Use of an Ingestible, Sensor-Based Digital Adherence System to Strengthen the Therapeutic Relationship in Serious Mental Illness
Bibliographic record
Abstract
Serious mental illness is a chronic condition that requires long-term pharmacological treatment. Adherence to oral antipsychotic medication has specific nuances that affects patients and physicians alike. For patients with serious mental illness, nonadherence increases their risk of hospitalization and relapse. Nonadherence is a formidable barrier for physicians in accurately assessing medication efficacy and helping patients achieve their fullest potential. A digital adherence system approved by the Food and Drug Administration can provide near-real time aripiprazole ingestion information. The system records ingestions through an embedded ingestible sensor in oral aripiprazole, which sends a transient local signal to a patch worn on the patient's torso that is then stored on a paired smartphone app. With patient permission, these data can be viewed remotely by their physician, along with a patient's mood, activity, and time spent resting. Such data are able to do the following: reveal broad patterns of medication adherence behavior to the patient as well as their physician; help physicians and patients understand and create more realistic expectations for adherence; promote discussion of treatment options; and minimize therapeutic appointment time devoted to determining actual adherence, thereby maximizing the time available to address each patient's distinctive reasons for their adherence pattern. Crucially, extra time created during appointments can be used to strengthen the therapeutic relationship, which may translate into both improvements in adherence and patient attitude toward their medication. Future investigations are needed to examine how this technology impacts the development of training and best practice guidelines for its use. Otherwise, the potential benefits of this technology may be lost, or worse, inadequate and inappropriate use may harm the therapeutic relationship.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".