Abstract

Background

Liver cancer (LC) screening, such as AFP test and abdominal ultrasound, is an effective way to prevent LC, one of the most common cancers worldwide. Despite the proven screening benefits, screening participation among high-risk populations for LC remains low. This suggests that targeted, systematic, and effective interventions should be provided to improve knowledge and awareness related to LC screening, enhance screening intentions, and thereby promote screening behaviors. Telephone is people’s main medium of daily communication and mHealth-based programs offer a potential and effective solution for promoting health behaviors. The purpose of this study is to develop and implement a mHealth (WeChat app) based intervention guided by Fogg’s Behavior Model (FBM) to augment the knowledge of LC prevention among people at risk of LC and enhance their motivation for screening, and to validate its effectiveness in improving LC screening.

Methods

We propose a two-arm, single-blind randomized controlled trial with 82 at-risk individuals of LC, delivering a 6-month mHealth-based intervention program with optional health counseling. Recruitment will be through tertiary hospitals and community organizations in 4 districts in Heng Yang. In total, 82 individuals at high risk for HCC will be randomized 1:1 to intervention or control (usual care) groups. The intervention group will receive intervention, whose contents are based on the FBM model, via multiple forms of media including PowerPoint presentation, multimedia video, health information booklet and screening message, which is delivered in the WeChat Applet. Control dyads will be provided with usual health education. Outcomes will be assessed at baseline and post-intervention.

Discussion

The findings of this study will provide evidence of the benefits of utilizing mHealth-based approaches in intervention development to enhance the effectiveness of screening adherence for high-risk people of LC. Further, the findings would provide reference to the potential incorporation of the targeted intervention in local community organizations.

Trial registration

Chinese Clinical Trial Registry (ChiCTR2400080530) Date registered: 31/1/2024.

Background

Liver cancer (LC) is one of the most prevalent malignancies in the world. The Global Cancer Statistics 2020 estimates that there are over 910,000 new cases and 830,000 deaths in 2020, with China accounting for nearly half of these cases [1]. It is estimated that over 1.4Ìýmillion new cases and 1.3Ìýmillion deaths from LC will occur globally by 2040, accounting for more than half of all occurrences [2].

According to relevant research, patients with early-stage LC have the highest survival rate, with a 5-year survival rate of more than 50% following aggressive therapy or surgical resection, which is much greater than that of patients with advanced-stage LC [3]. However, individuals with advanced LC have few therapy choices and typically die within 1–2 years [4]. Studies reveal that early screening, diagnosis and treatment of LC can greatly increase survival time and successfully minimize mortality. Compared to advanced LC, the 5-year survival rate of early-stage LC patients discovered with routine screening can be 70% greater [5, 6]. As a result, early screening is a significant technique for lowering mortality and improving survival for LC. International liver associations, such as the American Association for the Study of Liver Diseases (AASLD) [7], the European Association for the Study of the Liver [8], and the Asia-Pacific Association for the Study of the Liver (APASL) [9] all recommend abdominal ultrasound (US) with or without alpha-fetoprotein (AFP) testing every 6 months in high-risk patients. Patients with viral or non-viral cirrhosis, as well as HBV carriers without cirrhosis, are among those at high risk.

However, the utilization of LC screening is low. The Chinese Cancer Screening Program enrolled 408,742 patients at high risk of LC, with a participation rate of 37.5% [10]. In the United States, less than 30% of patients with cirrhosis are monitored for LC; a systematic review of 29 studies that included 118,799 patients calculated a combined surveillance rate of approximately 24.0% [11]. A retrospective, multicenter cohort study found the mean number of patients undergoing six-monthly surveillance was only 14.0% [12]. Previous studies have identified several factors that may contribute to this phenomenon, including a lack of information about LC screening and prevention, a lack of professional advice, and limited access to health care. The cost of screening, the difficulty of scheduling screening appointments, and a lack of transportation to screening sites all contribute to low screening adherence [13,14,15]. In addition, differences in LC surveillance have been linked to patient age, gender, ethnicity, and socioeconomic position [16,17,18]. These findings indicate that educational interventions to raise awareness of LC prevention among at-risk populations, as well as their awareness of self-efficacy and motivation to use LC screening, are required to increase their screening adherence.

