The utility of smartphone applications and wearable-based digital biomarkers in assessing quality of life in older women with breast cancer: a systematic review

Introduction

Breast cancer accounted for 2.3 million diagnoses and 670,000 deaths annually in 2022 and projected to increase in coming decades (1). This presents a significant disease burden worldwide as one of the most diagnosed of malignancies and second-leading cause of cancer death in women (2). Breast cancer is more prevalent in women over 65 years, who constitute over a third of new diagnoses and nearly half of deaths globally (3). For women over 70 years, these values are projected to double over the coming decades, a rise four times greater than in younger women under 70 years (4).

Older women with breast cancer face a unique set of challenges that affect their quality of life (QoL). These include frailty, comorbidities, functional decline, treatment-related toxicities, psychosocial issues, and differing treatment priorities—often valuing QoL over aggressive interventions (5). American Society of Clinical Oncology (ASCO) recommends comprehensive geriatric assessments (CGA) for patients over 65 to guide individualised care (6). These assessments which include domains such as cognitive and functional status, comorbidities, and nutrition, have been shown to correlate strongly with QoL and predict treatment outcomes. They help guide personalised treatment plans and are more informative than chronological age alone.

Older adults often present with pre-existing frailty and comorbidities, such as cardiac or renal issues, which increase chemotherapy toxicity risks. Many also prioritize maintaining QoL and independence over aggressive treatments with marginal survival benefits. Despite these needs, older adults remain significantly underrepresented in clinical trials, with only 0–2% participation in some studies, and many clinicians report challenges in addressing their specific needs (5).

Studies have shown that CGA and brief screening tools like the Geriatric 8 are closely linked to QoL, with strong predictive value over time using standardised measures like the European Organisation for Research and Treatment of Cancer QLG Core Questionnaire (EORTC QLQ-C30). Importantly, QoL itself is demonstrably independently predictive of survival and mortality in cancer patients (7,8). Despite these insights, routine QoL monitoring in oncology remains inconsistent and lacks standardization (9). Integrating continuous, digital assessment tools such as smartphone applications and wearable devices (such as smartwatches, fitness trackers, biosensors, etc.), which can collect objective quantifiable data (wearable digital biomarkers) could help fill this gap, offering a more personalised and proactive approach to care for older breast cancer patients. Wearable devices and machine learning offer a promising approach to assessing frailty and conducting real-time geriatric evaluations (10).

An increasing body of research supports the use of smartphone technologies in breast cancer care, demonstrating benefits such as enhanced self-management of symptoms and side effects, improved QoL, and potentially better survival outcomes (11-14). Similarly, wearable-based digital biomarkers like fitness trackers have proven useful in promoting physical activity and addressing barriers to engagement, particularly in managing cancer-related fatigue. These devices allow real-time self-monitoring, empowering patients to regulate their activity during exercise (15). Exercise is a well-recognised effective intervention for improving cancer related fatigue during chemotherapy in cancer patients (16).

In view of the increasing proportion of older women living with breast cancer, our review aims to (I) evaluate the feasibility of smartphone and wearable-based digital biomarkers, and (II) their use in assessing QoL for older women with breast cancer. We present this article in accordance with the PRISMA reporting checklist (available at https://abs.amegroups.com/article/view/10.21037/abs-25-32/rc) (17).

Methods

Search strategy and study selection

A systematic search was carried out on PubMed, Embase and Scopus on 24^th March 2025. The citations contained in pertinent systematic reviews and included articles derived from this search were also manually reviewed for further relevant publications. All publications were restricted to full text English articles from peer reviewed journals.

In line with medical consensus on the definition of an older woman, an age cut-off of greater than or equal to 65 years was employed (18). The search strategy, crafted in consultation with a clinical librarian, is detailed in Appendix 1. Researchers R.X.N.L. and A.W.X.L. independently executed the database searches and imported the results into Rayyan. Duplicates were then identified and removed, after which the same independent researchers screened remaining articles in two stages. In the first round of screening, only titles and abstracts were reviewed to assess potential relevance for full text review. The second stage then involved full text screening of identified articles. Any discrepancies were resolved through discussion with a third reviewer (C.L.K.C). The reference lists of all the relevant studies were also screened to ensure no study had been missed. As per the PRISMA guidelines, a flow diagram (Figure 1) has been developed to report the process of study selection.

