Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (856 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
A study that uses observations at multiple time points before and after an intervention (the interruption), in an attempt to detect whether the intervention has had an effect significantly greater than any underlying trend over time.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Interrupted Time Series; Time Series, Interrupted; ITS Studies.
Where it fits in the MeSH classification
- Broader topics
- Epidemiologic Studies
Research landscape
PubMed indexes 3,114 records tagged with the MeSH heading “Interrupted Time Series Analysis” (counts retrieved 2026-10-29). 2,181 of them (70.0%) were published from 2020 onward, and the busiest period so far is 2020 onward with 2,181 records.
Reviews account for 110 records (3.5%) and meta-analyses for 41. Randomized controlled trials number 13 (0.4%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 0 |
| 2000-2009 | 0 |
| 2010-2019 | 972 |
| 2020 onward | 2,181 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 110 | 3.5% |
| Meta-analyses | 41 | 1.3% |
| Randomized controlled trials | 13 | 0.4% |
Method: PubMed query "Interrupted Time Series Analysis"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in clinical research
Research methods determine how much a result can be trusted. Randomized trials guard against confounding, systematic reviews and meta-analyses combine many studies, and observational designs are valuable but can show only association. Reporting standards, trial registration and pre-specified outcomes reduce bias. When reading about a method, ask what problem it is designed to solve and which problems it does not solve.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Interrupted time series regression for the evaluation of public health interventions: a tutorial | 2017 | Research article Design not recorded in structured metadata | 2,538 | cc by PMID 27283160 · DOI · PMC5407170 |
| Psychometric network models from time-series and panel data | 2020 | Research article Design not recorded in structured metadata | 200 | cc by PMID 32162233 · DOI · PMC7186258 |
| Technology Delivered Interventions for Depression and Anxiety in Children and Adolescents: A Systematic Review and Meta-analysis | 2019 | Meta-analysis Design not recorded in structured metadata | 177 | cc by PMID 30229343 · DOI · PMC6479049 |
| A Comprehensive Simulation Study to Evaluate the Effect Size and Study Length Relationship in Single-Group Interrupted Time Series Analysis | 2026 | Research article Design not recorded in structured metadata | 1 | cc by PMID 40662481 · DOI · PMC13069133 |
Interrupted time series regression for the evaluation of public health interventions: a tutorial [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time.
- Closing excerpt
- Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (189 words)
Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 27283160 · DOI · PMC5407170
Psychometric network models from time-series and panel data [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)-an undirected network model of partial correlations-between observed variables of cross-sectional data or single-subject time-series data.
- Closing excerpt
- Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (201 words)
Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)-an undirected network model of partial correlations-between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 32162233 · DOI · PMC7186258
Technology Delivered Interventions for Depression and Anxiety in Children and Adolescents: A Systematic Review and Meta-analysis [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Depression and anxiety are common during adolescence.
- Closing excerpt
- Our findings suggest there is a benefit in using CBT based technology delivered interventions where access to traditional psychotherapies is limited or delayed.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (265 words)
Depression and anxiety are common during adolescence. Whilst effective interventions are available treatment services are limited resulting in many adolescents being unable to access effective help. Delivering mental health interventions via technology, such as computers or the internet, offers one potential way to increase access to psychological treatment. The aim of this systematic review and meta-analysis was to update previous work and investigate the current evidence for the effect of technology delivered interventions for children and adolescents (aged up to 18 years) with depression and anxiety. A systematic search of eight electronic databases identified 34 randomized controlled trials involving 3113 children and young people aged 6-18. The trials evaluated computerized and internet cognitive behavior therapy programs (CBT: n = 17), computer-delivered attention bias modification programs (ABM: n = 8) cognitive bias modification programs (CBM: n = 3) and other interventions (n = 6). Our results demonstrated a small effect in favor of technology delivered interventions compared to a waiting list control group: g = 0.45 [95% CI 0.29, 0.60] p < 0.001. CBT interventions yielded a medium effect size (n = 17, g = 0.66 [95% CI 0.42-0.90] p < 0.001). ABM interventions yielded a small effect size (n = 8, g = 0.41 [95%CI 0.08-0.73] p < 0.01). CBM and 'other' interventions failed to demonstrate a significant benefit over control groups. Type of control condition, problem severity, therapeutic support, parental support, and continuation of other ongoing treatment significantly influenced effect sizes. Our findings suggest there is a benefit in using CBT based technology delivered interventions where access to traditional psychotherapies is limited or delayed.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 30229343 · DOI · PMC6479049
A Comprehensive Simulation Study to Evaluate the Effect Size and Study Length Relationship in Single-Group Interrupted Time Series Analysis [4]
Abstract reading summary — source excerpts
- Opening excerpt
- Single-group interrupted time-series analysis (ITSA) is a popular non-experimental study design in healthcare research.
