The Mediating Role of Patient Activation in the Association Between Illness Perception and Self-Management Behavior Among Patients with Oral Cancer

Abstract

Objective

This study aimed to examine the relationships among illness perception, patient activation, and self-management behavior in oral cancer patients, and to explore the mediating role of patient activation between illness perception and self-management behavior.

Methods

A cross-sectional study was conducted among 220 oral cancer patients recruited from two tertiary hospitals in Shandong Province, China, using a convenience sampling method. Data were collected using the Brief Illness Perception Questionnaire (BIPQ), Patient Activation Measure (PAM), and the Cancer Patient Self-Management Scale. Pearson correlation analysis, multiple linear regression, and structural equation modeling (SEM) were performed to analyze the relationships among variables. The mediating effect was tested using the bootstrap method.

Results

A total of 220 valid questionnaires were collected. The mean scores for illness perception, patient activation, and self-management behavior were 41.69±12.23, 59.17±12.81, and 133.71±35.95, respectively. Several demographic and clinical characteristics were associated with self-management behavior in the regression analysis. Negative illness perceptions were significantly associated with lower self-management behavior (r= −0.875,P<0.01), whereas patient activation was positively correlated with self-management behavior (r=0.882,P<0.01). Negative illness perceptions were also associated with lower patient activation (r=−0.896,P<0.01). Structural equation modeling showed that patient activation partially mediated the relationship between negative illness perceptions and self-management behavior (β=−0.53; 95% CI: −0.87 to −0.19), with the mediating effect accounting for 56.58% of the total effect.

Conclusion

Patient activation plays a significant mediating role in the relationship between negative illness perceptions and self-management behavior in oral cancer patients, accounting for 56.58% of the total effect. This finding suggests that enhancing patient activation may partially reduce the adverse impact of negative illness perceptions on self-management behavior and represents an important target for clinical interventions aimed at improving self-management among oral cancer patients.

Introduction

Oral cancer is one of the most common malignancies of the head and neck region, characterized by high incidence and mortality. It represents a significant global public health burden, particularly in developing countries where incidence rates continue to rise.^1–3 It currently ranks as the 16th most common cancer worldwide.⁴ Between 1990 and 2021, the age-standardized incidence rate (ASIR) of lip and oral cavity cancer in China increased from 1.704 to 2.681 per 100,000 population, while the global ASIR rose from 4.270 to 4.880 per 100,000. Similarly, the age-standardized prevalence rate (ASPR) in China increased from 4.168 to 10.158 per 100,000, compared with an increase from 13.888 to 17.706 per 100,000 globally.⁵ Shandong Province has a notable burden of oral cancer, with tobacco and alcohol use remaining important regional risk factors.⁶ In addition, urban–rural disparities may limit patients’ access to screening, rehabilitation, and follow-up care.⁷ Therefore, examining self-management behavior among oral cancer patients in Shandong has practical clinical significance.

Most patients with oral cancer are diagnosed at a middle or advanced stage, which complicates treatment and leads to poor prognosis, thereby imposing a substantial burden on individuals and healthcare systems.^8,9 Oral cancer not only threatens survival but also profoundly affects physiological function and psychological well-being.^10,11 Following treatments such as surgical resection, radiotherapy, and chemotherapy patients often experience dysphagia, speech impairment, restricted masticatory function, and facial disfigurement, all of which significantly reduce quality of life.^12,13 Importantly, illness perceptions and self-management needs may vary across treatment phases, as patients face different psychological, functional, and behavioral demands before surgery, during postoperative recovery, and throughout adjuvant therapy.¹⁴ Therefore, treatment phase represents an important clinical context when evaluating patients’illness perceptions and self-management needs in oral cancer.¹⁵ Specific surgical procedures, including glossectomy, mandibulectomy, maxillectomy, and flap reconstruction, may further impair swallowing, speech, mastication, oral hygiene, wound care, and facial appearance.¹⁶ Specifically, glossectomy may impair tongue mobility and speech, thereby increasing difficulties in oral intake, oral hygiene, and functional rehabilitation.¹⁷ Mandibulectomy and maxillectomy may further compromise mastication, swallowing, and facial appearance, requiring dietary adaptation and continued functional rehabilitation, while flap reconstruction may impose additional demands related to wound care and postoperative functional recovery.¹⁸ Given that flap reconstruction may involve additional wound care, functional rehabilitation, and adaptation to changes in swallowing, speech, mastication, and oral hygiene, it was included as a clinically relevant treatment-related variable that may influence self-management behavior in patients with oral cancer.¹⁹ These functional changes create unique self-management challenges, such as maintaining oral hygiene, performing jaw-opening and swallowing exercises, modifying diet, monitoring symptoms, and adhering to follow-up care.²⁰ Furthermore, the high recurrence rate and prolonged treatment trajectory require patients to engage in continuous rehabilitation and health management after discharge, placing greater demands on their self-management abilities.²¹