Figure 1 Study selection process. QoL, quality of life.

Inclusion criteria are as follows: female participants; studies comparing older women ≥65 years to younger women, or including women of multiple age groups with clear representation of women ≥65 years of age; studies carried out in participants with a breast cancer diagnosis; studies involving smartphone or wearable devices, including native smartphone apps (internet-connected or offline); studies assessing QoL.

The following exclusion criteria were used: studies which do not fulfil inclusion criteria; cases of ductal carcinoma in situ; studies which do not clearly state the number of women ≥65 years of age; studies which included browser-only/web-portal interventions; review articles, editorials or case reports; articles with restricted access.

Data extraction

Data was extracted and double-checked by 2 reviewers (R.X.N.L. and J.P.J.M.L.) using a piloted modified worksheet including: country, year of study, total number of patients included in study, patient age, number of patients ≥65 years of age.

Critical appraisal

A system proposed by Harbour and Miller was employed to assess the included studies (19). The quality of the studies was assessed using the PRISMA statement (17). The level of evidence was assessed as level I through level VII using the guide derived by Harbour and Miller (19). Risk of bias was assessed using the Cochrane risk-of-bias assessment tool (20), and was done at a study and outcome level.

Results

Summary

A total of 4 studies met the inclusion criteria for this review (21-24). These studies were published between 2021 and 2024. In total, 380 women who were diagnosed with breast cancer (early, locally advanced or metastatic) were included in these studies. All four studies were related to using a smartphone application. No studies included the use of wearable-based digital biomarkers. The four studies of this review will now be referred to using their study number (#) as per Table 1.

General characteristics

Characteristics of the included 4 studies are presented in Table 1. Two studies were conducted in the United States, and one study each in Germany and Japan. Most were prospective studies (21-23), with one randomised controlled trial included (24). A summary of each study is presented in Table 2.

Level of evidence

One study was rated level II evidence (24) and three studies were rated level III evidence (21-23). Levels of evidence were determined according to the Harbour and Miller (2001) system. Under this framework, level II evidence refers to well-designed case-control or cohort studies with a low risk of confounding or bias and a moderate probability that the relationship is causal. Level III evidence includes non-analytic studies such as case reports, case series, or studies with higher risk of bias or confounding.

Risk of bias assessment

A summary of the risk of bias assessment for the included studies is given in Table 3. Cochrane RoB 2 was used to assess randomized controlled trials while ROBINS-I was used for non-randomized/observational studies. Of the four included studies, two were globally deemed at low risk of bias and two at moderate risk of bias. Areas of moderate risk of bias are primarily related to bias due to confounding.

Certainty of evidence

The certainty of evidence for each outcome family—feasibility/acceptability, QoL, psychological outcomes, and adherence—was assessed using the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach. Our findings are appended in Table 4.

Findings

We present the findings of this review in line with our objectives. Firstly, we evaluate the feasibility of smartphone and wearable-based digital biomarkers, and secondly, we explore their use in assessing QoL for older women with breast cancer.

Feasibility/acceptability

Only two studies (Study #3 and #4) aimed to evaluate the feasibility and acceptability of using a smartphone application to promote medication adherence among breast cancer patients (22), and for electronic patient-reported outcomes (PRO) (23).

Study #3 (22) investigated the feasibility and effectiveness of a smartphone application ‘Medisafe’, designed to promote oral medication adherence in breast cancer patients through reminder prompts and medication tracking. Of 100 participants, 14 (14%) were aged 70 years and above. The application was feasible, as evidenced by the successful completion of intervention by 78% of participants, who generated adherence reports every 4 weeks through Medisafe. Age, race, ethnicity, clinical stage, and type of medication were not associated with odds of intervention completion. The study also reported high levels of satisfaction with Medisafe, 85.4% of participants noting the application was easy to use, 75% would recommend the application to people. The self-reported nonadherence rates did not improve from baseline to postintervention in the overall study population. However, among patients with self-reported nonadherence at baseline, 26.3% reported adherence postintervention.

