The Internet of Things (IoT) refers to a physical world where smart devices or things and computers interconnect through wired or wireless networks to share and process information without human intervention. Health Internet of Things (HIoT) refers to a device that collects health related data from an individual. The explosive growth in HIoT to augment the delivery of healthcare is driving changes in clinical practice and patient-centered care, requiring new skills for providers. In this article I provide an overview of HIoT, factors fueling wearable market growth, and HIoT technology and usability challenges. Next, I present collaboration as a key to success in design and implementation of HIoT initiatives, identifying collaboration models useful to healthcare and consumer/patient teams. The discussion includes the technology adoption model (TAM) and associated adoption considerations. Finally, I identify implications for the nursing profession and provide HIoT recommendations for next steps that include engaging healthcare providers in the design and testing of HIoT products.
Key Words: collaboration, coproduction, data quality, digital health, design thinking, digital literacy, eHealth literacy, Healthcare IoT, HIoT, health literacy, Internet of Things, IoT, participatory design, person centered care, technology acceptance, TAM, wearable device
The Internet of Things (IoT) refers to a physical world where smart devices or things and computers are interconnected through wired or wireless networks...The Internet of Things (IoT) refers to a physical world where smart devices or things and computers are interconnected through wired or wireless networks to share and process information without human intervention (Dang, Piran, Han, Min, & Moon, 2019; Kelly, Cambell, Gong, & Scuffham, 2020). The device, or thing, has a sensor that transmits digital data through a network to computers that have been programmed to apply intelligence to the sensor data. A sensor can connect with computers through multiple technologies such as cellular or satellite networks, radio frequency identification (RFID), Bluetooth, Wi-Fi, Zigbee, Ethernet or LPWAN (low-power wide-area networks). Networking technology solutions are based on: a) required sensor data transmission rates and volumes, b) network sensor to computer reliability requirements, c) the complexity to install and manage the sensor connections within the network, and d) costs incurred for implementing and maintaining IoT applications and network technologies (Selvaraj & Sundaravaradhan, 2019; Holdowsky, Raynor, Mahto, & Cotteleer, 2015).
Defining Healthcare IoT (HIoT)
Kelly et al. (2020) define [healthcare] IoT [HIoT] as “any device that can collect health-related data from individuals, including computing devices, mobile phones, smart bands and wearables, digital medications, implantable surgical devices, or other portable devices, which can measure health and connect to the internet” (pg. 2). Examples of data collected include information about body motion and fall status; blood pressure; blood glucose level; body mass index (BMI); geographic location; heart rate; images; machine status; access to smart pill containers; sound; temperature; and many other categories of health-related data. These data can then be used to remotely monitor health status and detect patient conditions (Kelly et al, 2020; Senbekov et al, 2020; Dang et al, 2019; Wu & Luo, 2019).
HIoT devices are categorized as implanted, stationary, and wearable.HIoT devices are categorized as implanted, stationary, and wearable. According to Joung (2013) an implanted medical device is “partly or totally introduced, surgically or medically, into the human body and is intended to remain there after a procedure” (pg. 98). Examples of implanted IoT devices are pacemakers, defibrillators, and nerve stimulators that are closely monitored by clinical personnel (Ronte, Taylor, & Haughey, 2018).
Stationary IoT refer to devices such as X-ray and mammography, MRI scanners, and nuclear imaging machines that can transmit information to physicians and clinicians, and that can integrate data collected from the stationary device into other applications such as patient electronic health records (EHRs). In 2017 it was estimated by Ronte et al. (2018) that implanted and stationary medical device expenditures equaled $10.8 billion and would reach $35.9 billion by 2022.
HIoT wearable devices that track and communicate health status include smartwatches, wristbands, ear-worn (i.e., hearables), head-mounted display (e.g., Google Glass), smart clothing, and smart patches. Gartner Inc. forecasts global spending on wearables will reach $81.5 billion in 2021 and will exceed $93 billion in 2022, representing significant growth from $46.2 billion in 2019 (Rimol, 2021). Cisco Systems estimates that by 2022 connected wearables will total 1.105 billion devices (Cisco Public, 2019). Gartner attributes the growth in wearable HIoT to increased consumer interest in personal health and fitness; a desire to track COVID-19 symptoms; improved smart patch technologies that provide easy to use self-monitoring options; and a transition to remote work which significantly increased use of new ear-worn, wireless devices.
