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International Journal of Environmental Research and Public Health
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  • Review
  • Open Access

23 February 2021

Use of mHealth Technology for Patient-Reported Outcomes in Community-Dwelling Adults with Acquired Brain Injuries: A Scoping Review

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1
UT Southwestern Medical Center, Department of Physical Medicine & Rehabilitation, Dallas, TX 75390, USA
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UT Southwestern Medical Center, Department of Applied Clinical Research, Dallas, TX 75390, USA
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Department of Occupational Therapy, University of Pittsburgh, Pittsburgh, PA 15219, USA
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Shepherd Center, Atlanta, GA 30309, USA
Int. J. Environ. Res. Public Health2021, 18(4), 2173;https://doi.org/10.3390/ijerph18042173
This article belongs to the Special Issue Mobile Health and Mobile Rehabilitation for People with Disabilities
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Abstract

The purpose of our scoping review was to describe the current use of mHealth technology for long-term assessment of patient-reported outcomes in community-dwelling individuals with acquired brain injury (ABI). Following PRISMA guidelines, we conducted a scoping review of literature meeting these criteria: (1) civilians or military veterans, all ages; (2) self-reported or caregiver-reported outcomes assessed via mobile device in the community (not exclusively clinic/hospital); (3) published in English; (4) published in 2015–2019. We searched Ovid MEDLINE(R) < 1946 to 16 August 2019, MEDLINE InProcess, EPub, Embase, and PsycINFO databases for articles. Thirteen manuscripts representing 12 distinct studies were organized by type of ABI [traumatic brain injury (TBI) and stroke] to extract outcomes, mHealth technology used, design, and inclusion of ecological momentary assessment (EMA). Outcomes included post-concussive, depressive, and affective symptoms, fatigue, daily activities, stroke risk factors, and cognitive exertion. Overall, collecting patient-reported outcomes via mHealth was feasible and acceptable in the chronic ABI population. Studies consistently showed advantage for using EMA despite variability in EMA timing/schedules. To ensure best clinical measurement, research on post-ABI outcomes should consider EMA designs (versus single time-point assessments) that provide the best timing schedules for their respective aims and outcomes and that leverage mHealth for data collection.

1. Introduction

In the United States, approximately 3.5 million people experience a traumatic brain injury (TBI) [1] and 795,000 experience a stroke each year [2]. Acquired brain injury (ABI) including traumatic and non-traumatic brain injury (e.g., stroke) often results in secondary health issues [3,4,5], some of which may persist for years after the initial injury event [6,7]. Individuals with ABI often experience multiple barriers to receiving care and managing long-term symptoms, including inconsistent long-term follow-up with providers, limited access to community-based healthcare services [8,9], and costs associated with rehabilitative care and transportation [10].
Barriers to long-term clinical surveillance also hinder care. These challenges include reliance on self-report, length and breadth of available validated measures, complexity of administration, and ecological validity. Symptom-reporting also frequently relies on retrospective reporting, requiring individuals with ABI to think back over the past several months or longer, which make them prone to reporting errors as a result of impaired self-awareness or recall bias commonly associated with TBI-related cognitive impairment. However, self-report is still the most efficient and practical method for community-based clinical surveillance of patient-reported outcomes. Increasing measurement frequency and focusing on individuals’ immediate experiences, as done through Ecological Momentary Assessment (EMA) [11], may also reduce these limitations of self-reporting.
Given the ubiquity of smartphone use and the ability to access this technology in one’s community, mobile technology may have the potential to be a cost-effective way to improve and support symptom surveillance, access to care, self-management, and long-term outcomes. Most Americans, including individuals with brain injury [12], own smartphones [13]. Further, research suggests that mHealth technology, readily available in most people’s pockets, may effectively promote behavior change [14,15].
Through mobile health (mHealth) technology, healthcare providers can prospectively collect and/or share health-related information (e.g., tracking symptoms, documenting health-related activities/events), improving communication between patients and healthcare providers. If communication via mHealth occurs in only one direction (clinicians sending information out via a device or individuals with ABI inputting information into an app or device), this is considered a one-way system. A two-way systems allows for back and forth exchange of information and is preferred by ABI survivors, their family members, and clinicians [16].
To date, though, there is limited evidence to support the efficacy of mHealth technologies to improve functional outcomes following an ABI [17,18,19,20]. While thousands of mHealth applications are currently available for download, fewer than 1% of these are grounded in evidence-based knowledge [15,21]. Furthermore, only a minority of persons with brain injury report a clinician having discussed mHealth services with them following their injury [22,23]. And a recent survey of over 500 rehabilitation clinicians revealed only 23% felt knowledgeable about rehabilitation technology currently available and only 51% reported being comfortable using such technology in their practice [24]. But when presented with mHealth applications, most TBI and stroke survivors say that they would use these technologies and believe these applications would help improve their symptoms [16,25,26]. This demonstrates a critical gap in rehabilitative care: when individuals are willing to use mHealth technologies, but do not receive informed recommendations from trusted, knowledgeable healthcare providers, there is a greater risk that they may use applications inappropriate for their rehabilitation goals.
Studies using personal digital assistants (PDAs) and telephone-based interventions suggest that mHealth interventions are both feasible and potentially efficacious for managing chronic symptoms in individuals with brain injuries [23,27]. Two recent reviews on mHealth use after ABI found that while there is insufficient evidence to support the use of mHealth interventions as standard practice, emerging evidence does support their efficacy for improving goal attainment and social participation [20,28]. Both reviews highlight the importance of developing more personalized, patient-centered mHealth interventions sensitive to the needs and abilities of individuals with ABI. Evidence further supports the use of EMA for measuring psychological health outcomes [29]. The repeated sampling used in EMA captures patterns in an individual’s mood, activities, symptoms, and behaviors in real-time and in an individual’s natural environment [11]. EMA via mHealth could reveal moments for in situ intervention (Ecological Momentary Intervention [EMI]) [30], promoting stable long-term changes in behavior when combined with mHealth interventions.
MHealth technology may be promising for alleviating symptoms and improving long-term outcomes among individuals with chronic conditions. However, there remains insufficient evidence for definitive recommendation of their use after ABI. A previous review presented promising evidence for mHealth interventions in community-dwelling individuals with TBI [20], but no such review examines the evidence for mHealth technology to support collecting patient-reported outcomes after ABI. Therefore, the purpose of this review is to examine the currently available literature and evidence on the use of mHealth technology for long-term assessment of patient-reported outcomes in community-dwelling individuals with chronic ABI. We discuss the state of the science and limitations of these technologies and lay out recommendations for future research in this area.

