Digital Self-Management of Symptoms and Quality of Life for Patients With Advanced Cancer: A Randomized Clinical Trial.

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Learn more: PMC Disclaimer | PMC Copyright Notice JAMA Netw Open . 2026 Jun 1;9(6):e2615509. doi: 10.1001/jamanetworkopen.2026.15509 Digital Self-Management of Symptoms and Quality of Life for Patients With Advanced Cancer A Randomized Clinical Trial Wing-Lok Chan 1, ✉ , Inda Sung Soong 2 , Mei-Ying Lim 3 , Winnie Wing-Yan Tin 4 , Steven Wai-Kwan Siu 5 , Jeffrey Ng 6 , Carmen Leung 7 , Alex Leung 7 , Yin-Ling Tai 1 , Ching-Mei Lam 1 , Li-Yu Hou 1 , Raymond Lo 8 , Victor Ho-Fun Lee 1 , Kwok-Keung Yuen 5 , Dora Kwong 1 , Rina Hui 9 Author information Article notes Copyright and License information 1 Department of Clinical Oncology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region (SAR) 2 Department of Clinical Oncology, Pamela Youde Nethersole Eastern Hospital, Hong Kong SAR 3 Department of Clinical Oncology, Princess Margaret Hospital, Hong Kong SAR 4 Department of Clinical Oncology, Tuen Mun Hospital, Hong Kong SAR 5 Department of Clinical Oncology, Queen Mary Hospital, Hong Kong SAR 6 Department of Medicine and Geriatrics, Haven of Hope Hospital, Hong Kong SAR 7 Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR 8 Department of Medicine, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR 9 Centre of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR Accepted for Publication: April 6, 2026. Published: June 1, 2026. doi: 10.1001/jamanetworkopen.2026.15509 Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License , which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies. © 2026 Chan WL et al. JAMA Network Open . ✉ Corresponding Author: Wing-Lok Chan, MBBS, Department of Clinical Oncology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, 1/F Professorial Block, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong ( winglok@hku.hk ). Author Contributions: Dr Chan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Chan, Lim, Siu, C. Leung, Lo, Yuen. Acquisition, analysis, or interpretation of data: Chan, Soong, Lim, Tin, Ng, A. Leung, Tai, Lam, Hou, Lee, Kwong, Hui. Drafting of the manuscript: Chan. Critical review of the manuscript for important intellectual content: All authors. Statistical analysis: Chan, Hou. Obtained funding: Chan. Administrative, technical, or material support: Chan, Soong, Lim, Tin, Ng, C. Leung, A. Leung, Tai, Lam, Lee, Kwong. Supervision: Chan, Siu, Lo, Lee, Yuen, Kwong, Hui. Conflict of Interest Disclosures: Prof Hui reported receiving grant support to her institution from Amgen Inc, AstraZeneca, Boehringer Ingelheim, Chugai Pharmaceutical Co, Eli Lilly and Co, Merck Sharp & Dohme, Novartis AG, Olema Pharmaceuticals Inc, Relay Therapeutics Inc, and F. Hoffmann–La Roche AG; advisory board fees from Amgen Inc, AstraZeneca, CK Life Sciences International Inc, Daiichi Sankyo Co Ltd, Eli Lilly and Co, Gilead Sciences Inc, Janssen Pharmaceuticals Inc, Johnson & Johnson, Merck Sharp & Dohme, Novartis AG, Olema Pharmaceuticals Inc, F. Hoffmann–La Roche AG, Takeda Pharmaceutical Co Ltd, and Zai Lab Limited; and speaker honoraria from Amgen Inc, AstraZeneca, Daiichi Sankyo Co Ltd, Eli Lilly and Co, Janssen Pharmaceuticals Inc, Merck Sharp & Dohme, and Novartis AG outside the submitted work. No other disclosures were reported. Funding/Support: The digital symptom-monitoring application (SUPPORT+) was fully sponsored by the Bank of China (Hong Kong) Centenary Charity Programme. This trial was funded by the Health and Medical Research Fund of the Hong Kong government. Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Meeting Presentation: This study was presented at the Annual Meeting of the American Society of Clinical Oncology; June 1, 2026; Chicago, Illinois. Data Sharing Statement: See Supplement 3 . Additional Contributions: The authors thank all participants for their involvement in this study. ✉ Corresponding author. Received 2026 Feb 25; Accepted 2026 Apr 6; Collection date 2026 Jun. Copyright 2026 Chan WL et al. JAMA Network Open . This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies. PMC Copyright notice PMCID: PMC13227317  PMID: 42224222 Key Points Question Can a digital symptom-monitoring system with automated self-management guidance and nurse follow-up improve outcomes for community-dwelling patients with advanced cancer who are receiving palliative care? Findings In this randomized clinical trial of 1214 patients, a digital symptom-monitoring system better maintained health-related quality of life compared with usual care, with smaller declines in EuroQol 5-dimension 5-level utility and EuroQol visual analogue scale scores. Self-efficacy was more stable and unplanned hospitalizations and inpatient days were reduced, whereas performance status and emergency department visits did not differ significantly. Meaning Digital symptom monitoring with timely support can enhance community-based palliative care and health-related quality of life. This randomized clinical trial evaluates whether adding an application-facilitated symptom-monitoring intervention to usual palliative care can better maintain health-related quality of life (HRQOL), self-efficacy, and functional status and reduce acute hospital utilization among community-dwelling patients with advanced cancer. Abstract Importance Patients with advanced cancer experience substantial symptom burden that impairs health-related quality of life (HRQOL) and