Remote Monitoring and Self-Efficacy: Implications for Breast Cancer Survivorship Outcomes

ABSTRACT

Objectives: To examine whether self-efficacy in managing symptoms is associated with symptom burden and adjuvant endocrine therapy (AET) adherence among women with early-stage breast cancer and whether self-efficacy modifies the effects of a remote monitoring intervention.

Study Design: Post hoc analysis of a randomized trial evaluating app-based remote monitoring of self-reported symptoms and adherence for women prescribed AET.

Methods: Participants were randomly assigned to enhanced usual care (EUC), app only (weekly remote monitoring), or app plus feedback (with weekly tailored supportive text messages). Outcomes included 12-month symptom burden (Functional Assessment of Cancer Therapy–Endocrine Subscale [FACT-ES]) and adherence (≥ 80% of expected doses recorded using a connected pillbox). Multivariable regression evaluated associations of the PROMIS (Patient-Reported Outcomes Measurement Information System) Self-Efficacy for Managing Symptoms T score with symptom burden and adherence as well as differential intervention effects by self-efficacy level.

Results: Among 264 participants (mean age, 58.5 years; 64% White, 32% Black), mean (SD) baseline self-efficacy exceeded the normative mean of 50 (54.2 [9.9]). Higher self-efficacy was associated with lower symptom burden (eg, higher FACT-ES score: 0.6; 95% CI, 0.3-0.8) and greater odds of adherence (OR, 1.1; 95% CI, 1.0-1.1). Adherence benefits were greater at lower self-efficacy levels (eg, at a score of 30, 52.7% were adherent in app + feedback vs 17.8% in EUC; +34.9 percentage points; 95% CI, 1.0-68.9; P = .04), with effects diminishing and becoming nonsignificant at higher self-efficacy levels.

Conclusions: Without additional support, breast cancer survivors with low self-efficacy had poorer symptom control and lower adherence. Remote symptom monitoring with tailored messaging was most effective among women with lower self-efficacy levels, highlighting the need for screening and targeted interventions for patients most likely to benefit.

Takeaway Points

• In a post hoc analysis of a randomized trial of 264 women with early-stage breast cancer, the mean symptom-management self-efficacy score was 54.2, above the normative mean of 50.
• Lower self-efficacy was associated with higher symptom burden and lower medication adherence and was more common among racial or ethnic minority groups, individuals with lower incomes, and those with limited health literacy.
• Remote monitoring with tailored text messages significantly improved adherence for participants with lower self-efficacy, with effects diminishing as self-efficacy approached and exceeded the normative mean.
• Findings underscore the value of screening for self-efficacy and targeting digital interventions to those most likely to benefit.

Breast cancer remains one of the most common cancers among women, with 1 in 8 women in the US likely to develop it.¹ Hormone receptor–positive (HR+) breast cancer accounts for approximately 70% of all breast cancer cases.¹ For patients with HR+ tumors, standard care typically includes at least 5 years of adjuvant endocrine therapy (AET) with medications such as tamoxifen, letrozole (Femara), exemestane (Aromasin), or anastrozole (Arimidex).² AET works by blocking or lowering estrogen levels, thereby reducing the risk of cancer recurrence. When taken daily for 5 to 10 years, AET significantly decreases recurrence, hospitalizations, and mortality.^2-4 However, these benefits are often undermined by low adherence rates, which range from 42% to 78%.^5,6 Adherence challenges stem largely from difficult-to-manage and sometimes intolerable adverse events (AEs), including joint pain, fatigue, and hot flashes.^7-11 Because patients typically have fewer follow-up visits with providers during a typical course of AET than during active treatment phases, bothersome symptoms often go unaddressed, leading to missed doses or discontinuation and, ultimately, reducing survival benefits.^7-9 AET adherence, usually defined as taking more than 80% of doses for 5 or more years, is associated with lower risk of recurrence and mortality.^7-9

