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.

Crosswalking 4 Pain Impact Measures in a Nationally Representative Sample of Adults With Back Pain.

OBJECTIVE: To generate crosswalk equations and tables for 4 pain impact measures: the Impact Stratification Score (ISS), Oswestry Disability Index (ODI), Roland-Morris Disability Questionnaire (RMDQ), and the Pain, Enjoyment of Life and General Activity Scale (PEG). DESIGN: Cross-sectional survey assessing demographics and pain impact. Crosswalks were developed using item-response theory (IRT) cocalibrations and linear regressions between the ISS, ODI, RMDQ, and PEG. SETTING: Online panel. PARTICIPANTS: Population-based sample of United States adults aged 18 and older. Eligibility criteria were reporting current back pain, not reporting 2 fake health conditions, and having data for 2 or more pain measures (N=1530; 37% of sample). Crosswalks were developed (n=1030) and cross-validated in a subsample of 500 participants (n=125 randomly sampled from each ISS quartile). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: ISS, ODI, RMDQ, and the PEG. RESULTS: Associations of the ISS with the PEG and ODI met the criteria for IRT cocalibration. Other measure pairs were crosswalked using regression. Associations were strongest between the PEG and the ISS (r=0.87, normalized mean absolute error [NMAE]=0.38) and between the ODI and the ISS (r=0.85, NMAE=0.39). Associations were weakest between the PEG and the RMDQ (r=0.69, R2=0.48, NMAE: 0.55-0.58). Regression equations and IRT accounted for 48%-64% of the variance (NMAE: 0.38-0.58) in corresponding pain measures in the cross-validation sample. CONCLUSIONS: The crosswalks between the ISS and common legacy pain measures created in this study of a nationally representative sample of United States adults with back pain can be used to estimate 1 pain impact measure from another. Further evaluation in clinical samples is recommended.

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.

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.

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.

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.

Adapting summary scores for the PROMIS-29 v2.0 for use among older adults with multiple chronic conditions.

PURPOSE: The patient-reported outcomes measurement information system 29-item profile (PROMIS-29 v2.0) is a widely used health-related quality of life (HRQoL) measure. Summary scores for physical and mental HRQoL have recently been developed for the PROMIS-29 using a general population. Our purpose was to adapt these summary scores to a population of older adults with multiple chronic conditions. METHODS: We collected the PROMIS-29 v2.0 for 1359 primary care patients age 65+ with at least 2 of 13 chronic conditions. PROMIS-29 has 7 domains, plus a single-item pain intensity scale. We used exploratory factor analysis (EFA), followed by confirmatory factor analysis (CFA), to examine the number of factors that best captured these eight scores. We used previous results from a recent study by Hays et al. (Qual Life Res 27:1885-1891, 2018) to standardize scoring coefficients, normed to the general population. RESULTS: The mean age was 80.7, and 67% of participants were age 80 or older. Our results indicated a 2-factor solution, with these factors representing physical and mental HRQoL, respectively. We call these factors the physical health score (PHS) and the mental health score (MHS). We normed these summary scores to the general US population. The mean MHS for our population of was 50.1, similar to the US population, while the mean PHS was 42.2, almost a full standard deviation below the US population. CONCLUSIONS: We describe the adaptation of physical and mental health summary scores of the PROMIS-29 for use with a population of older adults with multiple chronic conditions.

Evaluating the PROMIS-29 v2.0 for use among older adults with multiple chronic conditions.

PURPOSE: The Patient-Reported Outcomes Measurement Information System 29-item profile (PROMIS-29 v2.0), which measures health-related quality of life (HRQoL), has had limited evaluation among older adults (age 65+) with multiple chronic conditions. Our purpose was to establish convergent validity for PROMIS-29 in this population. METHODS: We collected the PROMIS-29 v2.0 and the Veterans RAND 36 (VR-36) for 1359 primary care patients aged 65 + with at least 2 of 13 chronic conditions, oversampling those aged 80+. We conducted multiple analyses to examine score differences across subgroups, differential item functioning (DIF), and comparisons of PROMIS-29 v2.0 and VR-36 scores. RESULTS: The mean age was 80.7, and all patients had at least 2 of 13 chronic conditions. Older age, female sex, Hispanic ethnicity, and more chronic conditions were associated with worse physical health scores (PHS) and mental health scores (MHS) on the PROMIS-29 v2.0-findings which are in the expected direction. None of the 700 pairs of items met criteria for DIF. PHS and MHS were highly intercorrelated (r = 0.74, p < 0.001 for this and all other findings). PHS was more highly correlated with the VR-36 Physical Component Score (PCS) than the Mental Component Score (MCS) (r = 0.85 and 0.32, respectively), while MHS was highly correlated with both (r = 0.70 and 0.64, respectively). CONCLUSIONS: PROMIS-29 v2.0 demonstrates expected bivariate relationships with key person-level characteristics and does not show DIF. PROMIS-29 v2.0 scores are highly correlated with VR-36 scores. These results provide support for the validity of PROMIS-29 v2.0 as a measure of HRQoL among older adults with multiple chronic conditions.

