Clinical trial NCT06068309: Efficacy of CareAide® App in Improving Adherence in Adults With Chronic Diseases
Official title: Perception, Adherence, Clinical, Economical and Health-Related Quality of Life Outcomes of CareAide® App Usage in Chronic Diseases
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Adherence, Medication
Chronic Disease
Quality of Life
Economic Burden
Asthma
Hypertension
Heart Failure
Diabetes
Keywords: randomized controlled trial
mobile health app
digital health
medication adherence app
Patient perception
CareAide
clinical outcomes
Malaysia
adherence
Lead sponsor: University of Malaya
Sponsor class: OTHER
Collaborators: name: Caring Up Pte Ltd, Malaysia
name: Institute of Adherence to Medication, American University of Health Sciences, United States
First posted: 2023-10-05
Enrollment: 900
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SUPPORTIVE_CARE

Brief summary: The goal of this clinical trial is to study the impact of a medication adherence app, CareAide, in adult population diagnosed with chronic diseases in Malaysian population.

The main question\[s\] it aims to answer are:

1. Can CareAide make people take their medications better and improve their health?
2. Can CareAide improve the health of people with chronic diseases?
3. Does using CareAide make people's lives better?
4. Can CareAide save money when managing chronic diseases?
5. How do people feel about using CareAide?

Researchers will ask the participants to use the CareAide app for 6 months and compare the group that used the app with the other group which just received the usual treatment. Researchers will check if they do better with their medications, feel healthier, have a better life, and spend less money. They will check this twice, once after 3 months and again after 6 months.

adherence, clinical outcomes, quality of life and economic associated with the app with two follow ups at 3 month intervals.

Detailed description: According to WHO and Ministry of Health (MOH) Malaysia 2020 report, expenditure for chronic diseasesestimated involved around 0.65% of Malaysia's gross domestic product (GDP) which is about RM8.91 billion (Ministry of Health Malaysia, 2020). This showed that chronic diseases imposed a significant healthburden to our country. Malaysian population having good and easy access to effective medical therapyunder the public healthcare system, approximately 46% to 56% of patients with hypertension were non-adherent in terms of their medications. Therefore, CareAide App will be used to study and identify the effect, clinical outcome and cost effectiveness of mobile health app in managing chronic diseases.

The Comprehensive Adherence-enhancing Reminder and Engagement Aide (CAREAide) Trial aims to assess the effect of CareAide® app on adherence, clinical outcomes of patients with chronic diseases and user's perception and satisfaction with the app.

A 3-and 6- month open-label multicentre randomized controlled trial with two parallel groups will be conducted. This study will take place in the outpatient setting of the three hospitals \[i.e., Hospital Putrajaya, Hospital Pulau Pinang and Universiti Malaya Medical Center (UMMC)) in Malaysia\]. Both interventional and control group will be allocated to 1:1 ratio. Randomization number will be generated by using online number generated programme. Consultants reviewing patients at medical, cardiology,diabetes mellitus and asthma clinics will provide referral and notify independent researchers for eligibility and group assignments. Eligible patients will then be instructed to see pharmacists/researchers atoutpatient pharmacies to refill their prescriptions and subsequently enrol in the assigned study group either to receive an intervention with CareAide® or to receive normal clinical care. Information and training on the app will be provided by these researchers to the participants.Patients will be recruited at therespective outpatient clinics. At the usual care clinic appointments, patients have their demographics and baseline data of blood pressure, blood sugar, basic blood profiles, asthma control and review of theirdisease management collected by consultants/researchers. Patients who meet the study criteria will then be instructed to see pharmacists/researchers at outpatient pharmacy to refill their medications. The pharmacists/researchers will screen patients' inclusion and exclusion criteria for this study and will explainthat they may or may not be selected for the study and if they agree, proceed to provide written informedconsent. The adherence data from the screening will be collected to gauge the general adherence rate inthese group of patients. An independent researcher, who is not involved in the intervention and controlgroups, will use the random number generator software for randomisation sequences. The random numbers will be placed in opaque envelopes, which subsequently will be opened by pharmacists/researchers at the outpatient pharmacies. Participants and pharmacists/researchers are unblinded to group assignment and are aware of the intervention, but the study hypotheses are not disclosed to the participants. The assessor and statisticians are blinded to participant allocation. The study will be evaluating usual care compared with usual care plus pharmacist-driven patient-specific adherence interventions - CareAide® medication adherence digital application. Usual care provided bythese hospitals comprises clinic appointments every 3 to 6 months. Medication adherence: CareAide® effectiveness is measured with a medication adherence tool at baseline and follow-up time periods at 3months and 6 months using Morisky Medication Adherence Scale (MMAS) and proportion of days covered (PDC). MMAS-8 is a licensed validated questionnaire to assess medication adherence, where a higher score represents higher medication adherence and vice versa. Comprising of 8 items, the score ranges from 0 to 8, where a higher score represents a higher medication adherence. The internal consistency (Cronbach's alpha) of MMAS-8 is 0.83 along with a sensitivity of 93% and a specificity of 57%.

