Public perceptions of spatial computing in health: Opportunities and barriers for supporting self-care and wellbeing

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Design Cross-sectional online survey. Setting UK-wide, web-based survey conducted between January 2025 and August 2025. Participants Community-dwelling adults aged ≥ 18 years residing in the UK, including healthcare professionals. A total of 405 respondents completed the survey; 41 were healthcare professionals. Interventions No intervention was delivered. Participants completed a structured questionnaire assessing familiarity with spatial computing, perceived utility across self-care domains aligned to the Seven Pillars of Self-Care and perceived barriers to adoption. Primary and Secondary Outcome Measures Primary outcomes were self-reported familiarity with spatial computing technologies and perceived benefit across self-care domains. Secondary outcomes included perceived barriers to adoption and associations between demographic characteristics and familiarity. Analyses used descriptive statistics and exploratory inferential tests (χ², Fisher’s exact, Friedman and Wilcoxon signed-rank tests with Bonferroni correction). Results Most respondents (71.4%) reported familiarity with spatial computing technologies, although regular use was uncommon (4.7%). Oculus Quest (57.5%) and Apple Vision Pro (46.9%) were the most recognised platforms. Participants perceived strong potential for supporting health literacy, mental wellbeing and physical activity, particularly through guided mindfulness, avatar-led exercise and immersive patient education. Perceived benefit was lower for medication management and dietary guidance. Familiarity was statistically associated with gender (p < 0.001), age (p = 0.002) and ethnicity (p = 0.006), with higher awareness among men, younger adults and some minority ethnic groups. The most frequently cited barriers to adoption were high cost (56.5% rating as critical), lack of training (67.4% rating 4–5) and data privacy concerns (63.5%). Conclusions Spatial computing is viewed positively by the public and healthcare professionals as a tool to support self-care and aspects of healthcare delivery, particularly health literacy, mental wellbeing and physical activity. However, high cost, training gaps and privacy concerns remain substantial barriers. Targeted investment in evidence generation, workforce training and inclusive governance will be necessary to support equitable and responsible implementation. Health Policy Preventive Medicine Spatial computing Virtual reality Augmented reality Mixed reality Digital health Self-care Health literacy Primary care Patient education Adoption barriers Figures Figure 1 Figure 2 Summary Box What is already known on this topic Spatial computing (virtual, augmented and mixed reality) has demonstrated benefits in surgical training, rehabilitation and medical education. Evidence on public and healthcare professional perceptions of spatial computing in primary care and self-care is limited. Barriers such as cost, digital literacy and ethical concerns are recognised but underexplored in community settings. What this study adds Public familiarity with consumer VR platforms (e.g. Oculus Quest, Apple Vision Pro) is relatively high, but regular use is rare. Respondents endorsed strong potential for spatial computing to support health literacy, mental wellbeing and physical activity, but were less convinced about its role in medication adherence or dietary guidance. Significant gender, age and ethnicity differences exist in familiarity with spatial computing, with men, younger adults and some minority ethnic groups reporting higher awareness. Cost, lack of training and data privacy concerns were consistently ranked as the most important barriers to adoption. How this study might affect research, practice, or policy Developers and policymakers should prioritise co-designed applications that align with user preferences, particularly in health education, behaviour change and psychosocial support. Implementation in healthcare must be accompanied by investment in workforce training, affordability strategies and privacy safeguards. Addressing demographic disparities in digital confidence, particularly among women and older adults, will be essential for equitable uptake. Rigorous evaluation of clinical outcomes, cost-effectiveness and long-term engagement is required to guide responsible integration of spatial computing into health systems. Background Health systems face converging pressures of rising demand, workforce shortages and widening access gaps, trends that are particularly visible in UK primary care ( 1 ), where public dissatisfaction is closely tied to difficulties obtaining appointments and perceived staff shortfalls ( 1 ). Against this backdrop, the strategic case for technologies that expand capacity, enhance patient engagement and enable prevention is compelling. Spatial computing is an umbrella term encompassing virtual, augmented and mixed reality (VR/AR/MR). It allows digital content to be anchored in physical space, supporting embodied interactions that differ from conventional screen-based tools. In the UK policy context, digital technologies are repeatedly highlighted as levers for service redesign and workforce enablement ( 2 ). At the same time, the World Health Organization (WHO) situates self-care as a core pathway to universal health coverage, calling for evidence-based interventions that strengthen individuals’ ability to promote health, prevent disease and manage illness ( 3 ). Early applications of spatial computing in health are promising as randomised and controlled studies suggest that immersive VR can reduce acute and chronic pain in clinical settings, with growing methodological rigor and effect sizes that justify continued evaluation ( 4 , 5 ). In neurorehabilitation, successive Cochrane-aligned syntheses report benefits of VR as an adjunct to conventional therapy after stroke, including upper-limb function gains ( 6 , 7 ). In education and training, scoping and systematic reviews map expanding use across undergraduate and postgraduate curricula, with positive effects on skills acquisition and procedural rehearsal and mixed evidence on transfer to real-world performance ( 8 , 9 ). Conceptually, spatial computing has also been framed as a platform for next-generation, participatory “metaverse” health experiences ( 10 ). Yet translation beyond specialist contexts into primary care and community-based self-care remains limited. Real-world deployment hinges on integration with existing data infrastructure and workflows since most UK general practices use EMIS or TPP (SystmOne), which together cover more than 90% of English practices ( 11 ). Current national programmes enabling patient access to GP records through the NHS App illustrate the governance, interoperability and safety layers that any new modality must respect ( 12 , 13 ), whereas as parallel concerns focus on privacy, ethics and acceptability primarily because immersive systems capture high-granularity biometric, behavioural and spatial data (e.g., eye-tracking, body motion), raising distinctive risks around inference, re-identification and exploitation ( 14 – 16 ). Moreover, differential digital literacy persists across the population, with implications for equitable uptake of novel interfaces ( 17 ). In the UK, evidence and value expectations are increasingly codified since adoption decisions for digital health tools are guided by the National Institute for Health and Care Excellence Evidence Standards Framework (ESF), which specifies proportionate clinical- and economic-evidence requirements for deployment and scale-up ( 18 , 19 ). A further gap is conceptual. While “digital health” is often discussed generically, spatial computing affords embodied, situated experiences that may differentially influence key behaviours underpinning self-care (e.g., comprehension, motivation, adherence). The International Self-Care Foundation’s Seven Pillars of self-care framework (knowledge and health literacy; mental wellbeing; physical activity; healthy eating; risk avoidance; good hygiene; and the rational use of products and services) offers a structured lens for anticipating where immersive modalities could add value ( 20 ). Prior scholarship has proposed integrative frameworks for self-care and mapped measurement tools against these pillars, highlighting both the breadth of relevant behaviours and the scarcity of instruments that capture them comprehensively ( 21 , 22 ). This study addresses these evidence and implementation gaps by assessing the knowledge, attitudes, perceptions and behaviours of UK community-dwelling adults, including healthcare professionals, toward spatial computing in health and self-care. We examine familiarity and perceived utility across self-care domains, explore barriers such as cost, training and privacy and test for demographic differences relevant to equity. By situating public and professional perspectives within UK primary-care realities and established evidence standards, the study aims to inform co-design priorities, evaluation strategies and governance approaches for the responsible introduction of spatial computing into routine care. The primary aim of the METACARE study was to assess the knowledge, attitudes and practices (KAPB) of healthcare professionals and the general public regarding the potential implementation of spatial computing technologies in healthcare, particularly in primary care and self-care settings. The study focused on measuring familiarity with consumer and professional spatial computing platforms, perceived utility across the seven pillars of self-care and attitudes toward their integration into existing models of care. Specifically, we sought to examine public awareness and prior use of spatial computing tools, to identify perceived benefits and challenges to adoption from both healthcare professionals and community-dwelling adults and to explore how demographic characteristics such as age, gender and ethnicity shape familiarity and acceptance. Methods Study design This cross-sectional study aimed to explore the general knowledge, attitudes, perceptions and behaviours of a cross-section of UK adults, including health and care professionals, regarding the use of spatial computing technologies along with the socio-demographic factors which may affect them. The study employed a quantitative methodology using an electronic survey tool (eSurvey). Data collection This was an open eSurvey, accessible to anyone with the survey link. The voluntary survey required less than ten minutes to complete, though length may vary depending on the options selected. Accordingly, findings should be interpreted as descriptive and hypothesis-generating rather than estimates of population prevalence. Survey instrument A structured, self-administered electronic questionnaire was developed drawing on existing literature, expert input and public health frameworks on digital self-care. It included closed-ended items covering five domains: (i) familiarity and exposure to spatial computing devices (e.g. Apple Vision Pro, Microsoft HoloLens, Oculus Quest); (23) perceived utility of spatial computing for health and wellbeing (5-point Likert scale); (iii) perceived impact on self-care domains based on the Seven Pillars of Self-Care; (iv) barriers and ethical concerns related to adoption in healthcare; and (v) sociodemographic characteristics. A pilot survey was tested for clarity and usability before launch. The final version, hosted on Qualtrics, required approximately 10 minutes to complete. Adaptive questioning was used so, for example, having experienced specific symptoms or having consulted one or more HCPs regarding these symptoms, led to the display of specific questions relating to these experiences. In its longest version, the survey was composed of 33 questions, including 9 for socio-demographic information such as age, gender, employment status. Other questions related to knowledge, attitudes and experiences. In its longest version the survey was displayed over 13 pages, with a maximum of 6 questions per page. Respondents could not use a back button to revise their responses. The eSurvey was developed, revised and tested by the study team to ensure clarity, usability and technical functionality before dissemination. A copy of the full survey can be accessed in Supplementarty File 1. The link to the eSurvey was active on the Imperial College Qualtrics platform between 17 January 2025 and 1 August 2025. The survey included a link to the Participant Information Sheet (PIS) which informed potential respondents on the study’s aims, the protection of participants’ personal data, their right to withdraw from the study at any time, which data were stored, where and for how long, who the investigator was, the purpose of the study and survey length. Participants were recruited through convenience sampling. Most participants were recruited via Prolific Academic’s panel (24) which facilitates rapid access to diverse adult samples but does not employ probability-based sampling. As