Social Determinants of Health Needs and Health-related Quality of Life Among Surgical Patients: A Retrospective Analysis of 8512 Patients.

OBJECTIVE: To assess associations between social determinants of health (SDOH) needs and health-related quality of life (HRQOL) among surgical patients. BACKGROUND: Despite the profound impact of SDOH on health outcomes, studies examining the effect of SDOH needs on HRQOL among surgical patients are limited. METHODS: A retrospective study was conducted using responses from the SDOH needs assessment and the Patient-Reported Outcomes Measurement Information Systems Global Health instrument of adults seen in surgical clinics at a single institution. Patient characteristics including socioeconomic status (insurance type, education level, and employment status) were extracted. Stepwise multivariable logistic regression analyses were performed to identify independent predictors of global health scores. RESULTS: A total of 8512 surgical patients (mean age: 55.6±15.8 years) were included. 25.2% of patients reported one or more SDOH needs. The likelihood of reporting at least one SDOH need varied by patient characteristics and socioeconomic status variables. In fully adjusted regression models, food insecurity [odds ratio (OR), 1.53; 95% CI, 1.38-1.70 and OR, 1.49; 95% CI, 1.22-1.81, respectively], housing instability (OR, 1.27; 95% CI, 1.12-1.43 and OR, 1.39; 95% CI, 1.13-1.70, respectively) lack of transportation (OR, 1.46; 95% CI, 1.27-1.68 and OR, 1.25; 95% CI, 1.00-1.57, respectively), and unmet medication needs (OR, 1.31; 95% CI, 1.13-1.52 and OR, 1.61; 95% CI, 1.28-2.03, respectively) were independent predictors of poor physical and mental health. CONCLUSIONS: SDOH needs are independent predictors of poor patient-reported physical and mental health among surgical patients. Assessing and addressing SDOH needs should be prioritized in health care settings and by policymakers to improve HRQOL.

Patterns of Social Needs Predict Quality-of-Life and Healthcare Utilization Outcomes in Patients from a Large Hospital System.

BACKGROUND: Unmet social needs (SNs) often coexist in distinct patterns within specific population subgroups, yet these patterns are understudied. OBJECTIVE: To identify patterns of social needs (PSNs) and characterize their associations with health-related quality-of-life (HRQoL) and healthcare utilization (HCU). DESIGN: Observational study using data on SNs screening, HRQoL (i.e., low mental and physical health), and 90-day HCU (i.e., emergency visits and hospital admission). Among patients with any SNs, latent class analysis was conducted to identify unique PSNs. For all patients and by race and age subgroups, compared with no SNs, we calculated the risks of poor HRQoL and time to first HCU following SNs screening for each PSN. PATIENTS: Adult patients undergoing SNs screening at the Mass General Brigham healthcare system in Massachusetts, United States, between March 2018 and January 2023. MAIN MEASURES: SNs included: education, employment, family care, food, housing, medication, transportation, and ability to pay for household utilities. HRQoL was assessed using the Patient-Reported Outcomes Measurement Information System Global-10. KEY RESULTS: Six unique PSNs were identified: "high number of social needs," "food and utility access," "employment needs," "interested in education," "housing instability," and "transportation barriers." In 14,230 patients with HRQoL data, PSNs increased the risks of poor mental health, with risk ratios ranging from 1.07(95%CI:1.01-1.13) to 1.80(95%CI:1.74-1.86). Analysis of poor physical health yielded similar findings, except that the "interested in education" showed a mild protective effect (0.97[95%CI:0.94-1.00]). In 105,110 patients, PSNs increased the risk of 90-day HCU, with hazard ratios ranging from 1.09(95%CI:0.99-1.21) to 1.70(95%CI:1.52-1.90). Findings were generally consistent in subgroup analyses by race and age. CONCLUSIONS: Certain SNs coexist in distinct patterns and result in poorer HRQoL and more HCU. Understanding PSNs allows policymakers, public health practitioners, and social workers to identify at-risk patients and implement integrated, system-wide, and community-based interventions.

Patient-reported outcome measures for primary hyperparathyroidism: a systematic review of measurement properties.