Impact and Duration of Brief Surgeon-Delivered Smoking Cessation Advice on Attitudes Regarding Nicotine Dependence and Tobacco Harms for Patients with Peripheral Arterial Disease.

BACKGROUND: Despite the recognized benefits of smoking cessation, many clinicians question if a brief smoking cessation intervention can help dedicated smokers with peripheral arterial disease understand nicotine dependence and harms related to smoking. We investigated the impact and durability of a multimodal smoking cessation intervention on patient attitudes regarding nicotine dependence and the health effects of smoking. METHODS: We conducted a pilot cluster-randomized trial of a brief smoking cessation intervention at 8 vascular surgery practices between September 1, 2014 and August 31, 2015. Compared with control sites, patients at intervention sites received protocolized brief cessation counseling, medications, and referrals to a quitline. After their clinic visit and again at 3 months, participants completed a brief survey about patient attitudes regarding nicotine dependence and the health effects of smoking. Responses to questions were analyzed using chi-squared test and Student's t-test. RESULTS: All trial participants (n = 156) complete the initial survey, and 75 (45%) participants completed the follow-up survey. Intervention and control patients both reported a greater than 30-pack-year history (80% vs. 90%, P = 0.07) and previous failed quit attempts (77% vs. 78%, P = 0.8). Compared with usual care, patients in the intervention group were more likely to describe hearing advice to quit from their surgeon (98% vs. 77%, P < 0.001), and expressed "a lot" or "some" interest in quitting (95.4% vs. 85.7%, P = 0.05). Patients in the intervention group were also more likely to acknowledge their addictive behaviors, consistently scoring higher on question bank items regarding nicotine addiction (52.9 vs. 48.0, P = 0.006) and the negative health effects of smoking (scaled score 56.6 vs. 50.6, P = 0.001). When resurveyed 3 months after intervention, patients in the intervention group had larger declines in nicotine dependence and health effect domains, suggesting durable impact of the intervention on patient attitudes regarding nicotine addiction and smoking harms. CONCLUSIONS: Brief smoking cessation counseling by a vascular surgeon increases patient interest in smoking cessation and awareness of smoking harms, and this effect was durable 3 months after intervention. This evidence suggests that even brief counseling within a surgical clinic has the potential to impact patient desire to quit.

Effectiveness of Parent-Child Mediation in Improving Family Functioning and Reducing Adolescent Problem Behavior: Results from a Pilot Randomized Controlled Trial.

Parent-child mediation programs are intended to resolve or manage disputes and improve family functioning, but rigorous evaluations of their effectiveness are lacking. Families referred to a community-based mediation program (N = 111) were randomized to an intervention or wait-list control group, and completed three surveys over a 12-week period. With the exception of parent-reported child delinquency (which decreased more in the intervention group), this evaluation provides little support for the short-term effectiveness of parent-child mediation for improving family functioning and reducing child problem behaviors in general. Given that this is the first randomized controlled trial of a parent-child mediation program, additional evaluations involving larger samples and longer follow-ups are needed before firm conclusions can be drawn about the effectiveness of this intervention.

The Psychometric Performance of the PROMIS Smoking Assessment Toolkit: Comparisons of Real-Data Computer Adaptive Tests, Short Forms, and Mode of Administration.

INTRODUCTION: The PROMIS Smoking Initiative has developed six item banks for assessment related to cigarette smoking among adult smokers (Nicotine Dependence, Coping Expectancies, Emotional and Sensory Expectancies, Health Expectancies, Psychosocial Expectancies, and Social Motivations). This article evaluates the psychometric performance of the banks when administered via short form (SF), computer adaptive test (CAT), and by mode of administration (computer vs. paper-and-pencil). METHODS: Data are from two sources: an internet sample (N = 491) of daily and nondaily smokers who completed both SFs and CATs via the web and a community sample (N = 369) that completed either paper-and-pencil or computer administration of the SFs at two time points. First a CAT version of the PROMIS Smoking Assessment Toolkit was evaluated by comparing item administration rates and scores to the SF administration. Next, we considered the effect of computer versus paper-and-pencil administration on scoring and test-retest reliability. RESULTS: Across the domains approximately 5.4 to 10.3 items were administered on average for the CAT. SF and CAT item response theory-scores were correlated from 0.82 to 0.92 across the domains. Cronbach's alpha for the four- to eight-item SFs among daily smokers ranged from .80 to .91 and .82 to .91 for paper-and-pencil and computer administrations, respectively. Test-retest reliability of the SFs ranged from 0.79 to 0.89 across mode of administration. CONCLUSIONS: Results indicate that the SF and CAT and computer and paper-and-pencil administrations provide highly comparable scores for daily and nondaily smokers, but preference for SF or CAT administration may vary by smoking domain.

