Medication Non-adherenceAIM@BPNCT02454660
Improving Adherence and Outcomes by Artificial Intelligence-Adapted Text Messages
A study testing SMS (Text messages) in people with Medication Non-adherence, run by University of Michigan at 2 sites in United States.
Trial summary
Uncontrolled hypertension is a major cause of morbidity and mortality and many patients fail to take their antihypertensive medication as prescribed. The investigators propose to use artificial intelligence (AI) to allow short message service (SMS or text messages) interventions to adapt to patients' adherence needs… Read more about this study
- Who is it for?
- Ages 21 years and olderHealthy volunteers can take part
- Conditions the study is for
- Medication Non-adherence
- What is being tested?
- SMS (Text messages)
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 49 participants
- Where
- 2 sitesUnited States
- Study dates
- May 2015to 4 Nov 2016
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Patient must have Priority Health Care Health Insurance Coverage
- Patient must have PDC of < 0.5 for anti-hypertensive medications
Who cannot join
- No hypertension medicine currently taken
- Patient doesn't text message (no cell phone) in an average week
- No access to the internet
- Patient has heart failure which makes it difficult to catch breath and move around
- Patient uses artificial oxygen to breathe
- Patient is currently under treatment for cancer
Show all 11Show fewer
- Patient currently has kidney disease that requires dialysis
- Patient self reports a mental health diagnosis (from a health professional)
- Patient reports having schizophrenia
- Patient reports currently being treated bipolar disorder or manic-depressive illness or schizophrenia
- Patients reports ever been diagnosed with dementia or Alzheimer's disease
Trial locations and contacts
2 sites in 1 country.
United States2 sites
University of Michigan College of Pharmacy
Ann Arbor, Michigan, 48109
Spectrum Health
Grand Rapids, Michigan, 49503
About this study
Uncontrolled hypertension is a major cause of morbidity and mortality and many patients fail to take their antihypertensive medication as prescribed. The investigators propose to use artificial intelligence (AI) to allow short message service (SMS or text messages) interventions to adapt to patients' adherence needs and substantially improve medication taking. The aims of the study are to: (1) develop AI methods for adaptive decision-making in human-centered environments and demonstrate the feasibility of the resulting AI-enhanced SMS medication adherence intervention, (2) demonstrate that the intervention can "learn" by adapting the SMS message stream according to patients' medication taking over time, and (3) examine potential intervention impact as measured by improvements in medication adherence and systolic blood pressures. The investigators will recruit 100 patients with uncontrolled hypertension and antihypertensive medication non-adherence. Adherence and other covariates will be measured via surveys at baseline, 3- and 6 months; blood pressures will be measured at baseline and 6 months. Participants will be given an electronic pill-bottle adherence monitor. Participants will receive SMS messages designed to motivate antihypertensive medication adherence. Message content and frequency will adapt automatically using AI algorithms designed to automatically optimize expected pill bottle opening. For Aim 1, the first 25 patients will be enrolled to develop and test alternative RL algorithms and fine-tune the system parameters. For Aim 2, the investigators will examine changes in the probability distribution over message-types and compare that distribution with patients' reasons for non-adherence reported at baseline. For Aim 3, the investigators will examine changes in self-reported medication non-adherence and blood pressure and automatically-reported pill bottle openings. This pilot study will establish the feasibility and potential impact of this novel approach to mobile health messaging for self-management support. The results will be used to support an R01 application for a larger and more definitive trial of intervention impacts.
Read the detailed description
Self-management of chronic conditions involves complex behaviors, and patients vary in their adherence to these behaviors. The focus of this proposal is medication adherence because patients' failure to take their medications as prescribed is a major cause of excess morbidity and mortality and increased health care costs. Studies suggest that 33-50% of patients do not take their medications properly, contributing to nearly 100,000 premature deaths each year and $290 billion in health care costs. Adherence to antihypertensive medications is of particular importance in its own right, and hypertension can serve as an important tracer condition to better understand and improve medication adherence more generally. Uncontrolled hypertension is a major cause of stroke, coronary heart disease, heart failure and mortality, and medication non-adherence is a major cause of uncontrolled hypertension. For example, in a one-year study of ~5,000 hypertensive patients, most patients took their medications only intermittently with half of patients eventually discontinuing their medications against medical advise.
