ObjectiveTo conduct a systematic review and meta-analysis of the equivalence between electronic and paper administration of patient reported outcome measures (PROMs) in studies conducted subsequent to those included in Gwaltney et al’s 2008 review.MethodsA systematic literature review of PROM equivalence studies conducted between 2007 and 2013 identified 1,997 records from which 72 studies met pre-defined inclusion/exclusion criteria. PRO data from each study were extracted, in terms of both correlation coefficients (ICCs, Spearman and Pearson correlations, Kappa statistics) and mean differences (standardized by the standard deviation, SD, and the response scale range). Pooled estimates of correlation and mean difference were estimated. The modifying effects of mode of administration, year of publication, study design, time interval between administrations, mean age of participants and publication type were examined.ResultsFour hundred thirty-five individual correlations were extracted, these correlations being highly variable (I2 = 93.8) but showing generally good equivalence, with ICCs ranging from 0.65 to 0.99 and the pooled correlation coefficient being 0.88 (95 % CI 0.87 to 0.88). Standardised mean differences for 307 studies were small and less variable (I2 = 33.5) with a pooled standardised mean difference of 0.037 (95 % CI 0.031 to 0.042). Average administration mode/platform-specific correlations from 56 studies (61 estimates) had a pooled estimate of 0.88 (95 % CI 0.86 to 0.90) and were still highly variable (I2 = 92.1). Similarly, average platform-specific ICCs from 39 studies (42 estimates) had a pooled estimate of 0.90 (95 % CI 0.88 to 0.92) with an I2 of 91.5. After excluding 20 studies with outlying correlation coefficients (≥3SD from the mean), the I2 was 54.4, with the equivalence still high, the overall pooled correlation coefficient being 0.88 (95 % CI 0.87 to 0.88). Agreement was found to be greater in more recent studies (p < 0.001), in randomized studies compared with non-randomised studies (p < 0.001), in studies with a shorter interval (<1 day) (p < 0.001), and in respondents of mean age 28 to 55 compared with those either younger or older (p < 0.001). In terms of mode/platform, paper vs Interactive Voice Response System (IVRS) comparisons had the lowest pooled agreement and paper vs tablet/touch screen the highest (p < 0.001).ConclusionThe present study supports the conclusion of Gwaltney’s previous meta-analysis showing that PROMs administered on paper are quantitatively comparable with measures administered on an electronic device. It also confirms the ISPOR Taskforce´s conclusion that quantitative equivalence studies are not required for migrations with minor change only. This finding should be reassuring to investigators, regulators and sponsors using questionnaires on electronic devicesafter migration using best practices. Although there is data indicating that migrations with moderate changes produce equivalent instrument versions, hence do not require quantitative equivalence studi...

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The measures both have good psychometric properties and are suitable for use with patients with acute bacterial sinusitis. Both instruments are sensitive. The minimal important difference estimates for the Sinonasal Outcome Test-16 are quite high but are similar to estimates reported previously.

The Morisky Medication Adherence Scale (MMAS) has been widely used to measure medication adherence; whereas the Adherence to Refills and Medications Scale (ARMS) was developed to measure medication adherence among elderly population. The objective of the study was to assess medication adherence among young adults by using ARMS as well as MMAS and to evaluate determine which scales helps better in determining predictors of medication non adherence among young adults. METHODS: A cross-sectional survey was conducted in a sample of students attending a large university. A validated survey instrument was used to obtain information on socio-demographic variables, medication behavior, and factors responsible for non-adherence. Medication adherence was measured using MMAS (six questions) on a dichotomous (yes/no) scale with 'yes' being medically non-adherent and ARMS scale with 12 questions on a 4-point Likert scale. Reliability was assessed using Cronbach's alpha. Multiple regression analysis was conducted to assess predictors of non-adherence for both the scales. RESULTS: A total of 300 completed surveys were received (response rate 86%). Mean (SD) age of the sample was 23.4 (4.3) years. Participants indicated they were adherent with their medication therapy where mean (SD) ARMS score was 1.7 (0.4) (≤2 considered adherent) and MMAS scores was 2.6 (1.9) (≤3 considered adherent). Spearman correlation coefficient was 0.71 (p<0.0001). Predictors of medication non adherence using the ARMS were: forgetfulness (β:0.25, p<0.0001), time constraint (β:0.18, p=0.0004), social stigma (β:0.14, p<0.0001), gender (β:-0.09, p=0.01) and difficulty in remembering all medications (β:0.09, p=0.0003); whereas that using the MMAS were: forgetfulness (β:1.07, p=0.003) and difficulty in remembering all medications (β:1.41, p<0.0001). CONCLUSIONS: Medication adherence information obtained using ARMS were able to identify more predictors compared to MMAS in the cohort of young adults. Future application of the scales to different populations could be beneficial.

