Development and Usability Testing of An Intelligent and Integrated Older Care Model (SMART System) to Promote Integrated Home-based Older Care

doi:10.21203/rs.3.rs-4439416/v1

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Research Article

Development and Usability Testing of An Intelligent and Integrated Older Care Model (SMART System) to Promote Integrated Home-based Older Care

https://doi.org/10.21203/rs.3.rs-4439416/v1

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Background: Although integrated care has been proposed as a promising approach to actively responding to the global population aging, there is still no exploration of the meticulously integrated care model for older adults living at home. This study aimed to develop an Intelligent and Integrated Older Care Model (SMART System) and evaluate its usability among older individuals.

Methods: The development of our SMART system was guided by the knowledge-based Clinical Decision Support System architecture and the principle of neural reflexes, which included 5 phases: (1) functional design; (2) architecture and database design; (3) security measures design; (4) user interface and visualization design; (5) prototypes development and iteratively testing. We then conducted a cross-sectional study to evaluate the usability of the SMART system among older individuals by using the Health Information Technology Usability Evaluation Scale (Health-ITUES).

Results: The SMART system consisted of a Care Receiver App, a Professional Care Provider App, and a Cloud Platform. According to the assessment results and daily monitoring data, the SMART system can provide a comprehensive diagnosis of care problems and tailor interventions and implementation approaches to address the multifaceted care needs of older individuals. The interventions and implementation approaches generated by the SMART system, after being reviewed and adjusted by professional nurses, will be sent to the corresponding care providers to promote coordinated care services. The SMART system was perceived as useful and acceptable among older individuals with the mean scores for each item of the Health-ITUES (Older People Version) exceeding 3.00.

Conclusions: This study developed the SMART system to facilitate integrated home-based older care. The SMART system was demonstrated to be satisfactory and useful among older individuals.

Clinical Decision Support System

digital health

home-based

integrated care

neural reflexes

Population aging is a phenomenon that has affected the whole world [1]. In 2021, there were approximately 963 million individuals aged 65 or older, comprising 12.2% of the global population. It is projected that this number will reach 1.65 billion by 2050, accounting for 16.4% of the total population [2]. Aging is often accompanied by higher rates of frailty, chronic diseases, and disabilities, which leads to a rising demand for assistance in daily life and medical care [3]. Although most older people prefer to be cared for in their homes [4], the current home-based older care fails to meet their care needs due to fragmented care services, which was attributed to inadequate service coordination and limited communication among various care providers [5]. Therefore, there is an urgent need to explore a more integrated care model to meet the needs of older people for home-based care.

The World Health Organization (WHO) has proposed integrated care as a promising approach to actively responding to care fragmentation and population aging [6, 7]. Integrated care is a person-centered older care model to deliver comprehensive, coordinated, continuous, and proactive care of daily life, health promotion and disease prevention, early diagnosis and disease management, and rehabilitation throughout the entire life cycle [8]. The purpose of integrated care is to improve health status, minimize or prevent disability, and promote rehabilitation, therefore reducing both healthcare and long-term care costs [9]. Despite several attempts to implement integrated care [10, 11], there is still no exploration of the meticulously integrated care model for older adults living at home [12].

A crucial aspect of successfully implementing integrated care is customizing care interventions, which hinges on the timely identification and accurate diagnoses of care problems via continuous monitoring [13]. However, some issues, such as the scattered residences and heterogeneous conditions of older adults, limited older care resources, and extensive knowledge from diverse fields in older care, make real-time monitoring and accurate diagnoses challenging [14, 15]. Moreover, it necessitates collaboration among various care providers from different institutes of social care and healthcare sectors to effectively address the multifaceted care needs of older people [12]. By achieving that, uninterrupted information sharing is crucial to enhance communication across service sectors and improve care efficiency.

Fortunately, the ongoing advancement and widespread adoption of digital health technology, together with the Internet of Things (IoT), have emerged as enablers for continuous monitoring and information sharing [16, 17]. Furthermore, a knowledge-based Clinical Decision Support System (CDSS), although limited by the high cost of building and maintaining the knowledge base, is still predominantly adopted in the medical and nursing fields where practices are heavily based on evidence, knowledge, rules, and guidelines [18]. Therefore, it might be currently one of the solutions to deliver integrated older care in home-based settings through digital health technology and knowledge-based and tailored CDSS [19, 20]. Additionally, as automatic responses of the nervous system to stimuli, human neural reflexes play a crucial role in maintaining homeostasis and protecting the body from harm through coordination between different components [21]. Drawing on the principle of neural reflexes to design an integrated older care model could connect various care providers and promote coordinated care services, thereby satisfying older people’s multifaceted care needs.

Hence, this study aimed to develop an intelligent and integrated older care model using knowledge-based CDSS architecture and the principle of neural reflexes. Analogous to the neural reflex, our intelligent and integrated older care model consists of Sensors and Scales (servers as the receptor), a Mobile Phone Autonomous Response System (serves as the central nervous system in the spinal cord), a Remote Cloud Management Center (serves as the central nervous system in the brain), and a Total Care System (severs as the effector), in short, SMART system. Additionally, Wifi and 5G networks serve as Afferent Nerves or Sensory Nerves and Efferent Nerves or Motor Nerves. Our secondary goal was to evaluate its usability among older adults.