Study #4 (23) is a pilot trial that examined the feasibility of a smartphone application ‘Hibilog’, an electronic PRO monitoring tool for patients with metastatic breast cancer undergoing chemotherapy, that allows patients to report their symptoms. Of 71 participants, the response rate to Hibilog from registration to week 40 remained high at 80%, suggesting good compliance to using a smartphone application. The average response time was 5.5 minutes and the missing rate for each item was under 0.4%.

Medication adherence

Study #3 (22) used the questionnaire PROMIS-29, which was completed by 63 patients at baseline and postintervention. There were no statistically significant changes in T-scores for pre- and post-intervention for any of the eight domains assessed (anxiety, depression, fatigue, physical function, pain interference, pain intensity, sleep disturbance, ability to participate in social activities).

QoL outcomes

Three studies assessed the QoL of older women with breast cancer using a smartphone application.

Study #1 (21) is a prospective observational study that utilised a smartphone application to assess PRO and QoL in women with advanced or metastatic breast cancer receiving palbociclib in combination with endocrine therapy. Questionnaires assessing PRO included the Short Form Health Survey (SF-12) and the 10-Item Center for Epidemiological Studies Depression Scale (CES-D-10) were provided to patients via an application downloaded onto their smartphones. Patients completed the questions at daily, weekly, and cycle-based intervals. The study found that fatigue, pain, and depression (CES-D-10) levels were consistently low, and neutropenia did not significantly affect health status. Overall QoL and overall health (SF-12) remained stable during treatment, with most patients reporting “good”, “Very good” or “Excellent”.

Study #2 (24) is a randomised controlled pilot trial that aimed to assess the effect of cognitive behavioural therapy (CBT) using a smartphone application ‘Living Well’ on psychological outcomes in women with breast cancer and breast cancer survivors. The patients were randomised to receive either standard care or the intervention ‘Living Well’ in addition to standard care. Participants in the intervention group received 22 sessions of CBT-based content, each lasting 15–20 minutes. The study reported significant improvements in anxiety levels, illness perception and QoL in the group of patients who used ‘Living Well’. Reduction in distress was not statistically significant, as was the minimal change to depression.

Study #3 (22) reported that there were no statistically significant changes in baseline to postintervention (using the smartphone application Medisafe) T-scores for any of the eight subdomains assessed by PROMIS-29 (anxiety, depression, fatigue, physical function, pain interference, pain intensity, sleep disturbance and ability to participate in social activities).

Symptom burden

Only 1 study discussed the symptom burden on patients. In study #4 (23), the Hibilog application was downloaded onto patients’ smartphones which extracts 18 items from the Patient-Reported Outcome-Common Terminology Criteria for Adverse Events (PRO-CTCAE). This study revealed that the three most commonly reported symptoms interfering with daily life were fatigue (63%), numbness and tingling (48%), and pain (46%).

Discussion

To our knowledge, this is the first comprehensive systematic review reporting on the use of smartphone applications and wearable digital-based biomarkers in older women with breast cancer. Our review included four studies, all of which were related to using a smartphone application. No studies included the use of wearable-based digital biomarkers.

Feasibility of using smartphone applications

Recent studies suggest that smartphone applications can be feasible and well-accepted among older breast cancer patients, provided that adequate training and technical support are given and the interventions are designed to suit their specific needs. Additionally, study #3 (22), emphasised that older patients and those from racial and ethnic minority backgrounds were as likely to use the smartphone application as younger and White patients, suggesting that the digital divide can be overcome by individualised training and technical support, resources not always available in routine clinical practice. Grindrod et al. (25) suggested that e-health training courses for older adults may be helpful in supporting them to be capable and interested in using the smartphone applications.

Hawthorn et al. (26) highlighted the importance of making accommodations and addressing the specific needs of older adults. While many older adult users are comfortable with using smartphone applications, age-related changes such as impairment in cognition, hearing, vision and mobility must be considered. Prioritising usability features such as larger font size, simple navigation may improve the suitability and usability of smartphone applications.

