Skip to main content

Exploring the perspectives of health care professionals on digital health technologies in pediatric care and rehabilitation

Abstract

Background

Digital health technologies are increasingly used by healthcare professionals working in pediatric hospital and rehabilitation settings. Multiple factors may affect the implementation and use of digital health technologies in these settings. However, such factors have not been identified in a multidisciplinary, pediatric context. The objective of this study was to describe actual use and to identify the factors that promote or hinder the intention to use digital health technologies (mobile learning applications, virtual/augmented reality, serious games, robotic devices, telehealth applications, computerized assessment tools, and wearables) among pediatric healthcare professionals.

Methods

An online survey evaluating opinions, current use, and future intentions to use digital health technologies was completed by 108 professionals at one of Canada’s largest pediatric institutes. Mann-Whitney U tests were used to compare the attitudes of healthcare professionals who intend to increase their use of digital health technologies and those who do not. Linear regression analyses were used to determine predictors of usage success.

Results

Healthcare professionals reported mostly using mobile and tablet learning applications (n = 43, 38.1%), telehealth applications (n = 49, 43.4%), and computerized assessment tools (n = 33, 29.2%). Attitudes promoting the intention to increase the use of digital health technologies varied according to technology type. Healthcare professionals who wished to increase their use of digital health technologies reported a more positive attitude regarding benefits in clinical practice and patient care, but were also more critical of potential negative impacts on patient-professional relationships. Ease of use (β = 0.374; p = 0.020) was a significant predictor of more favorable usage success. The range of obstacles encountered was also a significant predictor (β = 0.342; p = 0.032) of less favorable evaluation of usage success. Specific factors that hinder successful usage are lack of training (β = 0.303; p = 0.033) and inadequate infrastructure (β = 0.342; p = 0.032).

Conclusions

When working with children, incorporating digital health technologies can be effective for motivation and adherence. However, it is crucial to ensure these tools are implemented properly. The findings of this study underscore the importance of addressing training and infrastructure needs when elaborating technology-specific strategies for multidisciplinary adoption of digital health technologies in pediatric settings.

Background

Digital health technologies are increasingly available to healthcare professionals working in hospital and rehabilitation settings [1,2,3,4]. Although there is no consensual definition of digital health technologies, they are typically described as any electronic technology or application that either directly or indirectly supports or provides healthcare, or promotes improved health [5, 6]. Such technologies include, but are not limited to, mobile and tablet learning applications, virtual or augmented reality (VR/AR), serious games, robotic devices, computerized assessment tools, wearables, and mobile health. Mobile health (mHealth) includes virtual care by using telehealth applications for video consultations during telehealth treatment or follow-up.

As digital health development and adoption progresses and access to virtual environments, particularly through VR and serious games, becomes more affordable, interest in using interactive and immersive systems and in exploring the therapeutic value of such systems has grown [7]. This includes exploring the therapeutic potential of such environments alongside other digital health technologies.

Digital health technologies in healthcare settings

Digital health technologies can assist healthcare professionals for diagnostic, evaluation, treatment, intervention, education, entertainment, and distraction purposes [8, 9]. Several studies have focused on their development and evaluation of their quality and utility (e.g., telehealth applications; [10,11,12]; mobile learning applications; [13,14,15]; VR; [16, 17], etc.). Implementation of digital health technologies results in positive outcomes in clinical settings such as fewer hospitalizations, more streamlined tasks, and improved healthcare efficiency and accessibility [18,19,20,21,22]. Additionally, digital health technologies have shown potential in decreasing reliance on specialized equipment and personnel, thereby improving the cost-effectiveness of healthcare services [10, 23, 24]. Specifically, in Canada, the cumulative economic benefits of digital health technologies amounted to 16 billion dollars in savings between 2007 and 2015, primarily through heightened clinician productivity and savings in transportation costs (e.g., with telehealth applications) [23].

Predicting digital health technology adoption

For digital health technologies to be successfully implemented, it is important to understand the factors that influence the adoption of innovations. The Technology Acceptance Model (TAM; [25]) is a widely used theoretical framework that seeks to explain and predict technology acceptance and usage by individuals. According to this model, users’ behavioral intentions towards technology are primarily determined by two key factors: perceived usefulness and perceived ease of use [26,27,28].

Perceived usefulness refers to the extent to which individuals believe that using a particular technology will enhance their performance or productivity [29]. Perceived ease of use refers to the degree to which individuals perceive the technology as easy to use [25]. According to this model, these two factors directly influence users’ attitudes and intentions towards adopting and using technology, which in turn predict their actual usage behavior. The TAM has been applied across various domains, including healthcare settings, where it has provided valuable insights into factors influencing the adoption and acceptance of digital health technologies among both healthcare professionals and patients [26, 30, 31].

Digital health technology needs in pediatric settings

In pediatric settings, digital health technologies present numerous potential advantages over conventional tools. The interactive nature of digital health technologies such as VR, AR and serious games, in particular, enhances motivation and engagement of pediatric patients participating in rehabilitation interventions [32, 33]. Given that children growing up in the current digital age typically have high digital literacy [7], health care systems could benefit from developing innovative digital health applications for care provision, with the goal of improving intervention uptake and compliance, as well as quality and efficiency of care. Digital health technology use in pediatric settings may thus be associated with distinct advantages, necessitating an approach and tailored strategies that consider the specific needs and characteristics of pediatric patients. Likewise, pediatric healthcare professionals may encounter unique challenges related to rapid changes in abilities depending on developmental stages, or family constraints and rules around technology use [34,35,36,37].