Two studies by Signal [19, 20] et al. of a randomized clinical trial of 1,800 patients with cirrhosis in the United States showed that 6 months after the intervention, screening adherence was 47.2% (p < 0.001) in the mailed outreach + navigation group, 44.5% (p < 0.001) in the mailed outreach group, and 24.3% in the usual care group. Specific interventions included providing each subject with booklets on LC risk information and screening guidelines, as well as receiving a telephone reminder to screen. Shaw et al. [21] combined targeted written materials with specific verbal information to create an educational intervention that was successful in improving adherence to outpatient HCC visits and liver imaging exams in patients with cirrhosis. This suggests that providing health education and patient navigation is essential to increasing the rates of LC screening. In a community-based setting, an Australian clinic created the Web Clinical Tools-Community-Based Management Model for Primary Care CHB, an interactive online clinical management tool. The intervention group showed 89% adherence to completing the recommended liver ultrasound within one month of the recommended screening date. Compared to control patients, the rate of attendance and ultrasound completion was considerably greater (p < 0.0001) [22]. Telephone reminders were shown to be effective in increasing hepatocellular carcinoma monitoring rates, according to another study [23]. Thus, while putting the intervention into practice, all of the aforementioned interventions should be taken into account.

However, approaches like using mHealth or multimedia interventions should also be taken into consideration in order to further increase the effectiveness of such interventions targeting people at high risk for LC. In addition to fostering engagement, boosting social support, and improving adherence to cancer screening, mobile health can offer health education to raise knowledge of cancer screening. The main focus of mHealth interventions is text messaging with information on cancer risk factors, screening advantages, and screening clinic locations [24, 25]. Alternatively, arranging screening appointments and sending out invitations and reminders for cancer screenings informed intervention receivers about cancer and removed any obstacles to screening via phone counseling [26, 27]. This research aims to design, develop, and evaluate a mHealth-based intervention program guided by Fogg’s Behavior Model (FBM) to enhance screening behaviors of LC high-risk populations. FBM is a comprehensive model that characterizes behavior change as the result of the convergence of three elements: prompts, ability, and motivation. FBM has been employed in various research projects to design an acceptable mHealth intervention for changing human behavior in the field of public health. For example, FBM has been used to build a health intervention program to encourage African American women to adopt healthy eating behaviors and engaging in physical exercise, because those target populations have a high prevalence of obesity [28]; the FBM-based intervention paradigm helps Malaysian teenagers with mental health issues get better [29]. Also, Alrige developed and validated a behavioral change-based messaging campaign during the COVID-19 pandemic to support FBM-based individual preventive health behaviors. This campaign successfully raised people’s motivation levels and their capacity to continue health-preventive behaviors with targeted messaging [30].

The aim of this study is to use FBM as a theoretical guide for a tailored mHealth intervention program to boost motivation and ability among individuals at risk for LC, in conjunction with regular screening reminders that never promote screening behaviors and increase screening adherence. Furthermore, the study’s findings will highlight the potential benefits of mHealth interventions in raising the rate of LC screening in order to effectively prevent the disease, which could lower its prevalence worldwide.

Study aim

The primary aim of this study is to evaluate the effectiveness of mHealth-based interventions guided by FBM to improve LC screening among high-risk populations, compared to traditional nursing interventions.

Methods

The preparation of this study protocol follows the guidelines set out by the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklist, and the completed SPIRIT checklist can be found in Additional File 1.

Study design

This is a single-center, two arms, single-blind, randomized controlled trial with a control group and an intervention group, which delivers a 6-month mHealth-based intervention program with optional health counseling to 82 people at high-risk for LC. FigureÌý1 shows a flow chart of the study design, including the five steps of participant recruitment, randomization, intervention, outcome evaluation, and data analysis.