such, participation required internet access, digital literacy, and familiarity with online research platforms, which may have influenced sample composition. No incentives were offered for participation beyond standard panel reimbursement where applicable. Personal and professional networks were mobilised to respond and further disseminate the eSurvey among potentially eligible participants. Informed consent was obtained from all participants at the beginning of the survey. They were informed that this was a voluntary survey. Data collected were stored on a secure database at Imperial College London and only accessible to the researcher team. All responses were pseudo-anonymised to ensure confidentiality. Data analysis Only questionnaires fully completed were included in the analysis. Duplicate entries from the same IP address within a 24-hour period were also eliminated before analysis. Descriptive statistics (frequencies and percentages) were used to summarise participant demographics and key response distributions. To assess relationships between demographic variables and familiarity with spatial computing, chi-square tests of independence were conducted. Where expected cell counts were fewer than five, Fisher’s exact test was used. Statistical significance was set at p<0.05. All inferential analyses were exploratory and hypothesis-generating; no causal inference was intended. Perceptions of spatial computing across multiple self-care domains were compared using Friedman tests, which account for repeated measures on ordinal variables. Post-hoc Wilcoxon signed-rank tests with Bonferroni correction were applied to identify specific differences between domain ratings. Similarly, comparisons of perceived adoption barriers were analysed using Friedman and Wilcoxon tests to identify statistically significant differences between ranked barrier severity scores. All analyses were performed using STATA, version 18 (StataCorp LP, College Station, TX, USA). The Checklist for Reporting Results of Internet ESurveys (CHERRIES) was used to guide reporting (25); Supplementarty File 2. Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Imperial College London Research Ethics Committee (reference number: 21IC7375). All participants were provided with a Participant Information Sheet and gave informed consent electronically before taking part in the survey. Results Demographic profile of respondents A total of 405 community-dwelling adults participated in the METACARE study ( tables 1 and 2), including 41 healthcare professionals (HCPs) and 364 non-HCPs. The average time taken to complete the survey as on average 5 min 30 sec. The full survey findings are illustrated in Supplementary Table S1. Detailed item-level distributions are provided in Supplementary Tables S2-S4. After accounting for missing values, the largest proportion of respondents were aged 31-40 years (30.6%), followed by those aged 18-30 years (22.0%), 41-50 years (18.3%), 51-60 years (16.3%) and 61 years or older (12.8%). Gender distribution was relatively balanced, with 49.6% identifying as female, 50.1% as male and 0.2% as another gender identity. The sample was predominantly White (81.0%), with smaller proportions identifying as Black/African/Caribbean British (6.2%), Asian/Asian British (5.9%), Mixed/Multiple ethnic groups (4.2%) and Other ethnic backgrounds (2.7%). While minor levels of item non-response were observed across demographic variables, the sample reflected broad representation across age bands, gender identities and racial/ethnic groups, supporting meaningful analysis of public perspectives on spatial computing in health ( Table 1 ). Table 1: Participants' demographics (N=405) Variable Frequency (Percentage) HCP (N=41) Non-HCP (N=364) Total What is your age? 18-30 11 (26.8%) 78 (21.4%) 89 (22.0%) 31-40 14 (34.1%) 110 (30.2%) 124 (30.6%) 41-50 4 (9.8%) 70 (19.2%) 74 (18.3%) 51-60 8 (19.5%) 58 (15.9%) 66 (16.3%) 61+ 4 (9.8%) 48 (13.2%) 52 (12.8%) What is your gender? Female 24 (58.5%) 177 (48.6%) 201 (49.6%) Male 17 (41.5%) 186 (51.1%) 203 (50.1%) Other 0 (0.0%) 1 (0.3%) 1 (0.2%) What is your ethnicity? White 30 (73.2%) 298 (81.9%) 328 (81.0%) Asian/Asian British 2 (4.9%) 22 (6.0%) 24 (5.9%) Black/African/Caribbean British 6 (14.6%) 19 (5.2%) 25 (6.2%) Mixed/Multiple ethnic groups 1 (2.4%) 16 (4.4%) 17 (4.2%) Other ethnic groups 2 (4.9%) 9 (2.5%) 11 (2.7%) Familiarity with spatial computing technologies Of the 405 survey participants, 71.4% reported being familiar with consumer-facing spatial computing or virtual reality devices (i.e., had used them briefly, regularly, or at least heard of them but never used them). Only a small minority (4.7%) indicated they regularly used such technologies. Around one-third (33.3%) had used a spatial computing device briefly, while an equal proportion (33.3%) had heard of the technologies but never used them. Just over a quarter (28.6%) reported being not familiar at all. This distribution suggests that, while general awareness is relatively high, hands-on experience with spatial computing in everyday contexts remains limited; Supplementary Table S1 . Exposure to spatial computing platforms When asked about specific devices, the most widely recognised platforms were the Oculus Quest (57.5%) and Apple Vision Pro (46.9%). Fewer participants were familiar with Google Cardboard (22.7%) or Microsoft HoloLens (11.6%). Notably, 22.5% of respondents indicated they had never heard of or used any spatial computing device. These findings illustrate a high level of public brand awareness for consumer VR products, particularly those marketed for entertainment, but lower visibility for enterprise-grade systems such as HoloLens, which are more relevant to professional healthcare settings; Supplementary Table S1 . Perceived benefits for managing health and wellbeing Participants showed strong optimism about spatial computing for health and wellbeing. The highest ratings were for avatar-guided physical activity (67.9% scoring 4–5), guided mindfulness (60.5%) and virtual support groups (56.6%), highlighting perceived value in mental health and peer support. Educational and behavioural applications were also endorsed, including VR simulations to understand health conditions (64.0%), interactive health education (57.5%), remote monitoring (53.6%) and customisable patient interfaces (52.6%). Overall, respondents viewed spatial computing as most valuable when it delivers immersive, visual and personalised interactions, particularly in mental health, exercise and health education ( Table 2 ). Lower enthusiasm for medication and lifestyle management features By contrast, participants were more cautious about spatial computing for structured health tasks. Fewer saw strong value in medication management (40.2%), visualising diet and nutrition (43.0%), or promoting adherence to lifestyle medicine (37.6%). This ambivalence may reflect limited exposure to such tools or doubts about their practicality in everyday health management; Supplementary Table S1. Barriers and Concerns Regarding Adoption Despite the generally positive outlook, respondents identified several obstacles that could slow the adoption of spatial computing in healthcare. High cost emerged as the most significant barrier, with over half of participants (56.5%) rating it as a major concern (5 out of 5). Other notable barriers included lack of training (67.4% rated 4 or 5), data privacy concerns (63.5%) and system complexity (63.0%). Participants also highlighted issues such as limited evidence of benefits, potential disruption to existing healthcare workflows and ethical considerations including patient consent, bias in algorithms and inequitable access. These findings highlight that while enthusiasm is high, successful integration will require addressing practical, technical and ethical challenges; Supplementary Table S1 . Summary of trends Overall, the results indicate that respondents perceived spatial computing to hold promise for self-care and health support, particularly in areas involving experiential learning, behaviour change and psychosocial engagement. Applications involving personalised health education, visualisation and remote interaction garnered the most enthusiastic support, while domains such as medication adherence and diet management were viewed more cautiously. The generally favourable response across a range of self-care domains highlights the emerging public readiness to engage with immersive technologies in health contexts, albeit with some reservations around specific functionalities. These perceptions will be critical in shaping future research, innovation priorities and implementation strategies for spatial computing in both clinical and community settings. Inferential analysis All inferential analyses were exploratory and not adjusted for multiple hypothesis testing unless otherwise stated. Demographics and familiarity with spatial computing technologies To explore whether familiarity with consumer-facing spatial computing technologies varied by demographic characteristics, bivariate analyses were conducted using chi-square (χ²) and Fisher’s exact tests where appropriate ( table 2) . For this analysis, "familiarity" was defined as a self-reported “yes” response to having knowledge of spatial computing or VR devices. Table 2: The association between demographics and familiarity with consumer-facing spatial computing or virtual reality devices Variable General level of familiarity with spatial computing technologies P-value Yes No What is your age? 0.002 18-30 70 (78.7%) 19 (21.4%) 31-40 91 (73.4%) 33 (26.6%) 41-50 56 (75.7%) 18 (24.3%) 51-60 47 (71.2%) 19 (28.8%) 61+ 25 (48.1%) 27 (51.9%) What is your gender? <0.001 Female 127 (63.2%) 74 (36.8%) Male 162 (79.8%) 41 (20.2%) Other 0 (0.0%) 1 (100.0%) What is your ethnicity? 0.006 Asian/Asian British 22 (91.7%) 2 (8.3%) British Black/African/Caribbean 23 (92.0%) 2 (8.0%) Mixed/Multiple ethnic groups 14 (82.4%) 3 (17.7%) Other ethnic groups 7 (63.6%) 4 (36.4%) White 223 (68.0%) 105 (32.0%) Gender was significantly associated with familiarity (p<0.001). A higher proportion of males (79.8%) reported familiarity compared to females (63.2%). Only one participant identified as “Other” gender and reported no familiarity. This pattern suggests a notable gender disparity in exposure to or engagement with emerging immersive technologies, potentially reflecting broader gender gaps in digital confidence and technology use. Age group was also significantly associated with familiarity (p=0.002). Familiarity tended to be higher among younger participants: 78.7% of those aged 18-30 and 73.4% of those aged 31-40 reported familiarity, compared with 48.1% among those aged 61 years or older. The data indicate a clear downward trend in familiarity with increasing age. Ethnicity was statistically associated with familiarity with spatial computing technologies (p=0.006). Higher reported familiarity was observed among participants identifying as British Black/African/Caribbean and Asian/Asian British compared with White participants, while familiarity was lowest among those reporting other ethnic backgrounds. However, these subgroup estimates were based on small cell sizes and should be interpreted with caution. The survey did not capture socioeconomic position, technology access pathways, occupational exposure or prior engagement with immersive technologies, all of which may confound observed associations. These patterns should not be interpreted as evidence of differential acceptability across ethnic groups, but rather as signals for further, adequately powered investigation. Familiarity with spatial computing varied significantly by gender, age and ethnicity. Men and younger adults reported higher awareness, while familiarity was also notable among some minority groups. Older adults and women, who showed lower familiarity, may be more affected by barriers such as complexity, lack of training and privacy concerns. Conversely, groups with higher familiarity may be more willing to engage but remain constrained by systemic barriers such as cost (critical for 56.5% of respondents) and limited evidence of benefit. Tailored training, support and trust-building for less familiar groups, alongside wider efforts to address cost and infrastructure, will be key for equitable adoption. Given the exploratory design and unequal subgroup sizes, these associations were not adjusted for multiple testing and should be interpreted as hypothesis-generating rather than confirmatory. Perceived Benefit of Spatial Computing A Friedman test compared perceptions of spatial computing across seven self-care domains (health literacy, mental wellbeing, physical activity, healthy eating, risk avoidance, hygiene and rational use). Ratings on a 5-point Likert scale showed significant variation in perceived benefit (Q(6)=275.86, p<0.001), indicating differences in how participants valued spatial computing across domains ( table 3 ). Post-hoc Wilcoxon tests (Bonferroni p<0.0024) showed that health literacy, mental wellbeing and physical activity were the highest-rated domains. Health literacy scored higher than healthy