Abstract
Obesity and overweight are major health concerns, with a 19.7% prevalence among Malaysian adults, as reported in the 2019 National Health and Morbidity Survey. This study assessed the effectiveness of an educational intervention on weight control intention using the Health Belief Model (HBM). A quasi-experimental design was employed, involving 140 participants equally divided into an intervention group (IG) and a control group (CG). Post-intervention, the IG showed significant improvement in perceived self-efficacy in dieting (mean score 3.96 ± 0.85) compared to the CG (3.76 ± 0.86, p = 0.003). Perceived self-efficacy in exercise also increased in the IG (4.12 ± 0.52) compared to the CG (3.51 ± 0.94, p < 0.001). While behavioral intention scores improved in the IG (4.00 ± 0.59), the difference was not significant (p = 0.300). This study highlights that educational interventions using HBM can effectively improve self-efficacy and influence weight control behaviors.
Keywords: Health belief model, Educational intervention, Weight control intention, Interventional study, Control group, Intervention group, Diet and physical activity
Subject terms: Patient education, Public health, Weight management
Introduction
Obesity and overweight have become major health concerns over the past 50 years, increasing the risk of illness, affecting the quality of life and raising financial burdens worldwide1. The National Health and Morbidity Survey (NHMS) identified an increasing trend of overweight and obesity prevalence among Malaysian adults aged 18 years and older2. The current prevalence of obesity among Malaysian adults was 19.7% as reported by NHMS 20193. It is deliberated as a multifactorial disease that is correlated to various comorbidities such as diabetes mellitus, hypertension, cardiovascular disease, stroke, sleep apnea, osteoarthritis and certain types of cancer3.
The Health Belief Model (HBM) is a health-specific social cognition model that predicts the reasons for change or maintain specific behaviour and addresses the impacts of beliefs on health4,5. The HBM is often considered effective due to its comprehensiveness and wide application across various health domains. One of the strengths of the Health Belief Model is its simplicity and ease of use. It provides a straightforward framework to understand how individuals perceive a health threat (e.g. obesity) and the corresponding behavioral response (e.g. engaging in weight management strategies). The HBM integrates essential components such as perceived susceptibility, severity, benefits, barriers, cues to action and self-efficacy. All these HBM subscales come into place affecting their belief to trigger a behavioural change.
When people considered themselves susceptible to developing a health problem (perceived susceptibility), believed that it would bring serious health complications (perceived severity), believed that changing health behaviour would bring potential positive aspects in reducing the severity of the condition (perceived benefits), believed that the benefits overweigh the obstacles of adopting a health-related behaviour (perceived barriers) and believed that they are able to perform a new health-related behaviour (perceived self-efficacy), then they are most likely to involve in a course of actions to reduce the health risks (cue to action)6.
A study evaluating the weight loss behaviour among female school students by utilizing HBM constructs reported that the overweight students had strongest intention to reduce weight. The study also revealed perceived severity, cues to action and perceived self-efficacy were the significant predictors of behavioural intention for weight reduction7. Perceived severity, perceived susceptibility, cue to action, perceived barriers and perceived benefits were reported as the significant predictors of BMI in a previous study conducted to evaluate the predictive power of the HBM among college students8.
The perceived barrier for weight loss was classified into six barriers, namely situational barrier, stress and depression barrier, social pressure, adverse effect of weight loss diet, food craving and the cost of the diet. Among the six barriers, the situational barrier is the most common barrier for adopting a healthy diet. Practicing a healthy diet is often interrupted when an individual joins a celebration or party9.
A previous study showed that one of the noteworthy variables in predicting behavioural intention is perceived benefits. The perceived benefits HBM subscale can be classified into three groups: emotional health, physical health and social health. The emotional health group had the higher ratings compared to others. It is accepted by the individual that there are positive outcomes of adopting a healthy diet and lifestyle. This will reduce their emotional burden, make them feel energetic and comfortable around others10.
Several experimental studies were conducted to evaluate the effectiveness of educational intervention developed based on HBM constructs. A previous study evaluating the effects of educational intervention based on HBM on improving dietary habits revealed that higher mean scores were obtained in the intervention group and almost all variables had significant differences between the control and intervention groups11. A previous study demonstrated the efficacy of lifestyle education utilizing the HBM to enhance behaviors related to obesity. The average scores of obesity-related behavior exhibited notable differences between the experimental and control groups immediately after the intervention and also remained significant two months post-intervention12.
Another previous study assessing the impacts of an educational program using the HBM, indicated that the experimental group exhibited a significant outcome of HBM constructs. This difference remained significant at both the 3-month and 6-month marks post-intervention. The results underscored the effectiveness of education based on HBM in enhancing behaviours related to obesity13.