The PDC method utilises pharmacy refill records. Fasting blood sugar, HbA1C, cardiovascular events (heart failure admission, mortality), asthma control and blood pressure, hospitalizations and emergency visits measurements will be based on the clinics attended by the participants. Health care utilization, quality of life and health care expenditures willbe measured for all the participants.

Intention to treat (ITT) analysis will be applied to evaluate the results to avoid any bias. The data analysiswill be using the SPSS version 29. Data will be coded and analysed. Descriptive or categorical data will beexpressed as mean ± standard deviation (SD) unless otherwise stated. Student t-test will be used foranalysis of normally distributed or continuous variables. Mann-Whitney test will be used for non-normallydistributed data. Categorical data will be analysed using Chi-square or Fischer's exact test. A value of P \<0.05 is considered statistically significant.

The prevalence of medication compliant is quantified at 3 months and 6 months follow-up. Independent t-test or Mann-Whitney test will be used to compare MMAS score rankings between the intervention andcontrol groups. Chi-Square test of independence will be used to determine the difference in number ofpatients with medication adherence between the intervention and control groups at 3 months and 6months follow-up. Ordinal logistic regression, with the MMAS adherence score ranking, will be used to examine the association between intervention and control groups.

For the perception and satisfaction towards the app, the frequency and percentage of respondents in each category (strongly disagree to strongly agree) will be calculated. The median of each statement will then be calculated to give an overview of perception and satisfaction towards the CareAide® app. Chi-square, Mann-Whitney U, Kriskall Wallis, Chi-square tests and logistic regression will be used where appropriate. Linear regression analysis will be conducted to determine the independent predictors of HRQoL.

An economic evaluation will be conducted from a payer and societal perspective. Costs and outcomes will be discounted at 3% per year as the results will be extrapolated to examine costs and outcome over a year. Cost effectiveness analysis will be estimated based on the results of cost data and the incremental cost per quality adjusted life year (QALY) gained. Total incremental cost effectiveness ratio (ICER) will be calculated by dividing the difference between the cost of CareAide intervention group and control group by the difference between the QALYs of CareAide intervention group and control group. Sensitivity analysis will be performed by bootstrapping technique to ensure or examine the robustness of ICER. Then, cost effectiveness acceptability curve (CEAC) will be created to estimate the proportion of the result being cost-effective in relation to the Willingness-to-Pay (WTP) threshold.

Eligibility criteria: Inclusion Criteria:

* Age: 18 years and above
* Diagnosed with selected non-communicable diseases (NCDs): hypertension, diabetes mellitus, heart failure or asthma for at least 6 months
* Prescription generated from one of the following specialty clinics: Medical, Cardiology, Diabetes Mellitus or Asthma clinics of study site.
* Medications are prescribed in previous 3 months and refill at the point of recruitment
* Morisky Medication Adherence Scale (MMAS) score \< 6 (i.e. Low adherence)
* More than three medications daily or two medications with multiple dosing intervals.
* One or more hospital admissions in the prior 24 months
* Owns a smartphone

Exclusion Criteria:

* Medications prescribed from other institution providers
* Existing mobile health app or medication reminder app user
* Pregnant
* Cognitively impaired
* Prisoners
* Bed-bound
* Severe diseases/comorbidities - terminal cancer, psychiatry, etc

