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Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

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Related Experiment Video

Updated: May 26, 2026

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
09:42

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation

Published on: November 8, 2013

Dropout in supervised small-group exercise programs: a 7-year retrospective cohort study.

Carlos Eduardo Rosa da Silva1, Wilian de Jesus Santana1, Vinicius Morales1

  • 1GETAFIS - São Judas Tadeu University, São Paulo, SP, Brazil.

Frontiers in Public Health
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

Supervised small-group exercise programs see high dropout rates, with nearly half leaving within a year. Male sex, overweight status, and weight-loss goals are key predictors of early discontinuation.

Keywords:
exercise dropoutolder peoplepersonalized small-group trainingsupervised programssurvival analysis

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A Training Program Using an Agility Ladder for Community-Dwelling Older Adults
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A Training Program Using an Agility Ladder for Community-Dwelling Older Adults

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Related Experiment Videos

Last Updated: May 26, 2026

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
09:42

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation

Published on: November 8, 2013

A Training Program Using an Agility Ladder for Community-Dwelling Older Adults
14:13

A Training Program Using an Agility Ladder for Community-Dwelling Older Adults

Published on: March 7, 2020

Area of Science:

  • Exercise science
  • Public health
  • Behavioral science

Background:

  • Dropout from exercise programs is a significant challenge for public health.
  • Supervised small-group training shows promise for improving adherence.
  • Limited evidence exists on the effectiveness of these programs in preventing dropout.

Purpose of the Study:

  • To determine the average participation duration in a supervised small-group training program.
  • To identify key factors predicting dropout among adult participants.
  • To analyze retrospective data from a personalized training studio.

Main Methods:

  • Retrospective cohort study of 587 participants (2018-2024) in São Paulo, Brazil.
  • Analysis of variables including sex, physical activity level, BMI, and exercise motivation.
  • Statistical methods included Kaplan-Meier survival analysis, Cox regression, and CHAID decision trees.

Main Results:

  • Mean participation was 13.8 months; 12-month survival rate was 51.7%.
  • Predictors of shorter participation included male sex, BMI ≥ 25 kg/m², and weight-loss as a primary goal.
  • Decision tree analysis identified male sex, weight-loss goals, and low physical activity as strong dropout predictors.

Conclusions:

  • Dropout in supervised small-group training is progressive, with high attrition in the first year.
  • Male sex, overweight status (BMI ≥ 25 kg/m²), and weight-loss goals are significant predictors of dropout.
  • Combined factors amplify dropout risk, highlighting the need for targeted interventions.