The development of the SMART system was based on our preliminary work on the development of the knowledge base for the SMART system following the first three steps of the Intervention Mapping (IM) framework, which has been published elsewhere [22]. In brief, we clearly described the care problems, risk factors, diagnostic criteria, long-term and short-term care objectives, evidence-based interventions, and specific implementation approaches in the SMART knowledge base, which were grounded in the latest literature, guidelines, and expert opinions in relevant fields. The current study progressed by employing the subsequent step of the IM framework: program production. A multidisciplinary team comprising professionals in nursing informatics, nursing research, gerontological care, software engineering, and interface designing was established to collaboratively advance the development and usability testing of the SMART system. We held weekly meetings throughout all phases to promptly resolve problems and issues raised during the development phase to ensure smooth ongoing progress.

2.1 Phase 1: Development of the SMART System

2.1.1 Functional Design

According to human neural reflexes, the Sensors and Scales collected data from older individuals through wearable devices, self-monitoring, and periodic assessments. The collected data were uploaded automatically to the Remote Cloud and Management Center via Wifi or 5G networks for comprehensive analysis to diagnose care problems and formulate customized interventions, which were subsequently sent to caregivers or professional care providers within the Total Care System as appropriate. The Mobile Phone Autonomous Response System was a set of simple algorithms that were pre-integrated into the SMART system to deal with simple but urgent care problems, similar to the coordinate reflex actions within the spinal cord that initiates a response, for instance, the withdrawal reflex from a painful stimulus in human neural reflexes.

The process enhanced the coordination among various caregivers and professional care providers and ensured high-quality, coordinated, and integrated care for older adults (see Fig. 1).

According to the overall objectives and functional design of the SMART system, the research team drafted the key functions and modules for the Cloud Platform, Care Receiver App, and Professional Care Provider App using XMind software version 8 (XMind, Ltd). Based on the initial draft, the multidisciplinary team collaborated in brainstorming to produce a more detailed document to guide the development of the SMART system (see Additional File 1).

2.1.2 Architecture and Database Design

To enhance the flexibility, scalability, and maintainability of the SMART system, we adopted MicroService architecture to guide the development. The MicroService architecture allowed us to decompose the SMART system into a set of small and single-responsibility viable units, each of which can be developed, operated, deployed, scaled, and managed independently [23]. This architecture not only can reduce task complexity and improve development efficiency but also enable zero-downtime releases in satisfying older people’s evolving care needs [24].

The Cloud Platform was designed to be compatible with mainstream browsers following a Browser/Server (B/S) architecture [25], while the Care Receiver App and Professional Care Provider App adopted a Client/Server (C/S) architecture with the application programming interface (API) gateway as the access way [26]. Besides, the Care Receiver App and Professional Care Provider App were programmed to be accessible on Android-based mobile phones to ensure their widespread popularity and affordability in China [27]. Once the current Apps can be stably used on Android-based mobile phones, we will extend compatibility to iOS-based mobile phones.

SQL databases were used in the SMART system to store, process, capture, retrieve, and manage substantial amounts of fully structured data, while NoSQL databases were employed for semi-structured and unstructured data [28]. The SMART system involved the storage of data, such as older people’s basic characteristics and comprehensive geriatric assessment results. Data from each module was stored in either Oracle or MongoDB as a database table as appropriate.

2.1.3 Security Measures Design

The SMART system involved the transmission of sensitive data, such as personal identification and health information, making it susceptible to external and internal threats. Multiple protective measures were therefore applied to ensure system security. Data access control was achieved through user authentication services and password protective mechanisms, including verifying password strength, limiting login attempts, and periodically changing passwords [29]. To secure data transmission and storage, the multidisciplinary team employed mutual authentication HTTPS to encrypt all sensitive data [30]. Additionally, database backup and recovery functions were included to ensure data availability at any time. The development of the SMART system adhered to international and national data standards (Additional File 1).

2.1.4 User Interface and Visualization Design

Considering that the main purpose of our SMART system was to provide integrated care for older people living at home, the user interface (UI) of the Care Receiver APP was meticulously crafted based on six user-friendly design principles (structure, simplicity, diverse presentation, feedback, consistency, and tolerance) to improve older people’s perceived usefulness and ease of use with the Care Receiver App, following the Technology Acceptance Model [31]. Axure RP8 (Axure Software Solutions, Inc., USA) was employed to draft the module interfaces with functional buttons annotated to assist interface designers in understanding the functionality of each icon. Based on the draft, the interface designers used Flinto version 26.0.5 (Flinto Inc., Australia) to document the planned UIs and their interactions. Distinctive design elements such as contrasting color blocks, large fonts, leading functions, and unique icons were used to signify various functions and statuses.

2.1.5 Prototypes Development and Iteratively Testing

The SMART system was developed using Java/. Ne because of its popularity, power, verbosity, and ease of maintenance, with Linux and UNIX as the operating environment. The software engineers used Android Studio to develop the alpha version of the SMART system based on the functional design and system architecture provided by the research team [32]. The research team then tested the alpha version repeatedly to streamline the delivery of care problems and personalized interventions. Following the agile approach, software engineers iteratively identified and solved technical issues throughout the development process to promote adaptive planning, evolutionary development, delivery quality, and continuous improvement [33]. After addressing technical issues or bugs in the alpha version, a beta version was formulated and tested among the research team until each module worked well.

2.2 Phase 2: Usability Testing of the SMART System

The usability testing of the SMART system’s beta version was designed based on the International Standards Organization (ISO) standard 9241-11, which defines usability as the extent to which users can use an App to achieve specific objectives with efficiency, satisfaction, and effectiveness in a specified context of usage [34]. The study was approved by the Institutional Review Committee of the Capital Medical University (Approval No. 2015SY49). It was also registered in ClinicalTrials.gov (registration number: ChiCTR-IOR-17010368) on 12th January, 2017. Given that older individuals are the core users of the SMART system, we only tested the usability of the Care Receiver App among older people.

