Pilot Feasibility Trial of a Supportive Care Digital Application for Patients with Advanced Nonsmall Cell Lung Cancer

Abstract

Background:

Patients with advanced nonsmall cell lung cancer (NSCLC) experience burdensome symptoms, psychological distress, and poor quality of life (QOL).

Objective:

We developed and pilot-tested a digital health application (“THRIVE”), consisting of six modules designed to improve patients’ symptom management and coping with NSCLC.

Design:

Randomized pilot feasibility trial.

Setting/Subjects:

Eligible patients included adults within 12 weeks of an advanced NSCLC diagnosis receiving care at a participating institution in the United States.

Measurements:

Participants completed baseline and 12-week assessments of QOL (Functional Assessment of Cancer Therapy-Lung), physical symptoms (MD Anderson Symptom Inventory; MDASI), psychological distress (Hospital Anxiety and Depression Scale), and coping (Brief COPE). The primary outcome was study feasibility, defined as ≥65% of approached patients consenting to participate; ≥70% of intervention participants completing ≥4 of 6 app modules; and ≥70% of the sample completing the 12-week assessments. We used the System Usability Scale (SUS) to assess intervention acceptability.

Results:

Of 232 patients approached, 135 (58.2%) provided consent, and 120 (51.7%) were randomized (Age_mean = 67.90 years, 61.7% female, 90.8% White). Among intervention participants, 70.5% (43/61) completed ≥4 modules, with 77.3% reporting above-average SUS usability ratings for THRIVE. Ninety-four (78.3%) participants completed the 12-week assessments.

Conclusions:

Although the enrollment rate was lower than anticipated, patients with advanced NSCLC who received THRIVE met the feasibility criterion for app completion and reported high acceptability. These results support conducting a follow-up efficacy trial of THRIVE for improving patients’ QOL, physical symptoms, and other psychosocial outcomes.

Key Message

This study demonstrates the feasibility and acceptability of the supportive care digital application “THRIVE” for patients with advanced nonsmall cell lung cancer, warranting follow-up efficacy testing of THRIVE for improving patients’ QOL, physical symptoms, and other psychosocial outcomes.

Introduction

Advances in cancer therapeutics have led to improved survival among patients with advanced lung cancer. Nonetheless, patients continue to experience a high burden related to lung cancer and its treatment, with both physical symptoms and psychological distress that impair their quality of life (QOL).^1–3 To help mitigate this burden, the American Society of Clinical Oncology and the National Comprehensive Cancer Network recommend that patients with advanced cancer receive early, integrated palliative care, defined as palliative care within eight weeks of diagnosis and throughout the course of illness, alongside oncology care.^4,5 These recommendations are supported by strong empirical evidence demonstrating that early, integrated palliative care for patients with advanced cancer improves QOL, symptom burden, mood, and end-of-life care.^6–10 Critical components of early palliative care include managing symptoms and enhancing effective coping skills.^11,12 However, early integrated palliative care is not widely accessible due to multiple barriers, including a shortage of specialty-trained palliative care clinicians.^7,13,14 Novel care delivery models, such as digital health applications (“apps”), have the potential to bridge this access gap by providing tailored supportive care to address the unmet needs of patients with advanced lung cancer.

Apps represent an innovative strategy to improve the accessibility and scalability of supportive care interventions.^15,16 Digital health interventions have demonstrated promising efficacy in addressing symptoms, including fatigue and insomnia, as well as depression and anxiety.^16–23 Regardless of age or prior computer experience, patients find such technologies acceptable with training.^24–26 However, available apps and online tools generally lack clarity regarding the sources of the intervention content, involvement of clinicians, and rigorous scientific testing needed to improve patient care.^27–29 Moreover, few digital health interventions are tailored to the supportive care needs of patients with advanced nonsmall cell lung cancer (NSCLC).^30,31 Patients with NSCLC commonly experience dyspnea and cough, as well as uncertainty about their prognosis, given that responses to novel therapies for lung cancer (i.e., immunotherapy, targeted therapy) can vary widely.^32–34 Therefore, the development of an evidence-based digital app for patients with advanced NSCLC has the potential to address unmet patient needs, enhance access to supportive care, and advance the field of palliative care in the oncology setting.