Multiple factors may affect acceptance and implementation of digital health technologies in pediatric health care settings. In addition, when hospitalized or in rehabilitation, children navigate through multiple services that employ different technologies, suggesting a need to comprehensively study a range of tools and factors at play. While some studies focus on a specific digital health technology such as VR or telemedicine [4, 38], on a specific intent such as improving capacity management in hospitals or improving speech in patients [39,40,41,42], or on particular healthcare professions such as occupational therapy or nursing [4, 38, 43], none consider the broader multidisciplinary care context or pediatric population. Previous studies have also reported current usage of digital health technologies, but only a few focus on the intentions behind their adoption [38, 44,45,46]. This underscores the need for further investigation to better comprehend how to promote the use of digital health technologies.

Objectives

The objective of this study was to explore the factors that promote and hinder the use of digital health technologies through a survey of multidisciplinary pediatric healthcare professionals. Specific objectives were to provide a description of the use of a variety of digital health technologies in a pediatric hospital and rehabilitation center and; (a) evaluate the attitudes that promote the intention to increase the use of digital health technologies and; (b) identify attitudes associated with, and obstacles that predict, usage success.

The hypotheses were: (a) the intention to increase the use of digital health technologies is associated with more positive attitudes; (b) more positive attitudes predict the best usage success, and conversely, technical problems, lack of training, inadequate infrastructure, time constraints, and other problems predict less usage success.

Methods

The data were collected as part of a feasibility study regarding the implementation of digital health technologies in pediatric hospitals and rehabilitation centers (the InteRV Project). The project was approved by the local human research ethics committee (CHU Sainte-Justine Azrieli Research Center, reference number 2021–2741). All participants provided informed consent for participation at the beginning of the survey.

Settings

A survey was completed by healthcare professionals at a Canadian pediatric hospital facility (CHU Sainte-Justine, including the Marie Enfant Rehabilitation Center, Montreal, Canada). CHU Sainte-Justine is one of Canada’s largest pediatric research institutes [47]. The affiliated rehabilitation center provides specialized services in the areas of adaptation-rehabilitation, integration and social participation [48].

Procedure

Specific services, units, and departments were targeted for survey dissemination to ensure the relevance of the survey for potential participants in terms of likelihood of current or projected use of digital health technologies, and to ensure data generalizability to other pediatric hospitals and rehabilitation centers. Choices were made by the research team in collaboration with clinical collaborators and the hospital directorship to ensure a global perspective. After consultation, the services, units and departments included psychology, nursing, special education, pain management team, psychiatry, speech therapy, physiotherapy, occupational therapy, neurotraumatology, anesthesia and intensive care, and orthopedics.

The survey was initially distributed to managers and service coordinators who shared it directly with their teams via email. It was also promoted in a newsletter for nurses at the hospital, thus broadening its reach within the institution. A total of approximately 1,558 healthcare professionals were reached through these means.

Participants

Participants (N = 108) were 75 healthcare professionals working at the hospital and 33 working at the rehabilitation center. To be included in the study, healthcare professionals had to be working in the targeted services, units and departments. The only exclusion criteria was completion of the survey. The most frequent occupations were nurse (28.7%, n = 31), physician (17.6%, n = 19), and occupational therapist (9.3%, n = 10). Most respondents were women (90.7%, n = 97) and were aged between 25 and 34 years (33.3%, n = 36) or between 35 and 44 years (32.4%, n = 35).

Measures

The custom-designed survey was built and distributed using the REDCap platform. Study data were also collected and managed using REDCap [49, 50]. The survey focused on the following digital health technologies: mobile and tablet learning applications, virtual or augmented reality, serious games, robotic devices, telehealth applications, computerized assessment tools, and wearables.

Completion time was approximately 10 min. A pilot test of the survey was completed by three research assistants and questions were adjusted based on their feedback. The survey consisted of a total of 20 questions and included information on current digital health technology use, usage intentions and attitudes towards digital health technologies, and obstacles encountered or perceived in relation to using digital health technologies. The nine questions relevant to the current study are in Additional file 1. Four-point scales with anchors on ‘Success, no obstacles’ and ‘Failure, too many obstacles’ were used for opinions related to usage success. Five-point Likert scales with anchors on ‘Total agreement’ and ‘Total disagreement’ were used for questions specific to attitudes (Cronbach’s alpha ranging from 0.790 to 0.902).

Statistical analyses

Analyses were run in IBM SPSS Statistics 29.0. Nonparametric statistics were used for analyzing group differences regarding questions related to attitudes because of the unequal group sizes and the ordinal nature of the data. For each digital health technology, respondents were divided into two groups: healthcare professionals who intend to increase their use of the technology and those who do not. Mann-Whitney U independent samples tests were used to compare the two groups on their attitudes regarding each digital health technology (Additional file 1, questions 8a, 8b, 8c, 8d, 8e, 8f). Due to the large number of tests conducted (6 Mann-Whitney U analyses for each digital health technology), Bonferroni correction was applied to Mann-Whitney U tests; p-values smaller than 0.0083 were considered significant.

