The impact of mHealth interventions on medication adherence in breast cancer: a systematic review of randomized controlled trials.

Background

Breast cancer is the most prevalent disease to receive a diagnosis. This is also the leading cause of cancer-related deaths among women worldwide (Coles et al., 2024 ; World Health Organization, 2024a ). About 2.3 million women worldwide had a breast cancer diagnosis in 2022, and 670,000 people died from the disease. It is estimated that the number of new cases of breast cancer worldwide will rise from 2.3 million in 2020 to over 3 million instances by 2040 (Cambridge University, 2024 ). Breast cancer incidence differs by race and ethnicity; such as Asian American/Pacific Islander women have the lowest incidence (88.3 per 100,000) and non-Hispanic white women have the highest rate (128.1 per 100,000) (Coles et al., 2024 ; Menon et al., 2024 ). Early detection and prompt treatment are necessary to improve outcomes and survival rates (American Cancer Society, 2024 ; Coles et al., 2024 ).

Due to significant breakthroughs in therapy and early identification, the death rate from breast cancer has decreased over the past 25 years in North America and numerous regions of Europe. Treatments for breast cancer include surgery, chemotherapy, radiation therapy (RT), endocrine therapy, targeted therapy, immunotherapy, and their combinations (26). During the procedure, any lymph nodes and the entire breast (mastectomy) or just the cancerous tissue (lumpectomy) may be removed (Clinic, 2023 ). Chemotherapy, targeted therapy, and hormone therapy are examples of systemic treatments for breast cancer (Menon et al., 2024 ). These therapies can reduce the risk of recurrence by up to 25% (Menon et al., 2024 ). Medication adherence in breast cancer is associated with patients’ incapacity to regularly take prescribed drugs as instructed (Yussof et al., 2022 ). Medication non-adherence in breast cancer patients may lead to less-than-ideal treatment outcomes, reduced therapeutic efficacy, an increased risk of disease progression or recurrence, and ultimately compromises the patient's quality of life and overall prognosis. Many factors, such as individuals, medications, and the protocols of those receiving breast cancer treatment, make it difficult for patients to take their medications consistently (Yussof et al., 2022 ; M. Zhao et al., 2022 ).

The World Health Organization (WHO) defined medication adherence as the degree to which a patient's medicine-taking habits align with a medical professional's approved recommendations (Burkhart & Sabaté, 2003 ). A key factor in determining the effectiveness of treatment is adherence to therapies. Adherence to prescribed medications is a potential challenge to the long-term management of chronic diseases. This is necessary to gain the full potential benefits of therapeutic treatment (Burnier, 2024 ). Inadequate adherence diminishes the maximum therapeutic benefits and, consequently, degrades the overall efficacy of healthcare systems (Brown & Bussell, 2011 ; Burkhart & Sabaté, 2003 ; Ge et al., 2023 ). One of the interventions that play a critical role in enhancing medication adherence is mobile health interventions. This is by providing digital solutions to handle patients not adhering to prescribed prescription regimens (Chiang et al., 2018 ; Hamilton et al., 2018 ; Henry et al., 2019 ; Schnall et al., 2015 ; Sharma et al., 2019 ).

The WHO defined mobile health (mHealth) as ‘medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistance, and other wireless devices' (World Health Organization, 2024a ). The use of voice calls, short messaging service (SMS), wireless data transmission, and mobile phone applications to enhance the delivery of healthcare is referred to as mHealth applications (Babirye et al., 2019 ). Mobile health interventions are becoming more prevalent in the literature (Shingleton & Palfai, 2016 ; J. Zhao et al., 2016 ). These have also been described to be the future of the medical field (Smith & Sverdlov, 2018 ). The main advantages of mHealth are its ability to increase efficiency, improve therapy adherence, reduce expenses, and save time with the least amount of risk (Grekin et al., 2019 ; Ventola, 2014 ). Moreover, mHealth applications offer a variety of features including follow-up management, diagnostic support, surgical planning, postoperative care, and patient and healthcare provider education (Hamilton et al., 2018 ; Zahmatkeshan et al., 2021 ).

Previous studies demonstrated that the widespread usage of mHealth applications has revolutionised healthcare services concerning the detection and diagnosis of both infectious and non-infectious disorders, including COVID-19, Human Immunodeficiency Virus (HIV), tuberculosis, cancer, hypertension, and others (Alwashmi, 2020 ; Schnall et al., 2015 ; Sharma et al., 2019 ). More studies have been conducted on mHealth applications to help with medication adherence, appointment reminders, disease surveillance, emergency tracking, and other aspects of treating and managing patients’ diseases (Chiang et al., 2018 ; Henry et al., 2019 ; Zha et al., 2020 ).

