First Use of an Ingestible Sensor to Manage Uncontrolled Blood Pressure in Primary Practice: The UK Hypertension Registry
Open accessAbstract
Background: Primary care physicians used a CE-marked ingestible sensor in a real world setting to assess patients with persistent hypertension. The objectives were to (1) characterize the pattern of their medication use, (2) differentiate medication use from pharmacologic unresponsiveness as a potential underlying cause for uncontrolled hypertension; (3) categorize and summarize subsequent management decisions; and (4) assess the usability and acceptability of passive electronic monitoring for managing hypertension. Methods: There were 167 patients with uncontrolled hypertension whilst chronically prescribed 2 to 5 antihypertensives; 40% were chronically prescribed ≥ 3 antihypertensives. Patients were instructed to ingest a pharmacologically inert tablet containing the ingestible sensor whenever they took their prescribed medications. A wearable sensor on the patient's torso passively recorded the dates/times of tablet ingestion. Taking adherence (i.e., the total number passively detected divided by the total number prescribed) and timing adherence were determined for a period of two weeks with no change in therapy. Findings: Taking adherence ranged from 53-100%; timing adherence ranged from 21-100%. Systolic blood pressure decreased from 154 ± 13 to 145 ± 18 mmHg (mean -9.7 mmHg; 95% CI: -12.5, -7.0 mmHg, p<0.001). Diastolic blood pressure decreased from 85 ± 11 to 80 ± 12 mmHg (mean -5.0 mmHg; 95% CI: -6.5, -3.5 mmHg, p<0.001). Thirty-two percent (32%) of participants achieved their treatment goals using their existing therapy. Subsequent management included: medication reviews and/or adherence counseling with no anti-hypertensive changes in 81%, anti-hypertensive changes in 13%, referrals to specialists in 1%, and no additional actions were taken in 6%. Practitioners found the offering easy to utilise, and the majority of patients expressed a positive experience in using it. Conclusion: In patients with uncontrolled blood pressure, the inclusion of an ingestible sensor in everyday practice helped practitioners to identify potential factors contributing to persistent hypertension and to determine subsequent patient-specific interventions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The uncontrolled prior studies did not focus on disease control.
“However, these uncontrolled studies did not focus on disease control.”
From Effectiveness of Digital Medicines to Improve Clinical Outcomes in Patients with Uncontrolled Hypertension and Type 2 Diabetes: Prospective, Open-Label, Cluster-Randomized Pilot Clinical Trial (2017) · cites this paper as [20]
The cited registry was uncontrolled, but its Introduction says it assessed patients with persistent hypertension, and its Discussion reports whether patients achieved blood-pressure control. The paper does not characterize prior uncontrolled studies as lacking a focus on disease control; its own study makes the contrary point. (LLM, conf=0.82)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredIn the prior hypertension registry study, 37% of participants achieved blood pressure control after 2 weeks with no adjustments to their antihypertensive medications.
“Additionally, 37% of participants achieved BP control after 2 weeks with no adjustments to their antihypertensive medications [20].”
From Effectiveness of Digital Medicines to Improve Clinical Outcomes in Patients with Uncontrolled Hypertension and Type 2 Diabetes: Prospective, Open-Label, Cluster-Randomized Pilot Clinical Trial (2017) · cites this paper as [20]
The Abstract reports that 32% of registry participants achieved their treatment goals using existing therapy, and the methods state that antihypertensive treatment was unchanged during the two weeks of sensor use. The Discussion mentions approximately 30% reaching their goals after two weeks without prescribing changes, but attributes that finding to a separate community-pharmacy study. Our copy does not support the claimed 37% for this registry. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA prior hypertension registry study conducted in several primary care centers in the United Kingdom demonstrated that a prior version of the DMO with no patient feedback (digital medicines plus patch) uncovered a root cause for uncontrolled hypertension in all participants after 2 weeks of use.
“A prior hypertension registry study conducted in several primary care centers in the United Kingdom demonstrated the ability of a prior version of the DMO with no patient feedback (ie, digital medicines plus patch) to uncover a root cause for uncontrolled HTN in all participants after 2 weeks of use.”
From Effectiveness of Digital Medicines to Improve Clinical Outcomes in Patients with Uncontrolled Hypertension and Type 2 Diabetes: Prospective, Open-Label, Cluster-Randomized Pilot Clinical Trial (2017) · cites this paper as [20]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredPrior clinical studies demonstrated the accuracy, safety, and feasibility of using the DMO in patients across a range of medical conditions, including hypertension and type 2 diabetes mellitus.