Parametric statistics were used to investigate predictors of usage success. Two distinct multiple regression models were run. The first regression model explored predictors of usage success among the attitudes and obstacles encountered. The second multiple regression model investigated predictors of usage success among technical problems, lack of training, inadequate infrastructures, lack of time, and other problems. To control for associations with technology experience, both multiple regression models included the variety of digital health technologies used, years of work experience, and the healthcare professional’s job position. Attitudes and usage success questions that were linked to multiple digital health technologies were combined into a single variable by averaging the scales. Statistical significance was assessed at the 0.05 level.

Results

A total of 154 respondents initiated the survey. Responses from 108 healthcare professionals were included. The remaining 46 respondents were excluded as they only completed the consent form without filling out the survey responses. The response rate (the proportion of respondents who completed the survey out of the total number of individuals in the sample group) ranged between 6.8% and 7.2%. The completion rate (the proportion of respondents who completed the survey out of the number of individuals who initiated it) was 70.1%.

Experience with digital health technologies

Demographic characteristics of the sample and statistics regarding digital health technology use are presented in Table S1 (Additional file 2). The proportion of healthcare professionals using digital health technologies in their work was 79.6% (n = 86), thus 20.4% reported not using any in their practice (n = 22). Healthcare professionals reported mostly using mobile and tablet learning applications (n = 43, 38.1%), telehealth applications (n = 49, 43.4%), and computerized assessment tools (n = 33, 29.2%). Some also used VR or AR (n = 16, 14.2%), serious games (n = 9, 8.0%), robotic devices (n = 11, 9.7%), or wearables (n = 5, 4.4%). They reported using them mostly for intervention (n = 55, 64.0%), evaluation (n = 51, 59.3%), and education (n = 31, 36.0%) purposes.

Attitudes promoting the intention to increase the use of digital health technologies

Table 1 presents the values of statistical differences between healthcare professionals who intend to increase their use of digital health technologies and those who do not. Additional file 3 includes Tables S2, S3, S4, S5, S6, S7 and S8 which present the detailed Mann-Whitney U statistics for each digital health technology. Across the majority of digital health technologies, healthcare professionals who plan on increasing their use were also more in agreement with statements 8d and 8e (Additional file 1) regarding the positive impact of digital health technologies and their harmful impact on patient-professional relationship. They were, however, less in agreement with statements 8c and 8f (Additional file 1) concerning the non-essential aspect of digital health technologies, and the limited therapeutic achievement they generate. Only healthcare professionals who intend to increase their use of serious games were more in agreement with statement 8b (Additional file 1) regarding ease of use. No significant results were found for 8a (Additional file 1) concerning the assets of the tools in practice, nor for computerized assessment tools, mobile tablets, and applications for all the questions (8a, 8b, 8c, 8d, 8e, 8f) (Additional file 1).

Table 1 Presence of statistical differences between professionals who intend and do not intend to increase their use of digital health technologies

Predictors of successful usage

Linear regression results pertaining to attitudes that promote or hinder successful digital health technologies use are presented in Table 2. Ease of use was a significant predictor of more favorable usage success. The range of obstacles encountered (from 0 to 5) was also a significant predictor of less favorable evaluation of usage success. The model demonstrated a moderate fit to the data (adjusted R² = 0.314). The correlation between the observed and predicted values was strong (R = 0.671).

Table 2 Predictors of successful usage (attitudes)

Linear regression results pertaining to obstacles that promote or hinder successful digital health technologies use are presented in Table 3. Lack of training and inadequate infrastructure were significant predictors of a less favorable evaluation of usage success. The model demonstrated a modest fit to the data (adjusted R²=0.181). The correlation between the observed and predicted values was moderate (R = 0.548).

Table 3 Predictors of successful usage (obstacles)

Discussion

The objective of the study was to better understand the attitudes promoting the use of digital health technologies and predictors of their successful usage in a pediatric multidisciplinary setting. The results indicate that the usage of digital health technology is influenced by various attitudes: viewing these technologies as essential, finding them easy to use, recognizing their positive impact on patients, acknowledging the risk of harming the patient-professional relationship, and appreciating their benefits for therapeutic achievement. The main predictor of successful usage was ease of use, and obstacles encountered included lack of training and inadequate infrastructure.

Attitudes promoting the intention to increase digital health technologies use

As expected, healthcare professionals who plan to increase their use of digital health technologies in their practice generally had a more positive attitude regarding their benefits for patient care compared to those who did not plan to incorporate digital health technologies into their practice. Nonetheless, they viewed the use of digital health technologies as more detrimental to patient-professional relationships and deemed them to be less essential for practice.

These results suggest that professionals who plan on using digital health technologies are aware of the potential drawbacks and acknowledge the obstacles involved in their implementation, but they remain reasonably enthusiastic about future use of these technologies. The fact that they acknowledge that digital health technologies such as mobile/tablet applications, VR or AR, serious games and telehealth applications, may be detrimental to patient-professional relationships could be explained by the considerable amount of equipment that needs to be handled in relation to these tools [51]. This could detract from efficient human interactions, as healthcare professionals may be preoccupied with setting up equipment. Although speculative, healthcare providers in this study appear to demonstrate humility by prioritizing patient benefits over patient-provider relationships and maintaining their therapeutic role. Training could address this issue and offer solutions to preserve both the patient-provider relationship and maximize outcomes [7, 52, 53]. To support this, technical aides or support professionals should be made available to assist with equipment set-up and guide healthcare professionals in the use of digital health technologies.