The use of mHealth to improve medication adherence in patients with a range of chronic illnesses, including diabetes, chronic pulmonary obstructive disease, and acquired immunodeficiency syndrome, has been addressed in previous studies (Anglada-Martinez et al., 2015 ; Hamine et al., 2015 ). According to a systematic review, mHealth technology may help with chronic disease treatment adherence (improvement in adherence behaviour in 56% of studies) (Hamine et al., 2015 ). In addition, a meta-analysis of randomised controlled trials demonstrated that mobile phone text messaging approximately doubles the odds of medication adherence among patients with chronic conditions (Thirumurthy & Lester, 2012 ). Regular communication and monitoring between healthcare providers and their patients have improved due to mHealth applications (Hardinge et al., 2015 ; Santo & Redfern, 2019 ). These have resulted in a decrease in the cost of obtaining healthcare services and an improvement in the quality of healthcare delivery (Chiang et al., 2018 ; Zha et al., 2020 ). With the use of mHealth interventions, patients may better manage and adhere to their prescriptions, keep track of their drug usage, and interact with their clinicians (Rowland et al., 2020 ). Moreover, a systematic review to determine the effectiveness of mHealth applications on adherence and symptoms related to oral anticancer medications (OAM), which demonstrated a potential role for mHealth applications in managing OAM side effects (Chow & Tan, 2024 ). Despite the use of mHealth interventions to improve medication adherence for patients with chronic conditions including breast cancer who face difficulties in adhering to therapy (Hardinge et al., 2015 ; Santo & Redfern, 2019 ; Thirumurthy & Lester, 2012 ; Yussof et al., 2022 ; M. Zhao et al., 2022 ), there has been no systematic review of the effectiveness of mHealth interventions in medication adherence among breast cancer patients regardless of their disease stage and the route of administration of anticancer medications. Therefore, this study aimed to conduct a systematic review of existing scientific literature of randomised clinical trials exploring the impact of using mHealth interventions for medication adherence in breast cancer patients.

Methods

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The review protocol was registered with the International Platform of Registered Systematic Reviews and Meta-analysis Protocol (INPLASY) under registration number: INPLASY2024100061.

Eligibility criteria

The researchers followed the Population, Intervention, Comparison, Outcome, and Study Design (PICOS) framework to define the inclusion criteria for this systematic review. Table 1 summarises the inclusion criteria.

P opulation: this review included breast cancer female patients, aged 18 years old or above.

I ntervention: using mHealth interventions to improve medication adherence.

C omparator: routine practice or standard care for improving medication adherence.

O utcome: improved medication adherence.

S tudy design: randomised controlled trial.

Randomised controlled trials (RCTs) were selected as the eligible study design for inclusion in this systematic review, as they are widely regarded as the gold standard for evaluating the effectiveness of interventional approaches. Randomisation minimises selection bias and confounding, thereby allowing a more accurate and reliable estimation of the true impact of the innovative interventions under investigation (Hariton & Locascio, 2018 ). This methodological approach is consistent with previous systematic reviews, which have restricted inclusion to RCTs in order to ensure high internal validity and to draw robust conclusions regarding intervention effectiveness (Bush et al., 2017 ; Cortés et al., 2021 ; Free et al., 2013 ; Primack et al., 2012 ).

Non-randomised controlled trials are often excluded from systematic reviews of interventional studies because the absence of randomisation increases the risk of bias and confounding, thereby weakening causal inference and internal validity (Sterne et al., 2016 ). Consequently, evidence-based frameworks such as Cochrane and GRADE prioritise randomised controlled trials as the most reliable design for estimating true intervention effects (Guyatt et al., 2008 ; Sterne et al., 2016 ). Moreover, preliminary studies, feasibility studies, and pilot studies have different objectives to RCTs and not powered to test a hypothesis of the outcome as RCTs (Abbott, 2014 ; Arain et al., 2010 ; Whitehead et al., 2014 ). Finally, when notable clinical, methodological, or statistical heterogeneity exists, a narrative synthesis may be more appropriate than meta-analysis in systematic reviews of interventional studies, as pooling heterogeneous data may influence the interpretation of effect estimates (Ioannidis, 2016 ).

Additional inclusion criteria were (i) studies published in peer-reviewed journals in the English language; (ii) full-text article accessibility; and (iii) studies published from inception until June 2025.

