Computerized Quality of Life Assessment: A Randomized Experiment to Determine the Impact of Individualized Feedback on Assessment Experience

Personality Feedback With Tailored Self-Care Recommendations Improves Self-Efficacy for Cancer Management: A Randomized Controlled Trial

OBJECTIVE

To test whether a personality feedback intervention improves three domains of cancer self-management: self-awareness, self-efficacy, and positive affect.

METHODS

From 11/2020-02/2021, 372 adults diagnosed with cancer participated in a randomized controlled trial (RCT) of an intervention that entailed reading a brief personality-related excerpt during an online survey. Eligibility included self-reported age ≥ 18 years, current or past cancer diagnosis, and ability to read English. The survey included a baseline assessment with a personality questionnaire, then randomized participants to one of two groups. The intervention group (n = 184) received a personality feedback report with tailored self-care tips, whereas the control group (n = 188) received a generic reading on personality theory. At the end of the survey, participants completed outcome measures of self-awareness (primary), self-efficacy for illness management, and positive affect. General linear models tested between-group differences in changes from baseline to post-test on each outcome.

RESULTS

There was no intervention effect on self-awareness (primary outcome) or positive affect. However, compared to controls, intervention participants experienced a greater increase in self-efficacy for illness management (d = 0.33, p = 0.002), including in 2 of 3 constituent domains: self-efficacy for managing symptoms (d = 0.36, p < 0.001) and self-efficacy for managing treatments/medication (d = 0.22, p = 0.035).

CONCLUSION

Despite the primary outcome's null results, this was the first RCT of a personality feedback intervention to show improvements in self-efficacy for managing chronic illness. Given the important role of self-efficacy in self-management, the intervention has implications for other cancer outcomes. Follow-up studies on longer-term outcomes such as health behaviors and quality of life should be explored.

TRIAL REGISTRATION

NCT04625439.

Using a modified Delphi procedure to select a PRO-CTCAE-based subset for patient-reported symptomatic toxicity monitoring in rectal cancer patients

PURPOSE

Standardized patient-reported outcomes (PRO) monitoring during and after rectal cancer treatment provides insight into treatment-related toxicities patients experience and improves health-related quality-of-life as well as overall survival. We aimed to select a subset of the PRO version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) for standardized monitoring of treatment-related symptomatic toxicities in rectal cancer.

METHODS

We used a mixed methods approach including a literature review, and semi-structured interviews with health care providers (HCPs) involved in rectal cancer care and rectal cancer patients. Results from literature and interviews were summarized and used in a modified Delphi procedure to select a PRO-CTCAE subset specific for rectal cancer.

RESULTS

Twenty-six PRO-CTCAE symptomatic toxicities were identified from literature. Fifteen HCPs from multiple disciplines (medical, radiation and surgical oncology), and a heterogeneous group of fifteen rectal cancer patients treated with chemotherapy and/or radiotherapy and/or surgery, participated in semi-structured interviews. Ten HCPs (67%) and nine patients (90%) participated in the first Delphi round. The final selected PRO-CTCAE core-subset contained 16 symptomatic toxicities: 'diarrhea', 'fecal incontinence', 'constipation','bloating of the abdomen', 'pain in the abdomen', 'vomiting', 'decreased libido', 'pain during vaginal sex', 'ability to achieve and maintain erection', 'fatigue', 'anxiety', 'feeling that nothing could cheer you up', 'urinary incontinence', 'painful urination', 'general pain', and 'hand-foot syndrome'.

CONCLUSION

Based on a comprehensive mixed methods study, a PRO-CTCAE subset for standardized treatment-related symptomatic toxicity monitoring in rectal cancer was identified. Assessment of the effectiveness and compliance of symptomatic toxicity monitoring using this subset is recommended.

Qualitative Health-Related Quality of Life and Natural Language Processing: Characteristics, Implications, and Challenges

OBJECTIVES

This article focuses on describing the main characteristics of the application of NLP in the qualitative assessment of quality of life, as well as its implications and challenges.