contributes to emergency department (ED) visits and hospitalizations. Evidence for application (app)-facilitated palliative care interventions remains limited. Objective To evaluate whether an app-facilitated palliative care intervention integrating digital symptom monitoring with nurse-led clinical follow-up can improve outcomes among patients with advanced cancer. Design, Setting, and Participants This multicenter randomized clinical trial was conducted at 6 palliative care clinics in Hong Kong from January 25, 2023, to February 5, 2025. Community-dwelling adults with advanced solid cancer who were no longer receiving systemic anticancer treatment were randomized 1:1 to digital symptom monitoring plus usual care or usual care alone and followed up for 18 weeks. Intervention Digital symptom monitoring combined weekly symptom reporting using the Integrated Palliative Care Outcome Scale, automated self-management guidance, and nurse-led follow-up for severe symptom alerts. Main Outcomes and Measures The primary outcome was change in HRQOL as measured by EuroQol 5-dimension 5-level (EQ-5D-5L) assessment. Secondary outcomes included self-efficacy (6-item Self-Efficacy for Managing Chronic Disease Scale), Eastern Cooperative Oncology Group performance status (ECOG PS), ED visits, and hospitalizations. Results Among 1214 randomized participants (590 to digital symptom monitoring and 624 to usual care; median age, 78 [range, 31-103] years; 617 [50.8%] male), including 821 caregivers (67.6%) as application users, HRQOL was better maintained with digital symptom monitoring at week 18. Mean changes from baseline favored the intervention compared with usual care for EQ-5D-5L utility (0.49 to 0.52 vs 0.50 to 0.38; mean difference in change, −0.15 [95% CI, −0.21 to −0.10]; P < .001) and EQ-5D visual analogue scale (63.16 to 65.72 vs 63.87 to 59.69; mean difference, −6.09 [95% CI, −8.67 to −3.51] points; P < .001). Self-efficacy was better maintained with the digital symptom monitoring intervention (5.29 to 5.34 vs 5.43 to 4.87; mean difference, −0.53 [95% CI, −0.78 to −0.27]; P < .001). Deterioration in ECOG PS (44 of 367 [12.0%] vs 66 of 379 [17.4%]; odds ratio [OR], 1.22 [95% CI, 0.83-1.79]; P = .31) and ED utilization (74 of 367 [20.2%] vs 97 of 379 [25.6%]; OR, 1.27 [95% CI, 0.97-1.67]; P = .09) were similar between groups. Hospitalization outcomes favored digital symptom monitoring, including fewer participants with worsening unplanned hospitalization episodes (63 of 367 [17.2%] vs 108 of 379 [28.5%]; OR, 1.59 [95% CI, 1.21-2.10]; P = .001) and fewer inpatient days during follow-up (mean [SD], 3.4 [8.9] vs 7.3 [15.5]). Conclusions and Relevance In this randomized clinical trial of patients with advanced cancer, an app-facilitated palliative care intervention helped maintain HRQOL and self-efficacy and reduced acute care use compared with usual care. Trial Registration ClinicalTrials.gov Identifier: NCT07475312 Introduction Patients with advanced cancer experience substantial multidimensional symptom burden that impairs health-related quality of life (HRQOL) and functional status and contributes to emergency department (ED) visits and unplanned hospitalizations. 1 , 2 Because most care occurs in the community, symptom exacerbations between scheduled visits may go unrecognized, leading to potentially preventable use of acute care services and further decline. 3 , 4 , 5 Digital symptom-monitoring interventions have improved patient-reported outcomes in populations receiving systemic anticancer treatment, where monitoring often focuses on treatment-related toxicities and relies primarily on patient self-report. 6 , 7 , 8 , 9 Whether these models can translate to populations receiving palliative care rather than cancer-directed therapy is uncertain. Patients receiving palliative care are often older, experience dynamic symptom trajectories, and are more dependent on caregivers for symptom observation and communication with clinicians. Evidence from multicenter randomized clinical trials demonstrating clinically meaningful outcomes in this setting remains limited. 10 , 11 The Integrated Palliative Outcome Scale (IPOS) was developed to capture the multidimensional symptom burden relevant to palliative care, encompassing physical, psychological, social, and informational concerns. 12 It also supports both patient and caregiver reporting. An application (app)-facilitated palliative care intervention integrating routine IPOS-based symptom reporting, automated self-management guidance, and nurse-led follow-up for severe symptoms (SUPPORT+; patent holder W.L.C.) was developed. This multicenter randomized clinical trial evaluated whether adding the app-facilitated intervention to usual palliative care could better maintain HRQOL, self-efficacy, and functional status and reduce acute hospital utilization among community-dwelling patients with advanced cancer. Methods Study Design This multicenter randomized clinical trial evaluated the app-facilitated symptom monitoring plus usual palliative care compared with usual palliative care alone among community-dwelling patients with advanced cancer (the trial protocol is provided in Supplement 1 ). The study protocol and materials were approved by the Hospital Authority Central Institutional Review Board and the University of Hong Kong–Hospital Authority Hong Kong West Cluster Institutional Review Board. Written informed consent was obtained from all participants. We followed the Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline. Study