Daily Physical Activity Patterns as a Window on Cognitive Diagnosis in the Baltimore Longitudinal Study of Aging (BLSA)

INTRODUCTION

Alzheimer’s disease (AD) dementia is a neurodegenerative disorder associated with cognitive impairment and loss of independence in daily functioning [1]. Difficulty performing everyday activities and consequent reduced participation may precede diagnoses of mild cognitive impairment (MCI) and AD [2]. To this end, measuring detailed daily activity patterns sequentially over time may hold important potential to enhance detection of MCI and AD.

Physical activity has long been considered a positive health behavior conferring reduced risk of multiple chronic diseases and debilitating health outcomes. The advent and proliferation of research and commercial accelerometers that provide continuous monitoring of daily movement/activity offers the opportunity to examine patterns of activity as indicators of emerging disease pathology [3]. Initial evidence using a hip-worn accelerometry protocol that best captures ambulation [4] suggests that daily higher-intensity activity patterns degrade among persons with mild AD compared to normal controls. Placement of accelerometers in large scale research studies has begun to move from the hip to the wrist [5, 6], which might be more sensitive for capturing upper extremity movement [7, 8] that may better reflect upper body movement characteristics of activities common to daily living [9, 10]. Yet, wrist-worn daily physical activity patterns between normal cognition, MCI, and AD diagnoses have not been well characterized. Additionally, fragmentation or “breaking up” of daily physical activity (Fig. 1), as a proxy of compensation due to physiological decline [11] and a marker of adverse health [11–17], has not been investigated among persons with MCI or AD. Further, accelerometry offers the opportunity to examine these associations by time of day, an important exploration since those who experience greater cognitive declines have been shown to be more fatigable [18]—a state related to waning physical activity engagement later in the day [19].

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Fig. 1

Physical activity fragmentation concept. Each green segment represents 1 minute of activity. Each black segment represents 1 sedentary minute. The top row shows an individual performing a 5-minute activity (upright individual) continuously and then resting (sitting individual). The middle and bottom row shows increasing sedentary/sitting/rest intervals, breaking up the 5 minutes of activity into shorter, more fragmented bouts. More sitting/resting intervals needed for activity completion is thus conceptualized as physical activity fragmentation.

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Fig. 2

Participant flow. APOE, apolipoprotein; CES-D, Center for Epidemiologic Studies-Depression; BMI, body mass index.

Thus, this study aims to identify and characterize objectively measured physical activity patterns between older adults with and without MCI/AD using wrist-worn accelerometry over a 24-hour day. We hypothesized that older adults with MCI/AD would have lower overall physical activity and more frequent rest periods identified as activity fragmentation [11, 15] throughout the day, compared to their counterparts with normal cognition.

METHODS

RESULTS

The mean age was 75 (SD = 11) years, 56% were women, and 68% were White among the total 585 participants (Table 1). Participants averaged 17.8 (2.5) years of education, 27% were APOE ɛ4 carriers, and had an average usual gait speed of 1.1 (0.2) m/s. Those with MCI/AD (n = 36) tended to be older and have slower gait speed compared to those with normal cognition (n = 549). The 5 participants with AD were slightly older (mean age of 89.3 years), had slower gait speed (mean 0.5 m/s), and had higher prevalence of cardiovascular disease history (40%) compared to those with MCI. Estimated AD onset for 3 of the 5 participants was within the same year of accelerometry collection, and 1 year before accelerometer collection for the other 2 participants.

Among the 585 participants, mean total activity counts/day were 2,105,145 (SD = 595,120), mean active minutes/day were 418 (107), and mean activity fragmentation was 25% (6%) (Table 2). Descriptively, those with MCI/AD had fewer total activity counts/day, fewer active minutes/day, and greater activity fragmentation. Participants with AD appeared to have slightly fewer activity counts/day, fewer active minutes/day, and great activity fragmentation compared to participants with MCI (Supplementary Table 1).

In linear regression models adjusting for age, sex, and race, those with MCI/AD had 209,000 fewer total activity counts/day than those with normal cognition (Table 3, Model 1). This difference was not statistically significant after full covariate adjustment (Table 3, Model 2). Between MCI/AD and normal cognitive diagnoses, there were no statistically significant differences in active minutes/day. However, persons with MCI/AD had 2.7% higher activity fragmentation than those with normal cognition (Table 3, Model 1)—which remained statistically significant after full covariate adjustment. In fully adjusted sensitivity analyses focused only on persons with MCI, the magnitude of the association with fragmentation remained similar but statistical significance was lost (p = 0.067) (Supplementary Table 2). Separate interactions with age, sex, and usual gait speed were not significant (p > 0.08).

Differences in diurnal (24-h) patterns of activity were assessed between MCI/AD and normal cognitive diagnoses. Descriptively, the diurnal patterns of those with MCI/AD appeared to be diminished by total amount (Fig. 3), with slight delays in awakening (6:00–7:00 am) and extended activity later in the night (11:00 pm–12:00 am) (Fig. 3) in MCI/AD versus normal cognition.

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Fig. 3

Diurnal patterns of physical activity by diagnosis status. MCI, mild cognitive impairment; AD, Alzheimer’s disease.

Using linear mixed effects models, time-of-day differences in each physical activity metric were tested by cognitive diagnosis (Table 4). During the overnight hours (12:00 am–5:59 am), mean total activity counts and active minutes did not differ between MCI/AD and normal cognition statuses. However, those with MCI/AD had 9.6% lower (95% Confidence Interval [CI]: –13.9, –5.2) activity fragmentation compared to those with normal cognition. During the morning hours (6:00 am–11:59 am), persons with MCI/AD had 116,000 fewer (95% CI: –178,000, –55,000) mean activity counts and 16 fewer (95% CI: –27, –4) active minutes, respectively, than those with normal cognition. Activity fragmentation was similar between diagnoses. During the afternoon (12:00 pm–5:59 pm), those with MCI/AD had 127,000 fewer (95% CI: –195,000, –59,000) mean activity counts, 21 fewer (95% CI: –34, –8) active minutes, and 3.4% higher (95% CI: 0.4, 6.4) activity fragmentation respectively, than those with normal cognition. During the evening hours (6:00–11:59 pm), there were no differences in any of the three accelerometer metrics.

