
==== Front
Asia Pac J Oncol Nurs
Asia Pac J Oncol Nurs
Asia-Pacific Journal of Oncology Nursing
2347-5625
2349-6673
Elsevier

S2347-5625(24)00191-4
10.1016/j.apjon.2024.100569
100569
Original Article
Development and psychometric assessment of a health action process approach (HAPA) inventory for home nutritional behavior among postoperative gastric cancer patients
Jiang Xiaohan a
Chen Jiamin b
Yuan Xiuhong c
Chen Yonghe d
Sun Qian a
Zhao Hui a
Xu Peirong a
Luo Ting a
Peng Junsheng pengjsh@mail.sysu.edu.cn
ad⁎
a School of Nursing, Sun Yat-sen University, Guangzhou, China
b Guangdong Province Chaozhou Health School, Chaozhou, China
c Department of Gastric Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
d Department of Gastric Surgery, Department of General Surgery, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
⁎ Corresponding author. pengjsh@mail.sysu.edu.cn
08 8 2024
10 2024
08 8 2024
11 10 10056912 6 2024
1 8 2024
1 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objective

The aim of this study was to develop and validate a health action process approach (HAPA) inventory for measuring cognitive belief factors influencing home nutritional behavior among postoperative gastric cancer patients.

Methods

Item pool of the inventory was constructed based on the HAPA, literature review, and qualitative interview. Expert consultations were used for item improvement. Then postoperative gastric cancer patients (n = 404) were surveyed to conduct item analysis, reliability and validity test of the inventory. Reliability was evaluated through internal, split-half, and test–retest reliability. Validity was assessed through content and construct validity.

Results

Starting with 44 items in the item pool, the final inventory comprised 23 items. The exploratory factor analysis identified six dimensions—namely, risk perception, outcome expectancy, self-efficacy, intention, action planning, coping planning. And the cumulative variance contribution rate was 70.676%. Confirmatory factor analysis showed the model fits well (χ2 = 370.794, df = 214, and χ2/df = 1.733, root mean square of approximation error = 0.054, comparative fit index = 0.943, Tucker–Lewis index = 0.933, and incremental fitting index = 0.944). The item and scale level content validity were 0.83–1.00, and 0.98, which was considered good. The reliability was acceptable (Cronbach's α = 0.922, split-half reliability = 0.781, test–retest reliability = 0.716).

Conclusions

The developed inventory was valid and reliable to assess HAPA-based cognitive belief factors of home nutritional behavior of postoperative gastric cancer patients. Future research is needed to examine the applicability of the inventory in patients across diverse cultural backgrounds and healthcare systems.

Keywords

Gastric cancer
Home nutritional behavior
Health action process approach
==== Body
pmcIntroduction

Gastric cancer is the fifth most common cancer (968,350 new cases, 4.9% of the total) and the fifth major cause of cancer death (659,853 deaths, 6.8% of the total) worldwide.1 Surgery plays a key role in the curative treatment of gastric cancer.2 For postoperative gastric cancer patients, a major challenge is the impact of the anatomy change on decreased oral intake, nutritional status, and ultimately, quality of life.3 The nutritional complications faced by these patients include weight loss, malnutrition, sarcopenia, and cachexia.4 It is believed that these nutritional complications can be prevented through better home nutritional behavior, including small and frequent meals (more than 6 meals a day), and adequate oral nutritional supplement to achieve sufficient energy and protein intake (similar to pre-illness levels).5,6 However, most postoperative gastric cancer patients do not have appropriate nutritional behavior, especially during their early period after surgery and discharge from hospital.3

Effective nutritional intervention for postoperative gastric cancer patients is needed to improve their nutritional behaviors and, subsequent health outcomes.7 Nutritional interventions are more effective when based on a behavior change theory, such as the health action process approach (HAPA).8 HAPA describes the factors influencing health behaviors and has been successfully applied in interventions promoting healthy diet, physical activity, and medication adherence.9 According to HAPA, the influencing factors of nutritional behavior include risk perceptions, outcome expectancies, self-efficacy, intention, action and coping planning.10

With growing interest in HAPA, and its proven effectiveness in various health behavior interventions, numerous HAPA-based assessments for health behaviors (e.g., exercise, smoking cessation and dental hygiene)11, 12, 13 have been developed. However, HAPA-based instruments specially designed for nutritional behavior among postoperative gastric cancer patients are lacking. Therefore, the aim of this study was to develop and validate a HAPA inventory for home nutritional behavior in postoperative gastric cancer patients, with a longer-term goal of providing validated measures to assess the effect of HAPA-based nutritional interventions in these patients.