Improving medication adherence requires addressing multiple challenges because patients typically have a variety of reasons for not taking their medication as prescribed, such as beliefs about their disease and its treatment, organizational challenges, and cost barriers. Moreover, as patients' regimens, health status, and social context change over time, adherence support interventions need to adapt, but most services lack the flexibility to do so.
Mobile health (mHealth) services such as patient text messaging or SMS have shown some promise in improving medication adherence. However, since almost all mHealth services are based on simplistic, deterministic protocols, these interventions lack the capacity to meet patients' complex changing needs. As a consequence, these rudimentary systems have demonstrated only modest effects that tend to decrease over time. The investigators propose to apply artificial intelligence (AI) methods, specifically Reinforcement Learning (one type of AI), to develop a model medication adherence system that can automatically adapt SMS communication to improve individual medication taking.
The proposed project is the result of a new multidisciplinary collaboration between UM experts from the College of Pharmacy, College of Engineering, and School of Medicine. Our long-term goal is to improve health outcomes using artificial intelligence (AI) enhanced mobile health tools. The objective in the proposed pilot study is to develop a Reinforcement Learning-based mHealth program focused on medication adherence among patients with poorly controlled hypertension. Our central hypotheses are that a SMS system that uses Reinforcement Learning (RL) will: be acceptable to patients, adapt to hypertension patients' unique adherence-related needs and preferences and changes in these needs over time, and improve medication adherence and blood pressure control. The specific aims are:
- Develop RL methods for adaptive decision-making in human-centered environments and demonstrate the feasibility of the resulting RL-based adaptive SMS medication adherence intervention,
- Demonstrate "learning" by the RL-base adaptive system using data showing adaptation of the SMS message stream according to variation across patients and over time in the reasons for non-adherence, and
- Examine the potential efficacy of the RL-based adaptive SMS intervention with respect to improvements in medication adherence and systolic blood pressure.
The results of this pilot project will include a novel AI/RL technology and evidence regarding its real-world use based on experience with a sample of adults with poorly controlled hypertension. These results will be used to support an R01 application for a larger and more definitive study of the intervention's impact on patients' health and long-term adherence behaviors. Over the longer term, this AI-enhanced mHealth self-management support infrastructure and unprecedented collaboration between investigators in Pharmacy, Medicine, and Computer Science will lay the foundation for a larger program of NIH-funded research using similar AI approaches to addressing behavior change challenges in a large number of health and healthcare problems.
- Conditions
- Medication Non-adherence
- Being tested
- SMS (Text messages)
- Keywords
- adherence, hypertension, mobile technology, intervention
- Medical subject headings
- Medication Adherence, Hypertension
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
SMS (Text messages)Behavioural
Up to 1 text message a day. The artificial agent will determine whether to send a message each day. If it sends a message, it will also determine which of five message types to send.
| Study group | Role | What they receive |
|---|---|---|
| SMS (Text messaging) | Experimental | This group will receive text messages during their entire enrollment period in the study. Receives: Behavioral: SMS (Text messages) |
| No SMS (No text messages) | No intervention | This group will not receive text messages during their entire enrollment period in the study. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Medication Adherence (Proportion Days Covered (PDC)) assessed by administrative insurance records
A measure of Proportion Days Covered (PDC) and is assessed administrative insurance records
Measured: 2 years
Secondary outcomes (3)
Self-reported medication adherence assessed via a questionnaire
Medication adherence is collected at these time points and assessed via a questionnaire.
