NSG6101: Mid Week Research Proposal Draft

Research Proposal Draft

By the due date assigned, write a 1-page paper addressing the sections below of the research proposal.

PICOT Question

In patients aged 60 and older with chronic diseases, does technology integration of wearable devices for medication adherence, compared to no intervention, to improve medication adherence rates and overall health outcomes over a 6-month period?

Methodology

Data Analysis Plans
• Describe plan for data analysis for demographic variables (descriptive statistical tests). Describe plan for data analysis of study variables (descriptive and inferential statistical tests)

Submission Details:

• 3 to 5 references within 5 years
• Provide full details and procedures
• no plagarism
• DONT ADD TO MY PAPER

Running head: RESEARCH PROJECT PROPOSAL
Chronic Disease Medication Management Using Wearable Devices
Adian Ladron De Guevara
South University: Online Division
Professor Judith Cornelius
NSG6101: Nursing Research Methods
December 01, 2023
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RESEARCH PROJECT PROPOSAL
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Research Project Proposal
PICOT Question
In patients aged 60 and older with chronic diseases, how does the integration of wearable
devices for medication adherence, compared to no intervention, is expected to result in increased
medication adherence rates as measured by objective measures such as electronic medication
monitoring systems or pill counts, leading to improved overall health outcomes over a 6-month
period?
Chronic Disease Medication Management Using Wearable Devices
Medication non-adherence is the dereliction of patients to take their medications as
prescribed by their physicians. This can involve taking lesser dosages or failing to take the
medications at the instructed time. This non-adherence presents as a consequential health risk to
patients with chronic diseases that can lead to increased healthcare costs, increased health risks,
and non-successful treatment. Utilizing technologies such as mobile apps for patient guidance
suggests a pivotal solution, increasing cohesion and thus enhancing general health results for
patients managing long-term illnesses (Volpi et al., 2021). The purpose of the PICOT question in
this particular context is to examine if using wearable devices to promote medication adherence
in individuals aged 60 and above who have chronic illnesses leads to enhanced rates of
medication adherence and overall health outcomes throughout a span of 6 months. The
integration of wearable devices for medication adherence has the potential to significantly
improve medication adherence rates and overall health outcomes over a 6-month period in
patients aged 60 and older with chronic diseases, compared to no intervention.
Background and Significance of the Problem
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Noncompliance with medication is a prevalent and consequential issue, especially among
elderly people with chronic illnesses. Non-adherence pertains to patients deviating from the
recommended drug regimen, which encompasses actions such as omitting doses, consuming
erroneous amounts, or completely ceasing therapy. This may have adverse impacts on health
outcomes and overall quality of life. Elderly individuals with chronic illnesses are more
susceptible to not following their prescribed pharmaceutical regimen due to variables such as
taking many medications, cognitive impairments, and complicated prescription schedules. The
repercussions of not adhering to medicine are significant. It may result in less effective illness
treatment, more frequent hospitalizations, and elevated healthcare expenses. Lack of adherence
may lead to the advancement of chronic illnesses, worsening of symptoms, and worse general
state of health. Moreover, it places a substantial strain on the healthcare system, since it results in
heightened consumption of healthcare resources and unwarranted healthcare expenses.
It is crucial to prioritize the management of medication non-adherence in elderly
individuals with chronic illnesses. Enhancing drug adherence rates may significantly influence
health outcomes and healthcare expenses. Patients who strictly follow their prescribed
medication regimens have a higher probability of regularly reaching therapeutic drug levels,
resulting in enhanced disease management and improved health outcomes. Consequently, this
leads to a decrease in hospitalizations, trips to the emergency room, and other expensive
healthcare treatments. Improving medication compliance may assist elderly individuals in
preserving their autonomy and overall well-being. Patients may get symptom alleviation,
enhanced physical functioning, and improved overall well-being by efficiently treating their
chronic diseases. Furthermore, enhanced compliance may result in a decrease in medication-
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associated adverse events and drug interactions, hence promoting a more secure healthcare
encounter for elderly patients.
Statement of the Problem and Purpose of the Study
This research aims to tackle the significant occurrence of medication non-adherence
among elderly individuals suffering from chronic illnesses. Medication non-adherence, also
known as medication non-compliance, pertains to patients deviating from the recommended
regimen of taking their prescriptions, leading to detrimental effects on health outcomes and
overall quality of life. Elderly individuals with chronic illnesses are more susceptible to noncompliance as a result of variables such as intricate drug schedules, cognitive impairments, and
the use of many medications. This issue results in less than ideal illness management, heightened
hospital admissions, and elevated healthcare expenses.
