A web-based intervention to promote healthy lifestyles in women under 45 years: a randomized controlled trial (RCT) for breast cancer prevention

Introduction

Breast cancer remains a global public health issue. Recent data indicate that 24.5% of all cancer cases diagnosed in women are breast cancer (1). Its incidence continues to rise (2) and is closely linked to lifestyle factors (3-7). Notably, the literature suggests that approximately 31.2% of new breast cancer cases are attributable to modifiable risk behaviors, underscoring the critical importance of investing in preventive strategies (8).

Background

Promoting adherence to healthy lifestyle habits is particularly crucial as a key measure to reduce the risk of developing breast cancer (6,7,9,10). Regular physical activity combined with maintaining a healthy diet are well-documented recommendations (11,12). A review by González-Palacios Torres et al. (13) suggests that adherence to the Mediterranean Diet has a protective effect on breast cancer risk, especially during menopause. Similarly, other authors extend the preventive capacity of this dietary pattern to other age groups, including premenopausal women (14,15). Moreover, these two behaviors collectively influence another highly prevalent global health issue—excess weight—which has a direct relationship with breast cancer development (9,10,16). In summary, lifestyle changes that promote a healthy diet and active living are crucial to reducing breast cancer incidence, with some authors asserting that primary prevention strategies are the most effective long-term approach to lowering breast cancer rates (17).

Rationale and knowledge gap

Adopting lifestyle changes in adulthood can be challenging. External regulations can motivate or stabilize behaviors, whether healthy or unhealthy (18). However, change is not impossible, particularly when individuals perceive a risk of developing a health problem if lifestyle improvements are not made (19). Regarding this study’s focus, evidence supports the benefits of interventions targeting these behaviors, as they may help reduce both the incidence and mortality of breast cancer (11). The literature reports effective interventions designed to promote adherence to healthy behaviors specifically tailored for young women to prevent breast cancer (20). Indeed, as noted by Mastrogiacomo et al. (21), there is a positive disposition among women to participate in lifestyle-related cancer prevention interventions involving behavior changes.

It is also essential to consider the most appropriate media for delivering interventions in terms of acceptance and usability for the target population. Recently, society has shown a preference for digital resources (22). Additionally, this technology has proven effective in various health-related domains, such as disease detection and prediction (23,24), supporting cancer survivors and their caregivers (25), and facilitating behavior change (26).

This study addresses the potential of digital technology-based interventions when implemented with young adults, aiming to empower them to have greater control over their health. Despite the extensive literature highlighting the benefits of using this technology, few studies focus on addressing the causes related to the onset of prevalent diseases in this population. Instead, the literature seems more interested in secondary prevention approaches or populations with elevated risk levels (27,28). Furthermore, young populations often have less proximity to healthcare services and are not typically recipients of preventive health advice. This research, therefore, seeks to resolve a controversial issue by demonstrating the potential of digital technology as a health-promoting tool for healthy, young adult populations.

The hypothesis I aimed to test was that a 12-week web-based intervention would significantly improve adherence to dietary and physical activity guidelines compared to a control group.

Objective

Given the above, specifically the burden of breast cancer, its preventability through healthy lifestyle adoption, and the demonstrated effectiveness of digital interventions in adulthood, this study aimed to address the effectiveness of a web-app-based intervention, using the Behavior Change Wheel model (BCW) in improving diet and physical activity among women aged 25–45 years residing in northern Spain. We present this article in accordance with the CONSORT reporting checklist (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-2/rc) (29).

Methods

Design

A randomized experimental study was conducted, including an intervention group (IG) and a control group without intervention (CG), based on the BCW model (30). The program was implemented between April and June 2022. This study was approved by the Research Ethics Committee of the Principality of Asturias (No. CEImPA 2021.341). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Participants

The study population consisted of women aged 25–45 years residing in Health Area VII of the Principality of Asturias (Spain).

The inclusion criteria were the capability to access the web-app. The exclusion criteria were: (I) pregnant women; (II) previous breast cancer diagnosis. An invitation to participate was sent via postal mail by the public health service to all women meeting the above characteristics (n=6,594). The mailing included an information sheet detailing the study’s purpose, the research team’s contact email, an informed consent form, and a prepaid envelope to return the signed consent form.