Measured: baseline, 3 months and 9 months
Pill bottle openings (how often medication was taken) assessed by records from pill bottle caps (MEMS readers)
proxy measure of how often medication was taken, assessed by records from pill bottle caps (MEMS readers)
Measured: 9 months
Medication Beliefs assessed via a questionnaire
A measurement of the patients beliefs about the medication they take will be collected at these time points and assessed via a questionnaire.
Measured: baseline, 3 months and 9 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 May 2015 (estimated)
- Study startMay 2015
- Primary completion4 Nov 2016
- Study completion4 Nov 2016
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Medication Non-adherenceStudies of the same condition, open and under way.
- More trials run by University of MichiganThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Investigator
- Enrolment
- 49 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Michigan
- Sponsor type
- University, hospital or other
- Collaborators
- Agency for Healthcare Research and Quality (AHRQ)
- Responsible party
- Principal investigator, Karen Farris, PhD., University of Michigan
- Investigators
- Karen Farris, PhD, Principal investigator, Univerity of Michigan, College of Pharmacy
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02454660
- Sponsor's protocol ID
- 1R21HS022336-01A1
- Acronym
- AIM@BP
- Status last verified
- Mar 2017
- First submitted
- 12 May 2015
- Last updated
- 11 Apr 2017
- Results posted
- No
- Data monitoring committee
- No
- Individual data sharing
- No
Publications
Lawn S, Schoo A. Supporting self-management of chronic health conditions: common approaches. Patient Educ Couns. 2010 Aug;80(2):205-11. doi: 10.1016/j.pec.2009.10.006.
PubMed 19931372Coleman MT, Newton KS. Supporting self-management in patients with chronic illness. Am Fam Physician. 2005 Oct 15;72(8):1503-10.
PubMed 16273817Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005 Aug 4;353(5):487-97. doi: 10.1056/NEJMra050100. No abstract available.
PubMed 16079372NEHI. Thinking outside the pillbox: a system-wide approach to improving patient medication adherence for chronic disease. http://www.nehi.net/publications/44/thinking_outside_the_pillbox_a_systemwide_approach_to_improving_patient_medication_adherence_for_chronic_disease, Accessed 09 12 12
Hill MN, Miller NH, Degeest S; American Society of Hypertension Writing Group; Materson BJ, Black HR, Izzo JL Jr, Oparil S, Weber MA. Adherence and persistence with taking medication to control high blood pressure. J Am Soc Hypertens. 2011 Jan-Feb;5(1):56-63. doi: 10.1016/j.jash.2011.01.001.
PubMed 21320699Munger MA, Van Tassell BW, LaFleur J. Medication nonadherence: an unrecognized cardiovascular risk factor. MedGenMed. 2007 Sep 19;9(3):58.
PubMed 18092064Vrijens B, Vincze G, Kristanto P, Urquhart J, Burnier M. Adherence to prescribed antihypertensive drug treatments: longitudinal study of electronically compiled dosing histories. BMJ. 2008 May 17;336(7653):1114-7. doi: 10.1136/bmj.39553.670231.25. Epub 2008 May 14.
PubMed 18480115Haynes RB, Ackloo E, Sahota N, McDonald HP, Yao X. Interventions for enhancing medication adherence. Cochrane Database Syst Rev. 2008 Apr 16;(2):CD000011. doi: 10.1002/14651858.CD000011.pub3.
PubMed 18425859Marx G, Witte N, Himmel W, Kuhnel S, Simmenroth-Nayda A, Koschack J. Accepting the unacceptable: medication adherence and different types of action patterns among patients with high blood pressure. Patient Educ Couns. 2011 Dec;85(3):468-74. doi: 10.1016/j.pec.2011.04.011. Epub 2011 May 19.