Other Factors Fueling Wearable Market Growth
Wearables provide a solution for consumers to fulfill personalized care and healthcare delivery priorities.Holdowsky et al. (2018) identified other trends impacting the growth of wearable devices, such as a global population expected to reach 1.6 billion aged 65 and over by 2050; cultural expectations for personalized healthcare that is fair, and easy to access and use; and technology innovations that expand healthcare delivery options. TranscendInsights, a market research firm, supports the Holdowsky et al. (2018) findings. In 2017, TranscendInsights surveyed over 2,500 respondents and found that personalized care from a patient perspective meant that providers and patients could easily share and receive medical history information and medical records, and that healthcare included mental and physical health. During the study period, 64% of respondents reported using a digital device and mobile app to manage their health (TranscendInsights, 2017). Microsoft (2020) found “81% of patients reported being unsatisfied with their current healthcare experience, and wanted access to services whenever they want, from wherever they are, and on whatever device they’re using" (p. 6). Wearables provide a solution for consumers to fulfill personalized care and healthcare delivery priorities.
Wu and Luo’s (2019) literature review of wearable technology applications highlighted the scope of HIoT devices available today to assist with healthcare tracking, reporting, and prevention. However, innovations in wearable devices continue to proliferate. Gee, Ho, & Raab (2021) of TIME magazine asked seven technology industry analysts and research experts to predict the future of wearables. Expert predictions included improved smart shoes that could charge your phone as you walk, shirts that give you directions, contact lenses that double up as your personal assistant, microchips in nail polish, buttons that track your steps and/or learn your habits, and earrings that can assess your moods. Dalvin Brown (2021) reports that Apple has patented smart buttons that trigger controls on Apple devices.
Not to be forgotten is how we might use our skin to display information.If this all seems ‘far off’ into the future, take note of current advancements at AI Silk, a company founded in 2015 that turns silk into sensors using a novel dyeing process (Kapfunde, 2019). Brown (2021) reports advancements in Nextiles’ smart fabrics that capture biometric data in a fabric that is machine washable. Also consider the WAREABLE clothing site (n.d.) that tracks smart clothes and other IoT product innovations. Not to be forgotten is how we might use our skin to display information. Takao Someya (2021) details how skin can display information that may someday replace your smartphone.
What we thought was slated for the future we now know may be here today or coming soon.Jeff Spry (2020) provides information about a breakthrough patent for a wearable glove that translates sign language into speech, potentially bringing another dimension to patient care and education. A review of the new glove in Nature Electronics by Zhou et al., (2020) suggests that the glove has a sign language and hand gesture recognition rate of up to 98.63% with less than 1 second lag time. What if our wearable device could smell? Marr (2021) reports findings about artificial intelligence company Aryballe, a startup that uses AI and olfaction technology to give machines a sense of smell. Marr (2021) posits that a potential use of the new technology could be to assist healthcare sensors to detect environmental issues and alert users. What we thought was slated for the future we now know may be here today or coming soon.
HIoT Technology and Usability Challenges
HIoT device research suggests that significant technology and usability factors remain a challenge. Core issues affecting data quality and device utility include:
- Differences in device data definitions, data capture, and algorithm capabilities where typically consumer devices are not as accurate as medically approved devices (Cho et al., 2021; Haghi et al., 2017).
- Variability in device features such as size, battery life, weight, and sensor capacity are not well understood and addressed with end users (Haghi et al., 2017).
- To use smart clothing, consumers require lightweight, easy to use fabric that is comfortable to wear (Cho et al., 2021; Haghi, Thurow, & Stoll, 2017).
- To work properly smart clothing must fit correctly, otherwise sensors cannot function properly. In some smart clothing designs, the clothing components must be removed before washing (Haghi et al., 2017).
- There is significant variability in how consumers wear tracking devices to record health patterns. This is attributed to device design issues, user error, and technical complications experienced with devices (Cho et al., 2021).
- The perceived value of device data by end users and clinical staff must be clear, and privacy versus risk tradeoffs must be well understood (Kelly et al., 2020).
- Network transmission reliability, device security, and computer storage and processing capabilities can impact data results (Selvaraj & Sundaravaradhan, 2019; Haghi et al., 2017).
Collaboration: A Potential Key to HIoT Success
Current HIoT challenges suggest that improved product and service design processes are needed. Collaboration among a diverse set of team members who bring different perspectives to identify problems and design solutions may provide a pathway to mitigate HIoT product and service shortcomings. A variety of collaborative frameworks have been successfully tested and applied in healthcare.
Current HIoT challenges suggest that improved product and service design processes are needed.Batalden et al. (2016) conceptualized a model of Coproduced Healthcare Service whereby patients and professionals work together to produce successful functional and clinical outcomes defined by all stakeholders. The model was successful in projects related to patient self-care, the design of patient group visits, and the creation of a learning network for inflammatory bowel disease. In a cancer patient digital information tool study for radiotherapy treatments (RT), Grynne, Browall, Fristedt, Ahlberg, and Smith (2021) used a Participatory Design (PD) methodology developed by Spinuzzi (2005) to facilitate collaboration between patients and medical teams to actively involve all stakeholders in the RT application design process.