Fig. 1

Flow diagram of this study

Theoretical framework

To change or promote a certain human behavior, theories of behavior change are needed. The Fogg behavior change model says that an individual’s behaviors are a function of their motivation, ability (how easy it is to do something), and any prompts that influence their behavior [31]. Additionally, the Fogg Behavior Change Model states that B = MAP, or that behavior (B) is a product of three factors, motivation (M), ability(A) and a prompt (P). It’s particularly the use of prompts (AKA triggers), which are often an environmental factor, that helps make this and similar models so popular in the digital environment [32] (Fig.Ìý2).

Fig. 2

Fogg Behavior Model

Randomization and blinding

Participants will be randomly allocated to either the intervention or control group. The control group will receive usual care (no intervention). Subjects will be randomly assigned Study ID numbers. A member of the research team will randomize the participant via a centralized, computerized randomization program in a uniform 1:1 allocation ratio (intervention: control). Participant allocation will be carried out by an independent researcher who is not involved in any part of the study. The investigators will be blinded to the randomization assignment.

Owing to the nature of the mHealth intervention program, we cannot blind the implementers of the intervention. Baseline information and clinical interviews are gathered and conducted prior to randomization. Management of the data will be done by the staff member being blinded. Statistical analysis of the results and resulting conclusions will also be blinded. The trial results will be analyzed by an independent statistician, and the results will be interpreted by the research team.

Study population

Intervention subjects will be recruited from both the hospital and the community. Prior to implementing the intervention, our researchers will contact the person in charge of the hospital or community to request assistance in enrolling study participants. Initially, a registered nurse from the hospital or a health professional from the community will check the appropriate medical examination records. Eligible subjects will be contacted by a dedicated member of the research team and be told about how to participate in the program. A full explanation of the program will be provided. All subjects can withdraw their participation at any time during the study. The reasons for declining to participate and discontinuing participation will be recorded. It is worth noting that the relevant participant’s data will receive protection.

Inclusion criteria

Participants will be eligible to take part if they are: (1) High-risk population for liver cancer: with hepatitis B virus (HBV) and/or hepatitis C virus (HCV) infection, chronic alcohol abuse, non-alcoholic steatohepatitis, cirrhosis of the liver due to various causes (including alcoholic liver disease, MAFLD), and a family history of LC; (2) Have reading, listening skills and communicate effectively; (3) Informed consent, volunteered to participate in the survey.

Exclusion criteria

Patients meeting 1 or more of the following criteria cannot be selected: (1) Have been diagnosed with liver cancer; (2) With mental disorders, dementia, etc. (3) The research subjects have participated in other studies of the same type.

Sample size calculation

The sample size was calculated using the following formula, N1 = N2 = 2[(u_α+u_β)/ (S/δ)] ², sing a two-sided test with α = 0.05, uα = 1.96, uβ = 1.282, δ = u1-u2, S/δ = 0.8. We calculated that a total of 64 participants across two arms would be recruited. By allowing for an estimated 20% attrition rate, a sample size of 82 will be used in this study.

Intervention

Control group

The control group will receive the usual care. For this study, usual care is defined as access to available information and health services to maintain a healthy lifestyle, including a monthly awareness program about liver cancer and the distribution of the Liver Cancer Screening Health Education Pamphlet. Participants in the control group receive a text message on the first day of each month reminding them to get screened. The messages at 2 and 4 months will have a link to complete the eating disorder screening and the message at 6 months will direct them to complete all follow-up assessments. Participants in the control group will be offered the text message intervention after the 6-month follow-up assessments are complete at no cost. However, they will not be able to receive health counseling. We will also assess the effectiveness of the intervention at baseline, month 3 and month 6 through the results of questionnaires, micro- and telephone follow-ups.

Intervention group

To better ensure the effectiveness of the intervention, the intervention will be implemented by nurses who have been working in the liver department for more than five years. At the same time, before the beginning of the experiment, the implementers of the intervention will be provided with detailed training to ensure that they understand the purpose of the intervention, the theoretical knowledge of the intervention, and the basic steps. At the same time, the implementers will be allowed to simulate the practice process and solve the problems that may arise. During this process, the hepatologists can assess the level of the skills and abilities of the implementers, and the operation will be supervised by the hepatologists.

