| 1. |
- Axén, Iben, et al.
(författare)
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Analyzing repeated data collected by mobile phones and frequent text messages : An example of Low back pain measured weekly for 18 weeks
- 2012
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Ingår i: BMC Medical Research Methodology. - : BioMed Central. - 1471-2288. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Background: Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such data from large groups of respondents by sending and receiving frequently repeated short questions and answers as text messages.The analysis of repeated data involves some challenges. Vital issues to consider are the within-subject correlation, the between measurement occasion correlation and the presence of missing values.The overall aim of this commentary is to describe different methods of analyzing repeated data. It is meant to give an overview for the clinical researcher in order for complex outcome measures to be interpreted in a clinically meaningful way.Methods: A model data set was formed using data from two clinical studies, where patients with low back pain were followed with weekly text messages for 18 weeks. Different research questions and analytic approaches were illustrated and discussed, as well as the handling of missing data. In the applications the weekly outcome “number of days with pain” was analyzed in relation to the patients’ “previous duration of pain” (categorized as more or less than 30 days in the previous year).Research questions with appropriate analytical methods1: How many days with pain do patients experience? This question was answered with data summaries.2: What is the proportion of participants “recovered” at a specific time point? This question was answered using logistic regression analysis.3: What is the time to recovery? This question was answered using survival analysis, illustrated in Kaplan-Meier curves, Proportional Hazard regression analyses and spline regression analyses.4: How is the repeatedly measured data associated with baseline (predictor) variables? This question was answered using generalized Estimating Equations, Poisson regression and Mixed linear models analyses.5: Are there subgroups of patients with similar courses of pain within the studied population?A visual approach and hierarchical cluster analyses revealed different subgroups using subsets of the model data.Conclusions: We have illustrated several ways of analysing repeated measures with both traditional analytic approaches using standard statistical packages, as well as recently developed statistical methods that will utilize all the vital features inherent in the data.
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| 2. |
- Ekelund, Ulf, 1960-, et al.
(författare)
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Physical activity assessed by activity monitor and doubly labeled water in children
- 2001
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Ingår i: Medicine & Science in Sports & Exercise. - : Ovid Technologies (Wolters Kluwer Health). - 0195-9131 .- 1530-0315. ; 33:2, s. 275-281
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: To validate the Computer Science and Application's (CSA) activity monitor for assessment of the total amount of physical activity during two school-weeks in 9-yr-old children and to develop equations to predict total energy expenditure (TEE) and activity energy expenditure (AEE) from activity counts and anthropometric variables.METHODS: A total of 26 children (15 boys and 11 girls, mean age 9.1 +/- 0.3 yr) were monitored for 14 consecutive days. TEE was simultaneously measured by the doubly labeled water method. Averaged activity counts (counts.min(-1)) were compared with data on: 1) TEE, 2) AEE = TEE minus basal metabolic rate (BMR; estimated from predictive equations), and 3) daily physical activity level (PAL = TEE/BMR).RESULTS: Physical activity determined by activity counts was significantly related to the data on energy expenditures: TEE (r = 0.39; P < 0.05), AEE (r = 0.54; P < 0.01), and PAL (r = 0.58; P < 0.01). Multiple stepwise regression analysis showed that TEE was significantly influenced by gender, body composition (body weight or fat free mass), and activity counts (R(2) = 0.54--0.60). AEE was significantly influenced by activity counts and gender (R(2) = 0.45). There were no significant differences between activity counts and PAL in discriminating among activity levels with "low" (PAL < 1.56), "moderate" (1.57 < or = PAL > or = 1.81), and "high" (PAL > 1.81) intensity.CONCLUSION: Activity counts from the CSA activity monitor seems to be a useful measure of the total amount of physical activity in 9-yr-old children. Activity counts contributed significantly to the explained variation in TEE and was the best predictor of AEE.
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| 3. |
- Nielsen, Rasmus Oestergaard, et al.