The goals of this study were to develop and pilot test a supportive care digital app, called “THRIVE,” designed to improve patients’ management of symptoms and coping with advanced NSCLC. We aimed to assess the feasibility (primary outcome) and acceptability of THRIVE as well as the preliminary effects of the intervention on patient-reported QOL, physical and psychological symptoms, and coping strategies (secondary outcomes).

Methods

Study design and app development

The study proceeded in two phases, consisting of: (1) development and refinement of the THRIVE app and (2) a pilot randomized trial to assess feasibility and acceptability of the study protocol and intervention. The first phase consisted of utilizing our evidence-based, early palliative care treatment guide and previous digital health interventions to develop an app tailored to the supportive care needs of patients with advanced NSCLC.^{6,9,11,12,35–38} Our multidisciplinary team, consisting of oncologists, palliative care clinicians, psychologists, and technology experts, developed a script for learning modules addressing the physical, social, emotional, functional, and existential well-being of this population. While further details about the content of THRIVE have been published previously, the intervention modules focused on patient education and skills for monitoring and managing physical symptoms; strengthening social support; coping with stress and mood symptoms; engaging in daily activities; and enhancing meaning and purpose in life.³⁹ For instance, skills for managing common lung cancer-related symptoms, such as dyspnea, and for coping with side effects of immunotherapy and targeted therapy, are included in the modules. In collaboration with our technology partner, Zco Corporation, we adapted the script to a self-administered, interactive, and patient-centered digital app (Fig. 1). We employed an iterative process that incorporated stakeholder feedback and numerous rounds of hands-on testing to develop and refine THRIVE, as described in the study protocol.³⁹

FIG. 1.

THRIVE Digital App Features.

The second phase of the study included a pilot randomized controlled trial among patients with advanced NSCLC to evaluate the feasibility and acceptability of THRIVE compared with usual oncology care. We also explored the preliminary effects of the intervention on patient-reported QOL, symptoms, psychological distress, and coping. The Dana-Farber/Harvard Cancer Center Institutional Review Board approved the study protocol, and all participants provided informed consent.

Participants

Eligible patients included adults (≥18 years) within 12 weeks of diagnosis of unresectable stage III or IV NSCLC who were unable to be treated with curative intent (per clinician documentation in the electronic health record; EHR); were receiving care at a participating institution (i.e., two academic medical centers and four community affiliates); had an Eastern Cooperative Oncology Group Performance Status (ECOG PS) = 0–2; and were able to read and respond to questions in English. To identify eligible patients, trained research staff reviewed the EHR and clinic schedules at the study sites. As this study was minimal risk, the treating oncology clinicians provided approval for research staff to approach all their eligible patients for enrollment. Recruitment occurred predominantly over the telephone, although research staff did approach patients in the clinic as well. Patients were excluded if they had a significant psychiatric or comorbid illness that prohibited their ability to provide consent and complete study activities, at the discretion of their oncology clinician.

Procedures

At baseline (prior to randomization) and again at 12 weeks (±2 weeks), participants completed self-report measures either by paper, verbally over the telephone, or electronically using Research Electronic Data Capture (REDCap).⁴⁰ Research staff, independent from the study team, used a computer-generated block randomization schema to randomize participants 1:1 to receive THRIVE plus usual oncology care versus usual oncology care alone, stratified by study site. Participants assigned to the intervention group received a study-issued tablet computer with THRIVE downloaded and completed a tutorial with research staff on how to operate the app. Research staff remained available throughout the study to address technological challenges, contacting intervention participants who had not engaged with the app for at least two weeks to troubleshoot any difficulties. Participants assigned to the control group had access to the supportive care services at the study sites as part of usual care and were also able to receive THRIVE at the conclusion of their study participation.

Study outcome measures and covariates

Feasibility outcomes (primary): The primary endpoint was to evaluate the feasibility of the study protocol, as defined by ≥65% of approached patients consenting to participate; ≥70% of participants in the intervention group completing ≥4 of 6 THRIVE app modules; and ≥70% of all enrolled participants completing the 12-week assessments post-baseline.

