How Single-Case Designs Are Revolutionizing Exercise Research
The secret to effective exercise science might lie in studying individuals, not just large groups.
Imagine a world where your fitness program is designed not from generalized guidelines, but from a deep, scientific understanding of what works specifically for you. This is the promise of single-case experimental designs (SCEDs), a powerful yet underappreciated approach in exercise science. While the vast majority of research compares large groups, SCEDs focus intensively on individuals, tracking their responses to interventions over time to build personalized, effective strategies for improving physical activity.
For the 80% of American adults who don't meet recommended exercise guidelines, the "one-size-fits-all" approach has clearly failed. SCEDs offer a more nuanced path forward, providing the scientific rigor to develop truly personalized exercise prescriptions.
This article explores how this innovative methodology is uncovering the secrets to sustainable physical activity, one person at a time.
Unlike traditional randomized controlled trials (RCTs) that compare average results across large groups, single-case experimental designs (SCEDs) involve repeatedly measuring one or a few participants' behaviors across different conditions, allowing each person to serve as their own control 7 .
The core principle is simple yet powerful: by collecting many measurements over time during baseline (no intervention) and intervention phases, researchers can detect whether changes in exercise behavior are truly caused by the intervention rather than other factors 3 .
The limitations of traditional group-based studies become particularly apparent when dealing with diverse populations like frail older adults 5 . As one analysis noted, "the average person presented in the results does not exist and cannot be equated with all participants or the rest of the population" 5 . SCEDs address this by:
They reveal how different people respond to the same exercise program, showing what works for some but not others 5 .
They're ideal for testing individualized exercise programs tailored to a person's specific needs, abilities, and limitations 5 .
Continuous data collection allows researchers and clinicians to adjust interventions in real-time based on participant response 5 .
They can be implemented with limited resources and in settings where recruiting large numbers is difficult, such as in nursing homes or with rare conditions 5 .
Researchers use several SCED configurations depending on their research question and the nature of the intervention:
The simplest form, involving a baseline (A) phase followed by an intervention (B) phase .
After baseline (A) and intervention (B) phases, the intervention is withdrawn (return to A) then reintroduced (B), strengthening causal inference 9 .
Intervention goals are gradually increased or decreased in stepwise fashion, with participant performance expected to match each new criterion 9 .
Two or more interventions are rapidly alternated in random sequence to compare their relative effectiveness 9 .
A systematic review published in 2019 critically examined the quality of single-case research targeting adults' exercise and physical activity, giving us a crucial snapshot of the field's strengths and weaknesses 1 .
The researchers conducted a comprehensive literature search between July and October 2017 across three major databases (PubMed, Web of Science, and PsycINFO), using 120 different search term combinations related to SCD and exercise/physical activity 1 . Their search identified 1,227 publications, but only 10 studies met their strict inclusion criteria 1 , highlighting how SCEDs remain underutilized in exercise promotion research.
Two published quality assessment tools were used to analyze the methodological quality of the included studies, evaluating factors like baseline characteristics, measurement reliability, blinding, and data analysis methods 1 .
The review revealed both encouraging signs and significant room for improvement in exercise SCED research:
| Assessment Tool | Average Score | Score Range | Overall Impression |
|---|---|---|---|
| Tool 1 | 10 out of 14 | 8–12 | Moderate to strong |
| Tool 2 | 13 out of 15 | 9–15 | Moderate to strong |
The analysis identified specific methodological areas where studies frequently fell short:
| Unmet Criterion | Percentage of Studies Not Meeting Criterion | Importance of Criterion |
|---|---|---|
| Assessor blinding | 100% | Reduces measurement bias |
| Treatment fidelity reporting | 100% | Ensures intervention delivered as intended |
| Inter-/intrarater reliability | 80% | Ensures consistent measurement |
| Appropriate statistical analyses | 60% | Provides complementary evidence to visual analysis |
For single-case designs to produce trustworthy evidence, researchers must employ specific methodological elements. Based on quality assessments and reporting standards, here are the essential components of a rigorous exercise SCED:
| Component | Function & Importance | Examples in Exercise Research |
|---|---|---|
| Repeated Measurements | Tracking behavior repeatedly across phases reveals patterns and establishes causality 7 . | Daily step counts, workout duration, exercise frequency |
| Stable Baseline | A representative pre-intervention period serves as comparison for judging intervention effects 7 . | 5+ measurements of normal activity before starting new program |
| Visual Analysis | Primary method for interpreting data patterns across phases 2 7 . | Graphing activity levels to detect changes between baseline and intervention |
| Methodological Replication | Repeating effects across participants, settings, or behaviors strengthens generalizability . | Testing same walking program with multiple participants |
| Randomization | Randomizing phase start times reduces bias and strengthens internal validity 7 . | Randomly determining when each participant begins intervention |
| Validated Measures | Using psychometrically sound tools ensures accurate data collection 1 . | Accelerometers, validated activity questionnaires |
Despite their potential, SCEDs face several challenges in exercise science. Quality concerns persist, particularly regarding assessor blinding, treatment fidelity, and appropriate statistical analysis 1 . There's also a lack of consensus on optimal data analysis methods, with ongoing debate about the role of statistical analysis alongside traditional visual analysis 2 4 7 .
Perhaps most importantly, questionable research practices can compromise the validity of findings, such as selectively reporting only positive results or manipulating graphical displays to emphasize effects 6 . One analysis found that published SCED studies showed larger effects than unpublished ones, suggesting potential publication bias 6 .
The future of SCEDs in exercise science will likely involve:
Single-case experimental designs represent a paradigm shift in how we study exercise behavior and intervention effectiveness. By focusing on the individual rather than group averages, SCEDs acknowledge what experienced fitness professionals have long understood: successful exercise programs must account for individual differences, preferences, and responses.
As research methodologies continue to improve and standards become more widely adopted, SCEDs hold tremendous promise for developing truly evidence-based, personalized exercise recommendations. Rather than replacing traditional group studies, SCEDs complement them, providing a more complete picture of how and why exercise interventions work—and for whom they might fail.
The next frontier in exercise science may not involve larger studies with thousands of participants, but rather smarter studies that deeply understand the needs and responses of individuals. As one research team noted, SCEDs "reduce the gap between researchers and participants, and provide immediate feedback during the intervention to ensure that adjustments can be made if necessary" 5 —bringing us closer to the ideal of exercise science that serves everyone, one person at a time.