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Multivariable Prediction Model Development and Validation for Dropout in Community-Based Going-Out Program for Older

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A new model predicts older adults dropping out of community programs. It identifies key factors like physical and cognitive function, but is more reliable for predicting who will stay than who will leave.

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Area of Science:

  • Gerontology
  • Public Health
  • Predictive Modeling

Background:

  • Community-based programs are vital for older adults' well-being.
  • Predicting and preventing dropout is crucial for program effectiveness and participant engagement.

Purpose of the Study:

  • To develop and validate a multivariable model for predicting dropout from community-based going-out programs for older adults.
  • To identify key predictors of program attrition in this demographic.

Main Methods:

  • Utilized a prospective cohort of 5905 older adults from the Study of Geriatric Syndromes.
  • Employed an extreme gradient boosting algorithm on training and validation datasets (6:2:2 ratio).
  • Evaluated model discrimination and calibration using receiver operating characteristic (ROC) and calibration plots on a test dataset.

Main Results:

  • The model achieved an area under the ROC curve of 0.701.
  • Key features identified included cognitive function, physical function, and willingness to engage in exercise/sport activities.
  • The model demonstrated high specificity (0.915) and negative predictive value (0.718) for dropout prediction.

Conclusions:

  • The developed predictive model shows reliability in identifying participants unlikely to drop out.
  • Physical and cognitive functions, alongside willingness for physical activity, appear to be primary predictors of program adherence.
  • Further refinement may be needed to improve prediction accuracy for participants likely to drop out.