A foundational premise of the IDEO design process is that complex problems are best solved collaboratively.IDEO, a global product and service design company, uses Design Thinking methods to help others innovate and solve problems. Tim Brown, Chair of IDEO, describes Design Thinking as “…a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success” (IDEO, p.1). A foundational premise of the IDEO design process is that complex problems are best solved collaboratively. Design Thinking has been successfully implemented at the Mayo Clinic (Waugh & Gibbs Howard, n.d.; Fullerton & Mullen, 2016), Children’s Health System of Texas (Liedtka & MacLaren, 2018), in the design of birth control product delivery, diabetes care management and devices, genomics testing devices, heart rate monitor design, Human Immunodeficiency Virus prevention services, nutrition branding, adaptive sleep plans for athletes, wearable breast pumps, and many other product and service applications.
The HIoT challenges highlight a need to include healthcare providers in HIoT design and implementation initiatives.The HIoT challenges highlight a need to include healthcare providers in HIoT design and implementation initiatives. Dykes & Chu (2020) suggested that nurses have not been included in technology design efforts, thus forcing them to construct technology workarounds. The Future of Nursing 2020-2030 Report (2021) recommends that nursing expertise should be used in “designing, generating, analyzing, and applying data to support initiatives focused on social determinants of health and health equity using diverse digital platforms, artificial intelligence, and other innovative technologies” (p.13). We are now at the forefront of HIoT product innovation and market growth. It would be prudent for technology developers to engage nurses in prototyping efforts.
HIoT Technology Adoption Considerations
While many technology adoption models have been proposed (for model overviews see Taherdoost, 2018; Sharma & Mishra, 2014; Holden & Karsh, 2010), the Technology Acceptance Model (TAM) theorized by Davis (1989) is noted as ‘the gold standard” framework for understanding technology adoption (Holden & Karsh, 2010). Holden and Karsh estimated that 30% to 40% of IT acceptance reviews were related to TAM. In their review, Shachak, Kuziemsky, & Petersen, (2019) found over 12,000 articles citing Davis’ TAM theory. The original TAM theory is based on a simple model of four factors: perceived usefulness (PU); perceived ease of use (PEOU); and attitude (ATT) towards use, which lead to a behavioral intention (BI) to use the technology. Davis (1989, pp. 320-322) defines the model factors as:
- Perceived usefulness refers to the degree to which a person believes that using a particular system would enhance his or her performance;
- Perceived ease of use is the degree to which a person believes that using a particular system would be free of effort;
- Attitude towards using the technology is a judgment and expectation about the outcome of using the technology.
Those three factors determine an individual’s Behavioral Intention (BI) to use the technology (i.e., “Acceptance”). The theory assumes that “behavioral intention is a direct determinant for actual system usage” (Ammenwerth, p. 69).
In healthcare research the TAM model has been extended to include multiple other factors such as subjective norms (social pressures), descriptive norms (will colleagues use the technology), self-efficacy beliefs, and familiarity/use of computers (Ketikidis, Dimitrovski, Lazuras, & Bath, 2012), image, voluntary use, job relevance, computer anxiety, technology enjoyment, nurse experience, EHR experience, computer skills (Ammenwerth, 2019), external variables such as user training, system traits, user participation in the system design and implementation (Taherdoost, 2017). Specific nursing studies using TAM tested factors such as teamwork, communication, feedback, hospital management, patient safety, training, technical infrastructures, tech support, equipment, optimism, innovativeness, insecurity, computer anxiety, self-efficacy, job relevance, social influence, and nurse work experience (Strudwick, 2015).
Given the vast number of additional variables and the great variability in construct definitions tested with the original TAM model and TAM model iterations (Holden & Karsh, 2010), it is not surprising that research findings are mixed. Explained model variances in nursing studies ranged from 24% to 87% (Ammenwerth, 2019; Ketikidis et al., 2012; Kowitlawakul, 2011; Strudwick, 2015). Across TAM healthcare studies, Holden and Karsh (2010) noted “Perhaps most impressive is that the relationship between PU [Perceived Usefulness] and intention to use or actual use of health IT is significant in every test, suggesting that to promote use and acceptance, the health IT must be perceived as useful” (p.165). Their findings of PU are consistent with other TAM research findings.