BACKGROUND: The quality of patient-reported outcome measures (PROMs) used to assess the outcomes of primary hyperparathyroidism (PHPT), a common endocrine disorder that can negatively affect patients' health-related quality of life due to chronic symptoms, has not been rigorously examined. This systematic review aimed to summarize and evaluate evidence on the measurement properties of PROMs used in adult patients with PHPT, and to provide recommendations for appropriate measure selection. METHODS: After PROSPERO registration (CRD42023438287), Medline, EMBASE, CINAHL Complete, Web of Science, PsycINFO, and Cochrane Trials were searched for full-text articles in English investigating PROM development, pilot studies, or evaluation of at least one PROM measurement property in adult patients with any clinical form of PHPT. Two reviewers independently identified studies for inclusion and conducted the review following the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) Methodology to assess risk of bias, evaluate the quality of measurement properties, and grade the certainty of evidence. RESULTS: From 4989 records, nine PROM development or validation studies were identified for three PROMs: the SF-36, PAS, and PHPQoL. Though the PAS demonstrated sufficient test-retest reliability and convergent validity, and the PHPQoL sufficient test-retest reliability, convergent validity, and responsiveness, the certainty of evidence was low-to-very low due to risk of bias. All three PROMs lacked sufficient evidence for content validity in patients with PHPT. CONCLUSIONS: Based upon the available evidence, the SF-36, PAS, and PHPQoL cannot currently be recommended for use in research or clinical care, raising important questions about the conclusions of studies using these PROMs. Further validation studies or the development of more relevant PROMs with strong measurement properties for this patient population are needed.

Comparison of patient-reported outcomes measurement information system (PROMIS®)-29 and PROMIS global physical and mental health scores.

PURPOSE: The Patient-Reported Outcomes Measurement and Information System (PROMIS®): includes the PROMIS-29 physical and mental health summary and the PROMIS global physical and mental health scores. It is unknown how these scores coincide with one another. This study examines whether the scores yield similar or different information. METHODS: The PROMIS-29 and the PROMIS global health items were administered to 5804 adults from Amazon's Mechanical Turk (MTurk) in 2021-2022 and to 4060 adults in the Ipsos KnowledgePanel (KP) in 2022. RESULTS: The median age of those in MTurk (KP) was 36 (54) and 53% (50%) were male. Mean T-scores on the PROMIS-29 and PROMIS global physical health scales were similar, but PROMIS global mental health was 3-4 points lower than the PROMIS-29 mental health summary score. Product-moment correlations ranged from 0.69 to 0.81 between the PROMIS-29 physical health and PROMIS global physical health scales and 0.56-0.69 between the mental health scales. Multi-trait multimethod analyses indicated that only a small proportion of the correlations between the two methods of measuring mental health were significantly more highly correlated with one another than correlations between physical and mental health. CONCLUSIONS: PROMIS-29 and PROMIS global mental health scales provide different information and, therefore, study conclusions may vary depending on which measure is used. Interpretation of results needs to consider that the PROMIS-29 mental health scale is a weighted combination of specific domains while the PROMIS global mental health scale is based on general mental health perceptions. Further comparisons of methods of assessing mental health are needed.

The PROMIS-16 reproduces the PROMIS-29 physical and mental health summary scores accurately in a probability-based internet panel.

PURPOSE: The Patient-Reported Outcomes Measurement Information System® (PROMIS)-16 assesses the same multi-item domains but does not include the pain intensity item in the PROMIS-29. We evaluate how well physical and mental health summary scores estimated from the PROMIS-16 reproduce those estimated using the PROMIS-29. METHODS: An evaluation of data collected from 4130 respondents from the KnowledgePanel. Analyses include confirmatory factor analysis to assess physical and mental health latent variables based on PROMIS-16 scores, reliability estimates for the PROMIS measures, mean differences and correlations of scores estimated by the PROMIS-16 with those estimated by the PROMIS-29, and associations between differences in corresponding PROMIS-16 and PROMIS-29 scores by sociodemographic characteristics. RESULTS: A two-factor (physical and mental health) model adequately fits the PROMIS-16 scores. Reliability estimates for the PROMIS-16 measures were slightly lower than for the PROMIS-29 measures. There were minimal differences between PROMIS physical and mental health summary scores estimated using the PROMIS-16 or the PROMIS-29. PROMIS-16 and PROMIS-29 score differences by sociodemographic characteristics were small. Using the PROMIS pain intensity item when scoring the PROMIS-16 produced similar estimates of physical and mental health summary scores. CONCLUSION: The PROMIS-16 provides similar estimates of the PROMIS-29 physical and mental health summary scores. The high reliability of these scores indicates they are accurate enough for use with individual patients.

Comparing patient-reported outcomes measurement information system® (PROMIS®)-16 domain scores with the PROMIS-29 and 5-item PROMIS cognitive function scores.

