Improving Adherence and Outcomes by Artificial Intelligence-Adapted Text Messages

Medication Non-adherence Clinical Trial

— AIM@BP  

Official title:

Improving Adherence and Outcomes by Artificial Intelligence-Adapted Text Messages

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT02454660
• Other study ID #: 1R21HS022336-01A1
• Status: Completed
• Phase: N/A
• First received: May 12, 2015
• Last updated: April 7, 2017
• Start date: May 2015
• Est. completion date: November 4, 2016

Study information

• Verified date: March 2017
• Source: University of Michigan
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical 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 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.

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:

1. 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,

2. 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

3. 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 49
• Est. completion date: November 4, 2016
• Est. primary completion date: November 4, 2016
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 21 Years and older

Eligibility

Inclusion Criteria:

• Patient must have Priority Health Care Health Insurance Coverage

• Patient must have PDC of < 0.5 for anti-hypertensive medications

Exclusion Criteria:

• 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

• 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

Related Conditions & MeSH terms

• Medication Non-adherence

Intervention

Behavioral:
• 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.

Locations

Country	Name	City	State
United States	University of Michigan College of Pharmacy	Ann Arbor	Michigan
United States	Spectrum Health	Grand Rapids	Michigan

Sponsors (2)

Lead Sponsor	Collaborator
University of Michigan	Agency for Healthcare Research and Quality (AHRQ)

Country where clinical trial is conducted

United States, 

References & Publications (12)

Coleman MT, Newton KS. Supporting self-management in patients with chronic illness. Am Fam Physician. 2005 Oct 15;72(8):1503-10. Review. — View Citation

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. — View Citation

Haynes 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. Review. Update in: Cochrane Database Syst Rev. 2014;11:CD000011. — View Citation

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. Review. — View Citation

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. Review. — View Citation

Marx G, Witte N, Himmel W, Kühnel 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. — View Citation

Munger MA, Van Tassell BW, LaFleur J. Medication nonadherence: an unrecognized cardiovascular risk factor. MedGenMed. 2007 Sep 19;9(3):58. Review. — View Citation

NEHI. 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

Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005 Aug 4;353(5):487-97. Review. — View Citation

Sabate 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

Vervloet 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. Review. — View Citation

Vrijens 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. — View Citation

* Note: There are 12 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Medication Adherence (Proportion Days Covered (PDC)) assessed by administrative insurance records	A measure of Proportion Days Covered (PDC) and is assessed administrative insurance records	2 years
Secondary	Self-reported medication adherence assessed via a questionnaire	Medication adherence is collected at these time points and assessed via a questionnaire.	baseline, 3 months and 9 months
Secondary	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)	9 months
Secondary	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.	baseline, 3 months and 9 months