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. Author manuscript; available in PMC: 2024 Mar 4.
Published in final edited form as: Distance Learn (Greenwich). 2023;20(1):9–18.

Creating a Tailored Info App to Promote Self-Management Skills in Persons with Chronic Health Conditions: Development Strategies and User Experience

Neil Patel 1, Drenna Waldrop 2, Raymond L Ownby 1
PMCID: PMC10911517  NIHMSID: NIHMS1966717  PMID: 38440090

Abstract

Persons with chronic health conditions, such as heart disease, diabetes, hypertension, and others, often experience self-management problems that are not disease specific. These include disrupted sleep, pain, memory issues, and challenges in working with healthcare providers. These patients may benefit from information and skill development for these problems, but simply providing them information in brief sessions during clinical appointments or in handouts or pamphlets may not have a substantial impact on their behavior. Providing information tailored to persons’ needs and individual characteristics has a greater impact on patients’ behavior and may increase these persons’ abilities to manage their health. Creating tailored information for each person, however, is labor intensive, making it difficult to use in everyday clinical practice. Computer-based tailoring is an alternative, allowing automated tailoring of information presented to patients based on their interactions with a computer app. The purpose of this article is to describe our process in developing a series of modules for chronic disease self-management for persons 40 years of age or older with one or more chronic health conditions.

Introduction

Providing information for patients in clinical settings on self-management of their health conditions is useful, but traditional formats of pamphlets or handouts are often not read and their recommendations are not implemented. One strategy to increase the impact of such information on patient behavior is tailoring. Tailoring information, defined as using various methods to create individualized communications for patients, aims to reduce the burden of self-management on health consumers by producing useful information using computer algorithms, made possible by recent technological advances.

Small studies have demonstrated the efficacy of computer tailored health communications in increasing attention during information processing (Ruiter et al., 2006). This ability to access such pertinent and personalized information is what drives larger change in health behaviors. As previous research (Petty & Cacioppo, 1986) has studied the effects of two key types of persuasion: (1) central route where the thoughtful consideration of the true merits of the information is taken into account and (2) peripheral route where the simple cues such as presentation and appearance influence the persuasion of the participant. Being able to integrate both into an effective presentation module will serve an enduring effect on participant understanding and behavior (Lustria et al., 2009; Lustria et al., 2013; Noar et al., 2007; Park et al., 2009). As a result, tailored information will be more likely to be used and acted upon.

In summary, tailored health information holds the promise of helping patients acquire and act on the information and skills they need to manage their health conditions. Computer-based tailoring may allow effective automated tailoring based on patients’ interactions with an application. The purpose of this paper is to describe our project in developing a series of computer-tailored modules on chronic disease self-management information for older adults.

The project

The goal of the project was the creation of a tailored information app focused on chronic disease self-management skills in older adults with multiple health conditions. This focus was in part due to the frequency with which older persons experience more than a single health condition; most Medicare beneficiaries, for example, are treated for more than one health condition (Centers for Medicare and Medicaid Services, 2012, 2018). Authors have thus argued that multimorbidity is the most common chronic condition (Tinetti et al., 2012) and that those with multimorbidity face special challenges that are not disease specific (Liddy et al., 2014).

Since chronic disease self-management addresses the problems and skills needed to deal with multiple issues like pain, stress, depression, fatigue, and sleep disturbance, self-management is a prime target to address the scalability issue with interventions (Zulman et al., 2015). While health conditions may require specific management skills, common issues such treatment adherence, sleep issues, stress, fatigue, and depression cut across multiple conditions and can be addressed in a single intervention.

How we created the app

In addition to a literature review, in the first phase of this project we completed a qualitative study to explore the specific information needs of older adults living with chronic illnesses. This study has been more extensively reported elsewhere (Jacobs et al., 2017). We found that while most participants had a general understanding of chronic illness, they often indicated that they would like to know more about their illnesses and treatments as well as help with managing symptoms. We thus aimed to create interventions with this understanding of older adults with chronic illness (Lorig et al., 2012; Ory et al., 2013; Van Gerven et al., 2003; Van Gerven et al., 2002). Intervention modules were created taking into account the principles of multimedia learning (Mayer, 2009) such as coherence, segmenting, pretraining, and redundancy of information. We also took note of the principles of Cognitive Load Theory (Paas et al., 2004; Van Merrienboer & Sweller, 2010) noting that it was essential that each content segment did not exceed learners’ ability to retain it. The structure of the modules was designed with level of literacy data, suitability of materials, and learner characteristics in mind specifically for patients with low literacy skills (Doak et al., 1996).