Methods

This study used an exploratory mixed-method design to develop the HAPA inventory for nutritional behavior in postoperative gastric cancer patients. According to the DeVellis's scale development guidelines,14 this study has three phases: (1) generation of the item pool, (2) item improvement through expert consultation, and (3) the psychometric tests of the inventory. The three phases are summarized in Fig. 1.Fig. 1 Summary of the three phases.

Fig. 1

Phase 1: Generation of the item pool

The item pool of the inventory was developed based on three sources: (1) HAPA, (2) findings from in-depth interviews with 15 postoperative gastric cancer patients, (3) comprehensive literature reviews of the cognitive belief factors of home nutritional behaviors in postoperative gastric cancer patients and HAPA assessment tools for other health behaviors.

The item pool of this inventory was based on the HAPA, which is promising for identifying the cognitive belief factors influencing health behaviors and helping to design targeted intervention to bridge the gap of intention and actual health behavior.9 HAPA characterizes six cognitive belief factors influencing health behaviors: risk perception, outcome expectancy, self-efficacy, intention, action plan and coping plan.8

For the in-depth interviews, participants were included if they met the following criteria: (1) histologically confirmed gastric adenocarcinoma; (2) received a partial or total gastrectomy; (3) age ≥18 years; (4) agreed to participate. The exclusion criteria included severe postoperative complications or comorbidities or having other malignancies within the last 5 years. The interview questions were developed based on the six cognitive belief factors identified by HAPA: “a) How was your home nutritional behavior changed after gastrectomy? b) Do you think the prescribed home nutritional behavior can bring you benefits or harm? c) When making decisions about your daily diet, how confident do you feel about following the prescribed home nutritional behavior? d) What kinds of home nutritional behaviors will you adopt or have you adopted? e) What action plans will you make or have you made to follow the prescribed postoperative home nutrition behavior? f) What challenges do you face in adhering to the prescribed postoperative home nutrition behavior?” The collected interview data were analyzed using the directed qualitative content analysis method with HAPA informing the initial direction of qualitative data analysis while allowing for the identification of new themes.15

For the literature review, we emphasized the cognitive beliefs of home nutritional behavior in postoperative gastric cancer patients and previous HAPA assessment tools for other health behaviors.

By comparing and analyzing the results derived from HAPA, literature review and qualitative interview, the tentative attributes of the inventory were identified and the item pool was constructed.

Phase 2: Item improvement through expert consultation

Two rounds of expert consultation were performed for item improvement. Fifteen external experts were consulted, including two nursing professors, four nutrition specialist nurses, four nursing manager, two nutritionists, two gastric surgeons, and one statistician. The importance of the scale items was scored using a 5-point Likert scale ranging from 5 (very important) to 1 (very unimportant). An average score of importance assignment > 4.00, and a coefficient of variation (the ratio of the standard deviation to the mean) of < 0.25, was considered the item selection standard.16 Respectively, we deleted and revised the items according to the scoring and advice from expert consultation.

Phase 3: Psychometric tests

Recruitment

From July 2021 to November 2022, convenience sampling was conducted in the Sixth Affiliated Hospital of Sun Yat-sen University and Sun Yat-sen University Cancer Center, which are two top-ranked diagnosis and treatment hospitals for gastric cancer. The specific inclusion criteria were as same as those for the in-depth interviews described above.

According to the Kendall criterion, the sample size of item analysis and exploratory factor analysis (EFA) should be 5–10 times the number of the scale items, and a 10% loss to follow-up was considered.17 A sample size of 150 for the item analysis and EFA was calculated. Because the data from the EFA could not be duplicated with the confirmatory factor analysis (CFA), considering the principle that the sample size of CFA should not be less than 200, a 10% loss to follow-up was considered.18 CFA required at least 223 participants, resulting in 373 participants needed for psychometric tests. Finally, we enrolled 404 participants: 152 cases from July 2021 to December 2021 for item analysis and EFA; 252 cases from January 2022 to November 2022 for CFA.

