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The mobile health (mhealth) app market continues to grow rapidly. However, with the exception of fitness apps and a few isolated cases, most mhealth apps have not gained traction. The barriers preventing patients and care providers from using these apps include: for patients, information that contradicts health care provider advice, manual data ent...
Context in source publication
Context 1
... would explicitly identify the patient's diagnosis to ensure that the patient is eligible for guideline or other evidence-based content. Further, the physician should make the diagnosis and the app should be 'prescribed' by the physician as part of a course of treatment for the patient (see Figure 3). The app should interoperate with the EMR to allow this prescription functionality to activate it only when authorized by a licensed provider. ...
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Citations
... Several studies demonstrate the positive impact of hApps on health-related behaviors including physical activity, diet change, and adherence to medication or therapy [4]. Clinician adoption plays a critical role in the uptake and success of hApps [5,6]. The COVID-19 pandemic has increased end-user interest in hApps, however, hApps face several barriers to wider adoption. ...
Forty-four percent of Canadians over the age of 20 have a non-communicable disease (NCD). Millions of Canadians are at risk of developing the complications of NCDs; millions have already experienced those complications. Fortunately, the evidence base for NCD prevention and behavior change is large and growing and digital technologies can deliver them at scale and with high fidelity. However, the current model of in-person primary care is not designed nor capable of operationalizing that evidence. New developments in artificial intelligence that can predict who will develop NCD or the complications of NCD are increasingly available, making the challenge of delivering disease prevention even more urgent. This paper presents findings from stakeholder engagement on a design architecture to address three initial barriers to large-scale deployment of health management and behavior change evidence: 1) the challenges of regulating mobile health apps, 2) the challenge of creating a value-based rationale for payers to invest in deploying mobile health apps at scale, and 3) the high cost of customer acquisition for delivering mobile health apps to those at risk.
... To be useful, educational content should be provided in a way that is easily accessible by users (Rowland et al. 2020), even among users in remote areas. However, there are several barriers when technology-based tools, such as serious game apps, including unreliable or non-uniform technology, lack of end-user education or literacy and numeracy limitations and interoperability of systems or software dependencies (Chindalo et al. 2016;Gurupur and Wan 2017). To overcome technology challenges when developing serious game apps, it is important to consider these barriers not only when developing the technology, but also when implementing it (Gurupur and Wan 2017). ...
Serious games (i.e., digital games designed for educational purposes) can foster positive learning attitudes and are increasingly used as educational tools. Foodbot Factory is a serious game application (app) that helps children learn about healthy eating based on Canada’s Food Guide principles and has demonstrated to increase nutrition knowledge among this group. This paper describes the process followed to expand Foodbot Factory’s educational content and integrate immersive technologies and innovative features into the app. The revision process, which was guided by the Obesity-Related Behavioral Intervention Trials model, included the following phases: first, an interdisciplinary team of nutrition scientists, education experts, and computer scientists analyzed data from the original pilot study, recently published literature, and feedback from stakeholders to define areas to improve Foodbot Factory. The five original Foodbot Factory modules were evaluated by the team during weekly meetings, where the educational content, interactive features, and other elements that required updates (e.g., aesthetics and accessibility) were identified. Second, prototypes were created and refined until a final version of Foodbot Factory was approved. Nineteen children tested the updated Foodbot Factory and found it “easy to use” (89%) and “fun” (95%). The new version of Foodbot Factory contains 19 learning objectives, including 13 original and six new objectives. Interactive engagement features in the updated Foodbot Factory included augmented reality incorporated into two learning modules; new mini-games were created, including a memory game; an overhaul of the aesthetics; (e.g., new food images); and accessibility features were included to support users with cognitive and vision disabilities.
... The current standard of care involves nurses providing general HF self-care education to patients and CPs during clinic visits and patients being provided standardized HF booklets, based on national guidelines, to take home. However, for patients and CPs to gain the benefits associated with HF education, the information must be simple to understand and specific to the patient [33,48]. Past studies have indicated that individualized education is key to help patients gain the skills needed for adequate self-care, as it accommodates their learning style and level of health literacy [33,49]. ...
Background
Heart failure (HF) affects many older individuals in North America, with recurrent hospitalizations despite postdischarge strategies to prevent readmission. Proper HF self-care can potentially lead to better clinical outcomes, yet many older patients find self-care challenging. Mobile health (mHealth) apps can provide support to patients with respect to HF self-care. However, many mHealth apps are not designed to consider potential patient barriers, such as literacy, numeracy, and cognitive impairment, leading to challenges for older patients. We previously demonstrated that a paper-based standardized diuretic decision support tool (SDDST) with daily weights and adjustment of diuretic dose led to improved self-care.