Self-management behavior refers to a range of proactive activities undertaken by individuals to manage chronic diseases or long-term health conditions, with the aim of maintaining physiological functioning, delaying disease progression, and improving quality of life.²² It primarily encompasses treatment adherence, symptom monitoring, functional rehabilitation, and lifestyle modification.²³ For patients with oral cancer, self-management also involves several disease-specific tasks, including maintaining specialized oral hygiene,^20,24 performing regular jaw-opening and swallowing exercises to prevent or alleviate trismus and dysphagia,^25,26 modifying food texture and dietary intake according to chewing and swallowing ability,²⁷ and monitoring treatment-related symptoms and attending regular follow-up visits. Effective self-management practices can improve treatment adherence, reduce complications, and promote recovery.²⁸

The Integrative Theory of Health Behavior Change (ITHBC), proposed by Ryan,²⁹ integrates the Health Belief Model, Social Facilitation Theory, and Self-Regulation Theory, emphasizing the central role of knowledge and beliefs, social support, and self-management in the development and maintenance of health behaviors. The theory posits that individuals’ cognitive appraisals (eg, their understanding and perception of illness) influence their confidence and capabilities (eg, patient activation), which in turn shape the initiation and maintenance of health behaviors. Although social support is an important component of the ITHBC, it was not included in the present model because the primary objective of this study was to examine the cognitive–motivational–behavioral pathway linking illness perception, patient activation, and self-management behavior, rather than to test all components of the ITHBC.

Illness perception refers to patients’ cognitive and emotional representations of their illness and its consequences, including beliefs about symptoms, causes, disease progression, consequences, and controllability.^30–32 Negative illness perceptions may undermine patients’ sense of control and self-efficacy, increase anxiety and fear, and consequently reduce engagement in self-management behaviors. In contrast, more adaptive illness perceptions are associated with greater adoption of health-promoting behaviors.³³ However, existing research has primarily focused on the direct association between illness perception and self-management behavior, with limited exploration of the underlying mechanisms.

Patient activation refers to the knowledge, skills, and confidence individuals possess in managing their health.³⁴ Higher patient activation has been associated with greater engagement in disease management and more consistent self-management behaviors.^35–37 Recent studies have also highlighted its relevance in head and neck cancer populations. Gao et al³⁸ reported that patients with head and neck cancer had a PAM-13 score of 63.48 ± 14.70, corresponding to activation level 3, suggesting that their self-management positivity still requires improvement. Manne et al³⁹ further found that activation among oral and oropharyngeal cancer survivors was associated with clinical and psychosocial factors. These findings support patient activation as a relevant and modifiable factor in oral cancer patients. Moreover, illness perception may influence patient activation by shaping patients’ cognitive appraisals and confidence, thereby indirectly affecting self-management behavior.⁴⁰ Therefore, patient activation was selected as the mediator in this study.

Moreover, illness perception may influence patient activation by shaping patients’ cognitive appraisals and confidence, thereby indirectly affecting behavioral outcomes.⁴¹

However, existing research has largely focused on the independent relationships between illness perception or patient activation and self-management behavior, with limited attention to the interactions among all three variables. Furthermore, these relationships have not been sufficiently examined in patients with oral cancer.

Guided by the Integrative Theory of Health Behavior Change, this study aims to examine the relationships among illness perception, patient activation, and self-management behavior in patients with oral cancer, and to construct a mediation model to investigate the mediating role of patient activation.

This study seeks to provide a theoretical basis for understanding the psychological mechanisms underlying self-management behavior in this population and to offer empirical evidence to inform the development of targeted interventions aimed at enhancing patient activation and improving health outcomes.

Research Hypothesis

Based on the Integrative Theory of Health Behavior Change, the following hypotheses were proposed:

H1: Illness perception significantly predicts self-management behavior in patients with oral cancer.

H2: Illness perception significantly predicts patient activation.

H3: Patient activation significantly predicts self-management behavior and mediates the relationship between illness perception and self-management behavior (see Figure 1).

Figure 1.

Research hypothesis diagram.

Notes: “+” indicates a hypothesized positive association, whereas “−” indicates a hypothesized negative association.

Methods

Study Participants

A convenience sampling approach was used to recruit patients with oral cancer who were hospitalized in two tertiary hospitals in Shandong Province, China, between October 2022 and December 2023.

The inclusion criteria were as follows: (1) age ≥18 years; (2) diagnosis of oral cancer according to established clinical guidelines for head and neck cancer;⁴² (3) conscious and cognitively intact, and able to read and complete the questionnaire independently or with assistance from researchers; and (4) awareness of their diagnosis and provision of written informed consent.

The exclusion criteria were as follows: (1) communication difficulties; (2) diagnosed psychiatric disorders or cognitive impairment; and (3) presence of other concurrent malignancies.

Sample Size Calculation: According to Kendall’s sample size estimation method, the sample size should be 5–10 times the number of study variables.⁴³ With 26 variables and allowing for a 10% rate of invalid questionnaires, the required sample size was estimated to be 145–289 participants. Considering that a sample size of at least 200 is generally considered adequate for structural equation modeling (SEM),⁴⁴ the target sample size was set at≥200. Ultimately, 220 valid questionnaires were included, meeting the sample size requirements.