PubMed 21600724Sabate E - World Health Organization. Adherence to long-term therapies: evidence for action. 2003. http://apps.who.int/medicinedocs/en/d/Js4883e/, Accessed 09 12 12
Elliott RA, Shinogle JA, Peele P, Bhosle M, Hughes DA. Understanding medication compliance and persistence from an economics perspective. Value Health. 2008 Jul-Aug;11(4):600-10. doi: 10.1111/j.1524-4733.2007.00304.x. Epub 2008 Jan 8.
PubMed 18194403Vervloet M, Linn AJ, van Weert JC, de Bakker DH, Bouvy ML, van Dijk L. The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: a systematic review of the literature. J Am Med Inform Assoc. 2012 Sep-Oct;19(5):696-704. doi: 10.1136/amiajnl-2011-000748. Epub 2012 Apr 25.
PubMed 22534082
Study at a glance
From the registry record, last updated by the sponsor on 11 Apr 2017. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Improving Adherence and Outcomes by Artificial Intelligence-Adapted Text Messages
- ClinicalTrials.gov ID
- NCT02454660
- Acronym / sponsor ID
- AIM@BP / 1R21HS022336-01A1
- Conditions
- Medication Non-adherence
- Keywords
- adherence, hypertension, mobile technology, intervention
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 49 participants (actual)
- Lead sponsor
- University of MichiganUniversity, hospital or other
- Collaborators
- Agency for Healthcare Research and Quality (AHRQ)
- Study start
- May 2015 (actual)
- Primary completion
- 4 Nov 2016 (actual)
- Study completion
- 4 Nov 2016 (actual)
- First posted
- 27 May 2015 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Uncontrolled hypertension is a major cause of morbidity and mortality and many patients fail to take their antihypertensive medication as prescribed. The investigators propose to use artificial intelligence (AI) to allow short message service (SMS or text messages) interventions to adapt to patients' adherence needs and substantially improve medication taking. The aims of the study are to: (1) develop AI methods for adaptive decision-making in human-centered environments and demonstrate the feasibility of the resulting AI-enhanced SMS medication adherence intervention, (2) demonstrate that the intervention can "learn" by adapting the SMS message stream according to patients' medication taking over time, and (3) examine potential intervention impact as measured by improvements in medication adherence and systolic blood pressures. The investigators will recruit 100 patients with uncontrolled hypertension and antihypertensive medication non-adherence. Adherence and other covariates will be measured via surveys at baseline, 3- and 6 months; blood pressures will be measured at baseline and 6 months. Participants will be given an electronic pill-bottle adherence monitor. Participants will receive SMS messages designed to motivate antihypertensive medication adherence. Message content and frequency will adapt automatically using AI algorithms designed to automatically optimize expected pill bottle opening. For Aim 1, the first 25 patients will be enrolled to develop and test alternative RL algorithms and fine-tune the system parameters. For Aim 2, the investigators will examine changes in the probability distribution over message-types and compare that distribution with patients' reasons for non-adherence reported at baseline. For Aim 3, the investigators will examine changes in self-reported medication non-adherence and blood pressure and automatically-reported pill bottle openings. This pilot study will establish the feasibility and potential impact of this novel approach to mobile health messaging for self-management support. The results will be used to support an R01 application for a larger and more definitive trial of intervention impacts.
Detailed description
Self-management of chronic conditions involves complex behaviors, and patients vary in their adherence to these behaviors. The focus of this proposal is medication adherence because patients' failure to take their medications as prescribed is a major cause of excess morbidity and mortality and increased health care costs. Studies suggest that 33-50% of patients do not take their medications properly, contributing to nearly 100,000 premature deaths each year and $290 billion in health care costs. Adherence to antihypertensive medications is of particular importance in its own right, and hypertension can serve as an important tracer condition to better understand and improve medication adherence more generally. Uncontrolled hypertension is a major cause of stroke, coronary heart disease, heart failure and mortality, and medication non-adherence is a major cause of uncontrolled hypertension. For example, in a one-year study of ~5,000 hypertensive patients, most patients took their medications only intermittently with half of patients eventually discontinuing their medications against medical advise.
