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A new look at nursing home residents’ depressive symptoms: the role of basic versus expanded everyday competence

Published online by Cambridge University Press:  29 September 2016

Mona Diegelmann ,
Hans-Werner Wahl ,
Oliver K. Schilling ,
Carl-Philipp Jansen ,
Katrin Classen  and
Klaus Hauer
Mona Diegelmann*
Affiliation:
Department of Psychological Aging Research, Institute of Psychology, Heidelberg University, 69115 Heidelberg, Germany
Hans-Werner Wahl
Affiliation:
Department of Psychological Aging Research, Institute of Psychology, Heidelberg University, 69115 Heidelberg, Germany
Oliver K. Schilling
Affiliation:
Department of Psychological Aging Research, Institute of Psychology, Heidelberg University, 69115 Heidelberg, Germany
Carl-Philipp Jansen
Affiliation:
Department of Psychological Aging Research, Institute of Psychology, Heidelberg University, 69115 Heidelberg, Germany
Katrin Classen
Affiliation:
Department of Psychological Aging Research, Institute of Psychology, Heidelberg University, 69115 Heidelberg, Germany
Klaus Hauer
Affiliation:
Department of Geriatric Research, Bethanien-Hospital/Geriatric Center at Heidelberg University, 69126 Heidelberg, Germany
*
Correspondence should be addressed to: Mona Diegelmann, Department of Psychological Aging Research, Institute of Psychology, Heidelberg University, Bergheimer Strasse 20, 69115 Heidelberg, Germany. Phone: +49(0)6221 548117; Fax: +49(0)6221 548112. E-mail: mona.diegelmann@psychologie.uni-heidelberg.de.
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Abstract

Background:

Depressive symptoms are highly prevalent in nursing home (NH) residents. The relationship between depressive symptoms and everyday competence in terms of basic (BaCo) and expanded everyday competence (ExCo; see Baltes et al., 2001) in the NH setting is, however, not clear. Applying Lewinsohn's depression model, we examined how residents’ BaCo and ExCo relate to their depressive symptoms. Furthermore, we investigated the mediating role of perceived control.

Methods:

Cross-sectional data from 196 residents (M age = 83.7 years, SD = 9.4 years) of two German NHs were analyzed. Study variables were assessed by the Geriatric Depression Scale-Residential (GDS-12R), maximal gait speed (BaCo), proxy ratings of residents’ in-home activity participation, and self-initiated social contact done by staff (ExCo). Structural equation modeling (SEM) was used and a simulation study was included to determine power and potential estimation bias.

Results:

At the descriptive level, one quarter of the residents showed symptoms of depression according to the GDS-12R cut-off criterion. Residents’ BaCo and ExCo were independently and equally strongly associated with their depressive symptoms in the SEM analysis. These findings were affected neither by cognitive impairment, sex, nor age. Perceived control mediated between BaCo but not ExCo and depressive symptoms.

Conclusion:

Future research needs to follow the connection between residents’ everyday competence and their depressive symptoms longitudinally to better understand the underlying mechanisms.

Keywords

nursing homedepressive symptomseveryday competencefunctional impairmentsocial activitystructural equation modeling
Type
Research Article
Information
International Psychogeriatrics , Volume 29 , Issue 1 , January 2017 , pp. 165 - 175
Copyright
Copyright © International Psychogeriatric Association 2016 

