Ethical perspectives on recommending digital technology for patients with mental illness.
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ABSTRACT:
SUBMITTER: Bauer M
PROVIDER: S-EPMC5293713 | biostudies-other | 2017 Dec
REPOSITORIES: biostudies-other
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Project description:The study examined multimodal technologies to identify correlates of violence among inpatients with serious mental illness.Twenty-eight high-risk inpatients were provided with smartphones adapted for data collection. Participants recorded their thoughts and behaviors by using self-report software. Sensors embedded in each device (microphone and accelerometers) and throughout the inpatient unit (Bluetooth beacons) captured patients' activity and location.Self-reported delusions were associated with violent ideation (odds ratio [OR]=3.08), damaging property (OR=8.24), and physical aggression (OR=12.39). Alcohol and cigarette cravings were associated with violent ideation (OR=5.20 and OR=6.08, respectively), damaging property (OR=3.71 and OR=4.26, respectively), threatening others (OR=3.62 and OR=3.04, respectively), and physical aggression (OR=6.26, and OR=8.02, respectively). Drug cravings were associated with violent ideation (OR=2.76) and damaging property (OR=5.09). Decreased variability in physical activity and noisy ward conditions were associated with violent ideation (OR=.71 and OR=2.82, respectively).Identifiable digital correlates may serve as indicators of increased risk of violence.
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Project description:Electronic monitoring devices (EMDs) have been trialled in interventions to improve inhaled corticosteroid adherence and clinical outcomes. This study sought to understand the perceptions and experiences of EMD end-users. Participants recruited into a six-month EMD study were invited to a semi-structured interview. Interviews were audio-recorded, transcribed verbatim and analysed using the framework approach. Twenty-eight participants (68% female, median age 47) were interviewed. Individuals described feeling responsible for their asthma control. Recent attacks motivated a desire to maintain control. Study participation led to increased awareness of asthma status and medication use. Several individuals were open to integrating digital monitoring data with other mHealth inputs, perceiving the potential to enhance communication with clinicians and empower self-management. Openness to data sharing was tied to expectations of transparent data use. Data supported integrating beliefs and habit formation to achieve behaviour change. There was a willingness for an integrated, platform-based approach to digital self-management.
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Project description:BackgroundSymptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms.AimsIn this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis.MethodSelf-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains.ResultsThe three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups.ConclusionsUsing a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.
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Project description:Increasing both access to and quality of mental healthcare is a global priority. One solution is to integrate technologies such as smartphone apps and sensors directly into care. Acknowledging many prior attempts and barriers, we introduce the Digital Clinic which is an already functioning clinic using smartphone apps to augment and extend care today at Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts. In this piece, we outline the theoretical foundation of the Digital Clinic and its emphasis on the therapeutic alliance, measurement-based care, and shared decision making. We explore both workflow and engagement challenges as well as solutions including a new care team member, the Digital Navigator, and the customization of technology. Acknowledging that the Digital Clinic is an evolving program, we offer details on our implementation in order to allow others to replicate, expand on, and improve these initial efforts.
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Project description:ObjectiveWhile lifestyle medicine can be highly effective for treating a range of mental illnesses these approaches are grossly underutilised and have not been systematically implemented into health care systems. Understanding the acceptability of lifestyle medicine is a critical first step to remediate this. This study evaluated the acceptability of lifestyle medicine relative to pharmacotherapy and psychotherapy, and explore perspectives of people with and without lived experience of mental illness.MethodsSix hundred and forty-nine adult Australian residents (62.6% female; 53.6% with a lifetime diagnosis of mental illness) completed an online survey based on the Theoretical Framework of Acceptability assessing the acceptability of lifestyle medicine, pharmacotherapy and psychotherapy for treating mental illness.ResultsMost participants felt positive about lifestyle medicine (76.9%) and felt that such approaches aligned with their personal values (74.9%). They understood how lifestyle medicine worked (86.4%) and believed it would be effective (69.6%). Lived experience of mental illness was associated with greater perceived burden and lower self-efficacy to engage in lifestyle medicine activities (both p < 0.001). While there was a clear preference for psychotherapy and lifestyle medicine over pharmacotherapy, pharmacotherapy was perceived as least effortful (p < .001) and participants were least confident in their ability to engage in lifestyle medicine (p < 0.05).ConclusionThe findings indicate strong acceptability of lifestyle medicine for mental illness, a preference for non-pharmacological treatment approaches, and an understanding of the challenges associated with making long-term healthy lifestyle modifications amongst people who have lived experience of mental illness.
