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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
Home health aides (HHAs) provide necessary hands-on care to older adults and those with chronic conditions in their homes. Despite their integral role, HHAs experience numerous challenges in their work, including their ability to communicate with other health care professionals about patient care while caring for patients and access to educational resources. Although technological interventions have the potential to address these challenges, little is known about the technological landscape and existing technology-based interventions designed for and used by this workforce.
We conducted a scoping review of the scientific literature to identify existing studies that have described, designed, deployed, or tested technology-based tools and apps intended for use by HHAs to care for patients at home. To complement our literature review, we conducted a landscape analysis of existing mobile apps intended for HHAs providing in-home care.
We searched the following databases from their inception to October 2020: Ovid MEDLINE, Ovid Embase, Cochrane Library, and CINAHL (EBSCO). A total of 3 researchers screened the yield using prespecified inclusion and exclusion criteria. In addition, 4 researchers independently reviewed these articles, and a fifth researcher arbitrated when needed. Among studies that met the inclusion criteria, data were extracted and summarized narratively. An analysis of mobile health apps designed for HHAs was performed using a predefined set of terms to search Google Play and Apple App stores. Overall, 2 researchers independently screened the resulting apps, and those that met the inclusion criteria were categorized according to their intended purpose and functionality.
Of the 8643 studies retrieved, 182 (2.11%) underwent full-text review, and 4.9% (9/182) met our inclusion criteria. Approximately half (4/9, 44%) of the studies were descriptive in nature, proposing technology-based systems (eg, web portals and dashboards) or prototypes without a technical or user-based evaluation of the technology. In most (7/9, 78%) papers, HHAs were just one of several users and not the sole or primary intended users of the technology. Our review of mobile apps yielded 166 Android and iOS apps, of which 48 (29%) met the inclusion criteria. These apps provided HHAs with one or more of the following functions: electronic visit verification (29/48, 60%), clocking in and out (23/48, 48%), documentation (22/48, 46%), task checklist (19/48, 40%), communication between HHA and agency (14/48, 29%), patient information (6/48, 13%), resources (5/48, 10%), and communication between HHA and patients (4/48, 8%). Of the 48 apps, 25 (52%) performed monitoring functions, 4 (8%) performed supporting functions, and 19 (40%) performed both.
A limited number of studies and mobile apps have been designed to support HHAs in their work. Further research and rigorous evaluation of technology-based tools are needed to assess their impact on the work HHAs provide in patient’s homes.
By 2060, the number of Americans aged >65 years is projected to reach approximately 95 million, making up almost one-fourth of the population in the United States. Most older adults, including those with multiple chronic conditions, prefer to stay in their homes and communities for as long as they can and avoid nursing homes, a concept referred to as “aging in place.” To do so, they require help at home from family caregivers and home health aides (HHAs). HHAs represent the sixth fastest growing occupation in the United States; at present, there are 2.3 million HHAs in the United States and are expected to grow by 1.5 million by 2030 [
However, despite being integral to patient care, HHAs are an overlooked and underutilized group of health care professionals who experience challenges in caring for patients at home. Most women and minorities of color who earn low wages work long hours, have erratic schedules, and have limited opportunities for career advancement [
Finally, although HHAs receive training for certification and maintenance, many of their courses are general and not disease specific, which may not meet the clinical needs of the older adults [
To meet these needs and inform future technological innovations for this workforce, a better understanding of the technology landscape is needed [
This scoping review is reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidance [
We conducted a scoping review using the 5-stage framework developed by Arksey and O’Malley [
A medical librarian (DD) performed a comprehensive literature search on October 28, 2020, of Ovid MEDLINE(R) ALL, from 1946 to October 27, 2020, Ovid Embase (from 1974 to October 27, 2020), Cochrane Library (Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and Cochrane Methodology Register), and CINAHL (EBSCO) from inception to October 2020. The first search was conducted using Ovid MEDLINE. Subject headings and keywords were adapted for other databases. No restrictions were applied on language, publication date, or article type. Additional records were identified by reviewing reference lists and using the “Cited by” and “View references” features in Scopus of the included studies. The full set of search terms for Ovid MEDLINE is presented in
This review was limited to studies that focused on technology-based tools, innovations, or interventions intended to be used by home health care workers (including HHAs, attendants, and personal care aides). Studies can be descriptive in nature (eg, overview of technology design), quasi-experimental, or randomized controlled trials. Only peer-reviewed studies published in the English language were included. Qualitative studies that did not discuss or propose an intervention, reviews, editorials, or scientific meeting abstracts were excluded. Studies that focused on other people who provide care at home (eg, nurses or family caregivers) were excluded. Studies that were conducted in nursing homes, long-term care centers, and acute rehabilitation centers were also excluded.