...alternative approaches to understanding technology adoption at a broader system level are needed.PEOU (Perceived Ease of Use) as a factor has resulted in mixed findings across studies. In nursing studies, computer skills, nursing experience, and factors such as system importance, endorsement, physician, and organizational support were identified as significant factors for behavioral intention to use a technology. Given the range of TAM model results, Shachak et al., (2019) suggested that alternative approaches to understanding technology adoption at a broader system level are needed. They proposed exploring “teamwork, multitasking, time constraints, workflow and interruptions” (Shachak et al., 2019, p. 2) that address system complexity, diverse user needs, and longitudinal technology adoption challenges.
...factors supporting intention to use a specific technology differ.TAM has been used to test consumer acceptance of wearable technologies such as fitness bands (Lunney, Cunningham, & Eastin, 2016), cardiac monitoring vests, (Tsai, Lin, Chang, Chang, & Lee, 2016) and smart clothes that monitor posture and vitals (Lin, Chou, Tsai, Lin, & Lee, 2016). Similar to healthcare team research, across these studies PU was a consistent factor for intention to use wearable technologies. However, differences in consumer adoption factors versus healthcare team adoption factors were found. Factors such as consumer health beliefs, information accuracy, reference groups (Cheung et al., 2019), perceived health outcomes (Lunney et al., 2016), perceived ubiquity of health information and attitude towards technology (Tsai et al., 2020) were identified as unique factors that predict consumer willingness to adopt wearable technology.
In sum, TAM findings for consumer and healthcare team adoption models suggest that factors supporting intention to use a specific technology differ. These differences indicate that strategies for design and implementation of HIoT products and services would benefit from targeting different antecedents, depending on target audience and type of HIoT device or service provided.
HIoT Implications for the Nursing Profession
...nurses need to address the impact of HIoT on the role of nursing in delivery of person-centered care.Given the rapid pace of the deployment of HIoT devices in healthcare settings, nurses need to address the impact of HIoT on the role of nursing in delivery of person-centered care. It is important to determine what changes in nursing education are needed to keep pace with evolving technological advancements.
The Future of Nursing 2020-2030 Report (2021) defines person-centered care as care models with the following attributes:
Personal choice and autonomy, customized [patient] care that meets individual abilities, needs, and preferences, care that addresses physical, mental and social needs, care teams that codesign [with patients] interventions, services, and policies with a focus on what the person and community want and need, maintain respect, deliver antidiscriminatory care, encourage prevention and health promotion, and improve patient health literacy abilities to empower and engage patients in healthcare decisions so they can self-manage their health (p.113-114).
Specifically, nurses did not perceive they could participate in technology decision processes.Nurses are on the front line of care. Implementation of person-centered care coupled with new HIoT innovations will require them to gain timely experience with and knowledge about a variety of HIoT devices (Hiremath, Yang, & Mankodiya, 2014; Carroll, 2020). Booth et al. (2021) postulated “that the rapid evolution of many modern-day technologies has superseded the abilities of many nurses to remain situationally aware of their presence in health(care) practice” (p. 398), and they suggested that technology ubiquity will drive a need for nurses to embrace new roles as ‘digital brokers and system navigators’ (p. 401). Mather, Cummings, and Gale (2019) studied nurses as stakeholders in a mobile technology application and concluded that nurses were not prepared for the digital future. Specifically, nurses did not perceive they could participate in technology decision processes. This suggests that a new kind of training and education will be needed to support contributions by nurses toward achieving person-centered digitally literate healthcare (Booth et al., 2021; Dykes & Chu, 2020; Huston, 2013; NASEM, 2021; Pepito & Locsin, 2018).
The new plan has placed a high priority on health literacy as a core objective for the nation.To effectively support patients in new models of HIoT driven healthcare, nurses will need to develop skills that include the ability to confidently navigate technology (Dykes & Chu, 2020; Huston, 2013; World Health Organization, 2020). They will need to acquire skills to assess a patient’s health literacy and digital literacy (i.e., eHealth literacy) capabilities (Brach & Harris, 2021; Cameron, 2011; Palumbo, 2021). Healthy People 2030 is the nation’s 10-year plan for addressing critical public health priorities and challenges. It is managed by the Department of Health and Human Services Office of Disease Prevention and Health Promotion ([U.S. DHHS], 2020). The new plan has placed a high priority on health literacy as a core objective for the nation. The plan focuses on how people use their health information to make well-informed healthcare decisions, and the role that both individuals and organizations contribute toward achieving health literacy objectives. The plan defines aspects of health literacy as presented below in Table 1.
Table 1. Healthy People 2030 Health Literacy Definitions
Personal Health Literacy is the degree to which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others.
Organizational Health Literacy is the degree to which organizations equitably enable individuals to find, understand, and use information and services to inform health-related decisions and actions for themselves.