Self-reported outcomes (secondary):

Intervention acceptability (intervention participants only): The System Usability Scale (SUS) is a 10-item measure that assesses an intervention’s user friendliness and ease of use, with a total score range of 0–100; higher scores indicate higher usability.⁴¹

QOL: The Functional Assessment of Cancer Therapy-Lung (FACT-L) is comprised of subscales evaluating physical, social, emotional, and functional well-being as well as lung cancer-specific symptoms. The FACT-L total score range is 0–136, with higher scores indicating better QOL.^42,43

Symptom burden: The MD Anderson Symptom Inventory (MDASI) includes two subscales that evaluate symptom severity and interference with daily activities, with each subscale’s mean score ranging from 0 to 10 and higher scores indicating worse symptoms.⁴⁴

Psychological distress: The Hospital Anxiety and Depression Scale (HADS) consists of two subscales that assess anxiety and depression symptoms, with each subscale score ranging from 0 to 21, and scores >7 on either subscale indicating clinically significant symptoms.^45,46

Coping strategies: The Brief Coping Orientation to Problems Experienced Inventory (Brief-COPE) assesses different types of coping strategies. We calculated two factors for approach-oriented coping (score range = 6–24) and avoidant coping (score range = 4–16), with higher scores indicating greater use of the respective coping strategies.^38,47,48

Covariates: Participants completed a sociodemographic questionnaire at baseline. Research staff extracted data from the EHR regarding each participant’s cancer diagnosis and treatment history, ECOG PS, emergency department visits, hospitalizations, and use of supportive care services.

Data analyses

We descriptively summarized sociodemographic and clinical characteristics, survey responses, and feasibility metrics. Visual inspection of the baseline data and medical events during the study period revealed disproportionate distributions of these factors by study group. As planned a priori, we conducted linear regression analyses to explore the preliminary effects of the study intervention versus usual care for each secondary self-reported outcome, except for the SUS, which only the intervention group completed. We adjusted the regression models to control for the study site, baseline scores of the outcome variable, and sample characteristics that were imbalanced between groups. We first performed the linear regression analyses using all available data, followed by multiple imputation analyses using predictive mean matching with 10 pooled datasets to address missing data in living participants who did not complete the 12-week self-report measures. Given the potential number of covariates in the linear regression models, we examined variation inflation factors (VIFs) to assess multicollinearity, with VIFs > 4 indicating potential multicollinearity concerns.⁴⁹ Finally, for this pilot trial, the sample size calculation was based on estimating reliable feasibility metrics for the study protocol rather than detecting significant differences in participant-reported outcomes.³⁹ All analyses were conducted using R version 4.3.3 in accordance with the intent-to-treat principle.

Results

Sample characteristics

Research staff screened the EHR of 3775 participants, identifying 232 potentially eligible patients who were offered study participation, of whom 135 (58.2%) consented and 120 were randomized (61 to the intervention group and 59 to the control group; Fig. 2). As shown in Table 1, enrolled participants (Age_mean = 67.90 years; 61.7% female; 90.8% White) primarily received chemotherapy ± immunotherapy (53.3%).

FIG. 2.

CONSORT Study Flow Diagram.

Table 1.

Descriptive Statistics by Group for Patient Baseline Characteristics, Self-Reported Measures, and Medical Events During Study