Understanding asthma-specific quality of life: moving beyond asthma symptoms and severity.

This study identifies the unique contributions of asthma severity, symptoms, control and generic measures of quality of life (QoL) to asthma-specific QoL, as measured by the 12-item RAND Negative Impact of Asthma on Quality of Life scale (RAND-IAQL-12).Using a sample of 2032 adults with asthma, we conducted multiple regression analyses that sequentially examined hypothesised predictors of asthma-specific QoL. The change in variance accounted for and total unique variance accounted for is calculated as hypothesised predictors are added in each step.Our results indicate that asthma severity and asthma symptoms are strong predictors of asthma-specific QoL only when not controlling for aspects of asthma control. In regression models that include other aspects of asthma control, the contributions of both asthma symptoms and severity were substantially reduced, with asthma control and aspects of QoL related to social roles and activities emerging as the strongest predictors of asthma-specific QoL.These findings suggest that researchers measuring the impact of asthma on QoL should also consider the importance of asthma control as measured by the RAND Asthma Control Measure (RAND-ACM) and generic QoL scales that measure aspects of daily life that are uniquely affected by asthma.

Quantifying 'problematic' DIF within an IRT framework: application to a cancer stigma index.

PURPOSE: DIF detection within an IRT framework is highly powerful, often identifying significant DIF that is of little clinical importance. This paper introduces two metrics for IRT DIF evaluation that can discern potentially problematic DIF among items flagged with statistically significant DIF. METHODS: Computation of two DIF metrics-(1) a weighted area between the expected score curves (wABC) and (2) a difference in expected a posteriori scores across item response categories (dEAP)-is described. Their use is demonstrated using data from a 27-item cancer stigma index fielded to four adult samples: (1) Arabic (N = 633) and (2) English speakers (N = 324) residing in Jordan and Egypt, and (3) English (N = 500) and (4) Mandarin speakers (N = 500) residing in China. We used IRTPRO's DIF module to calculate IRT-based Wald chi-square DIF statistics according to language within each region. After standard p value adjustments for multiple comparisons, we further evaluated DIF impact with wABC and dEAP. RESULTS: There were a total of twenty statistically significant DIF comparisons after p value adjustment. The wABCs for these items ranged from 0.13 to 0.90. Upon inspection of curves, DIF comparisons with wABCs >0.3 were deemed potentially problematic and were considered further for removal. The dEAP metric was also informative regarding impact of DIF on expected scores, but less consistently useful for narrowing down potentially problematic items. CONCLUSIONS: The calculations of wABC and dEAP function as DIF effect size indicators. Use of these metrics can substantially augment IRT DIF evaluation by discerning truly problematic DIF items among those with statistically significant DIF.

Spreading the word: A process evaluation of a voluntary AOD prevention program.

BACKGROUND AND OBJECTIVES: Research on voluntary after-school alcohol and other drug (AOD) prevention programs is limited. It is important to increase understanding of students' motivation to attend these types of programs and their tendency to transfer program information to peers. This paper summarizes efforts to evaluate process information for CHOICE, a voluntary after-school AOD prevention program for middle-school youth. METHODS: A survey administered to 1899 students aged 10-16 in seven schools assessed: (1) why students choose to attend CHOICE (2) barriers to attendance; and (3) how program information is disseminated to non-participants. Frequencies of responses from participants and non-participants were compared. RESULTS: Participants were motivated by several features, most notably, the demeanor of the group leaders and enjoyable curriculum content. Barriers to attendance were primarily logistic, but results also suggest that the promotion message should more effectively emphasize that CHOICE is appropriate for everyone. The majority of students knew about CHOICE, both through advertising and conversations with friends. Non-participants' detailed reports of what they heard from friends corresponded closely with what participants reported sharing. DISCUSSION AND CONCLUSIONS: The use of dynamic group leaders is critical to engaging students in voluntary programs. Offering the program on different days of the week or at different times (e.g., before school) may improve attendance rates. Peer networks represent a critical pathway for prevention information that can help increase program impact. SCIENTIFIC SIGNIFICANCE: These results can be used to inform modifications to existing voluntary after-school AOD prevention programs to obtain higher attendance rates and more widespread dissemination of the intervention message.