(U.S. DHHS, 2020, p. 1)
...health literacy and digital literacy skills will continue to become highly interrelated.Digital literacy (i.e., eHealth literacy) assesses individual skills needed to navigate and evaluate online content. The skills include the ability to “search, select, appraise, and apply online health information and health care related digital applications (van der Vaart & Drossaert, 2017, p. 2). With the proliferation of HIoT innovations, health literacy and digital literacy skills will continue to become highly interrelated. Patients and clinicians will need both types of literacy skills to successfully navigate new healthcare delivery models.
Patient ability to understand and use data generated from HIoT devices will depend on health literacy and digital literacy capabilities. It is important that nurse educators and healthcare organizations begin to experiment with new assessment tools. These tools must measure patient and clinician abilities to understand and use new forms of health data. Healthy People 2030 (U.S. DHHS, 2020) has identified six measurable health literacy objectives that can serve as guideposts for individual practitioners and organizations. They are identified in Table 2.
Table 2. Healthy People 2030 Health Literacy Objectives
HCHiIT-01. Increase the proportion of adults whose healthcare provider checked their understanding
HCIT-02. Decrease the proportion of adults who report poor communication with their healthcare provider
HCIT-03. Increase the proportion of adults whose healthcare provider involved them in decisions as much as they wanted
HCIT-d10. Increase the proportion of people who say their online medical record is easy to understand
HCIT-d11 Increase the proportion of adults with limited English proficiency who say their providers explain things clearly
HCIT-r01. Increase the health literacy of the population
(U.S. DHHS, 2020, p.1)
Digital literacy assessment tools such as the eHealth Literacy Scale (eHEALS) created by Norman & Skinner (2006), and The Digital Health Literacy Instrument (DHLI) created by van der Vaart & Drossaert (2017), are starting points for organizations and education providers to begin exploring how to best assess digital literacy/eHealth capabilities that are required to understand HIoT data. As HIoT innovations explode and take on new forms of data collection and analysis (Atluri, Cordina, Mango, Rao, & Velamoor, 2016), literacy assessment tools need to keep pace with the knowledge, skills, and abilities required to interpret and leverage data that is designed to personalize and improve individual healthcare outcomes.
Given the complexities of technology adoption it is recommended that healthcare teams begin their HIoT journey by taking the following actions:
- Determine what collaboration model may be a best fit for your organization and/or for your HIoT initiatives that include consumers/patients. Select a model to pilot test with interprofessional teams and stakeholders. As Batalden et al. (2016) did, ask, “What will encourage effective codesign?”
- Provide collaboration model training to all stakeholders (e.g., healthcare teams and consumers/patients) who will participate in the HIoT initiative.
- Begin a pilot with a small, well-defined problem that you are attempting to resolve using new HIoT devices or service offerings. Provide detailed training about the technologies and outputs expected from the HIoT initiative. Be specific about what measured outcomes you are trying to achieve. Prepare for setbacks during pilot testing. Feedback and open communication will help to overcome the downturns and should be part of the pilot project DNA.
- At the beginning of the HIoT initiative identify which adoption factors you are going to consider. The HIoT project references include surveys that can be used to guide your selection of adoption factors to address in HIoT design and implementation plans. Establish outcome measures that can signal adoption progress or adoption barriers.
- Celebrate successes and failures as collaboration improves over time.
Accountability comes with innovation, and it is the responsibility of HIoT product creators to include clinical personnel and consumers in the design of new innovations. As innovations are launched, HIoT product managers need to ensure that timely, relevant, transparent, and easy to understand HIoT training is available for frontline healthcare personnel. Alternatively, it is the responsibility of nurse educators and nurse leaders to ensure that nursing teams and students receive adequate training and skill development opportunities to learn how to be a productive and active participant in collaborative design and implementation efforts. An examination of critical technology adoption factors from both clinical team members and consumer/patient perspectives can help guide design and deployment of effective training and skill development initiatives.
Nancy Robert, PhD, MBA/DSS, BSN
Dr. Robert is an award-winning product innovator and patent holder experienced in the launch of digital products and services. She is the co-author of two books, multiple journal articles, and as a co-creator of The Doctor Development Project has published research and competency tools in the area of medical decision making. Dr. Robert is the Managing Partner for Polaris Solutions, a product and education consulting company.