The objective of this research is to examine the efficacy of using wearable devices to
enhance medication adherence among individuals aged 60 and above who suffer from chronic
illnesses. The objective of the research is to ascertain if the use of wearable devices may enhance
the rates of medication adherence and overall health outcomes throughout a span of 6 months.
The project aims to tackle the issue of medication non-adherence in this vulnerable group by
using wearable devices that provide reminders, tracking, and monitoring functionalities. The
primary objective is to ascertain an intervention that will effectively enhance medication
adherence rates and result in enhanced disease management, decreased hospitalizations, and
superior overall health outcomes for elderly individuals with chronic illnesses.
Literature Review
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Expansion in technology in recent years has since been integrated to improve patient
medical outcomes. Previous research has significantly outlined utilization of mobile applications,
in treating chronic patients, with a majority of the studies focusing on the impact the
technologies have on patient outcomes. However, how does this technology integration improve
medical adherence in chronic patients, and most importantly, what role does patient education
play in enhancing patient adherence and outcome?
As a primary theme in previous research, including mobile apps and the use of
telecommunication and digital tools, has improved medical adherence in chronically ill patients.
Peng et al. (2020) acknowledge that the use of mobile apps is more effective in improving
patient compliance than traditional ways, with these apps enabling chronic patients to have
electronic reminders to remain consistent in seeking medical attention or by providing a regular
schedule for medicine intake. In recent studies, Fan et al. (2022) further mention that mHealth
technologies have effectively reduced the time and location boundaries present initially, such
that the technology has enhanced communication networks, thus making patient adherence to
doctoral appointments more approachable and efficient which is often a problem with
chronically ill patients.
In addition, Ng et al. (2020) also mention that mobile apps provide patients with the
ability to receive messages, alerts, and information on their illness effectively through their
phones, which, especially for chronic patients, enables them to improve adherence factors such
as taking medication on time, filling their prescriptions or understanding any prescriptive
alterations made by doctors. Despite most of the research attributing to the benefits associated
with adapting technology to improve medical adherence in patients, Cheng et al. (2023) mention
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that these technology tools are only effective when utilized with other interventions, such that it
should be a combined approach rather than used in isolation to make an impact.
This research by Cheng et al. (2023) affirms the need for effective patient education in
ensuring medical technologies work efficiently. For instance, education on subject matters such
as the importance of utilizing these applications or involving regular communication schedules
between the patients and the medical professionals is vital as it provides a more advanced
approach to patient outcomes, especially for older adults with chronic illnesses. In addition,
Backes et al. (2021) mention that patient education and collaboration result in high patient
medical adherence rates. Patient education increases health literacy, resulting in the increased
need to adhere to medical insights and recommendations. For chronic patients especially, patient
education is influential in empowering them so that they get access to information on improving
their quality of life and be cautious of the signs that could lead to complications (Ghozalli, 2023).
This education and awareness can be done by tailoring a patient portal that regularly targets
engagement communication and treatment instructions.
However, Backes et al. (2021) conclude that despite technological advancements in
mobile apps significantly improving patient education for medication purposes, these can still
pose a barrier when patient use is limited and inadequate to these technologies. This situation
often presents a barrier to effectively using these mobile apps and digital tools, making them less
significant, especially in older, chronically ill patients. As a solution, however, the research
recommends that patient education begin with the infusion of technical knowledge to the patients
on how to use the app before the doctors apply a self-medication alternative to treatment, which
thus includes the inclusion of mobile apps and online tools to improve medical consistency. The
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more patients are engaged and in control of their care, the more they are likely to maintain a
medicative plan for their treatment.
Research Questions, Hypothesis, and Variables
In patients aged 60 and older with chronic diseases, how does the integration of wearable
devices for medication adherence, compared to no intervention, is expected to result in increased
medication adherence rates as measured by objective measures such as electronic medication
monitoring systems or pill counts, leading to improved overall health outcomes over a 6-month
period?
Hypothesis: Incorporating technology, including mobile apps and digital equipment,
improves medication obedience in individuals with chronic illnesses. In addition, functional
patient education using these technologies undoubtedly adds to improving cohesion and general
health results.
