Smart Pills for Psychosis: The Tricky Ethical Challenges of Digital Medicine for Serious Mental Illness

2018 ◽  
Vol 18 (9) ◽  
pp. 65-67 ◽  
Author(s):  
Anna K. Swartz
Keyword(s):  
Mental Illness ◽  
Serious Mental Illness ◽  
Ethical Challenges ◽  
Digital Medicine

scholarly journals Therapeutic Social Control of People with Serious Mental Illness: An Empirical Verification and Extension of Theory

2017 ◽  
Vol 8 (2) ◽  
pp. 108-122 ◽  
Author(s):  
Brea L. Perry ◽  
Emma Frieh ◽  
Eric R. Wright
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Social Control ◽  
Serious Mental Illness ◽  
Mental Health Treatment ◽  
Structured Interview ◽  
Ethical Challenges ◽  
Therapeutic Goals ◽  
Contraception Use ◽  
Client Behavior

Mental health services and psychiatric professional values have shifted in the past several decades toward a model of client autonomy and informed consent, at least in principle. However, it is unclear how much has changed in practice, particularly in cases where client behavior poses ethical challenges for clinicians. Drawing on the case of clients’ sexual behavior and contraception use, we examine whether sociological theories of “soft” coercion remain relevant (e.g., therapeutic social control; Horwitz 1982) in contemporary mental health treatment settings. Using structured interview data from 98 men and women with serious mental illness (SMI), we explore client experiences of choice, coercion, and the spaces that lie in between. Patterns in our data confirm Horwitz’s (1982) theory of therapeutic social control but also suggest directions for updating and extending it. Specifically, we identify four strategies used to influence client behavior: coercion, enabling, education, and conciliation. We find that most clients’ experiences reflect elements of ambiguous or limited autonomy, wherein compliance is achieved by invoking therapeutic goals. However, women with SMI disproportionately report experiencing intense persuasion and direct use or threat of force. We argue that it is critical to consider how ostensibly noncoercive and value-free interventions nonetheless reflect the goals and norms of dominant groups.

scholarly journals Autonomy and Capacity in Transitional Age Youth (TAY) with Serious Mental Illness: Challenges in Mental Health - A Case Report

2019 ◽  
Vol 6 (4) ◽  
pp. 1-5
Author(s):  
Timothy Van Deusen ◽  
Keyword(s):  
Mental Illness ◽  
Serious Mental Illness ◽  
Psychiatric Inpatient ◽  
Care Patient ◽  
Legal Issues ◽  
Ethical Challenges ◽  
Involuntary Hospitalization ◽  
Transitional Age Youth ◽  
Difficult Time ◽  
Transitional Age

Objective: An admission to a medical or psychiatric inpatient unit is a difficult time during a Transitional Age Youth (TAY)’s life. While some patients recognize the need for their admission, severely ill patients lack insight into their illnessand require involuntary hospitalization, which may impactthe patient’s quality of care, patient-doctor relationship and raise legal and ethical questions to patient’s autonomy, capacity, and their wishes. Methods: Describe the legal and ethical challenges of TAY with serious mental illness and multiple physical illnesses; illustrated by a clinical case. Results: TAYis affected by legal issues involved with treatment in this population, including a patient’s right to refuse treatment, involuntary commitment versus court-ordered treatment, advance directives, health care proxies, and confidentiality. Conclusions: It is critical to recognize the ethical and legal issues encountered by TAY with serious mental illness. Understanding these matters will improvethe provider’s care and enhance their ability to advocate for patients’ rights.

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

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 <i>adherence volatility</i> as <i>“the degree to which medication ingestion behavior fits expected behavior based on historically observed data”</i> 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 <i>adherence volatility</i> and develops a new type of contextual anomaly detection, which does not require an a priori definition of <i>normal</i> 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.

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

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jeffrey M. Cochran ◽  
Zahra Heidary ◽  
Jonathan Knights
Keyword(s):  
Mental Illness ◽  
Medication Adherence ◽  
Serious Mental Illness ◽  
Activity Rhythm ◽  
Activity Patterns ◽  
Improve Medication Adherence ◽  
Ingestion Rates ◽  
Patient Level ◽  
Activity Intensity ◽  
Digital Medicine

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.

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

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.

Leveraging Digital Medicine to Support Providers and Their Patients in Managing Serious Mental Illness

2021 ◽  
Vol 24 (S2) ◽  
pp. S-55-S-61
Author(s):  
Alexandria Skoufalos ◽  
Laetitia A. N'Dri ◽  
Dexter Waters
Keyword(s):  
Mental Illness ◽  
Serious Mental Illness ◽  
Support Providers ◽  
Digital Medicine

scholarly journals Development of a rest quality metric using a cluster-based analysis of accelerometer data and correlation with digital medicine ingestion data (Preprint)

2020 ◽  
Author(s):  
Zahra Heidary ◽  
Jeffrey Martin Cochran ◽  
Timothy Peters-Strickland ◽  
Jonathan Knights
Keyword(s):  
Mental Illness ◽  
Serious Mental Illness ◽  
Well Being ◽  
Sample Population ◽  
Accelerometer Data ◽  
Start Time ◽  
Adherence To Medication ◽  
Rest Periods ◽  
Digital Medicine

BACKGROUND Adherence to medication regimens and patient rest are two important factors in the well-being of patients with serious mental illness. Both of these behaviors are traditionally difficult to record objectively in unsupervised populations. OBJECTIVE A digital medicine system that provides objective time-stamped medication ingestion records was utilized in patients with serious mental illness. Accelerometer data from the digital medicine system was used to assess rest quality and thus allow for investigation into correlations between rest and medication ingestion. METHODS Longest daily rest periods were identified and then evaluated using a k-means clustering-based algorithm and distance metric to quantify the relative quality of patient rest during these periods. This accelerometer-derived quality of rest metric, along with other accepted metrics of rest quality, such as duration and start time of the longest rest periods, was compared to the objective medication ingestion records. Overall medication adherence classification based on rest features was not performed due to a lack of poorly adherent patients in the sample population. RESULTS Explorations of the relationship between these rest metrics and ingestion did seem to indicate that low-adherence patients experienced relatively low quality of rest; however, patients with better adherence did not necessarily exhibit consistent rest quality. This sample did not contain sufficient patients with poor adherence to draw more robust correlations between rest quality and ingestion behavior. The correlation of temporal outliers in these rest metrics with daily outliers in ingestion time was also explored. CONCLUSIONS This result demonstrates the ability of digital medicine systems to quantify patient rest quality, providing a framework for further work to expand the subject population, compare these rest metrics to gold-standard sleep measurements, and correlate these digital medicine biomarkers with objective medication ingestion data. CLINICALTRIAL All data used in this manuscript came from registered trials.

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