Shachak, A., Kuziemsky, C., & Petersen, C. (2019). Beyond TAM and UTAUT: Future directions for HIT implementation research. Journal of Biomedical Informatics, 100, 103315. https://doi.org/10.1016/j.jbi.2019.103315
Selvaraj, S. & Sundaravaradhan, S. (2019, November 1). Challenges and opportunities in IoT healthcare systems: A systematic review. SN Applied Sciences, 2,139. https://doi.org/10.1007/s42452-019-1925-y
Ammenwerth, Elske. (2019). Technology acceptance models in health informatics: TAM and UTAUT. Stud Health Technol Inform, 263, 64-71. https://doi.org/10.3233/SHTI190111
Atluri, V., Cordina, J., Mango, P., Rao, S. & Velamoor, S. (2016). How tech-enabled consumers are reordering the healthcare landscape. McKinsey&Company. Retrieved from: https://www.mckinsey.com/~/media/mckinsey/industries/healthcare%20systems%20and%20services/our%20insights/how%20tech%20enabled%20consumers%20are%20reordering%20the%20healthcare%20landscape/how-tech-enabled-consumers-are-reordering-healthcare-landscape-full-report.ashx
Batalden, M., Batalden, P., Margolis, P., Seid, M., Armstrong, G., Opipari-Arrigan, L., & Hartung, H. (2016). Coproduction of healthcare service. BMJ Quality Safety, 25(7), 509-517. https://doi.org/10.1136/bmjqs-2015-004315
Booth, R., Strudwick, G., McMurray, J. , Chan R., Cotton K., & Cook, S. (2021). The future of nursing informatics in a digitally-enabled world. In P. Hussey & M.A. Kennedy (Eds.), Introduction to nursing informatics (pp. 395-417). Springer. https://doi.org/10.1007/978-3-030-58740-6_16
Brach, C., & Harris, L.M. (2021). Healthy People 2030 health literacy definition tells organizations: Make information and services easy to find, understand, and use. Online Journal General Internal Medicine, 36, 1084-1085. https://doi.org/10.1007/s11606-020-06384-y
Brown, D. (2021, May 31). Did you get enough steps today? Maybe one day you'll ask your 'smart' shirt. The Washington Post. Retrieved from https://www.washingtonpost.com/technology/2021/05/03/smart-fabric-future-of-clothing/
Cameron, N. (2011). eHealth literacy 2.0: Problems and opportunities with an evolving concept. Journal of Medical Internet Research, 3(4), 1-4. Retrieved from: https://www.jmir.org/2011/4/e125/
Carroll, W. M. (2020, November 16). 2020: Emerging technology in global nursing care. Latest Resources. Retrieved from: https://www.himss.org/resources/2020-emerging-technology-global-nursing-care
Cheung, M. L., Chau, K.Y., Sum Lam, M. H., Tse, G., Ho, K.Y., Flint, S.W., Broom, D. R. Tso, E. K. H., & Lee, K.Y. (2019). Examining consumers' adoption of wearable healthcare technology: The role of health attributes. International Journal of Environmental Research and Public Health, 16(13). https://doi.org/10.3390/ijerph16132257
Cho, S., Ensari, I., Weng, C., Kahn, M. G., & Natarajan, K. (2021). Factors affecting the quality of person-generated wearable device data and associated challenges: Rapid systematic review. JMIR Mhealth Uhealth, 9(3):e20738 https://doi.org/10.2196/20738
CISCO PUBLIC. (2019). Cisco visual networking index: Global mobile data traffic forecast update, 2017-2022. [White paper]. CISCO. Retrieved from: https://s3.amazonaws.com/media.mediapost.com/uploads/CiscoForecast.pdf
Dang L. M., Piran M. J., Han D., Min K., & Moon H. (2019). A Survey on internet of things and cloud computing for healthcare. Electronics, 8(7),768. https://doi.org/10.3390/electronics8070768
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Dykes, S., & Chu, C.H. (2020). Now more than ever, nurses need to be involved in technology design: Lessons from the COVID-19 pandemic. Journal of Clinical Nursing, 30(7-8), e25-e28. https://doi.org/10.1111/jocn.15581
Fullerton, T., & Mullen, W. (2016, June 29). Design at Mayo [online case study # 09-034]. Yale School of Management. http://vol10.cases.som.yale.edu/design-mayo
Gee, M., Ho, A., & Raab, J. (2021). A day in the life of wearable tech. TIME. Retrieved from https://time.com/see-the-wearable-tech-of-the-future/
Grynne, A., Browall, M., Fristedt, S., Ahlberg, K., & Smith, F. (2021). Integrating perspectives of patients, healthcare professionals, system developers and academics in the co-design of a digital information tool. Plos one, 16(7), e0253448. https://doi.org/10.1371/journal.pone.0253448
Haghi, M., Thurow, K., & Stoll, R. (2017). Wearable devices in medical internet of things: Scientific research and commercially available devices. Healthcare Informatics Research, 23(1), 4–15. https://doi.org/10.4258/hir.2017.23.1.4