PURPOSE: This study evaluates the interpretability of Patient-Reported Outcomes Measurement Information System® (PROMIS®)-16 profile domain scores (physical function, ability to participate in social roles and activities, anxiety, depression, sleep disturbance, pain interference, cognitive function - abilities, and fatigue) compared to the PROMIS-29 scores and a 5-item PROMIS cognitive function score. The study aims to provide insights into using these measures in clinical and research settings. METHODS: Analyses were conducted using data from 4130 adults from a nationally representative, probability-based internet panel between September and October 2022. A subset of 1256 individuals with back pain was followed up at six months. We compared the PROMIS-16 profile with the corresponding domain scores from the PROMIS-29 and a custom five-item cognitive function measure. We evaluated (1) reliability through inter-item correlations within each domain and (2) criterion validity by comparing PROMIS-16 profile with the corresponding longer PROMIS measures: (a) standardized mean differences in domain scores, (b) correlations, and (c) concordance of change (i.e., got worse, stayed the same, got better) among those with back pain from baseline to six months later using the reliable change index. We report the Kappa coefficient of agreement and the frequency and percentage of participants with concordant classifications. RESULTS: Inter-item correlations for the PROMIS-16 domains ranged from 0.65 in cognitive function to 0.92 in pain interference. Standardized mean differences between PROMIS-16 and the scores for the corresponding longer PROMIS domains were minimal (< 0.2). Correlations among the corresponding domain scores ranged from 0.82 for sleep disturbance to 0.98 for pain interference. The percentage of concordance in change groups ranged from 63% for sleep disturbance to 88% for pain interference. Except for sleep disturbance, the change groups derived from the PROMIS-16 showed moderate to substantial agreement with scores estimated from the longer PROMIS measures (Kappa coefficients ≥ 0.41). CONCLUSION: The PROMIS-16 domain scores perform similarly to the longer PROMIS measures and can be interpreted in the same way. This similarity indicates that PROMIS-16 can be useful for research as a brief health-related quality-of-life profile measure.

The Patient-Reported Outcomes Measurement Information System^® (PROMIS^®)-16 Profile assesses eight health-related quality of life domains (physical function, ability to participate in social roles and activities, anxiety, depression, sleep disturbance, pain interference, cognitive function ­ abilities, and fatigue) using two items per domain. We evaluated the PROMIS-16 profile in a sample drawn from a nationally representative, probability-based internet panel. The study supports the reliability and criterion validity of the PROMIS-16, showing that the domain scores closely align with and have high concordance in change with the PROMIS-29 scores and a custom five-item cognitive function score. The PROMIS-16 has the potential to be a brief health-related quality-of-life profile measure in research and clinical settings.

Development of an ultra-short measure of eight domains of health-related quality of life for research and clinical care: the patient-reported outcomes measurement information system® PROMIS®-16 profile.

PURPOSE: We describe development of a short health-related quality of life measure, the patient-reported outcomes measurement information system® (PROMIS®)-16 Profile, which generates domain-specific scores for physical function, ability to participate in social roles and activities, anxiety, depression, sleep disturbance, pain interference, cognitive function, and fatigue. METHODS: An empirical evaluation of 50 candidate PROMIS items and item pairs was conducted using data from a sample of 5775 respondents from Amazon's Mechanical Turk (MTurk). Results and item response theory information curves for a subset of item pairs were presented and discussed in a stakeholder meeting to narrow the candidate item sets. A survey of the stakeholders and 124 MTurk adults was conducted to solicit preferences among remaining candidate items and finalize the measure. RESULTS: Empirical evaluation showed minimal differences in basic descriptive statistics (e.g., means, correlations) and associations with the PROMIS-29 + 2 Profile, thus item pairs were further considered primarily based on item properties and content. Stakeholders discussed and identified subsets of candidate item pairs for six domains, and final item pairs were agreed upon for two domains. Final items were selected based on stakeholder and MTurk-respondent preferences. The PROMIS-16 profile generates eight domain scores with strong psychometric properties. CONCLUSION: The PROMIS-16 Profile provides an attractive brief measure of eight distinct domains of health-related quality of life, representing an ideal screening tool for clinical care, which can help clinicians quickly identify distinct areas of concern that may require further assessment and follow-up. Further research is needed to confirm and extend these findings.

How Well Do Seven Self-Report Measures Represent Underlying Back Pain Impact?