The intervention group will receive usual care, plus a 6-month mHealth intervention program with text messages focused on three key areas for improving screening behavior, including knowledge and awareness of LC screening and prevention, screening motivation and screening prompts.

The FBM model intervention content. There are three elements: motivation, ability, and prompts. Additionally, the Fogg Behavior Change Model states that B = MAP, or that behavior (B) is a product of three factors, motivation (M), ability(A) and a prompt (P). B stands for motivation, which in this study refers to the factors that encourage participants to participate in screening activities or sustain healthy habits. A means ability, which in this study refers to the participants’ knowledge and perception of LC screening. The views and opinions of participants on LC screening are also included in this study. P stands for prompts, which in this study refers to any cue—such as outward reminders to screen or bodily symptoms—that affects screening behavior. The study’s core concept of screening behavior improvement is the enhancement of an individual’s knowledge and cognitive capacities through internal drives combined with external reminders, leading to health-preventative behavior.

B: The first section is motivation. There are three core motivators: pleasure/pain, hope/fear, and social acceptance/social rejection. In this study, we will use the hope/fear motivator to build the content of the intervention. The hope/fear motivation is sourced from the anticipation of something good (hope) or bad (fear) happening. For example, the hope of not being diagnosed with LC will motivate a person to screen routinely. Another example is that participants want early identification of negative body signals by regular screening, which could then be handled in a timely fashion.

Participants will be asked if they have ever been screened for LC. The participants’ inner feelings about LC and the obstacles they have faced in screening should be carefully considered. The participants’ main barriers should be carefully listened to, and any misconceptions should be cleared up. The participants should also be reassured and encouraged, given examples of similar screening experiences to boost their confidence, and given instructions to support themselves. There will be a 20–30-minute time limit. Motivational interviewing, experience sharing, and other techniques are the primary types of intervention. Enhancing motivation and fostering behavioral attitudes are the objectives.

A: The second factor in FBM is ability. To increase a person’s ability, the persuasive technology design must make the behavior easy to do. The author defined six elements that detract from simplicity: time, money, physical effort, brain cycles, social deviance, and nonroutine. It won’t take much time, money, or effort to carry out this activity, which will eventually turn into a regular occurrence in life. A in this study denotes the participants’ understanding and awareness of LC screening, particularly its significance. The pre-prepared manual, video, or PPT of knowledge related to LC and LC screening will be used to explain the information to the participants. This phase’s primary goals are to give participants a more thorough understanding of LC and information on LC screening. Each session lasts 45–60Ìýmin.

P: The third factor is prompts. Fogg defines prompts as “something that tells people to perform a behavior now. There are three types of prompts: facilitators, signals, and sparks. These prompts are a good fit if the participants are high in motivation and ability, but just need a reminder. This study aims to encourage screening behavior by giving participants an external reminder via text message using a WeChat group or WeChat app for routine screening.

Content and frequency of mHealth interventions

This study will be conducted at the Department of Hepatology of a tertiary hospital in Hengyang City, Hunan Province. The intervention process will be conducted for 6 months, encompassing an introductory session, weekly health education lectures or WeChat consultation sessions (0–2), and the maintenance phase of the intervention (3–6). Within WeChat groups and mini-programs, there will be capabilities for screen sharing and video conferencing accommodating up to 500 individuals.

The primary interventions will occur in small groups every weekend during the initial two months. The first two interventions will focus on capacity-building activities such as multimedia health talks or brochures aimed at enhancing the cognitive level of the participants. From week three to week seven, screening motivation among the participants will serve as a starting point for interventions which include motivational interviews, sharing exchange meetings, belief interventions, etc. Simultaneously, PowerPoint presentations or videos regarding liver cancer screening knowledge will be distributed within WeChat groups or mini-programs. Intervention materials such as Liver Cancer Screening Health Education Brochures or leaflets along with thematic animations will be utilized. Additionally, a discussion area function within the mini-program platform will be opened to address any consultation questions from participants at any time. Furthermore, a liver cancer and liver cancer screening knowledge question bank has been established for study subjects to enhance their cognitive level. TableÌý1 provides a summary of the content and frequency of mHealth interventions in this study.