Hiremath, S., Yang, G., & Mankodiya, K. (2014). Wearable internet of things: Concept, architectural components and promises for person-centered healthcare. 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies. https://doi.org/10.4108/icst.mobihealth.2014.257440
Holden, R. J., & Karsh, B. T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172. https://doi.org/10.1016/j.jbi.2009.07.002
Holdowsky, J., Raynor, M., Mahto, M., & Cotteleer, M. (2015, August 21). Inside the internet of things (IoT): A primer on the technolgies building the IoT. Deloitte Insights. Retrieved from: https://www2.deloitte.com/us/en/insights/focus/internet-of-things/iot-primer-iot-technologies-applications.html
Huston, C. (2013). The impact of emerging technology on nursing care: Warp speed ahead. OJIN: The Online Journal of Issues in Nursing, 18(2) Manuscript 1. https://doi.org/10.3912/OJIN.Vol18No02Man01
IDEO. (n.d.). Design thinking defined. Design Thinking. Retrieved from: https://designthinking.ideo.com/
Joung, Y. H. (2013). Development of implantable medical devices: From an engineering perspective. International Neurology Journal, 17(3), 98-106. https://doi.org/10.5213/inj.2013.17.3.98
Kapfunde, M. (2019, April 30). AI silk: Freeing the future by turning silk into sensors [Blog]. Succhi Inc. Retrieved from https://www.suuchi.com/ai-silk-freeing-the-future-by-turning-silk-into-sensors/
Kelly, J. T., Campbell, K. L., Gong, E. & Scuffham, P. The internet of things: Impact and implications for health care delivery. Journal of Medical Internet Research, 22(11), e20135. https://doi.org/10.2196/20135
Ketikidis, P., Dimitrovski, T., Lazuras, L., & Bath, P. A. (2012). Acceptance of health information technology in health professionals: An application of the revised technology acceptance model. Health informatics Journal, 18(2), 124-134. https://doi.org/10.1177%2F1460458211435425
Kowitlawakul, Y. (2011). The technology acceptance model: Predicting nurses’ intention to use telemedicine technology (eICU). The Computers, Informatics, Nursing, 29(7), 411-418. https://doi.org/10.1097/NCN.0b013e3181f9dd4a
Liedtka, J. & MacLaren, E. (2018, November 7). How children's health system of Texas is improving care with design thinking. Harvard Business Review, Retrieved from: https://hbr.org/2018/11/how-childrens-health-system-of-texas-is-improving-care-with-design-thinking
Lin, W. Y., Chou, W. C., Tsai, T. H., Lin, C. C., & Lee, M. Y. (2016). Development of a wearable instrument vest for posture monitoring and system usability verification based on the technology acceptance model. Sensors, 16(12). https://doi.org/10.3390/s16122172
Lunney, A., Cunningham, N. R., & Eastin, M. S. (2016). Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes. Computers in Human Behavior, 65, 114-120. https://doi.org/10.1016/j.chb.2016.08.007
Marr, B. (2021, May 10). Artificial intelligence is developing a sense of smell: What could a digital nose mean in practice? Forbes. Retrieved from: https://www.forbes.com/sites/bernardmarr/2021/05/10/artificial-intelligence-is-developing-a-sense-of-smell-what-could-a-digital-nose-mean-in-practice/?sh=19889d911544&mod=djemAIPro
Mather, C., Cummings, E., & Gale, F. (2019). Nurses as stakeholders in the adoption of mobile technology in Australian health care environments: Interview study. JMIR Nursing, 2(1), e14279. https://doi.org/10.2196%2F14279
Microsoft. (2020). Personalizing healthcare: Engaging patients in a digital age. Microsoft Health. Retrieved from: https://clouddamcdnprodep.azureedge.net/gdc/2339042/original
National Academies of Sciences, Engineering, and Medicine (NASEM). (2021). The Future of Nursing 2020-2030: Charting a path to achieve health equity. The National Academies Press. https://doi.org/10.17226/25982
Norman, C. D. & Skinner, H. A. (2006). eHealth literacy: Essential skills for consumer health in a networked world. Journal of Medical Internet Research, 8(2), e9. https://doi.org/10.2196/jmir.8.2.e9
Pepito, J. A. & Locsin, R. (2018). Can nurses remain relevant in a technologically advanced future? International Journal of Nursing Sciences, 6(1),106-110. https://doi.org/10.1016/j.ijnss.2018.09.013
Palumbo, R. (2021). Leveraging organizational health literacy to enhance health promotion and risk prevention: A narrative and interpretive literature review. Yale Journal of Biology and Medicine, 94(1),115-128. Retrieved from https://pubmed.ncbi.nlm.nih.gov/33795988/