BACKGROUND: The extent to which different measures of back pain impact represent an underlying common factor has implications for decisions about which one to use in studies of pain management and estimating one score from others. AIMS: To determine if different self-report back pain impact measures represent an underlying pain latent variable and estimate associations with it. METHOD: Seven pain impact measures completed by Amazon Mechanical Turk adults are used to estimate internal consistency reliability and associations: Oswestry Disability Index (ODI), Roland-Morris Disability Questionnaire (RMDQ), short form of the Örebro Musculoskeletal Pain Questionnaire (OMPQ), Subgroups for Targeted Treatment (STarT) Back Tool, the Graded Chronic Pain Scale (GCPS) disability score, PEG (Pain intensity, interference with Enjoyment of life, interference with General activity), and Impact Stratification Score (ISS). RESULTS: The sample of 1,874 adults with back pain had an average age of 41 and 52% were female. Sixteen percent were Hispanic, 7% non-Hispanic Black, 5% non-Hispanic Asian, and 71% non-Hispanic White. Internal consistency reliability estimates ranged from 0.710 (OMPQ) to 0.923 (GCPS). Correlations among the measures ranged from 0.609 (RMDQ with OMPQ) to 0.812 (PEG with GCPS). Standardized factor loadings on the pain latent variable ranged from 0.782 (RMDQ) to 0.870 (ISS). CONCLUSIONS: Scores of each measure can be estimated from the others for use in research.

Electronic health record (EHR)-based PROMIS measures among neurology clinic decedents and survivors: a retrospective cohort analysis.

BACKGROUND: In addition to their standard use to assess real-time symptom burden, patient-reported outcomes (PROs), such as the Patient-Reported Outcomes Measurement Information System (PROMIS), measures offer a potential opportunity to understand when patients are experiencing meaningful clinical decline. If PROs can be used to assess decline, such information can be used for informing medical decision making and determining patient-centered treatment pathways. We sought to use clinically implemented PROMIS measures to retrospectively characterize the final PROMIS report among all patients who completed at least one PROMIS assessment from December 2017-March 2020 in one large health system, stratified by decedents vs. survivors. We conducted a retrospective cohort analysis of decedents (N = 1,499) who received care from outpatient neurology clinical practice within a single, large health system as part of usual care. We also compared decedents to survivors (360 + days before death; N = 49,602) on PROMIS domains and PROMIS-Preference (PROPr) score, along with demographics and clinical characteristics. We used electronic health record (EHR) data with built-in PROMIS measures. Linear regressions assessed differences in PROMIS domains and aggregate PROPr score by days before death of the final PROMIS completion for each patient. RESULTS: Among decedents in our sample, in multivariable regression, only fatigue (range 54.48-59.38, p < 0.0029) and physical function (range 33.22-38.38, p < 0.0001) demonstrated clinically meaningful differences across time before death. The overall PROPr score also demonstrated statistically significant difference comparing survivors (0.19) to PROPr scores obtained 0-29 days before death (0.29, p < 0.0001). CONCLUSIONS: Although clinic completion of PROMIS measures was near universal, very few patients had more than one instance of PROMIS measures reported, limiting longitudinal analyses. Therefore, patient-reported outcomes in clinical practice may not yet be robust enough for incorporation in prediction models and assessment of trajectories of decline, as evidenced in these specialty clinics in one health system. PROMIS measures can be used to effectively identify symptoms and needs in real time, and robust incorporation into EHRs can improve patient-level outcomes, but further work is needed for them to offer meaningful inputs for defining patient trajectories near the end of life. Assessing symptom burden provides an opportunity to understand clinical decline, particularly as people approach the end of life. We sought to understand whether symptoms reported by patients can be used to assess decline in health. Such information can inform decision-making about care and treatments. Of eight symptoms that we assessed, patient reports of fatigue and physical function were associated with clinical decline, as was an overall score of symptom burden. Because few symptoms were associated with decline, patient-reported outcomes in clinical practice may not yet be robust enough for incorporation in prediction models and assessment of trajectories of decline.

Crosswalking the National Institutes of Health Impact Stratification Score to the PEG.

OBJECTIVE: To crosswalk the National Institutes of Health (NIH) Pain Consortium's Research Task Force proposed Impact Stratification Score (ISS) to the PEG (Pain Intensity, Interference With Enjoyment of Life, Interference With General Activity) Scale. DESIGN: Cross-sectional data collected in 2021. Ordinary least squares regression analyses of ISS and PEG. SETTING: Amazon Mechanical Turk workers. PARTICIPANTS: 1931 adults with back pain with an average age of 41 (range, 19-77); 48% were female, 16% Hispanic, 7% non-Hispanic Black, 5% non-Hispanic Asian, and 71% non-Hispanic White (N=1931). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: The Patient-Reported Outcomes Measurement Information System (PROMIS)-29+2 v2.1 survey that includes the ISS, and the 3-item PEG. RESULTS: The ISS and PEG had a correlation coefficient of 0.74. The ISS accounted for 55% of the adjusted variance in the PEG and the standardized average deviation between observed and predicted scores (normalized mean absolute error) was 0.53. Likewise, the PEG explained 55% of the variance in the ISS with a normalized mean absolute error of 0.52. CONCLUSIONS: This study provides a crosswalk between the ISS and PEG that can be used to predict one from the other. The regression equations can facilitate comparisons in studies that use different measures.