2.2.1 Study Design and Participants

To improve the efficiency of sample selection, we conducted a cross-sectional study in a geriatric ward of a comprehensive hospital in Beijing, China from December 2020 to February 2021 to test the usability of the Care Receiver App. Older adults were included consecutively if they: (1) were aged 60 or older; (2) were about to be discharged from the hospital and return home; (3) possessed normal communication and interaction abilities; (4) obtained at least a primary school education; (5) had an Android-based smartphone for Internet access; (6) expressed willingness to participate. Older individuals with dementia or other mental illness were excluded. All participants provided written informed consent on enrollment.

2.2.2 Measures

Demographic information of the older participants was collected using a predefined questionnaire, encompassing age, gender, education, monthly income, mobile phone use experience, and daily mobile phone use time. The perceived usability of the Care Receiver App among older individuals was measured using the Health Information Technology Usability Evaluation Scale (Health-ITUES) [35]. The Health-ITUES comprises 20 items from 4 domains: impact (3 items), perceived usefulness (9 items), perceived ease of use (5 items), and user control (3 items). Each item is rated from 1 (strongly disagree) to 5 (strongly agree) on a 5-point Likert scale. The total scores range from 20 to 100, with higher scores indicating better usability. This tool supports customization at the item level to align with the specific tasks and expectations of the health systems. The Chinese version of the Health-ITUES demonstrates satisfactory reliability with a Cronbach’s α coefficient of 0.74–0.90 [36]. To accurately reflect the perceived usability of the SMART system among older people, the Older People Version of the Health-ITUES was customized and distributed to the older participants after they completed the assigned tasks.

2.2.3 Testing Process

Once receiving approval from the relevant departments, we screened older individuals according to the predefined inclusion and exclusion criteria. After providing a detailed explanation of the study’s purpose, significance, and procedures, we assisted the older participants in downloading and installing the SMART system (Care Receiver App). Training materials, including instructional videos and user manuals, were made available until they felt confident in using the App. Subsequently, the older participants were encouraged to use the App for 24 hours to complete the assigned tasks before filling out the Health-ITUES (Older People Version). The specific tasks and more detailed testing process were presented in Additional File 1.

2.2.4 Sample Size Calculation

To achieve adequate statistical power, the sample size should be 5–10 times the number of items [37]. With a total of 20 items in the Health-ITUES, the study necessitated a minimum of 100 participants.

2.2.5 Statistical Analysis

Basic descriptive statistics were performed to describe older people’s characteristics and responses to the Health-ITUES. Continuous data was tested for normal distribution using the one-sample Kolmogorov-Smirnov test and expressed as medians with interquartile range (IQR) or mean ± standard deviation (SD) as appropriate. Categorical data was presented as frequencies and proportions (%). Statistical analysis was conducted using SPSS software version 26.0 (IBM Corp, Armonk, NY).

3.1 Phase 1: Development of the SMART System

3.1.1 Functions of the SMART System

The overall functional components and system architecture of the SMART system were illustrated in Fig. 2 and Additional File 1.

The SMART system has two main types of end users: care receivers (including older adults and/or their family members or their main caregivers) and professional care providers from both the social care and healthcare sectors. Therefore, we developed the Care Receiver App and Professional Care Provider App for the two types of end users, as well as the Cloud Platform.

The Care Receiver App enabled end users to record and upload health-related data of older adults to the Cloud Platform (see Additional File 2–12). To better understand the multi-dimensional care needs of older people, we included an assessment module using the Continuity Assessment Record and Evaluation (CARE) tool, which offered a continuous and comprehensive assessment of medical, functional, cognitive, and social support factors [38]. The CARE tool was structured into distinct sections with clear assessment statuses (to assess, in progress, and completed), allowing users to complete assessments at their own pace and save progress as needed. Moreover, we included a daily monitoring module to track older people’s blood glucose, blood pressure, heart rate, electrocardiogram, weight, diet, exercise, sleep, bowel movement, and urination through wearable devices or manual input, with historical monitoring data displayed graphically over different periods. Any abnormalities in the latest monitoring data were highlighted in varying colors on the main interface. A medication management module was also integrated to enable users to manually upload or scan prescribed medications and access medication history [39]. Besides data records, the Care Receiver App featured a reminder information module to display care problems, personalized interventions, and implementation approaches. The execution status of personalized interventions was indicated by a red dot. Additionally, a health information module was included to provide health-related knowledge on daily life topics such as health, wellness, exercise, and diet. Notably, to increase the ease of use of the Care Receiver APP, we added a read-out mode in the assessment and reminder information module.

Through the Professional Care Provider App, professional care providers were granted access to a list of older people, along with their assessment results and daily monitoring data, to understand their overall status. Meanwhile, nurses specialized in older care were designated as the primary care providers with the authority and responsibility to review and correct care problems, personalized interventions, and implementation approaches automatically generated by the Cloud Platform. The rationale behind the care problems and customized interventions and implementation approaches were accessible to professional nurses and other care providers, fostering transparency and interpretability in the decision-making process. Only the care problems, interventions, and implementation approaches that have undergone meticulous review and approval were delivered to care providers through the SMART system, ensuring the safety and effectiveness of care (see Additional File 2–12).

The Cloud Platform, functioning as the brain of the SMART system, was primarily structured for data analysis. By utilizing the preset knowledge base and diagnostic rules, the Cloud Platform diagnosed care problems according to the assessment results, daily monitoring data, and medication of older people, and further automatically matched personalized interventions and implementation approaches through amalgamating older people’s characteristics (e.g., demographics, lifestyle, preferences). Umeng Analytics and Kafka User Behavior Tracking were integrated to capture older people’s behavior and preferences, generating logs for personalized interventions and system improvements. In response to the continuously expanding knowledge in the field of older care, the Cloud Platform featured a knowledge maintenance function to modify knowledge items and diagnostic rules to ensure alignment with the latest medical and healthcare advancements. Meanwhile, the Cloud Platform could monitor the usage of the system.