(författare)
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Statement on methods in sport injury research from the 1st METHODS MATTER Meeting, Copenhagen, 2019
- 2020
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Ingår i: British Journal of Sports Medicine. - : BMJ PUBLISHING GROUP. - 0306-3674 .- 1473-0480. ; 54:15, s. 941-947
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Forskningsöversikt (refereegranskat)abstract
- High quality sports injury research can facilitate sports injury prevention and treatment. There is scope to improve how our field applies best practice methods-methods matter (greatly!). The 1st METHODS MATTER Meeting, held in January 2019 in Copenhagen, Denmark, was the forum for an international group of researchers with expertise in research methods to discuss sports injury methods. We discussed important epidemiological and statistical topics within the field of sports injury research. With this opinion document, we provide the main take-home messages that emerged from the meeting.
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| 4. |
- Nielsen, Rasmus Ostergaard, et al.
(författare)
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Statement on Methods in Sport Injury Research From the First METHODS MATTER Meeting, Copenhagen, 2019
- 2020
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Ingår i: Journal of Orthopaedic and Sports Physical Therapy. - : J O S P T. - 0190-6011 .- 1938-1344. ; 50:5, s. 226-233
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- SYNOPSIS: High-quality sports injury research can facilitate sports injury prevention and treatment. There is scope to improve how our field applies best-practice methods-methods matter (greatly!). The first METHODS MATTER meeting, held in January 2019 in Copenhagen, Denmark, was the forum for an international group of researchers with expertise in research methods to discuss sports injury methods. We discussed important epidemiological and statistical topics within the field of sports injury research. With this opinion document, we provide the main take-home messages that emerged from the meeting. Meeting participants agreed that the definition of sport injury depends on the research question and context. It was considered essential to be explicit about the goal of the research effort and to use frameworks to illustrate the assumptions that underpin measurement and the analytical strategy. Complex systems were discussed to illustrate how potential risk factors can interact in a nonlinear way. This approach is often a useful alternative to identifying single risk factors. Investigating changes in exposure status over time is important when analyzing sport injury etiology, and analyzing recurrent injury, subsequent injury, or injury exacerbation remains challenging. The choice of statistical model should consider the research question, injury measure (eg, prevalence, incidence), type and granularity of injury data (categorical or continuous), and study design. Multidisciplinary collaboration will be a cornerstone for future high-quality sport injury research. Working outside professional silos in a diverse, multidisciplinary team benefits the research process, from the formulation of research questions and designs to the statistical analyses and dissemination of study results in implementation contexts. This article has been copublished in the British Journal of Sports Medicine and the Journal of Orthopaedic & Sports Physical Therapy.
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| 5. |
- Nilsson, Andreas, 1973-, et al.
(författare)
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Comparison of equations for predicting energy expenditure from accelerometer counts in children
- 2008
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Ingår i: Scandinavian Journal of Medicine and Science in Sports. - Oxford : Blackwell. - 0905-7188 .- 1600-0838. ; 18:5, s. 643-650
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Tidskriftsartikel (refereegranskat)abstract
- Several prediction equations developed to convert body movement measured by accelerometry into energy expenditure have been published. The aim of this study was to examine the degree of agreement between three different prediction equations, when applied to data on physical activity in a large sample of children. We examined 1321 children (663 boys, 658 girls; mean age 9.6+/-0.4 years) from four different countries. Physical activity was measured by the MTI accelerometer. One equation, derived from doubly labeled water (DLW) measurements, was compared with one treadmill-based (TM) and one room calorimeter-based (CAL) equation (mixture of activities). Predicted physical activity energy expenditure (PAEE) was the main outcome variable. In comparison with DLW-predicted PAEE, both laboratory-derived equations significantly (P<0.001) overestimated PAEE by 17% and 83%, respectively, when based on a 24-h prediction, while the TM equation significantly (P<0.001) underestimated PAEE by 46%, when based on awake time only. In contrast, the CAL equation agreed better with the DLW equation under the awake time assumption. Predicted PAEE differ substantially between equations, depending on time-frame assumptions, and interpretations of average levels of PAEE in children from available equations should be made with caution. Further development of equations applicable to free-living scenarios is needed.
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