Zoekresultaten

Moderators of exercise effects on cancer-related fatigue

PURPOSE: Fatigue is a common and potentially disabling symptom in patients with cancer. It can often be effectively reduced by exercise. Yet, effects of exercise interventions might differ across subgroups. We conducted a meta-analysis using individual patient data of randomized controlled trials (RCTs) to investigate moderators of exercise intervention effects on cancer-related fatigue.METHODS: We used individual patient data from 31 exercise RCTs worldwide, representing 4,366 patients, of whom 3,846 had complete fatigue data. We performed a one-step individual patient data meta-analysis, using linear mixed-effect models to analyze the effects of exercise interventions on fatigue (z-score) and to identify demographic, clinical, intervention- and exercise-related moderators. Models were adjusted for baseline fatigue and included a random intercept on study level to account for clustering of patients within studies. We identified potential moderators by testing their interaction with group allocation, using a likelihood ratio test.RESULTS: Exercise interventions had statistically significant beneficial effects on fatigue (β= -0.17 [95% confidence interval (CI) -0.22;-0.12]). There was no evidence of moderation by demographic or clinical characteristics. Supervised exercise interventions had significantly larger effects on fatigue than unsupervised exercise interventions (βdifference= -0.18 [95%CI -0.28;-0.08]). Supervised interventions with a duration ≤12 weeks showed larger effects on fatigue (β= -0.29 [95% CI -0.39;-0.20]) than supervised interventions with a longer duration. CONCLUSIONS: In this individual patient data meta-analysis, we found statistically significant beneficial effects of exercise interventions on fatigue, irrespective of demographic and clinical characteristics. These findings support a role for exercise, preferably supervised exercise interventions, in clinical practice. Reasons for differential effects in duration require further exploration.

Series: Practical guidance to qualitative research

In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By ‘novice’ we mean Master’s students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The second article focused on context, research questions and designs, and referred to publications for further reading. This third article addresses FAQs about sampling, data collection and analysis. The data collection plan needs to be broadly defined and open at first, and become flexible during data collection. Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used. Data saturation determines sample size and will be different for each study. The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions. Analyses in ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory, and a descriptive summary, respectively. The fourth and final article will focus on trustworthiness and publishing qualitative research.

Making sense of the data

Charging an electric vehicle needs to be as simple as possible for the user. He needs to park his car, plug his vehicle and identify to start charging. There is no need to understand the technology and protocols needed to reach this simple task.For the students and researchers of the Amsterdam University of Applied Science (AUAS / HvA), there is a need to understand as deep as possible all the techniques involved in this technology.The purpose of this document is to give to the reader the information he needs to understand how an electric car can be charged and how he can use these knowledges to analyses and interpret data.

Factor Analysis: Dealing with Response Bias

This paper proposes an innovative method for factor analyzing data that potentially contains individual response bias. Past methods include the use of “ipsative” data, or, related to that, “ipsatized” data. Unfortunately, factor analysis as the main method used for analyzing the dimensionality of data, cannot be applied to ipsative data. In contrast, normalization of data as an alternative method to filter out response bias, is not hampered by the technical statistical issues inherent to applying multivariate techniques to ipsative data. Using high-quality data from a survey in Nepal that makes use of – among others – the High-Performance Organizations (HPO) framework, this paper shows that the traditional approach of directly applying Confirmatory Factor Analysis (CFA) starting from an existing model or theory, is inferior to our approach. Even applying Exploratory Factor Analysis (EFA) to the raw (non-normalized) data before using CFA, is unable to detect the optimal dimensionality, or structure, in the data. A better structure can be obtained by performing EFA on normalized data that corrects for response bias in the raw data. This paper convincingly shows that the newly identified structure is superior to the original structure suggested by the HPO framework. Applying a CFA using the newly detected structure on the raw data, gives excellent goodness-of-fit statistics, with more items retained, and no need of forced methods to improve the model fit. The findings suggest that existing models and questionnaires based on these models, are not necessarily as valid and reliable as empirical studies that make use of traditional analyses seem to suggest. When adopting existing instruments, researchers are advised to critically check the validity and reliability of these instruments – especially those vulnerable to response bias - and to apply the procedures laid out in this paper, in order to enhance the quality of their research, and to inform future researchers who consider using the same instruments or to warn them about the potential shortcomings of these instruments.

Data Studio

De Data-Driehoek

De meest gebruikte opbouw in business intelligence, predictive analitics en analytics modellen is de moeilijkheidsgraad: 1) descriptive, 2) diagnostic, 3) predictive en 4) prescriptive. Deze schaal vertelt iets over de volwassenheid van het gebruik van data door de organisatie. Een model dat niet op zichzelf staat en een achterliggende methode kent is de data driehoek van EDM (Figuur 1), welke in dit artikel zal worden toegelicht.