2. Materials and Methods

We conducted a scoping review of the literature, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRSIMA-ScR) [31] guidelines and 5-stage framework described by Arksey and O’Malley [32]. Stage 1, defining the research question and scope, begun at a meeting of authors during the American Congress of Rehabilitation Medicine Annual Meeting in 2018 and completed in the Summer of 2019.
Stage 2 included determining eligibility criteria and identifying relevant studies. Eligibility criteria for articles to be included in this review were: (1) Sample population was primarily acquired brain injury (traumatic brain injury, concussion, stroke, other brain injury); (2) Sample population included all ages and both civilians and veterans; (3) Assessment was collected via mobile phone/device (e.g., not just internet/computer-based), though could be done via app or text messaging; (4) Self-reported or caregiver-reported assessments of patient-reported outcomes (e.g., not direct collection of physiologic measures, activity data, etc.); (5) Assessment occurred in the community (e.g., not exclusively in a clinical setting); (6) Published in English; and (7) Published in or after 2015 (search conducted in August 2019 with the goal of examining research in the past 5 years). Systematic reviews, study protocol papers, and conference abstracts were excluded.
Stage 3 was identifying relevant studies through a database search. We worked with a research librarian to conduct a search on 16 August 2019 of the following databases: Ovid MEDLINE(R) < 1946 to 16 August 2019, MEDLINE InProcess, EPub, Embase, & PsycINFO. The exact search terms are presented in Supplemental Table S1. Our search strategy was limited to the English language, research involving humans, and publications from January 2015 to 15 August 2019.
Two reviewers independently examined all titles and abstracts for full text review, with a third reviewer making determinations when there were questions regarding inclusion or when the two reviewers did not have consensus. Two different reviewers completed full text review of identified articles for final inclusion. Another study author engaged in hand-searching reference lists of included articles and studies reviewed in previous systematic or scoping reviews for additional articles not identified in the database search.
Stage 4, data extraction, was completed by four authors who confirmed eligibility and completed data extraction for included articles (following pre-identified data elements in Table 1 and Table 2). Stage 5, summarizing and reporting results followed.
Table 1. Study characteristics.
Table 2. Mobile Health Study Results.

3. Results

Figure 1 outlines 5-stages of this review, including a summary of the number of articles identified from the initial database search and hand-searching (n = 54, Step 3) through the final number of articles included in the review (n = 12) [33,34,35,36,37,38,39,40,41,42,43,44].
Figure 1. Flow chart of study inclusion and exclusion.

3.1. Characteristics of Included Studies

Table 1 presents details of the sample, constructs measured, and study design. Table 2 presents details of the mHealth technology used, inclusion of EMA, purpose of the study, and results for the 12 included manuscripts (representing 11 distinct studies), by TBI (grey shaded rows) and stroke (white rows). Five manuscripts included only individuals with stroke; six manuscripts included only individuals with TBI, though two were from the same parent study; and one manuscript included individuals with stroke and TBI. Of the five unique studies focusing on TBI, three included adolescents with concussion [35,36,37], one included service members (injury severity unknown) [33], and the other (from which two manuscripts were derived) included adults with complicated mild to severe TBI [34,38]. The study including adult participants with either TBI or stroke did not specify injury severity [40]. Of the five studies focusing on stroke [39,41,42,43,44], two also included caregivers [41,44]. A total of 219 people with TBI and 171 people with stroke (plus 43 of their caregivers) participated in the 12 studies.

3.2. Summary of mHealth Technology Used and Constructs Measured

Ten studies used smartphone apps: five were one-way apps and five were two-way communication apps. Of the remaining two studies, one used text messaging linked to a web-based clinician management system and the other used a personal digital assistant. Constructs measured via mHealth included affect, anxiety, and/or depressive symptoms (five studies), daily and social activities (five studies), fatigue (three studies) post-concussive symptoms (three studies), disability (one study), stroke risk factors (one study), and cognitive exertion (one study).

3.3. Inclusion of Ecological Momentary Assessment via mHealth

Ten studies (11 papers: six in TBI, four in stroke, one in both) included EMA, with frequency and duration of EMA varying considerably. Four studies examined once a day measures (some at fixed times, some at random times), with a fifth study examining event-related reporting with a target of once a day for five of seven days each week. The remaining five studies examined multiple times per day measures (3, 5, and 10), some at fixed times and some at random times. Duration of EMA ranged from 6 days to 6 months: three studies included 6–7 days of multiple assessments per day, one study included multiple assessments per day for up to 18 days, one study included multiple assessments per day for two weeks, one study included daily measures for 3 weeks, two studies included daily measures for 8 weeks, one study included daily measures for 36 weeks, and one study included daily assessments for 6 months. Two studies examined the psychometric properties of the previously validated assessments when collected via EMA [34,41].

3.4. Traumatic Brain Injury Studies and Findings

Patient-reported outcomes after TBI measured mood, affect, anxiety, pain, fatigue, post-concussion symptoms (including post-concussive headache), activity, recovery, and overall well-being. Only one study compared patient-reported outcomes via the app (daily activity) to a biometric measure (data collected via accelerometer) [35].
For TBI, reported compliance with assessments ranged from 50–82% overall [33,34,35,37]. Pavliscsak et al. noted that those with behavioral problems, younger participants, and those with lower well-being were less compliant (many <50%) and took longer to respond to EMA prompts than those without behavioral problems, older participants, and those with higher well-being, regardless of presence or absence of TBI or PTSD [33]. Juengst et al. though finding an overall high average compliance for daily assessments over 8 weeks (73.4%), noted a bimodal distribution in compliance, suggesting that participants generally either did or did not complete daily reports rather than a middling compliance across all participants [34]. Finally, Worthen-Chaudhari et al., reported that using a gamified app may facilitate better compliance, particularly among adolescents [36].
Juengst et al. noted significant within person variability in daily mood, affect, and fatigue reporting over 6–8 weeks, despite no intervention, supporting the need for EMA—rather than a single time point—to capture these patient-reported outcomes [38]. Variability from day to day was greater for women and those reporting more symptoms. Sufrinko et al. also noted that participants reported fewer symptoms in the morning than the afternoon or evening [37], supporting EMA multiple times per day or at variable times from day to day to capture natural diurnal variation. They also found that symptom scores derived from EMA were better at predicting time to recovery after concussion than symptom scores obtained at clinic visits [37]. Worthen-Chaudhari et al. examined the effects of concussion symptom tracking as intervention, finding that those who more consistently reported their symptoms through the gamified app also noted more symptom improvement over time [36]. Wiebe et al. examined time-lagged relationships between concussion symptoms and both cognitive exertion and physical activity in youth concussion, finding that more cognitive exertion was associated with more concussion symptoms on the same day and for 2 days following and that, conversely, more physical activity was associated with fewer concussion symptoms on the same day and the 2 days following [35]. This is contrary to what Lenaert et al. reported in their study including both adults with TBI and stroke wherein they found that physical activity and fatigue predicted more negative affect on the same day; however, it did not predict negative affect on subsequent days [45].