All studies identified following the database search were uploaded to the web-based systematic review software package Covidence (Veritas Health Innovation). First, the title and abstract reviews of all studies were completed independently by 3 authors (JC, IO, and ND). Disagreements were discussed and resolved through consensus. A record was kept of all the studies excluded and the reason for exclusion in Covidence. All studies that met the inclusion criteria (189 studies) went through a full-text screening process by the 4 authors independently (JC, IO, EFK, and ND), and any disagreements on the eligibility of the studies were reviewed by a fifth author (MRS).
Data from the included studies were extracted using the following categories: (1) author, (2) country, (3) year of publication, (4) title of the study, (5) journal, (6) contribution, (7) technology innovation, (8) intended users, (9) study objective and systems goals, and (10) evaluation and assessment of innovation.
In total, 8643 studies were imported from our search of the peer-reviewed literature. Among these 8643 studies, 2452 (28.36%) were excluded because they were duplicates. We screened 6191 abstracts and excluded 6002 (96.94%) studies because they were not relevant to home health care. A total of 189 full-text studies were assessed for eligibility, and 7 studies were included. A medical librarian (DD) identified 13 additional studies from the citation chasing process; among these, 2 studies met the inclusion criteria. Taken together, we identified 9 full-text studies that met the inclusion criteria (
The characteristics of the 9 included studies are presented in
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram for the scoping literature review.
Results from the scoping review of literature.
Author, year, and countrya | Study title | Technology innovation | Intended users | HHAb role |
Ogawa et al [ |
A Java mobile phone-based “home helper” care report creation support system | Java mobile phone–based care report creation support system | Home helpers | Primary |
Scandurra et al [ |
Visualisation and interaction design solutions to address specific demands in shared home care | Design prototype based on participatory design | GPc, DNd, and HHSe personnel | Peripheral; one of many |
Paganelli et al [ |
An ontology-based system for context-aware and configurable services to support home-based continuous care | Emilia Romagna Mobile Health Assistance Network (ERMHAN) service platform | Patients, family members, home care teams (clinicians, GPs, nurses, etc), and social community members (eg, social workers and volunteers) | Peripheral; one of many |
Page et al [ |
Improving care delivery using health information technology in the home care setting: development of the home continuation care dashboard | Web dashboard intended to bridge the gap between physicians and home care case managers | Physicians, home care case managers, patients, and caregivers | Peripheral; one of many |
De Backere et al [ |
The OCareCloudS project: toward organizing care through trusted cloud services | OCarePlatform and cloud-based semantic system to offer information and knowledge-based services for older people and their informal and formal caregivers | Older patients residing at home and informal and formal caregivers | Secondary; 1 of 3 (patient is primary) |
De Backere et al [ |
The OCarePlatform: a context-aware system to support independent living | Sensor-based in-home system | Multiple formal and informal caregivers involved in a patient’s care | Peripheral; one of many |
Danilovich et al [ |
Design and development of a mobile exercise application for home care aides and older adult Medicaid home and community-based clients | Mobile exercise app. Content of the program itself seems static. Minimal data entry about patient (pain and mood) | Home HCAf and patients | Secondary; 1 of 2 (patient is primary) |
Bourikas et al [ |
Elderly support to inspired ageing (ESTIA) | Elderly Support to Inspired Ageing platform that enables medical and background information to be combined into a single server | Family, volunteers, older people, home care aides, hospitals | Primary |
Danilovich et al [ |
Translating Strong for Life into the Community Care Program: Lessons Learned | SFLg: Resistance Exercise Intervention: 35-minute DVD on warm-up and upper and lower extremity exercises for homebound older adult clients | Home HCA and patients | Secondary; 1 of 2 (patient is primary) |
aStudies are listed in chronological order based on the year published.
bHHA: home health aide.
cGP: general practitioner.
dDN: district nurse.
eHHS: home help service.
fHCA: health care aides.
gSFL: strong for life.