“Prior clinical studies demonstrated the accuracy, safety, and feasibility of using the DMO in patients across a range of medical conditions, including HTN and type 2 diabetes mellitus (T2DM)”
From Effectiveness of Digital Medicines to Improve Clinical Outcomes in Patients with Uncontrolled Hypertension and Type 2 Diabetes: Prospective, Open-Label, Cluster-Randomized Pilot Clinical Trial (2017) · cites this paper as [20]
The Introduction (page not available) reports prior ingestible-sensor studies involving people with hypertension and diabetes, and describes a low rate of side effects and no device-related serious adverse events. It does not report that those studies demonstrated accuracy, nor does it specify type 2 diabetes; the Results support usability in this hypertension registry, not feasibility across all the listed conditions. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPrior clinical studies suggested this DMO can identify reasons for uncontrolled hypertension and help patients achieve blood pressure control.
“and suggested this DMO can identify reasons for uncontrolled HTN and help patients achieve BP control [16-21].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Yu, S. M. (1998) · Maternal and Child Health Journal · doi:10.1023/a:1021801927353 · Cited by 3,876 · see citing works
Medication Adherence: Its Importance in Cardiovascular Outcomes
Ho, P. M., Bryson, C. L., & Rumsfeld, J. S. (2009) · Circulation · doi:10.1161/circulationaha.108.768986 · Cited by 1,488 · see citing works
Friedman, R. H. (1996) · American Journal of Hypertension · doi:10.1016/0895-7061(95)00353-3 · Cited by 506 · see citing works
Nunes, V. M. D. A., Neilson, J., O’Flynn, N., Calvert, N., Kuntze, S., Smithson, H., BENSON, J. A., Blair, J., Bowser, A., Clyne, W., et al. (2009) · University of Huddersfield Repository (University of Huddersfield) · Cited by 384 · see citing works
Adherence to antihypertensive treatment: a critical factor for blood pressure control
Yiannakopoulou, E. C., Papadopulos, J. S., Cokkinos, D. V., & Mountokalakis, T. D. (2005) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1097/00149831-200506000-00010 · Cited by 185 · see citing works
Parati, G., Omboni, S., Compare, A., Grossi, E., Callus, E., Venco, A., Destro, M., Villa, G., Palatini, P., Rosei, E. A., et al. (2013) · Trials · doi:10.1186/1745-6215-14-22 · Cited by 49 · see citing works
Lasserson, D. S., Buclin, T., & Glasziou, P. (2011) · Heart · doi:10.1136/hrt.2010.221473 · Cited by 43 · see citing works
Noble, K., Brown, K., Medina, M., Alvarez, F., Young, J., Leadley, S., Kim, Y. A., & DiCarlo, L. A. (2016) · Journal of the American Pharmacists Association · doi:10.1016/j.japh.2016.01.005 · Cited by 30 · see citing works
Hypertension Assessment and Management: Role for Digital Medicine
Godbehere, P., & Wareing, P. (2014) · The Journal of Clinical Hypertension · doi:10.1111/jch.12246 · Cited by 10 · see citing works
Self-citationOptimising hypertension treatment: NICE/BHS guideline implementation and audit for best practice
Arden, C. (2013) · British Journal of Cardiology · doi:10.5837/bjc.2013.s01 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Adherence in Hypertension: A Review of Prevalence, Risk Factors, Impact, and Management
Burnier, M., & Egan, B. M. (2019) · Circulation Research · doi:10.1161/circresaha.118.313220 · Cited by 878 · see citing works
The Internet of Things: Impact and Implications for Health Care Delivery
Kelly, J. T., Campbell, K. L., Gong, E., & Scuffham, P. (2020) · Journal of Medical Internet Research · doi:10.2196/20135 · Cited by 495 · see citing works
Dwivedi, R., Mehrotra, D., & Chandra, S. (2021) · Journal of Oral Biology and Craniofacial Research · doi:10.1016/j.jobcr.2021.11.010 · Cited by 417 · see citing works
Frias, J., Virdi, N., Raja, P., Kim, Y., Savage, G., & Osterberg, L. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7833 · Cited by 211 · see citing works