3.1.2 UIs of the SMART System

The UIs and logical relationships of the Care Receiver APP and Professional Care Provider APP were illustrated in Fig. 3 and Additional File 1. The Care Receiver APP was designed to incorporate various modules, color blocks, icons, and large fonts to facilitate easy access to specific modules and minimize the need for complex navigation. To reduce the cognitive load of the older people and increase their perceived usability of the Care Receiver App, we limited the number of text messages and replaced them with figures and videos for effective and visual information presentation.

3.1.3 Prototypes of the SMART system

The software engineers developed, tested, and refined the alpha version of the SMART system. They subsequently conducted independent tests on each module using simulated data 10–20 times to ensure that basic functionalities were operational. Following the identification and resolution of any technical issues or bugs in the alpha version, the beta version of the SMART system was launched. The research team conducted more than 100 rounds of testing on the beta version of the SMART system until a consensus was reached. Following a three-month testing period, the fully functional beta version demonstrated stability and was primed for usability testing.

3.2 Phase 2: Usability Testing of the SMART system

3.2.1 Characteristics of the older participants

A total of 110 eligible older adults were included in the usability testing. Table 1 summarized the characteristics of the older participants. The median age of the older individuals was 67.00 (64.00, 71.00) years with 67.27% being male. 52.73% of the older participants were reported to have limited experience in using mobile phones, while the rest (47.27%) exhibited moderate experience. Furthermore, older people tended to spend less time on their mobile phones every day, with only 10.00% using their mobile phones for more than 3 hours per day.

Table 1

Basic characteristics of the included older individuals
Characteristics	Older adults (n = 110)	Characteristics	Older adults (n = 110)
Age	67.00	Monthly income (￥)
	(64.00,71.00)	≤ 1000	16 (14.55%)
Male	74 (67.27%)	1001 ~ 3000	41 (37.27%)
Education		3001 ~ 5000	42 (38.18%)
Primary school and below	28 (25.45%)	> 5000	11 (10.00%)
Junior high school	51 (46.36%)	Daily mobile phone use time
Senior high school and above	31 (28.18%)	< 1 hour	52 (47.27%)
History of chronic diseases		1 ~ 3 hours	47 (42.73%)
Hypertension	75 (68.18%)	> 3 hours	11 (10.00%)
Diabetes mellitus	37 (33.64%)	Mobile phone use experience
Hyperlipidemia	31 (28.18%)	Less experience	58 (52.73%)
Stroke	38 (34.55%)	Moderate experience	52 (47.27%)
Coronary heart diseases	9 (8.18%)	Abundant experience	0 (0.00%)

3.2.2 Health-ITUES scores of the older participants

Older people’s scores of the Health-ITUES (Older People Version) were presented in Table 2. The mean total score was 65.75 ± 6.48, with a mean score on each item at 3.29 ± 0.32. The mean scores for each item related to the impact, perceived usefulness, and user control of the Care Receiver App all exceeded 3.00, while the mean score for each item related to the perceived ease of use was slightly below 3.00, at 2.95 ± 0.54. Meanwhile, findings from older people’s responses to items 7 and 9 of the Health-ITUES (Older People Version) indicated that 57.27% (63/110) and 40.00% (44/110) perceived the SMART system (Care Receiver App) as satisfactory and useful, respectively.

Table 2

Scores of the Health-ITUES (Older People Version)
Items of the Health-ITUES (Older People Version)	Scores
All domains (Cronbach’s α = 0.874)	3.29 ± 0.32
Impact	3.41 ± 0.43
1. I think the SMART system can be a positive addition to obtaining home-based care services.	3.70 ± 0.55
2. I think the SMART system can improve my quality of life.	3.31 ± 0.57
3. The SMART system is an important tool for me to access home-based care services.	3.23 ± 0.60
Perceived usefulness	3.42 ± 0.41
4. Using the SMART system makes it easier for me to apply for home-based care services.	3.33 ± 0.62
5. Using the SMART system enables me to apply for home-based care services more quickly.	3.37 ± 0.59
6. Using the SMART system makes it more likely to obtain the home-based care services that I need.	3.55 ± 0.54
7. Using the SMART system is useful for me to apply for home-based care services.	3.55 ± 0.57
8. I think the SMART system presents a more equitable process for applying home-based care services.	3.35 ± 0.57
9. I am satisfied with the SMART system for applying home-based care services.	3.34 ± 0.65
10. The SMART system allows me to obtain home-based care services promptly.	3.52 ± 0.63
11. Using the SMART system promotes the application of home-based care services.	3.68 ± 0.52
12. I can obtain home-based care services that I need whenever I use the SMART system.	3.07 ± 0.69
Perceived ease of use	2.95 ± 0.54
13. I am comfortable with my ability to use the SMART system.	3.05 ± 0.79
14. Learning to use the SMART system is easy for me.	3.14 ± 0.95
15. I can easily become skillful at using the SMART system.	2.60 ± 0.90
16. I find the SMART system easy to use.	2.84 ± 0.78
17. I can always remember how to log on to and use the SMART system.	3.11 ± 0.68
User control	3.34 ± 0.43
18. The SMART system gives messages that tell me how to fix problems.	3.29 ± 0.61
19. Whenever I make a mistake using the SMART system, I recover easily and quickly.	3.01 ± 0.76
20. The information (such as online help, on-screen messages, and other documentation) provided by the SMART system is clear.	3.73 ± 0.45

Based on digital health technology and the principle of neural reflexes, we developed the SMART system, a knowledge-based CDSS, to facilitate integrated home-based older care. Equipped with evidence-based interventions and a set of algorithms, the SMART system can provide a comprehensive diagnosis of care problems and tailor interventions and implementation approaches to address the multifaceted care needs of older individuals according to the assessment results and daily monitoring data. The interventions and implementation approaches generated by the SMART system, after being reviewed and adjusted by professional nurses, will be sent to the corresponding care providers to promote coordinated care services. The usability testing revealed that the SMART system had acceptable usability among older individuals. To the best of our knowledge, this is the first exploration of an integrated care model in the realm of home-based older care, which can provide a paradigm for the future advancements of integrated home-based older care models.