3.5. Stroke Studies and Findings

Patient-reported outcomes after stroke measured daily and social activities, disability, stroke risk factors, depression, anxiety, and fatigue. Only one study directly compared agreement between a measure (the modified Rankin Scale) assessed in the clinic to the same measure assessed via mHealth, noting 85% agreement if scores were dichotomized to reflect functionally independent versus dependent [41].
Similar to results from studies in TBI, three studies using mHealth for EMA demonstrated the importance and utility of this approach over single time point measures. Seo et al. found that daily reporting of stroke risk factors led to an overall shift in risk profiles [42], suggesting that daily tracking led to behavior change. Villain et al. used EMA measures to examine change in depression symptoms and activities of daily living participation over time, allowing them to examine early predictors of positive change over time. They found that more material support (e.g., provision of items or task assistance) and higher quality of moral support during the hospitalization led to later improving activities of daily living participation and decreasing depressive symptoms [43]. Lenaert et al. conclude that Experience Sampling Methods (conceptually equivalent to EMA) are needed to design personalized rehabilitation programs after stroke, after finding that more effortful activities or physical activities early in the day were associated with later fatigue, but that fatigue was highest when participants were resting or doing nothing [39]. Understanding differences between concurrent and time-lagged associations could inform use of energy conservation interventions and timing of EMI (in the moment interventions) to prevent and reduce fatigue post-stroke.
For stroke, reported compliance with assessments ranged from 50–77.3% overall. The consensus across studies was that mHealth was feasible and acceptable, with potential to improve symptoms and health. Fors et al. reported overall positive opinions about use of mHealth to track daily activities, from both patients with stroke and their family members [44]. In the absence of tech support, older participants struggled more with technical issues, with many relying on family members to send and receive text messages [44].

4. Discussion

The purpose of the current review was to examine recent (2015–2019) literature to describe use of mHealth technology, EMA design considerations, and findings in studies of patient-reported outcomes in community-dwelling individuals with chronic ABI. We found 12 manuscripts representing 11 distinct studies that used mHealth technology for community-based clinical symptom surveillance of affect, anxiety, and/or depression, daily and social activities, fatigue, post-concussive symptoms, disability, stroke risk factors, and cognitive exertion. There was variability across studies with respect to the constructs measured and frequency, duration, and timing schedule of EMA data collection. As a result, we cannot make recommendations as to the optimal content, timing, or mHealth platform for capturing accurate information and avoiding participant fatigue. However, personalization is a consistent theme across studies in this review and past studies on mHealth use after ABI [20,28], and individual differences will likely require personalized approaches that pair mHealth technology with unique needs and abilities of each person.
Despite differences across studies, we found consistency in advantages of using EMA that are not present when collecting single time-point measures, especially for those symptoms for which fluctuating frequently over short periods of time is common. To analyze EMA data meaningfully, it is important to organize the data using a timing schedule that aligns with the research question [46]. Our review uncovered a variety of EMA designs, including event-contingent assessments and random or fixed assessments either once or multiple times throughout the day. Another design that may be helpful to researchers is a combination of these approaches, such as event-related reporting plus either a fixed or random timing schedule as a complimentary way to collect data. For example, a researcher may ask the participant to report when feeling a panic attack (event-related reporting), but then also collect data throughout the day at fixed or random times to uncover the participant’s affect/mood leading up to or directly after that event. We also noticed that the number of days for data collection varied considerably amongst the EMA studies in our review. This can be especially important for research questions that are interested in capturing changes in effects over time. Researchers wishing to examine temporal changes in outcomes should consider the implications of nature within-person variability over the period of data collection that may mask or be misinterpreted as meaningful change if single time point measures are used. For example, we may observe day to day variability in mood across the week, so week-long daily data capture may provide more insight for pattern recognition over time. Future researchers should consider the aims and outcomes of the research study, the characteristics of the population, and the strengths and limitations of existing EMA literature when selecting an EMA design.
While papers included in our review clearly provide support for using mHealth for EMA at multiple, varied times per day, scarce detail is offered on sampling in varied environmental contexts. Assessment in natural environments is a core benefit of implementation of EMA in the community, and assessment of contextual factors influencing health, function, and participation is a key construct in rehabilitative care [47]. When EMA is used to assess aspects of psychological and behavioral function, implementation in the community may support identification of antecedent and proximal events influencing behavior in real world situations, thereby supporting self-awareness and delivery of timed-right, personalized interventions based on assessment [11,48,49]. However, some community settings and activities may present barriers to EMA. For example, an adolescent may not be able to receive or respond to notifications to complete an assessment when they are in school or adults may not be able to respond while at work. Likewise, the same may apply to an adult who is driving or engaged in childcare tasks. And regardless of setting, those without consistent access to high-speed connectivity may not have the network connectivity needed to support technical aspects of data collection and data transfer for some mHealth assessments [50]. Finally, implementing EMA in the community may raise concerns regarding remote monitoring of safety events in cases where collected data are not remotely monitored in real time but are instead stored and later viewed by healthcare providers during visits. And some patients may have added privacy concerns in cases in which sensitive health data is collected or location tracking is used [51].
In general, patient-oriented mHealth apps are designed to assist patients in managing their own health or to support the provision of healthcare services when a face-to-face encounter is not feasible [52,53,54,55]. Hundreds of thousands of mHealth apps exist, and many are effective in maintaining or improving health outcomes [56,57]. However, the majority of these apps are not designed for individuals with disabilities, and an even smaller number have been evaluated for accessibility by patients who experience upper extremity limitations, visual or cognitive deficits, aphasia, or emotional or behavioral disturbance, all common after ABI [58]. Our findings suggest that few mHealth apps have been designed specifically for collecting patient-reported outcomes in the ABI population, yet those that have demonstrate initial acceptability and potential efficacy. It is likely that evidence-based mHealth apps will be integrated in rehabilitation clinical practice in the near future, though currently it is challenging for rehabilitation practitioners and patients to identify evidence-based apps that are accessible and effective [59]. A 2018 systematic review of studies that applied mHealth evaluation methods or an assessment approach for mHealth apps revealed seven criteria for clinicians to use in assessing the appropriateness of mHealth apps: (1) design, (2) information/content, (3) usability, (4) functionality, (5) ethical issues, (6) security and privacy and (7) user-perceived value [60]. It is also paramount that rehabilitation practitioners include the individual patient in the decision to implement app use into the patient care plan [59], and that there be a practitioner following individuals with ABI who are using mHealth apps after discharge from clinical settings [16].
Healthcare providers may be afforded the opportunity to prospectively collect client information and share one-direction or bi-directional information with clients. Providers may use mHealth technology with an overarching aim of improving ABI recovery outcomes among their clients living in the community. While various mHealth technologies have been developed and tested, our study reveals that almost no articles have focused on use of this technology across the transition from acute or sub-acute care to the community. Although self-management largely occurs during community living, implementing mHealth technology across the care continuum has the capacity to improve adoption and use. However, only two papers in our review included mHealth technology started during the acute or sub-acute care period, and in one of these, only training was completed before transition to the community. Four articles excluded from our review for occurring exclusively in clinical settings, conversely, did not continue out into the community. Users may be described on a continuum of downloading and never using mHealth technology, discontinuing it immediately after use, or even using the technology in unexpected ways [61]. Careful consideration should be given to providing appropriate training to support use; our stroke findings suggest this may be especially true in older adults. Practitioners may be able to support use and adoption with strategies which include: (1) supporting the identification of evidence-based apps for self-assessment, (2) matching the technology to the specific user wants and needs, and (3) providing hands-on opportunities for practice and training with the technology itself. Furthermore, providers may be able to guide clients in accessibility interfaces and add-ons that may be needed considering the vast range of impairments (e.g., visual, motor, cognitive) people may experience after ABI [62]. These integration and training elements may be frequently over-looked but could be easily integrated into existing treatment plans based on client-centered needs across the care continuum. Introducing mHealth technology prior to discharge or with a provider familiar with unique needs of each client may be useful in supporting adoption and long-term use of the technology to support improved outcomes.