Self-efficacy for managing symptoms reflects a patient’s confidence in recognizing, managing, and coping with bothersome symptoms.¹² Rooted in Bandura’s social cognitive theory, self-efficacy is a well-established construct in behavioral science and oncology, representing an individual’s belief in their capacity to execute behaviors necessary to achieve desired health outcomes.¹³ In cancer care, patients are expected to manage complex treatment-related symptoms, and low self-efficacy can make this task difficult, leading to poorer symptom control and treatment adherence.¹⁴ Prior studies consistently show that higher self-efficacy is associated with better treatment adherence and lower symptom burden, as patients with greater confidence are more likely to use self-management strategies to address treatment-related AEs and stay on therapy.^12,15-18 Given the importance of adhering to AET and the notable role of bothersome symptoms in driving nonadherence,^7-11 women with low self-efficacy may benefit from additional support and monitoring when prescribed AET. Although symptom burden is a known barrier to adherence, interventions targeting symptom management have shown mixed results, with some showing improvements in symptom burden but not adherence¹⁹ and others showing short-term adherence improvements^20-22; however, none have demonstrated longer-term adherence improvements beyond 9 months.^23-27 Research is needed to understand how the effectiveness of remote monitoring and supportive messages differs for breast cancer survivors with varying self-efficacy levels for managing symptoms. Addressing these adherence barriers is crucial to improving health outcomes and reducing overall mortality among vulnerable populations of breast cancer survivors.

The THRIVE trial (NCT03592771) tested whether a web-enabled remote monitoring app could improve adherence and symptom management among women with HR+ breast cancer.²⁸ Although the overall trial found no significant difference in adherence between the intervention and usual care arms,²⁴ this post hoc analysis examines whether effectiveness differed based on patients’ baseline self-efficacy level. Specifically, we examine the relationship between self-efficacy, symptom burden, and adherence to AET among women with early-stage breast cancer. We further evaluate whether a remote monitoring intervention, with or without supportive text messaging, differentially impacts adherence and symptom burden among individuals with lower vs higher self-efficacy for managing symptoms. We hypothesize that lower self-efficacy is associated with poorer symptom control and lower AET adherence and that the remote monitoring intervention, particularly when paired with supportive feedback, mitigates these disparities. Specifically, we expect self-efficacy to both directly influence adherence and symptom burden without any intervention and to moderate the intervention’s impact, with the greatest benefit observed among participants with lower self-efficacy levels.

METHODS

Study Design and Participants

The THRIVE study evaluated whether remote monitoring interventions could improve AET adherence. In this nonblinded randomized controlled trial, consenting women at a multisite cancer center with early-stage breast cancer and a recent AET prescription were randomly assigned to 1 of 3 groups: (1) enhanced usual care (EUC), reporting symptoms at each clinic visit; (2) app only, receiving access to the study’s adherence and symptom monitoring app, with increasing or severe symptoms and missed doses reported in the app triggering follow-ups from the oncology team; or (3) app plus feedback, receiving additional weekly educational text messages about managing symptoms, adherence, and communication. The remote monitoring interventions were delivered over a 6-month period, which corresponds to the time frame when AET-related symptoms typically emerge and reach their peak severity.^29,30 All participants were asked to use an electronic adherence monitoring device (Wisepill Technologies) with their AET for 12 months and completed surveys at enrollment and at the 12-month follow-up.

Baseline Measures

An enrollment survey captured participants’ sociodemographic characteristics, including race/ethnicity, income level (≥ 100% vs < 100% of the federal poverty level), health literacy, residential location (nonmetro vs metro), educational attainment (high school degree or less vs some college or higher), and self-efficacy for managing symptoms. Health literacy was measured using a validated single question about how often they felt confident filling out medical forms independently.^31,32 Responses were categorized as higher (always) vs lower (never, rarely, sometimes, often) health literacy. Age, prior receipt of chemotherapy, and zip code were abstracted from each participant’s medical record at enrollment. Rural-Urban Commuting Area (RUCA) codes were used to categorize residential location as metro if the RUCA was 1 and nonmetro if the RUCA was 2 to 10.

Self-efficacy for managing symptoms was assessed using the PROMIS (Patient-Reported Outcomes Measurement Information System) Self-Efficacy for Managing Symptoms 4-item short form.³³ Raw scores (sum of item responses; range, 4-20) were converted to standardized T scores (mean [SD], 50 [10]) following PROMIS scoring guidelines.³³ Higher T scores indicate greater confidence in managing symptoms, and lower scores reflect less confidence.

Study Outcomes

Outcomes included symptom burden and AET adherence at 12 months. Symptom burden was captured using Functional Assessment of Cancer Therapy–Endocrine Subscale (FACT-ES) questions, which asked about 18 common AEs of endocrine treatments, including hot flashes and musculoskeletal pain.³⁴ Individual responses to 5-point Likert scales were used to calculate a composite score for symptom burden, with lower scores indicating greater symptom burden severity (range, 0-76).³⁵ AET adherence was electronically monitored over 12 months by the Wisepill pillbox and measured as at least 80% of the proportion of prescribed doses taken, excluding prescriber-advised pauses and hospitalization days.