The exclusion criteria were as: (i) ongoing studies; (ii) studies on non-human subjects; (iii) studies not published in English; (iv) RCTs on various cancers other than breast cancer; (v) study protocols, cross-sectional studies, descriptive studies, qualitative studies, cohort studies, reports, commentaries, preliminary studies, pilot studies, feasibility studies, editorials, book chapters, systematic reviews, conference abstracts, or meta-analysis; and (vi) articles with no accessible full text.

Search strategy

An extensive literature search was conducted between March 2024 and June 2025 to find studies about using mHealth interventions to improve medication adherence among breast cancer patients in the following databases: PubMed, Medline, Cochrane, and Scopus, from inception until June 2025 using keywords and Medical Subject Headings (MeSH) terms.

The search strategy was developed based on the review objectives and an extensive literature review (Anglada-Martinez et al., 2015 ; Cai et al., 2021 ; Chow & Tan, 2024 ; Hamine et al., 2015 ; Mehra et al., 2021 ; Yussof et al., 2022 ; Zahmatkeshan et al., 2021 ), and was subsequently reviewed by a health science librarian for validation, please see Supplemental Appendix 1 . Search keywords to identify the population, intervention, and outcomes approach were used to define the included studies. These are presented in Table 2 . The population was not restricted to any specific country or geographic location. Keywords were searched withing titles or abstracts and combined using Boolean operators (‘OR', ‘AND') and truncation. A supplemental search was done by hand-searching bibliography lists from all included papers and receiving email alerts for any newly published relevant papers from pre-specified databases to find other papers not identified through the electronic search.

Study selection

One reviewer (BFA) imported the search results from all databases into Mendeley reference software (Mendeley Reference Manager, 2024 ), where duplicates were removed. The study selection was done independently by two reviewers (BFA & RH). Any disagreement or uncertainty regarding the inclusion or the exclusion of the studies during the screening process was resolved by discussion between the reviewers, or consultation with a third reviewer (ZUB). The first phase was the title and abstract screening conducted by two reviewers (BFA & RH) then the selected studies were verified by the reviewer (ZUB). In the second phase, two reviewers did full-text screening independently (BFA & RH). The final list of studies was developed as a consensus of all reviewers to determine the reasons for exclusion.

Data extraction

Two reviewers (BFA & RH) completed data extraction independently using a standardised Excel spreadsheet (Microsoft Corp, Redmond, Washington). Any disagreement was resolved by discussion between the two reviewers or consultation with a third reviewer (ZUB). Extracted data included the first author, year of publication, study country, study design, study objectives, name of the mHealth intervention used, study duration, study participants, sample size, number of arms, medication adherence assessment tool, nature of intervention delivered, and a summary of major conclusions.

Quality assessment

Two reviewers (BFA & RH) independently assessed the quality of the included studies (n = 6), during the data extraction phase. As the included studies were randomised controlled trials, the Cochrane Risk of Bias Assessment Tool version 2 (RoB2) was used (Pack CRGS, 2024), which includes 5 main domains with 22 statements about bias arising from randomisation, bias due to deviations from intended interventions, bias due to missing outcomes, bias in the measurement of the outcome data, and bias in the selection of the reported result. The risk-of-bias judgments are ‘low risk of bias,' ‘some concerns,' or ‘high risk of bias’.

Data synthesis

A Synthesis Without Meta-Analysis (SWiM) was conducted on the included studies, due to the heterogeneity of the studies (Campbell et al., 2020 ). The SWiM is intended to facilitate transparent reporting of the synthesis of effect estimates when meta-analysis is not employed. It specifically elaborates reporting, synthesis, and presentation methods and results, and is likely to be most relevant to reviews that incorporate diverse sources of data not suitable for meta-analysis (Campbell et al., 2020 ). All included studies measured the adherence outcomes using various types of mHealth interventions (Graetz et al., 2024 ; Hershman et al., 2020 ; Kim et al., 2018 ; Park et al., 2022 ; Tan et al., 2020 ; Ziller et al., 2013 ).

Results

Characteristics of included studies

After removing duplicates, a final search strategy from all databases resulted in a total of 2025 studies. For the full-text screening phase, 124 studies were included. After the full-text screening, six studies were included for extraction, based on the inclusion and exclusion criteria of the study ( Figure 1 ). The extracted data are summarised in Table 3 .