OBJECTIVES: Scales measuring perceptions of pain can include numeric rating scales (NRS), visual analogue scales (VAS) and descriptor scales using words (adjectival). With the increased use of electronic data capture in clinical trial settings, such scales have been migrated to numerous platforms. After conducting a brief literature review, there was found to be a lack of research on the impact that migration of such pain scales can have on the efficacy of the scales. The objective of this study was to deploy three pain scales in different formats and on different platforms, and to explore preferences and perceived differences in patients who experience daily pain. METHODS: Twelve participants diagnosed with a range of conditions resulting in pain were interviewed in a qualitative interview setting. All participants completed three pain scales in different formats: NRS (11-, 9-, and 7-points; portrait/ landscape), VAS (horizontally/ vertically; portrait/ landscape) and adjectival scale (increasing/ decreasing severity). All scales were presented in three modes of administration: paper, handheld device and tablet device. Participants were asked about perceived differences and their preferences of the different scales, formats, and modes of administration. RESULTS: The NRS and adjectival scale were preferred equally (nϭ5). Participants expressed an almost unanimous preference for the 11-point NRS and increasing severity adjectival scale, across all administration modes. In contrast, the VAS was the least preferred scale (nϭ7) due to difficulty with interpretation. Participants expressed no usability problems with the tablet or handheld devices, but the tablet was preferred overall because of the bigger screen. CONCLUSIONS: Although the sample size makes generalising these findings difficult, this exploratory study suggests that the preferred pain scales may provide higher quality data as they are easier to interpret, and therefore minimise error and patient burden. Further evidence, particularly quantitative, is required to support these preliminary findings.

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Survey (nϭ12,000), a cross-sectional self-report survey which included the SF-12 Health Survey Version 2 (SF-12v2) to assess HRQoL. Differences between patients with and without asthma were identified using chi-square tests for categorical variables and t-tests for continuous variables. Multiple regression was used to assess the association of asthma with HRQoL while accounting for potential confounds, including age, gender, number of co-morbid conditions, possession of private insurance, alcohol use, exercise behavior, and cigarette smoking. RESULTS: Seventy-six percent (nϭ9108) of respondents were familiar with asthma. Physician diagnosis of asthma was reported by 4% of the sample (nϭ415), and the remaining 96% (nϭ11,585) served as the control group. Asthma patients were younger (36 vs. 41, pϽ0.001), more likely to be female (61% vs. 50%, pϽ.001), have private insurance (60% vs. 53% pϽ0.01), drink alcohol (68% vs. 60%, pϽ.01), and had more co-morbid conditions (0.73 vs. 0.32 pϽ0.001). Relative to controls, asthma patients had lower MCS (42.41 vs. 46.99, pϽ0.001), PCS (47.27 vs. 50.58, pϽ0.001) and SF-6D health utilities (0.66 vs. 0.73, pϽ0.001). Asthma was associated with significantly lower HRQoL after adjusting for age, gender, drinking, smoking, exercise, insurance, and comorbidities, with MCS lower by 2.59 points (pϽ0.001), PCS lower by 3.15 points (pϽ0.001), and SF-6D lower by 0.05 points (pϽ0.001). CONCLUSIONS: Asthma is associated with significant HRQoL burden, even after adjusting for covariates. This analysis may underestimate the burden of asthma, as undiagnosed asthma patients were likely included in the control group.

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