Rimol, M. (2021, January 12). Gartner forecasts global spending on wearable devices to total $81.5 billion in 2021. Press Releases. https://www.gartner.com/en/newsroom/press-releases/2021-01-11-gartner-forecasts-global-spending-on-wearable-devices-to-total-81-5-billion-in-2021
Ronte, H.,Taylor, K., & Haughey, J. (2018, July). Medtech and the internet of medical things: How connected medical devices are transforming health care. Deloitte Center for Health Solutions. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshc-medtech-iomt-brochure.pdf
Shachak, A., Kuziemsky, C., & Petersen, C. (2019). Beyond TAM and UTAUT: Future directions for HIT implementation research. Journal of Biomedical Informatics, 100, 103315. https://doi.org/10.1016/j.jbi.2019.103315
Selvaraj, S. & Sundaravaradhan, S. (2019, November 1). Challenges and opportunities in IoT healthcare systems: A systematic review. SN Applied Sciences, 2,139. https://doi.org/10.1007/s42452-019-1925-y
Senbekov, M., Saliev, T., Bukeyeva, Z., Almabayeva, A., Zhanaliyeva, M., Aitenova, N., Tosihibekov, Y. & Fakhradiyev, I. (2020). The recent progress and applications of digital technologies in healthcare: A review. International Journal of Telemedicine and Applications. https://doi.org/10.1155/2020/8830200
Sharma, R. & Mishra, R. (2014). A review of evolution of theories and models of technology adoption. International Medical Journal (IMJ), 6(2). Retrieved from: https://www.researchgate.net/publication/295461133_A_Review_of_Evolution_of_Theories_and_Models_of_Technology_Adoption/link/56caa1ea08ae5488f0d94ea7/download
Someya, T. (2021, May 27). Skin displays will give wearables their independence. IEEE Spectrum. Retrieved from: https://spectrum.ieee.org/consumer-electronics/audiovideo/skin-displays-will-give-wearables-their-independence
Spinuzzi, C. (2005). The methodology of participatory design. Technical Communication (Washington), 52(2), 163-174. Retrieved from: https://www.researchgate.net/publication/233564945_The_Methodology_of_Participatory_Design/link/578d21f908ae5c86c9a65576/download
Spry, J. (2020, July 2). Give a hand to this new wearable glove that translates sign language into real speech. SYFY WIRE. Retrieved from: https://www.syfy.com/syfywire/wearable-glove-translates-sign-language-to-speech
Strudwick, G. (2015). Predicting nurses' use of healthcare technology using the technology acceptance model: An integrative review. Computers, Informatics, Nursing, 33(5), 189-98. https://doi.org/10.1097/CIN.0000000000000142
Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960-967. https://doi.org/10.1016/j.promfg.2018.03.137
Tsai, T. H, Lin, W. Y, Chang, Y. S., Chang, P. C., & Lee, M. Y. (2020). Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults. PLoS ONE, 15(1):e0227270. https://doi.org/10.1371/journal.pone.0227270
TranscendInsights. (2017). Survey report: Patient expectations of medical information sharing & personalized healthcare. Retrieved from: https://www.hasatx.org/sites/default/files/transcend_insights_digital_health_survey_results_2.22.17_final.pdf
U.S. Department of Health and Human Services (DHHS). (2020). Health literacy in Healthy People 2030. Healthy People 2030. Retrieved from: https://health.gov/our-work/healthy-people/healthy-people-2030/health-literacy-healthy-people-2030
van der Vaart, R. & Drossaert, C. (2017). Development of the digital health literacy instrument: Measuring a broad spectrum of health 1.0 and health 2.0 skills. Journal of Medical Internet Research, 19(1), 1-13. https://doi.org/10.2196/jmir.6709
WAREABLE. (n.d.). Smart clothing. Trending. Retrieved from: https://www.wareable.com/smart-clothing
Waugh, C., & Gibbs Howard, S. (n.d.). How design thinking can make healthcare more human. Creative Confidence Series Chat. Retrieved from: https://www.ideou.com/blogs/inspiration/how-design-can-make-healthcare-more-human?_pos=1&_sid=1d9cbba5b&_ss=r
World Health Organization (WHO). (2020, April 7). WHO and partners call for urgent investment in nurses. News. Retrieved from: https://www.who.int/news/item/07-04-2020-who-and-partners-call-for-urgent-investment-in-nurses
Wu, M. & Luo, J. (2019, November 25). Wearable technology applications in healthcare: A literature review. Online Journal of Nursing Informatics (OJNI), 23(3). Retrieved from https://www.himss.org/resources/wearable-technology-applications-healthcare-literature-review
Zhou, Z., Chen, K., Li, X., Zhang, S., Wu, Y., Zhou, Y., Meng, K., Sun, C., He, Q,, Fan, E., Lin, Z., Tan, X., Deng, W., Yang, J., & Chen, J. (2020). Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays. Nature Electronics, 3, 571–578. https://doi.org/10.1038/s41928-020-0428-6