Electronic symptom monitoring: not everyone fits the mold
Authors: K.P. Loh, S.G. Mohile, M. Flannery
Journal: Annals of Oncology (2020)

The majority of individuals diagnosed with and dying from cancer are aged ≥65 years, yet this group of individuals is consistently underrepresented in clinical trials that establish the evidence base for oncology practice.1Ludmir E.B. Mainwaring W. Lin T.A. et al.Factors associated with age disparities among cancer clinical trial participants.JAMA Oncol. 2019; 5: 1769-1773Crossref PubMed Scopus (107) Google Scholar,2Freedman R.A. Foster J.C. Seisler D.K. et al.Accrual of older patients with breast cancer to alliance systemic therapy trials over time: Protocol A151527.J Clin Oncol. 2017; 35: 421-431Crossref PubMed Scopus (54) Google Scholar In particular, the proportion of individuals diagnosed with cancer aged ≥85 years is projected to increase in the future.3DeSantis C.E. Miller K.D. Dale W. et al.Cancer statistics for adults aged 85 years and older, 2019.CA Cancer J Clin. 2019; 69: 452-467Crossref PubMed Scopus (162) Google Scholar Aging is heterogeneous and there is a wide variation across older adults, some of whom may be very robust while others may be frail or vulnerable. It remains a challenge to clinicians to determine if interventions found to be effective in clinical trials are similarly effective in older adults with cancer. Therefore, subgroup analyses of older adults in clinical trials are an important contribution to oncology care. Technology-based interventions are becoming increasingly utilized in the health care system. While Internet use and ownership of electronic devices have increased in the general population, the pace at which older adults adopt these technologies is slower than their younger counterparts.4Tech Adoption Climbs Among Older Adults.https://www.pewinternet.org/2017/05/17/tech-adoption-climbs-among-older-adults/Date accessed: October 16, 2019Google Scholar We therefore applaud Nipp and colleagues for their study5Nipp R.D. Horick N.K. Deal A.M. et al.Differential effects of an electronic symptom monitoring intervention based on the age of patients with advanced cancer.Ann Oncol. 2020; 31: 123-130Abstract Full Text Full Text PDF PubMed Scopus (19) Google Scholar examining the differential effects of an electronic symptom monitoring intervention (STAR) based on age among adults with metastatic cancer. The original study6Basch E. Deal A.M. Kris M.G. et al.Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial.J Clin Oncol. 2016; 34: 557-565Crossref PubMed Scopus (1248) Google Scholar on which this secondary analysis was based included 766 patients (589 patients aged <70 years, 177 patients aged ≥70 years) with metastatic disease receiving chemotherapy; both the cancer stage and treatment were associated with high symptom burden. The intervention consisted of electronically monitoring 12 common symptoms, with trigger alerts for worsening or severe symptoms provided to the nursing staff. The symptom reports were also provided to nurses and oncologists at each clinic visits. Patients randomized to the intervention arm had better health-related quality of life (HRQoL), lower emergency room (ER) utilization (between-group difference, 7%; P = 0.02), lower risk of hospitalization (between-group difference, 4%; P = 0.08), were on chemotherapy longer (between-group difference, 1.9 months; P = 0.002), and had longer survival (31.2 versus 26.0 months; between-group difference, 5.0 months; P = 0.03).6Basch E. Deal A.M. Kris M.G. et al.Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial.J Clin Oncol. 2016; 34: 557-565Crossref PubMed Scopus (1248) Google Scholar,7Basch E. Deal A.M. Dueck A.C. et al.Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment.JAMA. 2017; 318: 197-198Crossref PubMed Scopus (1071) Google Scholar In this article, Nipp and colleagues demonstrated that among older patients, those randomized to the intervention arm had better HRQoL and lower risk of hospitalization, but not ER utilization (median time to ER visit: 17.61 months versus 21.98 months, Gray’s test P = 0.738) and survival (15.57 versus 12.94 months, log-rank, P = 0.512). Among younger patients, those randomized to the intervention arm had improvement in all of the aforementioned outcomes, including ER utilization (median time to ER visit: 50.73 months versus 21.72 months, Gray’s test P = 0.016) and survival (46.23 versus 30.14 months, log-rank, P = 0.004). The authors propose that the moderating effects of age on ER utilization and survival may have occurred due to a constellation of factors relevant to older adults (e.g. cognitive impairment, functional impairment) that may not be addressed by a symptom monitoring intervention. Indeed, the occurrence of symptoms triggers many downstream activities (e.g. taking pain medications for uncontrolled pain). In older adults, monitoring symptoms without providing adequate support for these downstream activities may not be effective. There are several other plausible explanations. First, symptom reporting may vary between older and younger adults, and studies have shown that older adults are less likely to report symptoms and more likely to report lower severity scores.8Cataldo J.K. Paul S. Cooper B. et al.Differences in the symptom experience of older versus younger oncology outpatients: a cross-sectional study.BMC Cancer. 