Limitations

Despite integrating a comprehensive search strategy, a major limitation of this study is that we have missed published articles that met the pre-defined inclusion criteria. This limitation may be associated with database selection, search terms used, and the search timeline. Additionally, we did not include articles written in languages other than English and this may have limited the findings of this study. We also did not include conference abstracts due to their limited information for data extraction. Although we intended to include studies that included individuals with all types of ABI, we did not find any studies that enrolled individuals with brain injuries other than TBI or stroke. Therefore, our findings might not be generalizable to people with brain injuries other than TBI and stroke.

5. Future Directions

Going forward, research on community-based EMA using information and communication technology, should focus on: (1) strategies for engaging survivors and their caregivers; (2) processes for adapting EMA to individual progress or decline; (3) clinician needs and acceptance (including impact on clinician workflow); (4) emerging capabilities in cloud-based artificial intelligence (AI), chat and natural language processing (NLP).
A key challenge to mHealth, particularly for self-assessment and self-management, is keeping the survivor (and caregiver) sufficiently engaged in processes that are often repetitive, like answering questions about their status or completing home exercise programs. At minimum, individuals need feedback on how they are doing and knowledge that someone is monitoring their performance, which reinforces accountability [63]. Beyond this minimum, other techniques for patient engagement could include the use of challenges and rewards to complete self-assessments. Such “gamification” in healthcare is more typically associated with interventions like exercise [64], smoking cessation [65], and weight loss [66] than self-assessments and may be more difficult to implement for people with brain injury. However, Worthen-Chaudhari et al.’s study, included in this review, demonstrates that gamification is potentially feasible in terms of compliance and effective for improving symptom management, at least for youth concussion [36]. They framed factors tracked by the app as “bad guys”, “power ups”, or “allies”; “bad guys” were factors that may trigger symptoms or maladaptive responses, “power ups” were effective strategies, and “allies” were members of a user’s social network who could send encouragement. Not only did they employ feedback and tracking for how often user’s conquered or did not conquer a “bad guy” each day, but they also employed social networking to improve compliance and symptom-management [36]. Still, research on engagement strategies, including challenges and virtual rewards for compliance, could yield insights into methods for increasing compliance and the types of patients with brain injury who may be most responsive. Updating the content, timing, and frequency of the assessments based on individual progress or decline is also critical and related in part to the challenge of engagement in ongoing self-assessments. A static EMA script or form, and the timing and frequency of assessments, may grow less useful for brain injury survivors as they improve. Additionally, reporting needs may differ based on available resources, including both internal and external resources. Research is needed on EMA programs that can adapt to patient progress (or regression) and reporting needs.
For clinicians, more research is needed on how they want to access data, how the data are presented, and impact on workflow. Research in various countries show that while clinicians embrace the potential of health technologies (mobile and otherwise), they remain especially concerned about how it may affect workflow [16,67,68,69]. When should they review patient data? How frequently? Will they be reimbursed for the time they spend reviewing remotely collected patient self-assessment data? In the United States, 2020 updates to mHealth and patient monitoring reimbursement policies by the Centers for Medicare and Medicaid Services (CMS, the largest government healthcare insurer in the U.S.) do not include coverage for remote patient self-assessment [70]. More evidence of effectiveness is needed for EMA to be reimbursed by public and private insurers in the U.S. and other countries.
Finally, rapid advances in cloud computing, AI, chat, predictive analytics and NLP offer new opportunities for EMA in the home and community. Gartner predicts that by 2021, customers will manage more than 85% their relationship with a business without interacting with a human, and that 15% of all customer service interactions will be completely handled by AI [71]. Furthermore, “[i]n the “post-app era,” chatbots will become the face of AI and bots will transform the way apps are built,” according to Gartner [72]. While AI, chat and NLP offer new capabilities, they also create complexity for EMA development and testing. Additionally, cloud-based AI solutions may not be feasible for low-resource patients or for localities lacking access to high-speed internet. Still, these technologies offer new capabilities for EMA, allowing assessments to be more detailed, adaptive (during an individual self-assessment and over a series of assessments), engaging and accessible by adding voice interaction to visual and tactile interfaces.