	THRIVE intervention (n = 61)	Usual care control (n = 59)
Variable	N (%)/Mean ± SD (range)	N (%)/Mean ± SD (range)
Age	67.27 ± 11.36 (28.67–83.59)	68.55 ± 10.95 (38.56–85.70)
Gender^a
Female	35 (57.4%)	39 (66.1%)
Male	26 (42.6%)	20 (33.9%)
Married/partnered^a	39 (63.9%)	40 (67.8%)
Race
Asian	1 (1.6%)	3 (5.1%)
Black	3 (4.9%)	3 (5.1%)
Other	0 (0.0%)	1 (1.7%)
White	57 (93.4%)	52 (88.1%)
Hispanic/Latino/a	0 (0.0%)	2 (3.4%)
College educated or more^a	29 (47.5%)	27 (45.8%)
ECOG performance status
0	13 (21.3%)	14 (23.7%)
1	37 (60.7%)	36 (61.0%)
2	11 (18.0%)	9 (15.3%)
Smoking history^a
Never	13 (21.3%)	24 (40.7%)
Prior	45 (73.8%)	32 (54.2%)
Current	3 (4.9%)	3 (5.1%)
Receipt of supportive care in the past month
Mental health care	20 (32.8%)	16 (27.1%)
Palliative care	10 (16.4%)	5 (8.5%)
Other supportive care^b	5 (8.2%)	1 (1.7%)
Treatment type^a
Chemotherapy ± immunotherapy	35 (57.4%)	29 (49.2%)
Immunotherapy only	8 (13.1%)	3 (5.1%)
Targeted therapy	18 (29.5%)	27 (45.8%)
Days from advanced cancer diagnosis to enrollment	35.64 ± 20.44 (10.00–103.00)	38.68 ± 20.89 (1.00–103.00)
Charlson comorbidity index^a	1.62 ± 1.19 (0.00–4.00)	1.02 ± 1.37 (0.00–5.00)
Self-reported measure
Quality of life (FACT-L)
Physical well-being	20.17 ± 6.13 (0.00–28.00)	20.37 ± 5.93 (4.00–28.00)
Social well-being	22.50 ± 5.80 (1.00–28.00)	23.07 ± 4.49 (12.83–28.00)
Emotional well-being	15.48 ± 5.15 (3.00–24.00)	16.00 ± 4.88 (3.00–24.00)
Functional well-being	16.08 ± 6.26 (0.00–27.00)	15.93 ± 6.39 (3.00–28.00)
Lung cancer subscale	19.46 ± 5.48 (8.00–28.00)	19.88 ± 5.35 (8.00–28.00)
Total FACT-L	93.68 ± 21.24 (36.83–131.00)	95.24 ± 20.90 (48.00–130.00)
Symptoms (MDASI)
Symptom severity	2.57 ± 1.83 (0.08–9.54)	2.80 ± 2.06 (0.00–7.92)
Symptom interference	3.31 ± 2.54 (0.00–10.00)	3.48 ± 2.78 (0.00–9.50)
Psychological distress (HADS)
Anxiety subscale	6.04 ± 4.47 (0.00–20.00)	5.77 ± 4.11 (0.00–20.00)
Depression subscale	5.22 ± 3.59 (0.00–15.00)	5.51 ± 3.87 (0.00–16.00)
Coping strategies (Brief COPE)
Approach-oriented coping	18.66 ± 3.77 (10.00–24.00)	19.85 ± 2.83 (13.00–24.00)
Avoidant coping	5.79 ± 1.89 (4.00–10.00)	5.59 ± 2.12 (4.00–13.00)
Medical events During study
ED visit	9 (14.8%)	8 (13.6%)
Hospitalization^a	19 (31.1%)	14 (23.7%)
Cancer progression^a	12 (19.7%)	9 (15.3%)
Treatment interruption^a	9 (14.8%)	3 (5.1%)

Group imbalances as determined visually.

Acupuncture, music or art therapy, therapeutic massage, and chaplain or spiritual counseling.

ECOG, Eastern Cooperative Oncology Group; chemo, chemotherapy; FACT-L, Functional Assessment of Cancer Therapy-Lung; MDASI, MD Anderson Symptom Inventory; HADS, Hospital Anxiety and Depression Scale; Brief COPE, Brief Coping Orientation to Problems Experienced Inventory.

Based on visual inspection of the data, we observed several imbalances between the study groups. The intervention group had a larger proportion of participants who were male (42.6% vs. 33.9%) and not married/partnered (36.1% vs. 32.2%) as well as had more comorbidities (CCI_mean = 1.62 vs. 1.02), hospitalizations (31.1% vs. 23.7%), cancer progression (19.7% vs. 15.3%), and treatment interruptions (14.8% vs. 5.1%) during the study. Compared with the usual care group, the intervention group also had a larger proportion of participants who smoked previously (73.8% vs. 54.2%), received immunotherapy only (13.1% vs. 5.1%), and received chemotherapy ± immunotherapy (57.4% vs. 49.2%), while having a smaller proportion of participants who never smoked (21.3% vs. 40.7%) and received targeted therapy (29.5% vs. 45.8%).