METHODS

The qualitative methodology allows analysing patient comments in unstructured free text and obtaining valuable information through manual analysis of these data. However, large amounts of data are a healthcare challenge since it would require a high number of staff and time resources that are not available in most healthcare organizations.

RESULTS

One potential solution to mitigate the resource constraints of qualitative analysis is the use of machine learning and artificial intelligence, specifically methodologies based on natural language processing.

Medical Maximizing Preferences and Beliefs About Cancer Among US Adults

Importance

Medical overutilization contributes to significant health care expenditures and exposes patients to questionably beneficial surgery and unnecessary risk.

Objectives

To understand public attitudes toward medical utilization and the association of these attitudes with beliefs about cancer.

Design, Setting, and Participants

In this cross-sectional survey study conducted from August 26 to October 28, 2020, US-based, English-speaking adults were recruited from the general public using Prolific Academic, a research participant platform. Quota-filling was used to obtain a sample demographically representative of the US population. Adults with a personal history of cancer other than nonmelanoma skin cancer were excluded. Statistical analysis was completed in July 2022.

Main Outcome and Measures

Medical utilization preferences were characterized with the validated, single-item Maximizer-Minimizer Elicitation Question. Participants preferring to take action in medically ambiguous situations (hereafter referred to as "maximizers") were compared with those who leaned toward waiting and seeing (hereafter referred to as "nonmaximizers"). Beliefs and emotions about cancer incidence, survivability, and preventability were assessed using validated measures. Logistic regression modeled factors associated with preferring to maximize medical utilization.

Results

Of 1131 participants (mean [SD] age, 45 [16] years; 568 women [50.2%]), 287 (25.4%) were classified as maximizers, and 844 (74.6%) were classified as nonmaximizers. Logistic regression revealed that self-reporting very good or excellent health status (compared with good, fair, or poor; odds ratio [OR], 2.01 [95% CI, 1.52-2.65]), Black race (compared with White race; OR, 1.88 [95% CI, 1.22-2.89]), high levels of cancer worry (compared with low levels; OR, 1.62 [95% CI, 1.09-2.42]), and overestimating cancer incidence (compared with accurate estimation or underestimating; OR, 1.58 [95% CI, 1.09-2.28]) were significantly associated with maximizing preferences. Those who believed that they personally had a higher-than-average risk of developing cancer were more likely to be maximizers (23.6% [59 of 250] vs 17.4% [131 of 751]; P = .03); this factor was not significant in regression analyses.

Conclusions and Relevance

In this survey study of US adults, those with medical maximizing tendencies more often overestimated the incidence of cancer and had higher levels of cancer-related worry. Targeted and personalized education about cancer and its risk factors may help reduce overutilization of oncologic care.

International development of a patient-centered core outcome set for assessing health-related quality of life in metastatic breast cancer patients

PURPOSE

For patients living with metastatic breast cancer (MBC), achieving best possible health-related quality of life, along with maximizing survival, is vital. Yet, we have no systemic way to determine if we achieve these goals. A Core Outcome Set (COS) that allows standardized measurement of outcomes important to patients, but also promotes discussing these outcomes during clinical encounters, is long overdue.

METHODS

An international expert group (EG) of patient advocates, researchers, medical specialists, nurse specialists, and pharmaceutical industry representatives (n = 17) reviewed a list of relevant outcomes retrieved from the literature. A broader group (n = 141: patients/patient advocates (n = 45), health care professionals/researchers (n = 64), pharmaceutical industry representatives (n = 28), and health authority representatives (n = 4)) participated in a modified Delphi procedure, scoring the relevance of outcomes in two survey rounds. The EG finalized the COS in a consensus meeting.