Classifying patients with non-specific chronic low back pain using the impact stratification score in an online convenience sample.

BACKGROUND: In 2014, the National Institute of Health Pain Consortium's research task force (RTF) on research standards for chronic low back pain (CLBP) proposed the Impact Stratification Score (ISS) as a patient-reported outcome measure that could stratify patients by the impact CLBP has on their lives. This work compares three newly developed ISS-based classifications to the RTF's original to provide an optimal recommendation. METHODS: The online sample included 1226 individuals from Amazon's Mechanical Turk who indicated having non-specific CLBP, average age of 40, 49% female, and 67% White. Participants completed the PROMIS-29 v2.1 profile survey that contains the 9 ISS items as well the Roland-Morris Disability Questionnaire (RMDQ) and Graded Chronic Pain Scale (GCPS). Other items included high-impact chronic pain; not working due to health problems; overall health; and number of healthcare visits for back pain in the past 6 months. Three new classifications were created using quartiles (Classification 2), latent profile analysis (Classification 3), and one modeled after the GCPS (Classification 4). Classifications were subsequently compared to the RTF-proposed classification (Classification 1) on several concurrent and prognostic criteria. RESULTS: Classification 1 had three CLBP severity groups, four in Classification 2, three in Classification 3, and four in Classification 4. All novel classifications improved upon the original. Classification 2 performed best at minimizing the classification of those with negative outcomes into the lowest severity groups at baseline (e.g., 11% with RMDQ ≥ 7) and 6 months (e.g., 8.2% had fair/poor health). Classification 4 performed best at maximizing classification of those with negative outcomes into the most severe group concurrently (e.g., 100% had GCPS grade ≥ 2) and at 6 months (e.g., 100% with RMDQ ≥ 7). CONCLUSIONS: We developed three ISS-based classification schemes and tested them against several outcomes. All three improved upon the original scheme. While appearing more optimal than other classifications in the lowest severity groups, Classification 2 presents some considerations and limitations. Given that Classification 4 was an improvement at the lowest end of severity and was the best at the highest end, it is our tentative recommendation that this approach be adopted to classify individuals with non-specific CLBP.

Effects of Excluding Those Who Report Having "Syndomitis" or "Chekalism" on Data Quality: Longitudinal Health Survey of a Sample From Amazon's Mechanical Turk.

BACKGROUND: Researchers have implemented multiple approaches to increase data quality from existing web-based panels such as Amazon's Mechanical Turk (MTurk). OBJECTIVE: This study extends prior work by examining improvements in data quality and effects on mean estimates of health status by excluding respondents who endorse 1 or both of 2 fake health conditions ("Syndomitis" and "Chekalism"). METHODS: Survey data were collected in 2021 at baseline and 3 months later from MTurk study participants, aged 18 years or older, with an internet protocol address in the United States, and who had completed a minimum of 500 previous MTurk "human intelligence tasks." We included questions about demographic characteristics, health conditions (including the 2 fake conditions), and the Patient Reported Outcomes Measurement Information System (PROMIS)-29+2 (version 2.1) preference-based score survey. The 3-month follow-up survey was only administered to those who reported having back pain and did not endorse a fake condition at baseline. RESULTS: In total, 15% (996/6832) of the sample endorsed at least 1 of the 2 fake conditions at baseline. Those who endorsed a fake condition at baseline were more likely to identify as male, non-White, younger, report more health conditions, and take longer to complete the survey than those who did not endorse a fake condition. They also had substantially lower internal consistency reliability on the PROMIS-29+2 scales than those who did not endorse a fake condition: physical function (0.69 vs 0.89), pain interference (0.80 vs 0.94), fatigue (0.80 vs 0.92), depression (0.78 vs 0.92), anxiety (0.78 vs 0.90), sleep disturbance (-0.27 vs 0.84), ability to participate in social roles and activities (0.77 vs 0.92), and cognitive function (0.65 vs 0.77). The lack of reliability of the sleep disturbance scale for those endorsing a fake condition was because it includes both positively and negatively worded items. Those who reported a fake condition reported significantly worse self-reported health scores (except for sleep disturbance) than those who did not endorse a fake condition. Excluding those who endorsed a fake condition improved the overall mean PROMIS-29+2 (version 2.1) T-scores by 1-2 points and the PROMIS preference-based score by 0.04. Although they did not endorse a fake condition at baseline, 6% (n=59) of them endorsed at least 1 of them on the 3-month survey and they had lower PROMIS-29+2 score internal consistency reliability and worse mean scores on the 3-month survey than those who did not report having a fake condition. Based on these results, we estimate that 25% (1708/6832) of the MTurk respondents provided careless or dishonest responses. CONCLUSIONS: This study provides evidence that asking about fake health conditions can help to screen out respondents who may be dishonest or careless. We recommend this approach be used routinely in samples of members of MTurk.