Data collection

The first author recruited patients and collected data in one research center (Sun Yat-sen University Cancer Center), and the second author in the other research center (the Sixth Affiliated Hospital of Sun Yat-sen University). Questionnaires were distributed face-to-face by the investigators, after an explanation of the purpose of the study, and after obtaining informed consent. All questionnaires were completed by the patients themselves, or with assistance from the investigators if the patient did not understand the questionnaire well or had a physical disability. The investigators collected the questionnaires immediately after patients filled them out, and then examined the completeness of the questionnaires.

Research tools

Patients' general characteristics questionnaire was used to investigate the patients’ demographic and sociological information, including sex, age, and education level, as well as disease-related information, such as tumor location. The first draft of the HAPA inventory was used for psychometric test.

Data analysis

The data were analyzed through SPSS version 25.0 and AMOS version 26.0 (IBM Corp., Armonk, NY, US). The demographic characteristics of the participants were calculated according to frequency (%), mean, and standard deviation using descriptive statistics. Details of the specific item analysis, validity and reliability testing methods are as follows:

Item analysis

In this study, critical ratio (CR), item-total correlation, Cronbach's α if item deleted, corrected item-total correlation (CITC), communalities value, and factor loading were used to analyze and screen the items.17,19

Validity test

Content and Construction validity were used to test the validity of the scale. Content validity was tested by six experts (2 nurses, 1 nursing professors, 1 nurse manager, 1 nutritionist, 1 gastric surgeon). Theoretical and operational definitions of the concepts were presented to the six experts and, thereafter, they were asked to rate the clarity and relevance of each item on a four-point scale ranging from 4 (strongly relevant) to 1 (weakly relevant). The item-level content validity index (I-CVI) and the scale-level CVI (S-CVI/Ave) were calculated separately. The content validity of the scale was considered good if I-CVI ≥ 0.78 and S-CVI/AVE ≥ 0.9.20

Construct validity of the scale was evaluated by exploratory and confirmatory factor analyses. First, the total data were randomly classified into data set A (n = 152) and data set B (n = 252). The EFA was conducted with data set A and the CFA was conducted with data set B. Prior to the EFA, the Kaiser-Meyer-Olkin (KMO) index and Bartlett's sphericity test were applied to confirm that the data were appropriate for factor analysis. If the cumulative explanatory variation of common factors extracted in the exploratory factor analysis was > 50%, χ2/df in the confirmatory factor analysis was < 3, the root mean square error of approximation (RMSEA) was＜0.1, and the confirmatory fit index (CFI), Tucker–Lewis index (TLI) and incremental fitting index (IFI) were > 0.9, the construct validity of the scale was considered to be good.21, 22, 23

Reliability test

Internal consistency reliability, split-half reliability, and test–retest reliability were used to test the reliability of the scale.17

For the test-retest reliability, a sample of 100 patients retook the scale 1 month after their first assessment. The test-retest reliability of the subscales of the questionnaire was assessed by Pearson correlation coefficients.

Results

Generation of the item pool

By comparing and analyzing the results derived from the literature review and interview data, the two attributes and eight elements were identified (Table 1). The item pool of 6 dimensions and 44 items with a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) were formed.Table 1 The attributes and elements of the inventory derived from the literature review and qualitative interview.

Table 1Attributes	Elements	Dimensions of theHAPA inventory	Number ofinitial items	Examples of items	
Motivation phase	Perceived increased nutritional risk	Risk perception	8	Item 1: “I'm more likely to become malnutrition” (extracted from literature review10)	
Outcome expectancy of appropriate nutritional behavior needs to be improved	Outcome expectancies	6	Item 5: “If I follow the prescribed postoperative nutrition behavior, it will help my postoperative recovery” (extracted from qualitative study)	
Task self-efficacy affects postoperative nutritional behavior	Self-efficacy	7	Item 16: “Even if I have to learn more about nutrition, I can stick to the prescribed postoperative nutrition behavior” (extracted from literature review8)	
Lack of intention to regulate individual's nutritional behavior	Intention	6	Item 7: “For the next few weeks, I intend to take adequate dose of oral nutritional supplement” (extracted from qualitative study)	
Volition phase	Lack of action plan	Action planning	4	Item 13: “I already have concrete plans how to arrange the prescribed postoperative nutrition behavior” (extracted from literature review10)	
Lack of coping plan to overcome barriers	Coping planning	6	Item 15: “I already have concrete plans what to do in difficult situations to stick to my intentions (such as gastrointestinal symptoms)”. (Extracted from qualitative study)	
Maintenance self-efficacy affects postoperative nutritional behavior	Self-efficacy	4	Item 18: “Even if the effect is not obvious in the short term (such as no weight gain), I can stick to the prescribed postoperative nutrition behavior”. (Extracted from qualitative study)	
Recovery self-efficacy affects postoperative nutritional behavior	Self-efficacy	3	Item 22: “Even if I meet some uncomfortable situation because of the disease or treatment, I can stick to the prescribed postoperative nutrition behavior”. (Extracted from qualitative study)	