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Project description:Digital technology is increasingly being used to enhance health care in various areas of medicine. In the area of serious mental illness, it is important to understand the special characteristics of target users that may influence motivation and competence to use digital health tools, as well as the resources and training necessary for these patients to facilitate the use of this technology.The aim of this study was to conduct a quantitative expert consensus survey to identify key characteristics of target users (patients and health care professionals), barriers and facilitators for appropriate use, and resources needed to optimize the use of digital health tools in patients with serious mental illness.A panel of 40 experts in digital behavioral health who met the participation criteria completed a 19-question survey, rating predefined responses on a 9-point Likert scale. Consensus was determined using a chi-square test of score distributions across three ranges (1-3, 4-6, 7-9). Categorical ratings of first, second, or third line were designated based on the lowest category into which the CI of the mean ratings fell, with a boundary >6.5 for first line. Here, we report experts' responses to nine questions (265 options) that focused on (1) user characteristics that would promote or hinder the use of digital health tools, (2) potential benefits or motivators and barriers or unintended consequences of digital health tool use, and (3) support and training for patients and health care professionals.Among patient characteristics most likely to promote use of digital health tools, experts endorsed interest in using state-of-the-art technology, availability of necessary resources, good occupational functioning, and perception of the tool as beneficial. Certain disease-associated signs and symptoms (eg, more severe symptoms, substance abuse problems, and a chaotic living situation) were considered likely to make it difficult for patients to use digital health tools. Enthusiasm among health care professionals for digital health tools and availability of staff and equipment to support their use were identified as variables to enable health care professionals to successfully incorporate digital health tools into their practices. The experts identified a number of potential benefits of and barriers to use of digital health tools by patients and health care professionals. Experts agreed that both health care professionals and patients would need to be trained in the use of these new technologies.These results provide guidance to the mental health field on how to optimize the development and deployment of digital health tools for patients with serious mental illness.
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Project description:BACKGROUND:The concepts of disease, illness and sickness capture fundamentally different aspects of phenomena related to human ailments and healthcare. The philosophy and theory of medicine are making manifold efforts to capture the essence and normative implications of these concepts. In parallel, socio-empirical studies on patients' understanding of their situation have yielded a comprehensive body of knowledge regarding subjective perspectives on health-related statuses. Although both scientific fields provide varied valuable insights, they have not been strongly linked to each other. Therefore, the article aims to scrutinise the normative-ethical implications of patient perspectives in building a bridge to the empirical ethics debates. MAIN TEXT:Three potential fields of tension between the illness and the disease perspective are presented. Consequently, findings from empirical research examining patient perspectives on illness are displayed and the practical implications and associated ethical issues which arise are discussed. This leads to the conclusion that an explicit and elaborate empirical-ethical methodology is needed to deal appropriately with the complex interaction between patients' views and the medico-professional view of disease. Kon's four-stage model of normative-empirical collaboration is then applied against the background of empirical data on patient perceptions. Starting from this exemplary approach, the article suggests employing empirical-ethical frameworks for further research on the conceptual and normative issues, as they help to integrate perspectives from the philosophy of medicine with socio-empirical research. CONCLUSION:The combination of theoretical and empirical perspectives suggested contributes to a more nuanced discussion of the normative impact of patients' actual understanding of illness. Further empirical research in this area would profit from explicitly considering potential ethical issues to avoid naturalistic fallacies or crypto-normative conclusions that may compromise healthcare practice. Vice versa, medico-theoretical debates could be enriched by integrating subjective views of those people who are immediately affected.
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Project description:ObjectiveThe need for clinical services in U.S. colleges exceeds the supply. Digital Mental health Interventions (DMHIs) are a potential solution, but successful implementation depends on stakeholder acceptance. This study investigated the relevance of DMHIs from students' perspectives.MethodsIn 2020-2021, an online cross-sectional survey using mixed methods was conducted with 479 students at 23 colleges and universities. Respondents reported views and use of standard mental health services and DMHIs and rated the priority of various DMHIs to be offered through campus services. Qualitative data included open-ended responses.FindingsAmong respondents, 91% reported having experienced mental health problems, of which 91% reported barriers to receiving mental health services. Students highlighted therapy and counseling as desired and saw flexible access to services as important. With respect to DMHIs, respondents had the most experience with physical health apps (46%), mental health questionnaires (41%), and mental well-being apps (39%). Most were unaware of or had not used apps or self-help programs for mental health problems. Students were most likely to report the following DMHIs as high priorities: a crisis text line (76%), telehealth (66%), websites for connecting to services (62%), and text/messaging with counselors (62%). They considered a self-help program with coach support to be convenient but some also perceived such services to be possibly less effective than in-person therapy.ConclusionsStudents welcome DMHIs on campus and indicate preference for mental health services that include human support. The findings, with particular focus on characteristics of the DMHIs prioritized, and students' awareness and perceptions of scalable DMHIs emphasized by policymakers, should inform schools looking to implement DMHIs.
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Project description: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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Project description: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.
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