This study compared physical activity patterns between older adults with and without an adjudicated MCI/AD diagnosis in their free-living, community settings. After adjusting for a multitude of sociodemographic, behavioral, and medical factors, participants with an MCI/AD diagnosis had similar overall amounts of physical activity engagement but accumulated their daily physical activity in a more fragmented manner compared to those with normal cognition. In time-of-day analyses, there were little physical activity patterning differences in the morning between those with MCI/AD diagnosis and those with normal cognition. Yet, those with MCI/AD had earlier afternoon declines in physical activity and more movement during late evening hours. Collectively, these results suggest small but potentially important differences in patterns of daily physical activity that may be informative when assessing progression of cognitive change.

This study did not find overall differences in physical activity between MCI/AD and normal cognition diagnoses. Yet, the magnitude of the differences support published evidence that shows lower accelerometer-measured physical activity associated with higher MCI/AD risk [4, 42]. Review studies suggest interventions that increase physical activity provide benefit towards preventing cognitive decline that leads to MCI/AD, though the strength of the evidence ranges widely [43–45]. These studies suggest that physical activity is a modifiable protective factor against declines into MCI/AD. However, the possibility that declines in cognition partially drive physical activity declines, should be considered. Observed deficits in instrumental activities of daily living have been observed in persons with MCI [46] that are progressively worse in those with AD [47], suggesting a related gradual disengagement or inability to perform more complex daily activities, even while maintaining the ability to perform basic activities of daily living. This is further supported by the burgeoning evidence suggesting poor adherence to physical activity interventions may be due to altered brain structure and function [48]—that reduces cognitive function and, in turn, the ability to plan, perform and adhere to complex physical activity behaviors [49].

Disengagement of complex movement in everyday activity might partly be captured within time-of-day differences measured by wrist accelerometry. Those with MCI/AD tended to have lower daily physical activity from morning to late evening but higher levels of physical activity later in the evening and overnight hours. This redistribution of normal to abnormal diurnal patterning supports published literature that suggest the degradation of circadian rest/activity rhythms might be symptoms of or contributors to MCI and AD [50–52]. Potential reasons for this redistribution of diurnal patterning include increased fatigue [53], daytime sleepiness that results in napping [54, 55], and insomnia [56]. Together, these results highlight the utility of time-of-day activity differences (as well as the limitations of undifferentiated 24-h monitoring) to potentially detect early signs of MCI/AD. Still, the bidirectionality of this relationship warrants further exploration as some studies observed abnormalities in rest-activity cycles as potential risk factors for MCI/AD [57, 58].

A novel finding of this study was the association between physical activity fragmentation and MCI/AD. Physical activity fragmentation is conceptualized as a behavioral compensatory mechanism to preserve energy [59] and maintain inde-pendent living in the face of functional decline and mobility loss that occurs with aging [60]. Our results suggest that activity fragmentation might be a more effective sign of MCI/AD progression than total physical activity. One reason is that activity fragmentation may capture subtle losses of motor function that occur with AD progression [61]. It is also possible that during waking periods, more fragmented activity manifests with behavioral signs of subtle cognitive deficits and other neuropsychiatric symptoms that occur with MCI/AD progression [62, 63]—factors that might contribute to the eventual replacement of routine activity with wandering, rest, and sedentariness. The finding that MCI/AD participants had lower activity fragmentation during typical sleep times (i.e., when active, more likely to remain active) supports this study’s diurnal pattern findings showing the flattening of overall diurnal curves with parallel increases in activity during normal sleep times. More longitudinal research is needed to disentangle the reasons fueling differential activity fragmentation patterns occurring with MCI/AD onset and progression.

Our findings should be interpreted within the context of the study’s limitations. The first limitation is that the sample consisted of a limited number of MCI and AD participants who were collapsed into one group to increase statistical power. The second limitation is that a higher proportion of participants with either MCI or AD with insufficient valid accelerometry collection appear to be excluded from the analysis, possibly attenuating the observed associations towards the null. The third limitation is that accelerometry does not capture context of movement behavior (e.g., standing, sitting), leading to a possible underestimation of the observed associations. The fourth limitation is that the cross-sectional study design does not account for temporality, allowing for reverse causation. The fifth limitation is that BLSA participants tend to have higher physical functioning and levels of education compared to the general older adult population, limiting generalizability. The sixth limitation is that defined intervals across the 24 h used in this study do not capture individual-level waking, napping, and sleeping times. The seventh limitation is that the study sample consists mainly of persons with MCI so results do not clearly translate to persons with AD. The strengths of the study include having a sample of participants with well-characterized MCI and adjudicated AD diagnoses, detailed physical activity patterns collected from wrist accelerometry using a 24/7 wear protocol, statistical adjustments for a large number of covariates, and reduced residual confounding due to the recruitment of older adults without initial functional decline and major disease.

Time-of-day and fragmented daily physical activity patterns were observed among older adults with MCI/AD diagnosis compared to counterparts with normal cognition. Future directions include exploring the temporal relationship these daily physical activity differences, in an effort to identify possible trajectories towards MCI/AD onset earlier in the pathological process to increase preventative and therapeutic efforts against AD. These findings also warrant further investigation into physical activity patterning among different subtypes of MCI and dementia.