Randomized Trial of a Smartphone Mobile App to Improve Symptoms and Adherence to Oral Therapy for Cancer

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  • a Massachusetts General Hospital, and
  • b Harvard Medical School, Boston, Massachusetts;
  • c University of Colorado-Anschutz Medical Campus, Denver, Colorado;
  • d Harvard T.H. Chan School of Public Health, Boston, Massachusetts;
  • e University of Colorado-Boulder, Boulder, Colorado;
  • f University of California-Los Angeles, Los Angeles, California;
  • g University of Miami, Coral Gables, Florida;
  • h Concerto HealthAI, Boston, Massachusetts; and
  • i Dana-Farber Cancer Institute, Boston, Massachusetts.

Background: Patients with cancer are increasingly prescribed oral therapies, bearing greater responsibility for self-management of treatment adherence and adverse events. We conducted a randomized trial to test the use of a smartphone mobile app to improve symptoms and adherence to oral cancer therapy. Materials and Methods: From February 18, 2015, through December 31, 2016, 181 patients with diverse cancers who were prescribed oral therapy were randomized to receive either the smartphone mobile app or standard care. The mobile app included a medication plan with reminders, a symptom-reporting module, and patient education. Primary outcomes were adherence (per electronic pill caps), symptom burden (per MD Anderson Symptom Inventory), and quality of life (per the Functional Assessment of Cancer Therapy–General). Participants also completed self-report measures of medication adherence, anxiety and depression symptoms, social support, quality of care, and healthcare utilization. Linear regression was used to assess intervention effects on adherence and change in self-report outcomes from baseline to week 12, controlling for baseline scores and social support. Results: Study groups did not differ across any outcome measure, with an overall mean adherence of 78.81% (SD, 26.66%) per electronic pill caps. However, moderation analyses showed that intervention effects on the primary adherence measure varied by baseline self-reported adherence and anxiety symptoms. Specifically, adherence rates per electronic pill caps were higher in patients randomized to the mobile app versus standard care within the subsamples of patients who reported baseline adherence problems (mean difference, –22.30%; 95% CI, –42.82 to –1.78; P=.034) and elevated anxiety (mean difference, –16.08%; 95% CI, –31.74 to –0.41; P=.044). Conclusions: Although the mobile app may not improve outcomes for all patients prescribed oral cancer therapy, the intervention may be beneficial for those with certain risk factors, such as difficulties with adherence or anxiety.

Background

Given advances in the study of tumor genetics, the delivery of cancer care has shifted considerably over the past 2 decades with the increased use of oral cancer therapies.1 Patients overwhelmingly prefer oral versus intravenous treatment due to the convenience of home administration, the mitigation of problems related to intravenous access, and an increased sense of control in taking medication.2,3 However, patients and oncology clinicians now encounter new challenges as cancer care is delivered at home.4,5

Although patients prescribed intravenous cancer therapy receive direct supervision in infusion centers, individuals prescribed oral therapy take their medications remotely with limited oversight and support from clinicians.6,7 The toxicities of oral cancer therapy can be equivalent to those of intravenous chemotherapy, including fatigue, nausea, and diarrhea,8 and the lack of regular contact with an oncology team may impact adherence to oral regimens5,9 because patients often must manage symptoms and adverse effects on their own.

Patient adherence to the prescribed dose and frequency of oral agents for cancer is vital to treatment efficacy. Poor adherence is associated with disease progression and worse survival.1014 Despite its importance for optimal outcomes, adherence to these medications varies widely, with rates ranging from as high as 100% to <50%.1518 A variety of patient-, clinician-, treatment-, and healthcare system–related factors are associated with poor adherence (eg, distress, poor communication, adverse effects, and medication costs).15,16,1820 Greater clinical support and research are needed to overcome the challenges of fragmentation in care from clinic to home, with specific attention to adherence and symptom management.21

However, evidence-based interventions to optimize adherence for patients prescribed oral cancer therapies are lacking.18,22 Mobile technology provides an opportunity for monitoring and support through a minimally burdensome, maximally accessible approach.23 Interventions delivered via mobile technologies can improve health behaviors in patients with cancer.24 Moreover, smartphones allow for repeated evaluation of adherence and symptoms in real time, ideally enhancing care for patients prescribed oral cancer therapies.