The studies were conducted in United States, n = 2; Germany, n = 1, Singapore, n = 1, Republic of Korea, n = 2), and most specifically conducted at outpatient breast surgical clinic (n = 1) (Park et al., 2022 ), clinics of cancer centre (s) or hospital(s) (n = 3) (Graetz et al., 2024 ; Hershman et al., 2020 ; Tan et al., 2020 ), hospital (n = 2) (Kim et al., 2018 ; Ziller et al., 2013 ). Four studies were conducted at a single centre (36,38,39,41), and two studies were conducted at more than one facility (Hershman et al., 2020 ; Tan et al., 2020 ). The sample size ranged from 61 to 348 patients and all included studies recruited adult females (> 18 years). The studies considered early breast cancer cases (stage I-III) receiving adjuvant hormonal therapy (n = 5) (Graetz et al., 2024 ; Hershman et al., 2020 ; Park et al., 2022 ; Tan et al., 2020 ; Ziller et al., 2013 ), and metastatic breast cancer (stage IV) cases receiving palliative chemotherapy (n = 1) (Kim et al., 2018 ). The mHealth intervention that was employed to improve the adherence; was a smart pill bottle paired with the Pillsy mobile application (n = 1) (Park et al., 2022 ), short messaging service (SMS) reminders or text messaging (n = 3) (Graetz et al., 2024 ; Hershman et al., 2020 ; Tan et al., 2020 ), phone call reminders (n = 1) (Ziller et al., 2013 ), and mobile game (n = 1) (Kim et al., 2018 ). The study duration ranged between three weeks to three years. Four studies had randomly assigned the patients into two arms (Hershman et al., 2020 ; Kim et al., 2018 ; Park et al., 2022 ; Tan et al., 2020 ), while two studies included patients into three arms (Graetz et al., 2024 ; Ziller et al., 2013 ).

Risk of bias assessment

All included RCTs were assessed by the Cochrane Risk of Bias assessment tool version 2 (RoB2) (Pack, CRGS). This tool used Y = yes; PY = probably yes; PN = probably no; N = no; NA = not applicable; NI = no information for each statement in each domain. All trials demonstrated adequate randomisation techniques, utilised appropriate allocation concealment, and used appropriate analysis to estimate the effect of assignment to intervention. Moreover, the description of dropout and withdrawal was stated clearly in all trials. As the intervention required overt participation, blinding the participants to treatment allocation was absent from all trials, except the trial that was conducted in Germany, which was blinded (Ziller et al., 2013 ). However, there was inadequate explanation of missing data in all trials, and five out of six trials (83%) showed some concerns in the domain of deviations from the intended intervention. The results of the assessment are presented in Supplemental Appendix 2 .

Description of mhealth interventions

Characteristics of interventions used in all trials are summarised in Table 4 .

Smart pill bottle paired with the Pillsy mobile application

Intervention description

Eligible breast cancer cases on adjuvant oral antiestrogen therapy from the outpatient breast surgical clinic of a single urban tertiary hospital in South Korea were randomised into two groups. The two groups were either to receive standard care of routine clinical follow-ups, without smart pill bottle reminders (control group), or a smart pill bottle and received the smart pill bottle reminder intervention for four weeks (28 days) with the time of medication intake being recorded automatically (Park et al., 2022 ). Briefly, the smart pill bottle beeps and blinks to remind a user when it is time for a dose. It syncs with the free Pillsy mobile application via Bluetooth. The mobile application has several functions, including recording the users’ doses when the bottle cap is opened, tracking the remaining medications (automated tracking), beeping to remind users when it is time for a dose, sending alarm reminders when users forget a dose (alarm reminders), and sending notifications to designated helpers when users miss a dose (sharing with designated helpers) (Park et al., 2022 ).

SMS reminders or text messaging

Intervention description

a. In multicancer clinics in the United States, the eligible breast cancer patients who were receiving adjuvant hormonal therapy received either an educational text message twice a week, on one randomly selected weekday and on one randomly selected day on the weekend, over a period of three years or no text messaging. The content of text messages focused on overcoming potential barriers to medication adherence. It included cues to action, statements related to the efficacy of the medication, reinforcements of the physician’s recommendation to take the medication, and words of support and encouragement (Hershman et al., 2020 ).

b. Eligible breast cancer patients received adjuvant hormonal therapy from the oncology clinics at the National University Hospital and the Ng Teng Fong General Hospital in Singapore, received either SMS or standard care (Tan et al., 2020 ). The SMS reminders were sent weekly on Monday and read: ‘Mdm <PATIENT NAME>, please be reminded to take your anticancer medicine as instructed by your doctor. Take one tablet once every day’ (Tan et al., 2020 ).

c. Breast cancer patients received adjuvant hormonal therapy in a large community comprehensive oncology centre serving western Tennessee, northern Mississippi, and eastern Arkansas who were eligible for the study assigned to either one – the standard care group or two – the application group, or three – the application plus feedback group. The Application group participants received a weekly text message reminder to log into the study application to report adherence and any new or changing symptoms in the prior seven days (Graetz et al., 2024 ).