Null hypothesis: There is no notable difference in medication obedience among
individuals with chronic diseases when using technologies like mobile phones, compared to
those without access to such digital interference. Moreover, patient learning through these digital
tools does not significantly impact obedience or overall health results.
Independent Variables: Medication adherence is the measure of how well people follow
their recommended drug regimes. The measurement will be conducted using objective methods
such as pill count or electronic medication monitoring devices. In order to put this variable into
action Intervention using wearable devices, the researchers will equip participants with wearable
devices capable of delivering medication reminders, monitoring adherence, and tracking drugtaking behavior. The researchers will quantify the frequency of use by documenting the
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participants’ interactions with the device for receiving reminders or logging medication intake.
Additionally, participants will be equipped with wearable devices that can deliver medication
reminders, monitor adherence, and track medication-taking behavior. The frequency of usage
will be assessed by tallying the instances in which individuals engage with the device to get
reminders or document medicine use.
Dependent Variables: The intervention under investigation, which comprises the use of
wearable devices to enhance medication adherence, is represented by the Wearable Device
variable. The operationalization of this will include the frequency of usage and if the wearable
gadget is present. In order to put this variable, Medication Adherence, into practice, the
researchers will gather data on the quantity of doses skipped, the proportion of prescribed doses
consumed, or the count of days in which drugs were taken accurately (Kuwabara, Su, Krauss,
2019). The acquisition of this information will be accomplished by means of pill counts or
electronic medication monitoring devices.
Theoretical Framework
Overview and Guiding Propositions
When selecting a theoretical framework for investigating the impact of technology
integration on medication adherence in patients with chronic illnesses and the influence of
patient education on boosting adherence and overall health outcomes, one viable option is the
Health Belief Model (HBM). The Health Belief Model is extensively used in healthcare research
to comprehensively comprehend and forecast the health behaviors of people (Peng et al., 2020).
The Health Belief Model is an all-encompassing framework that takes into account a wide range
of variables that have an effect on people’s health-related actions. It is well suited for
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comprehending the intricate dynamics of medicine adherence and technology incorporation since
it takes into account not only internal but also external signals to action and self-efficacy. Beliefs
and perceptions are emphasized in the paradigm as important contributors to health-related
actions. Researchers may learn more about the drivers of patient behavior by examining patients’
perceptions of the risk, severity, rewards, and obstacles associated with medication adherence
and technology usage.
Application of Theory to Study Focus
A collection of guiding propositions defines the theoretical ideas and linkages in the
Health Belief Model. The theories explain and predict health behavior. These ideas explain
medication adherence, technological usage, and health habits. Researchers and healthcare
providers may tailor treatments to patients’ attitudes, motives, and challenges to increase drug
adherence and health outcomes (Unni & Bae, 2022). The main Health Belief Model concepts are
applied to this research in multiple ways:
1. Perceived susceptibility suggests that people who consider themselves as vulnerable to a
health issue are more likely to adopt healthy habits. People who believe they may suffer
health effects without their prescription are more likely to take it.
2. Severity: People are more willing to act on major health issues. Awareness of the risks of
non-adherence to medicine may increase adherence.
3. Perceived benefits: When people see the benefits of a health habit, like taking
medication, they are more inclined to do so. Patients who understand the health
advantages and symptom alleviation of following dose guidelines may be more likely to
follow them.
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4. Perceived barriers: Difficulties may deter health-related activities. The problems may
include financial strain, bodily suffering, or unpleasant responses. Identifying and
removing these barriers helps change behavior. Reminders and instructional materials
may motivate people. Reminders, educational materials, and healthcare provider
guidance may improve drug adherence. Self-confidence may help patients take their
medications and use technology.
Methodology
Extraneous Variables
In any research study, it is crucial to identify and control extraneous variables to ensure
the internal validity of the findings. Extraneous variables are factors that may inadvertently
influence the dependent variable, in this case, medication adherence rates and overall health
outcomes. To control for extraneous variables, participants in the intervention and control groups
will be matched based on relevant demographic factors such as age, gender, and the specific
chronic disease being managed. Additionally, the research design itself, whether
phenomenological or ethnographic, will involve in-depth exploration and understanding of
individual experiences, allowing for the identification and control of potential confounding
variables. By closely monitoring and matching participants, the study aims to isolate the impact
of the technology intervention on medication adherence, thus enhancing the internal validity of
the research.