HHAs were the intended users of the technology for 33% (3/9) proposed technology interventions. Of these 3 studies, only 1 (33%) study by Ogawa et al [
The remaining 67% (6/9) of studies examined technology tools that were not designed primarily for HHAs. HHAs were one of several types of users. Although these tools supported HHAs, they targeted patients and general members of the health care team, such as physicians, nurses, and physical therapists, as the main users. For example, Page et al [
All (9/9, 100%) studies aimed to support HHAs in caring for patients at home. There was a wide range of distinct purposes of the technology proposed and tested. Overall, 78% (7/9) of studies proposed and described digital software platforms, ranging from web-based platforms to enhance communication in the home environment to a sensor-based in-home tool to alert caregivers of their patients’ falls and physical injuries. Only the study by Ogawa et al [
A total of 22% (2/9) of studies designed and tested a technological intervention intended to improve patients’ physical mobility. Both studies by Danilovich et al [
Of the 9 studies, 6 (67%) presented descriptions of the proposed technology and system. None of these studies included evaluations of the technology or data on feedback from the intended users, HHAs. A study discussed the development and testing of a system prototype among home help service personnel, nurses, and general practitioners. However, no follow-up user evaluations or deployment data were evaluated.
Only 22% (2/9) of studies collected and reported quantitative and qualitative data on user evaluation and deployment efforts from the perspective of users, one of whom was HHAs. These evaluation efforts focused on overall program satisfaction. For example, a study by Danilovich et al [
In another study by Danilovich et al [
On the basis of paucity of results from the scoping review, we conducted a landscape analysis of existing mobile apps that were designed for HHAs and could potentially assist them in their work caring for patients in the home.
Two authors (EFK and JC) searched for existing mHealth apps created for HHAs on the Google Play store (for Android apps) and the Apple App store (for iOS apps) using a predefined set of terms (
Our inclusion criteria for mobile apps included apps that (1) were available on the iOS Apple or Google Play stores, (2) were primarily designed for HHAs, and (3) supported HHAs with their work in patients’ homes. For example, we included apps with features for documentation, communication, and training. These are resources that HHAs may use while working directly with patients.
Our initial search yielded 686 Android apps and 289 Apple apps that were screened for inclusion. We created a custom-built Python script to automatically save all the search results as a list, which facilitated further analysis. In our first pass, we removed apps that were clearly not relevant to HHAs (eg, patient self-tracking apps, radio stations, or self-help books) This yielded 175 apps: 148 Android and 27 Apple apps. For each app, we collected the app name, ID, year released, last year updated, number of downloads, app description, and number of reviews. We then removed an additional 8 apps that were duplicated in the data set (ie, had both Android and iOS versions). A total of 167 apps underwent independent review by 2 authors (EFK and JC), who examined each app’s descriptions (found on the Google Play or the iOS Apple App stores) for their intended users (eg, HHA, medical professionals including HHA, or nurses) and purposes (eg, connecting providers with patients, providing task checklists, and GPS tracking). Apps that did not make HHAs one of the primary users were excluded.
After doing so, a total of 67 mobile apps met our inclusion criteria for further review.
We verified the completeness of the resulting set of apps in 2 ways. (1) For each relevant app, we used search engine optimization software (Semrush) that given the name and URL of a relevant app, provided a list of “competitor” apps that would be likely serve the same purpose or provide similar functionality. We reviewed the suggested competitor apps for all HHA-relevant apps to confirm whether they were already present in the set of apps or assess whether they met the inclusion criteria. (2) In addition, we used the built-in “recommended app” features provided by both Google Play and Apple App stores. We entered the name of each HHA-relevant app and noted any alternative or similar apps that were recommended by each platform. We then checked these recommended apps to see if they were already in our data set or assessed whether they should be included. Neither of these processes yielded new apps that were not already present in our data set, which increased the confidence that our search process discovered all relevant apps.
Since we sought to study apps that assisted HHAs with their work in a patient’s home, apps that only served HHAs before or after their patient visit were eliminated. Through our previous categorizations based on each mobile app’s description, we included apps that performed at least one of the following functions: (1) allowed HHAs to access their task checklist; (2) allowed HHAs to document their work of the day; (3) provided a place for HHAs to access their patient’s information; (4) facilitated communication between an HHA and their agency; (5) facilitated communication between an HHA and their patient; (6) provided resources such as training courses, information, and so on for HHAs; (7) helped with electronic visit verification; and (8) assisted HHAs with clocking in and out. After applying these criteria to the 67 apps, 48 (72%) remained that performed one or more of the 8 core functions (
An overview of the characteristics of these 48 unique apps is presented in
Flow diagram for the landscape analysis of mobile health apps. HHA: home health aide.
Results from the landscape analysis of mobile apps.