However, the application plus feedback group participants received additional weekly tailored text messages, including educational information and encouragement related to hormonal adherence, hormonal-related symptoms, lifestyle, social support, and patient-physician communication (Graetz et al., 2024 ).

Phone call reminders

Intervention description

Eligible breast cancer patients who received adjuvant hormonal therapy from the oncology department of the University Hospital of Giessen and Marburg in Germany were assigned into three groups: group one (the control group) – no intervention was performed and only standard information was provided, group two (the letter group) – whereby patients received a personalised motivational letter as a reminder, informative content in combination with a breast cancer information leaflet at weeks one, two, 10, 20 and 33, and at months 15, 18 and 21, group three (the telephone group) – patients were contacted by a study nurse at weeks one, two, 10, 20 and 33 and months 15, 18 and 21 via telephone. Employing a semi-structured interview technique, patients were reminded, informed, and motivated during the phone call. The call aimed to provide individualised information and feedback to questions and problems with medication or to provide contact with the treating oncologist if needed. The strategies were discussed that ensured the regular intake of the tablets (‘What will you do to ensure you don’t forget to take a tablet?’, ‘Where will you keep the tablets?', etc.) (Ziller et al., 2013 ).

Mobile game

Intervention description

Eligible metastatic breast cancer patients receiving palliative chemotherapy at the Chung-Ang University Hospital in Korea were randomly assigned to an education group using mobile gameplay (game group) or conventional education (control group) utilising an interactive web randomisation system. The study mobile game (ILOVEBREAST) was developed to improve self-management and to reduce the side effects of chemotherapy drugs, and it was installed on the participants’ smartphones. They were recommended to play the game for >30 minutes a day, three times a week. They were interviewed every week via cell phone for three weeks duration. The patients in the game group were checked with the records of access to the ILOVEBREAST game (Kim et al., 2018 ).

ILOVEBREAST (CLGAMES, Seoul, Korea) was developed to improve self-management and to reduce the side effects of chemotherapy drugs. Education for preventing side effects of anticancer drugs, support for the prevention of side effects of anticancer drugs, encouragement of mood and activity, and social game playing, which may facilitate participation in such activities in real life, and self-assessment using a personal avatar (Kim et al., 2018 ).

Description of medication adherence outcome from the included studies

There were variations in the ways to collect information for medication adherence methods as the methods varied between automatic smartphone records (Park et al., 2022 ), self-assessment adherence by certain questionnaires such as Simplified Medication Adherence Questionnaire (SMAQ), (Tan et al., 2020 ), the Korean version of the Medication Adherence Rating Scale (K-MARS) (Kim et al., 2018 ), and the self-administered questionnaire (Ziller et al., 2013 ), pillbox mobile data (Graetz et al., 2024 ), self-prescription refill count (Ziller et al., 2013 ), and self-reported discontinuation of treatment (Hershman et al., 2020 ). Furthermore, two trials employed biomarkers for adherence assessment: one employed blood hormonal assays (Tan et al., 2020 ), and the other used urine collection to measure the hormonal level (Hershman et al., 2020 ). Characteristics of medication adherence outcome used in all trials are summarised in Table 4 .

Automatic smartphone records

The medication adherence was calculated as an adherence percentage by dividing the number of days on which at least one bottle opening was registered by the total number of monitored days, multiplied by 100. It was found that at 28 days, a larger proportion of participants in the smart pill bottle reminders group displayed medication adherence compared to the control group (97.30% vs 88.33%). Mean medication adherence rates were higher among the bottle reminders group compared to the standard care group ( t = 2.96, p = 0.004) (Park et al., 2022 ).