Accepting an alpha risk of 0.05 and a power of 0.8 in a two-tailed test, 76 subjects are necessary in each to recognize as statistically significant a difference greater than or equal to 1 unit, assuming a 2.2 standard deviation.

A total of 414 women provided informed consent to participate. Each participant was assigned an alphanumeric code according to the order of receipt of consent. This code was used for random assignment (simple randomization method with Excel program), in a 1:1 ratio, between the IG (n=207) and CG (n=207). After the intervention began, an additional 37 women who met the eligibility criteria submitted their consent forms. Since the intervention had already started, these participants were included in the CG (n=244) to avoid introducing differences in intervention characteristics. The final study population consisted of 451 women (Figure 1). The code blinded the researchers after assigning participants to the IG or the CG.

Figure 1 Flow diagram of participants. CG POST, women from the control group after the intervention; CG PRE, women from the control group before the intervention; CG, control group; IG POST, women from the intervention group after the intervention; IG PRE, women from the intervention group before the intervention; IG, intervention group.

Measurement instruments and study variables

To achieve the study’s objective, the primary outcomes adherence to healthy dietary and physical activity recommendations, were assessed using the Motiva.Diaf questionnaire (31) (Cronbach’s α =0.802). This questionnaire comprises 12 multiple-choice items: 7 related to dietary recommendations and 5 related to physical activity recommendations, dichotomously coded (does not follow the recommendation =0; follows the recommendation =1). This questionnaire was validated in the same region where the study was conducted. Additionally, individuals over 18 years old, with no dietary or physical activity restrictions, participated in the validation process, and 55.7% were women. This alignment with the population of the current study, as well as its appropriate psychometric properties, justified its selection.

A composite quantitative variable, Combined Behaviors, was calculated by summing the scores for diet and physical activity, with a range from 0 (lowest adherence to healthy recommendations) to 12 (highest adherence to healthy recommendations).

Additionally, questions were included to assess personal variables such as age, marital status, education level, and living arrangements.

An ad hoc digital form was created to include items related to personal variables and those from the Motiva.Diaf questionnaire (31). Data were collected at two points: PRE-intervention phase (March 2022) and POST-intervention phase (June 2022).

Intervention

The educational intervention lasted 12 weeks and was based on the BCW model (30). The intervention tool was a web-app specifically designed and developed for this study (32). The app content included information on dietary and physical activity behaviors provided through tips, videos, and general information. Some content was continuously available throughout the intervention, while additional materials were progressively uploaded from Monday to Friday over the 12 weeks to maintain participants’ engagement with the web-app. Access to the app was restricted, and each participant was assigned a unique username and password of their choosing.

Participants in the IG received an educational intervention on diet and physical activity. The determinants addressed, intervention objectives, and the Theoretical Domains Framework (TDF) (29) components are summarized in Table 1. The intervention strategies included education, persuasion, and enablement. Participants in the CG did not have access to the web-app and did not receive any intervention. After completing the 12-week intervention and submitting the POST-questionnaires, the CG participants were granted full access to the web-app and all its educational materials.

Statistical analysis

The statistical analysis of the data collected in this study was performed using various methods. A description of all variables was carried out for both the IG and the CG before and after the intervention, using percentages, means (standard deviation), depending on the nature and distribution of the data.

An intention-to-treat analysis was conducted (33), as recommended, given the number of dropouts in the IG between the PRE and POST phases.

To compare behaviors between the IG and CG, chi-square or Student t-tests were used. Changes in each behavior within the IG and CG separately were analyzed using paired-samples t-tests. The choice of statistical tests was based on the nature of the variables.

Additionally, a multivariate linear regression was performed to determine which variables best predicted changes in combined behaviors (combined behaviors at POST). Independent variables included age, education level, marital status, living arrangements (alone or accompanied), group assignment, family history of cancer, family history of breast cancer, and combined behaviors at PRE.

The analyses were performed using IBM SPSS version 27.0^®, with results considered statistically significant at a P value ≤0.05.

Results

Personal variables

A total of 451 participants were included in the study (Figure 1). Table 2 presents the personal and anthropometric characteristics of the participants. The mean age was 38.82 years [standard deviation (SD) =5.3]. Most participants had a university education, were married or in a relationship, were employed, and lived with others. Additionally, 72.9% of the participants had a family history of cancer, and 33.9% had a family history of breast cancer.