A key strength of this study lies in the adoption of the principle of neural reflexes. In response to insufficient coordination between various care providers in different institutes across social care and healthcare sectors [40–42], we drew inspiration from the principle of neural reflexes to design our SMART system [21]. In the human neural reflex, the sensory receptor, sensory neuron, integration center, motor neuron, and effector organ work together to create automatic and involuntary responses to various stimuli quickly and effectively to maintain homeostasis and protect against potential harm. Similarly, the SMART system can transfer data collected by Sensors and Scales to the Cloud Platform through WiFi or 5G for comprehensive analysis and customized care plans, which are subsequently delivered to the appropriate care providers within the Total Care System via WiFi or 5G, thereby facilitating integrated and coordinated older care services. The Mobile Phone Autonomous Response System serves as a non-conditional reflex function to address emergencies.

The transparency and interpretability of the SMART system represent another notable strength [19]. As a knowledge-based CDSS, the SMART system relied on the pre-embedded knowledge base and rules for decision-making. After collecting data from older people, the SMART system could diagnose the existing or potential care problems based on their abnormal conditions, and further customize interventions and implementation approaches by considering their preferences, lifestyle, and other characteristics. The Professional Care Provider App allowed care providers to access the care problems, interventions, implementation approaches, and their underlying evidence. This feature enables professional care providers to understand the decision-making mechanism and make necessary improvements, thereby ensuring the accuracy and usefulness of the intervention measures provided.

Overall, the SMART system was perceived as useful among older people, with the mean score on each item of the Health-ITUES (Older People Version) above 3.00. Several possible reasons could account for this. Firstly, following the IM framework and nursing process, the SMART knowledge base was developed to incorporate evidence-based interventions tailored to older people’s multifaceted care needs [43]. Additionally, instead of the predefined interventions, the tailored approach could trigger specific care problems and personalized interventions, along with the specific implementation approaches for older people with real-time feedback [44, 45]. Furthermore, the design of our SMART system, rooted in the principle of neural reflexes, could provide continuous, coordinated, and integrated care interventions, which contributed to the perceived ease of usefulness among older individuals.

Meanwhile, the perceived ease of use of a system by users is another essential element in developing useful tools [46]. In our SMART system, we applied several strategies to enhance its ease of use, such as using large font sizes and distinct color blocks, incorporating a read-out mode to reduce visual fatigue and cognitive load, and replacing text with images, videos, and charts for improved comprehension [47]. Despite these efforts, the score of perceived ease of use among older people was relatively low. There are multiple reasons for this. First, the SMART system required multiple functions to satisfy older people’s multifaceted care needs, which, in turn, may overwhelm older people during the learning process, ultimately compromising its ease of use for this demographic [48]. Second, older people usually possess lower digital literacy, which contributed to their low perceived ease of use [49]. Moreover, it was difficult for hospitalized older participants to familiarize themselves with the Care Receiver App and complete the designated tasks in only 24 hours, as they also had some treatment tasks, which may result in a diminished perception of ease of use. Therefore, we will design a streamlined operating interface without compromising functionality. Concurrently, there is an urgent need to adopt some strategies to improve the digital literacy of older people.

This study is subject to several limitations. Firstly, although the results of the care needs assessment for older people were used to guide the development of the SMART system, their direct involvement in the development of the SMART system was absent, which might impede the usability of the Care Receiver App. However, the usability testing among older individuals yielded satisfactory results. Secondly, although we have designed separate portals for professional care providers and other caregivers on the Professional Care Provider App, the functional interfaces for other caregivers have not yet been completed, which will be further developed. Thirdly, the SMART system was initially designed to be merely compatible with Android-based smartphones, and older adults who used an iOS-based smartphone were excluded. Future iterations of the SMART system will guarantee compatibility with both Android and iOS smartphones through the use of WeChat mini-programs. Fourthly, a selection bias existed in the sampling process of the usability testing. Since we conducted usability tests during the prevalence of COVID-19, it was difficult to enter the elderly families for investigation, so we chose the elderly who were about to leave the hospital in the elderly general hospital. However, the hospital environment is also a scenario that can to some extent reflect availability. Fourth, due to the difficulties posed by the Coronavirus disease 2019 pandemic in conducting usability testing at the homes of older individuals, we included hospitalized older individuals who were about to be discharged and return home. While this approach may introduce a potential selection bias, it can also provide valuable insights into the perceived usability of the SMART system among older individuals. Lastly, using self-reported ratings for usability testing could lead to neglecting certain usability issues within the SMART system. Considering the feedback of older individuals when performing the assigned tasks can provide valuable insights to refine our SMART system in future studies.