Assessment of adherence using questionnaires

At six months, there was a higher percentage of adherence in the SMS group by using SMAQ (72.4%) as compared with the standard care (59.5%). The unadjusted odds ratio (OR) was 1.78 (95% CI 1.04 to 3.05, p = 0.034). After one year, the percentage of adherence in SMS group (68.9%) was higher than that of standard care (65.8%), and statistical significance was not achieved for this comparison (unadjusted OR = 1.15, 95% CI: 0.67–1.96 p = 0.617). Based on the mixed effects of SMS on adherence rate logistic regression analysis, the odds of adherence over one year in SMS was 2.35 times that of standard care (adjusted OR = 2.35, 95% CI: 1.01–5.49, p = 0.048) (Tan et al., 2020 ). Additionally, another trial used the Korean version of the Medication Adherence Rating Scale (K-MARS) for adherence assessment and by analysing the data; the game group also showed improved adherence to medications compared with the control group (K-MARS score, 7.6, SD 0.7 vs 6.5, SD 0.5; P < 0.001) (50). In contrast to a third trial, which employed a self-administered questionnaire for self-adherence assessment, the analysis revealed 98.1% (CI 92–100), 94.3% (CI 87–99), and 100% (CI 96–100) adherent patients for controls, letter, and phone-based intervention groups, respectively. The differences between the groups did not reach to the statistical significance (Ziller et al., 2013 ).

Pillbox mobile data

Adherence to hormonal therapy over one year was measured using the pillbox and was similar across groups: 76.6% for standard care, 73.4% for the application group (difference vs standard care, −3.3%; 95% CI, −11.4% to 4.9%; P = .43), and 70.9% for the application plus feedback (difference vs standard care, −5.7%; 95% CI, −13.8% to 2.4%; P = 0 .17) (51).

Biomarkers assessment for adherence

One trial by Tan et al. ( 2020 ), employed blood hormonal assays to assess medication adherence, and it was noted that after one year, the androstenedione level was similar in both groups (RMD = 1 .00, 95% CI: 0.88–1.13, p = 0.953). Although the estrone level at one year was higher in SMS as compared with standard care, the mean difference of 0.96 (95% CI: −0.43 to 1.35) was not significant ( p = 0.174) (Tan et al., 2020 ). Meanwhile, for the trial that employed urine collection to measure the hormonal level to check adherence failure at baseline and every three months for three years, there was no evidence of a statistically significant benefit of text messaging (TM) at any level of the urine assay (Hershman et al., 2020 ).

Self-prescription refill count

The prescription refills were achieved in 48% (CI 35–62), 64.7% (CI 51–76), and 62.7% (CI 48–74) in the control group, letter group, and phone group, respectively. The differences between the groups did not reach statistical significance (Ziller et al., 2013 ).

Self-reported discontinuation of treatment

In one trial by Hershman et al. ( 2020 ), the observed adherence at 36 months was 55.5% for the text message group and 55.4% for the no text message group. The primary analysis showed no difference in time to adherence failure by arm (3-year adherence failure: 81.9% text message, 85.6% no text message; HR, 0.89 [95% CI, 0.76 to 1.05]; p = 0 .18) (Hershman et al., 2020 ).

Discussion

This systematic review aimed to explore the impact of mHealth interventions on medication adherence among breast cancer patients. This review has the following main findings. First, a smart pill bottle paired with the Pillsy mobile application (Park et al., 2022 ), SMS reminders (49), and phone call reminders (Ziller et al., 2013 ) were associated with improved medication adherence compared to standard care interventions among breast cancer patients receiving hormonal therapy. In contrast, two trials found that text messaging did not improve adherence compared to regular care interventions among breast cancer patients receiving hormonal therapy (Graetz et al., 2024 ; Hershman et al., 2020 ). Additionally, a mobile game improved medication adherence compared to standard care interventions among breast cancer patients receiving palliative chemotherapy (Kim et al., 2018 ). Moreover, most of the included studies assessed adherence using self-reported adherence such as self-administered questionnaires (Kim et al., 2018 ; Tan et al., 2020 ; Ziller et al., 2013 ), self-prescription refill count (Ziller et al., 2013 ), and self-reported discontinuation of treatment (Hershman et al., 2020 ).

Prior research on women with breast cancer revealed limited benefit from standardising information or educational materials in terms of adherence to adjuvant endocrine therapy (Hadji et al., 2013 ; Neven et al., 2014 ). However, ensuring adherence to adjuvant hormone therapy has proven to be successful using reminder approaches (Graetz et al., 2018 ; Krok-Schoen et al., 2019 ). A review of interventional trials reported that interactive and personalised reminders significantly improved medication adherence (Kini & Ho, 2018 ). Thus, the current review expands on earlier findings by showing that SMS reminder intervention can effectively improve medication adherence among breast cancer survivors (49).

Despite these findings, studies assessing strategies to improve breast cancer patients’ adherence to hormone therapy are limited in number. A previous systematic review indicated that the educational interventions had no effect on medication adherence among breast cancer patients when compared to the standard care (Hurtado-de-Mendoza et al., 2016 ).