We conducted a randomized trial to test the use of a smartphone mobile app to promote adherence to oral therapy for cancer and symptom management. We hypothesized that patients prescribed oral cancer therapy who were assigned to the mobile app would demonstrate better medication adherence and report fewer symptoms and improved quality of life (QoL) compared with patients receiving standard care. Secondary outcomes included patient satisfaction with treatment and healthcare utilization. Finally, we planned to examine potential moderators of intervention effects to identify subgroups of patients who might benefit most from using such technology.

Materials and Methods

Study Design

We recruited patients with diverse malignancies who were prescribed oral therapy for cancer to participate in a 1:1 parallel-assignment randomized controlled trial of the mobile app intervention versus standard oncology care (ClinicalTrials.gov identifier: NCT02157519). The study was approved by the Dana-Farber/Harvard Cancer Center (DF/HCC) Institutional Review Board.

Participants

Patients receiving care at the Massachusetts General Hospital Cancer Center (or 2 satellite sites) were eligible to participate if they had a cancer diagnosis, a current prescription for oral cancer therapy (per electronic health record [EHR] documentation), and a smartphone using an iOS or Android operating system. Eligibility criteria also included age ≥18 years, ability to respond to surveys in English, and an ECOG performance status of 0 to 2.25 We excluded patients with comorbid acute psychiatric symptoms or cognitive impairment interfering with participation and those enrolled in oral therapy clinical trials.

Procedures

Research staff queried the EHR to identify potentially eligible patients and obtained permission from oncologists to approach patients at their upcoming clinic visits. After signing informed consent forms, enrolled patients completed baseline assessments via paper at their next clinic visit or via REDCap, an electronic HIPAA-compliant survey tool.26 The study statistician developed a computer-generated randomization scheme stratified by cancer type (hematologic vs solid malignancy). Independent from the study team, the DF/HCC Office of Data Quality then randomly assigned participants to either the mobile app intervention group or the standard oncology care control group. Study staff provided patients with an electronic pill bottle in which to store their oral cancer medication during the study. Participants then completed the postassessment measures 12 (±3) weeks after the baseline assessment.

Study Groups

Mobile App Intervention

The first phase of this research entailed an extensive, iterative process with key patient, clinician, and healthcare system stakeholders to develop the app.27 For the randomized trial, study staff met with each patient assigned to the intervention in order to download the app on the patient’s personal smartphone and orient the patient to the app functions. Patients were instructed to use the app for 12 weeks. The app included a personalized medication dosing schedule (with an optional reminder system that could accommodate various treatment regimens and cycles), an adherence and symptom reporting module, educational resources for symptom management and other cancer-related topics, and Fitbit integration for tracking physical activity.27 Patients received reminders to take their oral cancer medication and complete weekly adherence and symptom reports via push notifications (ie, pop-up messages). For these weekly reports, the app prompted participants to rate how well they took their oral cancer medication as prescribed using 2 scales (0%–100% and from “very poor” to “excellent”) and to record the level of severity of 17 different symptoms. Results of these weekly reports were transmitted via email to patients’ oncology clinicians, who could respond based on their clinical discretion.

Standard Oncology Care

Patients assigned to standard care alone did not receive the mobile app. These patients received care as usual from their oncology clinicians, completed baseline and post assessments, and were asked to store their oral cancer medication in the electronic pill bottle.

Measures

Primary Outcome Measures

Electronic Pill Caps

The MEMS Cap (AARDEX Group) and GlowCap (Vitality) are electronic pill bottle caps that record the date and time the bottle is opened, serving as a proxy for when patients take the medication. We switched from using GlowCaps to MEMSCaps during the study because of service changes with GlowCaps. Such electronic medication bottles are commonly used in adherence monitoring.18,28

MD Anderson Symptom Inventory

Patients completed the 19-item MD Anderson Symptom Inventory (MDASI),29 which consists of 2 subscales to assess symptom severity and interference in the last 24 hours on a scale of 0 to 10, with higher scores indicating worse symptoms.

Functional Assessment of Cancer Therapy–General

To measure QoL, we administered the Functional Assessment of Cancer Therapy–General (FACT-G),30 a 27-item questionnaire that assesses physical, social, emotional, and functional well-being during the previous week, with higher scores indicating better QoL.

