : QDU-HEC-2024404).

Consent to participate

All participants provided an informed written consent to participate in the study.

Consent for publication

Consent for publication is not applicable to this article as it does not contain any identifiable data.

Author contributions

Wenjing Liu: conceptualization, methodology, investigation, data curation, formal analysis, writing – original draft. Ting Liu: conceptualization, data curation, formal analysis, methodology, supervision, writing – review and editing. Linpei Dong: investigation, conceptualization, writing – original draft. Mengjie Yang: data curation, writing – original draft. Mengfan Zhang: validation, writing – original draft.

Funding

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

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The authors are unable to share the research data in a public data repository due to confidentiality and privacy concerns. Data are available upon reasonable request from the corresponding author.*

References

Ajzen

(1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes 50: 179–211.

Chen

Zhai

Wang

, et al. (2023) Application of temporal self-regulation theory in research field of health behavior. Chinese Journal of Health Education 39: 1131–1135.

Chicco

Magrì

Cingolani

, et al. (2021) Multidimensional impact of Mediterranean diet on IBD patients. Inflammatory Bowel Diseases 27: 1–9.

Cohen

Edmondson

Kronish

(2015) State of the art review: Depression, stress, anxiety, and cardiovascular disease. American Journal of Hypertension 28: 1295–1302.

Crooks

Misra

Arebi

, et al. (2022) The dietary practices and beliefs of British South Asian people living with inflammatory bowel disease: A multicenter study from the United Kingdom. Intestinal Research 20: 53–63.

Cucinotta

Romano

Dipasquale

(2021) Diet and nutrition in pediatric inflammatory bowel diseases. Nutrients 13: 655.

Dairo

Collett

Dawes

(2024) Development of a single-item physical activity intention measure for adults with intellectual disabilities: Evidence of validity and reliability. Disability and Health Journal 17: 101670.

Dash

Paul

(2021) CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change 173: 121092.

Dijkstra

Henseler

(2015) Consistent partial least squares path modeling. MIS Quarterly 39: 297–316.

10.

Dominguez Garcia

Mullan

Dorina

(2023) Predicting discretionary food consumption using temporal self-regulation theory and food reward sensitivity. Appetite 190: 107010.

11.

Dong

, et al. (2024) Reliability and validity of the Chinese version and brief version of the multidimensional self-control scale. Chinese Journal of Clinical Psychology 32: 100–106.

12.

Dorina

Mullan

Boyes

, et al. (2023) Utility of temporal self-regulation theory in health and social behaviours: A meta-analysis. British Journal of Health Psychology 28: 397–438.

13.

Fishman

Lushin

Mandell

(2020) Predicting implementation: Comparing validated measures of intention and assessing the role of motivation when designing behavioral interventions. Implementation science communications 1: 81.

14.

Godala

Gaszyńska

Durko

, et al. (2023) Dietary behaviors and beliefs in patients with inflammatory bowel disease. Journal of Clinical Medicine 12: 3455.

15.

Hall

Fong

(2007) Temporal self-regulation theory: A model for individual health behavior. Health Psychology Review 1: 6–52.

16.

Hall

Fong

(2015) Temporal self-regulation theory: A neurobiologically informed model for physical activity behavior. Frontiers in Human Neuroscience 9: 117.

17.

Hashash

Elkins

Lewis

, et al. (2024) AGA clinical practice update on diet and nutritional therapies in patients with inflammatory bowel disease: Expert review. Gastroenterology 166: 521–532.

18.

Hayes

Andrew

(2009) Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs 76: 408–420.

19.

Henseler

Ringle

Sarstedt

(2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 43: 115–135.

20.

Zhu

, et al. (2023) Advances on self-regulation in disease management of patients with chronic diseases. Medical Research and Education 40: 61–68.

21.

Jiang

(2023) The Effect of Consideration of Future Consequence on Healthy Eating Decisions: Based on Gain-and-Loss/Temporal Frame. Southwest University.

22.

Kamp

Pennings

Javelli

, et al. (2021) Dietary patterns, beliefs and behaviours among individuals with inflammatory bowel disease: A cross-sectional study. Journal of Human Nutrition and Dietetics 34: 257–264.

23.

Kutner

Nachtsheim

Neter

(2004) Applied linear regression model. Technometrics 26: 415–416.

24.

Kwasnicka

Dombrowski

White

, et al. (2016) Theoretical explanations for maintenance of behaviour change: A systematic review of behaviour theories. Health Psychology Review 10: 277–296.

25.

Liddelow

Mullan

Boyes

(2021) Understanding the predictors of medication adherence: Applying temporal self-regulation theory. Psychology and Health 36: 529–548.

26.

Limketkai

Hamideh

Shah

, et al. (2022) Dietary patterns and their association with symptoms activity in inflammatory bowel diseases. Inflammatory Bowel Diseases 28: 1627–1636.

27.

Marsh

Radford-Smith

Banks

, et al. (2022) Dietary intake of patients with inflammatory bowel disease aligns poorly with traditional Mediterranean diet principles. Nutrition & Dietetics 79: 229–237.

28.

