Characterization of activity behavior using a digital medicine system and comparison to medication ingestion in patients with serious mental illness

AbstractActivity 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.

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Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System (Preprint)

10.2196/preprints.21378 ◽

2020 ◽

Author(s):

Jonathan Knights ◽

Zahra Heidary ◽

Jeffrey M Cochran

Keyword(s):

Clinical Trial ◽

Mental Illness ◽

Medication Adherence ◽

Anomaly Detection ◽

Serious Mental Illness ◽

Clinical Trial Data ◽

Trial Data ◽

Reference Sequence ◽

Sequence Of Events ◽

Digital Medicine

BACKGROUND Adherence to medication is often represented in the form of a success percentage over a period of time. Although noticeable changes to aggregate adherence levels may be indicative of unstable medication behavior, a lack of noticeable changes in aggregate levels over time does not necessarily indicate stability. The ability to detect developing changes in medication-taking behavior under such conditions in real time would allow patients and care teams to make more timely and informed decisions. OBJECTIVE This study aims to develop a method capable of identifying shifts in behavioral (medication) patterns at the individual level and subsequently assess the presence of such shifts in retrospective clinical trial data from patients with serious mental illness. METHODS We defined the term adherence volatility as “the degree to which medication ingestion behavior fits expected behavior based on historically observed data” and defined a contextual anomaly system around this concept, leveraging the empirical entropy rate of a stochastic process as the basis for formulating anomaly detection. For the presented methodology, each patient’s evolving behavior is used to dynamically construct the expectation bounds for each future interval, eliminating the need to rely on model training or a static reference sequence. RESULTS Simulations demonstrated that the presented methodology identifies anomalous behavior patterns even when aggregate adherence levels remain constant and highlight the temporal dependence inherent in these anomalies. Although a given sequence of events may present as anomalous during one period, that sequence should subsequently contribute to future expectations and may not be considered anomalous at a later period—this feature was demonstrated in retrospective clinical trial data. In the same clinical trial data, anomalous behavioral shifts were identified at both high- and low-adherence levels and were spread across the whole treatment regimen, with 77.1% (81/105) of the population demonstrating at least one behavioral anomaly at some point in their treatment. CONCLUSIONS Digital medicine systems offer new opportunities to inform treatment decisions and provide complementary information about medication adherence. This paper introduces the concept of adherence volatility and develops a new type of contextual anomaly detection, which does not require an a priori definition of normal and allows expectations to evolve with shifting behavior, removing the need to rely on training data or static reference sequences. Retrospective analysis from clinical trial data highlights that such an approach could provide new opportunities to meaningfully engage patients about potential shifts in their ingestion behavior; however, this framework is not intended to replace clinical judgment, rather to highlight elements of data that warrant attention. The evidence provided here identifies new areas for research and seems to justify additional explorations in this area.

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Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System

JMIR Mental Health ◽

10.2196/21378 ◽

2020 ◽

Vol 7 (9) ◽

pp. e21378

Author(s):

Jonathan Knights ◽

Zahra Heidary ◽

Jeffrey M Cochran

Keyword(s):

Clinical Trial ◽

Mental Illness ◽

Medication Adherence ◽

Anomaly Detection ◽

Serious Mental Illness ◽

Clinical Trial Data ◽

Trial Data ◽

Reference Sequence ◽

Sequence Of Events ◽

Digital Medicine

Background Adherence to medication is often represented in the form of a success percentage over a period of time. Although noticeable changes to aggregate adherence levels may be indicative of unstable medication behavior, a lack of noticeable changes in aggregate levels over time does not necessarily indicate stability. The ability to detect developing changes in medication-taking behavior under such conditions in real time would allow patients and care teams to make more timely and informed decisions. Objective This study aims to develop a method capable of identifying shifts in behavioral (medication) patterns at the individual level and subsequently assess the presence of such shifts in retrospective clinical trial data from patients with serious mental illness. Methods We defined the term adherence volatility as “the degree to which medication ingestion behavior fits expected behavior based on historically observed data” and defined a contextual anomaly system around this concept, leveraging the empirical entropy rate of a stochastic process as the basis for formulating anomaly detection. For the presented methodology, each patient’s evolving behavior is used to dynamically construct the expectation bounds for each future interval, eliminating the need to rely on model training or a static reference sequence. Results Simulations demonstrated that the presented methodology identifies anomalous behavior patterns even when aggregate adherence levels remain constant and highlight the temporal dependence inherent in these anomalies. Although a given sequence of events may present as anomalous during one period, that sequence should subsequently contribute to future expectations and may not be considered anomalous at a later period—this feature was demonstrated in retrospective clinical trial data. In the same clinical trial data, anomalous behavioral shifts were identified at both high- and low-adherence levels and were spread across the whole treatment regimen, with 77.1% (81/105) of the population demonstrating at least one behavioral anomaly at some point in their treatment. Conclusions Digital medicine systems offer new opportunities to inform treatment decisions and provide complementary information about medication adherence. This paper introduces the concept of adherence volatility and develops a new type of contextual anomaly detection, which does not require an a priori definition of normal and allows expectations to evolve with shifting behavior, removing the need to rely on training data or static reference sequences. Retrospective analysis from clinical trial data highlights that such an approach could provide new opportunities to meaningfully engage patients about potential shifts in their ingestion behavior; however, this framework is not intended to replace clinical judgment, rather to highlight elements of data that warrant attention. The evidence provided here identifies new areas for research and seems to justify additional explorations in this area.

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A review of behavioral tailoring strategies for improving medication adherence in serious mental illness

Dialogues in Clinical Neuroscience ◽

10.31887/dcns.2016.18.2/jkreyenbuhl ◽

2016 ◽

Vol 18 (2) ◽

pp. 191-201 ◽

Cited By ~ 2

Keyword(s):

Mental Illness ◽

Medication Adherence ◽

Serious Mental Illness ◽

Psychiatric Symptoms ◽

Treatment Success ◽

Adverse Outcomes ◽

Daily Routine ◽

Improve Medication Adherence ◽

Novel Technologies ◽

Electronic Monitors

Nonadherence to psychopharmacological treatments poses a significant challenge to treatment success in individuals with serious mental illness, with upwards of 60% of people not taking their psychiatric medications as prescribed. Nonadherence is associated with adverse outcomes, including exacerbation of psychiatric symptoms, impaired functioning, increased hospitalizations and emergency room use, and increased health care costs. Whereas interventions using psychoeducation or cognitive approaches, such as motivational interviewing, have largely proven ineffective in improving adherence, approaches employing behavioral tailoring that incorporate medication taking into the daily routine and/or use environmental supports have shown promise. Recently, adherence-enhancing behavioral tailoring interventions that utilize novel technologies, such as electronic monitors and mobile phones, have been developed. Although interventions utilizing these platforms have the potential for widespread dissemination to a broad range of individuals, most require further empirical testing. This paper reviews selected behavioral tailoring strategies that aim to improve medication adherence and other functional outcomes among individuals with serious mental illness.

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