The findings of a previous study suggested that the modified HBM-based intervention was effective as a significant change in health knowledge and health behaviour was observed among students in intervention group14. According to an experimental study among female students, a significant difference was observed in the mean scores and BMI scores in the intervention group compared to control group. The findings denoted that the nutritional education based on HBM was effective in improving the BMI of the overweight female students15.
The previous studies underlined the need for educational intervention programs to prevent overweight and obesity problems among Malaysians. There is a lack of studies conducted on the HBM-based educational intervention in Malaysia to study the weight control intention among Malaysians. The findings of this study will serve as a reference for future studies involving the general population in Malaysia. The outcome of this study will be helpful in developing a more focused interventions for weight management. This study aims to investigate effectiveness of educational intervention on weight control intention associated with the reasons for action, dietary practices and physical activity via pre-post HBM Questionnaire (cue to action, perceived self-efficacy in dieting, perceived self-efficacy in exercise and behavioral intention of weight management).
Materials and methods
A quasi-experimental study was conducted to evaluate the effectiveness of an educational intervention on weight control intention. The present study protocol was reviewed and approved by The Human Research Ethics Committee of Universiti Sains Malaysia (USM/JEPeM/20,090,504). The study was performed in accordance with the relevant guidelines as approved by the committee board.
The sample size was calculated using a statistical calculator according to desired statistical power level (1 − β) of 0.80, significance level (α) of 0.05 (two-sided) for detecting a true difference in means between IG and CG and a standard deviation of 13.3 estimated from previously reported studies16–18. The minimum sample size required for this study was 67 participants for each group (e.g., a total sample size of 134, assuming equal group sizes). Informed consent was obtained from all participants once they agreed to answer the questionnaire.
Pre-survey
A convenience sampling technique was used to recruit the respondents by distributing the self-administered HBMQ link via Google Forms. The Google Form link was created and promoted in social media platforms to reach out to the public from all over the 13 states in Malaysia. The HBMQ is based primarily on the instrument used in a previous study by Saghafi-Asl et al.10. The questionnaire items were derived from a similar instrument utilized in previous studies by Park and McArthur et al.7,8. The format of the HBMQ includes rating all statements using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
This study included Malaysian adults aged 18 years and above from both genders, with BMI of 25 kg/m2 and greater or who have the intention to reduce body weight, are able to read and understand Malay or English and agree to participate in the survey. Respondents aged below 18, unable to read and understand English or those that declined to participate in the study were excluded.
The first page of the Google Form consisted the background information of the study and consent form. The participants were able to agree or decline their participation in the study prior to the survey. The participants who provided their consent to participate were directed to the section one of survey, while those who declined to participate were directed to the page thanking them for their time. After obtaining consent from the respondents, they were directed to the survey. The average time needed to complete the survey was approximately 10 to 15 min. The data of the completed survey were entered into Microsoft Excel program and cross-checked if the inclusion criteria have been met. The completed questionnaires list after the survey were sorted and assigned into intervention group (IG) and control group (CG).
Educational intervention
After completing pre-survey, the educational intervention was sent via email to the intervention group (IG). The average time needed for educational intervention was 5 min. The participants in the intervention group (IG) receiving educational intervention on overweight and obesity whereas, participants in the control group (CG) did not receive any educational intervention.
The educational intervention was carried out in the form of graphic visual representations of information in a video developed in bilingual (Malay and English) by two animators. This is intended to present information clearly and to increase knowledge retention by utilizing visually compelling information. HBM constructs were utilized to develop the video which included knowledge on overweight and obesity, which mainly aimed to provide understanding on the seriousness of the disease, health risks, key facts, classification and prevention, and control measures to promote excess body weight loss and maintain healthy weight.
The content of the video was verified by experts with different professional backgrounds consisted of lecturers, pharmacists and medical doctors. The video validation process began by sharing the video links to the experts, which allowed them to watch the content in both languages. Subsequently, the video was revised based on the feedback provided, ensuring that the video accurately conveyed the objective of the study and was linguistically appropriate for a diverse audience.
Post-survey
The effectiveness of educational intervention on overweight and obesity were evaluated by comparison between IG that received the educational intervention with CG which did not receive any educational intervention. The pre- and post-survey questionnaires were developed in separate Google Forms; only those who participated in the pre-survey will be allowed to complete the post-survey.
One week after the pre-survey, the HBMQ Google Form link for post-survey for both groups was sent via email. The first 75 participants who completed a post-survey after one week were included in the data analysis. The reason for the one week interval is to ensure knowledge retention among the respondents and to measure the effectiveness of intervention. To evaluate the effectiveness of educational intervention on weight control intention of the participants, the data related to the participants’ HBM was collected by the 3 sections from the same questionnaire after one week.