Primary feasibility outcomes and intervention acceptability

The consent rate (58.2%) was below the a priori threshold of ≥65% of approached patients enrolling in the study. Of the 61 participants assigned to the intervention group, 70.5% (n = 43) completed ≥4 of 6 THRIVE modules, meeting the feasibility metric for intervention completion (≥70%), and the majority (62.3%; n = 38) completed all six modules, with a mean of 32.90 (SD = 22.56) minutes spent on each module (Fig. 3). In addition, most participants completed the 12-week measures (78.3%; n = 94/120), surpassing the a priori feasibility metric for survey completion (≥70%). Forty-six participants in the intervention group (75.4%) and 48 participants in the usual care control group (81.4%) completed all surveys. Finally, with respect to perceptions of THRIVE’s acceptability, participants rated the intervention as highly usable relative to the max range on the SUS measure (M = 78.72, SD = 18.86, range = 35–100), with 77.3% (n = 34/44) of participants providing above-average usability ratings.⁵⁰

FIG. 3.

Completion Rates and Time Spent (Mean ± SD) on Each Module for Patients Randomized to the THRIVE App Intervention (N = 61).

Secondary self-reported outcomes

See Table 2 for a descriptive summary of the 12-week self-report measures. Table 3 shows the results of the regression models examining the effect of THRIVE versus usual care on the 12-week self-reported measures, controlling for study site, baseline scores of the outcomes, and imbalanced factors between study groups. In these models, THRIVE was associated with lower symptom severity on the MDASI at 12 weeks relative to the usual care control group (B = −0.55, 95% CI = −1.06, −0.05). Otherwise, study groups did not differ in their reported QOL, psychological distress, or coping strategies. The VIFs of the adjusted models were all <2.0, indicating no concern for multicollinearity.

Table 2.

Descriptive Statistics by Group for 12-Week Self-Reported Outcome Measures

	THRIVE intervention(n = 61)	Usual care control(n = 59)
Outcome measure	Mean ± SD (range)	Mean ± SD (range)
Quality of life (FACT-L)
Physical well-being	20.00 ± 6.07 (1.00–28.00)	19.99 ± 5.90 (1.00–28.00)
Social well-being	22.3 ± 5.22 (2.33–28.00)	22.84 ± 5.33 (7.00–28.00)
Emotional well-being	16.22 ± 4.62 (7.00–24.00)	17.95 ± 3.50 (9.00–24.00)
Functional well-being	17.04 ± 5.84 (4.00–28.00)	18.24 ± 6.62 (4.00–28.00)
Lung cancer subscale	20.36 ± 5.03 (5.83–28.00)	20.56 ± 5.01 (8.00–28.00)
Total FACT-L	96.08 ± 19.16 (57.2–132.83)	99.58 ± 20.84 (41.17–129.00)
Symptoms (MDASI)
Symptom severity	2.26 ± 1.87 (0.08–10.00)	2.72 ± 2.04 (0.00–8.08)
Symptom interference	2.72 ± 2.51 (0.00–9.67)	3.10 ± 2.55 (0.00–9.50)
Psychological distress (HADS)
Anxiety subscale	5.70 ± 4.03 (0.00–16.00)	4.94 ± 2.96 (1.00–11.67)
Depression subscale	4.72 ± 3.45 (0.00–12.00)	5.08 ± 4.08 (0.00–20.00)
Coping strategies (Brief COPE)
Approach-oriented coping	18.72 ± 2.96 (13.00–24.00)	18.89 ± 4.39 (6.00–24.00)
Avoidant coping	5.43 ± 1.66 (4.00–10.00)	5.28 ± 1.41 (4.00–9.00)

Table 3.

