Abstract
Activity patterns can be important indicators in patients with serious mental illness. Here, we utilized an accelerometer and electrocardiogram incorporated within a digital medicine system, which also provides objective medication ingestion records, to explore markers of patient activity and investigate whether these markers of behavioral change are related to medication adherence. We developed an activity rhythm score to measure the consistency of step count patterns across the treatment regimen and explored the intensity of activity during active intervals. We then compared these activity features to ingestion behavior, both on a daily basis, using daily features and single-day ingestion behavior, and at the patient-level, using aggregate features and overall ingestion rates. Higher values of the single-day features for both the activity rhythm and activity intensity scores were associated with higher rates of ingestion on the following day. Patients with a mean activity rhythm score greater than the patient-level median were also shown to have higher overall ingestion rates than patients with lower activity rhythm scores (p = 0.004). These initial insights demonstrate the ability of digital medicine to enable the development of digital behavioral markers that can be compared to previously unavailable objective ingestion information to improve medication adherence.
Highlights
Activity patterns and circadian rhythm are often disrupted in patients with serious mental illness (SMI)[1,2,3,4,5], and characterizing related behaviors could provide useful behavioral markers that enable better understanding and assessment of patients’ disease state
digital medicine system (DMS) data In this analysis, accelerometer-based step counts, measured at 1min intervals, and ECG-derived mean heart rates, measured at 5min intervals, were used to characterize patient activity patterns. These data were partitioned into 15-min intervals, with the total step count and mean heart rate calculated across each interval
Activity rhythm feature In order to characterize the consistency of activity patterns, the time-series of the 15-min resolution step count data for a given day and the two preceding days was analyzed using the Lomb–Scargle periodogram[20,21,22], which produced a power spectral density (P) and characteristic frequency for each day that had a sufficient amount of data
Summary
Activity patterns and circadian rhythm are often disrupted in patients with serious mental illness (SMI)[1,2,3,4,5], and characterizing related behaviors could provide useful behavioral markers that enable better understanding and assessment of patients’ disease state Wearable sensor data, such as accelerometer-derived step count and electrocardiogram- (ECG-) measured heart rate, have been used to quantify this activity markers[6,7,8,9]. The digital medicine system (DMS) utilized here[17] noninvasively records complementary information, such as step count and heart rate, that can provide further insight into patient behavior (Fig. 1) The combination of this behavioral data with objective ingestion data provides a unique opportunity to explore relationships between patterns of patient activity and medication ingestion behavior, which could both contextualize the behaviors that are associated with good or poor adherence and lead to the development of behavioral markers of adherence that could be more broadly applied
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