Single-Case Experimental Designs to Evaluate Novel Technology-Based Health Interventions

Technology-based interventions to promote health are expanding rapidly. Assessing the preliminary efficacy of these interventions can be achieved by employing single-case experiments (sometimes referred to as n-of-1 studies). Although single-case experiments are often misunderstood, they offer excel...

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Published inJournal of medical Internet research Vol. 15; no. 2; pp. e22 - 284
Main Authors Dallery, Jesse, Cassidy, Rachel N, Raiff, Bethany R
Format Journal Article
LanguageEnglish
Published Canada Journal of Medical Internet Research 01.02.2013
Gunther Eysenbach MD MPH, Associate Professor
Gunther Eysenbach
JMIR Publications
Subjects
Analysis
Behavior change
Behavioural changes
Clinical outcomes
Design
Efficacy
Experiments
Glucose
Health Behavior
Health promotion
Health Promotion - methods
Health Promotion - standards
Health Promotion - statistics & numerical data
Heuristic
Heuristics
Humans
Innovations
Internet
Intervention
Interventions
Medical technology
Medicine
New technology
Patient compliance
Quality Control
R&D
Reproducibility of Results
Research & development
Research Design
Research methods
Statistical analysis
Technology
Telemedicine - methods
Telemedicine - standards
Telemedicine - statistics & numerical data
Treatment Outcome
Tutorial
Variables
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Abstract Technology-based interventions to promote health are expanding rapidly. Assessing the preliminary efficacy of these interventions can be achieved by employing single-case experiments (sometimes referred to as n-of-1 studies). Although single-case experiments are often misunderstood, they offer excellent solutions to address the challenges associated with testing new technology-based interventions. This paper provides an introduction to single-case techniques and highlights advances in developing and evaluating single-case experiments, which help ensure that treatment outcomes are reliable, replicable, and generalizable. These advances include quality control standards, heuristics to guide visual analysis of time-series data, effect size calculations, and statistical analyses. They also include experimental designs to isolate the active elements in a treatment package and to assess the mechanisms of behavior change. The paper concludes with a discussion of issues related to the generality of findings derived from single-case research and how generality can be established through replication and through analysis of behavioral mechanisms.
AbstractList Technology-based interventions to promote health are expanding rapidly. Assessing the preliminary efficacy of these interventions can be achieved by employing single-case experiments (sometimes referred to as n-of-1 studies). Although single-case experiments are often misunderstood, they offer excellent solutions to address the challenges associated with testing new technology-based interventions. This paper provides an introduction to single-case techniques and highlights advances in developing and evaluating single-case experiments, which help ensure that treatment outcomes are reliable, replicable, and generalizable. These advances include quality control standards, heuristics to guide visual analysis of time-series data, effect size calculations, and statistical analyses. They also include experimental designs to isolate the active elements in a treatment package and to assess the mechanisms of behavior change. The paper concludes with a discussion of issues related to the generality of findings derived from single-case research and how generality can be established through replication and through analysis of behavioral mechanisms.
Technology-based interventions to promote health are expanding rapidly. Assessing the preliminary efficacy of these interventions can be achieved by employing single-case experiments (sometimes referred to as n-of-1 studies). Although single-case experiments are often misunderstood, they offer excellent solutions to address the challenges associated with testing new technology-based interventions. This paper provides an introduction to single-case techniques and highlights advances in developing and evaluating single-case experiments, which help ensure that treatment outcomes are reliable, replicable, and generalizable. These advances include quality control standards, heuristics to guide visual analysis of time-series data, effect size calculations, and statistical analyses. They also include experimental designs to isolate the active elements in a treatment package and to assess the mechanisms of behavior change. The paper concludes with a discussion of issues related to the generality of findings derived from single-case research and how generality can be established through replication and through analysis of behavioral mechanisms.Technology-based interventions to promote health are expanding rapidly. Assessing the preliminary efficacy of these interventions can be achieved by employing single-case experiments (sometimes referred to as n-of-1 studies). Although single-case experiments are often misunderstood, they offer excellent solutions to address the challenges associated with testing new technology-based interventions. This paper provides an introduction to single-case techniques and highlights advances in developing and evaluating single-case experiments, which help ensure that treatment outcomes are reliable, replicable, and generalizable. These advances include quality control standards, heuristics to guide visual analysis of time-series data, effect size calculations, and statistical analyses. They also include experimental designs to isolate the active elements in a treatment package and to assess the mechanisms of behavior change. The paper concludes with a discussion of issues related to the generality of findings derived from single-case research and how generality can be established through replication and through analysis of behavioral mechanisms.
Technology-based interventions to promote health are expanding rapidly. Assessing the preliminary efficacy of these interventions can be achieved by employing single-case experiments (sometimes referred to as n-of-1 studies). Although single-case experiments are often misunderstood, they offer excellent solutions to address the challenges associated with testing new technology-based interventions. This paper provides an introduction to single-case techniques and highlights advances in developing and evaluating single-case experiments, which help ensure that treatment outcomes are reliable, replicable, and generalizable. These advances include quality control standards, heuristics to guide visual analysis of time-series data, effect size calculations, and statistical analyses. They also include experimental designs to isolate the active elements in a treatment package and to assess the mechanisms of behavior change. The paper concludes with a discussion of issues related to the generality of findings derived from single-case research and how generality can be established through replication and through analysis of behavioral mechanisms. Adapted from the source document.
Audience Academic
Author Dallery, Jesse
Cassidy, Rachel N
Raiff, Bethany R
AuthorAffiliation 2 Rowan University Department of Psychology Glassboro, NJ United States
1 University of Florida Department of Psychology Gainesville, FL United States
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  givenname: Bethany R
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/23399668$$D View this record in MEDLINE/PubMed
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2013. This work is licensed under http://creativecommons.org/licenses/by/2.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Jesse Dallery, Rachel N. Cassidy, Bethany R. Raiff. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.02.2013. 2013
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Snippet Technology-based interventions to promote health are expanding rapidly. Assessing the preliminary efficacy of these interventions can be achieved by employing...
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SubjectTerms Analysis
Behavior change
Behavioural changes
Clinical outcomes
Design
Efficacy
Experiments
Glucose
Health Behavior
Health promotion
Health Promotion - methods
Health Promotion - standards
Health Promotion - statistics & numerical data
Heuristic
Heuristics
Humans
Innovations
Internet
Intervention
Interventions
Medical technology
Medicine
New technology
Patient compliance
Quality Control
R&D
Reproducibility of Results
Research & development
Research Design
Research methods
Statistical analysis
Technology
Telemedicine - methods
Telemedicine - standards
Telemedicine - statistics & numerical data
Treatment Outcome
Tutorial
Variables
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Title Single-Case Experimental Designs to Evaluate Novel Technology-Based Health Interventions
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