Statistical Analysis

We used a linear multivariate regression model to examine factors associated with self-efficacy for managing symptoms. Independent variables included self-reported race/ethnicity, income (less than the federal poverty level vs higher), health literacy, age (< 50, 50-64, ≥ 65 years), residential location (nonmetro vs metro), education (high school graduate or lower vs some college or higher), prior chemotherapy, and study arm assignment.

To assess whether baseline self-efficacy moderated the intervention’s effectiveness, we used a multivariate linear regression model with an interaction term between self-efficacy and intervention arm on symptom burden and a multivariate logistic regression model to examine the interaction on AET adherence. Marginal effects were calculated to quantify differences in outcomes by study arm and across self-efficacy levels.

The University of Tennessee Health Science Center Institutional Review Board approved the study, and the protocol has been previously described.²⁸

RESULTS

Baseline Characteristics

Among the 264 participants included (Table 1), the mean (SD) age was 58.5 (10.7) years. Most participants identified as White (64.0%), followed by Black (32.2%) and other racial/ethnic groups (3.8%), including Asian, American Indian, Hispanic, or mixed race. The majority had some college or higher education (80.7%). Lower health literacy was observed in 18.9% of participants, and 11.0% reported household income below the federal poverty level. Approximately one-third were married or living with a partner, and most resided in metro areas (76.1%). Regarding treatment, 24.2% had initiated tamoxifen, 67.0% anastrozole, and 8.7% exemestane or letrozole; 26.5% had received chemotherapy and 62.5% radiation. Most were diagnosed with stage I disease (70.1%); 11.4% had ductal carcinoma in situ, and 18.6% had stage II to III disease.

Self-Efficacy for Managing Symptoms

The mean (SD) baseline self-efficacy score was 54.2 (9.9) (range, 22.2-64.7). In adjusted analyses (Table 2), lower health literacy (−5.6; 95% CI, −8.5 to −2.6; P < .001), income below the federal poverty level (−4.1; 95% CI, −8.1 to −0.1; P = .045), older age (50-64 years: −3.7; 95% CI, –6.3 to –1.0; P < .01; ≥ 65 years: −4.5; 95% CI, –7.8 to –1.2; P < .01), and identifying as other race/ethnicity (−9.0; 95% CI, −15.7 to −2.3; P < .01) were significantly associated with lower self-efficacy scores. No statistically significant associations were observed for location, education, prior chemotherapy, or study arm.

Symptom Burden

In adjusted results, each 1-point increase in baseline self-efficacy was associated with a 0.57-point higher FACT-ES score (95% CI, 0.34-0.81; P < .001), indicating lower symptom burden at 12 months (eAppendix Table 1A [eAppendix available at ajmc.com]). Higher self-efficacy was consistently associated with higher FACT-ES scores across all study arms (Figure 1).

The intervention effects on symptom burden were statistically nonsignificant across different self-efficacy levels. At a lower self-efficacy level (eg, score of 30), the adjusted mean FACT-ES scores were 45.7 for EUC, 49.6 for app only (+4.0 vs EUC; 95% CI, −4.5 to 12.4; P = .36), and 50.6 for app plus feedback (+5.0 vs EUC; 95% CI, –3.85 to 13.80; P = .24). At a higher self-efficacy level (eg, score of 60), the means were 62.9 for EUC, 61.8 for app only (–1.1 vs EUC; 95% CI, –4.7 to 2.5; P = .54), and 63.4 for app plus feedback (+0.5 vs EUC; 95% CI, –3.2 to 4.2; P = .80) (Figure 1 and eAppendix Table 1B).

AET Adherence

In logistic regression, higher baseline self-efficacy was significantly associated with greater odds of AET adherence at 12 months (OR per 1-point increase, 1.1; 95% CI, 1.0-1.1; P < .01). Interaction terms indicated that the effect of self-efficacy was attenuated in the app-plus-feedback arms compared with EUC (eAppendix Table 2A).

