Associations Between Observed In‐Home Behaviors and Self‐Reported Low Mood in Community‐Dwelling Older Adults

Abstract

Objective Using novel monitoring technologies, we sought 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. Design Longitudinal cohort study of older adults whose homes were outfitted with activity sensors. The 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 157 adults, average age 84, followed for an average of 3.7 years. Measurements Mood was assessed by 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. We ascertained the association between global or weekly low mood and the four behavior measures, adjusting for baseline characteristics. Results 18,960 weekly observations of mood were analyzed; 2.6% involved low mood. Individuals who reported low mood more often showed no average differences in any behavior parameters compared to 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.

Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy

“…Time out of home has also been shown to relate to relevant behavioral and psychological changes that may be commonly experienced by MCI or dementia patients such as low mood. Thus, using 18,960 weekly observations of online reported mood ratings over a period of 120 weeks, it was found that during weeks of low mood, participants spent less time out of residence, but did not show changes in walking speed or movement about their home measured by room transitions (Thielke et al, 2014 ).…”

“Are You Sure?”

“…Similarly, subtle changes in medication use (Hayes, Larimer, Adami, & Kaye, 2009), walking speed (Dodge, Mattek, Austin, Hayes, & Kaye, 2012; Silbert, 2012), sleep patterns (Dodge et al, 2012), and overall activity level (Kaye et al, 2011) have been identified by in-home monitoring technologies as potential early signs of incipient cognitive decline. In a related finding, Thielke et al report associations between monitored in-home behaviors and self-reported low mood (Thielke et al, 2014). …”

Ubiquitous Real-World Sensing and Audiology-Based Health Informatics

“…For example, a comprehensive psychometric battery and behavioral assessment is completed with the intention of determining a change in cognitive status that also meets current standards of clinical diagnosis. The self-report and clinical outcome measures are a critical component for enabling ORCATECH to make connections between sensed behaviors and true health outcomes (Thielke et al, 2014).…”