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Related Experiment Video

Updated: Jul 25, 2025

Improving Strength, Power, Muscle Aerobic Capacity, and Glucose Tolerance through Short-term Progressive Strength Training Among Elderly People
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Aerobic Fitness as an Important Moderator Risk Factor for Loneliness in Physically Trained Older People: An

Samuel Encarnação1,2,3, Paula Vaz2, Álvaro Fortunato1,4

  • 1Department of Sport Sciences, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal.

Life (Basel, Switzerland)
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Physical fitness, including aerobic fitness and strength, significantly impacts loneliness in older adults. Higher fitness levels, particularly aerobic capacity, are linked to reduced feelings of loneliness.

Keywords:
agingartificial intelligencecardiorespiratory fitnessmental healthquality of lifewell-being

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Area of Science:

  • Gerontology
  • Computational Social Science
  • Exercise Science

Background:

  • Loneliness is a growing concern among the elderly population.
  • Understanding factors contributing to loneliness in older adults is crucial for public health.

Purpose of the Study:

  • To employ a machine learning algorithm to analyze the relationship between sociodemographic factors, physical fitness, physical activity levels, and sedentary behavior with loneliness in trained older adults.
  • To identify key predictors of loneliness in this demographic group.

Main Methods:

  • Utilized the UCLA Loneliness Scale and the Functional Fitness Test Battery for data collection.
  • Applied a naive Bayes machine learning algorithm with leave-one-out cross-validation (LOOCV) to a cohort of 23 physically trained older adults.
  • Assessed sociodemographic variables, physical fitness (aerobic fitness, hand grip strength, upper limb strength), physical activity levels, and sedentary behavior.

Main Results:

  • The naive Bayes model achieved 100% accuracy and F-1 score in identifying factors contributing to high loneliness.
  • Aerobic fitness (AF), hand grip strength (HG), and upper limb strength (ULS) were identified as the most relevant variables associated with increased loneliness.
  • The model demonstrated high precision in predicting loneliness.

Conclusions:

  • Machine learning, specifically the naive Bayes algorithm with LOOCV, effectively predicts loneliness in trained older adults.
  • Aerobic fitness emerged as the most significant factor in mitigating the risk of loneliness among the studied population.