We observed differential intervention effects on adherence by self-efficacy levels. At lower self-efficacy levels (eg, score of 30), 17.8% in EUC were adherent compared with 46.0% in app only (+28.2 percentage points vs EUC; 95% CI, –3.5 to 59.9; P = .08) and 52.7% in app plus feedback (+34.9 percentage points vs EUC; 95% CI, 1.0-68.9; P = .04). At higher self-efficacy levels (eg, score of 60), adherence rates were 62.7% in EUC, 55.5% in app only (−7.2 percentage points vs EUC; 95% CI, −23.8 to 9.4; P = .40), and 53.0% in app plus feedback (−9.7 percentage points vs EUC; 95% CI, −26.9 to 7.5; P = .27) (Figure 2 and eAppendix Table 2B).

DISCUSSION

In this post hoc analysis of a randomized trial of remote symptom monitoring, women with early-stage breast cancer prescribed AET had a mean self-efficacy score of 54.2, above the normative mean of 50.³⁶ Higher self-efficacy was associated with lower symptom burden and greater AET adherence at 12 months among participants in EUC who did not receive remote monitoring support. Importantly, remote monitoring with tailored text support significantly improved AET adherence among participants with lower self-efficacy, but benefits diminished and became statistically nonsignificant at higher self-efficacy levels. Together, these findings highlight self-efficacy as a potentially actionable predictor of adherence and response to remote monitoring, and could be leveraged to personalize survivorship care, optimize resource allocation, and support value-based oncology delivery.

Consistent with prior behavioral and cancer disparities research, sociodemographic disparities in self-efficacy for managing symptoms were evident. Participants identifying as members of racial or ethnic minority groups, those with lower income, and those with limited health literacy were more likely to report lower self-efficacy scores.^37-39 These patterns may reflect well-documented structural inequities, including differential access to culturally responsive care, communication barriers, prior negative health care experiences, and varying levels of social support, each of which may shape a patient’s perceived ability to manage treatment-related symptoms.^39-41 Because self-efficacy intersects with other social determinants of health, low self-efficacy can compound preexisting disparities in symptom burden, treatment adherence, and, ultimately, survivorship outcomes. From a policy and health system perspective, these disparities underscore the need for risk-stratified approaches that prioritize supportive services for those most likely to benefit.

Lower self-efficacy for symptom management has important clinical implications. Among EUC participants, those with lower self-efficacy reported substantially worse symptom burden and were more likely to be nonadherent to AET at 12 months (Figure 1 and Figure 2). These findings align with behavioral frameworks such as Bandura’s social cognitive theory, which highlights self-efficacy as a key determinant of health behavior and outcomes.¹³ Although the findings of numerous reviews^14,42,43 and cross-sectional studies^12,44,45 highlight self-efficacy as a modifiable driver of adherence to oral anticancer therapies, no intervention trials have specifically targeted individuals with lower self-efficacy related to symptom management. Our findings suggest that this group, which experiences both higher clinical burden and greater structural barriers, may represent an especially important population for targeted digital health strategies.

This study provides novel evidence that digital health interventions may confer differential benefits based on baseline self-efficacy. The app-plus-feedback intervention significantly improved adherence among participants with lower self-efficacy, with effect sizes diminishing and becoming nonsignificant as self-efficacy approached and exceeded the normative mean. This moderating effect suggests that self-efficacy may serve as a clinically meaningful segmentation variable for digital health implementation. Mechanistically, the intervention’s personalized text messages, symptom-triggered alerts, and connected monitoring features likely enhanced patients’ sense of support, reinforced effective symptom self-management, and facilitated timely provider intervention. These mechanisms may buffer the challenges faced by individuals with lower confidence in managing symptoms, many of whom also face overlapping socioeconomic or structural barriers. Notably, this pattern supports a precision-support paradigm: delivering more intensive monitoring and tailored communication to those who need it most rather than deploying universal digital interventions that dilute value, create unnecessary burden, or fail to reach populations at risk.

For health systems, these findings have direct relevance to current priorities around value-based cancer care, population health management, and efficient resource allocation. A brief, low-cost self-efficacy assessment, administered at treatment initiation, could serve as an early risk-stratification tool that is feasible to incorporate into existing workflows. Such screening could guide differentiated care pathways in which patients with low self-efficacy receive programmatic digital support, including remote symptom monitoring, enhanced communication touchpoints, and structured follow-up. This stratified model aligns with ongoing efforts in oncology to shift from one-size-fits-all survivorship care toward personalized models that emphasize patient-reported outcomes, risk-adjusted care delivery, and proactive management of treatment barriers.⁴⁶ Furthermore, remote symptom monitoring is increasingly supported through reimbursement mechanisms, including CMS remote patient monitoring and remote therapeutic monitoring codes, as well as enhanced survivorship planning and navigation services under programs such as the Oncology Care Model and its successors.^47,48 A risk-stratified approach that leverages psychosocial screening to direct digital intervention intensity could enhance cost-effectiveness, strengthen payer confidence in remote monitoring programs, and help health systems demonstrate value-added care, particularly in addressing disparities.