Item improvement through expert consultation

Of the 15 included experts aged 30–59 (44.93 ± 11.02) years with more than 7 years of work experience, 10 of them had titles of senior associate or higher. The authority coefficient of the experts was 0.79. The response rates of 2 rounds of expert consultation were 93.75% and 100%, respectively. The Kendall harmony coefficients were 0.141 and 0.318. After 2 rounds of expert consultation, 13 items were merged 10 items were deleted, and 27 items were modified (Table 2) and remaining in the first draft of the HAPA inventory, using a 5-point Likert rating system, involving: strongly disagree = 1, disagree = 2, uncertain = 3, agree = 4, and strongly agree = 5.Table 2 Examples of items improvement through expert consultation.

Table 2	Items	Expert advice	Results	
Example of merged items	“Even if the effect is not noticeable in the short term, I can still maintain a healthy diet	The items are duplicate, it is recommended to merge them	“Even if the effect is not obvious in the short term (such as no weight gain), I can stick to the prescribed postoperative nutrition behavior.”	
Even if my weight does not increase immediately, I can still maintain a healthy diet”	
Example of deleted item	“Malnutrition has a significant impact on my blood pressure and blood sugar levels”.	It is recommended to delete, as blood pressure and sugar levels are not so relevant to the inventory's main theme	Deleted the item	
Example of modified item	“I already have concrete plans to implement the prescribed postoperative nutrition behavior in various locations”.	It is recommended to add some examples to make the situations clearer, e.g. at home, work, out to dinner, etc.	“I already have concrete plans to implement the prescribed postoperative nutrition behavior in various locations (e.g. at home, work, out to dinner, etc.)”.	

Psychometric tests

Characteristics of the participants

A total of 404 postoperative gastric cancer patients, aged 52.77 ± 14.45 years, were enrolled in this study. 64.4% of them were male. The average body mass index (BMI) of the participants was 21.26 ± 2.68 kg/m2. 72.0% of the participants received partial gastrectomy. Participants’ characteristics are presented in Table 3.Table 3 Characteristics of study participants (N = 404).

Table 3Characteristics	n	%	
Sex	Male	260	64.4	
Female	144	35.6	
Age	< 40	19	4.7	
40∼49	57	14.1	
50∼59	143	35.4	
60∼69	114	28.2	
≥ 70	71	17.6	
BMI	< 18.5	67	16.6	
18.5–23.9	258	63.9	
≥ 24.0	79	19.6	
Work status	Employed	122	30.2	
Unemployeda	282	69.8	
Marital status	With spouse	340	84.2	
No spouse	64	15.8	
Education	Primary school or below	63	15.6	
Junior high school	104	25.7	
High school	100	24.8	
College or above	137	33.9	
Tumor location	Proximal	72	17.8	
Middle	126	31.2	
Distal	206	51.0	
Pathological stage	I	130	32.2	
II	90	22.3	
III	139	34.4	
IV	45	11.1	
Resection extend	Partial gastrectomy	291	72.0	
Total gastrectomy	113	28.0	
Number of comorbid chronic diseases	0	106	26.2	
1–2	164	40.6	
≥ 3	134	33.2	
a unemployed including homemaker and retired patients.

Item analysis

Data from 152 cases from July 2021 to December 2021 were selected for item analysis. Item 5 had an item-total correlation coefficient of 0.242, falling short of the desired threshold of 0.4.23 The Cronbach's α coefficient of the overall inventory was 0.902, and removing items 5 and 8 further increased this coefficient. The commonality of items 5 and 8 were 0.034 and 0.164, respectively, which were below the acceptable limit of 0.2.17 Additionally, their CITC were 0.167 and 0.370, again failing to meet the required standard of 0.4.23 Their factor loadings were 0.185 and 0.405, which should be more than 0.45.17 These findings indicated that items 5 and 8 had low homogeneity with other items (Table 4). Consequently, item analysis led to the removal of item 5 (“If I follow the appropriate postoperative nutritional behaviors, the food won't taste good”) and item 8 (“If I follow the appropriate postoperative nutritional behaviors, it will increase my financial burden”). After item analysis, the inventory included 25 items and 6 dimensions.Table 4 Results of Item analysis.