Participants Patients 18 years or older with advanced solid malignant neoplasms who had decided not to pursue further systemic anticancer treatment and were being followed up at palliative care clinics in 6 public hospitals in Hong Kong were consecutively recruited. Eligibility criteria included receipt of community-based care, smartphone ownership, willingness to receive notifications from the mobile app, and presence of at least 1 cancer-related symptom. Exclusion criteria included hematologic malignant disease, receipt of systemic anticancer therapy, estimated life expectancy less than 3 months, inadequate cognitive capacity to provide informed consent, or Eastern Cooperative Oncology Group performance status (ECOG PS) greater than 3. Life expectancy and cognitive capacity were assessed by treating clinicians using routine clinical judgment. For participants unable to use the application independently, a primary caregiver could serve as a proxy reporter. Randomization Participants were randomly assigned in a 1:1 ratio to the intervention group (app-facilitated symptom monitoring plus usual care) or the usual care group. Randomization was performed using computer-generated random permuted blocks of variable sizes (8, 10, and 15), stratified by age (>65 vs ≤65 years), long-term opioid use, and app user (patient vs caregiver proxy). Allocation was concealed from clinicians delivering routine palliative care. Owing to the nature of the intervention, participants and palliative care nurses were not masked. Outcome assessors and statisticians (W.L.C. and L.Y.H.) were masked until completion of the primary analysis. Procedures Baseline Assessment After consent, participants completed baseline assessments and data collection, including demographic characteristics, ECOG PS, caregiving support, prior palliative care experience, HRQOL using the EuroQol 5-dimension 5-level (EQ-5D-5L) assessment, and self-efficacy. Medical records from the Hospital Authority Computer Management System were reviewed to document comorbidities, cancer diagnosis, current medications used, and hospital admissions and ED visits during the 4 months before enrollment. The app was then installed on the participant’s or the caregiver’s smartphone. Usual Care Group Participants received standard outpatient palliative care, including scheduled clinic follow-up (generally every 4 to 12 weeks) and community-based nursing support as clinically indicated. They had access only to publicly available educational materials within the app. Intervention Group Participants assigned to the intervention group received usual palliative care supplemented by app-facilitated symptom monitoring for 18 weeks. Participants or caregiver proxies completed weekly symptom assessments using IPOS. The app provided symptom-specific self-management guidance. Reports indicating severe symptoms automatically generated alerts to trained palliative care nurses. Nurses reviewed alerts during office hours and contacted participants within 1 working day. Participants could also initiate contact using the app’s Nurse Consult function. Nurse-led follow-up included symptom assessment, red flag screening, and medication review (eMethods in Supplement 2 ). App use was paused during hospitalization and resumed after discharge. Outcomes Assessments were conducted at baseline and weeks 9 and 18 and included HRQOL, self-efficacy, ECOG PS, and experience with advance care planning. Health care utilization data, including ED visits and unplanned hospitalizations, were collected at week 18. The primary outcome was change in HRQOL, measured by the EQ-5D-5L utility score. This validated instrument assesses 5 dimensions (mobility, self-care, usual activities, pain and/or discomfort, and anxiety and/or depression), each rated on 5 levels ranging from 1 (no problem) to 5 (extreme problem). Utility scores range from –0.865 (worst health state) to 1 (perfect health) based on the Hong Kong value set. 13 , 14 , 15 , 16 , 17 The EQ visual analogue scale (VAS) captured self-rated health (range, 0 to 100; higher scores indicate better health). Both measures are widely used in advanced cancer, and the EQ-5D-5L is recommended by the National Institute for Health and Care Excellence for estimating quality-adjusted life-years. 18 Secondary outcomes included changes in self-efficacy, ED visits, unplanned hospitalization episodes and their duration, ECOG PS, and completion of advance directives. Self-efficacy was measured using the validated 6-item Self-Efficacy for Managing Chronic Disease Scale (SEMCD-6S), scored from 1 (not at all confident) to 10 (totally confident), with higher scores indicating greater confidence. 19 , 20 The Chinese version has demonstrated strong reliability. 20 Sample Size Calculation The sample size was calculated to detect a minimal important difference of 0.11 in the EQ-5D-5L utility score, based on published estimates in populations with cancer and assuming an SD of 0.30. 