Effect of telehealth on quality of life and psychological outcomes over 12 months (Whole Systems Demonstrator Telehealth Questionnaire Study): nested study of patient reported outcomes in a pragmatic, cluster randomised controlled trial

School of Health Sciences, City University London, London EC1A 7QN, UK.
BMJ (online) (Impact Factor: 17.45). 02/2013; 346(feb26 2):f653. DOI: 10.1136/bmj.f653
Source: PubMed


Objective To assess the effect of second generation, home based telehealth on health related quality of life, anxiety, and depressive symptoms over 12 months in patients with long term conditions. Design A study of patient reported outcomes (the Whole Systems Demonstrator telehealth questionnaire study; baseline n=1573) was nested in a pragmatic, cluster randomised trial of telehealth (the Whole Systems Demonstrator telehealth trial, n=3230). General practice was the unit of randomisation, and telehealth was compared with usual care. Data were collected at baseline, four months (short term), and 12 months (long term). Primary intention to treat analyses tested treatment effectiveness; multilevel models controlled for clustering by general practice and a range of covariates. Analyses were conducted for 759 participants who completed questionnaire measures at all three time points (complete case cohort) and 1201 who completed the baseline assessment plus at least one other assessment (available case cohort). Secondary per protocol analyses tested treatment efficacy and included 633 and 1108 participants in the complete case and available case cohorts, respectively.

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Available from: James G Barlow, Jan 03, 2014
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    • "Therefore, an important part of the evaluation design was to leave aspects of the design of the telehealth interventions to local teams. The protocol for the trial and evaluation has already been published [14], along with a detailed description of the interventions (Web Appendix [21], and Web Appendix 2 [20]). We summarise the main features below. "
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    ABSTRACT: The Whole Systems Demonstrator was a large, pragmatic, cluster randomised trial that compared telehealth with usual care among 3,230 patients with long-term conditions in three areas of England. Telehealth involved the regular transmission of physiological information such as blood glucose to health professionals working remotely. We examined whether telehealth led to changes in glycosylated haemoglobin (HbA1c) among the subset of patients with type 2 diabetes. The general practice electronic medical record was used as the source of information on HbA1c. Effects on HbA1c were assessed using a repeated measures model that included all HbA1c readings recorded during the 12-month trial period, and adjusted for differences in HbA1c readings recorded before recruitment. Secondary analysis averaged multiple HbA1c readings recorded for each individual during the trial period. 513 of the 3,230 participants were identified as having type 2 diabetes and thus were included in the study. Telehealth was associated with lower HbA1c than usual care during the trial period (difference 0.21% or 2.3 mmol/mol, 95% CI, 0.04% to 0.38%, p = 0.013). Among the 457 patients in the secondary analysis, mean HbA1c showed little change for controls following recruitment, but fell for intervention patients from 8.38% to 8.15% (68 to 66 mmol/mol). A higher proportion of intervention patients than controls had HbA1c below the 7.5% (58 mmol/mol) threshold that was targeted by general practices (30.4% vs. 38.0%). This difference, however, did not quite reach statistical significance (adjusted odds ratio 1.63, 95% CI, 0.99 to 2.68, p = 0.053). Telehealth modestly improved glycaemic control in patients with type 2 diabetes over 12 months. The scale of the improvements is consistent with previous meta-analyses, but was relatively modest and seems unlikely to produce significant patient benefit. Trial registration number International Standard Randomized Controlled Trial Number Register ISRCTN43002091.
    BMC Health Services Research 08/2014; 14(1):334. DOI:10.1186/1472-6963-14-334 · 1.71 Impact Factor
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    • "The positive QALY improvements found in this review can contribute to the evidence supporting the claim that telehealth is at least as effective as usual care [57]. However, the absence of a negative QALY effect might be due to publication bias. "
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    ABSTRACT: Background The quality-adjusted life-year (QALY) is a recognised outcome measure in health economic evaluations. QALY incorporates individual preferences and identifies health gains by combining mortality and morbidity into one single index number. A literature review was conducted to examine and discuss the use of QALYs to measure outcomes in telehealth evaluations. Methods Evaluations were identified via a literature search in all relevant databases. Only economic evaluations measuring both costs and QALYs using primary patient level data of two or more alternatives were included. Results A total of 17 economic evaluations estimating QALYs were identified. All evaluations used validated generic health related-quality of life (HRQoL) instruments to describe health states. They used accepted methods for transforming the quality scores into utility values. The methodology used varied between the evaluations. The evaluations used four different preference measures (EQ-5D, SF-6D, QWB and HUI3), and utility scores were elicited from the general population. Most studies reported the methodology used in calculating QALYs. The evaluations were less transparent in reporting utility weights at different time points and variability around utilities and QALYs. Few made adjustments for differences in baseline utilities. The QALYs gained in the reviewed evaluations varied from 0.001 to 0.118 in implying a small but positive effect of telehealth intervention on patient’s health. The evaluations reported mixed cost-effectiveness results. Conclusion The use of QALYs in telehealth evaluations has increased over the last few years. Different methodologies and utility measures have been used to calculate QALYs. A more harmonised methodology and utility measure is needed to ensure comparability across telehealth evaluations.
    BMC Health Services Research 08/2014; 14(1):332. DOI:10.1186/1472-6963-14-332 · 1.71 Impact Factor
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    • "Patient-reported outcomes (PRO) are increasingly used in clinical research; they have become essential criteria that have gained major importance especially in chronically ill patients. Consequently, nowadays these outcomes are often considered as main secondary endpoints or even primary endpoints in clinical studies [1-4]. Two main types of analytic strategies are used for PRO data: so-called classical test theory (CTT) and models coming from Item Response Theory (IRT). "
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    ABSTRACT: Background: Despite the widespread use of patient-reported Outcomes (PRO) in clinical studies, their design remains a challenge. Justification of study size is hardly provided, especially when a Rasch model is planned for analysing the data in a 2-group comparison study. The classical sample size formula (CLASSIC) for comparing normally distributed endpoints between two groups has shown to be inadequate in this setting (underestimated study sizes). A correction factor (RATIO) has been proposed to reach an adequate sample size from the CLASSIC when a Rasch model is intended to be used for analysis. The objective was to explore the impact of the parameters used for study design on the RATIO and to identify the most relevant to provide a simple method for sample size determination for Rasch modelling. Methods: A large combination of parameters used for study design was simulated using a Monte Carlo method: variance of the latent trait, group effect, sample size per group, number of items and items difficulty parameters. A linear regression model explaining the RATIO and including all the former parameters as covariates was fitted. Results: The most relevant parameters explaining the ratio's variations were the number of items and the variance of the latent trait (R2 = 99.4%). Conclusions: Using the classical sample size formula adjusted with the proposed RATIO can provide a straightforward and reliable formula for sample size computation for 2-group comparison of PRO data using Rasch models.
    BMC Medical Research Methodology 07/2014; 14(1):87. DOI:10.1186/1471-2288-14-87 · 2.27 Impact Factor
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