eating, hygiene, risk avoidance and rational use, but lower than physical activity and similar to mental wellbeing. Mental wellbeing and physical activity were both rated higher than all other domains. Healthy eating held an intermediate position, above risk avoidance and hygiene but below the top three. Risk avoidance ranked lowest, while hygiene and rational use clustered in the intermediate-low range with no significant difference between them. The final ranking placed health literacy, mental wellbeing and physical activity as the highest perceived benefits, followed by healthy eating, then hygiene practices and rational use of products, with risk avoidance lowest. Participants therefore viewed spatial computing as most valuable for improving literacy, wellbeing and physical activity ( Table 3 and Figure 1 ). Table 3: Pairwise Wilcoxon Signed-Rank Test Results Comparing Perceived Benefit Scores (1=Not at all, 5=Very much) of Spatial Computing Across Self-Care Pillars, with Bonferroni Correction (Significance Threshold: p<0.0024) Comparison Z Statistic p-value Interpretation Health literacy Mental wellbeing -2.177 0.0295 No significant difference than mental wellbeing Physical activity -3.542 0.0004 Health literacy was rated significantly lower than physical activity Healthy eating 5.407 <0.0001 Health literacy was rated significantly higher than healthy eating Risk avoidance 10.180 <0.0001 Health literacy was rated significantly higher than risk avoidance Good hygiene practices 7.249 <0.0001 Health literacy was rated significantly higher than good hygiene practices Rational use of products 5.739 <0.0001 Health literacy was rated significantly higher than rational use of products Mental wellbeing Physical activity -1.953 0.0508 No significant difference Healthy eating 6.478 <0.0001 Mental wellbeing was rated significantly higher than healthy eating Risk avoidance 10.447 <0.0001 Mental wellbeing was rated significantly higher than risk avoidance Good hygiene practices 7.798 <0.0001 Mental wellbeing was rated significantly higher than good hygiene practices Rational use of products 6.831 <0.0001 Mental wellbeing was rated significantly higher than rational use of products Physical activity Healthy eating 7.646 <0.0001 Physical activity was rated significantly higher than health eating Risk avoidance 10.813 <0.0001 Physical activity was rated significantly higher than risk avoidance Good hygiene practices 8.661 <0.0001 Physical activity was rated significantly higher than good hygiene practices Rational use of products 7.275 <0.0001 Physical activity was rated significantly higher than rational use of products Healthy eating Risk avoidance 5.605 <0.0001 Healthy eating was rated significantly higher than risk avoidance Good hygiene practices 3.104 0.0019 Healthy eating was rated significantly higher Rational use of products 0.373 0.7092 No significant difference Risk avoidance Good hygiene practices -3.465 0.0005 Risk avoidance was rated significantly lower than good hygiene practices Rational use of products -5.850 <0.0001 Risk avoidance was rated significantly lower than rational use of products Good hygiene Rational use of products -2.330 0.0198 No significant difference Barriers to Using Spatial Computing The Friedman test showed significant variation in how barriers were ranked (Q(9)=1000, p<0.0001). High cost was consistently the top concern, rated significantly higher than all others. Lack of training and limited evidence formed the next tier, comparable to data privacy and complexity. Potential disruption clustered with these mid-level concerns. Lower-ranked barriers included lack of evidence, ethical issues, hygiene and other considerations, which were generally seen as less critical. The final hierarchy was: (1) high cost; (2) lack of training and limited evidence; (3) data privacy, complexity and disruption; (4) lack of evidence; (5) ethical concerns; and (6) hygiene and other. These findings highlight cost, training and evidence gaps as the most pressing challenges, with privacy and technical issues moderately important and ethical or hygiene concerns less influential ( Table 4 ). Table 4: Pairwise Wilcoxon Signed-Rank Test Results Comparing Perceived Barrier Severity (1=Not at all, 5=Very much) to Spatial Computing Adoption, with Bonferroni Correction (Significance Threshold: p<0.0011) Comparison Z statistic p-value Interpretation High cost Lack of training 9.398 0.0001 High cost rated significantly higher than lack of training Limited evidence of benefits 11.633 0.0001 High cost rated significantly higher than limited evidence Potential disruption 13.220 0.0001 High cost rated significantly higher than potential disruption Data privacy concerns 7.376 0.0001 High cost rated significantly higher than data privacy Complexity 10.314 0.0001 High cost rated significantly higher than complexity Lack of evidence 12.688 0.0001 High cost rated significantly higher than lack of evidence Ethical concerns 13.020 0.0001 High cost rated significantly higher than ethical concerns Hygiene considerations 15.689 0.0001 High cost rated significantly higher than hygiene considerations Other 16.224 0.0001 High cost rated significantly higher than other Lack of training Limited evidence of benefits 4.472 0.0001 Lack of training rated significantly higher than limited evidence Potential disruption 8.300 0.0001 Lack of training w rated significantly higher than potential disruption Data privacy concerns -0.607 0.5441 No significant difference Complexity 0.537 0.5912 No significant difference Lack of evidence 6.218 0.0001 Lack of training rated significantly higher than lack of evidence Ethical concerns 8.164 0.0001 Lack of training rated significantly higher than ethical concerns Hygiene considerations 14.441 0.0001 Lack of training rated significantly higher than hygiene considerations Other 14.242 0.0001 Lack of training rated significantly higher than other Limited evidence of benefits Potential disruption 3.937 0.0001 Limited evidence rated significantly higher than potential disruption Data privacy concerns -4.524 0.0001 Limited evidence rated significantly lower than data privacy concerns Complexity -3.876 0.0001 Limited evidence rated significantly lower than complexity Lack of evidence 2.838 0.0045 No significant difference Ethical concerns 4.578 0.0001 Limited evidence rated significantly higher than ethical concerns Hygiene considerations 12.025 0.0001 Limited evidence rated significantly higher than hygiene considerations Other 12.836 0.0001 Limited evidence rated significantly higher than other Potential disruption Data privacy concerns -7.916 0.0001 Data privacy concerns rated significantly higher than potential disruption Complexity -7.659 0.0001 Complexity rated significantly higher than potential disruption Lack of evidence -1.894 0.0583 No significant difference Ethical concerns 1.688 0.0915 No significant difference Hygiene considerations 10.642 0.0001 Potential disruption rated significantly higher than hygiene considerations Other 11.690 0.0001 Potential disruption rated significantly higher than other Data privacy concerns Complexity 1.048 0.2948 No significant difference Lack of evidence 6.284 0.0001 Data privacy concerns were rated significantly higher than the lack of evidence Ethical concerns 9.070 0.0001 Data privacy concerns rated significantly higher than ethical concerns Hygiene considerations 14.324 0.0001 Data privacy concerns rated significantly higher than hygiene considerations Other 14.078 0.0001 Data privacy concerns rated significantly higher than other Complexity Lack of evidence 5.910 0.0001 Complexity rated significantly higher than lack of evidence Ethical concerns 8.344 0.0001 Complexity rated significantly higher than ethical concerns Hygiene considerations 14.176 0.0001 Complexity rated significantly higher than hygiene considerations Other 14.057 0.0001 Complexity rated significantly higher than other Lack of evidence Ethical concerns 3.414 0.0006 Lack of evidence rated significantly higher than ethical concerns Hygiene considerations 11.657 0.0001 Lack of evidence rated significantly higher than hygiene considerations Other 12.435 0.0001 Lack of evidence rated significantly higher than other Ethical concerns Hygiene considerations 8.965 0.0001 Ethical concerns rated significantly higher than hygiene considerations Other 9.974 0.0001 Ethical concerns rated significantly higher than other Hygiene considerations Other 5.034 0.0001 Hygiene considerations rated significantly higher than other Discussion Summary of principal findings This study surveyed 405 UK-based community-dwelling adults, including a small subgroup of health and care professionals, to examine awareness, perceptions, and perceived risks and benefits of spatial computing technologies in healthcare. Most participants reported general familiarity with virtual or augmented reality (VR/AR) devices, though only a small minority reported regular use. Among those familiar, the most recognised platforms were Oculus Quest and Apple Vision Pro-consumer-facing devices primarily marketed for entertainment, while familiarity with enterprise-focused devices such as Microsoft HoloLens was limited. Despite modest hands-on exposure, participants expressed strong optimism about the potential of spatial computing to support health and wellbeing, particularly through applications like guided mindfulness, avatar-led fitness demonstrations, immersive patient education and virtual peer support. Health literacy emerged as the most strongly endorsed self-care domain, followed by physical activity and mental wellbeing. However, enthusiasm waned when participants considered spatial computing for medication management or dietary guidance, suggesting variable acceptability across use cases. Inferential findings, which should be interpreted as exploratory signals rather than confirmatory evidence, highlighted a significant gender-based disparity with men being markedly more likely than women to report familiarity with spatial computing technologies. Statistically significant associations were also observed between familiarity with spatial computing and certain sociodemographic characteristics, including age and ethnicity. However, observed differences by ethnicity should be interpreted cautiously, as subgroup sizes were small and the survey did not collect data on socioeconomic status, technology access pathways or prior occupational exposure, which may confound apparent associations. Perceived barriers to adoption were also stratified. In particular, while higher familiarity was observed in some minority ethnic categories, these groups comprised relatively small numbers of participants, limiting the precision and generalisability of these estimates. High cost was consistently ranked as the top obstacle, followed by lack of training, limited evidence of benefits and concerns around data privacy and system complexity. Interpretation in the context of existing literature The findings of the METACARE study align with emerging literature that suggests spatial computing holds promise as an enabler of digital self-care, patient education and health engagement (Applications such as VR therapy, AR-assisted rehabilitation and immersive education have shown efficacy in clinical trials and pilot studies, particularly for chronic pain, phobias, post-stroke rehabilitation and mental health support. However, much of this literature is hospital-based or specialist-led. METACARE expands this evidence base by gauging public sentiment on the broader use of these tools in everyday, community-based self-care. Our findings corroborate the results of other studies (26-28) confirming that VR interventions in healthcare are generally well-received by users when aligned with behavioural and psychosocial goals. In the present study, support was strongest for uses that offer visualisation, simulation and interactive feedback, mechanisms that may promote cognitive engagement and enhance health literacy. The high ratings for avatar-led exercise and guided mindfulness reinforce the potential for immersive tools to facilitate adherence to health-promoting routines, especially when aligned with personalised or gamified models of care. However, scepticism around applications like medication adherence, diet tracking, or procedural support may reflect current limitations in usability and real-world integration. These more structured health behaviours require precision, continuity and seamless electronic health record (EHR) integration features not yet widely demonstrated by existing consumer-grade platforms. The relatively lower ratings for these domains may also reflect broader digital hesitancy or concern about over-reliance on immersive technologies in clinical care. The strong perception of spatial computing’s potential to improve health literacy is particularly salient. As self-care continues to be prioritised globally, tools that can enhance individuals’ ability to access, comprehend and act on health information are invaluable (22). Spatial computing can visualise anatomy, simulate disease progression, or demonstrate therapeutic actions offering more accessible