McAlpine

Mullan

(2022) The role of environmental cues in sugar-sweetened beverage consumption using a temporal self-regulation theory framework. Appetite 169: 105828.

29.

Monterrosa

Frongillo

Drewnowski

, et al. (2020) Sociocultural influences on food choices and implications for sustainable healthy diets. Food and Nutrition Bulletin 41: 59s–73s.

30.

Moran

Mullan

(2021) Exploring temporal self-regulation theory to predict sugar-sweetened beverage consumption. Psychology and Health 36: 334–350.

31.

Nilsen

Bang

Boe

, et al. (2020) The Multidimensional Self-Control Scale (MSCS): Development and validation. Psychological Assessment 32: 1057–1074.

32.

Prochaska

Velicer

(1997) The transtheoretical model of health behavior change. American Journal of Health Promotion 12: 38–48.

33.

Reznikov

Suskind

(2023) Current nutritional therapies in inflammatory bowel disease: Improving clinical remission rates and sustainability of long-term dietary therapies. Nutrients 15: 688.

34.

Sarstedt

Hair

Ringle

, et al. (2016) Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research 69: 3998–4010.

35.

Shang

Lin

Bian

, et al. (2019) Development and reliability and validity test of self-management behavior scale for patients with inflammatory bowel disease. Chinese Nursing Research 33: 2374–2380.

36.

Shi

Zhang

, et al. (2022) Investigation on current situation of dietary behavior and its influencing factors among patients with inflammatory bowel disese based on integrated change model. Nursing Journal of Chinese People's Liberation Army 39: 25–29.

37.

Strathman

Gleicher

Boninger

, et al. (1994) The consideration of future consequences: Weighing immediate and distant outcomes of behavior. Journal of Personality and Social Psychology 66: 742–752.

38.

Thongsunti

Silpakit

Rattananupong

, et al. (2024) Effect of a transtheoretical model-based intervention and motivational interviewing on hyperphosphatemia management via telehealth (TMT program) among hemodialysis patients during the COVID-19 pandemic. Frontiers in Public Health 12: 1361778.

39.

van Beek

Antonides

Handgraaf

MJJ

(2013) Eat now, exercise later: The relation between consideration of immediate and future consequences and healthy behavior. Personality and Individual Differences 54: 785–791.

40.

Verplanken

Orbell

(2003) Reflections on past behavior: A Self-report Index of Habit Strength. Journal of Applied Social Psychology 33: 1313–1330.

41.

Wang

Liu

, et al. (2023a) Global, regional and national burden of inflammatory bowel disease in 204 countries and territories from 1990 to 2019: A systematic analysis based on the Global Burden of Disease Study 2019. BMJ Open 13: e065186.

42.

Wang

Hua

, et al. (2023b) The application status and prospects of temporal self-regulation theory in health behaviors. Journal of Nursing 30: 43–48.

43.

Wang

Hua

, et al. (2023c) Using an integrated model of the theory of planned behavior and the temporal self-regulation theory to explain physical activity in patients with coronary heart disease. Frontiers in Psychology 14: 1049358.

44.

Wark

Samocha-Bonet

Ghaly

, et al. (2020) The role of diet in the pathogenesis and management of inflammatory bowel disease: A review. Nutrients 13: 135.

45.

Liang

Ran

, et al. (2018) Chinese consensus on diagnosis and treatment of inflammatory bowel disease (Beijing, 2018). Chinese Journal of Practical Internal Medicine 39: 796–813.

46.

Hao

, et al. (2025) Association of temporal self-regulation theory and its constructs with eating behavior: A systematic review and meta-analysis. Appetite 208: 107937.

47.

Xiao

Jin

Zhang

, et al. (2023) Applying the temporal self-regulation theory to understand sugar-sweetened beverage consumption among Chinese college students. Health Psychology and Behavioral Medicine 11: 2208213.

48.

Xiong

Zhang

Zeng

, et al. (2024) Experience of diet in patients with inflammatory bowel disease: A thematic synthesis of qualitative studies. Journal of Clinical Nursing 33: 3283–3293.

49.

Yin

, et al. (2022) Progress of research on dietary restriction behavior and its influencing factors in patients with inflammatory bowel disease. Chinese Journal of Gastroenterology 27: 439–443.

50.

Zeleke

Stephens

Gesesew

, et al. (2024) A community-based study of intention to use HIV self-testing among young people in urban areas of southern Ethiopia. Scientific Reports 14: 26519.

51.

Zhou

Long

(2004) Statistical tests and control methods for common method bias. Advances in Psychological Science 12: 942–950.

52.

Zhu

Lin

Chen

(2021) Research progress on dietary management for patients with inflammatory bowel disease. Modern Digestion and Intervention 26: 409–413.

53.

Zhu

Gao

Jia

, et al. (2024) Experience of dietary restriction in patients with inflammatory bowel disease: A qualitative study. Chinese Journal of Nursing 59: 50–56.

54.

Zhu

Zeid

Leiner

, et al. (2025) What motivates information (non-)seeking behaviors about a healthy diet? Journal of Health Psychology 30: 451–469.