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- Richard, Edo, et al.
(author)
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Healthy ageing through internet counselling in the elderly (HATICE) : a multinational, randomised controlled trial
- 2019
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In: The Lancet Digital Health. - 2589-7500. ; 1:8, s. e424-e434
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Journal article (peer-reviewed)abstract
- Background Although web-based interventions have been promoted for cardiovascular risk management over the past decade, there is limited evidence for effectiveness of these interventions in people older than 65 years. The healthy ageing through internet counselling in the elderly (HATICE) trial aimed to determine whether a coach-supported internet intervention for self-management can reduce cardiovascular risk in community-dwelling older people.Methods This prospective open-label, blinded endpoint clinical trial among people age 65 years or over at increased risk of cardiovascular disease randomly assigned participants in the Netherlands, Finland, and France to an interactive internet intervention stimulating coach-supported self-management or a control platform. Primary outcome was the difference from baseline to 18 months on a standardised composite score (Z score) of systolic blood pressure, LDL cholesterol, and body-mass index (BMI). Secondary outcomes included individual risk factors and cardiovascular endpoints. This trial is registered with the ISRCTN registry, 48151589, and is closed to accrual.Findings Among 2724 participants, complete primary outcome data were available for 2398 (88%). After 18 months, the primary outcome improved in the intervention group versus the control group (0.09 vs 0.04, respectively; mean difference -0.05, 95% CI -0.08 to -0.01; p=0.008). For individual components of the primary outcome, mean differences (intervention vs control) were systolic blood pressure -1.79 mm Hg versus -0.67 mm Hg (-1.12, -2.51 to 0.27); BMI -0.23 kg/m(2) versus -0.08 kg/m(2) (-0.15, -0.28 to -0.01); and LDL -0.12 mmol/L versus -0.07 mmol/L (-0.05, -0.11 to 0.01). Cardiovascular disease occurred in 30 (2.2%) of 1382 patients in the intervention versus 32 (2.4%) of 1333 patients in the control group (hazard ratio 0.86, 95% CI 0.52 to 1.43).Interpretation Coach-supported self-management of cardiovascular risk factors using an interactive internet intervention is feasible in an older population, and leads to a modest improvement of cardiovascular risk profile. When implemented on a large scale this could potentially reduce the burden of cardiovascular disease.
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| 2. |
- van Wanrooij, Lennard L., et al.
(author)
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Pooling individual participant data from randomized controlled trials : Exploring potential loss of information
- 2020
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:5
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Journal article (peer-reviewed)abstract
- Background Pooling individual participant data to enable pooled analyses is often complicated by diversity in variables across available datasets. Therefore, recoding original variables is often necessary to build a pooled dataset. We aimed to quantify how much information is lost in this process and to what extent this jeopardizes validity of analyses results. Methods Data were derived from a platform that was developed to pool data from three randomized controlled trials on the effect of treatment of cardiovascular risk factors on cognitive decline or dementia. We quantified loss of information using the R-squared of linear regression models with pooled variables as a function of their original variable(s). In case the R-squared was below 0.8, we additionally explored the potential impact of loss of information for future analyses. We did this second step by comparing whether the Beta coefficient of the predictor differed more than 10% when adding original or recoded variables as a confounder in a linear regression model. In a simulation we randomly sampled numbers, recoded those < = 1000 to 0 and those > 1000 to 1 and varied the range of the continuous variable, the ratio of recoded zeroes to recoded ones, or both, and again extracted the R-squared from linear models to quantify information loss. Results The R-squared was below 0.8 for 8 out of 91 recoded variables. In 4 cases this had a substantial impact on the regression models, particularly when a continuous variable was recoded into a discrete variable. Our simulation showed that the least information is lost when the ratio of recoded zeroes to ones is 1:1. Conclusions Large, pooled datasets provide great opportunities, justifying the efforts for data harmonization. Still, caution is warranted when using recoded variables which variance is explained limitedly by their original variables as this may jeopardize the validity of study results.
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