Information from the qualitative study enabled us to swiftly generate general content that addressed needs, skills, and disease related knowledge and may be used to improve health condition related knowledge and overall health literacy. Such content was developed from established domains of chronic disease management while adhering to the health literacy Abilities-Skills-Knowledge (ASK) model (Ownby et al., 2014; Ownby et al., 2021). Interventions and their elements were developed utilizing strategies aimed at persons of limited literacy skills (Doak et al., 1996) and while focusing on prose, document, and quantitative - the three basic literacy types as delineated by the Education Testing Service (Kirsch, 2001).

Module topics

In this project, we used Adobe® Captivate®, a readily available software program for the creation of multimedia learning apps without programming. It has expanded capacities for creating animations and allowing for individualized learning experiences (Figure 1). Individualization is possible through the use of structured dialogues that allow the author to create specific paths through the app’s screens based on users’ responses to questions. For example, if during assessment a participant indicated they experienced a specific problem, a variable could then be created so in a subsequent portion of the module that person received specific content relevant to their issue. It should be noted that other applications, both commercial (e.g., Articulate Storyline) and noncommercial (e.g., Moodle) are available and have similar capabilities, but we focus on Captivate in this article because we used it in our project.

Figure 1.

Figure 1.

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Adobe® Captivate® user interface

Adobe product screenshot reprinted with permission from Adobe.

User experience

Prior to further development, we tested the app’s usability with an iterative procedure, asking groups of likely users to interact with prototypes and eliciting their ongoing feedback by asking that they “think out loud” (Barnum & Dragga, 2001; Genov, 2005; Nielsen, 1989) while working through a prototype module. User comments were incorporated into subsequent versions of the interface. At the end of user testing, the interface was positively rated by users.

Structure of modules

Each module began with a section that provided a statement of purpose (Table 2 & Figure 2). Participants were then asked to complete an assessment of their current experience related to the module’s topic. After assessment, participants were provided with a general orientation to the topic that emphasized its importance, normalizing the experience of problems in the area. Each module then presented an overview of basic information about the problem that was targeted to help participants understand the problem’s origin and how to apply behavioral management strategies (Figure 3). Interactivity in this section was ensured by providing self-check about the material presented in the section. Questions branched to positive reinforcement for correct answers, additional feedback for incorrect responses, and the opportunity to review material presented relevant to an incorrectly answered question.

Table 2.

Examples from sleep module

Statement of purpose The purpose of this module is to give you some information about sleep and sleep disorders. We will explain and discuss just what sleep is and give an overview of what goes into getting a good night’s sleep. We will also review the factors that can interfere with getting sleep a good night’s sleep and make suggestions about what you can do to improve your sleep quality.
Assessment Insomnia Severity Index, a measure that asks the participant about problems getting to sleep, staying asleep, and waking up too early. The measure also asks about general satisfaction with sleep.(Morin et al., 2011)
Additional assessment As part of initial assessment, participants completed the Center for Epidemiological Studies-Depression measure, or CES-D (Radloff, 1977)
Overview of basic information (1) Sleep stages, (2) hypnogram, (3) sleep hygiene, and how to use a (4) sleep diary, including animations supported by illustrations for each sleep stage.
Self-check questions What is another word to describe the daily rhythm that makes you awake or sleepy?
(Correct answer: circadian)
Tailored information based on assessment Feedback on level of depression and its possible impact on sleep; techniques and skill building for sleep hygiene practices (e.g., no caffeine in the evening; no napping)
Conclusion/Recap In this module, we’ve gone over some important concepts about sleep. We talked about how sleep is related to your immune system and your mood. We went over how sleep is an active process in which you move through stages.
Assessment of relevance (Success in Tailoring Scale) The information in this module would be useful to someone like me.
After completing all modules Questionnaire based on the Technology Acceptance Model (Venkatesh, 2000) that provided participant ratings of their perception of the intervention’s overall usefulness, ease of use, and self-report of intent to use the intervention again in the future.
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Figure 2.

Figure 2.

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Sleep module statement of purpose

Adobe product screenshot reprinted with permission from Adobe.

Figure 3.

Figure 3.

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Basic information about sleep stage 1

Adobe product screenshot reprinted with permission from Adobe.

Modules then provided feedback to the participant’s responses, with links from the initial assessment to information and skill building later in the module (Figure 4). A final section provided a recap of the activity (Figure 5; this is only the first page of the summary). All participants were asked to provide ratings of each module on the Success in Tailoring scale, an eight-item measure assessing the extent to which participants viewed the material as relevant and useful to them.

Figure 4.

Figure 4.

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Sleep module tailored feedback to user

Adobe product screenshot reprinted with permission from Adobe.

Figure 5.

Figure 5.

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Conclusion

Adobe product screenshot reprinted with permission from Adobe.

