Does a physical activity program in the nursing home impact on depressive symptoms? A generalized linear mixed-model approach

ABSTRACT

Objectives: Physical activity (PA) may counteract depressive symptoms in nursing home (NH) residents considering biological, psychological, and person-environment transactional pathways. Empirical results, however, have remained inconsistent. Addressing potential shortcomings of previous research, we examined the effect of a whole-ecology PA intervention program on NH residents’ depressive symptoms using generalized linear mixed-models (GLMMs).

Method: We used longitudinal data from residents of two German NHs who were included without any pre-selection regarding physical and mental functioning (n = 163, M_age = 83.1, 53–100 years; 72% female) and assessed on four occasions each three months apart. Residents willing to participate received a 12-week PA training program. Afterwards, the training was implemented in weekly activity schedules by NH staff. We ran GLMMs to account for the highly skewed depressive symptoms outcome measure (12-item Geriatric Depression Scale–Residential) by using gamma distribution.

Results: Exercising (n = 78) and non-exercising residents (n = 85) showed a comparable level of depressive symptoms at pretest. For exercising residents, depressive symptoms stabilized between pre-, posttest, and at follow-up, whereas an increase was observed for non-exercising residents. The intervention group's stabilization in depressive symptoms was maintained at follow-up, but increased further for non-exercising residents.

Conclusion: Implementing an innovative PA intervention appears to be a promising approach to prevent the increase of NH residents’ depressive symptoms. At the data-analytical level, GLMMs seem to be a promising tool for intervention research at large, because all longitudinally available data points and non-normality of outcome data can be considered.

KEYWORDS:

• Nursing home
• depressive symptoms
• physical activity intervention
• multilevel models
• generalized linear mixed-models

Acknowledgments

We thank Katrin Claßen for her dedication and help in organizing the project and data acquisition. Moreover, we thank the participating residents and staff of both NHs, particularly their directors, Michael Thomas and Sonja Wendel, as well as Kurt Hoffmann, Ina Lebeda, Wolfgang Merkel, and Birgit Webster. We also thank all research assistants. We thank Christine Faller for her help in classifying residents’ medications and Stan Shatenstein for proofreading the manuscript.

Disclosure statement

The authors report no conflicts of interest.

Additional information

Funding

This research was supported by the European Commission [grant number Health-F3-2012-306058] as part of the project ‘innovAge – Social Innovations Promoting Active and Healthy Ageing’ and its subproject ‘Long-term Care in Motion’ as well as a scholarship of the Cusanuswerk to M. Diegelmann. We thank the European Commission and the Cusanuswerk for their support.