Analyzing repeated data collected by mobile phones and frequent text messages. An example of Low back pain measured weekly for 18 weeks
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 so...
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| Published in | BMC medical research methodology Vol. 12; no. 1; p. 105 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
23.07.2012
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2288 1471-2288 |
| DOI | 10.1186/1471-2288-12-105 |
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| 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 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. |
|---|---|
| AbstractList | 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. 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). 1: How many days with pain do patients experience? This question was answered with data summaries. 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. 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. 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. 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. 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.BACKGROUNDRepeated 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.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.METHODSA 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.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.CONCLUSIONSWe 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. 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. 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. 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. 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. Doc number: 105 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 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. 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 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. |
| ArticleNumber | 105 |
| Audience | Academic |
| Author | Axén, Iben Bodin, Lennart Kongsted, Alice Bergström, Gunnar Wedderkopp, Niels Jensen, Irene |
| AuthorAffiliation | 3 Institute of Regional Health Services Research, University of Southern Denmark, Winsloewparken 19.3, 5000, Odense, Denmark 1 Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet, Nobels v. 13, 171 77, Stockholm, Sweden 2 Nordic Institute of Chiropractic and Clinical Biomechanics, Clinical Locomotion Network, Forskerparken 10A, 5230, Odense M, Denmark |
| AuthorAffiliation_xml | – name: 1 Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet, Nobels v. 13, 171 77, Stockholm, Sweden – name: 2 Nordic Institute of Chiropractic and Clinical Biomechanics, Clinical Locomotion Network, Forskerparken 10A, 5230, Odense M, Denmark – name: 3 Institute of Regional Health Services Research, University of Southern Denmark, Winsloewparken 19.3, 5000, Odense, Denmark |
| Author_xml | – sequence: 1 givenname: Iben surname: Axén fullname: Axén, Iben email: iben.axen@ki.se organization: Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet – sequence: 2 givenname: Lennart surname: Bodin fullname: Bodin, Lennart organization: Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet – sequence: 3 givenname: Alice surname: Kongsted fullname: Kongsted, Alice organization: Nordic Institute of Chiropractic and Clinical Biomechanics, Clinical Locomotion Network – sequence: 4 givenname: Niels surname: Wedderkopp fullname: Wedderkopp, Niels organization: Institute of Regional Health Services Research, University of Southern Denmark – sequence: 5 givenname: Irene surname: Jensen fullname: Jensen, Irene organization: Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet – sequence: 6 givenname: Gunnar surname: Bergström fullname: Bergström, Gunnar organization: Institute of Environmental Medicine, Unit of Intervention and Implementation Research, Karolinska Institutet |
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| ContentType | Journal Article |
| Copyright | Axén et al.; licensee BioMed Central Ltd. 2012 COPYRIGHT 2012 BioMed Central Ltd. 2012 Axén et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2012 Axén et al.; licensee BioMed Central Ltd. 2012 Axén et al.; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Axén et al.; licensee BioMed Central Ltd. 2012 – notice: COPYRIGHT 2012 BioMed Central Ltd. – notice: 2012 Axén et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright © 2012 Axén et al.; licensee BioMed Central Ltd. 2012 Axén et al.; licensee BioMed Central Ltd. |
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| Keywords | Text Message Repeated Data Latent Class Analysis Spline Regression Pain Pattern |
| Language | English |
| License | http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| References_xml | – year: 2012 ident: CR23 publication-title: Multilevel and Longitudinal Modeling Using Stata – volume: 13 start-page: 13 year: 2005 ident: CR13 article-title: The epidemiology of low back pain in primary care publication-title: Chiropr Osteopat doi: 10.1186/1746-1340-13-13 – volume: 45 start-page: 357 issue: 4 year: 2011 ident: CR9 article-title: Overweight - a risk factor of overuse injuries in children? the childhood health, activity and motor performance school study - a 3-year controlled intervention study publication-title: Br J Sports Med doi: 10.1136/bjsm.2011.084038.133 – year: 2002 ident: CR11 publication-title: Statistical Analysis with Missing Data – volume: 9 start-page: 291 issue: 2 year: 1997 end-page: 319 ident: CR10 article-title: A person-oriented approach in research on developmental psychopathology publication-title: Dev Psychopathol doi: 10.1017/S095457949700206X – volume: 18 start-page: 208 issue: 4 year: 2000 end-page: 214 ident: CR14 article-title: Clinical findings in a population with back pain. Relation to one-year outcome and long-term sick leave publication-title: Scand J Prim Health Care doi: 10.1080/028134300448760 – year: 2007 ident: CR24 article-title: General linear mixed effects models publication-title: Longitudinal Data Analysis: A Handbook of Modern Statistical Methods – volume: 18 start-page: 10 issue: 1 year: 2010 ident: CR6 article-title: Comparison between data obtained through real-time data capture by SMS and a retrospective telephone interview publication-title: Chiropr Osteopat doi: 10.1186/1746-1340-18-10 – volume: 13 start-page: 51 issue: 1 year: 2009 end-page: 55 ident: CR17 article-title: Can rate of recovery be predicted in patients with acute low back pain? 