2013; 13: 6Crossref PubMed Scopus (70) Google Scholar, 9Jensen R.E. Potosky A.L. Moinpour C.M. et al.United States population-based estimates of patient-reported outcomes measurement information system symptom and functional status reference values for individuals with cancer.J Clin Oncol. 2017; 35: 1913-1920Crossref PubMed Scopus (86) Google Scholar, 10Mao J.J. Armstrong K. Bowman M.A. et al.Symptom burden among cancer survivors: impact of age and comorbidity.J Am Board Fam Med. 2007; 20: 434-443Crossref PubMed Scopus (164) Google Scholar, 11Walsh D. Donnelly S. Rybicki L. The symptoms of advanced cancer: relationship to age, gender, and performance status in 1,000 patients.Support Care Cancer. 2000; 8: 175-179Crossref PubMed Scopus (489) Google Scholar, 12Kirkova J. Walsh D. Rybicki L. et al.Symptom severity and distress in advanced cancer.Palliat Med. 2010; 24: 330-339Crossref PubMed Scopus (79) Google Scholar In this study, it is unclear if adherence to symptom reporting differs between younger and older adults (i.e. what percentage of patients entered symptoms and how frequent were symptoms reported). Secondly, since no specific guidance was provided to patients or health care providers in response to the symptoms, self-care activities and management plans may differ in the age groups (e.g. older adults may have a lower symptom threshold to present to the ER). Interestingly, the original study reported that patients in the intervention group who were computer-inexperienced (older, frailer, and more symptomatic) derived more benefits from the intervention compared with those of computer-experienced. The reason for the discrepancy is unclear and may be worth exploring further. It is worth noting that the vast majority of older adults who participated were aged <80 years (131/177; 79.6%), and they were mainly men (130/177; 73.5%) and diagnosed with genitourinary cancer (122/177; 68.9%). Therefore, the observed moderation effects may be most relevant to older men aged 70–79 years with genitourinary cancer. In addition, it is unclear if ‘frail’ or vulnerable older adults were enrolled as these factors were not measured. Nipp and colleagues have previously reported that age-related differential effects vary by diagnosis for a palliative care intervention, where moderation by age on use of coping strategies was found in patients with non-small-cell lung cancer, but not gastrointestinal cancer.13Nipp R.D. El-Jawahri A. Traeger L. et al.Differential effects of early palliative care based on the age and sex of patients with advanced cancer from a randomized controlled trial.Palliat Med. 2018; 32: 757-766Crossref PubMed Scopus (27) Google Scholar Therefore, studies of the ‘oldest-old’ with more inclusion of women and other cancer subtypes are needed. Nevertheless, this study reinforced that like younger adults, older adults were willing and able to participate and enroll on an electronic symptom monitoring study (enrollment rate: 76%–77%), and that older adults derived benefits from such an intervention. Many older adults value quality of life as much as (if not more than) survival,14Loh K.P. Mohile S.G. Epstein R.M. et al.Willingness to bear adversity and beliefs about the curability of advanced cancer in older adults.Cancer. 2019; 125: 2506-2513Crossref PubMed Scopus (33) Google Scholar and this study supports that older adults will enroll and participate in clinical trials focused on quality of life outcomes. These findings are consistent with our two ongoing nationwide geriatric oncology trials, where >1000 patients have enrolled (ClinicalTrials.gov Identifiers: NCT02107443 and NCT02054741).15Mohile S.G. Epstein R.M. Hurria A. et al.Communication with older patients with cancer using geriatric assessment: a cluster-randomized clinical trial from the National Cancer Institute Community Oncology Research Program.JAMA Oncol. 2019; ([Epub ahead of print]): 1-9https://doi.org/10.1001/jamaoncol.2019.4728Crossref PubMed Scopus (112) Google Scholar With the increasing use of technology devices in this population, there is a need for multimodal and personalized interventions moving forward. Considerations for clinical trial design and technology-based interventions (e.g. touch screen, font size) are crucial to target the unique needs of older adults with the goal of improving outcomes that are meaningful to them.16Loh K.P. Ramsdale E. Culakova E. et al.Novel mHealth app to deliver geriatric assessment-driven interventions for older adults with cancer: pilot feasibility and usability study.JMIR Cancer. 2018; 4: e10296Crossref PubMed Scopus (34) Google Scholar KPL is supported by the Wilmot Research Fellowship Award and National Cancer Institute of the National Institute of Health (K99 CA237744). SGM is supported by the National Institute of Aging of the National Institute of Health (K24 AG056589).