Table 4Item	CR	Item–total correlations	Cronbach's α if item deleted	CITC	Communality	Factor loading	Notes	
1	7.172∗∗	0.480∗∗	0.899	0.430	0.217	0.466	Reserve	
2	7.839∗∗	0.525∗∗	0.900	0.450	0.226	0.475	Reserve	
3	9.269∗∗	0.534∗∗	0.900	0.458	0.229	0.479	Reserve	
4	6.618∗∗	0.543∗∗	0.898	0.499	0.300	0.548	Reserve	
5	4.028∗∗	0.242∗∗	0.905	0.167	0.034	0.185	Delete	
6	6.193∗∗	0.534∗∗	0.899	0.494	0.307	0.554	Reserve	
7	6.421∗∗	0.595∗∗	0.898	0.560	0.382	0.618	Reserve	
8	6.091∗∗	0.439∗∗	0.901	0.370	0.164	0.405	Delete	
9	7.001∗∗	0.551∗∗	0.898	0.501	0.303	0.550	Reserve	
10	8.507∗∗	0.647∗∗	0.896	0.595	0.410	0.641	Reserve	
11	7.284∗∗	0.603∗∗	0.897	0.565	0.366	0.605	Reserve	
12	9.644∗∗	0.590∗∗	0.898	0.523	0.334	0.578	Reserve	
13	8.212∗∗	0.594∗∗	0.897	0.540	0.349	0.590	Reserve	
14	10.158∗∗	0.635∗∗	0.896	0.593	0.395	0.629	Reserve	
15	7.915∗∗	0.600∗∗	0.897	0.557	0.361	0.601	Reserve	
16	6.612∗∗	0.517∗∗	0.899	0.465	0.264	0.513	Reserve	
17	5.720∗∗	0.498∗∗	0.899	0.448	0.245	0.495	Reserve	
18	5.823∗∗	0.475∗∗	0.900	0.416	0.220	0.469	Reserve	
19	4.427∗∗	0.478∗∗	0.900	0.422	0.229	0.479	Reserve	
20	7.602∗∗	0.640∗∗	0.896	0.598	0.438	0.662	Reserve	
21	6.669∗∗	0.602∗∗	0.897	0.556	0.399	0.631	Reserve	
22	6.650∗∗	0.546∗∗	0.898	0.503	0.314	0.560	Reserve	
23	4.953∗∗	0.466∗∗	0.900	0.417	0.232	0.482	Reserve	
24	6.896∗∗	0.654∗∗	0.896	0.613	0.469	0.685	Reserve	
25	5.727∗∗	0.517∗∗	0.899	0.481	0.310	0.557	Reserve	
26	6.078∗∗	0.606∗∗	0.897	0.569	0.399	0.632	Reserve	
27	4.883∗∗	0.516∗∗	0.899	0.468	0.301	0.548	Reserve	
Notes: 1–3: Risk perception, 4–8: Outcome expectancy, 9–13: Intention, 14–16: Action planning, 17–19: Coping planning, 20–27: Self-efficacy.

∗∗P <0.01.

Validity

The content validity of the inventory was calculated based on the results of expert consultation. The item-level content validity index was 0.83–1.00, and the scale-level content validity index (S-CVI/AVE) was 0.98, which indicated that the content validity of the inventory was good.

In the exploratory factor analysis, the KMO index was 0.849 and the Bartlett spherical test reached statistical significance (χ2 = 2127.041, P < 0.001), indicating that the data were appropriate for factor analysis. After principal component analysis and varimax rotation, six factors were extracted, whose eigenvalue for the initial load matrix was > 1, and which accounted for 67.254% of the total variance. Two items with a communality of less than 0.4 were further deleted: “For the next few weeks, I intend to eat more healthily” and “I already have concrete plans how to deal with relapses into my old eating habit”. Consequently, 23 items and 6 dimensions were confirmed (Table 5). The cumulative proportion of variance was 70.676%.Table 5 Exploratory factor analysis.