Improving medication adherence requires addressing multiple challenges because patients typically have a variety of reasons for not taking their medication as prescribed, such as beliefs about their disease and its treatment, organizational challenges, and cost barriers. Moreover, as patients' regimens, health status, and social context change over time, adherence support interventions need to adapt, but most services lack the flexibility to do so.
Mobile health (mHealth) services such as patient text messaging or SMS have shown some promise in improving medication adherence. However, since almost all mHealth services are based on simplistic, deterministic protocols, these interventions lack the capacity to meet patients' complex changing needs. As a consequence, these rudimentary systems have demonstrated only modest effects that tend to decrease over time. The investigators propose to apply artificial intelligence (AI) methods, specifically Reinforcement Learning (one type of AI), to develop a model medication adherence system that can automatically adapt SMS communication to improve individual medication taking.
The proposed project is the result of a new multidisciplinary collaboration between UM experts from the College of Pharmacy, College of Engineering, and School of Medicine. Our long-term goal is to improve health outcomes using artificial intelligence (AI) enhanced mobile health tools. The objective in the proposed pilot study is to develop a Reinforcement Learning-based mHealth program focused on medication adherence among patients with poorly controlled hypertension. Our central hypotheses are that a SMS system that uses Reinforcement Learning (RL) will: be acceptable to patients, adapt to hypertension patients' unique adherence-related needs and preferences and changes in these needs over time, and improve medication adherence and blood pressure control. The specific aims are:
- Develop RL methods for adaptive decision-making in human-centered environments and demonstrate the feasibility of the resulting RL-based adaptive SMS medication adherence intervention,
- Demonstrate "learning" by the RL-base adaptive system using data showing adaptation of the SMS message stream according to variation across patients and over time in the reasons for non-adherence, and
- Examine the potential efficacy of the RL-based adaptive SMS intervention with respect to improvements in medication adherence and systolic blood pressure.
The results of this pilot project will include a novel AI/RL technology and evidence regarding its real-world use based on experience with a sample of adults with poorly controlled hypertension. These results will be used to support an R01 application for a larger and more definitive study of the intervention's impact on patients' health and long-term adherence behaviors. Over the longer term, this AI-enhanced mHealth self-management support infrastructure and unprecedented collaboration between investigators in Pharmacy, Medicine, and Computer Science will lay the foundation for a larger program of NIH-funded research using similar AI approaches to addressing behavior change challenges in a large number of health and healthcare problems.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| SMS (Text messaging) | Experimental | Behavioral: SMS (Text messages)This group will receive text messages during their entire enrollment period in the study. |
| No SMS (No text messages) | No intervention | –This group will not receive text messages during their entire enrollment period in the study. |
Who may qualify
- Ages
- 21 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Patient must have Priority Health Care Health Insurance Coverage
- Patient must have PDC of < 0.5 for anti-hypertensive medications
Key exclusion criteria (11 listed)
- No hypertension medicine currently taken
- Patient doesn't text message (no cell phone) in an average week
- No access to the internet
- Patient has heart failure which makes it difficult to catch breath and move around
- Patient uses artificial oxygen to breathe
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| SMS (Text messages)Up to 1 text message a day. The artificial agent will determine whether to send a message each day. If it sends a message, it will also determine which of five message types to send. | Behavioural | SMS (Text messaging) |
Primary outcome
Medication Adherence (Proportion Days Covered (PDC)) assessed by administrative insurance records
Time frame: 2 years
A measure of Proportion Days Covered (PDC) and is assessed administrative insurance records
3 secondary outcomes
Self-reported medication adherence assessed via a questionnaire
Time frame: baseline, 3 months and 9 months
Pill bottle openings (how often medication was taken) assessed by records from pill bottle caps (MEMS readers)
Time frame: 9 months
Medication Beliefs assessed via a questionnaire
Time frame: baseline, 3 months and 9 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Apr 2017.