The questionnaire consists of 41 questions which is comprised into 4 sections: (1) cue to action; (2) self-efficacy in dieting; (3) self-efficacy in physical activity; (4) behavioural intention of weight management. The average time needed to complete the post-questionnaire was estimated at around 10 min.
Statistical data analysis
For the descriptive analysis, the data was presented as frequency and percentages for categorical variables and continuous variables were presented as mean and standard deviation based on their normality distribution. For cross-sectional study data, Kruskal Wallis test was utilized to evaluate significant differences between HBM constructs and weight groups (underweight, normal weight, overweight and obese). The comparison of demographical variables between control group and intervention group were evaluated using chi-square test for pre- and post-survey data. The mean scores and standard deviation of the data were computed.
The independent t-test was used to determine the significant difference between control group and intervention group against the HBM constructs (Cue to action, self-efficacy in dieting and physical activity, and behavioural intention of weight management), before and after intervention. The paired t-test was employed to evaluate the effectiveness of intervention in each group separately. In this study, a p-value ≤ 0.05 was considered statistically significant.
Results
A total of 154 responses were collected and 140 recruited respondents were used for analysis after screening for the subject criteria and missing data. The respondents were divided equally into a control group (CG) or an intervention group (IG). Table 1 shows the demographic characteristics of the respondents in CG and IG. Most of the respondents were aged 18 to 24 years old in CG (n = 42, 60.0%) and IG (n = 37, 52.9%).
Table 1.
Frequency distribution of the control group and intervention group.
| Demographic data | Control group (CG) | Intervention group (IG) | p-value |
|---|---|---|---|
| Age (years) | 0.269 | ||
| 18–24 | 42 (60.0) | 37 (52.9) | |
| 25–34 | 16 (22.9) | 13 (18.6) | |
| 35–59 | 12 (17.1) | 20 (28.6) | |
| Gender | 1.000 | ||
| Male | 22 (31.4) | 22 (31.4) | |
| Female | 48 (68.6) | 48 (68.6) | |
| BMI category (kg/m2)a | 0.960 | ||
| Underweight | 4 (5.7) | 4 (5.7) | |
| Normal weight | 22 (31.4) | 25 (35.7) | |
| Overweight | 32 (45.7) | 30 (42.9) | |
| Obese | 12 (17.1) | 11 (15.7) | |
| Region of origin | 0.226 | ||
| Northern region | 33 (47.1) | 25 (35.7) | |
| East coast region | 19 (27.1) | 15 (21.4) | |
| Central region | 5 (7.1) | 13 (18.6) | |
| Southern region | 6 (8.6) | 8 (11.4) | |
| East Malaysia | 7 (10.0) | 9 (12.9) | |
| Education level | 0.478 | ||
| High School | 11 (15.7) | 11 (15.7) | |
| Diploma | 16 (22.9) | 9 (12.9) | |
| Undergraduate | 37 (52.9) | 43 (61.4) | |
| Postgraduate | 6 (8.6) | 7 (10.0) | |
| Marital status | 0.447 | ||
| Single | 53 (75.7) | 49 (70.0) | |
| Married | 17 (24.3) | 21 (30.0) | |
| Employment status | 0.407 | ||
| Student | 36 (51.4) | 31 (44.3) | |
| Unemployed | 7 (10.0) | 10 (14.3) | |
| Employed | 27 (38.6) | 27 (38.6) | |
| Retiree | 0 | 2 (2.9) | |
| Intention to reduce weight | 1.000 | ||
| No | 0 | 0 | |
| Yes | 70 (100.0) | 70 (100.0) | |
| Health issue | 0.730 | ||
| No | 65 (92.9) | 66 (94.3) | |
| Yesb | 5 (7.1) | 4 (5.7) |
The data is expressed as frequency (percentage). p-value is based on chi-square test.
aBMI category: underweight (< 18.5 kg/m2), normal-weight (18.5–24.9 kg/m2), Overweight (25.0–29.9 kg/m2), Obese (> 30 kg/m2).
bHealth issues include asthma, hypertension, hyperlipidaemia, gastritis, rheumatoid arthritis, epilepsy, hyperthyroidism, slipped (herniated) disc and gout.
Besides, the respondents were divided equally in terms of gender, with most female respondents (n = 48, 68.6%) in both groups. According to the data, the overweight and obese respondents in CG were 32 (45.7%) and 12 (17.1%), whereas in IG, 30 (42.9%) and 11 (15.7%), respectively. This study included underweight and normal-weight participants with an intention to reduce weight to address diverse perspectives on body image and promote healthy behaviors, while also recognizing the potential risks of over-perception of body weight.