Because individuals with low self-efficacy disproportionately include structurally marginalized groups, tailoring digital health interventions based on self-efficacy may also advance equity. Digital tools, when designed for inclusivity,⁴⁹ have the potential to extend supportive care beyond traditional clinic encounters, reducing geographic, transportation, communication, and navigation barriers. By providing timely, personalized support to patients at the highest risk for poor adherence, risk-stratified digital interventions may help narrow disparities in cancer survivorship outcomes. However, digital inequities remain a concern. Our sample required smartphone ownership and English proficiency, potentially excluding patients who face digital access and language barriers. These considerations highlight the need for future work to ensure that digital tools are available across languages and accessible to all, alongside policy efforts to reduce digital inequities.

Although higher self-efficacy was associated with better outcomes, it did not guarantee adherence. Notably, more than one-third of participants with self-efficacy scores higher than 60 remained nonadherent. This suggests that alternative factors, including treatment complexity, financial toxicity, work-related constraints, competing responsibilities, or unmet informational needs, may drive nonadherence in higher self-efficacy groups. For these individuals, strategies beyond symptom-focused support may be necessary, such as cost-transparency tools, behavioral economics–informed reminders, financial navigation, or workflow-integrated clinician nudges.

Strengths and Limitations

This study has several strengths, including a randomized design, a racially and socioeconomically diverse cohort, and its assessment of symptom burden and adherence, which are clinically meaningful and policy-relevant outcomes. These features enhance the internal validity and generalizability of the findings to real-world oncology practice. However, limitations warrant attention. This was a post hoc analysis not prespecified in the trial protocol,²⁸ which limits causal inference regarding effect modification. The sample size may have reduced power to detect differential effects across the full range of self-efficacy levels. The study included only English-speaking participants who owned smartphones, had early-stage disease, and were treated at a single large comprehensive cancer center, narrowing generalizability. The 12-month follow-up period also restricts assessment of long-term adherence, which is critical because AET is prescribed for at least 5 years. Future research should include non–English-speaking populations, incorporate implementation evaluation in community oncology settings, and extend follow-up to evaluate sustainability, scalability, and long-term value.

CONCLUSIONS

Overall, this study adds to a growing body of evidence linking self-efficacy to both symptom burden and adherence in breast cancer survivorship.^12,15-18 We found that, without additional support, breast cancer survivors with low self-efficacy had poorer symptom control and lower adherence. For health systems and payers, these results highlight self-efficacy as a pragmatic, measurable psychosocial factor that can guide risk stratification and personalized digital support. Targeted remote monitoring, directed specifically to individuals with lower self-efficacy, may enhance adherence, reduce symptom burden, and promote more equitable survivorship outcomes. In an era of value-based oncology, integrating brief psychosocial assessments into routine care and leveraging tailored digital tools represent promising strategies to deliver efficient, patient-centered, and equity-focused cancer care. n

Author Affiliations: Rollins School of Public Health (IG) and School of Medicine (XH), Emory University, Atlanta, GA; Medical College of Georgia (SH), Augusta, GA; University of Virginia Comprehensive Cancer Center and School of Medicine (RAK), Charlottesville, VA; West Cancer Center and Research Institute (GAV), Germantown, TN; Renown Health William N. Pennington Cancer Institute and University of Nevada, Reno (LSS), Reno, NV.

Source of Funding: This study was funded by the National Cancer Institute (R01CA218155). The funding agency had no role in the design or conduct of the study.

Author Disclosures: Dr Graetz reports receiving grants from PRIME Education, LLC, and Pfizer Inc outside the submitted work. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (IG, RAK, GAV, LSS); acquisition of data (IG, LSS); analysis and interpretation of data (IG, SH, XH, GAV, LSS); drafting of the manuscript (IG, SH, RAK, GAV); critical revision of the manuscript for important intellectual content (IG, SH, XH, RAK, LSS); statistical analysis (IG); provision of patients or study materials (IG, LSS); obtaining funding (IG, LSS); administrative, technical, or logistic support (IG, XH, LSS); and supervision (IG, LSS).