Furthermore, the current findings indicate that professionals who plan on using digital health technologies view digital health technologies as less essential to the attainment of therapeutic goals. This may be due to the perception that the current treatments are already effective enough to treat patients’ health conditions, and that the addition of digital health technologies may not significantly contribute to treatment outcomes. Previous studies on specific conditions (e.g. diabetes) report digital health technology advantages for patient motivation and entertainment, but not necessarily in terms of their therapeutic value [54]. A review suggests that intervention benefits for some digital health technologies may be more limited for some clinical populations (e.g. Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), eating disorders, psychosis, Post Traumatic Stress Disorder) [55]. However, work focusing on robotic devices indicates significant therapeutic value compared to traditional treatments for children living with physical conditions [56]. In line with this disparity, findings suggest that perceptions of therapeutic benefit need to be considered with respect to each individual technology.

Previous work also highlights discrepancies between patient and healthcare professional attitudes toward digital health technologies [57,58,59]. In general, healthcare professionals and managers exhibit more resistance towards technology compared to patients [43]. Importantly, however, the current findings suggest that professionals’ awareness of digital health technology disadvantages does not diminish their inclination toward their use. One explanation could be that they recognize the potential benefits for children’s motivation and adherence to treatment, which contribute to treatment outcome, and thus disregard the disadvantages.

Attitudes across digital health technology type

Attitudes promoting the intention to increase the use of digital health technologies varied according to technology type, supporting the need to collect data on individual tools and to elaborate strategies for adopting and implementing specific technologies. For example, healthcare professionals who intend to increase their use of VR/AR, robotic devices, and telehealth applications were more inclined to think that those technologies are non-essential, while there were no significant group differences for other technologies. Professionals may be more guarded in their opinion on potential benefits because of the novelty of those technologies and ongoing changes in their functionalities and applications [60].

Additionally, some digital health technologies such as VR and AR may not be suitable for all patients or medical conditions, and healthcare professionals may need to carefully evaluate each case to determine appropriate treatment options. For example, they may have concerns about the potential risks and side effects of the technology on children, such as cybersickness or disorientation [61]. Moreover, some might be concerned about their ability to use such technology effectively or its appropriateness based on the child’s developmental stage [34, 35].

There are also issues that are unique to pediatric populations who have complex conditions or needs, such as those with ASD or other cognitive or physical conditions. The challenges faced in integrating these technologies with such populations revolve around accessibility, usability, and appropriateness of the technology. For instance, children with ASD may require tailored interfaces or sensory-friendly designs to engage effectively with digital health tools [62], while those with severe physical or intellectual disabilities might have motor deficits or cognitive limitations that hinder their ability to engage and interact with technology [63, 64]. Additionally, concerns regarding data privacy and security are heightened when dealing with vulnerable populations [65].

Predictors of usage success

The easier healthcare professionals found digital health technologies to use, the more inclined they were to rate their usage success positively, whereas other attitudes were not significant predictors of usage success. A possible explanation for these results could be that healthcare professionals prioritize practical usability when evaluating the success of digital health technologies in their clinical practice, valuing efficiency and convenience in their workflow. This underscores the importance of familiarity and proficiency in using digital health technologies effectively. While this finding has been reported previously [7, 66], this study allows generalization to the multidisciplinary pediatric setting.

As expected, the range of obstacles encountered was also a significant predictor of usage success. The main obstacles to successful use and widespread adoption are inadequate infrastructure and lack of training. These findings align with existing literature, which has shown similar obstacles for a variety of specific technologies. For example, a previous review of facilitators and barriers to VR use in a healthcare setting similarly reported challenges related to environmental context and resources, such as treatment space issues, time to learn how to use, and time to use [7]. The current study extends these observations to a broader spectrum of digital health technologies in the pediatric healthcare setting, which have not been explored to date. Lack of training or lack of familiarity with a new digital health technology logically leads to insufficient skills to use it effectively and could lead to resistance to adopting technology or slow uptake. Appropriate training programs are thus essential to ensure digital health technologies do not have a negative impact, whether perceived or real, on patient care and provider-patient relationships.

The results also underscore the importance of upgrading infrastructures in pediatric settings to ensure healthcare professionals have the resources they need to use technology [67]. Each site may need to assess local infrastructure to ensure that it can accommodate the use of digital health technologies before acquiring new equipment or encouraging professionals to adopt and implement innovative tools.

