Examining demographic and psychosocial predictors of well-being in older pet owners

  • Cheryl A. Krause-Parello College of Nursing University of Colorado Denver Anschutz Medical Campus ED 2 North 13120 E. 19th Avenue, Aurora, CO 80045
  • Elsie Gulick Rutgers University
Keywords: Nursing, well-being, older adults


Background: Worldwide, older adults represent a significant proportion of the total population. Due to the international increase in the numbers of aging adults over the next several decades, it is important for nurses to assist this populace in aspects of healthy aging. There are known indicators of well-being both positive and negative that influence aging.

Objective: The objective of this study was to examine seven models consisting of demographic and psychosocial predictors of well-being among older adults.

Population: This quantitative descriptive design included 209 older pet-owning adults whose age ranged from 48 to 93 (M = 71.66; SD 9.14). The participants were recruited from senior housing facilities designed for older adults or attended a senior citizen community center.

Methods: Participants completed a demographics form and a loneliness, pet attachment, social support, and well-being scale. Demographic and psychosocial predictors of well-being were examined using hierarchical regression analysis (p < .05).

Results: The results revealed that age, gender, education, health, loneliness due to the loss, pet type, loneliness, social support, and pet attachment were significant predictors.

Interpretation: Older adults are at risk for less than optimal well-being due to situational factors such as loneliness and alternations in social support due to natural life transitions. Since well-being is a multidimensional construct that affects the world’s people it is important for nurses to investigate its components.

Conclusion: Internationally, nursing is focused on maintaining positive health and well-being throughout the lifespan. The findings supported both positive and negative components influence well-being. Appropriate interventions should be selected based on positive or negative predictors. Implications for clinical application are discussed.



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