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References

Allgaier, A. -K., Kramer, D., Mergl, R., Fejtkova, S. and Hegerl, U. (2011). Validität der geriatrischen depressionsskala bei altenheimbewohnern: vergleich von GDS-15, GDS-8 und GDS-4. Psychiatrische Praxis, 38, 280–286. doi: 10.1055/s-0030-1266105.Google Scholar
Asparouhov, T. and Muthén, B. O. (2010). Weighted least squares estimation with missing data. Technical Appendices. Available at: http://www.statmodel.com/download/GstrucMissingRevision.pdf; last accessed 3 November 2015.Google Scholar
Bach, M., Nikolaus, T., Oster, P. and Schlierf, G. (1995). Depressionsdiagnostik im alter: sie “Geriatric depression scale”. Zeitschrift für Gerontologie und Geriatrie, 28, 42–46.Google Scholar
Baltes, M. M., Maas, I., Wilms, H. -U., Borchelt, M. and Little, T. D. (2001). Everyday competence in old and very old age: theoretical considerations and empirical findings. In Baltes, P. B. and Mayer, K. U. (eds.), The Berlin Aging Study: Aging from 70 to 100: A Research Project of the Berlin-Brandenburg Academy of Sciences (pp. 384–402). Berkeley, CA, London: University of California Press.Google Scholar
Bjørkløf, G. H., Engedal, K., Selbæk, G., Kouwenhoven, S. E. and Helvik, A. -S. (2013). Coping and depression in old age: a literature review. Dementia and Geriatric Cognitive Disorders, 35, 121–154. doi: 10.1159/000346633.CrossRefGoogle ScholarPubMed
Blazer, D. G. (2003). Depression in late life: review and commentary. The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 58, 249–265. doi: 10.1093/gerona/58.3.M249.CrossRefGoogle Scholar
Brown, E. L., Raue, P. J., Halpert, K. D., Adams, S. and Titler, M. G. (2009). Evidence-based guideline detection of depression in older adults with dementia. Journal of Gerontological Nursing, 35, 11–15.CrossRefGoogle Scholar
Carstensen, L. L., Fung, H. H. and Charles, S. T. (2003). Socioemotional selectivity theory and the regulation of emotion in the second half of life. Motivation and Emotion, 27, 103–123. doi: 10.1023/A:1024569803230.Google Scholar
Clark, L. A. and Watson, D. (1991). Tripartite model of anxiety and depression: psychometric evidence and taxonomic implications. Journal of Abnormal Psychology, 100, 316–336. doi: 10.1037/0021-843X.100.3.316.Google Scholar
Conradsson, M., Rosendahl, E., Littbrand, H., Gustafson, Y., Olofsson, B. and Lövheim, H. (2013). Usefulness of the geriatric depression scale 15-item version among very old people with and without cognitive impairment. Aging and Mental Health, 17, 638–645. doi: 10.1080/13607863.2012.758231.Google Scholar
European Commission (DG ECFIN) and Economic Policy Committee (AWG) (2012). The 2012 Ageing Report: Economic and Budgetary Projections for the 27 EU Member States (2010–2060). Luxembourg: Publications Office of the European Union.Google Scholar
Freedman, V. A. and Spillman, B. C. (2014). The residential continuum from home to nursing home: size, characteristics and unmet needs of older adults. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 69 (Suppl. 1), 42–50. doi: 10.1093/geronb/gbu120.Google Scholar
Fries, B. E., Mehr, D. R., Schneider, D., Foley, W. J. and Burke, R. (1993). Mental dysfunction and resource use in nursing homes. Medical Care, 31, 898–920. doi: 10.1097/00005650-199310000-00004.Google Scholar
Gasior, K. et al. (2012). Facts and Figures on Healthy Ageing and Long-term Care: Europe and North America. Vienna: European Centre for Social Welfare Policy and Research.Google Scholar
Heckhausen, J., Wrosch, C. and Schulz, R. (2013). A lines-of-defense model for managing health threats: a review. Gerontology, 59, 438–447. doi: 10.1159/000351269.Google Scholar
Hyer, L., Carpenter, B., Bishman, D. and Wu, H. -S. (2005). Depression in long-term care. Clinical Psychology: Science and Practice, 12, 280–299. doi: 10.1093/clipsy.bpi031.Google Scholar
Jansen, C. -P., Claßen, K., Hauer, K., Diegelmann, M. and Wahl, H. -W. (2014). Assessing the effect of a physical activity intervention in a nursing home ecology: a natural lab approach. BMC Geriatrics, 14, 117. doi: 10.1186/1471-2318-14-117.Google Scholar
Kaup, B. A. et al. (2007). Depression and its relationship to function and medical status, by dementia status, in nursing home admissions. The American Journal of Geriatric Psychiatry, 15, 438–442. doi: 10.1097/JGP.0b013e31803c54f7.CrossRefGoogle ScholarPubMed
Kelly, A. et al. (2010). Length of stay for older adults residing in nursing homes at the end of life. Journal of the American Geriatrics Society, 58, 1701–1706. doi: 10.1111/j.1532-5415.2010.03005.x.CrossRefGoogle ScholarPubMed
Köhler, L., Schäufele, M., Hendlmeier, I. and Weyerer, S. (2010). Praktikabilität und reliabilität eines pflege- und verhaltensassessments (PVA) für stationäre pflegeeinrichtungen. Klinische Diagnostik und Evaluation, 3, 294–321.Google Scholar