Data Collection

Data were collected using a structured questionnaire during hospitalization. Each participant completed the questionnaire only once. Among patients undergoing surgical treatment, some completed the questionnaire preoperatively and others postoperatively, depending on their clinical condition and the timing of data collection. Patients who did not undergo surgery completed the questionnaire during hospitalization. Trained researchers administered the questionnaires in person. Prior to data collection, participants were informed of the study purpose and procedures, and it was emphasized that participation was voluntary and that they could withdraw at any time without consequences. Data collection commenced only after written informed consent was obtained.

Participants who were able to complete the questionnaire independently did so. For those with limited literacy or difficulty understanding the items, interviewer-administered surveys were conducted to ensure accurate comprehension. All questionnaires were completed and collected on-site.

To ensure data quality, completed questionnaires were checked immediately for missing or inconsistent responses, which were clarified on the spot. Data were double-entered independently by two researchers and cross-checked to ensure accuracy.

Research Instruments

General Information Questionnaire

A general information questionnaire was developed by the researchers based on a review of the relevant literature. It collected demographic and clinical characteristics, including gender, age, education level, marital status, occupation, monthly income, smoking status, comorbidities, long-term residence, perceived severity of illness, method of disease detection, cancer stage, and flap reconstruction. Perceived severity of illness reflects patients’subjective appraisal of disease seriousness, whereas cancer stage represents an objective clinical indicator of disease extent. Because these variables capture distinct and complementary dimensions of disease burden, both were included as separate predictors in the regression model. The full General Information Questionnaire, including all items and corresponding response options, is provided in Supplementary File 1 to facilitate replication of the study.

Cancer Patient Self-Management Scale

The Cancer Patient Self-Management Scale, developed by Cheng Lingling,⁴⁵ consists of 44 items across six dimensions: daily life management, symptom management, emotional management, communication with healthcare providers, information management, and self-management efficacy. Each item is rated on a 5-point Likert scale ranging from 1 (“never”) to 5 (“always”), with higher total scores indicating better self-management ability. The scale demonstrated excellent internal consistency in previous studies, with a Cronbach’s α coefficient of 0.959.

Brief Illness Perception Questionnaire (BIPQ)

The Brief Illness Perception Questionnaire (BIPQ), developed by Broadbent,⁴⁶ is used to assess patients’ cognitive and emotional representations of their illness. The instrument comprises nine items across three domains: illness perception (items 1–5), emotional response (items 6 and 8), and illness comprehensibility (item 7). Item 9 is an open-ended question. Items 1–8 are rated on a scale from 0 to 10, yielding a total score ranging from 0 to 80, with higher scores indicating more negative illness perceptions. The Cronbach’s α coefficient for the scale is 0.77.

Patient Activation Measure (PAM)

The Patient Activation Measure (PAM), developed by Hibbard,⁴⁷ consists of 22 items covering four dimensions: cognition, skills, actions, and beliefs. Each item is scored on a 4-point Likert scale. The raw score is converted into a standardized activation score ranging from 0 to 100 using the conversion algorithm provided by the developers, with higher scores indicating greater levels of patient activation. Based on established criteria, activation levels are categorized into four stages: Level 1 (≤47.0), Level 2 (47.1–55.1), Level 3 (55.2–67.0), and Level 4 (≥67.1). The scale has demonstrated good internal consistency, with a Cronbach’s α coefficient of 0.91.

Statistical Analysis

Data were analyzed using SPSS version 25.0 and AMOS version 26.0. Continuous variables were expressed as mean ± standard deviation (SD), while categorical variables were presented as frequencies and percentages. Pearson correlation analysis was performed to examine the relationships among illness perception, patient activation, and self-management behavior.

Structural equation modeling (SEM) was conducted using AMOS to test the hypothesized relationships. Confirmatory factor analysis (CFA) was first performed to evaluate the measurement model, followed by path analysis to assess the structural model. Model fit was evaluated using standard indices, including the chi-square to degrees of freedom ratio (χ²/df), the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the root mean square error of approximation (RMSEA).

The mediation effect was tested using the bootstrap method with 5,000 resamples. A bias-corrected 95% confidence interval (CI) that did not include zero was considered indicative of a statistically significant mediation effect. A two-tailed p value < 0.05 was considered statistically significant.

Results

Differences in Illness Perception, Patient Activation, and Self-Management Behavior by Demographic and Clinical Characteristics

A total of 220 valid questionnaires were collected for this study. Male participants accounted for 62.27% of the sample, while female participants accounted for 37.73%. Most participants were aged over 45 years, with those aged over 60 years representing the largest proportion (52.73%) (Table 1).

Table 1.