Address Correspondence to: Ilana Graetz, PhD, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA 30307. Email: ilana.graetz@emory.edu.

REFERENCES

1. Giaquinto AN, Sung H, Newman LA, et al. Breast cancer statistics 2024. CA Cancer J Clin. 2024;74(6):477-495. doi:10.3322/caac.21863

2. Waks AG, Winer EP. Breast cancer treatment: a review. JAMA. 2019;321(3):288-300. doi:10.1001/jama.2018.19323

3. Spring LM, Gupta A, Reynolds KL, et al. Neoadjuvant endocrine therapy for estrogen receptor–positive breast cancer: a systematic review and meta-analysis. JAMA Oncol. 2016;2(11):1477-1486. doi:10.1001/jamaoncol.2016.1897

4. Berry DA, Cronin KA, Plevritis SK, et al; Cancer Intervention and Surveillance Modeling Network (CISNET) Collaborators. Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med. 2005;353(17):1784-1792. doi:10.1056/NEJMoa050518

5. Murphy CC, Bartholomew LK, Carpentier MY, Bluethmann SM, Vernon SW. Adherence to adjuvant hormonal therapy among breast cancer survivors in clinical practice: a systematic review. Breast Cancer Res Treat. 2012;134(2):459-478. doi:10.1007/s10549-012-2114-5

6. Hershman DL, Kushi LH, Shao T, et al. Early discontinuation and nonadherence to adjuvant hormonal therapy in a cohort of 8,769 early-stage breast cancer patients. J Clin Oncol. 2010;28(27):4120-4128. doi:10.1200/JCO.2009.25.9655

7. Dumas E, Jochum F, Coussy F, et al. Explaining the relationships between age, endocrine therapy persistence, and risk of recurrence in hormone receptor–positive early breast cancer: a nationwide cohort study. J Clin Oncol. 2025;43(16):1863-1874. doi:10.1200/jco.24.01131

8. Font R, Espinas JA, Barnadas A, et al. Influence of adherence to adjuvant endocrine therapy on disease-free and overall survival: a population-based study in Catalonia, Spain. Breast Cancer Res Treat. 2019;175(3):733-740. doi:10.1007/s10549-019-05201-3

9. Makubate B, Donnan PT, Dewar JA, Thompson AM, McCowan C. Cohort study of adherence to adjuvant endocrine therapy, breast cancer recurrence and mortality. Br J Cancer. 2013;108(7):1515-1524. doi:10.1038/bjc.2013.116

10. Hu X, Krukowski RA, Stepanski E, et al. Racial differences in symptom burden and endocrine therapy adherence in breast cancer: a post hoc analysis of a randomized trial. J Natl Compr Canc Netw. 2025;23(10):e257054. doi:10.6004/jnccn.2025.7054

11. Hu X, Walker MS, Stepanski E, et al. Racial differences in patient-reported symptoms and adherence to adjuvant endocrine therapy among women with early-stage, hormone receptor–positive breast cancer. JAMA Netw Open. 2022;5(8):e2225485. doi:10.1001/jamanetworkopen.2022.25485

12. Shelby RA, Edmond SN, Wren AA, et al. Self-efficacy for coping with symptoms moderates the relationship between physical symptoms and well-being in breast cancer survivors taking adjuvant endocrine therapy. Support Care Cancer. 2014;22(10):2851-2859. doi:10.1007/s00520-014-2269-1

13. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall; 1986.

14. White LL, Cohen MZ, Berger AM, Kupzyk KA, Bierman PJ. Self-efficacy for management of symptoms and symptom distress in adults with cancer: an integrative review. Oncol Nurs Forum. 2019;46(1):113-128. doi:10.1188/19.ONF.113-128

15. Kimmick G, Edmond SN, Bosworth HB, et al. Medication taking behaviors among breast cancer patients on adjuvant endocrine therapy. Breast. 2015;24(5):630-636. doi:10.1016/j.breast.2015.06.010

16. Neugut AI, Hillyer GC, Kushi LH, et al. Non-initiation of adjuvant hormonal therapy in women with hormone receptor-positive breast cancer: the Breast Cancer Quality of Care Study (BQUAL). Breast Cancer Res Treat. 2012;134(1):419-428. doi:10.1007/s10549-012-2066-9