Applying the principle of neural reflexes, this study developed the SMART system, a knowledge-based CDSS, to facilitate integrated home-based older care by diagnosing care problems for older people and delivering personalized interventions to different care providers as appropriate. Our SMART system was demonstrated to be useful among older individuals. In the future, a rigorous large-scale randomized controlled trial is imperative to determine its effectiveness in achieving integrated older care in home-based settings.

WHO: World Health Organization; IoT: Internet of Things; CDSS: Clinical Decision Support System, IM: Intervention Mapping, B/S: Browser/Server, C/S: Client/Server ; API: application programming interface; UI: user interface; ISO: International Standards Organization; Health-ITUES: Health Information Technology Usability Evaluation Scale; IQR: interquartile range; SD: standard deviation; CARE: Continuity Assessment Record and Evaluation.

Ethics approval and consent to participate

The study was approved by the Institutional Review Committee of the Capital Medical University (Approval No. 2015SY49). All participants provided written informed consent on enrollment. It was also registered in ClinicalTrials.gov (registration number: ChiCTR-IOR-17010368) on 12th January, 2017.

Consent for publication

The written informed consent for publication was obtained from the participants, including professional care providers and older adults.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author at a reasonable request via [email protected].

Competing interests

The authors declare that they have no competing interests.

Funding

This research was supported by the Key Program of the National Natural Science Foundation of China (72034005). The funding source had no role in the study design, data collection, data analysis, data explanation, or manuscript writing.

Authors’ contributions

Rongrong Guo: Formal Analysis, Data Curation, Writing - Original Draft.

Huan Fan: Methodology, Investigation.

Shuqin Xiao: Methodology, Investigation.

Ziling Zheng: Formal Analysis, Data Curation.

Fangyu Yang: Methodology, Data Curation.

Yanyan Xiao: Data Curation.

Xue Yang: Data Curation.

Ying Wu: Conceptualization, Methodology, Writing - Review & Editing, Supervision.

Rongrong Guo, Huan Fan, and Shuqin Xiao made equal contributions to this manuscript. The corresponding author of this article is Ying Wu.

All authors reviewed the manuscript.

Acknowledgments

We would like to thank the whole multidisciplinary team for their effort to develop the SMART system. Additionally, we extend our sincere gratitude to all the older participants involved in the usability testing and gratefully acknowledge the support from the geriatric ward of a comprehensive hospital in Beijing, China.