Differences in Illness Perception, Patient Activation, and Self-Management Behavior According to Demographic and Clinical Characteristics Among Patients with Oral Cancer

Variable	Category	n	Illness Perception	Patient Activation	Self-Management Behavior
Gender	Male	137	60.124 ± 12.616	40.839 ± 12.302	137.818 ± 34.905
	Female	83	57.590 ± 13.042	43.096 ± 12.039	126.916 ± 36.820
Smoking	No	115	61.991 ± 11.901	38.539 ± 11.164	141.826 ± 33.495
	Yes	105	56.076 ± 13.104	45.143 ± 12.453	124.810 ± 36.587
Marital Status	Unmarried	13	64.308 ± 9.978	35.231 ± 7.737	146.000 ± 30.689
	Married	207	58.845 ± 12.916	42.097 ± 12.353	132.932 ± 36.178
Age	18–45	41	55.756 ± 13.991	44.976 ± 11.963	123.220 ± 39.336
	45–60	63	59.190 ± 14.088	42.794 ± 14.039	134.016 ± 36.111
	>60	116	60.362 ± 11.477	39.931 ± 10.996	137.241 ± 34.174
Education	Primary or below	72	55.667 ± 13.130	44.708 ± 12.656	120.583 ± 34.279
	Junior high	56	58.518 ± 12.535	43.161 ± 11.975	131.125 ± 33.847
	High school	67	60.642 ± 12.681	39.567 ± 11.867	141.612 ± 36.507
	College+	25	66.760 ± 8.983	35.400 ± 9.367	156.080 ± 28.234
Occupation	Worker	81	59.160 ± 13.621	41.444 ± 12.885	135.568 ± 37.720
	Farmer	108	57.787 ± 12.489	43.528 ± 11.709	126.361 ± 34.192
	Retired	17	62.941 ± 10.957	37.235 ± 11.138	154.235 ± 25.944
	Others	14	65.286 ± 10.745	34.357 ± 10.127	154.643 ± 32.712
Income	<1000	78	57.346 ± 12.138	43.474 ± 12.034	126.423 ± 35.748
	1000–3000	68	58.294 ± 13.942	42.971 ± 12.748	130.941 ± 36.118
	3000–5000	52	60.212 ± 13.028	39.712 ± 12.280	141.923 ± 34.590
	>5000	22	65.864 ± 8.582	36.091 ± 9.118	148.636 ± 33.176
Perceived Severity	Not severe	48	63.417 ± 10.665	38.042 ± 10.745	141.563 ± 32.984
	Moderate	65	60.415 ± 12.101	39.554 ± 11.279	140.923 ± 35.034
	Relatively severe	67	57.866 ± 13.356	43.373 ± 12.394	129.612 ± 35.307
	Very severe	40	54.225 ± 13.751	46.725 ± 13.286	119.400 ± 37.687
Residence	Rural	124	56.782 ± 13.101	43.685 ± 12.776	123.831 ± 34.595
	Town/county	61	62.082 ± 11.338	39.410 ± 10.598	144.295 ± 33.050
	City	35	62.543 ± 12.694	38.600 ± 11.843	150.229 ± 35.051
Detection	Incidental	123	56.984 ± 12.094	43.358 ± 11.984	125.106 ± 33.442
	Self-exam	47	58.596 ± 13.766	43.319 ± 13.244	133.340 ± 37.407
	Physical exam	46	64.674 ± 12.320	36.348 ± 10.571	154.913 ± 32.881
	Others	4	69.750 ± 5.439	32.750 ± 2.062	158.500 ± 20.761
Cancer Stage	I	43	62.326 ± 12.965	38.372 ± 11.812	145.140 ± 34.085
	II	110	61.236 ± 12.353	39.636 ± 12.003	137.991 ± 36.644
	III	53	54.943 ± 12.297	46.113 ± 11.520	121.811 ± 33.513
	IV	14	49.214 ± 9.349	51.286 ± 8.774	109.929 ± 22.169
Comorbidities	No	106	62.170 ± 12.048	39.094 ± 11.632	142.538 ± 33.737
	Yes	114	56.377 ± 12.912	44.105 ± 12.317	125.491 ± 36.134
Flap Reconstruction	No	162	60.784 ± 12.110	40.327 ± 11.974	138.519 ± 33.830
	Yes	58	54.655 ± 13.708	45.500 ± 12.213	120.259 ± 38.503

The total illness perception score among patients with oral cancer was 41.91±12.23, with a cognitive dimension score of 25.97±6.14, an emotional dimension score of 12.14±2.45, and a comprehension ability dimension score of 3.80±1.49. The total patient activation score was 59.17±12.81, with the distribution across activation levels as follows: Level 1 (8.2%), Level 2 (48.6%), Level 3 (12.2%), and Level 4 (31.0%). The total self-management behavior score was 133.71±35.95.

Independent-samples t-tests and one-way analysis of variance (ANOVA) were conducted to examine differences in self-management behavior according to demographic and clinical characteristics. Statistically significant differences in self-management behavior were observed according to gender, smoking status, education level, occupation, monthly income, perceived severity of illness, long-term residence, and method of disease detection (P<0.05).