Two controlled trials revealed that using text messaging to improve antiretroviral therapy (ART) adherence was found to be effective among adolescents (Hailey & Arscott, 2013 ; Mimiaga et al., 2019 ). Moreover, several reviews investigated the impact of text message reminders and other mobile phone strategies in promoting ART adherence and found the intervention to be effective among adults (Amankwaa et al., 2018 ; Free et al., 2013 ; Horvath et al., 2012 ). One review reporting the odds of adhering to ART was 1.6 times higher in those receiving scheduled SMS messages ( p < 0.0001) (Amankwaa et al., 2018 ). A systematic review of 16 randomised clinical trials also evaluated how text messaging on a mobile phone affected patients’ adherence to their chronic illness medications including HIV, cardiovascular disease (CVD), asthma, allergic rhinitis, diabetes mellitus, and epilepsy (Thakkar et al., 2016 ). The most popular technique for evaluating medication adherence was the self-reporting, and there was a statistically significant improvement in medication adherence (Thakkar et al., 2016 ). Although none of the trials included in the present review examined any of the social media applications as an intervention to improve medication adherence among breast cancer patients. It is crucial to note that modern mobile messaging systems, like Instagram, Snapchat, Telegram, Facebook Messenger, and WhatsApp, all have features that are comparable to or even better than those of SMS (Hall et al., 2015 ). We predict that this intervention, along with other smartphone interventions, will be used frequently for medication adherence. Supporting this, augmented smartphone applications providing real-time drug level information and visual representation of immunoprotection have been shown to significantly improve adherence to antiretroviral therapy (Perera et al., 2014 ). Another study revealed that a smartphone mobile application augmented with a medication plan with reminders, a symptom-reporting module, and patient education raised the adherence rates in patients who were receiving oral cancer therapy compared to standard care (Greer et al., 2020 ). Further research is recommended to examine the potential of smartphone applications to improve adherence to medications in breast cancer patients and other chronic illnesses.

Variations in sending SMS reminders time intervals were noticed in this review. Taking into account that daily administration of antihormonal aromatase is required and that medication adherence among breast cancer survivors is impeded by forgetting (Ali et al., 2017 ; Ell et al., 2009 ). Therefore, interventions based on weekly reminders would not be as helpful as those requiring daily reminders. One of the trials included in this review used 28 days duration of reminders (Park et al., 2022 ), which may be insufficient to evaluate long-term adherence and self-efficacy, as adjuvant endocrine therapies are typically prescribed for several years (Burstein et al., 2019 ). It was found in our review that one trial adopted weekly reminders (Tan et al., 2020 ) referring to a RCT in HIV patients in which the weekly reminders improved adherence compared to daily reminders (Pop-Eleches et al., 2011 ). Future studies should consider optimising reminder timing, frequency, and message content to improve SMS or text message reminders or phone call reminder interventions for improved effectiveness.

In this review, better medication adherence among breast cancer survivors with a smart pill bottle paired with the Pillsy mobile application was noticed compared to standard care (Park et al., 2022 ). However, regarding pill counts, patients could also forget to bring the appropriate number of tablets to the clinic. Additionally, pill dumping occurs with this strategy to keep up an impression of adherence (Osterberg & Blaschke, 2005 ). However, the findings from our study were consistent with other research, whereby showing that smart pill container treatments improved medication adherence in patients with two chronic diseases: Human Immunodeficiency Virus (HIV) (Ellsworth et al., 2021 ) and Multiple Myeloma (MM) (Mauro et al., 2019 ). When the smart pill container intervention was used, non-adherence decreased from 39% at baseline to 24% at week 12 after the intervention, indicating a decrease in non-adherence (Ellsworth et al., 2021 ). Moreover, median adherence for patients receiving lenalidomide was improved for the intervention group who received the smart pill compared with the control group (median = 100 % vs. 87.4%; P = 0.001) (Mauro et al., 2019 ).

It was noted in this review that the mobile game ILOVEBREAST was developed to educate patients with advanced breast cancer about the progress of their disease and the potential side effects of their medications. This also included improved medication adherence among metastatic breast cancer patients who were receiving palliative chemotherapy (Kim et al., 2018 ). Prior research has demonstrated a strong correlation between patient medication adherence and understanding of the course of the disease and adverse drug events (S. Zhao et al., 2015 ). In California, it has been reported that using a video-game intervention improved adherence and other behavioural outcomes for adolescents and young adults with malignancies including acute leukaemia, lymphoma, and soft-tissue sarcoma. (Kato et al., 2008 ). Additionally, a systematic review of health-related internet games revealed beneficial outcomes, including improved coping mechanisms for medical issues, improved adherence to therapy, and improved willingness to overcome difficult times during illness (Primack et al., 2012 ).

