Background Recruitment of health research participants through social media is becoming more common. In the United States, 80% of adults use at least one social media platform. Social media platforms may allow researchers to reach potential participants efficiently. However, online research methods may be associated with unique threats to sample validity and data integrity. Limited research has described issues of data quality and authenticity associated with the recruitment of health research participants through social media, and sources of low-quality and fraudulent data in this context are poorly understood. Objective The goal of the research was to describe and explain threats to sample validity and data integrity following recruitment of health research participants through social media and summarize recommended strategies to mitigate these threats. Our experience designing and implementing a research study using social media recruitment and online data collection serves as a case study. Methods Using published strategies to preserve data integrity, we recruited participants to complete an online survey through the social media platforms Twitter and Facebook. Participants were to receive $15 upon survey completion. Prior to manually issuing remuneration, we reviewed completed surveys for indicators of fraudulent or low-quality data. Indicators attributable to respondent error were labeled suspicious, while those suggesting misrepresentation were labeled fraudulent. We planned to remove cases with 1 fraudulent indicator or at least 3 suspicious indicators. Results Within 7 hours of survey activation, we received 271 completed surveys. We classified 94.5% (256/271) of cases as fraudulent and 5.5% (15/271) as suspicious. In total, 86.7% (235/271) provided inconsistent responses to verifiable items and 16.2% (44/271) exhibited evidence of bot automation. Of the fraudulent cases, 53.9% (138/256) provided a duplicate or unusual response to one or more open-ended items and 52.0% (133/256) exhibited evidence of inattention. Conclusions Research findings from several disciplines suggest studies in which research participants are recruited through social media are susceptible to data quality issues. Opportunistic individuals who use virtual private servers to fraudulently complete research surveys for profit may contribute to low-quality data. Strategies to preserve data integrity following research participant recruitment through social media are limited. Development and testing of novel strategies to prevent and detect fraud is a research priority.

Context. No information is available on oncology patients' level of stress and symptom burden during the coronavirus disease 2019 (COVID-19) pandemic. Objectives. To evaluate for differences in demographic and clinical characteristics, levels of social isolation and loneliness, and the occurrence and severity of common symptoms between oncology patients with low vs. high levels of COVID-19 and cancer-related stress. In addition, to determine which of these characteristics were associated with membership in the highstressed group. Methods. Patients were 18 years and older; had a diagnosis of cancer; and were able to complete an online survey. Results. Of the 187 patients in this study, 31.6% were categorized in the stressed group (Impact of Event ScaledRevised [score of $24]). Stressed group's Impact of Event ScaledRevised score exceeds previous benchmarks in oncology patients and equates with probable post-traumatic stress disorder. In this stressed group, patients reported occurrence rates for depression (71.2%), anxiety (78.0%), sleep disturbance (78.0%), evening fatigue (55.9%), cognitive impairment (91.5%), and pain (75.9%). Symptom severity scores equate with clinically meaningful levels for each symptom. Conclusion. We identified alarmingly high rates of stress and an extraordinarily high symptom burden among patients with cancer, exceeding those previously benchmarked in this population and on par with noncancer patients with post-traumatic stress disorder. Given that the COVID-19 pandemic will likely impact cancer care for an indefinite period, clinicians must exhibit increased vigilance in their assessments of patients' level of stress and symptom burden. Moreover, an increase in referrals to appropriate supportive care resources must be prioritized for high-risk patients. J Pain Symptom Manage 2020;60:e25ee34.

Background A large amount of inter-individual variability exists in the occurrence of symptoms in patients on chemotherapy (CTX). The purposes of this study, in a sample of oncology outpatients who were receiving CTX (n=582), were to identify subgroups of patients based on their distinct experiences with 25 commonly occurring symptoms and to identify demographic and clinical characteristics associated with subgroup membership. In addition, differences in QOL outcomes were evaluated. Methods Oncology outpatients with breast, gastrointestinal, gynecological, or lung cancer completed the Memorial Symptom Assessment Scale prior to their next cycle of CTX. Latent class analysis was used to identify subgroups of patients with distinct symptom experiences. Results Three distinct subgroups of patients were identified (i.e., 36.1% in Low class; 50.0% in Moderate class, 13.9% in All High class). Patients in the All High class were significantly younger, more likely to be female and Non-white, had lower levels of social support, lower socioeconomic status, poorer functional status, and a higher level of comorbidity. Conclusions Findings from this study support the clinical observation that some oncology patients experience a differentially higher symptom burden during CTX. These high risk patients experience significant decrements in QOL.

Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing chemotherapy. Using two different models of Pairwise Markov Random Fields (PMRF), we examined the nature and structure of interactions for three different dimensions of patients’ symptom experience (i.e., occurrence, severity, distress). Findings from this study provide the first direct evidence that the connections between and among symptoms differ depending on the symptom dimension used to create the network. Based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. Our findings can be used to guide the development of symptom management interventions based on the identification of core symptoms and symptom clusters within a network.

These findings provide insights into the most common symptom clusters in patients undergoing CTX for breast cancer. In addition, the most common symptoms within each cluster appear to be relatively stable across the two dimensions, as well as across time.

Background Allogeneic hematopoietic cell transplantation (HCT) continues to be associated with substantial rates of non-relapse mortality (NRM). Numerous factors influence glucose metabolism among HCT recipients. We hypothesized that “malglycemia”, defined as hyperglycemia, hypoglycemia or increased glycemic variability, is associated with increased mortality in HCT patients. Methods In a retrospective cohort study, Cox regression was used to assess the association of malglycemia after transplant with day 200 NRM. Results 66,062 blood glucose (BG) measurements from 1175 adult allogeneic HCT recipients between 2000 and 2005 at the Fred Hutchinson Cancer Research Center were evaluated (median 0.55 values per patient-day, range 0.09-3.62). Overall, there were 215 cases of NRM by day 200 post-HCT and 601 deaths from any cause throughout observation. After adjustment for previously identified factors associated with NRM, all three components of malglycemia were associated with increased NRM when individually modeled as time-dependent covariates. Specifically, the hazard ratio for death was 1.93 for BG>200 mg/dl (p=0.0009) and 2.78 for BG>300 (p=0.0004) compared with BG 101-150 mg/dl. A minimum BG ≤ 89 was associated with a risk of day 200 NRM 2.17-times that of a minimum BG > 89 (p<0.0001). The upper quartile of glucose variability was associated with a 14.57-fold increase in risk of NRM by day 200 relative to the first quartile (p<0.0001). Conclusions These retrospective data indicate that malglycemia is associated with mortality following HCT. The applicability of these findings to other situations and whether correcting malglycemia in HCT can lead to reductions in mortality remain to be determined.

Context: Patients with lung cancer who undergo chemotherapy (CTX) experience multiple concurrent symptoms. An evaluation of how these symptoms cluster together and how these symptom clusters change over time may provide insights into how to treat these multiple cooccurring symptoms. Objectives: The purposes of this study, in a sample of lung cancer patients (n=145) who were receiving chemotherapy (CTX) were to evaluate for differences in the number and types of symptom clusters at three time points (i.e., before CTX, the week after CTX, and two weeks after CTX) using ratings of symptom occurrence and severity and to evaluate for changes in these symptom clusters over time. Methods: At each of the three assessments, a modified version of the Memorial Symptom Assessment Scale was used to assess the occurrence and severity of the 38 symptoms. Exploratory factor analyses were used to extract the symptom clusters. Results: Across the two symptom dimensions (i.e., occurrence and severity) and the three assessments, six distinct symptom clusters were identified. However, only three of these clusters were relatively stable across both dimensions and across time (i.e., lung cancer specific, psychological, nutritional). Two additional clusters varied by time but not by symptom dimension (i.e., epithelial/gastrointestinal, epithelial). A sickness behavior cluster was identified at each assessment with the exception of the week before CTX using the severity dimension. Conclusion: These findings provide insights into the most common symptom clusters in patients undergoing CTX for lung cancer. The most common symptoms within each cluster appear to be relatively stable across the two dimensions, as well as across time.

No abstract