Current Status of Self-Management Behavior Among Oral Cancer Patients

The total score for self-management behavior among patients with oral cancer was 133.71±35.95, with a mean item score of 2.97±0.82. Among the six dimensions, self-efficacy had the highest mean item score (3.19±0.97), whereas information management had the lowest (2.74±0.98). The mean item scores, ranked from lowest to highest, were information management, communication with healthcare providers, symptom management, daily life management, psychological management, and self-efficacy (Table 2).

Table 2.

Self-Management Behaviours and Scores for Each Dimension Among Oral Cancer Patients

Project	Number of Entries	Score total	Score	Evenly Distributed
Dailylife management	11	(11–55)	33.58±10.01	3.04±0.93
Symptom management	7	(7–35)	20.45±6.95	2.91±1.01
Psychological management	9	(9–45)	27.81±8.90	3.08±1.01
Communicationwith Healthcare	4	(4–20)	11.57±3.04	2.88±0.78
Information management	3	(3–15)	8.25±2.90	2.74±0.98
Self-efficacy	10	(10–50)	32.05±9.44	3.19±0.97
Self-management behaviours	44	(44–220)	133.71±35.95	2.97±0.82

Multiple Linear Regression Analysis of Self-Management Behavior in Oral Cancer

The multiple linear regression analysis showed that female sex, monthly income, residence in a prefecture-level city, physical examination as the method of disease detection, illness perception, and patient activation were significantly associated with self-management behavior in patients with oral cancer. Illness perception was negatively associated with self-management behavior, whereas patient activation was positively associated with self-management behavior.

Multicollinearity was assessed using variance inflation factors (VIFs). The VIF values ranged from 1.081 to 5.626. Although the VIF values for illness perception (5.626) and patient activation (5.509) were moderately elevated, all VIF values were below 10, indicating that severe multicollinearity was not present (Table 3).

Table 3.

Multiple Linear Regression Analysis of Self-Management Behaviors in Patients with Oral Cancer

Variable	B	SE	β	t	P	VIF
Intercept	117.395	18.65	–	6.295	<0.001	–
Reference category:male
Female	−5.242	2.352	−0.071	−2.229	0.027	1.334
Smoking	−1.159	2.373	−0.016	−0.488	0.626	1.442
Education level	2.235	1.458	0.064	1.533	0.127	2.28
Reference category:worker
Others	−1.545	4.557	−0.011	−0.339	0.735	1.271
Retiree	2.974	4.378	0.022	0.679	0.498	1.404
Farmer	−2.089	2.997	−0.029	−0.697	0.486	2.305
Monthly income	−4.632	1.599	−0.128	−2.896	0.004	2.585
Perceived severity	0.453	1.051	0.013	0.43	0.667	1.189
Long-term residence reference category:
Prefecture-level city	9.607	3.787	0.098	2.537	0.012	1.971
Townships and county-level cities	4.991	2.936	0.062	1.7	0.091	1.774
Others	1.304	7.678	0.005	0.17	0.865	1.081
Physical examination	7.065	3.149	0.08	2.243	0.026	1.685
Oral self-examination	3.88	2.782	0.044	1.395	0.165	1.335
Cancer stage	−0.435	1.325	−0.01	−0.328	0.743	1.191
Comorbidities	−2.19	2.186	−0.031	−1.002	0.317	1.225
Flap presence	−2.663	2.331	−0.033	−1.142	0.255	1.084
Illness perception	−1.255	0.192	−0.427	−6.541	<0.001	5.626
Patient activation	1.237	0.181	0.441	6.827	<0.001	5.509

Notes: R²: 0.848; Adjusted R²: 0.834.

Correlations Among Illness Perception, Patient Activation, and Self-Management Behavior in Oral Cancer Patients

The correlation analysis revealed significant associations between illness perception, patient activation, and self-management behavior. Specifically, A significant negative correlation was observed between patients’ perception of their oral cancer and their self-management behavior (r=−0.875, P<0.01). Additionally, a significant positive correlation was found between patients’ Patient Activation and their self-management behavior (r=0.882, P<0.01). Furthermore, patients’ perception of their oral cancer was significantly negatively correlated with their level of Patient Activation (r=−0.896, P< 0.01). These findings suggest that higher levels of illness perception are associated with lower levels of patient activation and self-management behavior (Table 4).

Table 4.