Dogra S, Dunstan DW, Sugiyama T, Stathi A, Gardiner PA, Owen N. Active Aging and Public Health: Evidence, Implications, and Opportunities. Annu Rev Public Health. 2022;43:439-459. doi:10.1146/annurev-publhealth-052620-091107
United Nations Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Summary of Results. UNDESA/POP/2022/TR/NO.3
Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet. 2019;394(10206):1365-1375. doi:10.1016/S0140-6736(19)31786-6
Dostálová V, Bártová A, Bláhová H, Holmerová I. The needs of older people receiving home care: a scoping review. Aging Clin Exp Res. 2021;33(3):495-504. doi:10.1007/s40520-020-01505-3
Kehoe MacLeod K. Using More Equitable Integrated Care Programs to Reduce Fragmentation in Home Care. Int J Integr Care. 2022;22(3):17. Published 2022 Sep 13. doi:10.5334/ijic.6553
Rudnicka E, Napierała P, Podfigurna A, Męczekalski B, Smolarczyk R, Grymowicz M. The World Health Organization (WHO) approach to healthy ageing. Maturitas. 2020;139:6-11. doi:10.1016/j.maturitas.2020.05.018
Deschodt M, Laurent G, Cornelissen L, et al. Core components and impact of nurse-led integrated care models for home-dwelling older people: A systematic review and meta-analysis. Int J Nurs Stud. 2020;105:103552. doi:10.1016/j.ijnurstu.2020.103552
Tian Y, Wang S, Zhang Y, Meng L, Li X. Effectiveness of information and communication technology-based integrated care for older adults: a systematic review and meta-analysis. Front Public Health. 2024;11:1276574. Published 2024 Jan 5. doi:10.3389/fpubh.2023.1276574
Gröne O, Garcia-Barbero M; WHO European Office for Integrated Health Care Services. Integrated care: a position paper of the WHO European Office for Integrated Health Care Services. Int J Integr Care. 2001;1:e21.
Yu R, Tong C, Woo J. Effect of an integrated care model for pre-frail and frail older people living in community. Age Ageing. 2020;49(6):1048-1055. doi:10.1093/ageing/afaa087
Yan Wang N, Liu X, Kong X, et al. Implementation and impact of the World Health Organization integrated care for older people (ICOPE) program in China: a randomised controlled trial. Age Ageing. 2024;53(1):afad249. doi:10.1093/ageing/afad249
Takeda C, Guyonnet S, Sumi Y, Vellas B, Araujo de Carvalho I. Integrated Care for Older People and the Implementation in the INSPIRE Care Cohort. J Prev Alzheimers Dis. 2020;7(2):70-74. doi:10.14283/jpad.2020.8
Tavassoli N, de Souto Barreto P, Berbon C, et al. Implementation of the WHO integrated care for older people (ICOPE) programme in clinical practice: a prospective study. Lancet Healthy Longev. 2022;3(6):e394-e404. doi:10.1016/S2666-7568(22)00097-6
Franco P, Condon F, Martínez JM, Ahmed MA. Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition. Sensors (Basel). 2023;23(18):7936. Published 2023 Sep 16. doi:10.3390/s23187936
Kouroubali A, Kondylakis H, Logothetidis F, Katehakis DG. Developing an AI-Enabled Integrated Care Platform for Frailty. Healthcare (Basel). 2022;10(3):443. Published 2022 Feb 26. doi:10.3390/healthcare10030443
Chen C, Ding S, Wang J. Digital health for aging populations. Nat Med. 2023;29(7):1623-1630. doi:10.1038/s41591-023-02391-8
De Santis KK, Mergenthal L, Christianson L, Busskamp A, Vonstein C, Zeeb H. Digital Technologies for Health Promotion and Disease Prevention in Older People: Scoping Review. J Med Internet Res. 2023;25:e43542. Published 2023 Mar 23. doi:10.2196/43542
Guerra-Farfan E, Garcia-Sanchez Y, Jornet-Gibert M, Nuñez JH, Balaguer-Castro M, Madden K. Clinical practice guidelines: The good, the bad, and the ugly. Injury. 2023;54 Suppl 3:S26-S29. doi:10.1016/j.injury.2022.01.047
Westerbeek L, Ploegmakers KJ, de Bruijn GJ, et al. Barriers and facilitators influencing medication-related CDSS acceptance according to clinicians: A systematic review. Int J Med Inform. 2021;152:104506. doi:10.1016/j.ijmedinf.2021.104506
Fjellså HMH, Husebø AML, Storm M. eHealth in Care Coordination for Older Adults Living at Home: Scoping Review. J Med Internet Res. 2022;24(10):e39584. Published 2022 Oct 18. doi:10.2196/39584
Holt, Edwin. B. Reflex mechanisms and the physiology of the nerve [J]. Psychological Bulletin, 1914, 15(8):263-272. doi:10.1037/h0072724.
https://kd.nsfc.cn/fundingProjectInit
Berardi D, Giallorenzo S, Mauro J, Melis A, Montesi F, Prandini M. Microservice security: a systematic literature review. PeerJ Comput Sci. 2022;8:e779. Published 2022 Jan 5. doi:10.7717/peerj-cs.779
Michael Ayas H, Leitner P, Hebig R. An empirical study of the systemic and technical migration towards microservices. Empir Softw Eng. 2023;28(4):85. doi:10.1007/s10664-023-10308-9
Li J, Miao B, Wang S, et al. Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization. Brief Bioinform. 2022;23(4):bbac261. doi:10.1093/bib/bbac261
Woody SK, Burdick D, Lapp H, Huang ES. Application programming interfaces for knowledge transfer and generation in the life sciences and healthcare [published correction appears in NPJ Digit Med. 2020 Apr 9;3:56]. NPJ Digit Med. 2020;3:24. Published 2020 Feb 28. doi:10.1038/s41746-020-0235-5
Rajput ZA, Mbugua S, Amadi D, et al. Evaluation of an Android-based mHealth system for population surveillance in developing countries. J Am Med Inform Assoc. 2012;19(4):655-659. doi:10.1136/amiajnl-2011-000476
Pedrola A, Franch-Expósito S, Lahoz S, et al. PCIG: a web-based application to explore immune-genomics interactions across cancer types. Bioinformatics. 2022;38(8):2374-2376. doi:10.1093/bioinformatics/btac111
Krzyworzeka N, Ogiela L, Ogiela MR. Cognitive CAPTCHA Password Reminder. Sensors (Basel). 2023;23(6):3170. Published 2023 Mar 16. doi:10.3390/s23063170
Şeker Ö, Dalkılıç G, Çabuk UC. MARAS: Mutual Authentication and Role-Based Authorization Scheme for Lightweight Internet of Things Applications. Sensors (Basel). 2023;23(12):5674. Published 2023 Jun 17. doi:10.3390/s23125674
Davis, F.D & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. Int. J. Hum. Comput. Stud., 45, 19-45.
Bilardi J, Webb A, Trieu VH, Sharp G, McIntosh J, Temple-Smith M. Miscarriage Australia: the use of a human centered design approach to design and develop a website for those affected by miscarriage. Front Public Health. 2023;11:1128768. Published 2023 May 12. doi:10.3389/fpubh.2023.1128768
Herrmann S, Power B, Rashidi A, et al. Supporting Patient-Clinician Interaction in Chronic HIV Care: Design and Development of a Patient-Reported Outcomes Software Application. J Med Internet Res. 2021;23(7):e27861. Published 2021 Jul 30. doi:10.2196/27861
Ergonomic Requirements for Office Work With Visual Display Terminals (VDTs)-Part 11: Guidance on Usability. 1998. URL: https://www.iso.org/standard/16883.html [accessed 2021-11-19]
Yen PY, Wantland D, Bakken S. Development of a Customizable Health IT Usability Evaluation Scale. AMIA Annu Symp Proc. 2010;2010:917-921. Published 2010 Nov 13.
Schnall R, Cho H, Liu J. Health Information Technology Usability Evaluation Scale (Health-ITUES) for Usability Assessment of Mobile Health Technology: Validation Study. JMIR Mhealth Uhealth. 2018;6(1):e4. Published 2018 Jan 5. doi:10.2196/mhealth.8851
Liu J, Wang Y, Shi XY, et al. Analysis of Current Situation Regarding Scientific Fitness Literacy of Nurses in Sports Medicine Integration. Risk Manag Healthc Policy. 2022;15:1831-1841. Published 2022 Oct 1. doi:10.2147/RMHP.S378969
Centers for Medicare and Medicaid Services. (2012). The development and testing of the Continuity Assessment Record and Evaluation (CARE) Item Set. Final Report on the Development of the CARE Item Set, Vol. 1 of 3. Retrieved from https://www.cms.gov/Medicare/ Quality-Initiatives-Patient-Assessment-Instruments/Post-AcuteCare-Quality-Initiatives/Downloads/The-Development-and-Testi ng-of-the-Continuity-Assessment-Record-and-Evaluation-CAREItem-Set-Final-Report-on-the-Development-of-the-CARE-Item-SetVolume-1-of-3.pdf
Thompson W, McDonald EG. Polypharmacy and Deprescribing in Older Adults. Annu Rev Med. 2024;75:113-127. doi:10.1146/annurev-med-070822-101947
Kalánková D, Stolt M, Scott PA, Papastavrou E, Suhonen R; RANCARE COST Action CA15208. Unmet care needs of older people: A scoping review. Nurs Ethics. 2021;28(2):149-178. doi:10.1177/0969733020948112
Chen CC, Chiang YC, Lin YC, Cheng SH. Continuity of Care and Coordination of Care: Can they Be Differentiated?. Int J Integr Care. 2023;23(1):10. Published 2023 Feb 17. doi:10.5334/ijic.6467
Sterling MR, Lau J, Rajan M, Safford MM, Akinyelure OP, Kern LM. Self-reported gaps in care coordination and preventable adverse outcomes among older adults receiving home health care. J Am Geriatr Soc. 2023;71(3):810-820. doi:10.1111/jgs.18135
Bakhuys Roozeboom MC, Wiezer NM, Boot CRL, Bongers PM, Schelvis RMC. Use of Intervention Mapping for Occupational Risk Prevention and Health Promotion: A Systematic Review of Literature. Int J Environ Res Public Health. 2021;18(4):1775. Published 2021 Feb 11. doi:10.3390/ijerph18041775
Chen T, Shang C, Su P, et al. A Decision Tree-Initialised Neuro-fuzzy Approach for Clinical Decision Support. Artif Intell Med. 2021;111:101986. doi:10.1016/j.artmed.2020.101986
Dawson RM, Felder TM, Donevant SB, et al. What makes a good health 'app'? Identifying the strengths and limitations of existing mobile application evaluation tools. Nurs Inq. 2020;27(2):e12333. doi:10.1111/nin.12333
Giebel GD, Speckemeier C, Abels C, et al. Problems and Barriers Related to the Use of Digital Health Applications: Scoping Review. J Med Internet Res. 2023;25:e43808. Published 2023 May 12. doi:10.2196/43808
Chang M, Brainerd CJ. Association and dissociation between judgments of learning and memory: A Meta-analysis of the font size effect. Metacogn Learn. 2022;17(2):443-476. doi:10.1007/s11409-021-09287-3
Chen Y, Ji M, Wu Y, et al. An Intelligent Individualized Cardiovascular App for Risk Elimination (iCARE) for Individuals With Coronary Heart Disease: Development and Usability Testing Analysis. JMIR Mhealth Uhealth. 2021;9(12):e26439. Published 2021 Dec 13. doi:10.2196/26439
Estrela M, Semedo G, Roque F, Ferreira PL, Herdeiro MT. Sociodemographic determinants of digital health literacy: A systematic review and meta-analysis. Int J Med Inform. 2023;177:105124. doi:10.1016/j.ijmedinf.2023.105124