Name of the mobile appa | Year | Type | Primary users | Objective |
Domiciliary Care Toolkit | 2014 | Android | Home care providers (including HCWsb) | Supporting |
HHAeXchange | 2014 | Android | HCW | Both |
Verify Centre Home Health | 2015 | Android | HCW | Both |
Alora Plus | 2016 | Android | HCW | Both |
Connected Home Care | 2016 | Android | HCW | Both |
Electronic Visit Verification | 2016 | Android | HCW | Monitoring |
FreedomCare Plus | 2016 | Android | HCW | Monitoring |
MedFlyt | 2016 | Android | Home care providers (including HCWs) | Both |
PointClickCare Care at Home | 2016 | Android | HCW | Monitoring |
CareConnect | 2017 | Android | HCW | Supporting |
Axxess HomeCare | 2017 | Android | HCW | Both |
Caretap EVV | 2017 | Android | Home care providers (including HCWs) | Monitoring |
DCI Mobile EVV | 2017 | Android | HCW | Both |
eRSP Mobile Connect | 2017 | Android | Home care providers (including HCWs) | Both |
FormDox EVV for Aides | 2017 | Android | HCW | Both |
Ally Home Care | 2018 | Android | HCW | Monitoring |
August Systems Mobile for Caregivers | 2018 | Android | HCW | Both |
AuthentiCare 2.0 | 2018 | Android | HCW | Monitoring |
ClearCareGo Caregiver | 2018 | Android | HCW | Monitoring |
CliniqOS | 2018 | Android | Home care providers (including HCWs) | Both |
CrescendoConnect | 2018 | Android | Home care providers (not specific to HCWs) | Both |
Domiciliary Care Worker Gweithiwr Gofal Cartref | 2018 | Android | HCW | Supporting |
Helpers Home Care | 2018 | Android | Home care providers (including HCWs) | Both |
My EVV | 2018 | Android | HCW | Monitoring |
MyEzcare—EVV | 2018 | Android | HCW | Monitoring |
Mobile Caregiver+ | 2018 | Android | HCW | Monitoring |
Honor Care Pro | 2018 | Android | HCW | Both |
UCP Caregiver Staffing | 2018 | iOS | HCW | Monitoring |
BarbaraKares | 2019 | Android | HCW | Monitoring |
CareTime | 2019 | Android | HCW | Monitoring |
Cashe EVV | 2019 | Android | HCW | Both |
KorEvv | 2019 | Android | HCW | Monitoring |
MatrixCare for Home Care | 2019 | Android | HCW | Both |
myHRresults—At Work | 2019 | Android | HCW | Monitoring |
SwyftOps—Caregiver App | 2019 | Android | HCW | Monitoring |
Vertex EVV | 2019 | Android | HCW | Monitoring |
HomecareGPS Mobile | 2019 | iOS | HCW | Monitoring |
ServTracker Mobile Home Care | 2019 | iOS | HCW | Monitoring |
Moravia Shifts | 2020 | Android | HCW | Monitoring |
Netsmart Homecare Mobile Phone | 2020 | Android | HCW | Both |
BAYADA Home | 2021 | Android | HCW | Monitoring |
Careswitch | 2021 | Android | HCW | Both |
Visit Wizard Mobile | —c | Android | HCW | Both |
Best Care | — | Android | HCW | Monitoring |
Caregiver App | — | Android | HCW | Monitoring |
Caregiver Cloud Training | — | Android | Home care providers (including HCWs) | Supporting |
Time4Care | — | Android | HCW | Monitoring |
ViolaCare | — | Android | HCW | Monitoring |
aApps are listed in chronological order based on the year created or last updated.
bHCW: home care worker.
cMissing information.
Of the 48 apps, 25 (52%) apps focused on monitoring functions, 4 (8%) apps provided supporting functions, and 19 (40%) apps provided both. Of the 48 apps, 34 (71%) were developed and sponsored by software companies, 9 (19%) were developed and sponsored by individual home care agencies, 1 (2%) by government-partnered software companies, and 1 (2%) by government-partnered agencies. Specific developer information could not be found for 6% (3/48) of the apps. The apps that were developed between the years 2014 and 2021 were from 5 countries; most apps were from the United States (41/48, 85%), with the remainder from the United Kingdom, Ireland, Canada, and Ethiopia. The number of downloads ranged from 5 to >100,000, and user ratings ranged from 2 out of 5 stars to 5 out of 5 stars.
The most common feature provided by the apps was monitoring of HHAs at home (25/48, 52%). This included monitoring whether HHAs arrived at the patient’s home on time by logging HHAs’ work hours (16/25, 64%), keeping track of HHAs’ tasks (11/25, 44%), reporting their real-time GPS location in the patient’s home via electronic visit verification (17/25, 68%), and documenting information about the patient (11/25, 44%).
