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  • 1.
    Axén, Iben
    et al.
    Institute of Environmental Medicine, Unit of Intervention and Implementation Research in Worker Health, Karolinska Institutet, Stockholm, Sweden.
    Bodin, Lennart
    Institute of Environmental Medicine, Unit of Intervention and Implementation Research in Worker Health, Karolinska Institutet, Stockholm, Sweden.
    Searching for the optimal measuring frequency in longitudinal studies: an example utilizing short message service (SMS) to collect repeated measures among patients with low back pain2016In: BMC Medical Research Methodology, ISSN 1471-2288, E-ISSN 1471-2288, Vol. 16, no 1, article id 119Article in journal (Refereed)
    Abstract [en]

    Background: Mobile technology has opened opportunities within health care and research to allow for frequent monitoring of patients. This has given rise to detailed longitudinal information and new insights concerning behaviour and development of conditions over time.

    Responding to frequent questionnaires delivered through mobile technology has also shown good compliance, far exceeding that of traditional paper questionnaires. However, to optimize compliance, the burden on the subjects should be kept at a minimum.

    In this study, the effect of using fewer data points compared to the full data set was examined, assuming that fewer measurements would lead to better compliance.

    Method: Weekly text-message responses for 6 months from subjects recovering from an episode of low back pain (LBP) were available for this secondary analysis. Most subjects showed a trajectory with an initial improvement and a steady state thereafter.

    The data were originally used to subgroup (cluster) patients according to their pain trajectory. The resulting 4-cluster solution was compared with clusters obtained from five datasets with fewer data-points using Kappa agreement as well as inspection of estimated pain trajectories. Further, the relative risk of experiencing a day with bothersome pain was compared week by week to show the effects of discarding some weekly data.

    Results: One hundred twenty-nine subjects were included in this analysis. Using data from every other weekly measure had the highest agreement with the clusters from the full dataset, weighted Kappa = 0.823. However, the visual description of pain trajectories favoured using the first 18 weekly measurements to fully capture the phases of improvement and steady-state. The weekly relative risks were influenced by the pain trajectories and 18 weeks or every other weekly measure were the optimal designs, next to the full data set.

    Conclusions: A population recovering from an episode of LBP could be described using every other weekly measurement, an option which requires fewer weekly measures than measuring weekly for 18 weeks. However a higher measuring frequency might be needed in the beginning of a clinical course to fully map the pain trajectories.

  • 2.
    Axén, Iben
    et al.
    Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet, Stockholm, Sweden.
    Bodin, Lennart
    Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet, Stockholm, Sweden.
    Kongsted, Alice
    Clinical Locomotion Network, Nordic Institute of Chiropractic and Clinical Biomechanics, Odense, Denmark.
    Wedderkopp, Niels
    Institute of Regional Health Services Research, University of Southern Denmark, Odense, Denmark.
    Jensen, Irene
    Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet, Stockholm, Sweden.
    Bergström, Gunnar
    Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet, Stockholm, Sweden.
    Analyzing repeated data collected by mobile phones and frequent text messages: An example of Low back pain measured weekly for 18 weeks2012In: BMC Medical Research Methodology, ISSN 1471-2288, E-ISSN 1471-2288, Vol. 12, article id 105Article in journal (Refereed)
    Abstract [en]

    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 methods

    1: 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.

  • 3.
    Wahlgren, Lina
    et al.
    Örebro University, School of Health and Medical Sciences.
    Schantz, Peter
    Bikeability and methodological issues using the active commuting route environment scale (ACRES) in a metropolitan setting2011In: BMC Medical Research Methodology, ISSN 1471-2288, E-ISSN 1471-2288, Vol. 11, article id 6Article in journal (Refereed)
    Abstract [en]

    Background: Route environments can positively influence people’s active commuting and thereby contribute to public health. The Active Commuting Route Environment Scale (ACRES) was developed to study active commuters’ perceptions of their route environments. However, bicycle commuters represent a small portion of the population in many cities and thus are difficult to study using population-based material. Therefore, the aim of this study is to expand the state of knowledge concerning the criterion-related validity of the ACRES and the representativity using an advertisement-recruited sample. Furthermore, by comparing commuting route environment profiles of inner urban and suburban areas, we provide a novel basis for understanding the relationship between environment and bikeability.

    Methods: Bicycle commuters from Greater Stockholm, Sweden, advertisement- (n = 1379) and street-recruited (n = 93), responded to the ACRES. Traffic planning and environmental experts from the Municipality of Stockholm (n = 24) responded to a modified version of the ACRES. The criterion-related validity assessments were based on whether or not differences between the inner urban and the suburban route environments, as indicated by the experts and by four existing objective measurements, were reflected by differences in perceptions of these environments. Comparisons of ratings between advertisement-and street-recruited participants were used for the assessments of representativity. Finally, ratings of inner urban and suburban route environments were used to evaluate commuting route environment profiles.

    Results: Differences in ratings of the inner urban and suburban route environments by the advertisement-recruited participants were in accord with the existing objective measurements and corresponded reasonably well with those of the experts. Overall, there was a reasonably good correspondence between the advertisement-and street-recruited participants’ ratings. Distinct differences in commuting route environment profiles were noted between the inner urban and suburban areas. Suburban route environments were rated as safer and more stimulating for bicycle-commuting than the inner urban ones. In general, the findings applied to both men and women.

    Conclusions: The overall results show: considerable criterion-related validity of the ACRES; ratings of advertisement-recruited participants mirroring those of street-recruited participants; and a higher degree of bikeability in the suburban commuting route environments than in the inner urban ones.

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