No competing interests reported.

Additionalfile1.docx
Additional Files Additional File 1
AdditionalFile2.LogooftheSMARTsystem.png
Additional File 2. Logo of the SMART system.png
AdditionalFile3.IntroductionoftheSMARTsystem.mp4
Additional File 3. Introduction of the SMART system.mp4
AdditionalFile4.Registration.mp4
Additional File 4. Registration.mp4
AdditionalFile5.Loginandlogout.mp4
Additional File 5. Login and logout.mp4
AdditionalFile6.AssessmentmoduleoftheCareReceiverApp1.mp4
Additional File 6. Assessment module of the Care Receiver App1.mp4
AdditionalFile7.AssessmentmoduleoftheCareReceiverApp2.mp4
Additional File 7. Assessment module of the Care Receiver App2.mp4
AdditionalFile8.DailymonitoringmoduleoftheCareReceiverApp.mp4
Additional File 8. Daily monitoring module of the Care Receiver App.mp4
AdditionalFile9.MedicationmanagementmoduleoftheCareReceiverApp.mp4
Additional File 9. Medication management module of the Care Receiver App.mp4
AdditionalFile10.InterfacesoftheCareReceiverApp.mp4
Additional File 10. Interfaces of the Care Receiver App.mp4
AdditionalFile11.ReviewontheProfessionalCareProviderApp.mp4
Additional File 11. Review on the Professional Care Provider App.mp4
AdditionalFile12.Reviewanddelivery.mp4
Additional File 12. Review and delivery.mp4

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Editorial decision: Revision requested
26 Jun, 2024
Editor assigned by journal
20 Jun, 2024
Submission checks completed at journal
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18 May, 2024

You are reading this latest preprint version

Development and Usability Testing of An Intelligent and Integrated Older Care Model (SMART System) to Promote Integrated Home-based Older Care

Status:

Version 1

Abstract

Figures

1. Background

2. Methods

2.1 Phase 1: Development of the SMART System

2.1.1 Functional Design

2.1.2 Architecture and Database Design

2.1.3 Security Measures Design

2.1.4 User Interface and Visualization Design

2.1.5 Prototypes Development and Iteratively Testing

2.2 Phase 2: Usability Testing of the SMART System

2.2.1 Study Design and Participants

2.2.2 Measures

2.2.3 Testing Process

2.2.4 Sample Size Calculation

2.2.5 Statistical Analysis

3. Results

3.1 Phase 1: Development of the SMART System

3.1.1 Functions of the SMART System

3.1.2 UIs of the SMART System

3.1.3 Prototypes of the SMART system

3.2 Phase 2: Usability Testing of the SMART system

3.2.1 Characteristics of the older participants

3.2.2 Health-ITUES scores of the older participants

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1