Köhler, L., Weyerer, S. and Schäufele, M. (2007). Proxy screening tools improve the recognition of dementia in old-age homes: results of a validation study. Age and Ageing, 36, 549–554. doi: 10.1093/ageing/afm108.Google Scholar
Kovaleva, A. et al. (2012). Eine Kurzskala zur Messung von Kontrollüberzeugung: Die Skala Internale-Externale-Kontrollüberzeugung-4 (IE-4). GESIS-Working Papers 19: GESIS - Leibniz-Institut für Sozialwissenschaften.Google Scholar
Kuys, S. S., Peel, N. M., Klein, K., Slater, A. and Hubbard, R. E. (2014). Gait speed in ambulant older people in long term care: a systematic review and meta-analysis. Journal of the American Medical Directors Association, 15, 194–200. doi: 10.1016/j.jamda.2013.10.015.Google Scholar
Lawton, M. P. (1983a). Environment and other determinants of well-being in older people. The Gerontologist, 23, 349–357. doi: 10.1093/geront/23.4.349.Google Scholar
Lawton, M. P. (1983b). The varieties of wellbeing. Experimental Aging Research, 9, 65–72. doi: 10.1080/03610738308258427.Google Scholar
Lewinsohn, P. M., Hoberman, H., Teri, L. and Hautzinger, M. (1985). An integrative theory of depression. In Reiss, S. and Bootzin, R. R. (eds.), Theoretical Issues in Behavior Therapy (pp. 331–359). Orlando: Academic Press.Google Scholar
Meeks, S. and Depp, C. A. (2003). Pleasant events-based behavioral intervention for depression in nursing home residents: a conceptual and empirical foundation. Clinical Gerontologist, 25, 125–148. doi: 10.1300/J018v25n01_07.Google Scholar
Meeks, S., van Haitsma, K., Schoenbachler, B. and Looney, S. W. (2015). BE-ACTIV for depression in nursing homes: primary outcomes of a randomized clinical trial. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 70, 13–23. doi: 10.1093/geronb/gbu026.CrossRefGoogle ScholarPubMed
Mortenson, W. B., Miller, W. C., Backman, C. L. and Oliffe, J. L. (2012). Association between mobility, participation, and wheelchair-related factors in long-term care residents who use wheelchairs as their primary means of mobility. Journal of the American Geriatrics Society, 60, 1310–1315. doi: 10.1111/j.1532-5415.2012.04038.x.Google Scholar
Muthén, B. O. and Asparouhov, T. (2002). Latent variable analysis with categorical outcomes: multiple-group and growth modeling in Mplus. MPlus Web Notes 4.5. Available at: https://www.statmodel.com/download/webnotes/CatMGLong.pdf; last accessed 3 November 2015.Google Scholar
Muthén, B. O. and Muthén, L. K. (1998–2012). Mplus User's Guide. Los Angeles: Muthén & Muthén.Google Scholar
Muthén, B. O. and Muthén, L. K. (2013). IRT in Mplus. Available at: https://www.statmodel.com/download/MplusIRT2.pdf; last accessed 3 November 2015.Google Scholar
Muthén, L. K. and Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9, 599–620. doi: 10.1207/S15328007SEM0904_8.CrossRefGoogle Scholar
Park, N. S. (2009). The relationship of social engagement to psychological well-being of older adults in assisted living facilities. Journal of Applied Gerontology, 28, 461–481. doi: 10.1177/0733464808328606.CrossRefGoogle Scholar
Park, N. S., Zimmerman, S., Kinslow, K., Shin, H. J. and Roff, L. L. (2012). Social engagement in assisted living and implications for practice. Journal of Applied Gerontology, 31, 215–238. doi: 10.1177/0733464810384480.CrossRefGoogle Scholar
Parmelee, P. A., Lawton, M. P. and Katz, I. R. (1989). Psychometric properties of the geriatric depression scale among the institutionalized aged. Psychological Assessment: A Journal of Consulting and Clinical Psychology, 1, 331–338. doi: 10.1037/1040-3590.1.4.331.CrossRefGoogle Scholar
Raykov, T., Dimitrov, D. M. and Asparouhov, T. (2010). Evaluation of scale reliability with binary measures using latent variable modeling. Structural Equation Modeling: A Multidisciplinary Journal, 17, 265–279. doi: 10.1080/10705511003659417.Google Scholar
Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80, 1–28. doi: 10.1037/h0092976.Google Scholar
SAS Institute Inc. (2013). SAS/STAT® 9.4 User's Guide. Cary, NC: SAS Institute Inc.Google Scholar
Schafer, J. L. and Graham, J. W. (2002). Missing data: our view of the state of the art. Psychological Methods, 7, 147–177. doi: 10.1037//1082-989X.7.2.147.Google Scholar
Seino, S. et al. (2012). Is a composite score of physical performance measures more useful than usual gait speed alone in assessing functional status?. Archives of Gerontology and Geriatrics, 55, 392–398. doi: 10.1016/j.archger.2011.11.011.Google Scholar
Sutcliffe, C. et al. (2000). A new version of the geriatric depression scale for nursing and residential home populations: the geriatric depression scale (Residential) (GDS-12R). International Psychogeriatrics, 12, 173–181. doi: 10.1017/S104161020000630X.Google Scholar
Yesavage, J. A. and Sheikh, J. I. (1986). Geriatric depression scale (GDS): recent evidence and development of a shorter version. Clinical Gerontologist, 5, 165–173. doi: 10.1300/J018v05n01_09.CrossRefGoogle Scholar