- Closing excerpt
- The tables created from these analyses as well as a new community-contributed Stata package called POWER_ITSA will guide healthcare researchers in determining the most efficient way to achieve anticipated treatment effects in single-group ITSA studies.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (201 words)
Single-group interrupted time-series analysis (ITSA) is a popular non-experimental study design in healthcare research. However, little guidance is available to inform the power requirements of ITSA studies under most common usages. We performed simulations to estimate the number of time periods (ranging from 10 to 100) required for percentage increases in level and trend (from baseline), to achieve statistical significance (p p 80% and >90% power, when autocorrelation ranges from -0.90 to 0.90, and the intervention is introduced at 33%, 50% and 67% of the time series. Larger effect sizes were required for shorter studies, as well as with increasing autocorrelation, and when the intervention was introduced earlier or later than halfway in the time series. The required effect sizes were generally lower for estimating a change in the level of the time series as compared with the change in the trend, but the opposite was true when the number of time periods was larger. Simulations of studies with 10 time periods consistently produced unreliable estimates. The tables created from these analyses as well as a new community-contributed Stata package called POWER_ITSA will guide healthcare researchers in determining the most efficient way to achieve anticipated treatment effects in single-group ITSA studies.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40662481 · DOI · PMC13069133
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Use of interrupted time series analysis in evaluating health care quality improvements [5]
- Research article Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations [6]
- Research article Understanding and using time series analyses in addiction research [7]
- Research article Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review [8]
- Research article Creating effective interrupted time series graphs: Review and recommendations [9]
- Research article Heterogeneity in application, design, and analysis characteristics was found for controlled before-after and interrupted time series studies included in Cochrane reviews [10]
References
- Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. . 2017. PMID 27283160 · DOI · PMC5407170
- Epskamp S. Psychometric network models from time-series and panel data. . 2020. PMID 32162233 · DOI · PMC7186258
- Grist R, Croker A, Denne M, Stallard P. Technology Delivered Interventions for Depression and Anxiety in Children and Adolescents: A Systematic Review and Meta-analysis. . 2019. PMID 30229343 · DOI · PMC6479049
- Linden A. A Comprehensive Simulation Study to Evaluate the Effect Size and Study Length Relationship in Single-Group Interrupted Time Series Analysis. . 2026. PMID 40662481 · DOI · PMC13069133
- Penfold RB, Zhang F. Use of interrupted time series analysis in evaluating health care quality improvements. Acad Pediatr. 2013. PMID 24268083 · DOI
- Jandoc R, Burden AM, Mamdani M, Lévesque LE, Cadarette SM. Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol. 2015. PMID 25890805 · DOI
- Beard E, Marsden J, Brown J, Tombor I, Stapleton J, Michie S, et al.. Understanding and using time series analyses in addiction research. Addiction. 2019. PMID 31058392 · DOI
- Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, Cheng AC, et al.. Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review. J Clin Epidemiol. 2020. PMID 32109503 · DOI
- Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, Korevaar E, et al.. Creating effective interrupted time series graphs: Review and recommendations. Res Synth Methods. 2021. PMID 32657532 · DOI · PMC7818488
- Polus S, Pieper D, Burns J, Fretheim A, Ramsay C, Higgins JPT, et al.. Heterogeneity in application, design, and analysis characteristics was found for controlled before-after and interrupted time series studies included in Cochrane reviews. J Clin Epidemiol. 2017. PMID 28750849 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.