Strengths and limitations

The current study encompasses a broad spectrum of disciplines and digital health technologies, offering insights that can be applied across multiple pediatric healthcare centers. Findings can inform decision-makers on effective implementation strategies for these tools and promote the intent to increase their use. Additionally, it enriches conclusions gained from previous studies regarding predictors of usage success, underscoring the need for further changes. Nonetheless, some limitations should be considered. First, the study had a modest sample size and especially small sampling from the rehabilitation center. This may impact generalizability. The response rate for the survey was relatively low, potentially introducing bias and limiting the representativeness of the findings. Low response rate is attributed to the strategy of inviting participation through a weekly, institutional newsletter, which included an audience of approximately 1500 professionals, mostly nurses, which diminishes accuracy and breadth of response [68]. Additionally, 46 participants were excluded due to incomplete surveys, impacting the completion rate. The length of the questionnaire is likely to have contributed to the number of participants aborting the survey prematurely [69] and some respondents may have opened the survey multiple times. Second, a custom-made survey was used. Validated surveys for exploring technology use exist, for example ADOPT-VR [4]; however, a custom survey was chosen to address a broad range of technologies and ensure applicability to the pediatric healthcare setting where the study was conducted. Third, some nonparametric tests were based on unequal sample sizes (e.g. serious games, robotic devices) and this can weaken test accuracy, particularly when one group is much larger. Conclusions regarding these technologies should be interpreted with caution. Finally, other digital health technologies, such as those related to data management for example, were not addressed in this study. Including a wider range of technologies could have encouraged the participation of a greater number of healthcare professionals or managers in the study. Future research with multicentre studies and larger sample sizes should be conducted to further investigate the differences between healthcare professionals working in pediatric hospitals and rehabilitation centers, across various healthcare settings and geographical regions. They should also explore the key factors that contribute to inadequate infrastructure and lack of training and focus on strategies to mitigate such obstacles. Understanding these factors can inform strategies to promote their adoption, ultimately contributing to advancements in pediatric healthcare.

Conclusion

Considering the attitudes and perceptions of healthcare professionals regarding the integration of digital health technologies into pediatric care will help inform strategies to optimize their implementation and usage. While healthcare professionals intending to increase their use of such technologies generally hold a positive view of their benefits for patient care, they also express concerns about their potential negative impacts on patient-professional relationships and their perceived essentiality for practice. Given the differences between digital health technologies and the main barriers to their use, the findings emphasize the importance of establishing training and implementation tailored specifically to each type of technology and not assuming that the barriers or facilitators generalize across tools. Overall, the successful use of digital health technologies requires a comprehensive approach that carefully considers attitudes, infrastructure, training, and support necessary for their effective implementation and adoption in pediatric care.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

VR:

Virtual reality

AR:

Augmented reality

References

  1. Cai Y, Clinto M, Xiao Z. Artificial Intelligence Assistive Technology in Hospital professional nursing technology. J Healthc Eng. 2021;2021:e1721529.

    Article  Google Scholar 

  2. Daskalopoulou A, Palmer M. Persistent institutional breaches: technology use in healthcare work. Soc Sci Med. 2021;289:114399.

    Article  PubMed  Google Scholar 

  3. Iqbal MH, Aydin A, Brunckhorst O, Dasgupta P, Ahmed K. A review of wearable technology in medicine. J R Soc Med. 2016;109(10):372–80.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Levac D, Glegg S, Colquhoun H, Miller P, Noubary F. Virtual reality and active videogame-based practice, learning needs, and preferences: a Cross-canada Survey of physical therapists and occupational therapists. Games Health J. 2017;6(4):217–28.

    Article  PubMed  Google Scholar 

  5. Fatehi F, Samadbeik M, Kazemi A. What is Digital Health? Review of definitions. Stud Health Technol Inf. 2020;275:67–71.

    Google Scholar 

  6. Health C, for D. and R. What is Digital Health? FDA. 2020 Sep 22 [cited 2024 Jan 31]; https://www.fda.gov/medical-devices/digital-health-center-excellence/what-digital-health

  7. Glegg SMN, Levac DE, Barriers. Facilitators and interventions to support virtual reality implementation in Rehabilitation: a scoping review. PM R. 2018;10(11):1237–e12511.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Canada H, Notice. Health Canada’s Approach to Digital Health Technologies. 2018 [cited 2023 Nov 30]. https://www.canada.ca/en/health-canada/services/drugs-health-products/medical-devices/activities/announcements/notice-digital-health-technologies.html

  9. Pavel M, Jimison HB, Wactlar HD, Hayes TL, Barkis W, Skapik J, et al. The role of technology and engineering models in transforming healthcare. IEEE Rev Biomed Eng. 2013;6:156–77.

    Article  PubMed  Google Scholar 

  10. Langkamp DL, McManus MD, Blakemore SD. Telemedicine for Children with Developmental disabilities: a more effective clinical process Than Office-Based Care. Telemed E-Health. 2015;21(2):110–4.

    Article  Google Scholar 

  11. Shigekawa E, Fix M, Corbett G, Roby DH, Coffman J. The current state of Telehealth evidence: a Rapid Review. Health Aff (Millwood). 2018;37(12):1975–82.

    Article  PubMed  Google Scholar 

  12. Walle T, Erdal E, Mühlsteffen L, Singh HM, Gnutzmann E, Grün B et al. Completion rate and impact on physician–patient relationship of video consultations in medical oncology: a randomised controlled open-label trial. ESMO Open. 2020 Jan 1 [cited 2024 Jan 31];5(6). https://www.esmoopen.com/article/S2059-7029(20)32737-X/fulltext

  13. Arakawa Y, Haseda M, Inoue K, Nishioka D, Kino S, Nishi D, et al. Effectiveness of mHealth consultation services for preventing postpartum depressive symptoms: a randomized clinical trial. BMC Med. 2023;21(1):221.

