Subtle Changes in Medication-taking Are Associated With Incident Mild Cognitive Impairment

Abstract

Introduction: Medication-taking is a routine instrumental activity of daily living affected by mild cognitive impairment (MCI) but difficult to measure with clinical tools. This prospective longitudinal study examined in-home medication-taking and transition from normative aging to MCI. Methods: Daily, weekly, and monthly medication-taking metrics derived from an instrumented pillbox were examined in 64 healthy cognitively intact older adults (Mage=85.5 y) followed for a mean of 2.3 years; 9 transitioned to MCI during study follow-up. Results: In the time up to and after MCI diagnosis, incident MCI participants opened their pillbox later in the day (by 19 min/mo; β=0.46, P<0.001) and had increased day-to-day variability in the first pillbox opening over time (by 4 min/mo) as compared with stable cognitively intact participants (β=4.0, P=0.003). Discussion: Individuals who transitioned to MCI opened their pillboxes later in the day and were more variable in their medication-taking habits. These differences increased in the time up to and after diagnosis of MCI. Unobtrusive medication-taking monitoring is an ecologically valid approach for identifying early activity of daily living changes that signal transition to MCI.

Cognitive dispersion is related to subtle objective daily functioning changes in older adults with and without cognitive impairment

“…Lastly, no association between objective pillbox variables and cognitive performance was seen in this study despite prior findings demonstrating that MCI individuals often open their pillbox later in the day and have a more variable daily first time of pillbox opening compared to CN older adults. 19 Prior work demonstrated that among those with executive dysfunction, the complexity of daily life routines interacts with cognition to predict medication management in older adults. 38 Therefore, a shift toward more structured and simpler routines with reduced executive demands during the COVID-19 pandemic may have reduced group differences in these metrics and contributed to divergent findings.…”

“…Outcome variables were the nine continuous sensor variables that have demonstrated group differences between CN and MCI individuals. [17][18][19][20][21] The Benjamini-Hochberg procedure with the Stats_PADJUST extension for SPSS was utilized to correct for multiple comparisons with a false discovery rate set at 0.10.…”

“…10,[11][12] Additionally, our findings were generally consistent with prior work that found group differences between CN older adults and those with MCI on six of the nine passively assessed IADLs. [16][17][18][19][20][21] A lack of group differences seen in prior work for pillbox use variables and day-to-day variability in distance driven may be related to various factors, including cohort differences, different driving sensors being used, and a change in daily activity patterns due to the COVID-19 pandemic, which led to older adults spending more time at home, having more structured daily routines, and traveling less during lockdown periods. 33,34 Our results demonstrate that global cognitive performance is associated with some aspects of objectively assessed daily functioning, including computer-use duration, computer-use duration variability, and total distance driven.…”

“…In the hierarchical regression analyses, predictor variables included demographic variables (ie, age, sex, education) and global cognitive performance (Step 1) and iSD (Step 2). Outcome variables were the nine continuous sensor variables that have demonstrated group differences between CN and MCI individuals 17–21 . The Benjamini–Hochberg procedure with the Stats_PADJUST extension for SPSS was utilized to correct for multiple comparisons with a false discovery rate set at 0.10.…”

“…With dramatic advances in digital technologies, using passive sensors in one's own home environment represents a promising approach to capturing and monitoring subtle daily functioning changes early in the disease spectrum. 2,[14][15][16][17][18][19][20][21] Research conducted with passive sensor technologies has demonstrated the ability to detect subtle functional changes in important IADLs such as medication management, 19,20 driving, 18 and computer use 17,21 in older adults with MCI. Despite increasing interest in these technologies, no studies have yet examined the relationship between cognitive dispersion and real-world daily functioning as assessed by passive sensor-based activity monitoring.…”