Linear Regression Analyses of Between-Group Differences in Secondary Self-Reported Outcome Measures Using Intent-to-Treat and Multiple Imputation Analyses

	Linear regression	Linear regression with multiple imputation
Outcome measure	B [95% CI]	B [95% CI]
Quality of life (FACT-L)
Physical well-being	1.44 [−0.53, 3.41]	1.18 [−1.48, 3.84]
Social well-being	−0.42 [−1.88, 1.03]	0.31 [−1.88, 2.50]
Emotional well-being	−0.77 [−2.22, 0.69]	−0.91 [−2.64, 0.83]
Functional well-being	−0.003 [−2.10, 2.11]	0.14 [−2.96, 3.23]
Lung cancer subscale	0.36 [−0.97, 1.70]	0.22 [−1.69, 2.13]
Total FACT-L	0.38 [−5.17, 5.93]	1.15 [−6.09, 8.40]
Symptoms (MDASI)
Symptom severity	−0.55 [−1.06, −0.05]	−0.47 [−1.38, 0.43]
Symptom interference	−0.72 [−1.59, 0.15]	−0.64 [−1.66, 0.38]
Psychological distress (HADS)
Anxiety subscale	0.24 [−0.96, 1.44]	0.33 [−1.08, 1.75]
Depression subscale	−0.88 [−2.05, 0.30]	−0.77 [−2.55, 1.01]
Coping strategies (Brief COPE)
Approach-oriented coping	<0.01 [−1.47, 1.46]	0.18 [−1.58, 1.94]
Avoidant coping	−0.09 [−0.65, 0.47]	<0.01 [−0.69, 0.68]

The models controlled for study site (Massachusetts General Hospital Cancer Center and Dana-Farber Cancer Institute vs. community affiliates), baseline levels of the outcome, as well as sample characteristics that were imbalanced by group: age, gender [reference: female], CCI, smoking history [reference: patients who currently smoke], marital/partner status [reference: not married/ not partnered], treatment type [reference: chemotherapy ± immunotherapy], and the occurrence of a treatment interruption, hospitalization, and progression during study; VIFs of intent-to-treat adjusted models controlling for imbalances between groups were all <2.0.

95% CI, 95% confidence interval.

Missing data analysis

When excluding the nine deaths while on study from the missingness rate at 12 weeks, missingness was similar in both the intervention group (14.8%; n = 8/54) and the usual control group (14.0%; n = 8/57). To account for these missing data, we replicated the regression models of the effect of the THRIVE using multiple imputations with 10 pooled datasets while controlling for the aforementioned variables. The effects of THRIVE were largely the same as the available case analyses (Table 3). However, THRIVE’s effect on participant-reported symptom severity on the MDASI post-treatment was no longer significant (B = −0.47, 95% CI = −1.38, 0.43).

Discussion

Patients with advanced NSCLC experience marked physical and psychological symptoms that negatively impact their QOL. We developed and pilot-tested the THRIVE app specifically tailored to help patients manage symptoms and cope effectively with advanced NSCLC. Despite a lower-than-anticipated enrollment rate, the study met the a priori feasibility metrics for intervention and follow-up survey completion. THRIVE also showed promise for reducing symptom burden in patients with advanced NSCLC compared with usual care. However, the effect on symptom burden was no longer observed when imputing for missing data, and these findings come from a pilot study that was not powered to assess intervention effects.

The enrollment rate was below the a priori threshold, which is likely related to multiple factors. The study population has high cancer-related morbidity, and a significant proportion of those who did not provide consent declined due to illness or feeling unwell. Patient concerns with using technology also may have impacted the enrollment rate, and the COVID-19 pandemic limited in-person recruitment and digital app training. Nonetheless, the study achieved the feasibility metrics for completing the intervention modules, with high usability ratings and 12-week participant-reported measures. Moreover, THRIVE demonstrated preliminary effects in lowering symptom burden compared to usual care, likely due to the modules focused on skills for tracking and managing physical symptoms, reducing physiological stress, and engaging in life activities. These findings support the use of THRIVE as a strategy for increasing access to much-needed supportive care for patients with advanced NSCLC.

