This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
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.
This study aimed to identify HF apps and evaluate whether they met the criteria for optimal HF self-care.
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.
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%).
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.
Heart failure (HF) is the most important cardiovascular condition leading to hospitalization and rehospitalization in older adults, and it has a significant economic burden [
Mobile health (mHealth) apps have been developed to support patients with self-care [
Previous studies have reviewed current apps for HF self-care and found that there are limited number of apps available to support disease management [
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 [
We conducted an extensive search across Google Play and the App Store to identify all available apps for HF self-care. The search was facilitated with the use of following key terms: HF management, HF manager, HF self-care, HF, and HF tracker. Apps were included in the review if they (1) were HF specific and (2) contained a self-care component (ie, medication, symptom management, reminder system, and behavior tracking). Apps were excluded if they were intended for use in a conference, for education, or for reference purposes.
In accordance with Chindalo et al’s reference architecture, we developed a list of 25 major functions that would promote HF self-care for older adults [
Within each of the 25 functions, a list of descriptors was developed to help specify the components within the listed function. If the app included 1 of the descriptors, the feature was listed as present (
List of app features required for an adequate heart failure self-care app.
# | App feature | App descriptors |
1 | Prescribed | Physician prescribed for treatment; pharmacist recommendation |
2 | Diagnosisa | Patient predetermined diagnosis included (acute heart failure) |
3 | Patient demographics | Age; sex or gender; location |
4 | Patient sociocultural | Literacy; numeracy; socioeconomic status; culture/ethnicity; parental history |
5 | Patient symptoms | Shortness of breath; dizziness; orthopnea; leg edema or general swelling; paroxysmal nocturnal dyspnea |
6 | Patient behaviors | Smoking; exercise; fitness/movement; salt intake |
7 | Patient physiological observations | Heart rate; blood pressure; elevated jugular venous pressure; chest crackles; heart murmurs |
8 | Weighta | Management; monitoring; tracker |
9 | Comorbidities | Presence of other diseases (eg, diabetes and hypertension) |
10 | Drug list | List of medications |
11 | Laboratory results | Hemoglobin and hematocrit; creatinine and estimated glomerular filtration rate; brain natriuretic peptide; thyroid stimulating hormone; lipid profile |
12 | Diagnostic testing | Electrocardiogram; chest x-ray; echocardiogram |
13 | Behavior trackinga | Diet; exercise; patient-reported experience; compliance with medications |
14 | Education/recommendation | Behaviorally appropriate; culturally appropriate; health literacy appropriate; accredited/credible sources; evidence based |
15 | Self-carea | Self-maintenance; self-management: system provides patient with recommendation if clinical condition changes (eg, if weight increases, take extra Lasix); algorithm based or physician guidance; self-confidence |
16 | Health system utilization | Reviewed by family doctor; reviewed by nurse clinician, practitioner, or physician assistant; visit to EDb; hospitalization; seen by specialist |
17 | Notificationsa | Presence of reminder or notification |
18 | Integrations | Integrated into personal health record and electronic medical record; integrated into other health and fitness apps |
19 | Social supports | Connect/share results with caregiver or family; contact caregiver or family |
20 | Patient reported outcome measure/patient reported experience measure | Patient experience of care; app experience; quality of life; cognitive assessment; patient progress |
21 | Incentives to use | Easy access to provider; gamification; social aspect—connect with others |
22 | Predictive analytics | Length of stay, acuity of admission, comorbidities, ED visits, (readmissions); hospital admission risk prediction |
23 | Outcomes | Visit to family physician, specialist, or ED; hospitalized; death |
24 | Safety issues | Risk of falls; worsening kidney function; hyper- or hypokalemia |
25 | User interface | Easy to navigate functionality; simple to screen with minimal content on each page; features for visual (font size and color), hearing (audio cues), or general accessibility |
aStandard disease management feature for heart failure.
bED: emergency department.
Sample heart failure self-care confidence questions.