17. Liu Y, Malin JL, Diamant AL, Thind A, Maly RC. Adherence to adjuvant hormone therapy in low-income women with breast cancer: the role of provider-patient communication. Breast Cancer Res Treat. 2013;137(3):829-836. doi:10.1007/s10549-012-2387-8

18. Hurtado-de-Mendoza A, Carrera P, Parrott WG, Gómez-Trillos S, Perera RA, Sheppard VB. Applying the theory of planned behavior to examine adjuvant endocrine therapy adherence intentions. Psychooncology. 2019;28(1):187-194. doi:10.1002/pon.4931

19. Jacobs JM, Post K, Massad K, et al. A telehealth intervention for symptom management, distress, and adherence to adjuvant endocrine therapy: a randomized controlled trial. Cancer. 2022;128(19):3541-3551. doi:10.1002/cncr.34409

20. Singleton AC, Raeside R, Partridge SR, et al. Supporting women’s health outcomes after breast cancer treatment comparing a text message intervention to usual care: the EMPOWER-SMS randomised clinical trial. J Cancer Surviv. 2023;17(6):1533-1545. doi:10.1007/s11764-022-01209-9

21. Graetz I, McKillop CN, Stepanski E, Vidal GA, Anderson JN, Schwartzberg LS. Use of a web-based app to improve breast cancer symptom management and adherence for aromatase inhibitors: a randomized controlled feasibility trial. J Cancer Surviv. 2018;12(4):431-440. doi:10.1007/s11764-018-0682-z

22. Park HR, Kang HS, Kim SH, Singh-Carlson S. Effect of a smart pill bottle reminder intervention on medication adherence, self-efficacy, and depression in breast cancer survivors. Cancer Nurs. 2022;45(6):E874-E882. doi:10.1097/ncc.0000000000001030

23. Hershman DL, Unger JM, Hillyer GC, et al. Randomized trial of text messaging to reduce early discontinuation of adjuvant aromatase inhibitor therapy in women with early-stage breast cancer: SWOG S1105. J Clin Oncol. 2020;38(19):2122-2129. doi:10.1200/JCO.19.02699

24. Graetz I, Hu X, Kocak M, et al. Remote monitoring app for endocrine therapy adherence among patients with early-stage breast cancer: a randomized clinical trial. JAMA Netw Open. 2024;7(6):e2417873. doi:10.1001/jamanetworkopen.2024.17873

25. Hurtado-de-Mendoza A, Cabling ML, Lobo T, Dash C, Sheppard VB. Behavioral interventions to enhance adherence to hormone therapy in breast cancer survivors: a systematic literature review. Clin Breast Cancer. 2016;16(4):247-255.e3. doi:10.1016/j.clbc.2016.03.006

26. Tan EH, Wong ALA, Tan CC, et al. Improving medication adherence with adjuvant aromatase inhibitor in women with breast cancer: a randomised controlled trial to evaluate the effect of short message service (SMS) reminder. Breast. 2020;53:77-84. doi:10.1016/j.breast.2020.06.012

27. Lautner M, Lin H, Shen Y, et al. Disparities in the use of breast-conserving therapy among patients with early-stage breast cancer. JAMA Surg. 2015;150(8):778-786. doi:10.1001/jamasurg.2015.1102

28. Paladino AJ, Anderson JN, Krukowski RA, et al. THRIVE study protocol: a randomized controlled trial evaluating a web-based app and tailored messages to improve adherence to adjuvant endocrine therapy among women with breast cancer. BMC Health Serv Res. 2019;19(1):977. doi:10.1186/s12913-019-4588-x

29. Henry NL, Giles JT, Ang D, et al. Prospective characterization of musculoskeletal symptoms in early stage breast cancer patients treated with aromatase inhibitors. Breast Cancer Res Treat. 2008;111(2):365-372. doi:10.1007/s10549-007-9774-6

30. Zivian MT, Salgado B. Side Effects Revisited: Women’s Experiences With Aromatase Inhibitors. Breast Cancer Action; June 2008. Accessed July 10, 2025. https://www.bcaction.org/site-content/uploads/2011/03/AI-Report-June-2008-Final-ONLINE.pdf

31. Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36(8):588-594.