Digital app interventions are an area of growing interest due to the potential to increase access to personalized care, yet such platforms tailored for patients with advanced lung cancer are limited.⁵¹ Notably, Ciani and colleagues developed the Lung Cancer app (LuCApp) aimed at addressing the supportive care needs of patients with unresectable NSCLC or small cell lung cancer with input from oncologists and palliative care clinicians.³¹ The LuCApp is a digital app intervention consisting of patient education to promote self-management and monitoring of symptoms with set thresholds that trigger clinician alerts.³¹ While the LuCApp provides tailored supportive care for patients with advanced lung cancer, it requires a clinician response for app alerts within 24 hours. In contrast, we developed THRIVE to be self-administered to address both the access and scalability challenges of palliative care, while seeking to retain the critical palliative care components of symptom management and facilitation of effective coping skills.¹² Ji and colleagues developed a digital app for patients with NSCLC with obstructive lung disease focused on pulmonary rehabilitation, demonstrating improvements in exercise capacity, dyspnea, and QOL; these findings are encouraging for the future of digital interventions for symptom management in this population.³⁰ Similarly, THRIVE has the potential to alleviate symptom burden in patients with advanced NSCLC. Prior digital health interventions to treat psychological conditions and support cancer survivors have shown efficacy in managing symptoms and distress.^16,18–23 A fully powered randomized trial is needed to evaluate the efficacy of THRIVE for improving physical and psychological symptoms, coping strategies, and QOL.

Our study has several strengths. A multidisciplinary team developed THRIVE specifically to address the supportive care needs of patients with advanced NSCLC using evidence-based patient education and skills derived from early palliative care, cognitive behavioral therapy, and acceptance and commitment therapy.³⁹ Furthermore, the pilot randomized trial design and use of established feasibility metrics and well-validated self-reported outcome measures ensure study rigor. Nonetheless, several study limitations warrant consideration. First, the study sites included two Boston-based academic medical centers and four community affiliates with minimal sociodemographic diversity among the participants, limiting the generalizability of findings to other centers and populations. However, we were able to recruit patients from diverse educational backgrounds, with over half of the sample having less than a college education, and ages, with patients ranging from 28 to 85 years old. Furthermore, the study groups were imbalanced at baseline, which we accounted for using adjusted regression models to explore the preliminary effects of THRIVE. The provision of a tablet to participants, training on digital app use, and ongoing availability of technical support likely enabled study participation and engagement, which are important considerations for broader implementation. In addition, while the SUS is commonly used to assess usability, it does not provide insight into any specific challenges that participants may have experienced. The study team was not blinded to study group assignment, which may have introduced bias. Lastly, further research is needed to assess the long-term effects of THRIVE. Future work can explore the feasibility of THRIVE in patients with poorer performance status (ECOG 3–4), who may benefit from supportive care interventions that can be accessed without additional trips to a clinic. Despite these limitations, this rigorous pilot trial provides valuable evidence for a novel digital app to enhance access to supportive care.

Findings from this study demonstrate the feasibility and acceptability of THRIVE for patients with advanced NSCLC, warranting follow-up efficacy testing of the app in more diverse cancer care settings and populations. Given that access to early palliative care remains limited for patients with advanced cancer, this study serves as a critical step forward in improving the accessibility and scalability of supportive care interventions for this vulnerable population.

Footnotes

Authors’ Contributions

J.M.J., L.A.P., A.E.J., J.S.T., and J.A.G. conceptualized and designed this study. J.A.C., B.J., M.F., and M.H. collected the data. A.E.J., S.B.L., and J.A.G. conducted the statistical analysis. All authors interpreted the data. M.C.B., S.B.L., B.J., and J.A.G. drafted the study article, and all authors reviewed and edited the study article.

Funding Information

Funding for this study was provided by the National Comprehensive Cancer Network (NCCN), a nonprofit organization, through a grant from AstraZeneca Pharmaceuticals (PIs: J.A.G. and J.S.T.), and J.S.T. is supported by CRP-20-097-01-PCSM from the American Cancer Society. NCCN and AstraZeneca Pharmaceuticals were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. Research reported in this article was supported by NIH T32CA071345 (M.C.B.).

Author Disclosure Statement

J.M.J.: Consulting: OncoveryCare, Inc. and Reunion Neuroscience, Inc. J.B.: Advisory Board and Consulting: Pfizer and EMD Serono. A.E.J.: Consulting: Incyte Corporation, Tuesday Health, AAHPM. J.S.T.: Consulting: Thyme Care. J.A.G.: Consulting or Advisory Role: BeiGene; Speakers Bureau: GlaxoSmithKline; Research Funding: NCCN/AstraZeneca (Inst), Blue Note Therapeutics (Inst); Patents, Royalties, Other Intellectual Property: Royalties from Oxford University Press. The remaining authors have no conflicts of interest to disclose.

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