Secondary Outcome Measures

Morisky Medication Adherence Scale

The Morisky Medication Adherence Scale (MMAS-4)31 is a validated and sensitive 4-item self-report measure to assess medication-taking behavior over the past week, on which patients respond “yes” or “no” to each item to indicate any problems with adherence.

Functional Assessment of Chronic Illness Treatment–Treatment Satisfaction–Patient Satisfaction

The Functional Assessment of Chronic Illness Therapy–Treatment Satisfaction–Patient Satisfaction (FACIT-TS-PS)32 is a 29-item questionnaire that assesses patient satisfaction with treatment, clinicians, and communication. Higher scores indicate greater satisfaction. To reduce questionnaire burden, we only administered 5 subscales of the FACIT-TS-PS: clinician explanations, interpersonal treatment, comprehensiveness of care, nurse communication, and confidence and trust in the doctor and treatment team.

Resource Utilization Questionnaire

Patients completed an adapted 3-item resource utilization questionnaire that asked about the number of emergency department (ED) visits and hospitalizations in the past 3 months.

Potential Moderator Variables and Covariates

Sociodemographic and Clinical Factors

Participants reported their gender, race, ethnicity, marital status, education level, employment status, and income on a demographic questionnaire. Study staff collected data from the EHR regarding patient age, cancer diagnosis, ECOG performance status, cancer therapy, ED visits, and hospitalizations.

Hospital Anxiety and Depression Scale

The 14-item Hospital Anxiety and Depression Scale (HADS)33 includes 2 subscales that measure anxiety and depression symptoms in the past week. A threshold of >7 on either subscale indicates clinically significant anxiety or depression symptoms.

Multidimensional Scale of Perceived Social Support

The Multidimensional Scale of Perceived Social Support (MSPSS)34 is a 12-item questionnaire that assesses perceived social support on a scale of 1 to 7, with higher scores indicating greater perceived social support.

Statistical Analyses

SPSS Statistics, version 22.0 (IBM Corp) was used to conduct the analyses. Patient baseline characteristics were described using measures of central tendency or proportions. Based on the intent-to-treat principle, analyses were first conducted with all available participant data, and then with multiple imputation to account for any missing data. We examined between-group differences in outcomes from baseline to 12 weeks using general linear and logistic regression models for continuous and categorical outcomes, respectively. Unstandardized coefficients (B) and odds ratios were considered statistically significant based on a 2-sided α of 0.05 and the 95% confidence interval. Change in perceived social support on the MSPSS was included in all models because of the documented relationship between social support and adherence.18 Analyses also controlled for the baseline value of the outcome of interest. Using the effect size estimates from our prior pilot investigation,20 we had 80% power to detect a statistically significant improvement in mean adherence rates from 0.70 to 0.90 with a sample size of 150 patients (n=75 per group). We increased the target enrollment to 220 patients to ensure that at least 180 were randomized.

We also examined whether the mobile app intervention’s effects on adherence varied among subgroups of interest (per baseline demographics, self-reported adherence, and mood symptoms) based on prior work.18 To test moderators, we regressed the outcome on the interaction between study group and baseline characteristic, baseline characteristic, study group, baseline value of the outcome, and change in perceived social support. We then examined the effect of group assignment on the outcome within each subgroup.

Finally, we created a factor representing patient engagement with the mobile app by using principal components analysis to combine minutes of app use, days of app use, and number of completed symptom reports. We then calculated Pearson product-moment correlations to explore the relationships between app engagement and outcomes.

Results

Baseline Characteristics

From February 18, 2015, through December 31, 2016, study staff approached 500 potentially eligible patients in clinic, of whom 178 (35.6%) did not own a smartphone and 110 (22.0%) declined to participate (Figure 1). Of the 212 patients who enrolled, 181 completed baseline assessments and were randomly assigned to either the mobile app (n=91) or standard oncology care (n=90). The 31 patients who enrolled but failed to complete baseline measures did not differ significantly from those randomized with respect to age, gender, race, ethnicity, or cancer type (ie, solid vs hematologic malignancy). Of the 181 patients, due to an administrative error, baseline data on the MDASI were missing for 31 participants.

Figure 1.
Figure 1.

Study flow diagram.