A Review of PROM Implementation in Surgical Practice.

OBJECTIVE: To synthesize the current state of PROM implementation and collection in routine surgical practice through a review of the literature. SUMMARY OF BACKGROUND DATA: Patient-reported outcomes (PROs) are increasingly relevant in the delivery of high quality, individualized patient care. For surgeons, PROMs can provide valuable insight into changes in patient quality of life before and after surgical interventions. Despite consensus within the surgical community regarding the promise of PROMs, little is known about their real-world implementation. METHODS: The literature search was conducted in MEDLINE and Embase for studies published after 2012. We conducted a scoping review to synthesize the current state of implementation of PROs across all sizes and types of surgical practices. Studies were included if they met the following inclusion criteria: (1) patients ≥18 years 2) routine surgical practice, (3) use of a validated PRO instrument in the peri-operative period to report on general or disease-specific health-related quality of life, (4) primary or secondary outcome was implementation. Two independent reviewers screened 1524 titles and abstracts. FINDINGS: 16 studies were identified that reported on the implementation of PROMs for surgical patients. Sample size ranged from 41 patients in a single-center pilot study to 1324 patients in a study across 17 institutions. PROs were collected pre-operatively in 3 studies, post-operatively in 10, and at unspecified times in 4. The most commonly reported implementation outcomes were fidelity (12) and feasibility (11). Less than half of studies analyzed nonrespondents. All studies concluded that collection of PROMs was successful based on outcomes measured. CONCLUSIONS: The identified studies suggest that implementation metrics including minimum standards of collection pre- and postintervention, reporting for response rates in the context of patient eligibility and analysis of respondents and nonrespondents, in addition to transparency regarding the resources utilized and cost, can facilitate adoption of PROMs in clinical care and accountability for surgical outcomes.

Enhancing Patient-Centered Surgical Care With Mobile Health Technology.

From smartphones or wearables to portable physiologic sensors and apps, healthcare is witnessing an exponential growth in mHealth-digital health tools used to support medical and surgical care, as well as public health. In surgery, there is interest in harnessing the capabilities of mHealth to improve the quality of patient-centered care delivery. Digitally delivered surveys have enhanced patient-reported outcome measurement and patient engagement throughout care. Wearable devices and sensors have allowed for the assessment of physical fitness before surgery and during recovery. Smartphone-based digital phenotyping has introduced novel methods of integrating multiple data streams (accelerometer, global positioning system, call and text logs) to create multidimensional digital health footprints for patients following surgery. Yet, with all the technological sophistication and 'big data' mHealth provides, widespread implementation has been elusive. Do clinicians and patients find these data valuable or clinically actionable? How can mHealth become integrated into the day-to-day workflows of surgical systems? Do these data represent opportunities to address disparities of care or worsen them? In this review, we discuss experiences and future opportunities to use mHealth to enhance patient-centered surgical care.

Toward an asthma patient-reported outcome measure for use in digital remote monitoring.

OBJECTIVE: To develop and test a patient-reported outcome measure (PROM) for suitability in digital remote asthma symptom monitoring to identify uncontrolled asthma. METHODS: We modified a 5-item PROM that does not require a license, the asthma control measure (ACM), from a one-month to one-week lookback period, and evaluated it using the 5-item asthma control questionnaire (ACQ-5). We recruited subjects with asthma through MTurk, an online platform. RESULTS: In a sample of 498 subjects, the ACM identified uncontrolled asthma with sensitivity 0.99 and specificity 0.65. The two measures correlated with r = 0.81. CONCLUSION: The ACM modified to a weekly lookback period can differentiate patients with well-controlled asthma from those with uncontrolled asthma. This PROM does not require a license and can be used in digital remote monitoring interventions.

Unpacking the impact of chronic pain as measured by the impact stratification score.