- Background: PubMed 19931372Lawn S, Schoo A. Supporting self-management of chronic health conditions: common approaches. Patient Educ Couns. 2010 Aug;80(2):205-11. doi: 10.1016/j.pec.2009.10.006.
- Background: PubMed 16273817Coleman MT, Newton KS. Supporting self-management in patients with chronic illness. Am Fam Physician. 2005 Oct 15;72(8):1503-10.
- Background: PubMed 16079372Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005 Aug 4;353(5):487-97. doi: 10.1056/NEJMra050100. No abstract available.
- BackgroundNEHI. Thinking outside the pillbox: a system-wide approach to improving patient medication adherence for chronic disease. http://www.nehi.net/publications/44/thinking_outside_the_pillbox_a_systemwide_approach_to_improving_patient_medication_adherence_for_chronic_disease, Accessed 09 12 12
- Background: PubMed 21320699Hill MN, Miller NH, Degeest S; American Society of Hypertension Writing Group; Materson BJ, Black HR, Izzo JL Jr, Oparil S, Weber MA. Adherence and persistence with taking medication to control high blood pressure. J Am Soc Hypertens. 2011 Jan-Feb;5(1):56-63. doi: 10.1016/j.jash.2011.01.001.
- Background: PubMed 18092064Munger MA, Van Tassell BW, LaFleur J. Medication nonadherence: an unrecognized cardiovascular risk factor. MedGenMed. 2007 Sep 19;9(3):58.
- Background: PubMed 18480115Vrijens B, Vincze G, Kristanto P, Urquhart J, Burnier M. Adherence to prescribed antihypertensive drug treatments: longitudinal study of electronically compiled dosing histories. BMJ. 2008 May 17;336(7653):1114-7. doi: 10.1136/bmj.39553.670231.25. Epub 2008 May 14.
- Background: PubMed 18425859Haynes RB, Ackloo E, Sahota N, McDonald HP, Yao X. Interventions for enhancing medication adherence. Cochrane Database Syst Rev. 2008 Apr 16;(2):CD000011. doi: 10.1002/14651858.CD000011.pub3.
- Background: PubMed 21600724Marx G, Witte N, Himmel W, Kuhnel S, Simmenroth-Nayda A, Koschack J. Accepting the unacceptable: medication adherence and different types of action patterns among patients with high blood pressure. Patient Educ Couns. 2011 Dec;85(3):468-74. doi: 10.1016/j.pec.2011.04.011. Epub 2011 May 19.
- BackgroundSabate E - World Health Organization. Adherence to long-term therapies: evidence for action. 2003. http://apps.who.int/medicinedocs/en/d/Js4883e/, Accessed 09 12 12
- Background: PubMed 18194403Elliott RA, Shinogle JA, Peele P, Bhosle M, Hughes DA. Understanding medication compliance and persistence from an economics perspective. Value Health. 2008 Jul-Aug;11(4):600-10. doi: 10.1111/j.1524-4733.2007.00304.x. Epub 2008 Jan 8.
- Background: PubMed 22534082Vervloet M, Linn AJ, van Weert JC, de Bakker DH, Bouvy ML, van Dijk L. The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: a systematic review of the literature. J Am Med Inform Assoc. 2012 Sep-Oct;19(5):696-704. doi: 10.1136/amiajnl-2011-000748. Epub 2012 Apr 25.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Karen Farris, PhD principal investigator
Univerity of Michigan, College of Pharmacy
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing SMS (Text messages) in people with Medication Non-adherence, run by University of Michigan at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 11 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 11 Apr 2017. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Medication Adherence (Proportion Days Covered (PDC)) assessed by administrative insurance records (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT02454660, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Apr 2017; AzTrials last refreshed its copy on 29 Oct 2026.