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A randomised controlled clinical trial publication-title: BMC Med doi: 10.1186/1741-7015-10-22 – year: 2010 ident: CR18 article-title: Correlations between pain and function in a longitudinal low back pain cohort publication-title: Disability and rehabilitation – volume: 4 start-page: 1 year: 2008 end-page: 32 ident: CR2 article-title: Ecological momentary assessment publication-title: Annu Rev Clin Psychol doi: 10.1146/annurev.clinpsy.3.022806.091415 – volume: 24 start-page: 182 issue: 2 year: 2003 end-page: 199 ident: CR1 article-title: Patient compliance with paper and electronic diaries publication-title: Control Clin Trials doi: 10.1016/S0197-2456(02)00320-3 – year: 1973 ident: CR28 publication-title: Pattern Classification and Scene Analysis – volume: 17 start-page: 11 year: 2009 ident: CR7 article-title: The Nordic back pain subpopulation program - individual patterns of low back pain established by means of text messaging: a longitudinal pilot study publication-title: Chiropr Osteopat doi: 10.1186/1746-1340-17-11 – year: 2001 ident: CR15 publication-title: Biostatistics in Clinical Trials – year: 2010 ident: CR19 publication-title: Introducing Survival and Event History Analysis – volume: 25 start-page: 450 issue: 7 year: 2002 end-page: 454 ident: CR16 article-title: Can patient reactions to the first chiropractic treatment predict early favorable treatment outcome in persistent low back pain? publication-title: J Manip Physiol Ther doi: 10.1067/mmt.2002.126473 – volume: 19 start-page: 716 issue: 6 year: 1974 end-page: 723 ident: CR25 article-title: A new look at the statistical model identification publication-title: IEEE Trans Autom Control doi: 10.1109/TAC.1974.1100705 – volume: 27 start-page: 2409 issue: 21 year: 2002 end-page: 2416 ident: CR21 article-title: Episodes of low back pain: a proposal for uniform definitions to be used in research publication-title: Spine doi: 10.1097/00007632-200211010-00016 – year: 2008 ident: CR3 publication-title: The Swedish population's use of the internet and telephones - an individ survey 2008 – volume: 68 start-page: 427 year: 2006 end-page: 436 ident: CR12 article-title: A primer on the Use of Modern Missing-Data Methods in Psychosomatic Medicine Research publication-title: Psychosom Med doi: 10.1097/01.psy.0000221275.75056.d8 – ident: CR5 – volume: 3 start-page: 1 year: 1974 end-page: 27 ident: CR27 article-title: A dendrite method for cluster analysis publication-title: Comm Stat doi: 10.1080/03610928308827180 – year: 2006 ident: CR22 publication-title: Applied Multilevel Analysis doi: 10.1017/CBO9780511610806 – volume: 65 start-page: 454 issue: 4 year: 2012 end-page: 461 ident: CR4 article-title: The use of weekly text messaging over 6 months was a feasible method for monitoring the clinical course of low back pain in patients seeking chiropractic care publication-title: J Clin Epidemiol doi: 10.1016/j.jclinepi.2011.07.012 – year: 2011 ident: CR26 publication-title: D S: Cluster Analysis doi: 10.1002/9780470977811 – volume: 65 start-page: 454 issue: 4 year: 2012 ident: 759_CR4 publication-title: J Clin Epidemiol doi: 10.1016/j.jclinepi.2011.07.012 – volume: 19 start-page: 716 issue: 6 year: 1974 ident: 759_CR25 publication-title: IEEE Trans Autom Control doi: 10.1109/TAC.1974.1100705 – volume: 27 start-page: 2409 issue: 21 year: 2002 ident: 759_CR21 publication-title: Spine doi: 10.1097/00007632-200211010-00016 – volume: 18 start-page: 10 issue: 1 year: 2010 ident: 759_CR6 publication-title: Chiropr Osteopat doi: 10.1186/1746-1340-18-10 – volume: 68 start-page: 427 year: 2006 ident: 759_CR12 publication-title: Psychosom Med doi: 10.1097/01.psy.0000221275.75056.d8 – volume-title: Introducing Survival and Event History Analysis year: 2010 ident: 759_CR19 – volume: 25 start-page: 450 issue: 7 year: 2002 ident: 759_CR16 publication-title: J Manip Physiol Ther doi: 10.1067/mmt.2002.126473 – volume: 12 start-page: 99 year: 2011 ident: 759_CR20 publication-title: BMC Musculoskelet Disord doi: 10.1186/1471-2474-12-99 – volume-title: The Swedish population's use of the internet and telephones - 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| Snippet | Background
Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such data from large groups of... Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such data from large groups of respondents by... Background Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such data from large groups of... Doc number: 105 Abstract Background: Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such... BACKGROUND: Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such data from large groups of... Background : Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such data from large groups of... Abstract Background Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such data from large... |
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| SubjectTerms | Adult Analysis Back pain Backache Cellular telephones Chiropractic medicine Cluster Analysis Convalescence Correspondence Data Collection - methods Data entry Data Interpretation, Statistical Data mining Female Health Sciences Humans Incidence Linear Models Logistic Models Low back pain Low Back Pain - epidemiology Male Measurement Medical research Medicine Medicine & Public Health Medicine, Experimental Middle Aged Neural networks Poisson Distribution Questions and answers Repeated measures (Research method) Sports injuries Statistical methods Statistical Theory and Methods Statistics for Life Sciences Studies Text Messaging Theory of Medicine/Bioethics |
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| Title | Analyzing repeated data collected by mobile phones and frequent text messages. An example of Low back pain measured weekly for 18 weeks |
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