BACKGROUND: In 2014, the National Institute of Health Pain Consortium's research task force on research standards for chronic low back pain (CLBP) proposed a measure that could be used to stratify patients by the impact CLBP has on their lives, namely the Impact Stratification Score (ISS). This study examines the dimensionality of the ISS and support for its single total score, and evaluates its overall psychometric properties. METHODS: The sample included 1677 chiropractic patients being treated for CLBP and chronic neck pain, had an average age of 49, 71% female, and 90% White. Study participants completed the PROMIS-29 v2.1 profile survey that contains the 9 ISS items. The ISS was evaluated using item-total correlations, Cronbach's alpha, factor analysis (i.e., correlated factors and bifactor models), and item response theory (IRT). Reliability indices and item properties were evaluated from bifactor and IRT models, respectively. RESULTS: Item-total correlations were high (0.64-0.84) with a Cronbach's alpha of 0.93. Eigenvalues suggested the possibility of two factors corresponding to physical function and pain interference/intensity. Bifactor model results indicated that data were essentially unidimensional, primarily reflecting one general construct (i.e., impact) and that after accounting for 'impact' very little reliable variance remained in the two group factors. General impact scores were reliable (omegaH = .73). IRT models showed that items were strong indicators of impact and provided information across a wide range of the impact continuum and offer the possibility of a shorter 8-item ISS. Finally, it appears that different aspects of pain interference occur prior to losses in physical function. CONCLUSIONS: This study presents evidence that the ISS is sufficiently unidimensional, covers a range of chronic pain impact and is a reliable measure. Insights are obtained into the sequence of chronic pain impacts on patients' lives.

Support for the Reliability and Validity of the National Institutes of Health Impact Stratification Score in a Sample of Active-Duty U.S. Military Personnel with Low Back Pain.

OBJECTIVE: Evaluate the Impact Stratification Score (ISS) measure of low back pain impact that assesses physical function, pain interference, and pain intensity. DESIGN: Secondary analyses of a prospective comparative effectiveness trial of active-duty military personnel with low back pain. SETTING: A Naval hospital at a military training site (Pensacola, Florida) and two military medical centers: 1) Walter Reed National Military Medical Center (Bethesda, Maryland); and 2) San Diego Naval Medical Center. SUBJECTS: The 749 active-duty military personnel had an average age of 31 years, 76% were male, and 67% were white. METHODS: Participants completed questionnaires at baseline, 6 weeks later, and 12 weeks later. Measures included the ISS, Roland-Morris Disability Questionnaire (RMDQ), PROMIS-29 v1.0 satisfaction with social role participation scale, and single-item ratings of average pain, feeling bothered by low back pain in the past week, and a rating of change in low back pain. RESULTS: Internal consistency reliability for the ISS was 0.92-0.93 at the three time points. The ISS correlated 0.75 to 0.84 with the RMDQ, 0.51 to 0.78 with the single-item ratings, and -0.64 to -0.71 with satisfaction with social role participation. The ISS was responsive to change on the three single items. The area under the curve for the ISS predicting improvement on the rating of change from baseline to 6 weeks later was 0.83. CONCLUSIONS: This study provides support for the reliability and validity of the ISS as a patient-reported summary measure for acute, subacute, and chronic low back pain. The ISS is a useful indicator of low back impact.

Crosswalking the Patient-Reported Outcomes Measurement Information System Physical Function, Pain Interference, and Pain Intensity Scores to the Roland-Morris Disability Questionnaire and the Oswestry Disability Index.

OBJECTIVE: To link scores from 2 condition-specific measures for chronic low back pain (CLBP), the Oswestry Disability Index (ODI) and the Roland-Morris Disability Questionnaire (RMDQ), to Patient Reported Outcomes Measurement Information System (PROMIS) physical function, pain interference, and pain intensity scores. DESIGN: Ordinary least squares regression analyses of existing data to link the PROMIS scores with the ODI and RMDQ. SETTING: Not applicable. PARTICIPANTS: Samples of adults with CLBP (N=2279) obtained from the Center for Excellence in Research for Complementary and Integrative Health (CERC) Study (n=1677), the Assessment of Chiropractic Treatment for Low Back Pain and Smoking Cessation in Military Active Duty Personnel (ACT) (n=384), and the pain subsample of the PROMIS 1 Wave 2 Pain and Depression study (PROMIS 1 W2) (n=218). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: PROMIS physical function, pain interference, and pain intensity (CERC, ACT, and PROMIS 1 W2), ODI (CERC and PROMIS 1 W2), and RMDQ (ACT and PROMIS 1 W2). RESULTS: In predicting PROMIS scores, the ODI model R2 values ranged from 0.26-0.56 and the RMDQ model R2 values ranged from 0.13-0.50. ODI and RMDQ models were the least precise in predicting the PROMIS pain intensity score (R2 value range, 0.13-0.41) relative to the other PROMIS scores. Models with the 3 PROMIS scores as predictors yielded R2 values ranging from 0.64-0.68 and 0.46-0.58 for the ODI and RMDQ, respectively. Models using combined data from 2 studies (ie, PROMIS 1 W2 and ACT, or PROMIS 1 W2 and CERC) tended to be more precise than models using only a single study sample. CONCLUSIONS: Model results reported here can be used to translate PROMIS physical function, pain interference, and pain intensity scores to and from the ODI and RMDQ. The empirical linkages can facilitate comparisons across CLBP interventions and broaden interpretation of study results.