14 With a 2-sided significance level of .05, 157 evaluable participants per group were required to provide 90% power. Allowing for 20% attrition, the planned sample size was 394 participants (197 per group). Enrollment was extended because attrition due to death and loss to follow-up was higher than anticipated. Statistical Analysis Analyses followed the intention-to-treat principle. For HRQOL (EQ-5D-5L utility score and EQ-VAS), self-efficacy (SEMCD-6S), and ECOG PS, analyses included all observed data among participants alive at each follow-up point. Outcomes were not imputed after death. Primary analyses of EQ-5D-5L utility and EQ-VAS scores were conducted using linear mixed-effects models with fixed effects for treatment group, time (weeks 9 and 18), and the treatment × time interaction, adjusting for baseline values. Between-group differences in mean changes from baseline were reported with 95% CIs and P values. Responder analyses classified participants as improved, stable, or worsened using minimally important difference thresholds of 0.11 for EQ-5D-5L utility and 6 points for EQ-VAS. Absolute risk differences with 95% CIs were calculated separately for improvement and worsening. The overall intervention effect across the 3 ordered categories was assessed using ordinal logistic regression, yielding odds ratios (ORs) and P values. Prespecified subgroup analyses were performed by adding treatment × subgroup interaction terms to the primary linear mixed-effects models. Self-efficacy was analyzed as a continuous outcome using linear mixed-effects models. Changes in SEMCD-6S categories, ECOG PS, ED visits, and unplanned hospitalizations were classified as improved, stable, or worsened and analyzed using ordinal logistic regression. Cross-sectional distributions were compared using χ 2 tests. Overall survival was summarized descriptively using Kaplan-Meier methods, with between-group comparisons performed using log-rank tests. Consistent with a protocol amendment approved by the institutional review boards, a complete case sensitivity analysis restricted to participants with observed assessments at baseline and at weeks 9 and 18 was performed. All tests were 2 sided, with P < .05 considered statistically significant. Analyses were conducted using R, version 4.5.2 (R Project for Statistical Computing). Results Between January 25, 2023, and February 5, 2025, 1375 patients were screened; 1214 were enrolled and randomized to app-facilitated symptom monitoring plus usual care (590 participants) or usual care alone (624 participants) ( Figure 1 ). During follow-up, 343 participants died and 96 were lost to follow-up. Primary analyses followed the intention-to-treat principle and included all observed outcome data among participants alive at each follow-up point. A complete case cohort of 633 participants (315 in the app-facilitated symptom monitoring group and 318 in the usual care group) was used for sensitivity analyses. Figure 1. CONSORT Diagram of Participant Flow Through Trial. Open in a new tab The median age was 78 (range, 31-103) years, 597 participants (49.2%) were female, and 617 (50.8%) were male. The most common cancer types were gastrointestinal (445 [36.7%]), lung (23.3%), and breast or gynecologic (168 [13.8%]). Most app users were caregivers (821 [67.6%] overall; 421 [71.4%] in the app-facilitated symptom monitoring group 400 [64.1%] in usual care). Baseline characteristics are shown in Table 1 . Table 1. Participant Characteristics. Characteristic Participant group, No. (%) Overall (N = 1214) Intervention group (n = 590) Control group (n = 624) Sex Female 597 (49.2) 302 (51.2) 295 (47.3) Male 617 (50.8) 288 (48.8) 329 (52.7) Age, y Mean (SD) 76.7 (12.1) 77.0 (12.5) 76.4 (11.7) Median (range) 78.0 (31-103) 79.0 (31-102) 77.5 (37-103) User of the app Patient 393 (32.4) 169 (28.6) 224 (35.9) Caregiver proxy 821 (67.6) 421 (71.4) 400 (64.1) Caregiver available in the daytime Yes 1001 (82.5) 490 (83.1) 511 (81.9) No 213 (17.5) 100 (16.9) 113 (18.1) Primary cancer type GI tract 445 (36.7) 231 (39.2) 214 (34.3) Lung 283 (23.3) 121 (20.5) 162 (26.0) Breast and gynecologic 168 (13.8) 78 (13.2) 90 (14.4) Urologic 150 (12.4) 66 (11.2) 84 (13.5) Other 177 (14.6) 99 (16.8) 78 (12.5) Marital status Single 60 (4.9) 29 (4.9) 31 (5.0) Married 781 (64.3) 376 (63.7) 405 (64.9) Divorced 86 (7.1) 38 (6.4) 48 (7.7) Widowed 287 (23.6) 147 (24.9) 140 (22.4) Educational level Primary or below 631 (52.0) 306 (51.9) 325 (52.1) Secondary 439 (36.2) 203 (34.4) 236 (37.8) Diploma or above 144 (11.9) 81 (13.7) 63 (10.1) Employment status Employed 53 (4.4) 25 (4.2) 28 (4.5) Unemployed or retired 1080 (89.0) 527 (89.3) 553 (88.6) Other 81 (6.7) 38 (6.4) 43 (6.9) Baseline ECOG PS a 0-1 446 (36.7) 218 (36.9) 228 (36.5) 2 445 (36.7) 215 (36.4) 230 (36.9) 3 323 (26.6) 157 (26.6) 166 (26.6) Religious affiliation Buddhism 208 (17.1) 107 (18.1) 101 (16.2) Catholicism 64 (5.3) 29 (4.9) 35 (5.6) Christianity 202 (16.6) 93 (15.8) 109 (17.5) None 673 (55.4) 331 (56.1) 342 (54.8) Other 67 (5.5) 30 (5.1) 37 (5.9) Use of opioids 190 (15.7) 79 (13.4) 111 (17.8) Palliative care experience Signed the advanced directive 219 (18.0) 89 (15.1) 130 (20.8) Discussed care plan with family 418 (34.4) 175 (29.7) 243 (38.9) Set a will 242 (19.9) 124 (21.0) 118 (18.9) Ever discussed dying place with family 337 (27.8) 174 (29.5) 163 (26.1) Open in a new tab Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; GI, gastrointenstinal. a Higher scores indicate worse status. HRQOL At week 18, EQ-5D-5L utility was better maintained in the app-facilitated symptom monitoring group than in the usual care group (mean [SD], 0.49 [0.43] to 0.52 [0.43] vs 0.50 [0.43] to 0.38 [0.52]; mean difference, −0.15 [95% CI, −0.21 to −0.10]; P < .001) ( Figure 2 A and D and eTable 1 in Supplement 2 ). EQ-VAS scores demonstrated a comparable pattern, with higher scores observed in the app-facilitated symptom monitoring group over time (mean [SD], 63.16 [19.56] to 65.72 [19.25] vs 63.87 [19.17] to 59.69 [21.57]; mean difference, −6.09 [95% CI, −8.67 to −3.51]; P < .001) ( Figure 2 B and E and eTable 1 in Supplement 2 ). Figure 2. Model-Based Mean Change and Score Distribution From Baseline at Week 9 and Week 18. Open in a new tab A to C, Line graphs show changes in mean scores for EuroQol 5-dimension 5-level (EQ-5D-5L) utility (range, −0.865 to 1, with higher scores indicating better health), EQ visual analogue scale (VAS) for self-rated health (range, 0 to 100, with higher scores indicating better health), and 6-item Self-Efficacy for Managing Chronic Disease Scale (scores range from 1 to 10, with higher scores indicating greater confidence). D to F, Box plots show distribution of scores from panels A to C. Boxes indicate IQRs; dots, means; horizontal lines, medians; and whiskers, range. Responder analyses showed a greater likelihood of improvement and less worsening with app-facilitated symptom monitoring. For EQ-5D-5L utility (≥0.11 change threshold), there were 11.6% more participants with improvement (71 of 367 [19.3%] vs 29 of 379 [7.7%]) and 15.1% fewer with worsening (139 of 367 [37.9%] vs 201 of 379 [53.0%]) in the app-facilitated symptom monitoring group, corresponding to higher odds of a more positive outcome compared with usual care (OR, 2.04 [95% CI, 1.29-2.22]; P < .001) ( Table 2 ). For EQ-VAS (≥6-point threshold), app-facilitated symptom monitoring showed 10.1% more participants with improvement (98 of 367 [26.7%] vs 63 of 379 [16.6%]) and 12.0% fewer with worsening (141 of 367 [38.4%] vs 191 of 379 [50.4%]) compared with usual care (OR, 1.69 [95% CI, 1.29-2.22]; P < .001) ( Table 2 ). Table 2. Changes in HRQOL, Self-Efficacy, ED Visits, Hospitalization Episodes, and Duration of Hospitalization a . End point Participant group, No./total No. (%) Risk difference (95% CI), % Odds ratio (95% CI) b P value b Intervention Control Primary EQ-5D-5L index, change ≥0.11 Baseline to week 9 Improved 96/410 (23.4) 72/459 (15.7) 7.7 (2.4 to 13) 1.37 (1.07 to 1.76) .01 Stable 169/410 (41.2) 201/459 (43.8) NA Worsened 145/410 (35.4) 186/459 (40.5) −5.2 (−11.6 to 1.3) Baseline to week 18 Improved 71/367 (19.3) 29/379 (7.7) 11.7 (6.8 to 16.5) 2.04 (1.29 to 2.22) <.001 Stable 157/367 (42.8) 149/379 (39.3) NA Worsened 139/367 (37.9) 201/379 (53.0) −15.2 (−22.2 to −8.1) EQ-VAS, change ≥6 Baseline to week 9 Improved 125/410 (30.5) 109/459 (23.7) 6.7 (0.8 to 12.7) 1.41 (1.10 to 1.81) .006 Stable 137/410 (33.4) 146/459 (31.8) NA Worsened 148/410 (36.1) 204/459 (44.4) −8.3 (−14.8 to −1.8) Baseline to week 18 Improved 98/367 (26.7) 63/379 (16.6) 10.1 (4.2 to 16.0) 1.69 (1.29 to 2.22) <.001 Stable 128/367 (34.9) 125/379 (33.0) NA Worsened 141/367 (38.4) 191/379 (50.4) −12.0 (−19.1 to −4.9) Secondary Self-efficacy Baseline to week 9 Improved 144/410 (35.1) 138/459 (30.1) 5.1 (−1.2 to 11.3) 1.23 (0.94 to 1.61) .13 Stable 25/410 (6.1) 30/459 (6.5) NA Worsened 241/410 (58.8) 291/459 (63.4) −4.6 (−11.1 to 1.9) Baseline to week 18 Improved 137/367 (37.3) 91/379 (24.0) 13.3 (6.8 to 19.9) 1.75 (1.30 to 2.36) <.001 Stable 21/367 (5.7) 26/379 (6.9) NA Worsened 209/367 (56.9) 262/379 (69.1) −12.2 (−19.1 to −5.3) ED visits, baseline to week 18 Improved 96/367 (26.2) 87/379 (23.0) 3.2 (−3.0 to 9.4) 1.27 (0.97 to 1.67) .09 Stable 197/367 (53.7) 195/379 (51.5) NA Worsened 74/367 (20.2) 97/379 (25.6) −5.4 (−11.4 to 0.6) Hospitalization episodes, baseline to week 18 Improved 100/367 (27.2) 83/379 (21.9) 5.3 (−0.8 to 11.5) 1.59 (1.21 to 2.10) .001 Stable 204/367 (55.6) 188/379 (49.6) NA Worsened 63/367 (17.2) 108/379 (28.5) −11.3 (−17.3 to −5.4) Days in hospital, baseline to week 18 Improved 111/367 (30.2) 85/379 (22.4) 7.8 (1.5 to 14.1) 1.84 (1.40 to 2.42) <.001 Stable 188/367 (51.2) 165/379 (43.5) NA Worsened 68/367 (18.5) 129/379 (34.0) −15.5 (−21.7 to −9.3) ECOG PS, baseline to week 18 Improved 4/367 (1.1) 15/379 (4.0) −2.9 (−5.1 to −0.6) 1.22 (0.83 to 1.79) .31 Stable 319/367 (86.9) 298/379 (78.6) NA Worsened 44/367 (12.0) 66/379 (17.4) −5.4 (−10.5 to −0.4) Open in a new tab Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; ED, emergency department; EQ-5D-5L, EuroQol 5-dimension 5-level assessment; NA, not applicable; VAS, visual analogue scale. a Percentages were calculated among participants who were alive and assessed at each time point (week 9, 410 vs 459; week 18, 367 vs 379). Deaths and missing assessments are reported in Figure 1. ED visits and hospitalization change categories compare the follow-up period with the 4-month pre-enrollment period and are presented descriptively. b Applies to the overall ordinal variable (improved, stable, and worsened). Across all 5 EQ-5D-5L domains, a significantly greater proportion of participants in the app-facilitated symptom monitoring group experienced improvement or stability during the 18-week follow-up (eg, OR for mobility, 1.69 [95% CI, 1.21-2.38; P = .002]; OR for self-care, 1.58 [95% CI, 1.07-2.37; P = .02]) (eTable 2 in Supplement 2 ). Prespecified subgroup analyses showed no significant effect modification for EQ-5D-5L utility. Caregiver-reported assessments were associated with lower EQ-VAS scores than patient-reported assessments (eTables 3 and 4 in Supplement 2 ). Self-Efficacy At week 18, SEMCD-6S scores were better maintained in the app-facilitated symptom monitoring group than in the usual care group (mean [SD], 5.29 [2.38] to 5.34 [2.26] vs 5.43 [2.26] to 4.87 [2.30]; mean difference, −0.53 [95% CI, −0.78 to −0.27]; P < .001) ( Figure 2 C and F and eTable 1 in Supplement 2 ). In responder analyses, 13.3% more participants showed improvement