AdLip: Human Coach-supported Digital/AI Personal Health Assistant to Improve Adherence to Lipid-Lowering Medication

Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age21-84
SponsorNational University of Singapore

About this trial

Investigators hypothesize that the use of a human coach-supported digital/AI personal health assistant (app) will improve adherence to cholesterol-lowering medications (statins with or without ezetimibe) among patients with hyperlipidaemia and suboptimal LDL-C control, when compared to standard care.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

Between 21 to 84 years old

Prescribed statins with or without ezetimibe for hyperlipidaemia.

Medication non-adherence as defined by the "Extent to Non-adherence" sub-scale of the DOSE Non-Adherence Measure), with a score > 1 (range from 0-15)

Singapore residents (citizens, permanent residents, or long-term pass holders).

Disqualifiers

Does not read or understand English. Current use of smartphone medication adherence app(s) that include statins.

Concurrent use of PCSK9 Inhibitors in addition to statins and/or ezetimibe

Participation in another study that uses medications that could affect lipid levels

Severe renal impairment defined as chronic kidney disease stage 4 and above.

Trial design

Design model

Parallel

Treatments tested in this trial

  • Human Coach-supported Digital/AI Personal Health Assistant to Improve Adherence to Lipid-Lowering Medications

    Behavioral

    Receive personalised feedback and educational content curated by local clinicians and pharmacists through the CADENCE D-PHA app coupled with six sessions of human coaching on top of usual clinical care for lipid management over 6 months

Treatment groups

450 Participants
are divided into 2 treatment groups
Group A: Standard careNo intervention 0 interventions
Group B: Standard care + Human Coach-supported Digital/AI Personal Health AssistantExperimental treatment 1 intervention

Trial outcomes

Primary outcomes

1

Evaluate effectiveness of adherence to lipid-lowering medication

Evaluate effectiveness of adherence to lipid-lowering medication through the use of pill counts

Time frame
6 and 12 months
2

Evaluate effectiveness of adherence to lipid-lowering medication

Evaluate effectiveness of adherence to lipid-lowering medication through the use of Medication Adherence Report Scale-5 (MARS-5). The score ranges between 5 and 25, with higher scores indicating higher reported adherence.

Time frame
6 and 12 months

Secondary outcomes

1

Blood LDL-cholesterol levels

Blood LDL-cholesterol levels compared with those receiving standard care.

Time frame
6 and 12 months
2

Cardiovascular Risk score

Different in Singapore-modified Framingham Risk Scores between intervention and control groups. The risk scores range between -5 and 20 for men and between 0 and 27 for women. Higher scores indicate higher 10-year coronary artery disease risk.

Time frame
6 and 12 months
3

Changes in health motivation and attitudes

Changes in health motivation and attitudes using Motivation and Attitude toward Changing Health (MATCH) Scale. It consists of 9 items rated on five-point Likert scale. Higher average scores indicate greater motivation and attitude.

Time frame
6 and 12 months
4

Changes in self-care efficacy

Changes in self-care efficacy using Self-efficacy for Appropriate Medication Use Scale (SEAMS) in low-literacy patients with chronic disease. The scores range between 13 to 39, with higher scores indicating higher self-efficacy for medication adherence

Time frame
6 and 12 months

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

National University of Singapore

Lead sponsor

National University Polyclinics, Singapore

Collaborator

Nanyang Technological University

Collaborator

National Healthcare Group Polyclinics

Collaborator

Consortium for Clinical Research and Innovation, Singapore (CRIS)

Collaborator

National University Heart Centre, Singapore

Collaborator