6. Conclusions

Consistent with similar findings of preliminary evidence on the use of mHealth technology for intervention after TBI [20], this scoping review showed promising early evidence for the feasibility of using mHealth technology for patient-reported outcomes in community-dwelling adults with chronic ABI. The advantage of mHealth technology for community-based assessment is its ability to easily and efficiently conduct EMA—and eventually, EMI—in individuals’ natural environments, while still maintaining contact with and oversight from a trained clinician. ABI survivors and their caregivers have noted the importance of and their preference for a “human component” of mHealth [16]—that is, they want a person to actually receive the responses they punch into an app and they want to get responses in return. Technology continues to develop faster than the research on its feasibility, usability, and efficacy, but the importance of clinicians having available evidence to support clinical recommendations for use of mHealth to support ABI survivors cannot be understated.

Supplementary Materials

The following are available online at https://www.mdpi.com/1660-4601/18/4/2173/s1. Table S1: Database Search Strategy Terms.

Author Contributions

Conceptualization: S.B.J., J.A.W., T.W., C.L.O., and J.M. Methodology: S.B.J., A.N., J.A.W., T.W., C.L.O., B.W., C.-L.N.K., and J.M. Investigation: S.B.J., J.A.W., T.W., S.P.B., P.-S.W., C.L.O., B.W., L.T., and J.M. Writing—Original Draft Preparation: S.B.J., A.N., J.A.W., B.W., J.M., S.P.B. Writing—Review & Editing: S.B.J., A.N., J.A.W., T.W., S.P.B., P.-S.W., C.L.O., B.W., L.T., and J.M. Project Administration: S.B.J. and J.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Acknowledgments