This inclusive approach allows for a comprehensive examination of weight-related intentions and behaviors, contributing to a more nuanced understanding of the factors influencing individuals across different weight categories. The study strongly discourages unhealthy weight reduction behaviors and emphasizes the promotion of safe and sustainable approaches to weight management.
In comparison, most of the respondents in both groups were single (CG: n = 53, 75.7% and IG: n = 49, 70.0%), from the northern region (CG: n = 33, 47.1% and IG: n = 25, 35.7), had an undergraduate level education (CG: n = 37, 52.9% and IG: n = 43, 61.4). About 36 (51.4%) respondents in CG were students; in IG, about 31 (44.3%) student respondents were reported. However, both groups had an equal distribution of employed respondents (n = 27, 38.6%). The retiree participants consist of 2 (2.9%) only in the intervention group. All of the respondents in both groups had the intention to reduce weight. A small proportion of respondents from CG and IG reported having health issues (n = 5, 7.1%) and (n = 4, 5.7%). There were no significant differences observed between the two study groups in terms of demographic variables.
The comparison of mean scores and standard deviation of respondents’ scores of HBM subscales between CG and IG, before and after intervention were presented in Table 2. The mean scores of the cue to action subscale of the respondents in CG and IG showed no significant difference before and after the intervention (p = 0.974 and 0.408, respectively).
Table 2.
Comparison of pre- and post-intervention.
| Subscale | Before intervention | After intervention | p-value |
|---|---|---|---|
| Cue to action | |||
| Control group | 3.85 ± 0.89 | 3.82 ± 0.97 | 0.733 |
| Intervention group | 3.85 ± 0.63 | 3.68 ± 0.93 | 0.081 |
| p-value | 0.974 | 0.408 | |
| Perceived self- efficacy in dieting | |||
| Control group | 3.62 ± 0.83 | 3.76 ± 0.86 | 0.225 |
| Intervention group | 3.76 ± 0.58 | 3.96 ± 0.85 | 0.003 |
| p-value | 0.265 | 0.098 | |
| Perceived self-efficacy in exercise | |||
| Control Group | 3.59 ± 0.85 | 3.51 ± 0.94 | 0.437 |
| Intervention Group | 3.63 ± 0.71 | 4.12 ± 0.52 | < 0.001 |
| p-value | 0.730 | < 0.001 | |
| Behavioral intention | |||
| Control Group | 3.90 ± 0.76 | 3.87 ± 0.92 | 0.672 |
| Intervention Group | 3.82 ± 0.67 | 4.00 ± 0.59 | 0.028 |
| p-value | 0.503 | 0.300 |
The data is expressed as mean ± SD. p-value is based on independent and paired t-tests.
The mean scores of perceived self- efficacy in dieting before the intervention in CG and IG groups, were 3.62 ± 0.83 and 3.76 ± 0.58, respectively. The results of independent t-test demonstrated no significant difference (p = 0.265). However, after the education intervention, respondents’ scores were changed to 3.76 ± 0.86 in CG and 3.96 ± 0.85 in IG, which implied a significant difference between the two groups (p = 0.003).
The mean scores of perceived self-efficacy in exercise among the respondents in both study groups showed no significant difference before the intervention (p = 0.730). Nevertheless, the mean score of perceived self-efficacy in exercise was 3.51 ± 0.94 in CG and 4.12 ± 0.52 in IG after the intervention, which revealed a significant difference between the two groups (p < 0.001).
For behavioural intention subscale, the mean score of the CG and IG before the intervention were 3.90 ± 0.76 and 3.82 ± 0.67 which changed to 3.87 ± 0.92 and 4.00 ± 0.59, respectively. However, the results showed no significant difference between the CG and IG (p = 0.503 and p = 0.300).
Discussion
The control and intervention groups represented an equal distribution of respondents regarding their demographic characteristics. The scores of the HBM subscales (perceived self- efficacy in dieting, perceived self-efficacy in exercise, and behavioural intervention) in intervention group have improved and showed a significant difference after the intervention.
This underlines that the educational intervention effectively improved health knowledge and instilled a behavioural change through diet and exercise in the respondents for weight management. The findings are consistent with previous studies conducted in different populations whereby a significant increase in the mean score of HBM subscales was observed in the intervention group after the education, compared with their scores before the intervention19–22.
