Evaluating digital medicine ingestion data from seriously mentally ill patients with a Bayesian Hybrid Model

Knights, Jonathan; Heidary, Zahra; Peters-Strickland, Timothy; Ramanathan, Murali

doi:10.1038/s41746-019-0095-z

Cited by 12 publications

(12 citation statements)

References 22 publications

(22 reference statements)

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“…The digital medicine system (DMS) measures ingestion of a medication using an ingestible sensor that is either embedded within a tablet 14 , 15 or embedded within an inert tablet and co-encapsulated with an oral medication tablet. 16 After ingestion of the tablet, the sensor is activated by stomach fluid and sends a signal to a wearable sensor patch attached to the patient’s torso.…”

Section: Introductionmentioning

confidence: 99%

Hummingbird Study: Results from an Exploratory Trial Assessing the Performance and Acceptance of a Digital Medicine System in Adults with Schizophrenia, Schizoaffective Disorder, or First-Episode Psychosis

Fowler¹,

Cope²,

Knights³

et al. 2021

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Purpose Symptoms of psychotic disorders can complicate efforts to accurately evaluate patients’ medication ingestion. The digital medicine system (DMS), composed of antipsychotic medication co-encapsulated with an ingestible sensor, wearable sensor patches, and a smartphone application, was developed to objectively measure medication ingestion. We assessed performance and acceptance of the DMS in subjects with psychotic disorders. Methods This was an 8-week open-label, single-arm, multicenter, Phase 4 pragmatic study (NCT 03568500; EudraCT #2017-004602-17). Eligible adults were diagnosed with schizophrenia, schizoaffective disorder, or first-episode psychosis; were receiving aripiprazole, quetiapine, olanzapine, or risperidone; and could use the DMS with the application downloaded on a personal smartphone. The primary endpoint was good patch coverage, defined as the proportion of days over the assessment period where ≥80.0% of patch data was available, or an ingestion was detected. Exploratory endpoints included a survey on user satisfaction, used to assess acceptance of the DMS. Safety analyses included the incidence of treatment-emergent adverse events (TEAEs). Results From May 25, 2018 to March 22, 2019, 55 subjects were screened and 44 were enrolled. Good patch coverage was achieved on 63.4% of days assessed and the DMS generated an adherence metric of ≥80.0%, reflecting the percentage of ingestion events expected when good patch coverage was reported. Most subjects (53.5%) were satisfied with the DMS. Medical device skin irritations were the only TEAEs reported. Conclusion The DMS had sufficient performance in, and acceptance from, subjects with psychotic disorders and was generally well tolerated.

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Section: Introductionmentioning

confidence: 99%

Hummingbird Study: Results from an Exploratory Trial Assessing the Performance and Acceptance of a Digital Medicine System in Adults with Schizophrenia, Schizoaffective Disorder, or First-Episode Psychosis

Fowler¹,

Cope²,

Knights³

et al. 2021

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“…The patient-worn patch then transmits data to a mobile device and subsequently to a secure cloud infrastructure where it can be made available to clinical (and nonclinical) care teams to aid in decision making. We have previously provided descriptions of these studies [ 18 ] but recapitulated below for clarity.…”

Section: Methodsmentioning

confidence: 99%

“…The information is the passed on to a secure cloud infrastructure where it can be made available to appropriate members of the patient’s care team. Image reproduced under creative commons license from the article published in [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…Previously published work has demonstrated that a first-order Markov model could describe digital medicine ingestion data at the population level [ 18 ]; however, this work did not address the temporal evolution of Markov chains at the individual level or provide a mechanism by which such information might be used proactively by clinicians, patients, or their support teams to aid in treatment decisions. This study addresses these 2 additional components.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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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“…Digital medicine system: coencapsulated antipsychotic medication with miniature ingestible event marker in tablet (MIT) and compatible medical device. Image reprinted from an article in npj Digital Medicine 32. DM, digital medicine; HCP, healthcare professional.…”

Section: Methods and Analysismentioning

confidence: 99%

Hummingbird Study: a study protocol for a multicentre exploratory trial to assess the acceptance and performance of a digital medicine system in adults with schizophrenia, schizoaffective disorder or first-episode psychosis

et al. 2019

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IntroductionIn patients with schizophrenia, medication adherence is important for relapse prevention, and effective adherence monitoring is essential for treatment planning. A digital medicine system (DMS) has been developed to objectively monitor patient adherence and support clinical decision making regarding treatment choices. This study assesses the acceptance and performance of the DMS in adults with schizophrenia, schizoaffective disorder or first-episode psychosis and in healthcare professionals (HCPs).Methods/AnalysisThis is a multicentre, 8-week, single-arm, open-label pragmatic trial designed using coproduction methodology. The study will be conducted at five National Health Service Foundation Trusts in the UK. Patients 18–65 years old with a diagnosis of schizophrenia, schizoaffective disorder or first-episode psychosis will be eligible. HCPs (psychiatrists, care coordinators, nurses, pharmacists), researchers, information governance personnel, clinical commissioning groups and patients participated in the study design and coproduction. Intervention employed will be the DMS, an integrated system comprising an oral sensor tablet coencapsulated with an antipsychotic, non-medicated wearable patch, mobile application (app) and web-based dashboard. The coencapsulation product contains aripiprazole, olanzapine, quetiapine or risperidone, as prescribed by the HCP, with a miniature ingestible event marker (IEM) in tablet. On ingestion, the IEM transmits a signal to the patch, which collects ingestion and physical activity data for processing on the patient’s smartphone or tablet before transmission to a cloud-based server for viewing by patients, caregivers and HCPs on secure web portals or mobile apps.Ethics and disseminationApproval was granted by London - City and East Research Ethics Committee (REC ref no 18/LO/0128), and clinical trial authorisation was provided by the Medicines and Healthcare products Regulatory Agency. Written informed consent will be obtained from every participant. The trial will be compliant with the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use guidelines and the Declaration of Helsinki.Trial registration numberNCT03568500; EudraCT2017-004602-17; Pre-results.

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Evaluating digital medicine ingestion data from seriously mentally ill patients with a Bayesian Hybrid Model

Cited by 12 publications

References 22 publications

Hummingbird Study: Results from an Exploratory Trial Assessing the Performance and Acceptance of a Digital Medicine System in Adults with Schizophrenia, Schizoaffective Disorder, or First-Episode Psychosis

Hummingbird Study: Results from an Exploratory Trial Assessing the Performance and Acceptance of a Digital Medicine System in Adults with Schizophrenia, Schizoaffective Disorder, or First-Episode Psychosis

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

Hummingbird Study: a study protocol for a multicentre exploratory trial to assess the acceptance and performance of a digital medicine system in adults with schizophrenia, schizoaffective disorder or first-episode psychosis

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