A protocol for chronic pain outcome measurement enhancement by linking PROMIS-29 scale to legacy measures and improving chronic pain stratification.

BACKGROUND: Substantial investment has gone into research on the efficacy and effectiveness of pharmaceutical and nonpharmacologic interventions for chronic pain. However, synthesizing this extensive literature is challenging because of differences in the outcome measures used in studies of similar or competing interventions. The absence of a common metric makes it difficult to replicate findings, pool data from multiple studies, resolve conflicting conclusions, or reach consensus when interpreting findings. METHODS: This study has a seven-member Advisory Council of chronic pain experts. Preliminary analyses will be performed on data from several large existing datasets; intermediate analyses will be performed using primary data collected from Amazon's Mechanical Turk (MTurk); and cross-validation will use primary data collected from a nationally-representative, probability-based panel. Target sample size for both primary datasets is 1500. The three study aims are as follows: Aim 1 will develop and evaluate links between the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS®-29) and legacy measures used for chronic pain such as the Roland-Morris Disability Questionnaire (RMDQ) and the Oswestry Disability Index (ODI). We will assess the best method of score linking and create crosswalk tables. Aim 2 will evaluate and refine the Impact Stratification Score (ISS) based on 9 PROMIS-29 items and proposed by the NIH Research Task Force on chronic low back pain. We will evaluate the ISS in terms of other indicators of condition severity and patient prognosis and outcomes and identify cut-points to stratify chronic pain patients into subgroups. Aim 3 will evaluate the strengths and limitations of MTurk as a data collection platform for estimating chronic pain by comparing its data to other data sources. DISCUSSION: The accomplishment of Aims 1 and 2 will allow direct comparison of results across past and future studies of chronic pain. These comparisons will help us to understand different results from seemingly similar studies, and to determine the relative effectiveness of all pharmaceutical and nonpharmacologic interventions for chronic pain across different trials. Aim 3 findings will provide valuable information to researchers about the pros and cons of using the MTurk platform for research-based data collection. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04426812 ; June 10, 2020.

Feasibility of Distinguishing Performance Among Provider Groups Using Patient-reported Outcome Measures in Older Adults With Multiple Chronic Conditions.

OBJECTIVE: To examine minimum sample sizes and follow-up times required for patient-reported outcome-based performance measures (PMs) to achieve acceptable reliability as PMs. PARTICIPANTS: We used 2 groups of patients age 65+ with at least 2 of 13 chronic conditions. The first was a sample of Medicare Advantage beneficiaries, who reported health-related quality of life (HRQoL) at baseline and 2 years. The second was a sample of primary care patients, who reported HRQoL at baseline and 6 months. MEASURES: Medicare Advantage beneficiaries completed the Veterans RAND 12-Item Short Form (VR-12), while the primary care sample completed the Patient-Reported Outcomes Measurement Information System 29-Item Profile Measure (PROMIS-29). We constructed binary candidate PMs indicating stable or improved physical or mental HRQoL at follow-up, and continuous PMs measuring mean change over time. RESULTS: In the Medicare Advantage sample, with a sample size per entity profiled of 160, the most promising PM achieved a reliability of 0.32 as a PM. A sample size of 882 per entity would have been needed for this PM to achieve an acceptable reliability of 0.7. In the prospective sample, with a sample size of 27 per clinic, the most promising PM achieved a reliability of 0.16 as a PM. A sample size of 341 patients (at the clinic level) would have been needed for this PM to achieve a reliability of 0.7. CONCLUSIONS: Achieving acceptable reliability for these PMs and conditions would have required minimum sample sizes of 341 at the clinic level or 880 at the health plan level. These estimates can guide the design of future patient-reported outcome-based PMs.