    Article  PubMed  PubMed Central  Google Scholar 

  14. de Arriba Muñoz A, García Castellanos MT, Cajal MD, Beisti Ortego A, Ruiz IM, Labarta Aizpún JI. Automated growth monitoring app (GROWIN): a mobile health (mHealth) tool to improve the diagnosis and early management of growth and nutritional disorders in childhood. J Am Med Inf Assoc. 2022;29(9):1508–17.

    Article  Google Scholar 

  15. Yoshizaki A, Murata E, Yamamoto T, Fujisawa TX, Hanaie R, Hirata I, et al. Improving children’s Sleep habits using an interactive smartphone app: community-based intervention study. JMIR MHealth UHealth. 2023;11(1):e40836.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ozturk CS, Toruner EK. Effectiveness of virtual reality in anxiety and Pain Management in Children and adolescents receiving Cancer Treatment: a systematic review and Meta-analysis of Randomized controlled trials. J Med Syst. 2023;47(1):103.

    Article  PubMed  Google Scholar 

  17. Roostaei M, Babaee M, Alavian S, Jafari N, Rayegani SM, Behzadipour S. Effects of a multi-component virtual reality program on motor skills and functional postural control in children with hemiplegic cerebral palsy. Heliyon. 2023 Sep 1 [cited 2024 Jan 31];9(9). https://www.cell.com/heliyon/abstract/S2405-8440(23)07091-3

  18. Cady R, Finkelstein S, Kelly A. A telehealth nursing intervention reduces hospitalizations in children with complex health conditions. J Telemed Telecare. 2009;15(6):317–20.

    Article  PubMed  Google Scholar 

  19. Durant DJ, Fallwell N, Martinez L, Gonzalez A, Guerrazzi-Young C. A free Mobile Application improves the efficiency of Hand Hygiene Observation Collection: experiences at a Pediatric Hospital in South Texas. Jt Comm J Qual Patient Saf. 2023;49(2):111–9.

    PubMed  Google Scholar 

  20. Keasberry J, Scott IA, Sullivan C, Staib A, Ashby R. Going digital: a narrative overview of the clinical and organisational impacts of eHealth technologies in hospital practice. Aust Health Rev. 2017;41(6):646–64.

    Article  PubMed  Google Scholar 

  21. King J, Patel V, Jamoom EW, Furukawa MF. Clinical benefits of Electronic Health Record Use: National findings. Health Serv Res. 2014;49(1pt2):392–404.

    Article  PubMed  Google Scholar 

  22. Rosenbaum PL, Silva M, Camden C. Let’s not go back to ‘normal’! Lessons from COVID-19 for professionals working in childhood disability. Disabil Rehabil. 2021;43(7):1022–8.

    Article  PubMed  Google Scholar 

  23. Gheorghiu B, Hagens S. Cumulative Benefits of Digital Health Investments in Canada calculating quality, access and productivity benefits on a national scale. ETELEMED 2017 Ninth Int Conf EHealth Telemed Soc Med. 2017.

  24. Sundar P, Kasprzak S, Halsall T, Woltman H. Using web-based technologies to Increase Evaluation Capacity in Organizations Providing Child and Youth Mental Health Services. Can J Program Eval. 2010;25(1):91–112.

    Article  Google Scholar 

  25. Davis FD, Perceived, Usefulness. Perceived ease of Use, and user Acceptance of Information Technology. MIS Q. 1989;13(3):319–40.

    Article  Google Scholar 

  26. Bertrand M, Bouchard S. Applying the technology acceptance model to VR with people who are favorable to its use. J Cybertherapy Rehabil. 2008;1(2):200–7.

    Google Scholar 

  27. Fetscherin M, Lattemann C. User acceptance of virtual worlds. J Electron Commer Res. 2008;9.

  28. Tokel ST, İsler V. Acceptance of virtual worlds as learning space. Innov Educ Teach Int. 2015;52(3):254–64.

    Article  Google Scholar 

  29. Venkatesh V, Davis FD. A theoretical extension of the Technology Acceptance Model: four Longitudinal Field studies. Manag Sci. 2000;46(2):186–204.

    Article  Google Scholar 

  30. Ammenwerth E. Technology Acceptance Models in Health Informatics: TAM and UTAUT. Stud Health Technol Inf. 2019;263:64–71.

    Google Scholar 

  31. Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. J Biomed Inf. 2010;43(1):159–72.

    Article  Google Scholar 

  32. Brom C, Šisler V, Slussareff M, Selmbacherová T, Hlávka Z. You like it, you learn it: affectivity and learning in competitive social role play gaming. Int J Comput-Support Collab Learn. 2016;11(3):313–48.

    Article  Google Scholar 

  33. Phelan I, Furness PJ, Dunn HD, Carrion-Plaza A, Matsangidou M, Dimitri P, et al. Immersive virtual reality in children with upper limb injuries: findings from a feasibility study. J Pediatr Rehabil Med. 2021;14(3):401–14.

    PubMed  PubMed Central  Google Scholar 

  34. Kumpulainen K, Sairanen H, Nordström A. Young children’s digital literacy practices in the sociocultural contexts of their homes. J Early Child Lit. 2020;20(3):472–99.