in self-efficacy (137 of 367 [37.3%] vs 91 of 379 [24.0%]) and 12.2% fewer experienced worsening (209 of 367 [56.9%] vs 262 of 379 [69.1%]) with app-facilitated symptom monitoring (OR, 1.75 [95% CI, 1.30-2.36]; P < .001) ( Table 2 ). ECOG PS, ED Visits, and Hospitalization Deterioration in ECOG PS at week 18 occurred less frequently in the app-facilitated symptom monitoring group than in the usual care group (44 of 367 [12.0%] vs 66 of 379 [17.4%]), although the between-group difference was not statistically significant (OR, 1.22 [95% CI, 0.83-1.79]; P = .31) ( Table 2 , Figure 3 A, and eTable 5 in Supplement 2 ). Figure 3. Component Bar Graphs Showing Secondary Outcomes at Baseline and 18 Weeks. Open in a new tab ECOG PS indicates Eastern Cooperative Oncology Group performance status (range, 1 to 4, with higher scores indicating worse status). ED utilization was similar between groups, with worsening observed in 74 of 367 participants (20.2%) in the app-facilitated symptom monitoring group and 97 of 379 (25.6%) in the usual care group (OR, 1.27 [95% CI, 0.97-1.67]; P = .09) ( Table 2 , Figure 3 B, and eTable 6 in Supplement 2 ). In contrast, hospitalization outcomes favored app-facilitated symptom monitoring. Compared with usual care, 11.3% fewer participants in the app-facilitated symptom monitoring group experienced worsening unplanned hospitalization episodes (63 of 367 [17.2%] vs 108 of 379 [28.5%]; OR, 1.59 [95% CI, 1.21-2.10]; P = .001) ( Table 2 , Figure 3 C, and eTable 7 in Supplement 2 ), and 15.5% fewer participants experienced worsening inpatient days (68 of 367 [18.5%] vs 129 of 379 [34.0%]; OR, 1.84 [95% CI, 1.40-2.42]; P < .001). Mean (SD) inpatient days during follow-up were also lower with app-facilitated symptom monitoring (3.4 [8.9] vs 7.3 [15.5] days) (eTable 8 in Supplement 2 ). Completion of Advance Directives Completion of advance directives did not differ between groups. Among participants without an advance directive at baseline, 25 of 519 (4.8%) in the app-facilitated symptom monitoring group and 18 of 530 (3.4%) in the usual care group completed one during follow-up (eTable 9 in Supplement 2 ). IPOS Score in Intervention Group Among 315 participants in the app-facilitated symptom monitoring group with complete data, 271 (86.0%) submitted regular IPOS reports, generating 3167 symptom reports during 18 weeks (eTable 10 in Supplement 2 ). Severe symptoms were reported in 1140 submissions (36.0%), most commonly pain (389 [12.3%]), weakness or lack of energy (270 [8.5%]), poor appetite (224 [7.1%]), and drowsiness (135 [4.3%]). Among participants with evaluable baseline and week 18 assessments, 57 (21.0%) improved from severe to nonsevere symptom levels, whereas 23 (8.5%) worsened from nonsevere to severe levels during follow-up. All severe symptom alerts triggered nurse contact. Most responses involved medication advice (580 of 1140 [50.9%]) or symptom management guidance (540 of 1140 [47.4%]), and 24 alerts (2.1%) resulted in recommendations for ED visits or hospitalization. A total of 763 alerts (66.9%) required nurse telephone follow-up, with a median call duration of 13.5 minutes (range, 10-45 minutes). Mortality During the Study Period Overall survival did not differ between groups (median, 43.3 [95% CI, 37.1-47.1] vs 44.0 [95% CI, 36.9-51.4] weeks; P = .98), and among participants who died during follow-up (n = 343), the median time from enrollment to death was 50 (IQR, 31-74) days, with similar timing between groups (eTable 11 and eFigure 1 in Supplement 2 ). Complete Case Cohort Analysis Results from complete-case sensitivity analyses were consistent with the primary intention-to-treat findings. Among participants with complete data, app-facilitated symptom support was associated with better maintenance of HRQOL, self-efficacy, and performance status, as well as fewer unplanned hospitalizations and shorter inpatient stays compared with usual care (eTables 12 to 21 and eFigures 2 and 3 in Supplement 2 ). Discussion In this multicenter randomized clinical trial, adding app-facilitated symptom monitoring to usual palliative care was associated with better maintenance of HRQOL and self-efficacy among community-dwelling patients with advanced cancer. Participants allocated to app-facilitated symptom monitoring also experienced fewer unplanned hospital admissions and shorter inpatient stays. These findings support a shift from episodic, visit-based symptom management toward proactive identification of deterioration and timely response between scheduled encounters in palliative care. Advancement Beyond Existing Evidence Previous multicenter randomized clinical trials, including the PRO-TECT (Patient-Reported Outcomes To Enhance Cancer Treatment) trial 6 and eSMART (Electronic Symptom Management Using the Advanced Symptom Management System Remote Technology) study, 7 demonstrated that electronic patient-reported outcome monitoring improves symptom control, functioning, and HRQOL during chemotherapy. Qualitative evaluations 21 further highlighted enhanced patient-clinician communication and care experiences. More recent studies 22 , 23 have suggested that multicomponent approaches pairing automated self-management support with clinician or nurse follow-up outperform symptom tracking alone. Evidence in populations receiving palliative care and no anticancer therapy remains limited and largely focused on feasibility. 