RESULTS

The final MBC COS includes 101 variables about: (1) health-related quality of life (HRQoL, n = 26) and adverse events (n = 24); (2) baseline patient characteristics (n = 9); and (3) clinical variables (n = 42). Many outcome that cover aspects of HRQoL relevant to MBC patients are included, e.g. daily functioning (including ability to work), psychosocial/emotional functioning, sexual functioning, and relationship with the medical team.

CONCLUSION

The COS developed in this study contains important administrative data, clinical records, and clinician-reported measures that captures the impact of cancer. The COS is important for standardization of clinical research and implementation in daily practice and has received accreditation by the International Consortium for Health Outcomes Measurement (ICHOM).

Key considerations to reduce or address respondent burden in patient-reported outcome (PRO) data collection

Patient-reported outcomes (PROs) are used in clinical trials to provide evidence of the benefits and risks of interventions from a patient perspective and to inform regulatory decisions and health policy. The collection of PROs in routine practice can facilitate monitoring of patient symptoms; identification of unmet needs; prioritisation and/or tailoring of treatment to the needs of individual patients and inform value-based healthcare initiatives. However, respondent burden needs to be carefully considered and addressed to avoid high rates of missing data and poor reporting of PRO results, which may lead to poor quality data for regulatory decision making and/or clinical care.

Visualization formats of patient-reported outcome measures in clinical practice: a systematic review about preferences and interpretation accuracy

PURPOSE

The use of Patient-Reported Outcome Measures (PROMs) for individual patient management within clinical practice is becoming increasingly important. New evidence about graphic visualization formats for PROMs scores has become available. This systematic literature review evaluated evidence for graphic visualization formats of PROMs data in clinical practice for patients and clinicians, for both individual and group level PROMs data.

METHODS

Studies published between 2000 and 2020 were extracted from CINAHL, PubMed, PsychInfo, and Medline. Studies included patients ≥ 18 years old in daily clinical practice. Papers not available in English, without full-text access, or that did not specifically describe visualization of PROMs data were excluded. Outcomes were: visualization preferences; interpretation accuracy; guidance for clinical interpretation.

RESULTS

Twenty-five out of 789 papers were included for final analysis. Most frequently studied formats were: bar charts, line graphs, and pie charts. Patients preferred bar charts and line graphs as these were easy and quick for retrieving information about their PROMs scores over time. Clinicians' interpretation accuracy and preferences were similar among graphic visualization formats. Scores were most often compared with patients' own previous scores; to further guide clinical interpretation, scores were compared to norm population scores. Different 'add-ons' improved interpretability for patients and clinicians, e.g. using colors, descriptions of measurement scale directionality, descriptive labels, and brief definitions.

CONCLUSION

There was no predominant graphical visualization format approach in terms of preferences or interpretation accuracy for both patients and clinicians. Detailed clarification of graph content is essential.

Effectiveness of eHealth and mHealth Interventions Supporting Children and Young People Living With Juvenile Idiopathic Arthritis: Systematic Review and Meta-analysis

BACKGROUND

Juvenile idiopathic arthritis (JIA) management aims to promote remission through timely, individualized, well-coordinated interdisciplinary care using a range of pharmacological, physical, psychological, and educational interventions. However, achieving this goal is workforce-intensive. Harnessing the burgeoning eHealth and mobile health (mHealth) interventions could be a resource-efficient way of supplementing JIA management.

OBJECTIVE

This systematic review aims to identify the eHealth and mHealth interventions that have been proven to be effective in supporting health outcomes for children and young people (aged 1-18 years) living with JIA.

METHODS

We systematically searched 15 databases (2018-2021). Studies were eligible if they considered children and young people (aged 1-18 years) diagnosed with JIA, an eHealth or mHealth intervention, any comparator, and health outcomes related to the used interventions. Independently, 2 reviewers screened the studies for inclusion and appraised the study quality using the Downs and Black (modified) checklist. Study outcomes were summarized using a narrative, descriptive method and, where possible, combined for a meta-analysis using a random-effects model.