This work represents a collaboration of American Congress of Rehabilitation Medicine Community Group members, including members of the Measurement Interdisciplinary Special Interest Group Long-term Community Assessment Task force, the Brain Injury Interdisciplinary Special Interest Group Chronic Brain Injury Task Force, The Stroke Interdisciplinary Special Interest Group, and the Technology Networking Group.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Coronado, V.G.; McGuire, L.C.; Sarmiento, K.; Bell, J.; Lionbarger, M.R.; Jones, C.D.; Geller, A.I.; Khoury, N.; Xu, L. Trends in Traumatic Brain Injury in the U.S. and the Public Health Response: 1995–2009. J. Saf. Res. 2012, 43, 299–307. [Google Scholar] [CrossRef]
  2. Virani, S.S.; Alonso, A.; Benjamin, E.J.; Bittencourt, M.S.; Callaway, C.W.; Carson, A.P.; Chamberlain, A.M.; Chang, A.R.; Cheng, S.; Delling, F.N.; et al. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation 2020, 141, 139–596. [Google Scholar] [CrossRef] [PubMed]
  3. Corrigan, J.D.; Hammond, F.M. Traumatic Brain Injury as a Chronic Health Condition. Arch. Phys. Med. Rehabil. 2013, 94, 1199–1201. [Google Scholar] [CrossRef] [PubMed]
  4. Edwards, D.F.; Hahn, M.G.; Baum, C.M.; Perlmutter, M.S.; Sheedy, C.; Dromerick, A.W. Screening Patients with Stroke for Rehabilitation Needs: Validation of the Post-Stroke Rehabilitation Guidelines. Neurorehabilit. Neural Repair 2006, 20, 42–48. [Google Scholar] [CrossRef] [PubMed]
  5. Masel, B.E.; DeWitt, D.S. Traumatic Brain Injury: A Disease Process, Not an Event. J. Neurotrauma 2010, 27, 1529–1540. [Google Scholar] [CrossRef]
  6. McMillan, T.M.; Teasdale, G.M.; Stewart, E. Disability in Young People and Adults after Head Injury: 12-14 Year Follow-up of a Prospective Cohort. J. Neurol. Neurosurg. Psychiatry 2012, 83, 1086–1091. [Google Scholar] [CrossRef]
  7. Andelic, N.; Howe, E.I.; Hellstrøm, T.; Sanchez, M.F.; Lu, J.; Løvstad, M.; Røe, C. Disability and Quality of Life 20 Years after Traumatic Brain Injury. Brain Behav. 2018, 8, e01018. [Google Scholar] [CrossRef] [PubMed]
  8. Graven, C.; Brock, K.; Hill, K.; Joubert, L. Are Rehabilitation and/or Care Co-Ordination Interventions Delivered in the Community Effective in Reducing Depression, Facilitating Participation and Improving Quality of Life after Stroke? Disabil. Rehabil. 2011, 33, 1501–1520. [Google Scholar] [CrossRef]
  9. Centers for Disease Control and Prevention. Report to Congress on Traumatic Brain Injury in the United States: Epidemiology and Rehabilitation. National Center for Injury Prevention and Control; Division of Unintentional Injury Prevention: Atlanta, GA, USA, 2015. [Google Scholar]
  10. Turner, B.; Fleming, J.; Cornwell, P.; Worrall, L.; Ownsworth, T.; Haines, T.; Kendall, M.; Chenoweth, L. A Qualitative Study of the Transition from Hospital to Home for Individuals with Acquired Brain Injury and Their Family Caregivers. Brain Inj. 2007, 21, 1119–1130. [Google Scholar] [CrossRef] [PubMed]
  11. Shiffman, S.; Stone, A.A.; Hufford, M.R. Ecological Momentary Assessment. Annu. Rev. Clin. Psychol. 2008, 4, 1–32. [Google Scholar] [CrossRef]
  12. Baker-Sparr, C.; Hart, T.; Bergquist, T.; Bogner, J.; Dreer, L.; Juengst, S.; Mellick, D.; O’Neil-Pirozzi, T.M.; Sander, A.M.; Whiteneck, G.G. Internet and Social Media Use After Traumatic Brain Injury: A Traumatic Brain Injury Model Systems Study. J. Head Trauma Rehabil. 2018, 33, E9–E17. [Google Scholar] [CrossRef]
  13. Street, L.N.W.; Washington, S.; Inquiries, D. 20036 202 419 4300|M. 202 419 4349|F. 202 419 4372|M. Mobile Fact Sheet. Pew Res. Cent. Internet Sci. Tech. 2017. Available online: http://www.pewinternet.org/fact-sheet/mobile (accessed on 2 February 2017).
  14. Ernsting, C.; Dombrowski, S.U.; Oedekoven, M.; Sullivan, J.L.O.; Kanzler, M.; Kuhlmey, A.; Gellert, P. Using Smartphones and Health Apps to Change and Manage Health Behaviors: A Population-Based Survey. J. Med. Internet Res. 2017, 19, e101. [Google Scholar] [CrossRef] [PubMed]
  15. Free, C.; Phillips, G.; Galli, L.; Watson, L.; Felix, L.; Edwards, P.; Patel, V.; Haines, A. The Effectiveness of Mobile-Health Technology-Based Health Behaviour Change or Disease Management Interventions for Health Care Consumers: A Systematic Review. PLoS Med. 2013, 10, e1001362. [Google Scholar] [CrossRef]
  16. Osborne, C.L.; Juengst, S.B.; Smith, E.E. Identifying User-Centered Content, Design, and Features for Mobile Health Apps to Support Long-Term Assessment, Behavioral Intervention, and Transitions of Care in Neurological Rehabilitation: An Exploratory Study: Br. J. Occup. Ther. 2020. [Google Scholar] [CrossRef]
  17. Kwan, V.; Bihelek, N.; Anderson, V.; Yeates, K. A Review of Smartphone Applications for Persons with Traumatic Brain Injury: What Is Available and What Is the Evidence? J. Head Trauma Rehabil. 2019, 34, E45–E51. [Google Scholar] [CrossRef]
  18. Lynch, E.A.; Jones, T.M.; Simpson, D.B.; Fini, N.A.; Kuys, S.S.; Borschmann, K.; Kramer, S.; Johnson, L.; Callisaya, M.L.; Mahendran, N.; et al. Activity Monitors for Increasing Physical Activity in Adult Stroke Survivors. Cochrane Database Syst. Rev. 2018, 7, CD012543. [Google Scholar] [CrossRef]
  19. Zhou, X.; Du, M.; Zhou, L. Use of Mobile Applications in Post-Stroke Rehabilitation: A Systematic Review. Top. Stroke Rehabil. 2018, 489–499. [Google Scholar] [CrossRef]
  20. Juengst, S.B.; Hart, T.; Sander, A.M.; Nalder, E.J.; Pappadis, M.R. Mobile Health Interventions for Traumatic Brain Injuries. Curr. Phys. Med. Rehabil. Rep. 2019, 7, 341–356. [Google Scholar] [CrossRef]
  21. Anthes, E. Mental Health: There’s an App for That. Nat. News 2016, 532, 20. [Google Scholar] [CrossRef] [PubMed]
  22. Plackett, R.; Thomas, S.; Thomas, S. Professionals’ Views on the Use of Smartphone Technology to Support Children and Adolescents with Memory Impairment Due to Acquired Brain Injury. Disabil. Rehabil. Assist. Technol. 2017, 12, 236–243. [Google Scholar] [CrossRef] [PubMed]
  23. Wong, D.; Sinclair, K.; Seabrook, E.; McKay, A.; Ponsford, J. Smartphones as Assistive Technology Following Traumatic Brain Injury: A Preliminary Study of What Helps and What Hinders. Disabil. Rehabil. 2017, 39, 2387–2394. [Google Scholar] [CrossRef]