Table 1 Content and frequency of mHealth interventions

Data collection and study outcomes

Upon receipt of the informed consent, participants in both groups will be asked to complete the self-developed questionnaire for the collection of demographic data and screening adherence questionnaire for live cancer. The WeChat applet will automatically remind patients to complete the outcome measurements in 12 and 24 weeks. If the participant does not return on time, researchers will telephonically inquire about the reasons. We will also report the numbers and reasons of participants lost to follow-up.

TableÌý2 shows the summary of the outcome measures for the study. The primary outcome is screening participation, which includes screening adherence, and a change in screening behavior from the baseline to 12 and 24 weeks. Screening behavior will be measured by a self-designed screening adherence questionnaire for liver cancer and the screening participation rate. The secondary outcomes include the score of aMAP, social support and self-efficacy. The aMAP Model (age-Male-ALBI-Platelets, aMAP) is used to assess an individual’s risk of developing HCC and includes five indicators, gender, age, bilirubin (TBil), albumin (ALB), and platelets (PLT) [33].

Table 2 Assessment time points for primary and secondary outcomes

Three assessments will be performed. The first assessment will be focused on obtaining the data before intervention and sociodemographic variables (including age and sex), i.e., in week 0 (baseline situation, immediately before intervention), whereas the second assessment will be conducted at the end of the intervention, i.e., in week 12. Moreover, one follow-up assessment will be done: 3 months after intervention. FigureÌý1 shows a flow chart of the study design.

Demographic data, including age, gender, education level, monthly household income, marital status, employment status, family history of liver cancer, health insurance status, knowledge level and screening adherence to liver cancer, will also be collected by means of an author-developed questionnaire. Furthermore, we will conduct a semi-structured interview to understand participants’ satisfaction, perceptions and experiences in the intervention groups. The timeline for enrollment, intervention, and assessment is shown in Fig.Ìý3.

Fig. 3

Description of the research period. T0: Baseline survey phase, fill in socio-demographic information; T1: 1 month after the intervention; T3: 3 months after the intervention; T6: 6 months after the intervention

All adverse events will be reported to the ethics committee as required during the 24-week study period. Adverse events are defined as medical occurrences resulting in hospitalization, disability or death.

Data analysis

Statistical analyses will be performed with SPSS v.26.0. Statistical significance will be set at p < 0.05. The test Shapiro-Wilk test will be used to study the normality of the sample.

Appropriate descriptive statistics will be calculated, the continuous variables will be performed using means ± SD for normal distribution variables, and medians and interquartile ranges for non-normal distribution variables. The categorical variables will be described using percentage frequencies. To evaluate statistically significant differences intergroup in the variables at baseline, the independent Samples T-test (normal distribution) or the Mann-Whitney U test (non-normal distribution) and χ² tests or Fisher’s Precision Test will be used, such as sociodemographic factors and differences between intervention and control groups. To study the effects of the interventions on the outcome measures at three time points (T0, T3 and T6), repeated measures Analysis of Variance will be conducted. The Bonferroni correction will be employed for pairwise post-hoc comparisons to further analyze significant interactions. The Greenhouse– Geisser adjustment will be applied to correct for the lack of sphericity (Mauchly’s sphericity test, p < 0.05) whenever necessary. For non-normal distribution variables, the Friedman test will be used. Three-time points (T0, T3 and T6) will be used to examine effectiveness in improving secondary outcomes, including social support and self-efficacy, with statistical significance represented as p values (two-sided) of less than 0.05.

Ethical considerations

This study will comply with the ethical principles of the Declaration of Helsinki, and the Human Research Ethics has been approved by Hengyang Medical School, University of South China (2023NHHL002). This trial has been registered in the Chinese Clinical Trial Registry (ChiCTR2400080530). All participants will be required to provide written informed consent.

