Roxanne E Jensen

Bio: Roxanne E Jensen is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Health Information National Trends Survey & Cancer. The author has an hindex of 4, co-authored 13 publications receiving 51 citations.

Symptom Clusters in Breast Cancer Survivors: A Latent Class Profile Analysis.

Abstract: Objectives To identify symptom clusters in breast cancer survivors and to determine sociodemographic and clinical characteristics influencing symptom cluster membership. Sample and setting The authors performed a cross-sectional secondary analysis of data obtained from a community-based cancer registry-linked survey with 1,500 breast cancer survivors 6-13 months following a breast cancer diagnosis. Methods and variables Symptom clusters were identified using latent class profile analysis of four patient-reported symptoms (pain, fatigue, sleep disturbance, and depression) with custom PROMIS® short forms. Results Four distinct classes were identified. Implications for nursing Common symptom clusters may lead to better prevention and treatment strategies that target a group of symptoms. Results also suggest that certain factors place patients at high risk for symptom burden, which can guide tailored interventions.

A reporting checklist for HealthMeasures’ patient-reported outcomes: ASCQ-Me, Neuro-QoL, NIH Toolbox, and PROMIS

Abstract: ASCQ-Me®, Neuro-QoL™, NIH Toolbox®, and PROMIS®, which are health-related quality of life measures collectively known as HealthMeasures, have experienced rapid uptake in the scientific community with over 1700 peer-reviewed publications through 2018. Because of their proliferation across multiple research disciplines, there has been significant heterogeneity in the description and reporting of these measures. Here, we provide a publication checklist to promote standardization and comparability across different reports. This checklist can be used across all HealthMeasures systems. Checklist Development: Authors drafted a draft checklist, circulated among the HealthMeasures Steering Committee and PROMIS Health Organization until the members reached consensus. Checklist: The final checklist has 21 entries in 4 categories: measure details, administration, scoring, and reporting. Most entries (11) specify necessary measure-specific details including version number and administration language(s). Administration (4 entries) reminds authors to include details such as use of proxy respondents and the assessment platform. Scoring (3 entries) is necessary to ensure replication and cross-study comparisons. Reporting (3 entries) reminds authors to always report scores on the T-score metric. Consistent documentation is necessary to ensure transparent and reproducible methods and support the accumulation of evidence across studies. This checklist promotes standardization and completeness in documentation for ASCQ-Me, Neuro-QoL, PROMIS, and NIH Toolbox measures.

Characterizing the US Population by Patterns of Mobile Health Use for Health and Behavioral Tracking: Analysis of the National Cancer Institute's Health Information National Trends Survey Data.

Abstract: Background: Multiple types of mobile health (mHealth) technologies are available, such as smartphone health apps, fitness trackers, and digital medical devices. However, despite their availability, some individuals do not own, do not realize they own, or own but do not use these technologies. Others may use mHealth devices, but their use varies in tracking health, behaviors, and goals. Examining patterns of mHealth use at the population level can advance our understanding of technology use for health and behavioral tracking. Moreover, investigating sociodemographic and health-related correlates of these patterns can provide direction to researchers about how to target mHealth interventions for diverse audiences. Objective: The aim of this study was to identify patterns of mHealth use for health and behavioral tracking in the US adult population and to characterize the population according to those patterns. Methods: We combined data from the 2017 and 2018 National Cancer Institute Health Information National Trends Survey (N=6789) to characterize respondents according to 5 mutually exclusive reported patterns of mHealth use for health and behavioral tracking: (1) mHealth nonowners and nonusers report not owning or using devices to track health, behaviors, or goals; (2) supertrackers track health or behaviors and goals using a smartphone or tablet plus other devices (eg, Fitbit); (3) app trackers use only a smartphone or tablet; (4) device trackers use only nonsmartphone or nontablet devices and do not track goals; and (5) nontrackers report having smartphone or tablet health apps but do not track health, behaviors, or goals. Results: Being in the mHealth nonowners and nonusers category (vs all mHealth owners and users) is associated with males, older age, lower income, and not being a health information seeker. Among mHealth owners and users, characteristics of device trackers and supertrackers were most distinctive. Compared with supertrackers, device trackers have higher odds of being male (odds ratio [OR] 2.22, 95% CI 1.55-3.19), older age (vs 18-34 years; 50-64 years: OR 2.83, 95% CI 1.52-5.30; 65+ years: OR 6.28, 95% CI 3.35-11.79), have an annual household income of US $20,000 to US $49,999 (vs US $75,000+: OR 2.31, 95% CI 1.36-3.91), and have a chronic condition (OR 1.69, 95% CI 1.14-2.49). Device trackers also have higher odds of not being health information seekers than supertrackers (OR 2.98, 95% CI 1.66-5.33). Conclusions: Findings revealed distinctive sociodemographic and health-related characteristics of the population by pattern of mHealth use, with notable contrasts between those who do and do not use devices to track goals. Several characteristics of individuals who track health or behaviors but not goals (device trackers) are similar to those of mHealth nonowners and nonusers. Our results suggest patterns of mHealth use may inform how to target mHealth interventions to enhance reach and facilitate healthy behaviors.