Extraneous factors might potentially influence the relationship between the independent
and dependent variables, while they are not the primary focus of the inquiry. These variables
may inadvertently introduce bias or confusion, so distorting the results. In order to ensure the
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validity and reliability of research, it is necessary to identify and control any extraneous
influences. Possible extraneous influences in the study on medication adherence among older
chronic disease patients using wearable devices may include:
1. Age: The participants’ age might affect their capacity to use the wearable device
or their general health state, which may in turn affect medication adherence.
2. Health literacy: Individuals with differing degrees of health literacy may possess
distinct comprehension or perception of the instructions accompanying the
wearable device, therefore impacting their compliance with medication.
3. Social support: The existence or lack of a robust network of assistance from
family, friends, or caregivers might impact the adherence to medicine,
irrespective of the intervention of wearable devices.
4. Medication complexity: The intricacy of the medication regimen, encompassing
the quantity of drugs, frequency of dose, and directions for administration, might
influence medication adherence, regardless of the wearable device intervention.
In order to reduce the influence of irrelevant factors, researchers might use methods like
randomization, matching individuals based on pertinent attributes, or statistical analytic
techniques like regression analysis or analysis of covariance to manage their impact (Volpi et al.,
2021). This guarantees that any noticeable disparities in medication adherence may be more
definitively as described to the intervention of the wearable device.
One research design that can be used for this study is a pre-post intervention study. In this
design, participants would be assessed for their medication adherence rates and overall health
outcomes before the intervention (use of wearable devices) is implemented. Then, after a 6-
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month period of using the wearable devices, participants would be reassessed to determine any
changes in medication adherence rates and overall health outcomes. This design allows for
within-group comparisons to assess the effectiveness of the intervention over time. Objective
measures, such as electronic medication monitoring systems or pill counts, can be used to assess
medication adherence rates, and validated questionnaires or scales can be used to evaluate
overall health outcomes. It is important to note that without a control group, it may be
challenging to attribute any observed changes solely to the intervention, as there could be other
factors influencing the outcomes. However, this design can still provide valuable information
about the impact of wearable devices on medication adherence rates and overall health outcomes
in older patients with chronic diseases.
Instruments
One instrument that can be used for this study to collect quantitative data is an electronic
medication monitoring system. Electronic medication monitoring systems use technology to
track and record when medication doses are taken. These systems typically involve a device,
such as a pill dispenser with built-in sensors or a smartphone app, that automatically records the
date and time of medication administration. This data can be collected over the course of the
study to objectively measure medication adherence rates. By analyzing the data from the
electronic medication monitoring system, researchers can obtain quantitative information on the
frequency and consistency of medication adherence (Creswell, 2013). This instrument provides
precise and reliable data that can be used to assess the effectiveness of the wearable devices in
improving medication adherence rates in older patients with chronic diseases.
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The validity and reliability of an electronic medication monitoring system as a tool for
collecting quantitative data can be assessed through various methods. Validity encompasses
different aspects, including content validity, construct validity, and criterion validity. Content
validity ensures that the system accurately captures and records medication adherence data,
considering all relevant aspects of adherence (Polit and Beck, 2017). Construct validity refers to
the system’s ability to show a strong relationship with other measures of medication adherence,
indicating that it effectively captures the intended construct. Criterion validity involves
comparing the system’s results with established gold standard measures of medication adherence
to provide evidence of its accuracy.
On the other hand, reliability focuses on the consistency and stability of the
measurements obtained. Test-retest reliability ensures that the system yields consistent results
when measuring the same individual’s medication adherence multiple times under similar
conditions. Internal consistency indicates that the system’s various components or items reliably
measure the same construct. Inter-rater reliability evaluates the system’s consistency in
measurements obtained across different raters or observers (Polit and Beck, 2017). To assess the
validity and reliability of the electronic medication monitoring system, researchers can conduct
validation studies comparing its results with established measures of medication adherence and
analyze the consistency of measurements over time or across different raters. These studies
provide evidence regarding the instrument’s validity and reliability for collecting quantitative
data on medication adherence.