Abbreviations: EHR, electronic health record; MGH, Massachusetts General Hospital.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 18, 2; 10.6004/jnccn.2019.7354

Mean patient age was 53.30 years (SD, 12.91), 53.6% were women, and most were white (88.4%) and partnered (80.1%) (Table 1). The most frequent diagnoses were hematologic malignancies (33.1%), followed by non–small cell lung cancer (18.2%). Most were prescribed oral targeted therapies (66.9%), and average time on treatment at enrollment for the sample was 12.70 months (SD, 20.87). Approximately one-fifth of the participants (21.5%) reported adherence problems on the MMAS-4 at baseline.

Table 1.

Baseline Sociodemographic and Clinical Characteristics

Table 1.

Intervention Effects on Primary and Secondary Outcomes

Study groups did not differ with respect to the primary outcome of adherence per electronic pill caps. In addition, patient-reported adherence (MMAS-4), symptom burden, QoL, satisfaction with treatment, and healthcare utilization did not differ significantly between groups (Table 2).

Table 2.

Study Group Differences in Primary and Secondary Outcomes

Table 2.

Moderation Effects on Adherence Outcome

As shown in Table 3, intervention effects on adherence per electronic pill caps varied by self-reported adherence (group × baseline MMAS-4 interaction: B=26.04; 95% CI, 6.97–45.10; P=.008) and anxiety symptoms (group × baseline HADS–anxiety interaction: B=17.55; 95% CI, 0.08–35.02; P=.049). Specifically, among the subgroup of patients who reported adherence problems on the baseline MMAS-4, those assigned to the mobile app had better adherence per pill caps on average versus those assigned to standard care (86.23% vs 63.94%; P=.034). Similarly, among patients with elevated anxiety on the baseline HADS–anxiety subscale, mean adherence in the mobile app group was higher than in the standard care group (85.46% vs 69.39%; P=.044). No demographic factors, HADS–depression scores, or time on oral cancer therapy moderated intervention effects on adherence.

Table 3.

Interaction Effects of Group Assignment With Self-Reported Adherence and Anxiety Symptoms

Table 3.

Mobile App Engagement and Outcomes

On average, patients assigned to the mobile app intervention completed 14.70 (SD, 14.12) symptom reports during the study. Patients used the app for a mean of 57.43 minutes (SD, 64.71; range, 0–299 minutes), accessing the app on 21.76 discrete days (SD, 21.24). Minutes and days of app use and the number of completed symptom reports accounted for 83.6% of the variance in the app engagement factor. App engagement, per the combined factor, was not related to any baseline characteristic of the participants assigned to the intervention group. However, greater app engagement was associated with a higher proportion of medications taken per electronic pill cap (r = 0.29; P=.022) and fewer ED visits resulting in hospitalizations per chart review (r = –0.22; P=.048) during the study.

Missing Data Analyses

The rate of missing data at postassessment was 6.6% for the self-report questionnaires and 6.1% for the electronic pill cap data. To account for these missing data and the missing baseline MDASI data, we replicated all analyses using multiple imputation with 10 pooled datasets (supplemental eTables 1 and 2, available with this article at JNCCN.org). Findings were generally consistent with the available patient analyses, showing that study groups did not differ with respect to the primary and secondary outcomes. Although the results of the subgroup analyses were similar for the effect of the intervention among patients who reported baseline adherence problems (P=.040), the moderator effect of those patients with elevated anxiety became nonsignificant (P=.074).

Discussion

We evaluated a novel smartphone mobile app for adherence and symptom management among individuals with diverse malignancies who were prescribed oral therapy for cancer. Patients who received the intervention did not experience improvements in adherence, symptoms, QoL, perceptions of quality of care, or healthcare utilization compared with those who received standard care alone. However, among the subgroups of participants who reported baseline adherence problems or elevated anxiety, the mobile app was associated with higher adherence, as measured by electronic pill caps, in the intervention group versus the control group. Moreover, within the intervention group, greater engagement with the mobile app was associated with improved adherence and fewer ED visits leading to hospitalization.