RESULTS

Of the 301 studies identified in the search strategy, 15 (5%) fair-to-good-quality studies met the inclusion criteria, which identified 10 interventions for JIA (age 4-18.6 years). Of these 10 interventions, 5 (50%) supported symptom monitoring by capturing real-time data using health applications, electronic diaries, or web-based portals to monitor pain or health-related quality of life (HRQoL). Within individual studies, a preference was demonstrated for real-time pain monitoring over recall pain assessments because of a peak-end effect, improved time efficiency (P=.002), and meeting children's and young people's HRQoL needs (P<.001) during pediatric rheumatology consultations. Furthermore, 20% (2/10) of interventions supported physical activity promotion using a web-based program or a wearable activity tracker. The web-based program exhibited a moderate effect, which increased endurance time, physical activity levels, and moderate to vigorous physical activity (standardized mean difference [SMD] 0.60, SD 0.02-1.18; I²=79%; P=.04). The final 30% (3/10) of interventions supported self-management development through web-based programs, or apps, facilitating a small effect, reducing pain intensity (SMD -0.14, 95% CI -0.43 to 0.15; I²=53%; P=.33), and increasing disease knowledge and self-efficacy (SMD 0.30, 95% CI 0.03-0.56; I²=74%; P=.03). These results were not statistically significant. No effect was seen regarding pain interference, HRQoL, anxiety, depression, pain coping, disease activity, functional ability, or treatment adherence.

CONCLUSIONS

Evidence that supports the inclusion of eHealth and mHealth interventions in JIA management is increasing. However, this evidence needs to be considered cautiously because of the small sample size, wide CIs, and moderate to high statistical heterogeneity. More rigorous research is needed on the longitudinal effects of real-time monitoring, web-based pediatric rheumatologist-children and young people interactions, the comparison among different self-management programs, and the use of wearable technologies as an objective measurement for monitoring physical activity before any recommendations that inform current practice can be given.

Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001)

BACKGROUND

Women undergoing cancer-related mastectomy and reconstruction are facing multiple treatment choices where post-surgical satisfaction with breasts is a key outcome. We developed and validated machine learning algorithms to predict patient-reported satisfaction with breasts at 2-year follow-up to better inform the decision-making process for women with breast cancer.

METHODS

We trained, tested, and validated three machine learning algorithms (logistic regression (LR) with elastic net penalty, Extreme Gradient Boosting (XGBoost) tree, and neural network) to predict clinically important differences in satisfaction with breasts at 2-year follow-up using the validated BREAST-Q. We used data from 1553 women undergoing cancer-related mastectomy and reconstruction who were followed-up for two years at eleven study sites in North America from 2011 to 2016. 10-fold cross-validation was used to train and test the algorithms on data from 10 of the 11 sites which were further validated using the additional site's data. Area-under-the-receiver-operating-characteristics-curve (AUC) was the primary outcome measure.

RESULTS

Of 1553 women, 702 (45.2%) experienced an improved satisfaction with breasts and 422 (27.2%) a decreased satisfaction. In the validation set (n = 221), the algorithms showed equally high performance to predict improved or decreased satisfaction with breasts (all P > 0.05): For improved satisfaction AUCs were 0.86-0.87 and for decreased satisfaction AUCs were 0.84-0.85.

CONCLUSION

Long-term, individual patient-reported outcomes for women undergoing mastectomy and breast reconstruction can be accurately predicted using machine learning algorithms. Our algorithms may be used to better inform clinical treatment decisions for these patients by providing accurate estimates of expected quality of life.

Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning

Patient-reported assessments are transforming many facets of health care, but there is scope to modernize their delivery. Contemporary assessment techniques like computerized adaptive testing (CAT) and machine learning can be applied to patient-reported assessments to reduce burden on both patients and health care professionals; improve test accuracy; and provide individualized, actionable feedback. The Concerto platform is a highly adaptable, secure, and easy-to-use console that can harness the power of CAT and machine learning for developing and administering advanced patient-reported assessments. This paper introduces readers to contemporary assessment techniques and the Concerto platform. It reviews advances in the field of patient-reported assessment that have been driven by the Concerto platform and explains how to create an advanced, adaptive assessment, for free, with minimal prior experience with CAT or programming.