For perceived self-efficacy in the dieting subscale, the mean score in the intervention group changed from 3.76 ± 0.58 to 3.96 ± 0.85 after the education, which indicated a significant difference. The findings corroborate the data reported by a previous study conducted among students whereby the mean self-efficacy score in dietary behaviour had increased significantly by 18.85% in the intervention group23.
Another study evaluating the association between self-efficacy and dietary behaviour reported a positive correlation24. In contrast, a prior investigation conducted by Shojaei, Sarallah et al. determined that achieving behavioral change through educational intervention is a challenging and enduring process. This conclusion was drawn from the observation of the intervention group, where no significant difference in dietary behavior was found before and after the educational intervention16.
The mean score of perceived self-efficacy in exercise by respondents in the intervention group reported prominent improvements, increasing from 3.63 ± 0.71 to 4.12 ± 0.52 after the educational intervention. This implies that the respondents in the intervention group were more confident after the educational intervention and motivated to behavioural change through physical activity for better weight management.
This result is similar to a previous study conducted among students, which described a significant difference between knowledge and their practices in terms of physical activity in the intervention group after educational intervention22. The findings of the current study are similar to those of the study by Nourian et al., which showed that the mean score of perceived self-efficacy in physical activity significantly increased in the intervention group17. The increase in mean score denotes an increase in the knowledge of the respondents after the educational intervention.
For behavioural intention subscale, the mean score in the intervention group changed from 3.82 ± 0.67 to 4.00 ± 0.59 after the education, which indicated a significant increase. The findings suggested that the respondents in intervention group intended to reduce and manage their weight by visiting a dietitian to control their diet and involving in physical activities regularly for the next six months.
This results corroborate the findings reported by a previous study evaluating the effectiveness of nutritional education program which was developed based on HBM constructs20. The study reported a significant difference (p = 0.01) in nutritional behaviour between IG and CG after intervention which implied that nutritional education was effective in enhancing their knowledge and nutrional practice.
Another study conducted among female students concluded that nutritional education based on HBM had a positive correlation on the nutrition behavior of students24. The educational intervention was effective in creating a positive behavioural intention as the students in the intervention group obtained 92.66% score for the behaviour subscale. A significant positive correlation between the mean scores of behaviour and self-efficacy and knowledge of learned material was reported18,24. This suggested that improving the knowledge on weight management had positive impact on adopting a positive health behaviour.
However, cue to action was the only HBM subscale that had an indirect association with educational intervention and its mean score decreased after education in the intervention group. This indicates that the educational intervention did not positively impact the cue to the action of respondents and they may not understand the significance of cue to action for weight management.
This finding is supported by a previous study that reported no significant difference in the mean scores of cues to action in the intervention group before and after educational intervention17. The study also reported that young adults who learned that a family member had a disease with obesity being a risk factor were still less likely to be involved in weight loss or physical activity, contrary to the HBM17.
The findings of the present study revealed that the educational intervention was effective in increasing perceived self- efficacy in dieting, perceived self-efficacy in exercise and behavioural intervention of the respondents in intervention group compared with the no-intervention control group.
Emphasizing educational interventions that target enhancing confidence in managing dietary habits and engaging in regular physical activity is crucial. Encouraging individuals to set realistic goals, such as visiting a dietitian and engaging in regular physical activities, can serve as powerful cues to action. However, the observed decrease in cue to action scores after education highlights a potential gap in understanding the importance of cues for weight management. Future programs should integrate targeted strategies to enhance awareness of cues and their role in influencing health behaviors. This could involve interactive sessions, visual aids or personalized feedback to reinforce the relevance of cues in the context of weight management.
The present study findings offer practical implications for designing effective weight reduction programs by focusing on boosting self-efficacy, aligning behavioral intentions with achievable goals and addressing potential gaps in understanding cues to action. These insights contribute to the development of more tailored interventions to support individuals in weight management strategy.
Conclusion
The findings of the present study revealed that the educational intervention had a significantly positive effect on perceived self-efficacy in dieting, perceived self-efficacy in exercise and behavioural intervention of the respondents in the intervention group in comparison with the no-intervention control group. The study demonstrated that the educational intervention was effective in improving knowledge about weight management and weight control intention.
According to the findings of the study, it is evident that educational intervention influenced the behavioural change of the respondents in the intervention group through diet and physical activity. This implies that the respondents deliberate on behavioural changes when they have a better understanding of the risks of being overweight and obese.
Author contributions
The authors confirm their sufficient contribution to the study and take responsibility for the study concept, design, analysis and writing of the manuscript. All authors certify that they have reviewed the results and approved the final version of the manuscript.
Funding
Special thanks to Universiti Sains Malaysia for the financial assistance through Short Term Grant (304/PFARMASI/6315675).
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.