ways of communicating complex health information, particularly for people with lower baseline literacy or linguistic barriers (29). Regarding barriers, the predominance of cost concerns echoes similar findings from studies on digital health tool adoption (30, 31). Spatial computing hardware remains expensive and unless scaled through national programmes or insurance models, may deepen inequities. Similarly, the perceived lack of training and evidence highlights the importance of parallel investments in workforce development and robust clinical trials to validate new applications. The gender gap in familiarity, where men were significantly more likely to report prior knowledge or use of spatial computing, echoes wider digital confidence disparities (17). Addressing this imbalance may require more inclusive design, gender-sensitive training resources and targeted community engagement. While age was not statistically significant, the trend of declining familiarity among older adults deserves attention, especially since this demographic often has the most to gain from digital interventions supporting independent living and chronic disease management. Strengths and limitations of this study To our knowledge, our study is among the first UK-based investigations to systematically examine public and healthcare professional perceptions of spatial computing specifically in relation to self-care and primary care contexts. The survey instrument was theoretically grounded, drawing on established public health and self-care frameworks, including the Seven Pillars of Self-Care, and was refined through pilot testing to ensure clarity and usability. The inclusion of both community-dwelling adults and healthcare professionals, alongside detailed demographic data, enabled exploration of equity-relevant differences in familiarity and perceived utility. Analytically, the use of non-parametric, rank-based methods allowed appropriate comparison of perceived benefits and barriers across multiple self-care domains while respecting the ordinal nature of Likert-scale data. The principal limitation of this study was that it employed convenience sampling via an online survey platform, which necessarily favours individuals with internet access, higher digital literacy and greater engagement with emerging technologies. As such, the sample cannot be considered representative of the UK population, and groups most at risk of digital exclusion, including some older adults, individuals from socioeconomically deprived backgrounds and those with disabilities, are likely under-represented. Findings should therefore be interpreted as reflecting the views of a digitally engaged segment of the public rather than population-level attitudes. Second, familiarity, attitudes and perceived benefits were self-reported and may be subject to recall bias or social desirability bias. Reported familiarity does not necessarily equate to sustained or competent use, particularly given that regular use of spatial computing devices was uncommon in this sample. Third, although statistically significant associations were observed between familiarity and demographic variables such as gender, age and ethnicity, some subgroup sizes, particularly within ethnic minority categories and among healthcare professionals, were small. These analyses were exploratory and unadjusted for multiple testing; accordingly, observed associations should be treated as hypothesis-generating rather than confirmatory. Finally, the cross-sectional design precludes any inference about causality or temporal change. Perceptions captured at a single time point may evolve rapidly as spatial computing technologies mature, costs change and evidence accumulates. Longitudinal and mixed-methods studies, as well as purposefully stratified samples, will be essential to assess how familiarity, acceptability and equity implications develop over time and in real-world implementation settings. Implications for practice, policy and research The findings of this study have several important implications. First, they highlight the growing public receptivity to immersive technologies in healthcare, particularly in domains such as mental wellbeing, physical activity and health education. Policymakers and digital health developers should capitalise on this receptivity by prioritising co-designed applications that reflect user preferences and address their concerns especially regarding privacy, usability and equity. Second, integration into self-care and primary care pathways will require technical innovation coupled to robust evidence of impact. Trials evaluating cost-effectiveness, clinical outcomes and behavioural changes resulting from spatial computing interventions are urgently needed. Future studies should also explore long-term engagement and potential harms, including over-reliance, cybersickness, or exacerbation of digital exclusion. Third, our data highlight the need for targeted digital literacy initiatives, particularly for women and older adults who appear less familiar with these technologies. Community-based workshops, primary care integration pilots and educational campaigns could help narrow this gap and promote equitable uptake. Finally, the ethical and regulatory context must evolve in tandem. Concerns around data privacy, algorithmic bias, consent and equitable access remain salient. As spatial computing platforms become increasingly intertwined with AI decision-support systems, safeguards must be established to ensure transparency, accountability and trustworthiness. National frameworks for immersive health technologies, analogous to those developed for telehealth and mobile apps, will be essential to guide ethical deployment. By providing the first UK-wide snapshot of public and professional perceptions of spatial computing framed explicitly through a self-care lens, this study establishes a baseline against which future implementation trials, policy evaluations and equity-focused deployment strategies can be assessed. Conclusion Spatial computing represents a promising frontier in the evolution of digital health, with strong perceived value for self-care, patient education and public health engagement. This study offers timely and actionable insights into how the UK public perceives these tools, highlighting optimism for immersive applications that support mental health, health literacy and behavioural change. At the same time, it highlights important barriers, including cost, training gaps and digital confidence disparities, that must be addressed to ensure equitable adoption. As spatial computing matures, its integration into mainstream healthcare must be guided by inclusive design, rigorous evaluation and robust governance. Abbreviations AR – Augmented reality CHERRIES – Checklist for Reporting Results of Internet E-Surveys EHR – Electronic health record GP – General practitioner HCP – Healthcare professional ICREC – Imperial College Research Ethics Committee KAPB – Knowledge, attitudes, perceptions and behaviours MR – Mixed reality NHS – National Health Service NIHR – National Institute for Health and Care Research PIS – Participant information sheet SCARU – Self-Care Academic Research Unit UK – United Kingdom VR – Virtual reality Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Imperial College London Research Ethics Committee (reference number: 21IC7375). All participants were provided with a Participant Information Sheet and gave informed consent electronically before taking part in the survey. Consent for publication Not applicable. Availability of data and materials De-identified data supporting the findings of this study are available from the corresponding author on reasonable request. The data are not publicly available due to ethical restrictions related to participant confidentiality and consent. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Austen El-Osta is supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration (ARC) Northwest London. The views expressed are those of the authors and not necessarily those of the NHS or the NIHR or the Department of Health and Social Care. Authors’ contributions AE-O conceived the study, led the study design, oversaw data collection, conducted the analysis and drafted the manuscript. CSQ, AA, MA and SA contributed to survey development, interpretation of findings and critical revision of the manuscript. All authors reviewed and approved the final manuscript and agree to be accountable for all aspects of the work. AE-O is the guarantor. Acknowledgements The authors thank all participants who took part in the survey. The authors also acknowledge the support of the Self-Care Academic Research Unit (SCARU), Imperial College London. Patient and public involvement Patients and members of the public were not involved in the design, conduct, reporting or dissemination plans of this research. Twitter: @austenelosta @ImperialSCARU References The King’s Fund (2024) Public satisfaction with the NHS and social care in 2023 NHS. The Topol Review (2025) [Available from: https://topol.digitalacademy.nhs.uk/the-topol-review?utm_source=chatgpt.com World Health Organization (2021) WHO Guideline on self-care interventions for health and well-being Smith V, Warty RR, Sursas JA, Payne O, Nair A, Krishnan S et al (2020) The effectiveness of virtual reality in managing acute pain and anxiety for medical inpatients: systematic review. J Med Internet Res 22(11):e17980 Goudman L, Jansen J, Billot M, Vets N, De Smedt A, Roulaud M et al (2022) Virtual reality applications in chronic pain management: systematic review and meta-analysis. JMIR Serious Games 10(2):e34402 Cochrane (2025) Virtual reality gives stroke rehab a new dimension Laver KE, Lange B, George S, Deutsch JE, Saposnik G, Chapman M, Crotty M (2025) Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev 6(6):Cd008349 Mallari B, Spaeth EK, Goh H, Boyd BS (2019) Virtual reality as an analgesic for acute and chronic pain in adults: a systematic review and meta-analysis. J Pain Res 12:2053–2085 Magalhães R, Oliveira A, Terroso D, Vilaça A, Veloso R, Marques A et al (2024) Mixed Reality in the Operating Room: A Systematic Review. J Med Syst 48(1):76 Qiu CS, Majeed A, Khan S, Watson M (2022) Transforming health through the metaverse. J R Soc Med 115(12):484–486 Edwards L, Pickett J, Ashcroft DM, Dambha-Miller H, Majeed A, Mallen C et al (2023) UK research data resources based on primary care electronic health records: review and summary for potential users. BJGP Open. ;7(3) NHS Access to patient records through the NHS App 2023 [Available from: https://transform.england.nhs.uk/information-governance/guidance/access-to-patient-records-through-the-nhs-app/?utm_source=chatgpt.com PRSB (2023) Transfer of Care Madary M, Metzinger TK (2016) Real virtuality: A code of ethical conduct. Recommendations for good scientific practice and the consumers of VR-technology. Front Rob AI 3:180932 Giaretta A (2024) Security and privacy in virtual reality: a literature survey. Virtual Reality 29(1):10 Lake K, Mc Kittrick A, Desselle M, Bo APL, Abayasiri RAM, Fleming J et al (2024) Cybersecurity and privacy issues in extended reality health care applications: Scoping review. JMIR XR Spat Comput (JMXR) 1(1):e59409 Ofcom (2023) Adults’ Media Use and Attitudes report 2023 NICE (2022) Evidence standards framework for digital health technologies Unsworth H, Dillon B, Collinson L, Powell H, Salmon M, Oladapo T et al (2021) The NICE Evidence Standards Framework for digital health and care technologies – Developing and maintaining an innovative evidence framework with global impact. Digit HEALTH 7:20552076211018617 ISF. International Self-care Forum Homepage (2025) [Available from: https://isfglobal.org/ El-Osta A (2019) The Self-Care Matrix: a unifying framework for self-care. Int J Self Help Self Care 10:38–56 El-Osta A, Sasco ER, Barbanti E, Webber I, Alaa A, Karki M et al (2023) Tools for measuring individual self-care capability: a scoping review. BMC Public Health 23(1):1312 Hietasalo P, Seppä L, Lahti S, Niinimaa A, Kallio J, Aronen P et al (2009) Cost-effectiveness of an experimental caries‐control regimen in a 3.4‐yr randomized clinical trial among 11–12‐yr‐old Finnish schoolchildren. Eur J Oral Sci 117(6):728–733 Prolific, London UK [Available from: https://www.prolific.com Eysenbach G (2004) Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J Med Internet Res 6(3):e34–e Prétat T, Ming Azevedo P, Lovejoy C, Hügle T (2025) Effectiveness and user experience of a virtual reality intervention in a cohort of patients with chronic musculoskeletal pain syndromes. PLOS Digit Health 4(3):e0000788 Kouijzer M, Kip H, Bouman YHA, Kelders SM (2023) Implementation of virtual reality in healthcare: a scoping review on the implementation process of virtual reality in various healthcare settings. Implement Sci Commun 4(1):67 Iqbal AI, Aamir A, Hammad A, Hafsa H, Basit A, Oduoye MO et al (2024) Immersive Technologies in Healthcare: An In-Depth Exploration of Virtual Reality and Augmented Reality in Enhancing Patient Care, Medical Education, and Training Paradigms. J Prim Care Community Health 15:21501319241293311 