Objective
The aim of this study is to better understand the self-care challenges that older patients with HF and their informal care providers (CPs) face on a daily basis, leading to the conversion of the SDDST into a user-centered mHealth app.
Methods
We recruited 14 patients (male: 8/14, 57%) with a confirmed diagnosis of HF, aged ≥60 years, and 7 CPs from the HF clinic and the cardiology ward at the Hamilton General Hospital. Patients were categorized into 3 groups based on the self-care heart failure index: patients with adequate self-care, patients with inadequate self-care without a CP, or patients with inadequate self-care with a CP. We conducted semistructured interviews with patients and their CPs using persona-scenarios. Interviews were transcribed verbatim and analyzed for emerging themes using an inductive approach.
Results
Six themes were identified: usability of technology, communication, app customization, complexity of self-care, usefulness of HF-related information, and long-term use and cost. Many of the challenges patients and CPs reported involved their unfamiliarity with technology and the lack of incentive for its use. However, participants were supportive and more likely to actively use the HF app when informed of the intervention’s inclusion of volunteer and nurse assistance.
Conclusions
Patients with varying self-care adequacy levels were willing to use an mHealth app if it was simple in its functionality and user interface. To promote the adoption and usability of these tools, patients confirmed the need for researchers to engage with end users before developing an app. Findings from this study can be used to help inform the design of an mHealth app to ensure that it is adapted for the needs of older individuals with HF.
... usability, user engagement, provision of information). [22][23][24] The framework was customized for patients with gestational prediabetes with the consultation of an experienced clinician (K.K.) and a researcher who has experience with the gestational prediabetic population (Y.Q.). The framework uses features found in the literature for the prevention of diabetes in pregnancy and also includes behavior change techniques that are known to be effective in changing behavior along with preferred patient features, as found in the literature. ...
... 28 Hospitals need to decide to build their apps in-house, work with an existing vendor, or prescribe apps to patients to render them effective. 23 In either case, there is a significant cost for systems integration. ...
This study evaluates mobile apps using a theory-based evaluation framework to discover their applicability for patients at risk of gestational diabetes. This study assessed how well the existing mobile apps on the market meet the information and tracking needs of patients with gestational diabetes and evaluated the feasibility of how to integrate these apps into patient care. A search was conducted in the Apple iTunes and Google Play store for mobile apps that contained keywords related to the following concepts of nutrition: diet, tracking, diabetes, and pregnancy. Evaluation criteria were developed to assess the mobile apps on five dimensions. Overall, the apps scored well on education and information functions and scored poorly on engagement functions. There are few apps that provide comprehensive evidence-based educational content, tracking tools, and integration with electronic health records. This study demonstrates the need to develop apps that have comprehensive content, tracking tools, and ability to bidirectionally share data.
... An evaluation framework was developed to evaluate the quality of the patient-provider relationship in CBT mHealth apps based on a reference architecture for health app design [19], (see Table 1). The evaluation framework is composed of 20 measures aimed at measuring the evidence-based support of CBT mHealth apps and their ability to enhance the patient-provider relationship. ...
... The evaluation framework is composed of 20 measures aimed at measuring the evidence-based support of CBT mHealth apps and their ability to enhance the patient-provider relationship. These 20 measures were based on properties from Chindalo et al. reference architecture which distinguishes features such as explicitly identifying patient's diagnosis, enabling interoperability with EMRs, identifying and tracking process and proxy metrics for diseases as well as identifying and tracking important outcome measures [19]. These concepts fit with Albrecht et al. framework which provides details on evidence-based criteria that should be considered when evaluating mobile applications [20]. ...
... 50 CBT mHealth apps were identified from the Apple iTunes and Google Play app stores using the search terms "Cognitive Behavioural Therapy" or "CBT. " The rationale for the use of the health app design reference architecture over other popular frameworks used for mHealth App reviews is described previously [19]. ...
Background
Mobile health apps (mHealth apps) are increasing in popularity and utility for the management of many chronic diseases. Although the current reimbursement structure for mHealth apps is lagging behind the rapidly improving functionality, more clinicians will begin to recommend these apps as they prove their clinical worth. Payors such as the government or private insurance companies will start to reimburse for the use of these technologies, especially if they add value to patients by providing timely support, a more streamlined patient experience, and greater patient convenience. Payors are likely to see benefits for providers, as these apps could help increase productivity between in-office encounters without having to resort to expensive in-person visits when patients are having trouble managing their disease.
Key findings
To guide and perhaps speed up adoption of mHealth apps by patients and providers, analysis and evaluation of existing apps needs to be carried out and more feedback must be provided to app developers. In this paper, an evaluation of 35 mHealth apps claiming to provide cognitive behavioural therapy was conducted to assess the quality of the patient-provider relationship and evidence-based practices embedded in these apps. The mean score across the apps was 4.9 out of 20 functional criteria all of which were identified as important to the patient-provider relationship. The median score was 5 out of these 20 functional criteria.