Applying PRO Reference Values to Communicate Clinically Relevant Information at the Point-of-care

Abstract: Introduction The inclusion of reference values for common patient-reported outcomes (PROs) measures in clinical care settings provides a clinically relevant context for an individual patient's PRO scores. PRO reference values are currently not reported in clinical care settings. This is a missed opportunity, as clinicians are familiar with the presence and interpretation of reference values, commonly provided alongside laboratory test results. Incorporating PRO reference values into clinical PRO reporting requires: an understanding of the clinical purpose, the availability of an appropriate reference value, and graphical representation. Methods for pro score interpretation We present reference value terminology adapted for PROs and discuss important differences between using reference values in the PRO score interpretation compared to other types of clinical measures from clinical chemistry. We outline the basic methodological approaches in obtaining a PRO reference sample and calculating reference intervals. Lastly, we provide recommendations on how to present and use PRO reference values in clinical care settings. Discussion There is a strong, long-standing discipline behind reference value development and application in psychology and medicine, allowing for both providers and patients to understand comparisons and identify what is "out of range." PRO reference values can be communicated in a wide range of ways within clinical care settings and are adaptable as required to different patient populations or clinical care situations. However, a notable adoption barrier is the expense and methodological expertise needed to establish and apply PRO reference values effectively in clinical encounters.

Spiritual peace and life meaning may buffer the effect of anxiety on physical well-being in newly diagnosed cancer survivors.

Abstract: OBJECTIVE To assess the extent to which spiritual well-being moderates the relationship between anxiety and physical well-being in a diverse, community-based cohort of newly diagnosed cancer survivors. METHODS Data originated from the Measuring Your Health (MY-Health) study cohort (n = 5506), comprising people assessed within 6-13 months of cancer diagnosis. Life meaning/peace was assessed using the 8-item subscale of the Spiritual Well-Being Scale (FACIT-Sp-12). Anxiety was measured with an 11-item PROMIS Anxiety short form, and physical well-being was assessed using the 7-item FACT-G subscale. Multiple linear regression models were used to assess relationships among variables. RESULTS Life meaning and peace was negatively associated with anxiety, b = -0.56 (P < .001) and positively associated with physical well-being, b = 0.43 (P = <.001) after adjusting for race, education, income, and age. A significant interaction between life meaning/peace and anxiety emerged (P < .001) indicating that spiritual well-being moderates the relationship between anxiety and physical well-being. Specifically, for cancer survivors high in anxiety, physical well-being was dependent on levels of life meaning/peace, b = 0.19, P < .001. For those low in anxiety, physical well-being was not associated with levels of life meaning/peace, b = 0.01, P = .541. Differences in cancer clinical factors (cancer stage at diagnosis, cancer type) did not significantly impact results. CONCLUSIONS Further research is needed to assess how spiritual well-being may buffer the negative effect of anxiety on physical well-being. A clinical focus on spiritual well-being topics such as peace and life meaning may help cancer survivors of all types as they transition into follow-up care.

Internet, phone, mail and mixed-mode surveys: the tailored design method: reseña

Abstract: Resena de la obra de Don A. Dillman, Jolene D. Smyth y Leah Melani Christian: Internet, Phone, Mail and Mixed-Mode Surveys. The Tailored Design Method. New Jersey: John Wiley and Sons

Item response theory

Abstract: A must-have reference resource for quantitative management researchers, the Dictionary contains over 100 entries covering the fundamentals of quantitative methodologies; covering both analysis and implementation and examples of use, as well as detailed graphics to aid understanding.

Minimal important change (MIC): a conceptual clarification and systematic review of MIC estimates of PROMIS measures

Abstract: We define the minimal important change (MIC) as a threshold for a minimal within-person change over time above which patients perceive themselves importantly changed. There is a lot of confusion about the concept of MIC, particularly about the concepts of minimal important change and minimal detectable change, which questions the validity of published MIC values. The aims of this study were: (1) to clarify the concept of MIC and how to use it; (2) to provide practical guidance for estimating methodologically sound MIC values; and (3) to improve the applicability of PROMIS by summarizing the available evidence on plausible PROMIS MIC values. We discuss the concept of MIC and how to use it and provide practical guidance for estimating MIC values. In addition, we performed a systematic review in PubMed on MIC values of any PROMIS measure from studies using recommended approaches. A total of 50 studies estimated the MIC of a PROMIS measure, of which 19 studies used less appropriate methods. MIC values of the remaining 31 studies ranged from 0.1 to 12.7 T-score points. We recommend to use the predictive modeling method, possibly supplemented with the vignette-based method, in future MIC studies. We consider a MIC value of 2–6 T-score points for PROMIS measures reasonable to assume at this point. For surgical interventions a higher MIC value might be appropriate. We recommend more high-quality studies estimating MIC values for PROMIS.

The promise of Immuno-oncology: implications for defining the value of cancer treatment.

Abstract: The rapid development of immuno-oncology (I-O) therapies for multiple types of cancer has transformed the cancer treatment landscape and brightened the long-term outlook for many patients with advanced cancer. Responding to ongoing efforts to generate value assessments for novel therapies, multiple stakeholders have been considering the question of “What makes I-O transformative?” Evaluating the distinct features and attributes of these therapies, and better characterizing how patients experience them, will inform such assessments. This paper defines ways in which treatment with I-O is different from other therapies. It also proposes key aspects and attributes of I-O therapies that should be considered in any assessment of their value and seeks to address evidence gaps in existing value frameworks given the unique properties of patient outcomes with I-O therapy. The paper concludes with a “data needs catalogue” (DNC) predicated on the belief that multiple key, unique elements that are necessary to fully characterize the value of I-O therapies are not routinely or robustly measured in current clinical practice or reimbursement databases and are infrequently captured in existing research studies. A better characterization of the benefit of I-O treatment will allow a more thorough assessment of its benefits and provide a template for the design and prioritization of future clinical trials and a roadmap for healthcare insurers to optimize coverage for patients with cancers eligible for I-O therapy.