Developing a Digital Medicine System in Psychiatry: Ingestion Detection Rate and Latency Period
Open accessAbstract
BACKGROUND: A digital medicine system (DMS) has been developed to measure and report adherence to an atypical antipsychotic, aripiprazole, in psychiatric patients. The DMS consists of 3 components: ingestible sensor embedded in a medication tablet, wearable sensor, and secure mobile and cloud-based applications. An umbrella study protocol was designed to rapidly assess the technical performance and safety of the DMS in multiple substudies to guide the technology development. METHODS: Two sequential substudies enrolled 30 and 29 healthy volunteers between March-April 2014 and February-March 2015, respectively, to assess detection accuracy of the ingestible sensor by the DMS and the latency period between ingestion and detection of the ingestion by the wearable sensor or the cloud-based server. RESULTS: The first substudy identified areas for improvement using early versions of the wearable sensor and the mobile application. The second substudy tested updated versions of the components and showed an overall ingestion detection rate of 96.6%. Mean latency times for the signal transmission were 1.1-1.3 minutes (from ingestion to the wearable sensor detection) and 6.2-10.3 minutes (from the wearable sensor detection to the server detection). Half of transmissions were completed in < 2 minutes, and ~90% of ingestions were registered by the smartphone within 30 minutes of ingestion. No serious adverse events, discontinuations, or clinically significant laboratory/vital signs findings were reported. CONCLUSIONS: The DMS implementing modified versions of the smartphone application and the wearable sensor has the technical capability to detect and report tablet ingestion with high accuracy and acceptable latency time. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02091882.
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Ingestible sensors combined with or embedded in tablets may accurately determine taking adherence.
“Ingestible sensors combined with or embedded in tablets may accurately determine taking adherence[23,24].”
From Adherence is associated with a favorable outcome after lung transplantation (2019) · cites this paper as [23]
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References 13 · Cited by 24
- Authors
- Profit, D., Rohatagi, S., Zhao, C., Hatch, A., Docherty, J. P., & Peters-Strickland, T. S.
- Affiliations
- Otsuka (United States) (US)
- Corresponding author
- Profit, Deborah
- Venue
- The Journal of Clinical Psychiatry
- Publisher
- Physicians Postgraduate Press, Inc
- Published
- 2016-07-05
- Volume
- 77
- Issue
- 09
- Pages
- e1095-e1100
- ISSN
- 0160-6689 (ISSN-L)
- OpenAlex
- W2464751096
- Cited by
- 24
- References
- 13
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 1 · 2019: 5 · 2018: 4 · 2017: 2 · 2016: 2
- FWCI
- 5.29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journal of Clinical Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- medication adherence, aripiprazole, atypical antipsychotics, ingestible sensors, wearable sensors, mobile application, cloud applications, latency, psychiatric patients, technology safety
- MeSH terms
- Cloud Computing, Adolescent, Adult, Aged, Mental Disorders, Female, Humans, Male, Medical Informatics Applications, Middle Aged, Psychiatry, Sensitivity and Specificity, Tablets, Time Factors, Biosensing Techniques, Medication Adherence, Young Adult, Mobile Applications
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