Conclusion
Overall, the apps reviewed were mostly stand-alone apps that do not enhance the patient-provider relationship, improve patient accountability or help providers support patients more effectively between visits. Large improvements in patient experience and provider productivity can be made through enhanced integration of mHealth apps into the healthcare system.
... Previous studies have reviewed the current apps for HF selfcare and found that there is a limited number available to support disease management [12,17]. Nevertheless, these studies were unable to effectively evaluate overall app and features quality due to its lack of disease specificity within the rating scales design [18,19]. For example, the commonly used mobile application rating scale (MARS) was able to assess app quality, but its was unable to provide any feedback regarding the quality and usability of the app features specific for the disease population (older adults with HF) [12,15,18,19] (Table 5). ...
... Nevertheless, these studies were unable to effectively evaluate overall app and features quality due to its lack of disease specificity within the rating scales design [18,19]. For example, the commonly used mobile application rating scale (MARS) was able to assess app quality, but its was unable to provide any feedback regarding the quality and usability of the app features specific for the disease population (older adults with HF) [12,15,18,19] (Table 5). This led to more generic health apps (e.g. ...
... To address this gap, we conducted a systematic review of all the apps currently available exclusively for HF selfcare. We used Chindalo et al's peer reviewed reference architecture to define the app design requirements [19]. Contrary to the other rating scales, this architecture allows us to combine the evaluative components related to the aesthetics, usability and HF selfcare to effectively evaluate whether the current HF apps are meeting the endusers selfcare needs and capabilities [19]. ...
BACKGROUND
Heart failure (HF) is chronic disease that affects over 1% of Canadians and is associated with a significant economic burden (2.8 billion/year). Many mobile health (mHealth) applications have been developed to help support patients self-care in the home setting, but the quality of the apps available, according to the older adults needs or capabilities, has yet to be established.
OBJECTIVE
The purpose of this study was to determine the number of HF apps available and evaluate whether they met the criteria for optimal HF self-care
METHODS
We conducted a systematic search all the apps available exclusively for HF self-care across the Google Play and Apple iTunes app stores. We then evaluated the apps according to a list of 25 major functions pivotal to promote HF self-care
RESULTS
Seventy-four apps for HF self-care were identified, but only 21 apps were listed as being both HF and self-care specific. None of the apps had all of the 25 of the listed features required for an adequate HF self-care app and only 41% (31/74) had the key weight management feature present. HF Storylines received the highest functionality score (18/25).
CONCLUSIONS
Our findings suggest that the current apps available are not adequate for HF patients to use. This highlights the need for mHealth apps to refine their development process as user needs and capabilities should be identified during the design stage to ensure the usability of an intervention.
... Previous studies have reviewed current apps for HF self-care and found that there are limited number of apps available to support disease management [12,17]. Nevertheless, these studies were unable to effectively evaluate app quality because of their lack of disease specificity within the rating scale design [18,19]. For example, the commonly used Mobile Application Rating Scale (MARS) was able to provide an overall assessment of the quality of apps with respect to engagement, functionality, aesthetics, information, and subjective opinion, but it does not evaluate the usability or effectiveness of the app features specific for the disease population [12,15,18,19]. ...
... Nevertheless, these studies were unable to effectively evaluate app quality because of their lack of disease specificity within the rating scale design [18,19]. For example, the commonly used Mobile Application Rating Scale (MARS) was able to provide an overall assessment of the quality of apps with respect to engagement, functionality, aesthetics, information, and subjective opinion, but it does not evaluate the usability or effectiveness of the app features specific for the disease population [12,15,18,19]. Therefore, generic health apps (eg, WebMD) receive higher app quality scores using the MARS even if they do not have crucial app features (eg, weight management) for proper self-care [17]. ...
... To address this gap, we conducted a systematic search of all the apps currently available exclusively for HF self-care. We used Chindalo et al's peer-reviewed mHealth app reference architecture to define the app design requirements [19]. Contrary to other rating scales, this architecture allows us to combine the evaluative components related to the aesthetics, usability, and HF self-care to effectively evaluate whether the current HF apps are meeting the end user's self-care needs and capabilities [19]. ...
Background
Heart failure (HF) is a chronic disease that affects over 1% of Canadians and at least 26 million people worldwide. With the continued rise in disease prevalence and an aging population, HF-related costs are expected to create a significant economic burden. Many mobile health (mHealth) apps have been developed to help support patients’ self-care in the home setting, but it is unclear if they are suited to the needs or capabilities of older adults.