10 , 11 , 24 , 25 The present trial adds clinically meaningful evidence from a community palliative care setting using an IPOS-based monitoring model combined with automated self-management advice and nurse follow-up for severe symptoms. Unlike chemotherapy-focused programs that primarily target treatment-related toxicities, app-facilitated symptom monitoring addressed multidimensional symptom burden and evaluated outcomes central to palliative care, including functional status and hospital use. These outcomes align with patient priorities to maintain HRQOL and independence while minimizing caregiver burden. Mechanisms and the Role of Caregivers Caregiver involvement was a defining feature of this system use, with approximately 70% of symptom reports submitted by caregivers. Because IPOS supports both patient and proxy reporting, caregiver input was feasible when patients were unable to engage directly. This is particularly important in palliative care settings, where patients are often older, frailer, and more dependent on caregivers for symptom observation and communication with clinicians than those receiving systemic therapy. Caregiver engagement may have facilitated earlier recognition of deterioration and more consistent home-based management. However, caregiver-centered outcomes, including caregiver burden and bereavement outcomes, were not assessed in this study and warrant consideration in future research. Implementation Considerations and Future Directions This trial provides practical insights into implementing digital symptom monitoring in palliative care, where low technology literacy and functional dependency are common. Initial onboarding was resource intensive, with many patients and caregivers requiring prolonged training and repeated demonstrations. These findings underscore the need for simplified interfaces, dyadic design that supports both patients and caregivers, and dedicated staff support during early adoption. Engagement declined with clinical deterioration. Nearly one-third of participants died within 18 weeks, and rapid decline often reduced the relevance of digital reporting once patients entered periods of intensive clinical care. This pattern suggests that digital symptom monitoring may be most feasible and beneficial when introduced earlier in the disease course, before substantial functional decline. Sustained delivery of the intervention required dedicated nursing input. A large proportion of symptom reports triggered nurse follow-up, and telephone consultations were frequently required. These findings indicate reliance on trained personnel and protected time for follow-up. Workforce planning, training in remote symptom management, and clear escalation pathways are essential for scalability. Future research should evaluate the optimal timing of initiation, including earlier introduction in the disease course or while patients are receiving anticancer treatment, assess caregiver-centered outcomes, and examine cost-effectiveness by balancing reductions in hospital use against staffing, training, and technical resources. 26 , 27 , 28 Implementation studies are also needed to identify effective strategies for integrating digital monitoring into routine care pathways. Emerging technologies, including artificial intelligence–enhanced features, may help prioritize alerts, detect urgent symptoms in real time, deliver more tailored self-management advice, and support triage for emergency situations, with the potential to reduce nursing workload and improve scalability. 29 , 30 Limitations This study has several limitations. Although EQ-5D-5L is suitable for populations with frailty, it may not capture the full spectrum of symptom-specific changes in HRQOL. Attrition was substantial due to progressive deterioration, limiting sustained engagement. High rates of mortality and loss to follow-up indicate that many participants were already in very advanced stages of illness at enrollment. The 18-week observation period was relatively short and may not capture longer-term effects. The trial was also not blinded to patients or health care professionals, and awareness of group assignment may have influenced reporting. Although the intervention involved active clinical decision-making by palliative care nurses, including symptom management and medication-related guidance, detailed data on specific clinical actions (such as medication dose adjustments) were not systematically captured. Finally, because digital reporting and nurse follow-up were delivered as an integrated intervention, the contribution of individual components could not be distinguished. Conclusions In this randomized clinical trial of community-dwelling patients with advanced cancer receiving palliative care, the app-facilitated symptom monitoring model was associated with better maintenance of HRQOL, self-efficacy, and functional status. These findings support integration of structured digital symptom monitoring with nurse-led follow-up into routine palliative care services and suggest that earlier introduction in the disease course may further enhance benefit. Supplement 1. Trial Protocol jamanetwopen-e2615509-s001.pdf (2MB, pdf) Supplement 2. eTable 1. Mean Scores and Differences in Mean Changes of EQ-5D-5L and Self-Efficacy From Baseline Between Arms at Each Visit eTable 2. Proportion of Patients in the Intervention Group and Usual Care Group With Changes in the 5 Domains of EQ-5D-5L eTable 3. Preplanned Subgroup Analysis of HRQOL, as Measured by EQ-5D-5L Utility eTable 4. Preplanned Subgroup Analysis of HRQOL, as Measured by EQ-VAS eTable 5. ECOG Performance in