Although this study highlights the potential for the mobile app to promote medication-taking among patients at greater risk for nonadherence, we failed to observe a benefit of the intervention in improving the prespecified outcomes. Despite the study’s comprehensive and iterative process in developing the app with key stakeholders,27 the intervention simply may not have been effective. Several other factors may have contributed to the null findings: (1) most patients had high self-reported adherence at baseline with little room for improvement, (2) the sample was heterogeneous with respect to cancer types and oral treatments, and (3) clinicians were not required to follow up with patients regarding weekly adherence and symptom reports, but rather could respond based on clinical judgment. In contrast, recent trials have shown that automated remote monitoring of patients during routine cancer care can lead to lower symptom burden and use of acute care as well as improved QoL.35,36 Yet in those studies, investigators provided results of symptom monitoring to clinicians at the point of care35 or included protocols for clinician follow-up for severe or worsening symptoms.36

Medication adherence interventions for patients with cancer are limited, with most studies suffering from small sample sizes and nonrandomized designs.18 In the few existing randomized trials, investigators have examined the use of text messaging and automated voice response systems for adherence and symptom management.3740 Some of these studies were pilot trials showing feasibility and preliminary efficacy for patient adherence.3739 However, the largest multisite study to date (which used automated calls to remind patients to take their medications, assess symptoms, and refer to a symptom management toolkit) showed no difference in adherence to oral oncolytic agents between study groups.40 Attributing the null findings to high rates of adherence in that sample, those investigators suggested that future research ought to identify subgroups of patients who may benefit from interventions to sustain adherence. The results of our study led to a similar conclusion. Although we extend the literature by showing that certain patients (ie, those who report adherence difficulties or elevated anxiety) appear to have higher medication adherence when using the mobile technology and that greater app engagement was associated with better outcomes, further study is needed to confirm these findings.

This study has several limitations worth noting. Although we used the current gold standard for measuring adherence (ie, electronic pill caps), such monitoring in the control group may have raised awareness and improved adherence. Moreover, measuring adherence for patients with interval dosing schedules (eg, 2 weeks on, 1 week off) was challenging in defining periods when patients were supposed to take the medications.41 Study staff had to compare the pill cap data against EHR documentation of planned medication breaks to ensure that patients were not penalized for missing doses on those days. We did not track clinician responses to the weekly adherence and symptom reports, and therefore could not assess clinician engagement with the intervention. Finally, the study was conducted at an academic institution with a fairly homogeneous patient population demographically, limiting the generalizability of findings to other populations and care settings.

Conclusions

Treatment adherence is of public health significance and remains a challenge for the healthcare system, which strives to optimize patient outcomes. Future research should focus on testing mobile health interventions in patients at risk for poor adherence and on standardizing alerts and protocols for clinicians to address problems with adherence and symptoms in real time and at the point of care. Such standardization for remote monitoring and clinician response to patient reports would ideally enhance engagement with mobile health interventions and sustain adherence over the long term. Proactive, systematic monitoring through mobile apps would not only reinforce the importance of adherence for patients but also promote effective, bidirectional communication between patients and clinicians about the administration of oral cancer therapy.

Acknowledgments

Use of the ©MMAS is protected by U.S. copyright laws. Permission for use is required. A license agreement is available from Donald E. Morisky, MMAS Research LLC, 14725 NE 20th Street, Bellevue, WA 98007, or from dmorisky@gmail.com.

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Submitted June 9, 2019; accepted for publication September 3, 2019.

Author contributions: Study concept and design: Greer, Buzaglo, Lennes, Safren, Pirl, Temel. Data curation: Greer, Jacobs, Pensak, Nisotel, Fishbein, MacDonald, Ream, Walsh. Formal data analysis and interpretation: Greer, Jacobs, Muzikansky. Data interpretation: Pensak, Buzaglo, Muzikansky, Safren, Pirl, Temel. Manuscript writing—original draft: Greer, Jacobs. Manuscript writing—review and editing: Penzak, Nisotel, Fishbein, MacDonald, Ream, Walsh, Buzaglo, Muzikansky, Lennes, Safren, Pirl, Temel.

Disclosures: Dr. Greer has disclosed that he receives grant/research support from and is a consultant for Gaido Health/BCG Digital Ventures. The remaining authors have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.

Funding: Research reported in this publication was funded through the Patient-Centered Outcomes Research Institute (PCORI) (IHS-1306-03616).

Disclaimer: The statements presented in this work are solely the responsibility of the authors and do not necessarily represent the views of the PCORI, its Board of Governors, or Methodology Committee.

Correspondence: Joseph A. Greer, PhD, Massachusetts General Hospital, Yawkey Center, Suite 10B, 55 Fruit Street, Boston, MA 02114. Email: jgreer2@mgh.harvard.edu

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Supplementary Materials

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    Study flow diagram.

    Abbreviations: EHR, electronic health record; MGH, Massachusetts General Hospital.

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