Correlation Between Disease Perception, Patient Activation and Self-Management in Patients with Oral Cancer

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
Self-management behaviors	1	–	–	–	–	–	–	–	–	–	–	–	–	–	–	–
2. Daily life management	0.885**	1	–	–	–	–	–	–	–	–	–	–	–	–	–	–
3. Symptom management	0.725**	0.492**	1	–	–	–	–	–	–	–	–	–	–	–	–	–
4. Psychological management	0.954**	0.808**	0.645**	1	–	–	–	–	–	–	–	–	–	–	–	–
5. Medical communication	0.787**	0.650**	0.447**	0.765**	1	–	–	–	–	–	–	–	–	–	–	–
6. Information management	0.805**	0.651**	0.499**	0.748**	0.675**	1	–	–	–	–	–	–	–	–	–	–
7. Self-efficacy	0.937**	0.776**	0.597**	0.882**	0.730**	0.779**	1	–	–	–	–	–	–	–	–	–
8. Patient activation	0.882**	0.814**	0.505**	0.869**	0.730**	0.701**	0.854**	1	–	–	–	–	–	–	–	–
9. Positive cognition	0.835**	0.764**	0.503**	0.816**	0.702**	0.688**	0.794**	0.905**	1	–	–	–	–	–	–	–
10. Skills	0.867**	0.792**	0.511**	0.857**	0.698**	0.678**	0.846**	0.978**	0.861**	1	–	–	–	–	–	–
11. Action	0.824**	0.781**	0.459**	0.818**	0.685**	0.633**	0.786**	0.955**	0.836**	0.895**	1	–	–	–	–	–
12. Belief	0.803**	0.733**	0.427**	0.783**	0.708**	0.688**	0.789**	0.920**	0.830**	0.856**	0.861**	1	–	–	–	–
13. Illness perception	−0.875**	−0.800**	−0.585**	−0.861**	−0.701**	−0.659**	−0.814**	−0.896**	−0.861**	−0.887**	−0.838**	−0.789**	1	–	–	–
14. Perceived cognition	−0.877**	−0.794**	−0.593**	−0.869**	−0.699**	−0.663**	−0.813**	−0.874**	−0.842**	−0.865**	−0.823**	−0.763**	0.983**	1	–	–
15. Comprehension ability	−0.597**	−0.551**	−0.404**	−0.579**	−0.486**	−0.446**	−0.553**	−0.669**	−0.602**	−0.663**	−0.627**	−0.611**	0.740**	0.651**	1	–
16. Emotional dimension	−0.762**	−0.716**	−0.486**	−0.735**	−0.619**	−0.570**	−0.718**	−0.819**	−0.806**	−0.813**	−0.750**	−0.730**	0.897**	0.821**	0.636**	1

Note: **P< 0.01.

Analysis of the Mediating Effect of Patient Activation on the Relationship Between Illness Perception and Self-Management Behavior

To investigate the mediating role of patient activation in the relationship between illness perception and self-management behavior, a structural equation model (SEM) was developed, using illness perception as the independent variable, patient activation as the mediating variable, and self-management behavior as the dependent variable. The model was fitted using the maximum likelihood estimation method in AMOS 26.0.

The initial model was tested and refined iteratively, resulting in a final model with satisfactory fit indices, as shown in Figure 2. The model’s goodness-of-fit indices and corresponding values are summarized in Table 5.

Figure 2.

Structural equation model of illness perception, patient activation, and self-management behavior.

Table 5.

Fit Indices for Structural Equation Modelling

Fit Indices	χ2/df	RMSEA	NFI	TLI	GFI	RFI	IFI	CFI
Fit Values	2.338	0.078	0.959	0.969	0.912	0.946	0.976	0.976
Fit Standards	<3.0	<0.08	>0.90	>0.90	>0.90	>0.90	>0.90	>0.90

Notes: χ²/df, chi-square to degrees of freedom ratio.

Abbreviations: RMSEA, root mean square error of approximation; NFI, normed fit index; TLI, Tucker–Lewis index; GFI, goodness-of-fit index; RFI, relative fit index; IFI, incremental fit index; CFI, comparative fit index.

The mediation effect was assessed using the bootstrap method with 5,000 resamples. The mean path value of the mediation effect was calculated, and the 95% confidence interval (CI) was estimated. A mediation effect was considered significant if the 95% CI did not include zero. In the pathway from illness perception to self-management behavior via patient activation, patient activation exhibited a partial mediating effect (B=0.556×(−0.952)= −0.529, P<0.01), accounting for 56.58% of the total effect (−0.529/-0.935=0.5658), with patient activation serving as the mediator (Table 6).

Table 6.

Mediation Effect Test of Patient Activation in Oral Cancer Patient

Project	Effect Size	SE	95% CI	Relative Effect (%)
Direct Effect	−0.406	0.180	−0.769–−0.051	43.42%
Mediation Effect	−0.529	0.169	−0.866–−0.189	56.58%
Total Effect	−0.935	0.016	−0.965–−0.904	100%

Abbreviations: SE, standard error; CI, confidence interval.