    Article  Google Scholar 

  35. Saçkes M, Trundle KC, Bell RL. Young children’s computer skills development from kindergarten to third grade. Comput Educ. 2011;57(2):1698–704.

    Article  Google Scholar 

  36. Tena RR, Gutiérrez MP, Cejudo M. del CL. Technology use habits of children under six years of age at home. Ens Aval E Políticas Públicas Em Educ. 2019;27:340–62.

  37. Saleh S, Alameddine M, Farah A, El Arnaout N, Dimassi H, Muntaner C, et al. eHealth as a facilitator of equitable access to primary healthcare: the case of caring for non-communicable diseases in rural and refugee settings in Lebanon. Int J Public Health. 2018;63(5):577–88.

    Article  PubMed  Google Scholar 

  38. Kowitlawakul Y. The Technology Acceptance Model: Predicting nurses’ intention to use Telemedicine Technology (eICU). CIN Comput Inf Nurs. 2011;29(7):411.

    Article  Google Scholar 

  39. Foreman C, Stephens K, Swanson-Biearman B, Acree C, Whiteman K. Using technology to improve Capacity Management at a Pediatric Hospital. J Nurs Adm. 2020;50(11):605–11.

    Article  PubMed  Google Scholar 

  40. Delemere E, Gitonga I, Maguire R. Utility, barriers and facilitators to the use of connected health to support families impacted by paediatric cancer: a qualitative analysis. Support Care Cancer. 2022;30(8):6755–66.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lin Y, Lemos M, Neuschaefer-Rube C. Digital Health and Digital Learning Experiences Across Speech-Language Pathology, Phoniatrics, and Otolaryngology: Interdisciplinary Survey Study. JMIR Med Educ. 2021;7(4):e30873.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Vis C, Mol M, Kleiboer A, Bührmann L, Finch T, Smit J, et al. Improving implementation of eMental Health for Mood disorders in Routine Practice: systematic review of barriers and facilitating factors. JMIR Ment Health. 2018;5(1):e9769.

    Article  Google Scholar 

  43. Safi S, Thiessen T, Schmailzl KJ. Acceptance and Resistance of New Digital technologies in Medicine: qualitative study. JMIR Res Protoc. 2018;7(12):e11072.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Tennant M, McGillivray J, Youssef GJ, McCarthy MC, Clark TJ. Feasibility, acceptability, and clinical implementation of an immersive virtual reality intervention to address Psychological Well-being in children and adolescents with Cancer. J Pediatr Oncol Nurs off J Assoc Pediatr Oncol Nurses. 2020;37(4):265–77.

    Article  Google Scholar 

  45. Barzekar H, Ebrahimzadeh F, Luo J, Karami M, Robati Z, Goodarzi P. Adoption of Hospital Information System among nurses: a Technology Acceptance Model Approach. Acta Inf Med AIM J Soc Med Inf Bosnia Herzeg Cas Drustva Za Med Inf BiH. 2019;27(5):305–10.

    Google Scholar 

  46. Siebert JN, Gosetto L, Sauvage M, Bloudeau L, Suppan L, Rodieux F, et al. Usability testing and Technology Acceptance of an mHealth app at the point of Care during simulated Pediatric In- and out-of-hospital cardiopulmonary resuscitations: study nested within 2 Multicenter Randomized controlled trials. JMIR Hum Factors. 2022;9(1):e35399.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Our Hospitals - CHU Sainte-. Justine Foundation | CCHF. [cited 2024 Jan 31]. https://childrenshospitals.ca/childrens-hospitals/chu-sainte-justine-foundation/

  48. Mission du CRME. [cited 2023 Nov 30]. https://readaptation.chusj.org/fr/Decouvrez-nous/Qui-sommes-nous-/Notre-mission

  49. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inf. 2019;95:103208.

    Article  Google Scholar 

  50. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42(2):377–81.

    Article  Google Scholar 

  51. Rodrigues DA, Roque M, Mateos-Campos R, Figueiras A, Herdeiro MT, Roque F. Barriers and facilitators of health professionals in adopting digital health-related tools for medication appropriateness: a systematic review. Digit Health. 2024;10:20552076231225133.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ardito V, Golubev G, Ciani O, Tarricone R. Evaluating barriers and facilitators to the uptake of mHealth apps in Cancer Care using the Consolidated Framework for Implementation Research: scoping Literature Review. JMIR Cancer. 2023;9(1):e42092.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Whitelaw S, Pellegrini DM, Mamas MA, Cowie M, Van Spall HGC. Barriers and facilitators of the uptake of digital health technology in cardiovascular care: a systematic scoping review. Eur Heart J - Digit Health. 2021;2(1):62–74.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Glegg SMN. Facilitating interviews in qualitative research with visual tools: a typology. Qual Health Res. 2019;29(2):301–10.

    Article  PubMed  Google Scholar 

  55. Hollis C, Falconer CJ, Martin JL, Whittington C, Stockton S, Glazebrook C, et al. Annual Research Review: Digital health interventions for children and young people with mental health problems – a systematic and meta-review. J Child Psychol Psychiatry. 2017;58(4):474–503.