Primary outcomes:
measure: Morisky Medication Adherence Scale (MMAS) Score
timeFrame: 6 Months
measure: Proportion of days covered (PDC)
timeFrame: 6 Months
Secondary outcomes:
measure: Clinical Parameter of Hypertension: Blood Pressure
timeFrame: 6 Months
measure: Clinical Parameter of Heart Failure: Ejection fraction
timeFrame: 6 Months
measure: Clinical Parameter of Diabetes: Glycated hemoglobin
timeFrame: 6 Months
measure: Clinical Parameter of Diabetes: Glucose levels
timeFrame: 6 Months
measure: Clinical Parameter of Asthma: Forced Expiratory Volume in 1 second
timeFrame: 6 Months
measure: Clinical Parameter of Asthma: Peak Expiratory Flow
timeFrame: 6 Months
measure: Clinical Parameter of Asthma: Asthma Control Questionnaire-7 (ACQ-7) Score
timeFrame: 6 Months
measure: Total Medical Cost
timeFrame: 6 Months
measure: Incremental Cost-Effectiveness Ratio (ICER)
timeFrame: 6 Months
measure: Health-related quality of life (HRQoL): EuroQol-5D (EQ-5D-5L) Questionnaire
timeFrame: 6 Months
measure: Health-related quality of life (HRQoL): Short-form (SF-36) Survey Instrument
timeFrame: 6 Months
measure: Perception & Satisfaction Questionnaire (PSQ)
timeFrame: 6 Months
Other outcomes:
Locations:
city: George Town
country: Malaysia
facility: Hospital Pulau Pinang
state: Penang
city: Kuala Lumpur
country: Malaysia
facility: University Malaya Medical Centre
city: Putrajaya
country: Malaysia
facility: Hospital Putrajaya
Study officials:
affiliation: School of Pharmaceutical Sciences, Universiti Sains Malaysia (USM), Penang, Malaysia
name: Ong Siew Chin, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Hospital Putrajaya, Malaysia
name: Navin Kumar Loganadan, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Hospital Pulau Pinang, Malaysia
name: Jaya Muneswarao M Devudu
role: PRINCIPAL_INVESTIGATOR
affiliation: Faculty of Pharmacy, University of Malaya, 50603 Kuala Lumpur, Malaysia
name: Izyan A Wahab, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Faculty of Pharmacy, University of Malaya, 50603 Kuala Lumpur, Malaysia
name: Kayatri Govindaraju, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Faculty of Pharmacy, University of Malaya, 50603 Kuala Lumpur, Malaysia
name: Rajat Rana, Pharm.D
role: PRINCIPAL_INVESTIGATOR
affiliation: School of Pharmaceutical Sciences, Universiti Sains Malaysia (USM), Penang, Malaysia
name: Ng Chow Kyn, MPharm Clin
role: PRINCIPAL_INVESTIGATOR
affiliation: Faculty of Pharmacy, University of Malaya, 50603 Kuala Lumpur, Malaysia
name: Mohamed Syamir Shukeri, MCH
role: PRINCIPAL_INVESTIGATOR
affiliation: Faculty of Pharmacy, University of Malaya, 50603 Kuala Lumpur, Malaysia
name: Baharudin Ibrahim, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Faculty of Pharmacy, University of Malaya, 50603 Kuala Lumpur, Malaysia
name: Hasniza Zaman Huri, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Asche C, LaFleur J, Conner C. A review of diabetes treatment adherence and the association with clinical and economic outcomes. Clin Ther. 2011 Jan;33(1):74-109. doi: 10.1016/j.clinthera.2011.01.019.
citation: Baker-Eveleth, L., & Stone, R. W. (2020). User's perceptions of perceived usefulness, satisfaction, and intentions of mobile application. International Journal of Mobile Communications, 18(1), 1-18.
citation: Charan J, Biswas T. How to calculate sample size for different study designs in medical research? Indian J Psychol Med. 2013 Apr;35(2):121-6. doi: 10.4103/0253-7176.116232.
citation: Cano Martin JA, Martinez-Perez B, de la Torre-Diez I, Lopez-Coronado M. Economic impact assessment from the use of a mobile app for the self-management of heart diseases by patients with heart failure in a Spanish region. J Med Syst. 2014 Sep;38(9):96. doi: 10.1007/s10916-014-0096-z. Epub 2014 Jul 4.