Intervention and Usual Care Arms at Baseline and Week 18 eTable 6. Emergency Department Visits in Intervention and Usual Care Arms During the Study Period eTable 7. Hospitalization Episodes in Intervention and Usual Care Arms During the Study Period eTable 8. Hospitalization Duration in Intervention and Usual Care Arms During the Study Period eTable 9. Advance Directive Status at Baseline, Week 18, and New Completions eTable 10. Alerts and Nurse Responses in Intervention Group in Complete-Data Cohort eTable 11. Median Time to Death for Patients Who Died Within Study Periods eTable 12. Proportion of Patients in the Intervention Arm and Control Arm With Changes in HRQOL, Self-Efficacy, Emergency Department Visit, and Hospitalization Episodes and Duration (Complete-Case Cohort) eTable 13. Mean Scores and Differences in Mean Changes of EQ-5D-5L and Self-Efficacy From Baseline Between Arms at Each Visit (Complete-Case Cohort) eTable 14. Proportion of Patients in the Intervention Group and Usual Care Group With Changes in the 5 Domains of EQ-5D-5L (Complete-Case Cohort) eTable 15. Preplanned Subgroup Analysis of HRQOL, as Measured by EQ-5D-5L Index (Complete-Case Cohort) eTable 16. Preplanned Subgroup Analysis of HRQOL, as Measured by EQ-VAS (Complete-Case Cohort) eTable 17. ECOG Performance in Intervention and Usual Care Arms at Baseline and Week 18 (Complete-Case Cohort) eTable 18. Emergency Department Visits in Intervention and Usual Care Arms During the Study Period (Complete-Case Cohort) eTable 19. Hospitalization Episodes in Intervention and Usual Care Arms During the Study Period (Complete-Case Cohort) eTable 20. Hospitalization Duration in Intervention and Usual Care Arms During the Study Period (Complete-Case Cohort) eTable 21. Completion of Advance Directive in Both Groups (Complete-Case Cohort) eFigure 1. Survival Curves for All Participants eFigure 2. Model-Based Mean Change and Score Distribution From Baseline at Week 9 and Week 18 (Complete-Case Cohort) eFigure 3. ECOG Performance Status (A), Episodes of Emergency Department (ED) Visits (B), and Hospitalization (C) in Intervention and Usual Care Groups at Baseline and Week 18 (Complete-Case Cohort) eMethods. Intervention Nurse Workflow and Guidance jamanetwopen-e2615509-s002.pdf (1.2MB, pdf) Supplement 3. Data Sharing Statement jamanetwopen-e2615509-s003.pdf (22.9KB, pdf) References 1. Teunissen SC, Wesker W, Kruitwagen C, de Haes HC, Voest EE, de Graeff A. Symptom prevalence in patients with incurable cancer: a systematic review. J Pain Symptom Manage. 2007;34(1):94-104. doi: 10.1016/j.jpainsymman.2006.10.015 [ DOI ] [ PubMed ] [ Google Scholar ] 2. Henson LA, Maddocks M, Evans C, Davidson M, Hicks S, Higginson IJ. 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Preplanned Subgroup Analysis of HRQOL, as Measured by EQ-5D-5L Utility eTable 4. Preplanned Subgroup Analysis of HRQOL, as Measured by EQ-VAS eTable 5. ECOG Performance in Intervention and Usual Care Arms at Baseline and Week 18 eTable 6. Emergency Department Visits in Intervention and Usual Care Arms During the Study Period eTable 7. Hospitalization Episodes in Intervention and Usual Care Arms During the Study Period eTable 8. Hospitalization Duration in Intervention and Usual Care Arms During the Study Period eTable 9. Advance Directive Status at Baseline, Week 18, and New Completions eTable 10. Alerts and Nurse Responses in Intervention Group in Complete-Data Cohort eTable 11. Median Time to Death for Patients Who Died Within Study Periods eTable 12. Proportion of Patients in the Intervention Arm and Control Arm With Changes in HRQOL, Self-Efficacy, Emergency Department Visit, and Hospitalization Episodes and Duration (Complete-Case Cohort) eTable 13. Mean Scores and Differences in Mean Changes of EQ-5D-5L and Self-Efficacy From Baseline Between Arms at Each Visit (Complete-Case Cohort) eTable 14. Proportion of Patients in the Intervention Group and Usual Care Group With Changes in the 5 Domains of EQ-5D-5L (Complete-Case Cohort) eTable 15. Preplanned Subgroup Analysis of HRQOL, as Measured by EQ-5D-5L Index (Complete-Case Cohort) eTable 16. Preplanned Subgroup Analysis of HRQOL, as Measured by EQ-VAS (Complete-Case Cohort) eTable 17. ECOG Performance in Intervention and Usual Care Arms at Baseline and Week 18 (Complete-Case Cohort) eTable 18. Emergency Department Visits in Intervention and Usual Care Arms During the Study Period (Complete-Case Cohort) eTable 19. Hospitalization Episodes in Intervention and Usual Care Arms During the Study Period (Complete-Case Cohort) eTable 20. Hospitalization Duration in Intervention and Usual Care Arms During the Study Period (Complete-Case Cohort) eTable 21. Completion of Advance Directive in Both Groups (Complete-Case Cohort) eFigure 1. Survival Curves for All Participants eFigure 2. Model-Based Mean Change and Score Distribution From Baseline at Week 9 and Week 18 (Complete-Case Cohort) eFigure 3. ECOG Performance Status (A), Episodes of Emergency Department (ED) Visits (B), and Hospitalization (C) in Intervention and Usual Care Groups at Baseline and Week 18 (Complete-Case Cohort) eMethods. Intervention Nurse Workflow and Guidance jamanetwopen-e2615509-s002.pdf (1.2MB, pdf) Supplement 3. Data Sharing Statement jamanetwopen-e2615509-s003.pdf (22.9KB, pdf) Articles from JAMA Network Open are provided here courtesy of American Medical Association ACTIONS View on publisher site Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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