Popov V, Mateju N, Jeske C, Lewis KO (2024) Metaverse-based simulation: a scoping review of charting medical education over the last two decades in the lens of the 'marvelous medical education machine'. Ann Med 56(1):2424450 Cheng A, Fijacko N, Lockey A, Greif R, Abelairas-Gomez C, Gosak L et al (2024) Use of augmented and virtual reality in resuscitation training: A systematic review. Resusc Plus 18:100643 Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44–56 Additional Declarations The authors declare no competing interests. Supplementary Files SFile1SurveyExport.docx SFile2CHERRIESChecklist.docx Supplementry File 2: CHERRIES Checklist SFile3rawdatafile.csv Supplementry File 2 SupplementaryTablesS1S4.docx Supplementry File 4: Supplementary Table S1-S4 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8720861","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581808895,"identity":"71842983-a29a-41ee-ba3d-51db46cdbb27","order_by":0,"name":"Austen El-Osta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie2RwUoDMRCGJwS2l0iuU5TuK2QRBFHwVeLJa8HLHmSJFHPqA9THEF9gyoB72QeoLIgieO6xIIqJFi+S1mMP+S6TDHzM/AlAJrObCPoug2sXi4LBT9tscqKCoOZrRf5bQbu+blO0k0RQN42+fb25H9dPB3oCYrkCPkwpSIUl6BixP/f9rLtUyCCHU+Cj9FbK8IcnhKjseauAAfYB+DRllKSXJHyD5eM8KJ9WlWHK+ybFkIKgSDQLERRnw1Ao4pTkYhUXJmYZ3nUxy4NVFQt/PDUXyfijdvLyHF5Mj9r2rR9f2bPQ4cWqPqlcypF/z8Jt+ciEnslkMplfvgAtBVO5eaETNQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8772-4938","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Austen","middleName":"","lastName":"El-Osta","suffix":""},{"id":581808896,"identity":"1d8fa680-5674-49ed-9fcb-db821cc6e00f","order_by":1,"name":"Connor Qiu","email":"","orcid":"https://orcid.org/0000-0002-7314-1504","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Connor","middleName":"","lastName":"Qiu","suffix":""},{"id":581808897,"identity":"bbb7c515-be0f-42e7-b370-766529ce3d0c","order_by":2,"name":"Aos Alaa","email":"","orcid":"https://orcid.org/0000-0001-6130-5092","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Aos","middleName":"","lastName":"Alaa","suffix":""},{"id":581808898,"identity":"ace615c7-222d-4679-b1b5-5171b6fb6b54","order_by":3,"name":"Mohammed Adam","email":"","orcid":"https://orcid.org/0000-0002-4228-2494","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Adam","suffix":""},{"id":581808899,"identity":"981a4249-067e-4ec1-866e-1adaa2967853","order_by":4,"name":"Sami Altalib","email":"","orcid":"https://orcid.org/0000-0001-7404-8486","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Sami","middleName":"","lastName":"Altalib","suffix":""}],"badges":[],"createdAt":"2026-01-28 12:40:50","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8720861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8720861/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101406079,"identity":"0b4ee533-9bd9-4f2f-b7aa-1b2be0c46ecb","added_by":"auto","created_at":"2026-01-29 10:42:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":269760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePairwise comparison of perceived benefits of spatial computing across self-care domains based on Wilcoxon signed-rank tests with Bonferroni correction for multiple comparisons.\u003c/strong\u003e The matrix displays upper-triangle comparisons only. Green upward triangles (▲) indicate that the self-care domain on the x-axis (Pillar 1) was rated significantly higher than the domain on the y-axis (Pillar 2); red downward triangles (▼) indicate the opposite. Grey open circles (○) denote no statistically significant difference. Domains are ordered consistently across axes to facilitate interpretation. Adjusted significance threshold derived using Bonferroni correction across all pairwise comparisons.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8720861/v1/aa1e595beba293b06ce70c5a.png"},{"id":101406009,"identity":"5b9bdb81-fd08-4769-bce3-e6716c6475e2","added_by":"auto","created_at":"2026-01-29 10:42:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":591659,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePairwise comparison of perceived barriers to adoption of spatial computing based on Wilcoxon signed-rank tests with Bonferroni correction for multiple comparisons.\u003c/strong\u003e The matrix displays upper-triangle comparisons only. Green upward triangles (▲) indicate that the barrier on the x-axis (Barrier 1) was rated significantly more important than the barrier on the y-axis (Barrier 2); red downward triangles (▼) indicate the reverse. Grey open circles (○) denote no statistically significant difference. Barriers are ordered by overall perceived importance, with higher-salience barriers positioned toward the top and right of the matrix. Adjusted significance threshold derived using Bonferroni correction across all pairwise comparisons.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8720861/v1/d9b2fb882373f6f5746c5ede.png"},{"id":101406126,"identity":"b3ec9c91-830c-475c-8f21-e30337a4827a","added_by":"auto","created_at":"2026-01-29 10:42:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2761265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8720861/v1/4fee538a-2597-4ec7-918e-495ba5b4c8dc.pdf"},{"id":101406077,"identity":"4ef2aaa2-e2c7-4969-a88d-88639800fc15","added_by":"auto","created_at":"2026-01-29 10:42:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29961,"visible":true,"origin":"","legend":"","description":"","filename":"SFile1SurveyExport.docx","url":"https://assets-eu.researchsquare.com/files/rs-8720861/v1/4718ad2876780343bc2a94f8.docx"},{"id":101405972,"identity":"3416c677-c0c1-4125-b845-c5f2ab20737f","added_by":"auto","created_at":"2026-01-29 10:42:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25579,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementry File 2: CHERRIES Checklist\u003c/p\u003e","description":"","filename":"SFile2CHERRIESChecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-8720861/v1/dc96e65fae623261eb0c9e03.docx"},{"id":101406025,"identity":"b42b3f92-26a4-4c02-870d-921a6f366bb2","added_by":"auto","created_at":"2026-01-29 10:42:22","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":473491,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementry File 2\u003c/p\u003e","description":"","filename":"SFile3rawdatafile.csv","url":"https://assets-eu.researchsquare.com/files/rs-8720861/v1/2f8b3861b41b7ec12744ac6b.csv"},{"id":101406066,"identity":"331c69a9-1e16-4c2e-874f-f43218482b01","added_by":"auto","created_at":"2026-01-29 10:42:26","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":58694,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementry File 4: Supplementary Table S1-S4\u003c/p\u003e","description":"","filename":"SupplementaryTablesS1S4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8720861/v1/04b178897784a254e8c0d283.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePublic perceptions of spatial computing in health: Opportunities and barriers for supporting self-care and wellbeing\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Summary Box","content":"\u003cp\u003e\u003cstrong\u003eWhat is already known on this topic\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eSpatial computing (virtual, augmented and mixed reality) has demonstrated benefits in surgical training, rehabilitation and medical education.\u003c/li\u003e\n \u003cli\u003eEvidence on public and healthcare professional perceptions of spatial computing in primary care and self-care is limited.\u003c/li\u003e\n \u003cli\u003eBarriers such as cost, digital literacy and ethical concerns are recognised but underexplored in community settings.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat this study adds\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003ePublic familiarity with consumer VR platforms (e.g. Oculus Quest, Apple Vision Pro) is relatively high, but regular use is rare.\u003c/li\u003e\n \u003cli\u003eRespondents endorsed strong potential for spatial computing to support health literacy, mental wellbeing and physical activity, but were less convinced about its role in medication adherence or dietary guidance.\u003c/li\u003e\n \u003cli\u003eSignificant gender, age and ethnicity differences exist in familiarity with spatial computing, with men, younger adults and some minority ethnic groups reporting higher awareness.\u003c/li\u003e\n \u003cli\u003eCost, lack of training and data privacy concerns were consistently ranked as the most important barriers to adoption.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eHow this study might affect research, practice, or policy\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eDevelopers and policymakers should prioritise co-designed applications that align with user preferences, particularly in health education, behaviour change and psychosocial support.\u003c/li\u003e\n \u003cli\u003eImplementation in healthcare must be accompanied by investment in workforce training, affordability strategies and privacy safeguards.\u003c/li\u003e\n \u003cli\u003eAddressing demographic disparities in digital confidence, particularly among women and older adults, will be essential for equitable uptake.\u003c/li\u003e\n \u003cli\u003eRigorous evaluation of clinical outcomes, cost-effectiveness and long-term engagement is required to guide responsible integration of spatial computing into health systems.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eHealth systems face converging pressures of rising demand, workforce shortages and widening access gaps, trends that are particularly visible in UK primary care (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), where public dissatisfaction is closely tied to difficulties obtaining appointments and perceived staff shortfalls (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Against this backdrop, the strategic case for technologies that expand capacity, enhance patient engagement and enable prevention is compelling. Spatial computing is an umbrella term encompassing virtual, augmented and mixed reality (VR/AR/MR). It allows digital content to be anchored in physical space, supporting embodied interactions that differ from conventional screen-based tools. In the UK policy context, digital technologies are repeatedly highlighted as levers for service redesign and workforce enablement (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). At the same time, the World Health Organization (WHO) situates self-care as a core pathway to universal health coverage, calling for evidence-based interventions that strengthen individuals\u0026rsquo; ability to promote health, prevent disease and manage illness (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Early applications of spatial computing in health are promising as randomised and controlled studies suggest that immersive VR can reduce acute and chronic pain in clinical settings, with growing methodological rigor and effect sizes that justify continued evaluation (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In neurorehabilitation, successive Cochrane-aligned syntheses report benefits of VR as an adjunct to conventional therapy after stroke, including upper-limb function gains (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In education and training, scoping and systematic reviews map expanding use across undergraduate and postgraduate curricula, with positive effects on skills acquisition and procedural rehearsal and mixed evidence on transfer to real-world performance (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConceptually, spatial computing has also been framed as a platform for next-generation, participatory \u0026ldquo;metaverse\u0026rdquo; health experiences (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Yet translation beyond specialist contexts into primary care and community-based self-care remains limited. Real-world deployment hinges on integration with existing data infrastructure and workflows since most UK general practices use EMIS or TPP (SystmOne), which together cover more than 90% of English practices (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Current national programmes enabling patient access to GP records through the NHS App illustrate the governance, interoperability and safety layers that any new modality must respect (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), whereas as parallel concerns focus on privacy, ethics and acceptability primarily because immersive systems capture high-granularity biometric, behavioural and spatial data (e.g., eye-tracking, body motion), raising distinctive risks around inference, re-identification and exploitation (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Moreover, differential digital literacy persists across the population, with implications for equitable uptake of novel interfaces (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In the UK, evidence and value expectations are increasingly codified since adoption decisions for digital health tools are guided by the National Institute for Health and Care Excellence Evidence Standards Framework (ESF), which specifies proportionate clinical- and economic-evidence requirements for deployment and scale-up (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA further gap is conceptual. While \u0026ldquo;digital health\u0026rdquo; is often discussed generically, spatial computing affords embodied, situated experiences that may differentially influence key behaviours underpinning self-care (e.g., comprehension, motivation, adherence). The International Self-Care Foundation\u0026rsquo;s Seven Pillars of self-care framework (knowledge and health literacy; mental wellbeing; physical activity; healthy eating; risk avoidance; good hygiene; and the rational use of products and services) offers a structured lens for anticipating where immersive modalities could add value (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Prior scholarship has proposed integrative frameworks for self-care and mapped measurement tools against these pillars, highlighting both the breadth of relevant behaviours and the scarcity of instruments that capture them comprehensively (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study addresses these evidence and implementation gaps by assessing the knowledge, attitudes, perceptions and behaviours of UK community-dwelling adults, including healthcare professionals, toward spatial computing in health and self-care. We examine familiarity and perceived utility across self-care domains, explore barriers such as cost, training and privacy and test for demographic differences relevant to equity. By situating public and professional perspectives within UK primary-care realities and established evidence standards, the study aims to inform co-design priorities, evaluation strategies and governance approaches for the responsible introduction of spatial computing into routine care.