A comparison between the IG and CG showed homogeneity in the personal characteristics of the women in both groups (Table 2). No differences were observed between the women who completed the study and those who dropped out in terms of the total behavior score (P=0.18), nor in dietary behaviors (P=0.09) or physical activity (P=0.77) separately.

No significant differences were found in adherence to behavioral recommendations between the IG and the CG during the PRE phase (Table 3).

Effectiveness of the intervention

The IG demonstrated a positive and statistically significant improvement in adherence to recommendations (Table 4).

In addition, a statistically significant positive change in overall consideration was observed in the IG group, as demonstrated by the analysis of the combined behavior scores (Table 5).

However, no differences were observed in the IG based on age, body mass index (BMI), marital status, living alone or with others, or education level, nor in combined behaviors (P=0.65; P=0.87; P=0.84; P=0.47; P=0.69), dietary behaviors (P=0.40; P=0.37; P=0.70; P=0.36; P=0.57), or physical activity (P=0.78; P=0.40; P=0.91; P=0.73; P=0.95).

As shown in Table 6, significant differences were observed in dietary behavior between the PRE and POST phases in both groups. Additionally, significant differences were observed between IG and CG in the physical activity scores after the intervention.

Accepting an alpha risk of 0.05, in a two-tailed test with 207 in IG and 244 in CG, the statistical power was 22%, 6% and 52% to recognize as statistically significant a difference of means in the total score, nutrition, and physical activity, respectively.

A multivariate linear regression analysis was conducted with combined behaviors in POST as the dependent variable. Independent variables included age, education level, marital status, profession, living arrangement (alone or accompanied), group assignment (CG or IG), family history of cancer or breast cancer, and combined behaviors in PRE. The analysis revealed that belonging to the combined behaviors score in PRE significantly influenced the POST score (standardized β=0.645; 95% CI: 0.773–0.886), with an R² of 67%.

Discussion

This study was designed to evaluate the effectiveness of a web-app-based intervention aimed at improving two behaviors—diet and physical activity—that are associated with breast cancer. The results confirm the intervention’s effectiveness in enhancing adherence to healthy recommendations, as can be seen in Tables 4-6. Notably, no significant differences were observed between the IG and the CG in predictors or behavioral variables before the intervention. This allows the observed effects to be attributed to the intervention, confirming its efficacy.

Although a wide variety of digital health resources are currently available (33), this study chose a web-app as the primary intervention tool for two reasons: its widespread use among the population and evidence supporting its effectiveness in similar populations (34-36), even in addressing various aspects of breast cancer (25,37). Additionally, web-apps offer advantages over other tools, such as easy updates and the ability to integrate diverse content formats, including text, infographics, images, and videos.

The literature extensively documents the use of web-apps to promote healthy lifestyles among cancer survivors (37-40), but they are not exclusively used for this population or those with chronic diseases. For example, a review by Singh et al. (41) identified nine studies focused on the general population. These studies highlight the effectiveness of digital interventions, including web-apps, in increasing daily steps, moderate physical activity, fruit and vegetable consumption, and reducing calorie and saturated fat intake.

Following the educational intervention, women in the IG significantly improved their mean scores for adherence to both dietary and physical activity recommendations. This finding aligns with a pragmatic randomized pilot trial conducted in a similar population within the same region, which also used a web app (20). However, the results from the previous study may be considered more effective. This may be due to the study being conducted on a cohort of familiar women, thus creating a stronger connection with the research team, unlike the current study, which used a pragmatic approach on a more heterogeneous and unknown population. These characteristics suggest that the intervention presented here is likely to be more representative of what could be observed if it were integrated into routine clinical practice. Other previous studies, aimed at testing the efficacy of a web-based intervention designed to improve breast health behaviors, have found positive results (42). While these studies do not include the same behaviors, the interpretation of their results supports the benefits of using web-based tools to raise awareness about breast cancer in women.

However, the present intervention was shortened from six to three months, and the equally favorable results suggest that future interventions may consider this shorter duration. Although slight improvements were observed in the CG, these may be attributed to the Hawthorne effect (43).

Another notable finding was a higher dropout rate in the IG compared to the CG, consistent with the previous study (20). This may be explained by the IG participants’ knowledge that they would lose web-app access after completing the POST questionnaires, while CG participants were about to gain access. This expectation may have motivated CG participants to remain in the study. Future interventions should address this issue to ensure participant retention in the IG.