A total of 2 reviewers (SW and KK) completed a preliminary screening of apps available in both Google Play and the App Store using the key terms mentioned previously. Following the search, apps were reviewed according to their title and summary description (
Once screening was completed, a calibration session was held among the 8 reviewers (SW, KK, AG, AD, HW, HS, NN, and DL). During the calibration session, each reviewer was asked to evaluate and score the same 5 apps before the start of the session (
After the calibration session, the remaining apps were assigned for evaluation, where 2 different reviewers evaluated each app. Each evaluation was completed on the revised Google Sheet with the respective app assignments. Once all the app evaluations were completed, the data were combined into a summary sheet for review. Each app score was then reviewed by the 2 assigned reviewers to ensure that they were within the 85% agreement rate. To determine if the scores met the 85% agreement rate, we conducted interrater agreement statistic and reviewed the kappa value. If the apps did not meet the 85% agreement threshold, the 2 reviewers completed an in-person or virtual evaluation session to review the app discrepancies. Both reviewers were required to provide evidence (ie, screenshot or quote) to support the presence of the feature, and a discussion was held until consensus was achieved. All supporting evidence was sent to SW for a final review. Following consensus, a postreview screening was then completed to filter out any apps that were not HF or self-care specific. The remaining apps within the inclusion criteria were analyzed through a descriptive analysis to assess the app search’s findings.
Reviewers evaluated each app based on the description, screenshots, videos, and reviews available on each app store website. Owing to the limited resources and to ensure a consistent method of app evaluation, we did not download the apps. Our rationale for not downloading apps was also based on the premise that users decide to download an app after reviewing it externally [
In accordance with the guidelines for app review before download, reviewers also extracted the following data from each app: number of downloads, date of last update, cost, and developer [
Each reviewer selected was equipped with postsecondary experience in the electronic health or health technology field to allow them to effectively evaluate the respective apps. Reviewers were required to follow the training protocol in accordance with the mHealth design architecture as well as attend the calibration session previously described [
Preliminary screening identified a total of 74 apps as HF self-care apps within the combined app store searches (
Conceptual study design of the mobile health app review.
Features present in the reviewed heart failure self-care apps (N=74).
App feature | Apps, n (%) |
Education/recommendations | 67 (90) |
Self-care | 51 (68) |
User interface | 51 (68) |
Diagnosis | 40 (54) |
Notifications | 33 (44) |
Weight | 31 (41) |
Patient demographics | 28 (37) |
Patient symptoms | 27 (36) |
Patient physiological observations | 26 (35) |
Behavior tracking | 22 (29) |
Patient behaviors | 19 (25) |
Drug list | 18 (24) |
Patient reported outcome measure/patient reported experience measure | 18 (24) |
Incentives to use | 18 (24) |
Comorbidities | 9 (12) |
Lab results | 9 (12) |
Diagnostic testing | 9 (12) |
Social supports | 9 (12) |
Prescribed | 8 (10) |
Health system utilization | 8 (10) |
Outcomes | 7 (9) |
Predictive analytics | 6 (8) |
Patient socio-cultural | 6 (8) |
Safety issues | 0 (0) |
Following the postreview screening, 21 apps were listed as both HF and self-care specific. Moreover, 53 apps were excluded for the following reasons: (1) HF specific but not for self-care (n=9), (2) used for self-care but not specifically for HF (n=16), and (3) neither HF nor self-care specific but used for general cardiac education (n=28). As there is an increasing number of apps for entertainment or novelty purposes, from the 53 apps excluded, 12 were shortlisted for this reason. From the 21 HF self-care apps included, more than 50% (12/21, 57%) of the apps had 10 features or more (
Many apps include features such as patient demographics, patient symptoms, education, self-care, notifications, and, most notably, weight. Unexpectedly, less than 50% (9/21) of the apps included a patient behavior or a behavior tracking feature, both of which are vital for adequate HF self-care (
Features present in filtered heart failure self-care apps (N=21).