Despite the volume of research available on this topic, however, a review of all studies for oncological wearable remote monitors found that less than 10% were randomised controlled trials with almost half being observational. Few trials demonstrated actual utility of wearable to improvement of outcomes, rather focusing on feasibility of their involvement. Other limitations highlighted, of particular relevance to this paper, were the technologically literate younger participant population involved across studies, and restriction to a singular metric or measurement modality, rather than multi-sensor devices (17). While feasibility and acceptability were well established, evidence for long-term clinical outcomes (e.g., improved survival, reduced hospitalizations) remains limited. Some prior randomised trials have suggested that electronic PRO monitoring may prolong survival in advanced cancer patients, likely due to earlier detection of symptoms and timely medical response. However, none of the four studies included in this review directly assessed survival or cost-effectiveness.

Assessing QoL using smartphone applications

Of the four studies included in this review, two studies (#1 and #4) focused on monitoring patients’ symptoms and symptom reporting in patients undergoing chemotherapy. Two studies (#2 and #3) assessed the effect of an intervention delivered via the smartphone application on patients’ QoL. A few studies have established that use of smartphone application improves QoL in patients with cancer (27,28). A systematic review published in 2019 (27) revealed that interventions using the mobile health applications showed a positive effect by promoting weight loss, improving the QoL, and decreasing stress.

Multiple randomised trials have investigated the effectiveness of electronic patient-reported outcome measures (ePROMs) in cancer care (29-31). These studies have shown that ePROMs can enhance patient satisfaction, strengthen communication between patients and healthcare providers, and help sustain or improve QoL. More recently, randomised controlled trials involving cancer patients receiving chemotherapy have even suggested that ePROMs may contribute to longer overall survival (30-33). These results highlight the significant role ePROMs can play in improving clinical outcomes and informing patient treatment decisions. By integrating patients’ self-reported experiences and monitoring their symptoms more closely, clinicians can deliver more tailored and responsive care. As a result, ePROMs are emerging as a transformative communication tool in oncology (34). This led to the European Society for Medical Oncology clinical practice guidelines publication which strongly recommend use of ePROM in cancer patients undergoing systemic drug therapy in 2022 (35).

However, despite these promising developments, disparities in access to smartphones and digital literacy exist—most notably among older adults (36). In the United Kingdom, a report stated that access to smartphones and tablets is also considerably lower in adults over the age of 65 years old (37). Additionally, Moon et al. stated that only 55% of patients aged 66–75 years and 31% in those above 75 years had access to a smartphone, in comparison to 97% in those aged 45 years and under (36). Poorer e-health literacy was associated with being older, lower educational attainment, and lack of access to a mobile device (36). These issues with access to smartphones are further exacerbated by issues with long-term engagement with the older adults. In study #4, while the overall compliance with ePROMs was high, exploratory analyses showed a decline in response rates among participants aged 65 and older after 10 weeks. This is supported by studies which have concluded that older adults are less likely to own or use mobile health apps, use the internet or seek information online (38,39). And when they do, they may experience greater difficulty navigating interfaces or understanding app functionalities.

Interestingly, a pilot randomised controlled trial to test the use of a web-based application to improve breast cancer symptoms and medication adherence suggested that older adults are more likely to engage with web-based application (40). However, the study was an unpowered small pilot trial which recruited patients from a single clinic and required patients to have internet access via a computer or mobile device. Other literature highlights the importance of impact of social support from family, friends, professional caregivers and peers, in influencing the acceptance of technology and willingness to engage (16). Another review examining the adoption of innovative assistive technologies (IAT), including smartphones and wearable devices like activity trackers, among individuals with cognitive impairment and their caregivers, found that caregivers had a significant role in facilitating IAT implementation (41). Older women, who make up a significant proportion of the breast cancer population, often face barriers related to digital literacy, device access, and confidence in using technology. These disparities limit the accessibility and engagement with smartphone applications. This underscores the need for age-tailored intervention strategies.