  24. Morris, J.; Jones, M.; Thompson, N.; Wallace, T.; DeRuyter, F. Clinician Perspectives on MRehab Interventions and Technologies for People with Disabilities in the United States: A National Survey. Int. J. Environ. Res. Public. Health 2019, 16, 4220. [Google Scholar] [CrossRef]
  25. Bradley, S.E.; Haun, J.; Powell-Cope, G.; Haire, S.; Belanger, H.G. Qualitative Assessment of the Use of a Smart Phone Application to Manage Post-Concussion Symptoms in Veterans with Traumatic Brain Injury. Brain Inj. 2020, 34, 1031–1038. [Google Scholar] [CrossRef]
  26. Jenkins, C.; Burkett, N.-S.; Ovbiagele, B.; Mueller, M.; Patel, S.; Brunner-Jackson, B.; Saulson, R.; Treiber, F. Stroke Patients and Their Attitudes toward MHealth Monitoring to Support Blood Pressure Control and Medication Adherence. mHealth 2016, 2. [Google Scholar] [CrossRef] [PubMed]
  27. Scherer, M.J. Assessing the Benefits of Using Assistive Technologies and Other Supports for Thinking, Remembering and Learning. Disabil. Rehabil. 2005, 27, 731–739. [Google Scholar] [CrossRef]
  28. Kettlewell, J.; Phillips, J.; Radford, K.; dasNair, R. Informing Evaluation of a Smartphone Application for People with Acquired Brain Injury: A Stakeholder Engagement Study. BMC Med. Inform. Decis. Mak. 2018, 18, 33. [Google Scholar] [CrossRef] [PubMed]
  29. Yang, Y.S.; Ryu, G.W.; Choi, M. Methodological Strategies for Ecological Momentary Assessment to Evaluate Mood and Stress in Adult Patients Using Mobile Phones: Systematic Review. JMIR MHealth UHealth 2019, 7, e11215. [Google Scholar] [CrossRef]
  30. Heron, K.E.; Smyth, J.M. Ecological Momentary Interventions: Incorporating Mobile Technology into Psychosocial and Health Behaviour Treatments. Br. J. Health Psychol. 2010, 15, 1–39. [Google Scholar] [CrossRef] [PubMed]
  31. Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef] [PubMed]
  32. Arksey, H.; O’Malley, L. Scoping Studies: Towards a Methodological Framework. Int. J. Soc. Res. Methodol. 2005, 8, 19–32. [Google Scholar] [CrossRef]
  33. Pavliscsak, H.; Little, J.R.; Poropatich, R.K.; McVeigh, F.L.; Tong, J.; Tillman, J.S.; Smith, C.H.; Fonda, S.J. Assessment of Patient Engagement with a Mobile Application among Service Members in Transition. J. Am. Med. Inform. Assoc. 2016, 23, 110–118. [Google Scholar] [CrossRef] [PubMed]
  34. Juengst, S.B.; Graham, K.M.; Pulantara, I.W.; McCue, M.; Whyte, E.M.; Dicianno, B.E.; Parmanto, B.; Arenth, P.M.; Skidmore, E.R.D.; Wagner, A.K. Pilot Feasibility of an MHealth System for Conducting Ecological Momentary Assessment of Mood-Related Symptoms Following Traumatic Brain Injury. Brain Inj. 2015, 29, 1351–1361. [Google Scholar] [CrossRef] [PubMed]
  35. Wiebe, D.J.; Nance, M.L.; Houseknecht, E.; Grady, M.F.; Otto, N.; Sandsmark, D.K.; Master, C.L. Ecologic Momentary Assessment to Accomplish Real-Time Capture of Symptom Progression and the Physical and Cognitive Activities of Patients Daily Following Concussion. JAMA Pediatr. 2016, 170, 1108–1110. [Google Scholar] [CrossRef]
  36. Worthen-Chaudhari, L.; McGonigal, J.; Logan, K.; Bockbrader, M.A.; Yeates, K.O.; Mysiw, W.J. Reducing Concussion Symptoms among Teenage Youth: Evaluation of a Mobile Health App. Brain Inj. 2017, 31, 1279–1286. [Google Scholar] [CrossRef] [PubMed]
  37. Sufrinko, A.M.; Howie, E.K.; Charek, D.B.; Elbin, R.J.; Collins, M.W.; Kontos, A.P. Mobile Ecological Momentary Assessment of Postconcussion Symptoms and Recovery Outcomes. J. Head Trauma Rehabil. 2019, 34, E40–E48. [Google Scholar] [CrossRef] [PubMed]
  38. Juengst, S.B.; Terhorst, L.; Kew, C.L.; Wagner, A.K. Variability in Daily Self-Reported Emotional Symptoms and Fatigue Measured over Eight Weeks in Community Dwelling Individuals with Traumatic Brain Injury. Brain Inj. 2019, 33, 567–573. [Google Scholar] [CrossRef] [PubMed]
  39. Lenaert, B.; Neijmeijer, M.; van Kampen, N.; van Heugten, C.; Ponds, R. Poststroke Fatigue and Daily Activity Patterns During Outpatient Rehabilitation: An Experience Sampling Method Study. Arch. Phys. Med. Rehabil. 2020, 101, 1001–1008. [Google Scholar] [CrossRef]
  40. Lenaert, B.; Colombi, M.; van Heugten, C.; Rasquin, S.; Kasanova, Z.; Ponds, R. Exploring the Feasibility and Usability of the Experience Sampling Method to Examine the Daily Lives of Patients with Acquired Brain Injury. Neuropsychol. Rehabil. 2017, 754–766. [Google Scholar] [CrossRef]
  41. Cooray, C.; Matusevicius, M.; Wahlgren, N.; Ahmed, N. Mobile Phone-Based Questionnaire for Assessing 3 Months Modified Rankin Score After Acute Stroke: A Pilot Study. Circ. Cardiovasc. Qual. Outcomes 2015, 8, 125–130. [Google Scholar] [CrossRef]
  42. Seo, W.K.; Kang, J.; Jeon, M.; Lee, K.; Lee, S.; Kim, J.H.; Oh, K.; Koh, S.B. Feasibility of Using a Mobile Application for the Monitoring and Management of Stroke-Associated Risk Factors. J. Clin. Neurol. Seoul Korea 2015, 11, 142–148. [Google Scholar] [CrossRef]
  43. Villain, M.; Sibon, I.; Renou, P.; Poli, M.; Swendsen, J. Very Early Social Support Following Mild Stroke Is Associated with Emotional and Behavioral Outcomes Three Months Later. Clin. Rehabil. 2017, 31, 135–141. [Google Scholar] [CrossRef] [PubMed]
  44. Fors, U.; Kamwesiga, J.T.; Eriksson, G.M.; von Koch, L.; Guidetti, S. User Evaluation of a Novel SMS-Based Reminder System for Supporting Post-Stroke Rehabilitation. BMC Med. Inform. Decis. Mak. 2019, 19, 122. [Google Scholar] [CrossRef] [PubMed]
  45. Lenaert, B.; Meulders, A.; van Heugten, C.M. Tired of Pain or Painfully Tired? A Reciprocal Relationship between Chronic Pain and Fatigue. Pain 2018, 159, 1178–1179. [Google Scholar] [CrossRef] [PubMed]
  46. Shiffman, S. Conceptualizing Analyses of Ecological Momentary Assessment Data. Nicotine Tob. Res. Off. J. Soc. Res. Nicotine Tob. 2014, 16 (Suppl. 2), S76–87. [Google Scholar] [CrossRef]
  47. World Health Organization. Towards a Common Language for Functioning, Disability and Health: ICF; World Health Organization: Geneva, Switzerland, 2002. [Google Scholar]
  48. Runyan, J.D.; Steenbergh, T.A.; Bainbridge, C.; Daugherty, D.A.; Oke, L.; Fry, B.N. A Smartphone Ecological Momentary Assessment/Intervention “App” for Collecting Real-Time Data and Promoting Self-Awareness. PLoS ONE 2013, 8, e71325. [Google Scholar] [CrossRef] [PubMed]