    Article  PubMed  Google Scholar 

  56. Deineko VV, Krysyuk OB, Safonov LV, Shurygin SN. [Modern opportunities and prognosis of physical rehabilitation of children with cerebral palsy]. Zh Nevrol Psikhiatr Im S S Korsakova. 2020;120(6):88–91.

    Article  CAS  PubMed  Google Scholar 

  57. Alameddine M, Tamim H, Hadid D, Cheaito MA, Makki M, Maatouk H, et al. Patient attitudes toward Mobile device use by Health Care providers in the Emergency Department: cross-sectional survey. JMIR MHealth UHealth. 2020;8(3):e16917.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Gauthier-Beaupré A, Grosjean S. Understanding acceptability of digital health technologies among francophone-speaking communities across the world: a meta-ethnographic study. Front Commun. 2023 [cited 2024 Feb 2];8. https://www.frontiersin.org/articles/https://doi.org/10.3389/fcomm.2023.1230015

  59. Jarva E, Oikarinen A, Andersson J, Tuomikoski A, Kääriäinen M, Meriläinen M, et al. Healthcare professionals’ perceptions of digital health competence: a qualitative descriptive study. Nurs Open. 2022;9(2):1379–93.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Tieri G, Morone G, Paolucci S, Iosa M. Virtual reality in cognitive and motor rehabilitation: facts, fiction and fallacies. Expert Rev Med Devices. 2018;15(2):107–17.

    Article  CAS  PubMed  Google Scholar 

  61. Tychsen L, Foeller P. Effects of Immersive virtual reality headset viewing on Young Children: visuomotor function, Postural Stability, and Motion Sickness. Am J Ophthalmol. 2020;209:151–9.

    Article  PubMed  Google Scholar 

  62. Valencia K, Rusu C, Quiñones D, Jamet E. The impact of technology on people with Autism Spectrum disorder: a systematic literature review. Sensors. 2019;19(20):4485.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Lissitsa S, Madar G. Do disabilities impede the use of information and communication technologies? Findings of a repeated cross-sectional study – 2003–2015. Isr J Health Policy Res. 2018;7:66.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Keptner KM, Heath M. The impact of resistance on telemedicine use for people with disabilities. J Telemed Telecare. 2023;1357633X231213412.

  65. Paul M, Maglaras L, Ferrag MA, Almomani I. Digitization of healthcare sector: a study on privacy and security concerns. ICT Express. 2023;9(4):571–88.

    Article  Google Scholar 

  66. Nadav J, Kaihlanen AM, Kujala S, Laukka E, Hilama P, Koivisto J, et al. How to Implement Digital Services in a way that they integrate into routine work: qualitative interview study among Health and Social Care professionals. J Med Internet Res. 2021;23(12):e31668.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Tolley C, Seymour H, Watson N, Nazar H, Heed J, Belshaw D. Barriers and opportunities for the Use of Digital Tools in Medicines optimization across the interfaces of Care: stakeholder interviews in the United Kingdom. JMIR Med Inf. 2023;11(1):e42458.

    Article  Google Scholar 

  68. Gingras MÈ, Belleau H, Avantages. et désavantages du sondage en ligne comme méthode de collecte de données: une revue de la littérature. Montréal: INRS Centre - Urbanisation Culture Société; 2015 [cited 2024 Apr 2]. https://espace.inrs.ca/id/eprint/2678/

  69. Vehovar V, Manfreda KL. The SAGE Handbook of Online Research Methods. SAGE Publications Ltd; 2017 [cited 2024 Apr 2]. https://methods.sagepub.com/book/the-sage-handbook-of-online-research-methods-second-edition

Download references

Acknowledgements

We would like to thank Karine Tadros, Stéphanie Fiset, Michelle Bourassa and Jérôme Gauvin-Lepage for their input in the study design and revision of the protocol. We would also like to thank the healthcare professionals who participated in this study and Hamza Zarglayoun who tested the survey prior to data collection.

Funding

This work was supported by a TRAUMA Grant from the CHU Sainte-Justine Foundation.

Author information

Authors and Affiliations

Authors

Contributions

Author I.R. led the data collection process, oversaw survey distribution, conducted result analysis, and interpreted findings. Additionally, I.R. took the lead in drafting the manuscript. J.S. and E.N. provided significant contributions to the analysis and played major roles in manuscript writing. D.L.B. and A.B. were responsible for protocol and survey development. D.L. contributed to manuscript writing and assistance with revisions. M.H.B. supervised the study. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Miriam H. Beauchamp.

Ethics declarations

Ethics approval and consent to participate

The project was approved by the local human research ethics committee (CHU Sainte-Justine Azrieli Research Center, reference number 2021–2741). All participants provided informed consent for participation at the beginning of the survey.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Additional file 1: Appendix 1:

Survey questions

Additional file 2: Table S1:

Demographic characteristics and digital health technology use among participants

Additional file 3: Tables S2–S8

: Comparisons between professionals who intend and do not intend to increase their use of digital health technologies

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roy, I., Salles, J., Neveu, E. et al. Exploring the perspectives of health care professionals on digital health technologies in pediatric care and rehabilitation. J NeuroEngineering Rehabil 21, 156 (2024). https://doi.org/10.1186/s12984-024-01431-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12984-024-01431-9

Keywords