For example, FormDox EVV (
Another example is My EVV (
FormDox EVV interface.
My EVV interface.
Of the 48 apps, 4 (8%) focused solely on providing support for HHAs. Of the 4 apps, 3 (75%) them provided information or training resources for HHAs and 1 (25%) app assisted with communication between HHAs and their agencies. However, none of the apps included other supporting functions, such as assisting with communication between HHAs and their patients or providing information about the patient to the HHA.
An example of a supporting app is the Domiciliary Care Toolkit (
Domiciliary Toolkit interface.
Of the 48 apps, 19 (40%) were apps that both supported and monitored HHAs. The 19 apps included features for keeping track of tasks (n=8, 42%), for HHAs to use for documentation (n=11, 58%), for electronic visit verification (n=12, 63%), for assisting with clocking in and out (n=7, 37%), for HHAs to find information about the patient (n=6, 32%), for facilitating communication between the HHA and patient (n=4, 21%), for facilitating communication between the HHA and agency (n=12, 63%), and for resources (n=1, 5%).
For example, the app MedFlyt (
MedFlyt interface.
Our findings illustrate the need for increased research on technological interventions for HHAs and the further development of mobile technologies that support HHAs with their work in patients’ homes. The scoping review of the peer-reviewed literature yielded only 9 studies, and in most of them, HHAs were not the primary intended users of the technology. In addition, very few studies have assessed the feasibility and effectiveness of this technology among HHAs. The landscape analysis revealed only 4 existing apps that were solely focused on supporting HHAs, with most apps designed for agencies to monitor HHAs rather than assisting them with their work. The lack of studies from the scoping review that included data on user feedback and the dearth of research on how mobile technologies impact HHAs’ work suggests an urgent need for research that rigorously evaluates technology-based tools to measure their effect on HHAs’ caregiving in patients’ homes.
Research in the field of human-computer interaction has long acknowledged the importance of actively engaging the eventual users of technologies in their design [
Although prior work [
Rather, our landscape analysis suggests that many of the existing apps have been primarily developed with home care agencies in mind, providing functionality that primarily serves the administrative needs of the agency (eg, electronic visit verification) rather than providing on-the-job support for HHAs. Compounding these concerns, the COVID-19 pandemic has accelerated and amplified the use of technology in HHAs’ work. For example, home care agencies have been rapidly transitioning to using digital tools for remote training, scheduling, and monitoring HHAs’ work, but it remains unclear whether, or to what extent, these technology tools meet HHAs’ needs or impact patient care [
Notably, several recent reviews of mHealth apps relate to and build on some of our main findings. A scoping review by Vaughan et al [
Our study has a few limitations. First, new studies and mobile apps may have been published or released after we collected data for our scoping review and landscape analysis, which may signal an underrepresentation of existing studies and apps. Second, mobile apps available at the time of data analysis may have been discontinued. Finally, our study examined mobile app descriptions on the Google Play or the Apple stores between 2019 and 2021, and our categorization of these apps depends on the accuracy of these descriptions. Future studies should verify publicly facing descriptions of these apps to confirm that they accurately represent the intended use of the products.
Our findings suggest that despite the integral role of HHAs in patient care and their exposure to and use of technology, few studies of technology-based interventions designed for this workforce exist and those that do lack rigorous evaluations. In addition, although many apps for the workforce are in use, most are designed from the perspective of the home care agency, not the HHA, and serve to monitor HHAs rather than support them in providing care to patients. Taken together, there is an urgent need for research that centers on the needs and perspectives of HHAs and using human-centered methods to engage HHAs in the design of technologies that truly support their essential caregiving work. Such approaches will also likely make HHAs feel more included and valued in the health care system, addressing the challenges identified in prior work. Therefore, more rigorous evaluations of both existing and new technologies, including clinical trials that effectively measure the impact of the technology on both HHAs and patients for whom they care, are warranted.
Search strategy terms for scoping review.
Full results from the scoping review of literature.
Search strategy terms for landscape analysis.
Full results from the landscape analysis of mobile apps.
home health aide
mobile health
Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews
The authors would like to acknowledge Andrea Gallardo for her contributions to the landscape analysis. This work was supported by funding from the Robert Wood Johnson Foundation (76487), the National Science Foundation (2026577), and the National Heart, Lung, and Blood Institute (grant K23HL15060).
None declared.