Discussion

Current Status of Illness Perception, Patient Activation, and Self-Management Behavior in Oral Cancer Patients

The present study found that patient activation and self-management behavior among oral cancer patients were at moderate levels, indicating considerable potential for improvement in disease management. Compared with other cancer populations, patients with oral cancer exhibited relatively lower levels of self-management behavior, which may be attributable to disease-specific characteristics.⁴⁸

Oral cancer and its treatments (eg, surgery, radiotherapy, and chemotherapy) often result in functional impairments such as dysphagia, speech difficulties, and facial disfigurement.⁴⁹ These complications not only increase the complexity of daily self-care but also impose substantial psychological burdens, which may hinder patients’ motivation and ability to engage in effective self-management behaviors.^50–52

In addition, patients in this study demonstrated relatively high illness perception scores, suggesting a predominance of negative cognitive and emotional representations of the disease. This finding is consistent with previous studies indicating that cancer patients tend to perceive their illness as highly threatening and difficult to control, thereby increasing level of anxiety and fear.⁵³ The mean patient activation score corresponded to Level 3 according to PAM criteria. However, nearly half of the participants (48.6%) were classified as Level 2, indicating considerable room for improvement in patient activation.³⁵

Relationships Among Illness Perception, Patient Activation, and Self-Management Behavior

The findings indicate that illness perception is negatively associated with both patient activation and self-management behavior, whereas patient activation is positively associated with self-management behavior. These results suggest that more negative perceptions of illness are linked to reduced engagement in health management and poorer self-management outcomes. In contrast, higher levels of patient activation are associated with a greater likelihood of engaging in sustained and effective self-management behaviors.^53,54 Despite the strong correlations, illness perception, patient activation, and self-management behavior represent conceptually distinct constructs. The BIPQ assesses patients’ cognitive and emotional representations of illness, the PAM assesses their knowledge, skills, and confidence in managing health, and the Cancer Patient Self-Management Scale assesses actual self-management behaviors. Thus, although these constructs are theoretically closely related, they represent different cognitive, motivational, and behavioral domains. Nevertheless, some conceptual proximity among the constructs may have contributed to the high correlations, and these associations should therefore be interpreted cautiously.

From a theoretical perspective, these findings are consistent with the Integrative Theory of Health Behavior Change (ITHBC), which emphasizes the interplay between cognitive appraisals, emotional responses, and self-regulatory processes in shaping health behaviors.⁵⁵ Negative illness perceptions may undermine patients’ sense of control and self-efficacy, intensify emotional distress, and consequently diminish motivation to engage in health-promoting behaviors.⁵⁶ In contrast, patient activation—reflecting individuals’ knowledge, skills, and confidence in managing their health—can enhance self-efficacy and facilitate active participation in disease management, thereby promoting better self-management outcomes.⁵⁷

Further analysis indicated that the skills dimension of patient activation exhibited the strongest association with self-management behavior. This finding suggests that the mere provision of health-related knowledge may be insufficient to drive meaningful behavioral change. Instead, interventions should prioritize the development of practical skills and the enhancement of patients’ capacity for behavioral implementation, thereby translating knowledge into sustained self-management practices.

The Mediating Mechanism of Patient Activation

The present study identified patient activation as a significant partial mediator in the relationship between illness perception and self-management behavior, accounting for 56.58% of the total effect. This finding suggests that illness perception influences self-management behavior not only directly but also indirectly through its impact on patient activation.

From a mechanistic perspective, illness perception appears to affect self-management behavior through both direct and indirect pathways. Directly, more negative illness perceptions may lead to maladaptive cognitive and emotional responses, such as avoidance, helplessness, and reduced perceived control, which in turn hinder engagement in self-management behaviors.^58,59 Indirectly, illness perception may shape patients’ cognitive appraisals and emotional states, thereby influencing their level of patient activation—encompassing knowledge, skills, and confidence—which ultimately affects their ability to engage in effective self-management.^60,61

These findings provide empirical support for the Integrative Theory of Health Behavior Change (ITHBC), highlighting that cognitive and emotional representations of illness can influence health behaviors through self-regulatory mechanisms. In this context, patient activation functions as a key intermediary linking cognition to behavior by facilitating the translation of health-related knowledge and beliefs into actionable self-management practices.

Importantly, the mediating effect observed in this study suggests that enhancing patient activation may buffer the adverse impact of negative illness perceptions on self-management behavior. Even among patients with unfavorable illness perceptions, improving their levels of activation may promote more adaptive behavioral responses.

Therefore, patient activation not only plays a critical role in shaping self-management behavior but also represents a modifiable intervention target. Clinical interventions should move beyond solely addressing patients’ illness perceptions and instead focus on strengthening patient activation through strategies such as skills training, self-efficacy enhancement, and structured behavioral support, thereby promoting sustainable improvements in self-management outcomes.

The novelty of this study lies in applying the ITHBC framework specifically to patients with oral cancer and integrating illness perception, patient activation, and self-management behavior into a mediation model. By identifying patient activation as a partial mediator accounting for 56.58% of the total effect, this study provides additional evidence for a cognitive–motivational–behavioral pathway underlying self-management and identifies patient activation as a potentially modifiable target for intervention.