32. Chew LD, Griffin JM, Partin MR, et al. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561-566. doi:10.1007/s11606-008-0520-5

33. Gruber-Baldini AL, Velozo C, Romero S, Shulman LM. Validation of the PROMIS measures of self-efficacy for managing chronic conditions. Qual Life Res. 2017;26(7):1915-1924. doi:10.1007/s11136-017-1527-3

34. Sheppard VB, Dash C, Nomura S, et al. Physical activity, health-related quality of life, and adjuvant endocrine therapy–related symptoms in women with hormone receptor–positive breast cancer. Cancer. 2020;126(17):4059-4066. doi:10.1002/cncr.33054

35. Long Y, Xie S, Liu Q, et al. The experiences of adjuvant endocrine therapy for women breast cancer survivors: a literature review. Medicine (Baltimore). 2023;102(51):e36704. doi:10.1097/MD.0000000000036704

36. Yussof I, Mohd Tahir NA, Hatah E, Mohamed Shah N. Factors influencing five-year adherence
to adjuvant endocrine therapy in breast cancer patients: a systematic review. Breast. 2022;62:22-35. doi:10.1016/j.breast.2022.01.012

37. Spencer JC, Reeve BB, Troester MA, Wheeler SB. Factors associated with endocrine therapy non-adherence in breast cancer survivors. Psychooncology. 2020;29(4):647-654. doi:10.1002/pon.5289

38. Sheppard VB, Sutton AL, Hurtado-de-Mendoza A, et al. Race and patient-reported symptoms in adherence to adjuvant endocrine therapy: a report from the Women’s Hormonal Initiation and Persistence Study. Cancer Epidemiol Biomarkers Prev. 2021;30(4):699-709. doi:10.1158/1055-9965.EPI-20-0604

39. Cocozza T, Smith R, Lopez AM, et al. Multilevel factors influencing perceived barriers to adjuvant endocrine therapy among breast cancer patients at medication onset. Int J Environ Res Public Health. 2025;22(5):734. doi:10.3390/ijerph22050734

40. Wheeler SB, Reeder-Hayes KE, Carey LA. Disparities in breast cancer treatment and outcomes: biological, social, and health system determinants and opportunities for research. Oncologist. 2013;18(9):986-993. doi:10.1634/theoncologist.2013-0243

41. Reeder-Hayes KE, Wheeler SB, Mayer DK. Health disparities across the breast cancer continuum. Semin Oncol Nurs. 2015;31(2):170-177. doi:10.1016/j.soncn.2015.02.005

42. Lambert LK, Balneaves LG, Howard AF, Gotay CC. Patient-reported factors associated with adherence to adjuvant endocrine therapy after breast cancer: an integrative review. Breast Cancer Res Treat. 2018;167(3):615-633. doi:10.1007/s10549-017-4561-5

43. Toivonen KI, Williamson TM, Carlson LE, Walker LM, Campbell TS. Potentially modifiable factors associated with adherence to adjuvant endocrine therapy among breast cancer survivors: a systematic review. Cancers (Basel). 2020;13(1):107. doi:10.3390/cancers13010107

44. Post KE, Ahmad Z, Jankauskaite G, et al. Managing symptom distress: key factors for patients on adjuvant endocrine therapy for breast cancer. J Pain Symptom Manage. 2024;67(1):88-97. doi:10.1016/j.jpainsymman.2023.10.001

45. Baik SH, Oswald LB, Buitrago D, et al. Cancer-relevant self-efficacy is related to better health-related quality of life and lower cancer-specific distress and symptom burden among Latina breast cancer survivors. Int J Behav Med. 2020;27(4):357-365. doi:10.1007/s12529-020-09890-9

46. Mayer DK, Alfano CM. Personalized risk-stratified cancer follow-up care: its potential for healthier survivors, happier clinicians, and lower costs. J Natl Cancer Inst.2019;111(5):442-448. doi:10.1093/jnci/djy232

47. Chong A, Honig B, Fowler E, Rawal P, Fogler S, Blackwell K. Role of oncology in the Centers for Medicare & Medicaid Services Innovation Center’s Comprehensive Specialty Strategy. JCO Oncol Pract. 2025;21(8):1079-1083. doi:10.1200/op-24-00746

48. Joo JH, Lieu N, Tang Y, Browne DS, Agusala B, Liao JM. Trends in utilization of remote monitoring in the United States. Health Aff Sch. 2025;3(6):qxaf115. doi:10.1093/haschl/qxaf115

49. Anderson JN, Krukowski RA, Paladino AJ, et al. THRIVE intervention development: using participatory action research principles to guide a mHealth app-based intervention to improve oncology care. J Hosp Manag Health Policy. 2021;5:5. doi:10.21037/jhmhp-20-103