Literature DB >> 24635020

Associations between observed in-home behaviors and self-reported low mood in community-dwelling older adults.

Stephen M Thielke1, Nora C Mattek, Tamara L Hayes, Hiroko H Dodge, Ana R Quiñones, Daniel Austin, Johanna Petersen, Jeffrey A Kaye.   

Abstract

OBJECTIVES: To ascertain the association between self-report of low mood and unobtrusively measured behaviors (walking speed, time out of residence, frequency of room transitions, and computer use) in community-dwelling older adults using novel monitoring technologies.
DESIGN: Longitudinal cohort study of older adults whose homes were outfitted with activity sensors. Participants completed Internet-based weekly health questionnaires with questions about mood.
SETTING: Apartments and homes of older adults living in the Portland, Oregon, metropolitan area. PARTICIPANTS: Adults, average age 84, followed for an average of 3.7 years (n = 157). MEASUREMENTS: Mood was assessed according to self-report each week. Walking speed, time spent out of residence, and room transitions were estimated using data from sensors; computer use was measured by timing actual use. The association between global or weekly low mood and the four behavior measures was ascertained, adjusting for baseline characteristics.
RESULTS: Eighteen thousand nine hundred sixty weekly observations of mood were analyzed; 2.6% involved low mood. Individuals who reported low mood more often had no average differences in any behavior parameters from those who reported low mood less often. During weeks when they reported low mood, participants spent significantly less time out of residence and on the computer but showed no change in walking speed or room transitions.
CONCLUSION: Low mood in these community-dwelling older adults involved going out of the house less and using the computer less but no consistent changes in movements. Technologies to monitor in-home behavior may have potential for research and clinical care. © Published 2014. This article is a U.S. Government work and is in the public domain in the U.S.A.

Entities:  

Keywords:  behaviors; monitoring; mood; psychomotor; sensors; technologies

Mesh:

Year:  2014        PMID: 24635020      PMCID: PMC4007277          DOI: 10.1111/jgs.12744

Source DB:  PubMed          Journal:  J Am Geriatr Soc        ISSN: 0002-8614            Impact factor:   5.562


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1.  Relationships between mood state, time estimation, and selected movement speed.

Authors:  Kumi Naruse
Journal:  Percept Mot Skills       Date:  2004-10

2.  Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale.

Authors:  M D Miller; C F Paradis; P R Houck; S Mazumdar; J A Stack; A H Rifai; B Mulsant; C F Reynolds
Journal:  Psychiatry Res       Date:  1992-03       Impact factor: 3.222

3.  Geriatric Depression Scale.

Authors:  J A Yesavage
Journal:  Psychopharmacol Bull       Date:  1988

4.  The Clinical Dementia Rating (CDR): current version and scoring rules.

Authors:  J C Morris
Journal:  Neurology       Date:  1993-11       Impact factor: 9.910

Review 5.  Actigraphy and motion analysis: new tools for psychiatry.

Authors:  M H Teicher
Journal:  Harv Rev Psychiatry       Date:  1995 May-Jun       Impact factor: 3.732

Review 6.  Psychomotor symptoms of depression.

Authors:  C Sobin; H A Sackeim
Journal:  Am J Psychiatry       Date:  1997-01       Impact factor: 18.112

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Authors:  Ann M Berger; Kimberly Wielgus; Melody Hertzog; Patricia Fischer; Lynne Farr
Journal:  Support Care Cancer       Date:  2009-04-19       Impact factor: 3.603

8.  Black bile and psychomotor retardation: shades of melancholia in Dante's Inferno.

Authors:  David A J Widmer
Journal:  J Hist Neurosci       Date:  2004-03       Impact factor: 0.529

Review 9.  Psychomotor symptoms in depression: a diagnostic, pathophysiological and therapeutic tool.

Authors:  Didier Schrijvers; Wouter Hulstijn; Bernard G C Sabbe
Journal:  J Affect Disord       Date:  2007-12-20       Impact factor: 4.839

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Authors:  Ania Korszun; Elizabeth A Young; N Cary Engleberg; Christine B Brucksch; John F Greden; Leslie A Crofford
Journal:  J Psychosom Res       Date:  2002-06       Impact factor: 3.006

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Authors:  Ju Wang; J Bauer; M Becker; P Bente; L Dasenbrock; K Elbers; A Hein; M Kohlmann; G Kolb; C Lammel-Polchau; M Marschollek; M Meis; H Remmers; H M zu Schwabedissen; M Schulze; E-E Steen; R Haux; K-H Wolf
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