App feature | Apps, n (%) |
Diagnosis | 20 (95) |
User interface | 20 (95) |
Self-care | 19 (90) |
Notifications | 18 (85) |
Education/recommendations | 17 (80) |
Weight | 17 (80) |
Patient demographics | 13 (61) |
Patient symptoms | 13 (61) |
Patient physiological observations | 11 (52) |
Drug-list | 11 (52) |
Behavior tracking | 10 (47) |
Incentives to use | 10 (47) |
Patient behaviors | 9 (42) |
Integrations | 8 (38) |
Patient reported outcome measure/patient reported experience measure | 8 (38) |
Social supports | 5 (23) |
Health system utilization | 4 (19) |
Prescribed | 4 (19) |
Patient sociocultural | 3 (14) |
Lab results | 3 (14) |
Comorbidities | 2 (9) |
Diagnostic testing | 0 (0) |
Predictive analytics | 0 (0) |
Outcomes | 0 (0) |
Safety issues | 0 (0) |
With respect to cost, the majority of apps could be downloaded for free; however, 2 apps had an associated cost. For consistency, apps were not downloaded. As a result, we found that the 2 apps with a download cost had relatively lower scores (score of 5=US $50 and score of 8=US $7) and did not list their number of downloads.
Each app’s last update varied from 2013 to 2019; however, most of the recently updated apps received higher scores. One of the most recently updated apps (HF Storylines) obtained the highest total app score of 18.
Total score of the filtered heart failure self-care apps and their corresponding number of downloads, last updates, and cost (N=21).
App name | Total app score | Number of downloads | Last updated | Cost (US $) | Developer |
Heart Failure | 2 | 1000-5000 | December 22, 2014 | 0 | Leon Do |
HF Coach | 5 | —a | April 29, 2016 | 50 | Etectera Edutainment Inc |
HF Defender | 7 | 1000-5000 | November 10, 2016 | 0 | Cardio Fortress |
HF Buddy | 8 | — | June 08, 2016 | 0 | Singapore Health Services |
HF Monitoring | 8 | — | February 22, 2017 | 0 | Van Phuc Nguyen |
HF Tracker | 8 | — | October 31, 2014 | 0 | Rebecca Boxer |
HF Path | 8 | — | January 04, 2017 | 7 | American Heart Association |
HF Log | 9 | — | February 09, 2016 | 0 | Narnar LLC |
HF Buddy | 9 | 500-1000 | April 08, 2016 | 0 | — |
Heart Scribe | 10 | 50-100 | July 30, 2016 | 0 | Rohan Tanjea |
Heart Failure Health | 11 | — | — | 0 | Self-Care Catalyst Inc |
Health Plus | 12 | — | May 12, 2016 | 0 | Hany Assaad |
Heart Lessons | 12 | — | Apr 17, 2017 | 0 | Palo Alto Medical Foundation for Health Care, Research and Education |
My HF | 12 | 1000-5000 | September 22, 2016 | 0 | Les Laboratoires Servier |
WOW ME 200mg | 12 | 100-500 | July 24, 2013 | 0 | AtantiCare Regional Medical Center Inc |
HF Self- Management | 13 | — | August 11,2015 | 0 | — |
Pulsario | 14 | — | October 07, 2016 | 0 | Cardio Fortress Inc |
My Heart Mate | 14 | — | November 14, 2016 | 0 | Elevator Entertainment |
Heart Partner | 16 | — | October 07, 2016 | 0 | Novartis Pharmaceuticals |
Medly | 17 | — | Ongoing | — | University Health Network |
HF Storylines | 18 | 500-1000 | March 10, 2017 | 0 | HF Society of America |
aNot available.