Objective
This study aimed to identify HF apps and evaluate whether they met the criteria for optimal HF self-care.
Methods
We conducted a systematic search of all apps available exclusively for HF self-care across Google Play and the App Store. We then evaluated the apps according to a list of 25 major functions pivotal to promoting HF self-care for older adults.
Results
A total of 74 apps for HF self-care were identified, but only 21 apps were listed as being both HF and self-care specific. None of the apps had all 25 of the listed features for an adequate HF self-care app, and only 41% (31/74) apps had the key weight management feature present. HF Storylines received the highest functionality score (18/25, 72%).
Conclusions
Our findings suggest that currently available apps are not adequate for use by older adults with HF. This highlights the need for mHealth apps to refine their development process so that user needs and capabilities are identified during the design stage to ensure the usability of the app.
... The lack of certainty in the interventions independent sustainability is one of the leading factors responsible for this resistance [4]. Electronic health apps may hold great promise for beter health tracking [5], providing education [6], changing and enforcing health behaviors [7], and monitoring treatment adherence [8], however despite these beneits, they are still not being used [9]. This can be atributed due to nine key design barriers that are outlined in Chindalo et al. literature review [9] (Table 1). ...
... Electronic health apps may hold great promise for beter health tracking [5], providing education [6], changing and enforcing health behaviors [7], and monitoring treatment adherence [8], however despite these beneits, they are still not being used [9]. This can be atributed due to nine key design barriers that are outlined in Chindalo et al. literature review [9] (Table 1). ...
... To ensure that resources being spent on an application are adequately being used and the above barriers are addressed, the needs, wants and expectations of the health apps primary stakeholders should be evaluated [10]. However, it is this lack of stakeholder consideration within app design that builds the three prime issues with innovation, which we describe below [1,9]. ...
... A common rationale behind these multiple-components methods is to provide easy to use tools to assist app assessment in various application areas. Some examples are: the "Mobile Apps Rating Scale" (MARS), that organizes quality features into five components: engagement, functionality, aesthetics, information, and subjective quality [83]; the "Health Apps by Design" framework, that addresses quality elements related to self-management support, physician leadership, communication, information technology, and the general mHealth ecosystem [85]; the Organization for the Review of Care and Health Applications (ORCHA) framework, that includes 24 items related to data governance, clinical efficacy, and user experience and engagement [86]; as well as other multi-component approaches that combine miscellaneous elements, e.g. quality of app specifications, development team, interfaces, clinical content, and ease of use [84]. ...
The need to characterize and assess health apps has inspired a significant amount of research in the past years, in search for methods able to provide potential app users with relevant, meaningful knowledge. This article presents an overview of the recent literature in this field and categorizes - by discussing some specific examples - the various methodologies introduced so far for the identification, characterization, and assessment of health apps. Specifically, this article outlines the most significant web-based resources for app identification, relevant frameworks for descriptive characterization of apps' features, and a number of methods for the assessment of quality along its various components (e.g., evidence base, trustworthiness, privacy, or user engagement). The development of methods to characterize the apps' features and to assess their quality is important to define benchmarks and minimum requirements. Similarly, such methods are important to categorize potential risks and challenges in the field so that risks can be minimized, whenever possible, by design. Understanding methods to assess apps is key to raise the standards of quality of health apps on the market, towards the final goal of delivering apps that are built on the pillars of evidence-base, reliability, long-term effectiveness, and user-oriented quality.
... However, uptake of mhealth apps is particularly poor [6]. In a recently accepted paper, we developed a reference architecture for the design and development of mhealth apps for chronic disease management that addresses many of the barriers identified in the literature [7]. Based on this reference architecture, we developed screening criteria for what should be in a mhealth app for optimal management of patients with diabetes. ...
... Our search focused on diabetes management and prevention apps using search terms such as diabetes, diabetes management and diabetes tracker; 201 apps were identified. Using our mhealth reference architecture [7], we devised 15 major functions that a diabetes management app should perform (Table 1) because they enable care that is supported by guidelines and by patient engagement best practices. Each major function also has about 5 to 10 descriptors; e.g., when "Patient Information" is a major app function, information such as "date of birth" or "sex" is a descriptor. ...
mHealth apps are not being used. Over 45,000 mhealth apps are languishing in mobile app stores. We evaluated over 200 diabetes mobile apps found in the Apple and Google app stores using a framework that we recently published. None of the apps met all 15 criteria identified by our framework. The largest number of apps fell into the category of Type 1 diabetes blood sugar and medication trackers. Other types of apps included educational apps such as recipe apps, guideline dissemination apps, simple diabetes education apps, etc. There is a need for more Type 2 apps and for all types of apps that are better integrated into EMRs for more holistic care that can be prescribed by clinicians and monitored and supported by the health care team.