Wearable-based digital biomarkers

The following discussion on wearables is presented as a commentary on the evidence gap identified in this review, rather than a finding derived from the included studies. It aims to highlight potential opportunities and directions for future research in this emerging area. A recent scoping review on application of wearables for remote monitoring of oncology patients (42) showed that most studies among oncology patients are focused on assessing user feasibility of wearables and mostly feature multiple cancer types. Despite breast cancer being the most prevalent specific type in publications, our study did not include any article that explored the feasibility and use of wearable-based digital biomarkers in older women. A recent pilot study by Barillaro et al. (43) assessed the compliance of breast cancer patients with fitness tracker monitoring program during radiotherapy. The fitness tracker remotely and continuously monitored heart rate and steps as markers to assess radiation induced fatigue status of patients. Further machine learning-based extrapolation of the functional trajectories could predict impending fatigue, and timely intervention could be undertaken, adding to the potential of such a device (43). This is one of many studies that have demonstrated utility of wearable devices to measure physiological activity across multiple cancers, most commonly breast cancer which accounted for over a third of studies. Correlations have been found between these markers and performance, fatigue, QoL, hospitalization, and survival (44). Within the limitations of a small sample size of 36 patients, their study confirmed the feasibility of continuous biomedical monitoring of breast cancer patients by fitness tracker. This potentially can aid healthcare professionals in evaluating radiation induced fatigue trajectories and provide personalised care strategies to improve patient outcomes.

A recent observational study (45) concluded that older adults have overall high adherence to wearable use. In fact, wrist-worn wearables for tracking physical activity are gaining popularity, even among older adults. Recent estimates suggest that 17% of U.S. adults aged 50 years or older already use activity watches/wearable trackers regularly (46). A recent meta-analysis (47) on older adults’ experiences with using wearable devices showed overall high levels of acceptability. It also suggested that a support structure should be placed to foster motivation, encourage peer engagement and adapt to the user’s preferences, in order for the successful integration of the device into the user’s daily life (47). Factors like cognitive functioning and patient demographics, however, may represent potential barriers to equal uptake of wearables in older adults (45).

Strengths and limitations of the present review

The main limitation of the present study is that there is a small number of articles included, with only four studies exploring the use of smartphone applications in assessing QoL in older women with breast cancer, and no studies have been found to explore the use of wearable-based digital biomarkers.

Secondly, most of the studies were conducted in Western countries, which may limit the applicability of the data in our study to other countries. Older women in different parts of the world may have significantly different experiences. Conducting research specific to individual countries would be valuable, as differences in healthcare systems, as well as broader societal issues such as race and socioeconomic status, could influence patient experiences.

Thirdly, given that the elderly population constitutes a substantial proportion of patients with this condition, the relatively small representation of older adults ≥65 years old in the included studies of 90/380, with 2 studies with (14% and 10% representation of ≥65 years) may limit the generalisability of our findings to this demographic, particularly as age-related factors (e.g., comorbidities, pharmacologic response, or frailty) could influence both treatment efficacy and adverse events. This constrains the external validity of our findings and limits our ability to perform meaningful subgroup analyses by age. With such small sample sizes, any age-stratified comparisons would be statistically underpowered and potentially unreliable.

Lastly, a few potentially eligible studies could not be retrieved due to restricted access, including paywalled and non-digitized papers, despite comprehensive efforts to obtain them through institutional subscriptions, interlibrary loan requests and direct author contact. Their exclusion may introduce selection bias if their findings differed from accessible studies and influenced the completeness and representativeness of the evidence base. However, given their small number and possible overlap with included studies, the overall impact on conclusions is likely minimal.

Conclusions

Smartphone applications show promising utility in the care of the older breast cancer patients, especially for improving symptom monitoring, medication adherence and QoL. This group of patients present unique complexities and differing treatment priorities compared to younger patients, which necessitate individualised interventions. Despite the growing interest, the current body of evidence in older patients remains limited.

In conclusion, while smartphone applications offer meaningful potential in improving breast cancer care in older patients, their successful implementation in this population must consider the complexity and needs. Future research should focus on conducting age-inclusive well-designed research to assess the long-term clinical outcomes and cost-effectiveness of smartphone applications and wearable-based digital biomarkers in older breast cancer patients. Integration of these tools into future breast cancer pathways could significantly contribute to personalized and proactive cancer management.

Acknowledgments

We would like to thank Ms. Wong Suei Nee, librarian at the National University of Singapore, for her assistance in creating the search strategy.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://abs.amegroups.com/article/view/10.21037/abs-25-32/rc

Peer Review File: Available at https://abs.amegroups.com/article/view/10.21037/abs-25-32/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://abs.amegroups.com/article/view/10.21037/abs-25-32/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.

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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