\u003c/p\u003e \u003cp\u003e The primary aim of the METACARE study was to assess the knowledge, attitudes and practices (KAPB) of healthcare professionals and the general public regarding the potential implementation of spatial computing technologies in healthcare, particularly in primary care and self-care settings. The study focused on measuring familiarity with consumer and professional spatial computing platforms, perceived utility across the seven pillars of self-care and attitudes toward their integration into existing models of care. Specifically, we sought to examine public awareness and prior use of spatial computing tools, to identify perceived benefits and challenges to adoption from both healthcare professionals and community-dwelling adults and to explore how demographic characteristics such as age, gender and ethnicity shape familiarity and acceptance.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study aimed to explore the general knowledge, attitudes, perceptions and behaviours of a cross-section of UK adults, including health and care professionals, regarding the use of spatial computing technologies along with the\u0026nbsp;socio-demographic factors which may affect them. The study employed a quantitative methodology using an electronic survey tool (eSurvey). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was an open eSurvey, accessible to anyone with the survey link. The voluntary survey required less than ten minutes to complete, though length may vary depending on the options selected. Accordingly, findings should be interpreted as descriptive and hypothesis-generating rather than estimates of population prevalence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvey instrument\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA structured, self-administered electronic questionnaire was developed drawing on existing literature, expert input and public health frameworks on digital self-care. It included closed-ended items covering five domains: (i) familiarity and exposure to spatial computing devices (e.g. Apple Vision Pro, Microsoft HoloLens, Oculus Quest); (23) perceived utility of spatial computing for health and wellbeing (5-point Likert scale); (iii) perceived impact on self-care domains based on the Seven Pillars of Self-Care; (iv) barriers and ethical concerns related to adoption in healthcare; and (v) sociodemographic characteristics. A pilot survey was tested for clarity and usability before launch. The final version, hosted on Qualtrics, required approximately 10 minutes to complete.\u003c/p\u003e\n\u003cp\u003eAdaptive questioning was used so, for example, having experienced specific symptoms or having consulted one or more HCPs regarding these symptoms, led to the display of specific questions relating to these experiences. In its longest version, the survey was composed of 33 questions, including 9 for socio-demographic information such as age, gender, employment status. Other questions related to knowledge, attitudes and experiences. In its longest version the survey was displayed over 13 pages, with a maximum of 6 questions per page. Respondents could not use a back button to revise their responses. The eSurvey was developed, revised and tested by the study team to ensure clarity, usability and technical functionality before dissemination. A copy of the full survey can be accessed in \u003cstrong\u003eSupplementarty File 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe link to the eSurvey was active on the Imperial College Qualtrics platform between 17 January 2025 and 1 August 2025. The survey included a link to the Participant Information Sheet (PIS) which informed potential respondents on the study’s aims, the protection of participants’ personal data, their right to withdraw from the study at any time, which data were stored, where and for how long, who the investigator was, the purpose of the study and survey length. Participants were recruited through convenience sampling. Most participants were recruited via Prolific Academic’s panel (24) which facilitates rapid access to diverse adult samples but does not employ probability-based sampling. As such, participation required internet access, digital literacy, and familiarity with online research platforms, which may have influenced sample composition. No incentives were offered for participation beyond standard panel reimbursement where applicable.\u0026nbsp;Personal and professional networks were mobilised to respond and further disseminate the eSurvey among potentially eligible participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants at the beginning of the survey. They were informed that this was a voluntary survey. \u0026nbsp;Data collected were stored on a secure database at Imperial College London and only accessible to the researcher team. All responses were pseudo-anonymised to ensure confidentiality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnly questionnaires fully completed were included in the analysis. Duplicate entries from the same IP address within a 24-hour period were also eliminated before analysis. Descriptive statistics (frequencies and percentages) were used to summarise participant demographics and key response distributions. To assess relationships between demographic variables and familiarity with spatial computing, chi-square tests of independence were conducted. Where expected cell counts were fewer than five, Fisher’s exact test was used. Statistical significance was set at p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003eAll inferential analyses were exploratory and hypothesis-generating; no causal inference was intended. Perceptions of spatial computing across multiple self-care domains were compared using Friedman tests, which account for repeated measures on ordinal variables. Post-hoc Wilcoxon signed-rank tests with Bonferroni correction were applied to identify specific differences between domain ratings. Similarly, comparisons of perceived adoption barriers were analysed using Friedman and Wilcoxon tests to identify statistically significant differences between ranked barrier severity scores. All analyses were performed using STATA, version 18 (StataCorp LP, College Station, TX, USA). The Checklist for Reporting Results of Internet ESurveys (CHERRIES) was used to guide reporting (25); \u003cstrong\u003eSupplementarty File 2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Imperial College London Research Ethics Committee (reference number: 21IC7375). All participants were provided with a Participant Information Sheet and gave informed consent electronically before taking part in the survey.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic profile of respondents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 405 community-dwelling adults participated in the METACARE study (\u003cstrong\u003etables 1 and 2),\u0026nbsp;\u003c/strong\u003eincluding 41 healthcare professionals (HCPs) and 364 non-HCPs. The average time taken to complete the survey as on average 5 min 30 sec. The full survey findings are illustrated in\u003cstrong\u003eSupplementary Table S1.\u0026nbsp;\u003c/strong\u003eDetailed item-level distributions are provided in\u003cstrong\u003eSupplementary Tables S2-S4.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter accounting for missing values, the largest proportion of respondents were aged 31-40 years (30.6%), followed by those aged 18-30 years (22.0%), 41-50 years (18.3%), 51-60 years (16.3%) and 61 years or older (12.8%). Gender distribution was relatively balanced, with 49.6% identifying as female, 50.1% as male and 0.2% as another gender identity. The sample was predominantly White (81.0%), with smaller proportions identifying as Black/African/Caribbean British (6.2%), Asian/Asian British (5.9%), Mixed/Multiple ethnic groups (4.2%) and Other ethnic backgrounds (2.7%). While minor levels of item non-response were observed across demographic variables, the sample reflected broad representation across age bands, gender identities and racial/ethnic groups, supporting meaningful analysis of public perspectives on spatial computing in health (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Participants\u0026apos; demographics (N=405)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (Percentage)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHCP (N=41)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HCP (N=364)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is your age?\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e89 (22.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (34.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110 (30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124 (30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is your gender?\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24 (58.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e177 (48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e201 (49.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e186 (51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e203 (50.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is your ethnicity?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;White\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 (73.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e298 (81.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e328 (81.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Asian/Asian British\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Black/African/Caribbean British\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mixed/Multiple ethnic groups\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other ethnic groups\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFamiliarity with spatial computing technologies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 405 survey participants, 71.4% reported being familiar with consumer-facing spatial computing or virtual reality devices (i.e., had used them briefly, regularly, or at least heard of them but never used them). Only a small minority (4.7%) indicated they regularly used such technologies. Around one-third (33.3%) had used a spatial computing device briefly, while an equal proportion (33.3%) had heard of the technologies but never used them. Just over a quarter (28.6%) reported being not familiar at all. This distribution suggests that, while general awareness is relatively high, hands-on experience with spatial computing in everyday contexts remains limited; \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure to spatial computing platforms\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen asked about specific devices, the most widely recognised platforms were the Oculus Quest (57.5%) and Apple Vision Pro (46.9%). Fewer participants were familiar with Google Cardboard (22.7%) or Microsoft HoloLens (11.6%). Notably, 22.5% of respondents indicated they had never heard of or used any spatial computing device. These findings illustrate a high level of public brand awareness for consumer VR products, particularly those marketed for entertainment, but lower visibility for enterprise-grade systems such as HoloLens, which are more relevant to professional healthcare settings; \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived benefits for managing health and wellbeing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants showed strong