Most of the interventions used in the included trials primarily focused on addressing forgetfulness part such as SMS reminders or text messaging, (Graetz et al., 2024 ; Hershman et al., 2020 ; Tan et al., 2020 ), and phone call reminders (Ziller et al., 2013 ), which is a significant aspect of medication adherence. However, non-adherence is a multifactorial issue influenced by various factors, including psychological, social, and economic aspects (Gast & Mathes, 2019 ). Therefore, it is highly recommended that future research should focus on all factors that influence medication adherence, to completely address the non-adherence issue.

In our review, there were variations in the medication adherence assessment methods, including automatic smartphone records (Park et al., 2022 ), self-assessment adherence by certain questionnaires such as SMAQ (Tan et al., 2020 ), K-MARS (Kim et al., 2018 ), and self-administered questionnaires (Ziller et al., 2013 ), pillbox mobile data (Graetz et al., 2024 ), self-prescription refill counts (Ziller et al., 2013 ), and self-reported discontinuation of treatment (Hershman et al., 2020 ). Furthermore, two trials employed biomarkers for adherence assessment: one employed blood hormonal assays (Tan et al., 2020 ), and the other urine collection to measure the hormonal level (Hershman et al., 2020 ). Moreover, in this review, all participants in the included trials had to use their phones and no study phones were provided for any (Graetz et al., 2024 ; Hershman et al., 2020 ; Kim et al., 2018 ; Park et al., 2022 ; Tan et al., 2020 ; Ziller et al., 2013 ). However, some issues were not taken into consideration that might have an impact on the effectiveness of the interventions, such as undeliverable messages, damaged phones, and unsuitable mobile networks. Therefore, more research is required to fully understand the unexpected practical problems related to using phones on medication adherence.

Several included trials were assessed as having ‘some concerns' according to the Cochrane Risk of Bias 2 tool, particularly related to deviations from intended interventions, missing data, and selective reporting (Pack CRGS, 2024 ). Although these concerns do not necessarily indicate a high risk of bias, they may affect the certainty and robustness of the reported intervention effects. In particular, lack of blinding and reliance on self-reported adherence outcomes could have led to overestimation of the intervention benefits (Pack CRGS, 2024 ). Therefore, although the findings suggest potential benefits of mHealth interventions for improving medication adherence, the results should be interpreted cautiously. Future clinical trials with more rigorous methodological designs and improved reporting are needed to strengthen the evidence base and confirm these findings.

Strengths and limitations

The methods employed in this review are reproducible, screening of trials and data extraction were carried out by two authors, with disagreement settled with a third reviewer through discussion. However, this study is not devoid of limitations, such as, we limited our studies retrieval to PubMed, Medline, Cochrane, and Scopus search engines, English-language peer-reviewed published studies, and the grey literature was not included in the review, hence, some relevant information may have been missed. Although across-referencing was also performed, which added a few studies to our review, we still cannot claim that we had access to all the relevant articles related to this topic. Despite following PRISMA guidelines to report this systematic review and using Cochrane Risk-of-Bias version 2 for quality assessment, there is a potential for publication bias. Additionally, high heterogeneity was noted among the included RCTs as there were variations in interventions, outcome measurements (medication adherence), and follow-up times that might have influenced the pooled effect size of the intervention.

Conclusion

This review found that mHealth interventions, such as telephone reminders, smart pill bottles with mobile applications, and mobile games can improve medication adherence in breast cancer patients, although SMS alone showed inconsistent effects. Adherence assessment methods varied across studies, including smartphone records, self-reported questionnaires, and biomarkers. Key factors influencing the effectiveness of mHealth interventions include the timing and frequency of reminders, patient preferences, measurement methods, and duration of follow-up. Future research should explore combined approaches, such as pairing phone calls with messaging applications like WhatsApp. These findings provide practical guidance for designing scalable mHealth interventions to enhance medication adherence in breast cancer care.

Statement of ethics

This systematic review was designed according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). It was registered with the International Platform of Registered Systematic Reviews and Meta-analysis Protocol (INPLASY) [Registration number: INPLASY2024100061]. Ethical approval was not required as our study did not include confidential participants’ data and interventions.

Supplementary Material

Appendix 1.docx

Appendix2.docx

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/20523211.2026.2633827 .

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Associated Data

Supplementary Materials

Appendix 1.docx

Appendix2.docx