Clinical Implications

The findings of this study have several important implications for clinical practice. Healthcare providers should not only address patients’ illness perceptions but also place greater emphasis on enhancing patient activation. Interventions combining cognitive support, structured education, skills training, and nurse-led coaching may help modify maladaptive illness perceptions, enhance patient activation, and improve self-management behaviors.³⁷ These findings suggest that self-management behavior in patients with oral cancer is associated with both individual contextual characteristics and modifiable psychological factors. From a clinical perspective, particular attention should be given to patients who may require additional self-management support, while routine assessment of illness perception and patient activation may help guide more individualized interventions. Patients undergoing flap reconstruction may face greater self-management challenges because of increased postoperative care demands, including wound care, donor-site management, and functional impairments in swallowing, speech, mastication, and oral hygiene. These challenges may increase the complexity of daily self-management and reduce patients’confidence in health management. Therefore, individualized support, including wound-care education, oral hygiene guidance, swallowing and jaw-opening exercises, dietary management, rehabilitation training, and nurse-led coaching, is recommended to enhance patients’ self-management capacity. In perioperative care, oral surgeons, oncology teams, and nurses should routinely assess patients’illness perceptions and patient activation to identify those at risk of suboptimal self-management and provide individualized support, particularly for patients with greater postoperative functional and self-care needs. In clinical practice, oral surgeons and head and neck oncology teams can incorporate assessments of illness perception and patient activation into preoperative evaluation to identify patients requiring additional support. During hospitalization, individualized education should focus on wound care, oral hygiene, swallowing and jaw-opening exercises, dietary management, and symptom monitoring. Before discharge, individualized self-management plans should be developed with patients and caregivers, including rehabilitation tasks, warning signs, and follow-up schedules. During follow-up, multidisciplinary teams should reassess patients’ self-management difficulties, reinforce behavioral skills, and provide timely additional support when needed. Incorporating these assessments into standard care may facilitate more targeted and effective disease management for patients with oral cancer.

Limitations and Future Research Directions

This study has several limitations. First, the cross-sectional design precludes causal inference among illness perception, patient activation, and self-management behavior; longitudinal studies are needed to further validate the proposed mediation pathways.

Second, convenience sampling from only two tertiary hospitals in Shandong Province may limit the generalizability of the findings. In addition, participants were surveyed at different treatment stages. Among patients undergoing surgical treatment, some completed the questionnaire preoperatively and others postoperatively, while patients who did not undergo surgery completed the questionnaire during hospitalization. These differences in treatment stage may have influenced illness perceptions, patient activation, and self-management behaviors. Future studies should consider treatment stage in stratified or adjusted analyses.

Third, all data were collected using self-report questionnaires, which may have introduced response bias. Patients may have over-reported positive self-management behaviors, while their illness perceptions may have been influenced by their emotional state during hospitalization. Future studies should combine self-report measures with objective clinical indicators to improve the robustness of the findings.

Despite these limitations, this study provides insight into the potential psychological mechanisms linking illness perception and self-management behavior and may inform future research and targeted interventions.

Conclusions

This study found that negative illness perceptions were associated with poorer self-management behavior in patients with oral cancer, whereas higher patient activation was associated with better self-management behavior. Patient activation partially mediated the association between illness perception and self-management behavior, accounting for 56.58% of the total effect. In addition, self-management behavior was associated with several demographic and clinical characteristics, including female sex, monthly income, residence in a prefecture-level city, and physical examination as the method of disease detection. These findings highlight patient activation as a potentially modifiable mechanism and an important target for improving self-management in this population.

Clinical interventions should move beyond general health education and combine cognitive support aimed at addressing maladaptive illness perceptions with structured education, practical skills training, self-efficacy enhancement, and nurse-led coaching to strengthen patient activation. In perioperative oral cancer care, oral surgeons, oncology teams, and nurses may incorporate routine assessment of illness perceptions and patient activation and provide individualized support according to patients’ needs, including oral hygiene guidance, wound-care education, swallowing and jaw-opening exercises, dietary management, and functional rehabilitation.

Overall, this study advances the understanding of the psychological mechanisms underlying self-management behavior in patients with oral cancer and provides a theoretical and practical basis for developing targeted interventions to improve self-management and health outcomes.

Funding Statement

This study was supported by the Science and Technology Development Project of the Affiliated Hospital of Shandong Second Medical University (No.2023FYM029) and the Shandong Province Medical and Health Science and Technology Project (No. 202514020952). The funders had no role in the design of the study; collection, analysis, or interpretation of data; or in writing the manuscript.

Generative AI Statement

The authors confirm that no generative artificial intelligence was used in the preparation of this manuscript. Any alternative text accompanying figures in this article was generated by Frontiers with the assistance of AI, and reasonable efforts have been made to verify its accuracy, including review by the authors where applicable. Please contact the publisher if any errors are identified.

Data Sharing Statement

The datasets are available from the corresponding author upon reasonable request.

Ethics Approval and Consent to Participate

This study was reviewed and approved by the Ethics Committee of Linyi People’s Hospital (Approval No. YX200751). Written informed consent was obtained from all participants prior to data collection. This study complies with the Declaration of Helsinki.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be interpreted as a potential conflict of interest.