Computerized adaptive technology for the assessment of HRQOL of PD and CKD patients

This study was designed as a pilot study to see whether electronic patient-reported outcome measures using computer adaptive technology (CAT) could be successfully implemented in clinics caring for chronic kidney disease (CKD) and peritoneal dialysis (PD) patients. The results demonstrate the feasibility of using CAT on an iPad to assess the symptom burden and health-related quality of life of both PD and CKD patients.

SF-12 or SF-36 in pituitary disease? Toward concise and comprehensive patient-reported outcomes measurements

PURPOSE

Pituitary diseases severely affect patients' health-related quality of life (HRQoL). The most frequently used generic HRQoL questionnaire is the Short Form-36 (SF-36). The shorter 12-item version (SF-12) can improve efficiency of patient monitoring. This study aimed to determine whether SF-12 can replace SF-36 in pituitary care.

METHODS

In a longitudinal cohort study (August 2016 to December 2018) among 103 endoscopically operated adult pituitary tumor patients, physical and mental component scores (PCS and MCS) of SF-36 and SF-12 were measured preoperatively, and 6 weeks and 6 months postoperatively. Chronic care was assessed with a cross-sectional study (N = 431). Mean differences and agreement between SF-36 and SF-12 change in scores (preoperative vs. 6 months) were assessed with intraclass correlation coefficients (ICC) and limits of agreement, depicting 95% of individual patients.

RESULTS

In the longitudinal study, mean differences between change in SF-36 and SF-12 scores were 1.4 (PCS) and 0.4 (MCS) with fair agreement for PCS (ICC = 0.546) and substantial agreement for MCS (ICC = 0.931). For 95% of individual patients, the difference between change in SF-36 and SF-12 scores varied between -14.0 and 16.9 for PCS and between -7.8 and 8.7 for MCS. Cross-sectional results showed fair agreement for PCS (ICC = 0.597) and substantial agreement for MCS (ICC = 0.943).

CONCLUSIONS

On a group level, SF-12 can reliably reproduce MCS in pituitary patients, although PCS is less well correlated. However, individual differences between SF-36 and SF-12 can be large. For pituitary diseases, alternative strategies are needed for concise, but comprehensive patient-reported outcome measurement.

Validation of a Mobile Version of the American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form: An Observational Randomized Crossover Trial

Background

The American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES) questionnaire is an effective tool for evaluating shoulder joint function. The development and usage of a mobile version of the ASES questionnaire has the potential to save time, money, and effort.

Objective

The aim of this study is to assess the equivalence between the paper and mobile versions of the ASES questionnaire and their acceptability among patients.

Methods

The paper and mobile versions of the ASES questionnaire were used to evaluate the shoulder joint function of 50 patients with shoulder pain. This study included patients from the shoulder clinic of Sun Yat-sen Memorial Hospital. The intraclass correlation coefficient (ICC) and Bland-Altman method were used to evaluate the agreement (reliability) of the scores obtained by the two methods (paper versus mobile).

Results

Of the 50 patients recruited from March 2018 to May 2019, 46 (92%) completed the study. There was a high agreement between the paper and mobile versions of the ASES questionnaire (ICC=0.979, 95% CI 0.943-0.987; P<.001). The mean difference between the scores of the mobile and paper versions was 1.0, and only 1/46 (2%) had a difference greater than the minimal clinically important difference of 12 points. About 75% of patients preferred the mobile version to the paper version.

Conclusions

Our study shows that the mobile version of the ASES questionnaire is comparable to the paper version, and has a higher patient preference. This could prove to be a useful tool for epidemiological studies and patient follow-up over longer periods of time.