Description of the Intervention
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The intervention involves the incorporation of wearable technology, such as
smartwatches or electronic pill dispensers, to promote medication adherence in patients aged 60
and above who have chronic conditions. These devices are allocated to patients and configured
by healthcare experts who provide instruction on their use. Patients utilize the gadgets to monitor
their compliance with medicine intake during a duration of 6 months. The devices gather data on
medication adherence, which is then accessible and assessed by healthcare practitioners
(Creswell, 2013). The purpose is to enhance medication adherence rates, as assessed by objective
metrics such as electronic medication monitoring systems or pill counts, with the ultimate aim of
achieving better overall health outcomes in this group. To execute the intervention of
incorporating wearable devices for medication compliance in patients aged 60 and above with
chronic illnesses, the following measures might be undertaken:
Step One: Wearable device selection and dissemination (1 week)
The healthcare practitioner or a qualified healthcare expert will choose suitable wearable
devices that are compatible with electronic medication monitoring systems. These gadgets may
encompass smartwatches or computerized pill dispensers. Upon being chosen, the gadgets will
be allocated to qualified patients.
Step Two: Installation and instruction for device configuration and patient education (1-2
days)
A healthcare practitioner, such as a nurse or pharmacist, will have the responsibility of
configuring the wearable devices for each patient. This may include the configuration of
medication reminders, synchronization of the devices with electronic medication monitoring
systems, and ensuring that patients possess a comprehensive understanding of how to efficiently
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use the devices. Training for patients may include the demonstration of correct medicine
administration and use of the device’s functionalities.
Step Three: Continuous monitoring and data gathering have been conducted for a period
of 6 months.
During the 6-month period, patients will use the devices to monitor and record their
compliance with medication. The wearable gadgets will autonomously gather data on medication
compliance, including the precise timing and date of each administered dosage. Healthcare
professionals may obtain this data via electronic medication monitoring devices.
Step Four: Continuous monitoring and data gathering have been conducted for a period of
6 months.
Upon the conclusion of the 6-month duration, healthcare practitioners will evaluate the
data obtained from the wearable devices and electronic medication monitoring systems. The
medication adherence rates will be evaluated using objective methods such as electronic
medication monitoring devices or pill counts. The healthcare professionals will thereafter assess
the medication adherence rates and overall health outcomes of patients who got the intervention
in comparison to those who did not (Creswell, 2013). This assessment will ascertain the efficacy
of the intervention in enhancing medication adherence rates and overall health outcomes within
the specified population.
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At each level, certain tasks are assigned to ensure intervention precision. Healthcare
practitioners or qualified healthcare professionals choose and distribute wearable gadgets. This
ensures eligible patients get relevant devices. Nurses or pharmacists configure the devices and
advise patients. They are qualified to set up devices, synchronize them with electronic
medication monitoring systems, and teach patients on their usage. Patients monitor and gather
data after device configuration. They use the devices to track medication compliance for six
months. Patients are responsible for utilizing the devices, although doctors may help them with
data. Finally, doctors or researchers evaluate outcomes. Data is examined, medication adherence
rates are measured objectively, and intervention and non-intervention health outcomes are
compared (Creswell, 2013). Executing these steps and involving relevant parties in each activity
ensures the intervention’s legitimacy.
Data Collection Procedures
Medical history gathering is crucial when joining a research project for numerous
reasons. Demographic data helps researchers comprehend study participants. Age, gender, and
ethnicity may affect drug adherence and health. The medical history describes participants’
chronic conditions and their severity. This information helps researchers understand how the
circumstances being researched may affect drug adherence and health outcomes. To correctly
measure medication adherence, participants’ medication history and regimen must be known. It
helps researchers discover drugs, doses, and combinations that may impact adherence. Data
collection will occur over a 6-month period. The following procedures will be followed:
Baseline Assessments: All participants will complete baseline assessments, which
include gathering demographic information, medical history, and medication adherence rates.
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This step establishes a starting point for comparison and provides a baseline understanding of
participants’ characteristics.
Intervention Group: Participants in the intervention group will receive wearable devices
and training. The intervention aims to improve medication adherence rates. At the 6-month mark,
the participants will be interviewed to explore their experiences and perspectives on using the
devices. This qualitative step helps gain insights into the intervention’s impact and participants’
perceptions.
Control Group: Participants in the control group receive no intervention. At the 6-month
mark, they will be interviewed to explore their medication adherence practices and any changes
in their experiences. This qualitative step helps understand factors influencing medication
adherence in the absence of the intervention.
Medical Record Review: At the 6-month mark, medical records of all participants will
be reviewed to assess changes in medication adherence rates and health outcomes. This
quantitative step provides objective data on adherence rates and allows for an analysis of the
intervention’s impact on health outcomes.