Medication adherence in hypertension
Poulter, N. R., Borghi, C., Parati, G., Pathak, A., Toli, D., Williams, B., & Schmieder, R. E. (2019) · Journal of Hypertension · doi:10.1097/hjh.0000000000002294 · Cited by 172 · see citing works
Digital pills: a scoping review of the empirical literature and analysis of the ethical aspects
Martani, A., Geneviève, L. D., Poppe, C. J., Casonato, C., & Wangmo, T. (2020) · BMC Medical Ethics · doi:10.1186/s12910-019-0443-1 · Cited by 52 · see citing works
Adherence Measurements in Clinical Trials and Care
Shader, R. I. (2017) · Clinical Therapeutics · doi:10.1016/j.clinthera.2017.12.005 · Cited by 29 · see citing works
Sulkowski, M., Luetkemeyer, A. F., Wyles, D. L., Martorell, C. T., Muir, A. J., Weisberg, I. S., Gordon, S. C., McLain, R. W., & Huhn, G. D. (2020) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.15707 · Cited by 26 · see citing works
Bittner, B., Chiesi, C. S., Kharawala, S., Kaur, G., & Schmidt, J. (2019) · Medical Devices Evidence and Research · doi:10.2147/mder.s198943 · Cited by 25 · see citing works
Internet of Medical Things – The Future of Healthcare
Wal, P., Wal, A., Verma, N., Karunakakaran, R., & Kapoor, A. (2022) · The Open Public Health Journal · doi:10.2174/18749445-v15-e221215-2022-142 · Cited by 20 · see citing works
Review of glaucoma medication adherence monitoring in the digital health era
Erras, A., Shahrvini, B., Weinreb, R. N., & Baxter, S. L. (2021) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2020-317918 · Cited by 19 · see citing works
Mohsen, M. M., Riad, N. A., Badawy, A. I., Gafar, S. E. A. E., El-Hammed, B. M. A., & Eltomy, E. M. (2019) · American journal of nursing research · doi:10.12691/ajnr-8-1-3 · Cited by 13 · see citing works
Чазова, И. Е., Solntseva, T. D., Анатольевна, С. О., Ageev, F. T., Фофанова, Т. В., Bragina, A. E., & Trushina, O. I. (2024) · Systemic Hypertension · doi:10.38109/2075-082x-2024-2-5-15 · Cited by 10 · see citing works
Cabling, M. L., Drago, F., Turner, J. W., Hurtado‐de‐Mendoza, A., & Sheppard, V. B. (2022) · Health and Technology · doi:10.1007/s12553-022-00707-1 · Cited by 5 · see citing works
IMPACT OF AI IN INTERNET OF MEDICAL THINGS FOR HEALTH CARE DELIVERY
Nathani, N., & Hasan, Z. (2021) · International Journal of Engineering Technologies and Management Research · doi:10.29121/ijetmr.v8.i8.2021.1012 · Cited by 3 · see citing works
Digital Medicines to Measure Drug Ingestion Adherence
Virdi, N. S. (2018) · Updates in hypertension and cardiovascular protection · doi:10.1007/978-3-319-76593-8_7 · Cited by 2 · see citing works
Iot In Healthcare: Challenges and Opportunities for Improved Patient Outcomes
Chattopadhyay, S. (2019) · INFORMATION TECHNOLOGY IN INDUSTRY · doi:10.17762/itii.v7i3.813 · Cited by 1 · see citing works
A rapid review on internet of things and healthcare: A global transformation
Arora, S. P., Naqvi, W. M., Pathan, H., Fating, T., & Patil, A. (2024) · AIP conference proceedings · doi:10.1063/5.0244858
- Authors
- R, N., N, M., RJ, W., P, G., SC, T., R, F., W, X., Y, K., I, S., S, L., & L, D.
- Venue
- Journal of Community Medicine & Health Education
- Publisher
- OMICS Publishing Group
- Published
- 2017
- Volume
- 07
- Issue
- 01
- ISSN
- 2161-0711 (ISSN-L)
- OpenAlex
- W2598668318
- Cited by
- 18
- References
- 10
- Citations by year
- 2024: 2 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 5 · 2018: 1 · 2017: 2
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Journal of Community Medicine & Health Education (publishedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Blood Pressure and Hypertension Studies
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- ingestible sensors, uncontrolled hypertension, medication adherence, antihypertensive drugs, timing of drug administration, primary care, blood pressure control, medication review, patient acceptability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review