How we evaluated the modules

We recruited individuals who would be potential users of the app, with inclusion criteria including low levels of health literacy based on a screening procedure described in greater detail elsewhere (Ownby et al., 2017). All participants were 40 years of age or older and had at least one chronic health condition for which they were treated. After completing a baseline visit for assessment of current health and physical status, measures of cognitive and academic skills, and self-report of mood, stress, and health-related quality of life, participants completed each of the self-management modules in research offices over two to three weeks. They then completed a follow-up assessment, part of which were questions about the participants’ views of the app using a questionnaire based on the Technology Acceptance Model (Venkatesh, 2000) and the Success in Tailoring Scale, developed for this study. We then asked participants to complete a semi-structured interview to assess their responses to the app, its contents, and usability. In a second follow-up visit three months later, we again completed assessments and the interview.

Outcomes

Participants in this study were three hundred and four individuals (mean age 57.57 years; mean education 11.85 years; 144 men and 157 women; 40 whites and 261 nonwhites). Participants reported from one to 16 health conditions, with an average of 6.3 conditions (SD = 2.7). The most common conditions reported included arthritis (n = 171), hypertension (n = 201), elevated cholesterol levels (n = 145), gastroesophageal reflux (GERD; n = 91), depression (n = 163), and diabetes (n = 73). Results of ratings and interviews from the first follow-up (immediately after completing the app) are reported here; results from the three-month follow-up were similar.

TAM ratings:

Participants rated the apps on a 0 to 6 scale, with higher values indicating more positive evaluations. The mean rating on the Usefulness subscale was 4.8 and 5.1 on the Usability subscale. The mean rating on the Behavioral Intent subscale (indicating a willingness to use the app again in the future) was 4.8.

SIT ratings:

Participants rated the app’s relevance and usefulness to them on the Success in Tailoring Scale, on a scale from 1 to 5, with a lower rating indicating greater agreement with statements such as “The information in this app would work for someone like me.” Their average rating on the eight items was 1.8, indicating moderate to strong agreement that the information in the app was relevant and useful to them. One item on the SIT asked the participants how strongly they agreed or disagreed with the statement “I want to try out some of the things I learned in the modules” Participants” average rating on this item was 1.8, again suggesting substantial willingness to act on information they learned during interactions with the app.

Interview results:

The first 136 follow-up interviews were coded according participants’ reports of using techniques described in each module as well as the technique used. The most commonly cited module was stress (32%), with numerous participants indicating they had tried techniques such as relaxation and deep breathing as strategies for coping with the stress related to their daily lives and coping with their health. The next most often cited module was adherence (18%) which included topics such as strategies for treatment adherence and working with providers. The most often cited techniques from this module were remembering to ask providers questions about treatment, especially medications, and making list of issues to discuss with participants.

An important finding in interviews was participants’ surprise that they could question providers about the reasons for a prescribed treatment and how to understand its effects. Participants’ comments on the contents of the adherence module suggest that it may have helped them feel as though they could become more active partners in their health care. They may have felt as though they could ask questions about their treatment in order to better understand its purpose, how to judge whether it was effective, and how to cope with any adverse effects that might arise. Finally, a less-frequently cited module was sleep. Approximately ten percent of participants cited trying some of the techniques, and several participants asked for paper copies of a sleep log to record their sleep habits. They indicated that they would take the logs to their providers to discuss their sleep issues and seek further assistance.

Conclusion

In this report, we demonstrate that while computer-based tailoring of health care information may require substantial effort on the front end (development, programming, and deployment), the impact on individuals’ health may be important. In another project, we showed that a tailored information application promoting medication adherence could be cost effective, with reduced cost of care (doctor visits, hospitalizations) more than offsetting increased costs for app development and deployment. (Ownby et al., 2013) Results of our current study suggest that our chronic disease self-management app may similarly be helpful, as long-term use might offset initial development and deployment expenses.

Limitations of our approach are important to recognize. Most important, developing app content that is reliable and likely to helpful requires that content experts are available to assist. In addition, while currently-available software for app development does not require sophisticated programming skills, applications require some degree of familiarity. Further, the simplicity of the software must be balanced with its limitations: apps can be developed, but are less sophisticated and less complex than those that require more extensive programming skills. A final advantage of app development software, however, is the ability to revise and update content without the intervention of expert programmers.

In this report, we hoped to explain how currently-available, off the shelf software can be used to develop apps that provide patients with individually-tailored information about managing their chronic health conditions. While this development process requires resources, including computer and software support, multiple resources are available from both the software publisher and other online resources, such as YouTube. Given the potential power of helping those in need better understand how to manage their healthcare, we hope that this report will inspire others to develop tailored information apps in other areas of need.

Table 1.

Chronic Disease Self-Management Modules

Sleep
Pain and/or Physical Discomfort
Fatigue
SOB
Memory
Depression
Anger
Stress
Adherence
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Funding:

This project was supported by grants MD MD010368 from the US National Institute on Minority Health and Health Disparities and HL096578 from the US National Heart Lung and Blood Institute to Dr. Ownby.

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