Data Analysis Plans
This study’s quantitative research on wearable devices for medication adherence in older
chronic illness patients may be analyzed as follows: Clean and organize the data. Outliers,
missing values, and incorrect variable coding should be removed. A master dataset should
contain all important variables for analysis. Create descriptive statistics for the variables of
interest. For medication adherence and health outcomes, calculate mean, median, standard
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deviation, and range. This summarizes the data and shows central tendency and variability.
Wearable device and control group medication adherence rates should be compared. Determine
whether the two groups have significant adherence differences using t-tests or chi-square testing.
To quantify observable differences, calculate effect sizes like Cohen’s d. By assessing the
relationship’s strength and direction using Pearson’s correlation coefficient. This will establish
whether increased adherence rates enhance health. If needed, analyze subgroups. Check whether
age, gender, or chronic conditions affect the intervention’s impact.
To investigate subgroup interactions or treatment effects, use statistical tests like
ANOVA or regression analysis. Perform regression analysis to find predictors or confounders
that may affect drug adherence or health outcomes. The model includes demographics, medical
history, and other confounding factors. Measure relationship strength using regression
coefficients and p-values. Sensitivity analyses evaluate results robustness. Test alternate
assumptions or analytic methods to check whether the findings hold. The stability and
generalizability of the results are assessed (Polit and Beck, 2017). Assess the data analysis
outcomes against the study question and goals. Discuss the study’s shortcomings, clinical
significance, and consequences. Based on findings, suggest further research or initiatives.
Perform sensitivity analysis to assess robustness. Try several strategies to test conclusions under
numerous assumptions or analytic methodologies. Results stability and generalizability are
assessed. Assess data analysis results against research objectives. Discuss the findings’ clinical
relevance, implications, and limits. Make outcome-based recommendations for research or
therapy.
Ethical Issues
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Limitations of Proposed Study
Implications for Practice
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References
Backes, C., Moyano, C., Rimaud, C., Bienvenu, C., & Schneider, M. P. (2021). Digital
Medication Adherence Support: Could Healthcare Providers Recommend Mobile Health
Apps? Frontiers in medical technology, 2, 616242.
https://doi.org/10.3389/fmedt.2020.616242
Cheng, C., Donovan, G., Al-Jawad, N., & Jalal, Z. (2023). The use of technology to
improve medication adherence in heart failure patients: a systematic review of
randomized controlled trials. Journal of Pharmaceutical Policy and Practice, 16(1), 81.
Fan, K., & Zhao, Y. (2022). Mobile health technology: a novel tool in chronic disease
management. Intelligent Medicine, 2(1), 41-47.
Ghozali, M. (2023). Is integrating video into tech-based patient education effective for
improving medication adherence? – A review. Paladyn, Journal of Behavioral Robotics,
14(1), 20220109. https://doi.org/10.1515/pjbr-2022-0109
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Kuwabara, A., Su, S., & Krauss, J. (2019). We are utilizing Digital Health Technologies
for Patient Education in Lifestyle Medicine. American journal of lifestyle
medicine, 14(2), 137–142. https://doi.org/10.1177/1559827619892547
Ng, R., Carter, S. R., & El-Den, S. (2020). The impact of mobile applications on
medication adherence: a systematic review. Translational behavioral medicine, 10(6),
1419-1435.
Peng, Y., Wang, H., Fang, Q., Xie, L., Shu, L., Sun, W., & Liu, Q. (2020). Effectiveness
of Mobile Applications on Medication Adherence in Adults with Chronic Diseases: A
Systematic Review and Meta-Analysis. Journal of managed care & specialty pharmacy,
26(4), 550–561. https://doi.org/10.18553/jmcp.2020.26.4.550
Unni, E., & Bae, S. (2022). Exploring a New Theoretical Model to Explain the Behavior
of Medication Adherence. Pharmacy (Basel, Switzerland), 10(2), 43.
https://doi.org/10.3390/pharmacy10020043
Volpi, S. S., Biduski, D., Bellei, E. A., Tefili, D., McCleary, L., Alves, A. L. S., & De
Marchi, A. C. B. (2021). Using a mobile health app to improve patients’ adherence to
hypertension treatment: a non-randomized clinical trial. PeerJ, 9,
e11491. https://doi.org/10.7717/peerj.11491
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