The improvements were more pronounced in dietary behaviors than in physical activity. This may be due to the lower baseline scores for some dietary recommendations and the fact that dietary changes are more knowledge-driven, while physical activity requires physical capabilities. Since the intervention focused on promoting knowledge, behaviors requiring primarily cognitive improvements had greater potential for modification. This aligns with the theoretical framework used, which posits that strategies enhancing psychological capability, such as educational interventions, facilitate the adoption of health-related behaviors dependent on this capability (44).

Dietary behavior is a critical area for breast cancer prevention (7,9). Studies like those by Buja et al. (45) indicate that a healthy diet is associated with a significant reduction in breast cancer risk. Likewise, physical inactivity is recognized as a risk factor for breast cancer (4,5). Body weight, influenced by diet and physical activity, also plays a crucial role. The literature points to a positive association between high BMI and breast cancer risk (9,10,46).

For these reasons, the present study addressed both behaviors. As detailed in the web-app design article, effective and safe resources were included, such as video tips from a nutritionist, written guidance, and steps for preparing healthy recipes (32). Regarding physical activity, walking was the primary recommendation due to its physical and emotional benefits and its safety in unsupervised interventions (47). Alternative activities were also offered via videos on the web-app (48,49).

The results suggest that belonging to the IG and baseline adherence to healthy behaviors were the strongest predictors of POST behaviors. The first predictor demonstrates the intervention’s effectiveness and justifies its replication. The second reflects behavioral continuity, which aligns with the self-determination theory. This theory suggests that extrinsically motivated behaviors can exhibit temporal stability when aligned with the individual’s context and social environment (18).

Limitations

This study focused on a specific geographic area, and sociodemographic characteristics may have influenced the results. However, this allowed the intervention content to be tailored to the population. Future interventions should consider these characteristics in their design. Another limitation is related to technology use. While web-apps are widely accessible, some individuals may lack access or experience technophobia, leading to non-participation. On the other hand, the use of technology is a strength, facilitating remote access to the intervention, which may increase participation and reduce costs. Another important limitation is derived from the self-reporting of data, which can lead to a loss of objectivity in responses, as well as a social desirability bias. In the CG, as previously mentioned, an improvement in the observed results after the intervention was seen, which is likely a consequence of the Hawthorne effect. Finally, the number of dropouts observed in the IG should also be considered. This may have occurred because women in this group were informed that the use of the tool would not continue after completing the POST questionnaire. In future studies, this information should be withheld to prevent dropouts.

Future directions

Future interventions should adopt the effective design demonstrated in this study, but consider the following improvements: reducing the intervention duration, modifying the POST evaluation timeline to minimize IG dropout, using objective measurement systems, and tailoring the intervention to the specific characteristics of the population. Additionally, it is recommended to conduct follow-up assessments over time to evaluate behavior sustainability.

It is important to note that, despite the magnitude and significance of breast cancer, no program specifically targeting women in this age group has been found. This indicates that such a program could be included in the common service portfolio of Primary Care. Furthermore, this aligns with the Digital Health Strategy of Spain’s National Health System (Web), which suggests incorporating digital technology into clinical practice to empower the population to take greater control over their health, delaying or preventing health problems through behavioral measures. However, to scale these results to larger populations, it is necessary to identify the specific needs of the target population to design a tailored intervention.

Conclusions

The results demonstrate that a web app-based intervention, designed using the BCW framework and tailored for young women, effectively improved behaviors related to diet and physical activity. These behaviors are associated with breast cancer prevention, confirming the intervention’s utility in promoting healthy lifestyles.

From a pragmatic perspective, the results suggest that this type of digital intervention can be incorporated into clinical practice as a complement to usual care. Policymakers and health professionals should prioritize the development and use, respectively, of digital interventions like the one presented in this study. In this sense, a clear opportunity for this intervention lies in its potential to be included in the common service portfolio of Primary Care.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the CONSORT reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-2/rc

Trial Protocol: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-2/tp

Data Sharing Statement: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-2/dss

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-2/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-2/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was approved by the Research Ethics Committee of the Principality of Asturias (No. CEImPA 2021.341). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. A total of 414 women provided informed consent to participate.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/⁴.⁰/.

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