Self-care is pivotal for HF patients to prevent worsening of HF, yet the majority of current HF apps available are neither HF or self-care specific. To our knowledge, this is the first study to identify and evaluate apps exclusively for HF self-care. We found 21 apps that were both HF and self-care specific. From the 21 apps, few contained key features such as behavior tracking. Apps that included the self-care feature were also listed as only being capable of self-maintenance. Thus, patients would be able to, at most, follow their treatment regimen but would not be able to respond to any changes. Potential features to expand on self-care could include medication titration algorithms to adjust medication doses according to weight fluctuations or the use of telehealth services to connect with a physician to modify their treatment regime [
Our findings suggest that the current available apps are not able to support patients adequately with HF self-care; instead, are in need of further redesign or development. Many developers may have limited resources to accommodate all 25 features in a single app. Therefore, to appropriately engage patients in self-care, apps should at minimum have the following functions: (1) diagnosis, (2) weight, (3) behavior tracking, (4) self-care, and (5) notifications. However, from our systematic search, none of the apps even had these 5 functional features. Not only are these app features key for HF self-care but they can also be easily transferable to other health conditions, such as diabetes or asthma, as it captures the essential components for treatment management. The specifics detailing each feature will differ depending on the condition, but it provides a sufficient baseline category to allow the consumer, researcher, or clinician to incorporate key components for the app evaluation. The consumer, researcher, or clinician may list similar or different components within the 5 features, but with this, they are able to incorporate their perspective within 5 wider categories, while maintaining its relevance for multiple audiences. A prime example of an effective mHealth app is BlueStar from WellDoc Diabetes Management. The BlueStar app is a digital therapeutic for diabetes mellitus type 2 that serves as a virtual coach for patients, providing tailored guidance and facilitating the coordination of diabetes care with their existing care team [
One surprising finding from this app search was that the lowest-scoring apps had a relatively higher number of downloads compared with the highest-scoring app (
Nevertheless, it is also important to note that the fact that lower-scoring apps had higher downloads can also be attributed to several external factors promoting public app awareness. This includes factors such as effective marketing, links to a national health body, or the use of Web-based search engine optimization [
The limitations of our study were as follows: (1) the apps were not downloaded but were reviewed based on app description, screenshots, videos, and reviews from current/past users, and there are good reasons to believe that the quality of the assessment is not severely compromised, as mentioned above; (2) descriptions for review varied in detail and quality (eg, download data not available for Apple iOS apps); (3) we were only able to review the number of downloads but could not quantify active app use; and (4) actual HF patients were not consulted to define the criteria for adequate HF self-care app.
Future studies should involve end users to better understand their needs with the design of an app to ensure the uptake and usability of an intervention. Specifically, the use of engagement strategies with HF patients and health care providers would be strongly desirable to ensure the findings of this study are congruent with what is experienced in reality. In our app evaluation, we incorporated app user reviews to assess user perspectives, but this method is limited to the feedback available and the quality of the responses on the Web. This study also did not evaluate the value of the 5 functional criteria for HF self-care compared with the remaining categories. Future studies should aim to understand the relative importance of each criteria in relation to patient outcomes, potentially with the use of focus groups or user testing to develop priority weightings for each function. In addition to this, as the potential rise in scale modification for disease specificity could lead to inappropriate features being selected for app shortlisting, there is a need to also evaluate the priority features in relation to other common chronic conditions (ie, diabetes and asthma). We believe there is value in the inclusion of the 5 minimum features for app evaluation, as it allows for specific components to be embedded within wider priority categories and provides a baseline mode to manage the use of multiple modified scales. However, before any scales can be managed or reviewed, future studies need to confirm the reliability of the 5 features for the management of priority app categories.
In summary, our study was the first to specifically evaluate HF self-care apps according to the criteria essential to promote HF self-care for older adults. We found that there was a lack of usable apps to promote HF self-care for older adults, and this is mainly because of the lack of a patient-centered design. With a rise in the aging population, identifying features pivotal for patient self-care will be crucial to increase their user experience and ensure the longevity of the app’s use.
Total features present in preliminary and postreview screening apps. PROMS: patient reported outcome measure; PREMS: patient reported experience measure.
heart failure
hazard ratio
mobile health
Mobile Application Rating Scale
The authors would like to thank Everett McKay for providing his ongoing expertise throughout the design and conduct of this project.
None declared.