Table 5Item No.	Items	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5	Factor 6	
Factor 1: Self-efficacy	
16	Even if I have to learn more about nutrition, I can stick to the prescribed postoperative nutrition behaviora	0.662						
17	Even if I have to pay more attention to my diet, I can stick to the prescribed postoperative nutrition behaviora	0.652						
18	Even if the effect is not obvious in the short term (such as no weight gain), I can stick to the prescribed postoperative nutrition behaviora	0.640						
19	Even if I have to make nutrition plans by myself, I can stick to the prescribed postoperative nutrition behaviora	0.671						
20	Even if the food (and/or nutritional supplements) don't taste good, I can stick to it	0.658						
21	Even if it takes time to adjust to the postoperative changes, I can stick to the prescribed postoperative nutrition behaviora	0.632						
22	Even if I meet some uncomfortable situation because of the disease or treatment, I can stick to the prescribed postoperative nutrition behaviora	0.662						
23	Even if without the help of others (family or friends), I can stick to the prescribed postoperative nutrition behaviora by myself	0.709						
Factor 2: Coping planning	
13	I already have concrete plans when to especially watch out in order to stick to the prescribed postoperative nutrition behavior		0.866					
14	I already have concrete plans what to do if something intervenes		0.883					
15	I already have concrete plans what to do in difficult situations to stick to my intentions (such as gastrointestinal symptoms)		0.925					
Factor 3: Intentions	
7	For the next few weeks, I intend to take adequate dose of oral nutritional supplement			0.849				
8	For the next few weeks, I intend to get enough energy and protein (that is, try to keep the similar intake as the intake before the illness)			0.819				
9	For the next few weeks, I intend to eat small and frequent meals			0.789				
Factor 4: Action planning	
10	I already have concrete plans how to arrange the prescribed postoperative nutrition behavior				0.845			
11	I already have concrete plans when to start the prescribed postoperative nutrition behavior				0.774			
12	I already have concrete plans to implement the prescribed postoperative nutrition behavior in various locations (e.g., at home, work, out to dinner, etc.)				0.781			
Factor 5: Risk perception	
1	I'm more likely to become malnutrition					0.679		
2	Malnutrition can hinder recovery after surgery					0.858		
3	Inappropriate postoperative nutritional behavior can lead to the occurrence or development of malnutrition					0.860		
Factor 6: Outcome expectancy	
4	If I follow the prescribed postoperative nutrition behaviora, I will not be worried about malnutrition						0.815	
5	If I follow the prescribed postoperative nutrition behaviora, it will help my postoperative recovery						0.828	
6	If I follow the prescribed postoperative nutrition behaviora, it will improve my quality of life						0.638	
a For postoperative gastric patients, the prescribed postoperative nutrition behavior includes small and frequent meals (more than 6 meals a day), and adequate oral nutritional supplement in order to achieve adequate energy and protein intake per day (that is, keep the total daily intake similar as the intake before illness).

The construct validity of the scale was further verified by CFA. The results showed that each fitting index in the model met the statistical requirements, indicating that the overall fit of the model was good, with χ2 = 370.794, df = 214, and χ2/df = 1.733. Additionally, the RMSEA, CFI, TLI and IFI were 0.054, 0.943, 0.933, and 0.944, respectively. All indexes were within the acceptable range. The path coefficients ranged from 0.68 to 0.90; all path coefficients were statistically significant (P < 0.01). The model structure is shown in Fig. 2.Fig. 2 Confirmatory factor analysis (CFA) measurement invariance (MI) model fit plot.

Fig. 2

Reliability

Cronbach's α for this scale was 0.915. The split-half reliability was 0.757. The test–retest reliability was 0.835 (all the above P < 0.001).

Discussion

In this study, we developed and validated the HAPA inventory for cognitive beliefs of home nutritional behavior in postoperative gastric cancer patients. The 23-item inventory is a comprehensive tool including all HAPA constructs which are risk perception, outcome expectancy, self-efficacy, intention, action planning, coping planning.9

When constructing the item pool, qualitative research was conducted to clarify the structural composition of the scale and expanded the operational definition of the HAPA concepts in home nutritional behavior of postoperative gastric cancer patients. Previous related studies on home nutritional behaviors of postoperative gastric cancer patients and HAPA assessment tools were combined to develop the items. These items were generated from the previous HAPA-based scales11, 12, 13 and perspective of postoperative gastric cancer patients, and verified by the published literature in the field of expertise. This approach not only conforms to the principles of measurement tool development, but also ensures the content validity of the tool.24