  49. Smyth, J.M.; Stone, A.A. Ecological Momentary Assessment Research in Behavioral Medicine. J. Happiness Stud. 2003, 4, 35–52. [Google Scholar] [CrossRef]
  50. Burke, L.E.; Shiffman, S.; Music, E.; Styn, M.A.; Kriska, A.; Smailagic, A.; Siewiorek, D.; Ewing, L.J.; Chasens, E.; French, B.; et al. Ecological Momentary Assessment in Behavioral Research: Addressing Technological and Human Participant Challenges. J. Med. Internet Res. 2017, 19, e77. [Google Scholar] [CrossRef]
  51. Lui, J.H.L.; Marcus, D.K.; Barry, C.T. Evidence-Based Apps? A Review of Mental Health Mobile Applications in a Psychotherapy Context. Prof. Psychol. Res. Pract. 2017, 48, 199–210. [Google Scholar] [CrossRef]
  52. Edbrooke-Childs, J.; Smith, J.; Rees, J.; Edridge, C.; Calderon, A.; Saunders, F.; Wolpert, M.; Deighton, J. An App to Help Young People Self-Manage When Feeling Overwhelmed (ReZone): Protocol of a Cluster Randomized Controlled Trial. JMIR Res. Protoc. 2017, 6, e213. [Google Scholar] [CrossRef]
  53. English, L.L.; Dunsmuir, D.; Kumbakumba, E.; Ansermino, J.M.; Larson, C.P.; Lester, R.; Barigye, C.; Ndamira, A.; Kabakyenga, J.; Wiens, M.O. The PAediatric Risk Assessment (PARA) Mobile App to Reduce Postdischarge Child Mortality: Design, Usability, and Feasibility for Health Care Workers in Uganda. JMIR MHealth UHealth 2016, 4. [Google Scholar] [CrossRef] [PubMed]
  54. Hebden, L.; Cook, A.; van der Ploeg, H.P.; Allman-Farinelli, M. Development of Smartphone Applications for Nutrition and Physical Activity Behavior Change. JMIR Res. Protoc. 2012, 1, e9. [Google Scholar] [CrossRef]
  55. Mena, L.; Felix, V.; Ostos, R.; Gonzalez, J.; Cervantes, A.; Ochoa, A.; Ruiz, C.; Ramos, R.; Maestre, G. Mobile Personal Health System for Ambulatory Blood Pressure Monitoring. Comput. Math. Methods Med. 2013. [Google Scholar] [CrossRef] [PubMed]
  56. Ahmed, I.; Ahmad, N.S.; Ali, S.; Ali, S.; George, A.; Danish, H.S.; Uppal, E.; Soo, J.; Mobasheri, M.H.; King, D.; et al. Medication Adherence Apps: Review and Content Analysis. JMIR MHealth UHealth 2018, 6, e62. [Google Scholar] [CrossRef] [PubMed]
  57. Byambasuren, O.; Sanders, S.; Beller, E.; Glasziou, P. Prescribable MHealth Apps Identified from an Overview of Systematic Reviews. NPJ Digit. Med. 2018, 1. [Google Scholar] [CrossRef] [PubMed]
  58. Wellbeing, Recovery and Mental Health. Available online: https://www.cambridge.org/core/books/wellbeing-recovery-and-mental-health/26DC535A216270DB76A571337DC20F1D (accessed on 16 December 2020).
  59. Rowland, S.P.; Fitzgerald, J.E.; Holme, T.; Powell, J.; McGregor, A. What Is the Clinical Value of MHealth for Patients? NPJ Digit. Med. 2020, 3, 1–6. [Google Scholar] [CrossRef] [PubMed]
  60. Nouri, R.; Niakan Kalhori, S.R.; Ghazisaeedi, M.; Marchand, G.; Yasini, M. Criteria for Assessing the Quality of MHealth Apps: A Systematic Review. J. Am. Med. Inform. Assoc. 2018, 25, 1089–1098. [Google Scholar] [CrossRef]
  61. Pham, Q.; Graham, G.; Carrion, C.; Morita, P.P.; Seto, E.; Stinson, J.N.; Cafazzo, J.A. A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer MHealth Apps for Chronic Conditions: Scoping Review. JMIR MHealth UHealth 2019, 7, e11941. [Google Scholar] [CrossRef]
  62. Jones, M.; Morris, J.; Deruyter, F. Mobile Healthcare and People with Disabilities: Current State and Future Needs. Int. J. Environ. Res. Public. Health 2018, 15. [Google Scholar] [CrossRef] [PubMed]
  63. Ryan, C.; Bergin, M.; Wells, J.S. Theoretical Perspectives of Adherence to Web-Based Interventions: A Scoping Review. Int. J. Behav. Med. 2018, 25, 17–29. [Google Scholar] [CrossRef] [PubMed]
  64. Burkow, T.M.; Vognild, L.K.; Johnsen, E.; Bratvold, A.; Risberg, M.J. Promoting Exercise Training and Physical Activity in Daily Life: A Feasibility Study of a Virtual Group Intervention for Behaviour Change in COPD. BMC Med. Inform. Decis. Mak. 2018, 18, 136. [Google Scholar] [CrossRef] [PubMed]
  65. Rajani, N.B.; Weth, D.; Mastellos, N.; Filippidis, F.T. Use of Gamification Strategies and Tactics in Mobile Applications for Smoking Cessation: A Review of the UK Mobile App Market. BMJ Open 2019, 9, e027883. [Google Scholar] [CrossRef] [PubMed]
  66. Timpel, P.; Cesena, F.H.Y.; da Silva Costa, C.; Soldatelli, M.D.; Gois, E.; Castrillon, E.; Díaz, L.J.J.; Repetto, G.M.; Hagos, F.; Castillo Yermenos, R.E.; et al. Efficacy of Gamification-Based Smartphone Application for Weight Loss in Overweight and Obese Adolescents: Study Protocol for a Phase II Randomized Controlled Trial. Ther. Adv. Endocrinol. Metab. 2018, 9, 167–176. [Google Scholar] [CrossRef]
  67. Moeckli, J.; Cram, P.; Cunningham, C.; Reisinger, H.S. Staff Acceptance of a Telemedicine Intensive Care Unit Program: A Qualitative Study. J. Crit. Care 2013, 28, 890–901. [Google Scholar] [CrossRef] [PubMed]
  68. Sittig, D.F.; Krall, M.A.; Dykstra, R.H.; Russell, A.; Chin, H.L. A Survey of Factors Affecting Clinician Acceptance of Clinical Decision Support. BMC Med. Inform. Decis. Mak. 2006, 6, 6. [Google Scholar] [CrossRef]
  69. Blumenthal, J.; Wilkinson, A.; Chignell, M. Physiotherapists’ and Physiotherapy Students’ Perspectives on the Use of Mobile or Wearable Technology in Their Practice. Physiother. Can. 2018, 70, 251–261. [Google Scholar] [CrossRef]
  70. Centers for Medicare & Medicaid Services (CMS), HHS Medicare Program. Revisions to Payment Policies under the Physician Fee Schedule, Clinical Laboratory Fee Schedule, Access to Identifiable Data for the Center for Medicare and Medicaid Innovation Models & Other Revisions to Part B for CY 2015. Final Rule with Comment Period. Fed Regist. 2014, 79, 67547–68010. Available online: https://pubmed.ncbi.nlm.nih.gov/25507411/ (accessed on 25 September 2020).
  71. 4 Trends in Gartner Hype Cycle for Customer Service and Customer Engagement. Available online: //www.gartner.com/smarterwithgartner/4-trends-gartner-hype-cycle-customer-service-customer-engagement/ (accessed on 5 January 2021).
  72. Gartner Top Strategic Predictions for 2018 and Beyond. Available online: //www.gartner.com/smarterwithgartner/gartner-top-strategic-predictions-for-2018-and-beyond/ (accessed on 5 January 2021).
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