citation: Chia YC, Devaraj NK, Ching SM, Ooi PB, Chew MT, Chew BN, Mohamed M, Lim HM, Beh HC, Othman AS, Husin HS, Mohamad Gani AH, Hamid D, Kang PS, Tay CL, Wong PF, Hassan H. Relationship of an adherence score with blood pressure control status among patients with hypertension and their determinants: Findings from a nationwide blood pressure screening program. J Clin Hypertens (Greenwich). 2021 Mar;23(3):638-645. doi: 10.1111/jch.14212. Epub 2021 Feb 14.
citation: Consort - The CONSORT Flow Diagram. (2021). Retrieved 11 December 2021, from http://www.consort-statement.org/consort-statement/flow-diagram.
citation: Eysenbach G; CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions. J Med Internet Res. 2011 Dec 31;13(4):e126. doi: 10.2196/jmir.1923.
citation: Islam, S., Peiffer, R., Chow, C., Maddison, R., Lechner, A., & Holle, R. et al. (2020). Cost-effectiveness of a mobile-phone text messaging intervention on type 2 diabetes-A randomized-controlled trial. Health Policy and Technology, 9(1), 79-85
citation: Vahatalo I, Kankaanranta H, Tuomisto LE, Niemela O, Lehtimaki L, Ilmarinen P. Long-term adherence to inhaled corticosteroids and asthma control in adult-onset asthma. ERJ Open Res. 2021 Feb 8;7(1):00715-2020. doi: 10.1183/23120541.00715-2020. eCollection 2021 Jan.
citation: Li J, Sun L, Hou Y, Chen L. Cost-Effectiveness Analysis of a Mobile-Based Intervention for Patients with Type 2 Diabetes Mellitus. Int J Endocrinol. 2021 Jul 1;2021:8827629. doi: 10.1155/2021/8827629. eCollection 2021.
citation: Lins L, Carvalho FM. SF-36 total score as a single measure of health-related quality of life: Scoping review. SAGE Open Med. 2016 Oct 4;4:2050312116671725. doi: 10.1177/2050312116671725. eCollection 2016.
citation: Melin J, Bonn SE, Pendrill L, Trolle Lagerros Y. A Questionnaire for Assessing User Satisfaction With Mobile Health Apps: Development Using Rasch Measurement Theory. JMIR Mhealth Uhealth. 2020 May 26;8(5):e15909. doi: 10.2196/15909.
citation: Perez-Jover V, Sala-Gonzalez M, Guilabert M, Mira JJ. Mobile Apps for Increasing Treatment Adherence: Systematic Review. J Med Internet Res. 2019 Jun 18;21(6):e12505. doi: 10.2196/12505.
citation: Purcell R, McInnes S, Halcomb EJ. Telemonitoring can assist in managing cardiovascular disease in primary care: a systematic review of systematic reviews. BMC Fam Pract. 2014 Mar 7;15:43. doi: 10.1186/1471-2296-15-43.
citation: Shang P, Liu GG, Zheng X, Ho PM, Hu S, Li J, Jiang Z, Li X, Bai X, Gao Y, Xing C, Wang Y, Normand SL, Krumholz HM. Association Between Medication Adherence and 1-Year Major Cardiovascular Adverse Events After Acute Myocardial Infarction in China. J Am Heart Assoc. 2019 May 7;8(9):e011793. doi: 10.1161/JAHA.118.011793.
citation: Tsuji S, Ishikawa T, Morii Y, Zhang H, Suzuki T, Tanikawa T, Nakaya J, Ogasawara K. Cost-Effectiveness of a Continuous Glucose Monitoring Mobile App for Patients With Type 2 Diabetes Mellitus: Analysis Simulation. J Med Internet Res. 2020 Sep 17;22(9):e16053. doi: 10.2196/16053.
citation: Wood AM, White IR, Thompson SG. Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals. Clin Trials. 2004;1(4):368-76. doi: 10.1191/1740774504cn032oa.
citation: Juniper EF, Bousquet J, Abetz L, Bateman ED; GOAL Committee. Identifying 'well-controlled' and 'not well-controlled' asthma using the Asthma Control Questionnaire. Respir Med. 2006 Apr;100(4):616-21. doi: 10.1016/j.rmed.2005.08.012. Epub 2005 Oct 13.
citation: Nau DP. Proportion of days covered (PDC) as a preferred method of measuring medication adherence. Springfield, VA: Pharmacy Quality Alliance. 2012;6:25.
citation: Morisky DE, Ang A, Krousel-Wood M, Ward HJ. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens (Greenwich). 2008 May;10(5):348-54. doi: 10.1111/j.1751-7176.2008.07572.x.
citation: Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986 Jan;24(1):67-74. doi: 10.1097/00005650-198601000-00007.
citation: Morisky DE, DiMatteo MR. Improving the measurement of self-reported medication nonadherence: response to authors. J Clin Epidemiol. 2011 Mar;64(3):255-7; discussion 258-63. doi: 10.1016/j.jclinepi.2010.09.002. Epub 2010 Dec 8. No abstract available.