The items were verified using the Delphi expert consultation method. Experts from different majors in the field of health management of gastric cancer provided suggestions for item optimization from different perspectives, which strengthens the content validity of the scale.25

In this study, we used a variety of methods to verify whether our HAPA questionnaire had the appropriate psychometric properties. Exploratory and confirmatory factor analyses supported the evidence for the construct validity of the scale.17 This scale extracted six factors through EFA, and the cumulative explanatory variables of the six factors were 70.676%. The model was tested using CFA, which showed a good fit between the model and the data. An RMSEA less than 0.06 is considered a close fit, the CFI, IFI, and TLI of the obtained model are all greater than 0.90, and the values of χ2/df and RMSEA also supported the acceptable fit of the model. As for the content validity, the I-CVI for the 23 items being 0.83–1.00, S-CVI/AVE being 0.98 (I-CVI ≥ 0.78, and S-CVI/AVE ≥ 0.9 indicate good content validity). The good content validity of the scale indicated that the content actually measured by the scale was highly consistent with the content to be measured.20 The questionnaire also showed good reliability with Cronbach's α greater than 0.9. The split-half reliability was 0.947, indicating that the internal consistency of the scale was good. The test–retest reliability of the scale was 0.716 (> 0.7), demonstrating that the scale has good cross- time stability.17

From the above results, it can be seen that the inventory we developed is an effective and reliable tool for evaluating the HAPA-related factors influencing home nutritional behavior of postoperative gastric cancer patients. Numerous studies have shown that postoperative gastric cancer patients suffer from inadequate nutritional behavior and therefore leading to inadequate nutritional intake, malnutrition, and ultimately, influencing patients' prognosis.3,5 How to better manage nutritional behavior is one of the important issues faced by postoperative gastric cancer patients.6 In the practice of nutrition management in these patients, the inventory we developed can capture significant cognitive belief determinants of improving patients’ nutritional behavior based on the HAPA framework. And based on this, we could design targeted interventions to improve their nutritional behavior.8 And this inventory could also provide validated measures in assessing the effect of the HAPA-based intervention.

Limitations

This study has several limitations. First, owing to the lack of gold standards, we did not test the criterion validity. Future research should aim to establish or identify appropriate gold standards for testing criterion validity to strengthen the reliability of the findings. Moreover, the investigations were conducted only in two tertiary hospitals, which may limit the wider applicability of the findings. Future studies should consider including a more diverse range of hospitals, including secondary and primary care settings, to enhance the generalizability of the results.

Conclusions

This study developed and validated an inventory for measuring the significant cognitive belief determinants of adequate nutritional behavior for postoperative gastric cancer patients. This inventory contains 23 items with six dimensions (risk perception, outcome expectancy, self-efficacy, intention, action planning, coping planning), yielding valid and reliable results. The applicability of this inventory in different social or cultural back grounds should be further investigated.

CRediT authorship contribution statement

Xiaohan Jiang: Conceptualization, Methodology, Data curation, Formal analysis, Writing - Original and Revised draft preparation. Jiamin Chen: Conceptualization, Methodology, Formal analysis, Writing- Original draft preparation, Xiuhong Yuan: Conceptualization, Methodology, Formal analysis, Writing- Original draft preparation, Yonghe Chen: Methodology, Formal analysis, Writing- Original draft preparation, Qian Sun: Methodology, Formal analysis, Writing- Revised draft preparation. Hui Zhao: Methodology, Investigation, Formal analysis; Peirong Xu: Formal analysis, Writing- Original draft preparation. Ting Luo, Methodology, Investigation, Formal analysis. Junsheng Peng: Conceptualization, Methodology, Supervision, Resources, and Project administration. All authors had full access to all the data in the study, and the corresponding author had final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Ethics statement

The study protocol was approved by the Research Ethics Committee of Sun Yat-sen University (IRB No. L2022SYSU-HL-045). All enrolled patients were informed about the aim and methodology of the study, and informed consent was obtained from them.

Funding

This research was supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 82203187 ). The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declaration of generative AI and AI-assisted technologies in the writing process

No AI tools/services were used during the preparation of this work.

Declaration of competing interest

The authors declare no conflict of interest.

Acknowledgments

This manuscript is a revision of the first author's doctoral thesis from Sun Yat-sen University in 2024. We are thankful to all the postoperative gastric cancer patients who participated in the study.
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