Research dissemination

Research reports will be disseminated to healthcare and academic professionals through scientific forums, including peer-reviewed publications and presentations at national and international conferences.

Discussion

The aim of this study was to design and execute a mHealth (smartphone app–WeChat app) based intervention informed by FBM to improve LC screening motivation and knowledge among individuals at risk for LC. Additionally, to assess how well a mHealth-based intervention works in comparison to traditional interventions to improve LC screening among high-risk individuals.

LC screening is an effective method of detecting precancerous lesions for early detection of LC, which increases the availability of treatment options and the likelihood of cure of cancer, thereby decreasing the mortality rate of LC [34]. However, the low uptake of LC screening is influenced by a number of factors, including inadequate knowledge and awareness of LC screening, different cultural beliefs and financial burden [35,36,37]. Despite the consensus that routine surveillance is beneficial for early detection of LC and may confer a survival advantage, only a minority of patients receive guideline-concordant routine surveillance, and previous studies have shown that the rate of LC screening ranges from 11 to 64% [16]. In China, the participation rate in screening for people at high risk of LC is only 37.5% [10]. A systematic review showed that the combined surveillance rate for LC in the United States was approximately 24.0% [11], all of which suggests that LC screening is grossly inadequate. Therefore, it will be urgent and crucial to develop and implement interventions to effectively promote LC screening. With the popularization of social media applications, new health promotion and public health interventions incorporating mHealth technologies are playing an increasingly important role in the field of chronic disease prevention and control [38]. mHealth technologies provide health behavioral intervention content, a rich form of expression and accessible promotion channels [39]. Uy et al. [40] evaluated the effectiveness of an mHealth intervention (text message reminders) for cancer screening, and showed that the absolute screening rate for the text message intervention was 1-15% higher, and the relative screening rate for the intervention subjects was 4-63% higher than that of the control subjects. Therefore, mHealth-based interventions should be used in the development of interventions to increase awareness of the importance of screening in LC prevention among high-risk populations. This study proposes a mHealth intervention based on the Fogg Behavioral Model to improve its effectiveness in disseminating information about LC prevention and encouraging the use of LC screening among people at high risk for LC.

The results of this study will provide further evidence of the benefits of utilizing a mHealth approach in intervention development to improve the effectiveness of health promotion interventions. Indeed, mHealth interventions have been shown to be effective in increasing cancer awareness among ethnic minorities and improving their self-efficacy to undergo cancer screening [41, 42]. Studies have similarly shown [43] that mHealth-based interventions significantly improved total quality of life scores for postoperative breast cancer patients, resulting in significant improvements in social and family status, physical status, functional status, and emotional status. Our intervention could help increase screening participation and bring more health benefits.

This research has some limitations. Firstly, there is a limited geographical region (this study was only conducted in Hunan Province). Secondly, the study will recruit only Chinese people in Hunan Province as subjects. In addition, any long-term effect of the mHealth intervention on screening behavior of high-risk populations for LC will be difficult to show through this proposed project because of the limited timeframe. Nevertheless, with the scarcity of data on the effectiveness of mHealth-based and multimedia interventions on increasing the intention of high-risk populations to take up screening, this study would provide useful data to fill this research gap, serving as a case study for the implementation of such interventions targeting high-risk population for LC.

Conclusion

Increased utilization of LC screening, especially among high-risk populations, is vital to combating the increasing prevalence of LC worldwide. With previous studies shown to exhibit a low screening uptake, it is of great importance to implement interventions to enhance their knowledge of LC and its preventive measures. The proposed intervention, using a combination of mHealth and multimedia approaches, would potentially enable them to acquire a better understanding of the preventive measures of LC and show greater self-efficacy in undergoing screening. If effective, the results will provide high-quality evidence to inform future translational research to scale up the program and provide potential clinical importance.

Study status

The study has started enrolling participants in April 2024 and recruited 50 participants through hospitals and community centers as of August 2024. Intervention and follow-up of patients in this randomized controlled trial will end 6 months after enrollment, up until the last subjects are enrolled.