optimism about spatial computing for health and wellbeing. The highest ratings were for avatar-guided physical activity (67.9% scoring 4\u0026ndash;5), guided mindfulness (60.5%) and virtual support groups (56.6%), highlighting perceived value in mental health and peer support. Educational and behavioural applications were also endorsed, including VR simulations to understand health conditions (64.0%), interactive health education (57.5%), remote monitoring (53.6%) and customisable patient interfaces (52.6%). Overall, respondents viewed spatial computing as most valuable when it delivers immersive, visual and personalised interactions, particularly in mental health, exercise and health education (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLower enthusiasm for medication and lifestyle management features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy contrast, participants were more cautious about spatial computing for structured health tasks. Fewer saw strong value in medication management (40.2%), visualising diet and nutrition (43.0%), or promoting adherence to lifestyle medicine (37.6%). This ambivalence may reflect limited exposure to such tools or doubts about their practicality in everyday health management; \u003cstrong\u003eSupplementary Table S1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBarriers and Concerns Regarding Adoption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the generally positive outlook, respondents identified several obstacles that could slow the adoption of spatial computing in healthcare. High cost emerged as the most significant barrier, with over half of participants (56.5%) rating it as a major concern (5 out of 5). Other notable barriers included lack of training (67.4% rated 4 or 5), data privacy concerns (63.5%) and system complexity (63.0%). Participants also highlighted issues such as limited evidence of benefits, potential disruption to existing healthcare workflows and ethical considerations including patient consent, bias in algorithms and inequitable access. These findings highlight that while enthusiasm is high, successful integration will require addressing practical, technical and ethical challenges; \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSummary of trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the results indicate that respondents perceived spatial computing to hold promise for self-care and health support, particularly in areas involving experiential learning, behaviour change and psychosocial engagement. Applications involving personalised health education, visualisation and remote interaction garnered the most enthusiastic support, while domains such as medication adherence and diet management were viewed more cautiously. The generally favourable response across a range of self-care domains highlights the emerging public readiness to engage with immersive technologies in health contexts, albeit with some reservations around specific functionalities. These perceptions will be critical in shaping future research, innovation priorities and implementation strategies for spatial computing in both clinical and community settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInferential analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll inferential analyses were exploratory and not adjusted for multiple hypothesis testing unless otherwise stated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographics and familiarity with spatial computing technologies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore whether familiarity with consumer-facing spatial computing technologies varied by demographic characteristics, bivariate analyses were conducted using chi-square (\u0026chi;\u0026sup2;) and Fisher\u0026rsquo;s exact tests where appropriate (\u003cstrong\u003etable 2)\u003c/strong\u003e. For this analysis, \u0026quot;familiarity\u0026quot; was defined as a self-reported \u0026ldquo;yes\u0026rdquo; response to having knowledge of spatial computing or VR devices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: The association between demographics and familiarity with consumer-facing spatial computing or virtual reality devices\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"608\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral level of familiarity with spatial computing technologies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is your age?\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"6\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e18-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70 (78.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e31-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91 (73.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e41-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56 (75.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e51-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (71.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e61+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (51.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is your gender?\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e127 (63.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e162 (79.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41 (20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is your ethnicity?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"6\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Asian/Asian British\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (91.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;British Black/African/Caribbean\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (92.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mixed/Multiple ethnic groups\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (82.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other ethnic groups\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;White\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e223 (68.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGender was significantly associated with familiarity (p\u0026lt;0.001). A higher proportion of males (79.8%) reported familiarity compared to females (63.2%). Only one participant identified as \u0026ldquo;Other\u0026rdquo; gender and reported no familiarity. This pattern suggests a notable gender disparity in exposure to or engagement with emerging immersive technologies, potentially reflecting broader gender gaps in digital confidence and technology use.\u003c/p\u003e\n\u003cp\u003eAge group was also significantly associated with familiarity (p=0.002). Familiarity tended to be higher among younger participants: 78.7% of those aged 18-30 and 73.4% of those aged 31-40 reported familiarity, compared with 48.1% among those aged 61 years or older. The data indicate a clear downward trend in familiarity with increasing age.\u003c/p\u003e\n\u003cp\u003eEthnicity was statistically associated with familiarity with spatial computing technologies (p=0.006). Higher reported familiarity was observed among participants identifying as British Black/African/Caribbean and Asian/Asian British compared with White participants, while familiarity was lowest among those reporting other ethnic backgrounds. However, these subgroup estimates were based on small cell sizes and should be interpreted with caution. The survey did not capture socioeconomic position, technology access pathways, occupational exposure or prior engagement with immersive technologies, all of which may confound observed associations. These patterns should not be interpreted as evidence of differential acceptability across ethnic groups, but rather as signals for further, adequately powered investigation.\u003c/p\u003e\n\u003cp\u003eFamiliarity with spatial computing varied significantly by gender, age and ethnicity. Men and younger adults reported higher awareness, while familiarity was also notable among some minority groups. Older adults and women, who showed lower familiarity, may be more affected by barriers such as complexity, lack of training and privacy concerns. Conversely, groups with higher familiarity may be more willing to engage but remain constrained by systemic barriers such as cost (critical for 56.5% of respondents) and limited evidence of benefit. Tailored training, support and trust-building for less familiar groups, alongside wider efforts to address cost and infrastructure, will be key for equitable adoption.\u003c/p\u003e\n\u003cp\u003eGiven the exploratory design and unequal subgroup sizes, these associations were not adjusted for multiple testing and should be interpreted as hypothesis-generating rather than confirmatory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived Benefit of Spatial Computing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Friedman test compared perceptions of spatial computing across seven self-care domains (health literacy, mental wellbeing, physical activity, healthy eating, risk avoidance, hygiene and rational use). Ratings on a 5-point Likert scale showed significant variation in perceived benefit (Q(6)=275.86, p\u0026lt;0.001), indicating differences in how participants valued spatial computing across domains (\u003cstrong\u003etable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003ePost-hoc Wilcoxon tests (Bonferroni p\u0026lt;0.0024) showed that health literacy, mental wellbeing and physical activity were the highest-rated domains. Health literacy scored higher than healthy eating, hygiene, risk avoidance and rational use, but lower than physical activity and similar to mental wellbeing. Mental wellbeing and physical activity were both rated higher than all other domains. Healthy eating held an intermediate position, above risk avoidance and hygiene but below the top three. Risk avoidance ranked lowest, while hygiene and rational use clustered in the intermediate-low range with no significant difference between them.\u003c/p\u003e\n\u003cp\u003eThe final ranking placed health literacy, mental wellbeing and physical activity as the highest perceived benefits, followed by healthy eating, then hygiene practices and rational use of products, with risk avoidance lowest. Participants therefore viewed spatial computing as most valuable for improving literacy, wellbeing and physical activity (\u003cstrong\u003eTable 3 and Figure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Pairwise Wilcoxon Signed-Rank Test Results Comparing Perceived Benefit Scores (1=Not at all, 5=Very much) of Spatial Computing Across Self-Care Pillars, with Bonferroni Correction (Significance Threshold: p\u0026lt;0.0024)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eZ Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth literacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant difference than mental wellbeing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth literacy was rated significantly lower than physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealthy eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth literacy was rated significantly higher than healthy eating\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth literacy was rated significantly higher than risk avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood hygiene practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth literacy was rated significantly higher than good hygiene practices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRational use of products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth literacy was rated significantly higher than rational use of products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental wellbeing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealthy eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental wellbeing was rated significantly higher than healthy eating\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental wellbeing was rated significantly higher than risk avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood hygiene practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental wellbeing was rated significantly higher than good hygiene practices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRational use of products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMental wellbeing was rated significantly higher than rational use of products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealthy eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhysical activity was rated significantly higher than health eating\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhysical activity was rated significantly higher than risk avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood hygiene practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhysical activity was rated significantly higher than good hygiene practices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRational use of products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhysical activity was rated significantly higher than rational use of products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy eating\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealthy eating was rated significantly higher than risk avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood hygiene practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealthy eating was rated significantly higher\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRational use of products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk avoidance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood hygiene practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk avoidance was rated significantly lower than good hygiene practices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRational use of products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk avoidance was rated significantly lower than rational use of products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGood hygiene\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRational use of products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eBarriers to Using Spatial Computing \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Friedman test showed significant variation in how barriers were ranked (Q(9)=1000, p\u0026lt;0.0001). High cost was consistently the top concern, rated significantly higher than all others. Lack of training and limited evidence formed the next tier, comparable to data privacy and complexity. Potential disruption clustered with these mid-level concerns. Lower-ranked barriers included lack of evidence, ethical issues, hygiene and other considerations, which were generally seen as less critical. The final hierarchy was: (1) high cost; (2) lack of training and limited evidence; (3) data privacy, complexity and disruption; (4) lack of evidence; (5) ethical concerns; and (6) hygiene and other. These findings highlight cost, training and evidence gaps as the most pressing challenges, with privacy and technical issues moderately important and ethical or hygiene concerns less influential (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Pairwise Wilcoxon Signed-Rank Test Results Comparing Perceived Barrier Severity (1=Not at all, 5=Very much) to Spatial Computing Adoption, with Bonferroni Correction (Significance Threshold: p\u0026lt;0.0011)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eZ statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh cost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh cost rated significantly higher than lack of training\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited evidence of benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh cost rated significantly higher than limited evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePotential disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh cost rated significantly higher than potential disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData privacy concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh cost rated significantly higher than data privacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh cost rated significantly higher than complexity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh cost rated significantly higher than lack of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh cost rated significantly higher than ethical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh cost rated significantly higher than hygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh cost rated significantly higher than other\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eLack of training\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited evidence of benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of training rated significantly higher than limited evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePotential disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of training w rated significantly higher than potential disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData privacy concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of training rated significantly higher than lack of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of training rated significantly higher than ethical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of training rated significantly higher than hygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of training rated significantly higher than other\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eLimited evidence of benefits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePotential disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited evidence rated significantly higher than potential disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData privacy concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited evidence rated significantly lower than data privacy concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited evidence rated significantly lower than complexity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited evidence rated significantly higher than ethical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited evidence rated significantly higher than hygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited evidence rated significantly higher than other\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotential disruption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData privacy concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData privacy concerns rated significantly higher than potential disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity rated significantly higher than potential disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePotential disruption rated significantly higher than hygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePotential disruption rated significantly higher than other\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eData privacy concerns\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo significant difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData privacy concerns were rated significantly higher than the lack of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData privacy concerns rated significantly higher than ethical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData privacy concerns rated significantly higher than hygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData privacy concerns rated significantly higher than other\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplexity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity rated significantly higher than lack of evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity rated significantly higher than ethical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity rated significantly higher than hygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplexity rated significantly higher than other\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eLack of evidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of evidence rated significantly higher than ethical concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of evidence rated significantly higher than hygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of evidence rated significantly higher than other\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthical concerns\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthical concerns rated significantly higher than hygiene considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthical concerns rated significantly higher than other\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHygiene considerations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene considerations rated significantly higher than other\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eSummary of principal findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study surveyed 405 UK-based community-dwelling adults, including a small subgroup of health and care professionals, to examine awareness, perceptions, and perceived risks and benefits of spatial computing technologies in healthcare. Most participants reported general familiarity with virtual or augmented reality (VR/AR) devices, though only a small minority reported regular use. Among those familiar, the most recognised platforms were Oculus Quest and Apple Vision Pro-consumer-facing devices primarily marketed for entertainment, while familiarity with enterprise-focused devices such as Microsoft HoloLens was limited.\u003c/p\u003e\n\u003cp\u003eDespite modest hands-on exposure, participants expressed strong optimism about the potential of spatial computing to support health and wellbeing, particularly through applications like guided mindfulness, avatar-led fitness demonstrations, immersive patient education and virtual peer support. Health literacy emerged as the most strongly endorsed self-care domain, followed by physical activity and mental wellbeing. However, enthusiasm waned when participants considered spatial computing for medication management or dietary guidance, suggesting variable acceptability across use cases.\u003c/p\u003e\n\u003cp\u003eInferential findings, which should be interpreted as exploratory signals rather than confirmatory evidence, highlighted a significant gender-based disparity with men being markedly more likely than women to report familiarity with spatial computing technologies. Statistically significant associations were also observed between familiarity with spatial computing and certain sociodemographic characteristics, including age and ethnicity. However, observed differences by ethnicity should be interpreted cautiously, as subgroup sizes were small and the survey did not collect data on socioeconomic status, technology access pathways or prior occupational exposure, which may confound apparent associations. Perceived barriers to adoption were also stratified. In particular, while higher familiarity was observed in some minority ethnic categories, these groups comprised relatively small numbers of participants, limiting the precision and generalisability of these estimates. High cost was consistently ranked as the top obstacle, followed by lack of training, limited evidence of benefits and concerns around data privacy and system complexity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation in the context of existing literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of the METACARE study align with emerging literature that suggests spatial computing holds promise as an enabler of digital self-care, patient education and health engagement (Applications such as VR therapy, AR-assisted rehabilitation and immersive education have shown efficacy in clinical trials and pilot studies, particularly for chronic pain, phobias, post-stroke rehabilitation and mental health support. However, much of this literature is hospital-based or specialist-led. METACARE expands this evidence base by gauging public sentiment on the broader use of these tools in everyday, community-based self-care.\u003c/p\u003e\n\u003cp\u003eOur findings corroborate the results of other studies (26-28) confirming that VR interventions in healthcare are generally well-received by users when aligned with behavioural and psychosocial goals. In the present study, support was strongest for uses that offer visualisation, simulation and interactive feedback, mechanisms that may promote cognitive engagement and enhance health literacy. The high ratings for avatar-led exercise and guided mindfulness reinforce the potential for immersive tools to facilitate adherence to health-promoting routines, especially when aligned with personalised or gamified models of care.\u003c/p\u003e\n\u003cp\u003eHowever, scepticism around applications like medication adherence, diet tracking, or procedural support may reflect current limitations in usability and real-world integration. These more structured health behaviours require precision, continuity and seamless electronic health record (EHR) integration features not yet widely demonstrated by existing consumer-grade platforms. The relatively lower ratings for these domains may also reflect broader digital hesitancy or concern about over-reliance on immersive technologies in clinical care.\u003c/p\u003e\n\u003cp\u003eThe strong perception of spatial computing’s potential to improve health literacy is particularly salient. As self-care continues to be prioritised globally, tools that can enhance individuals’ ability to access, comprehend and act on health information are invaluable (22). Spatial computing can visualise anatomy, simulate disease progression, or demonstrate therapeutic actions offering more accessible ways of communicating complex health information, particularly for people with lower baseline literacy or linguistic barriers (29).\u003c/p\u003e\n\u003cp\u003eRegarding barriers, the predominance of cost concerns echoes similar findings from studies on digital health tool adoption (30, 31). Spatial computing hardware remains expensive and unless scaled through national programmes or insurance models, may deepen inequities. Similarly, the perceived lack of training and evidence highlights the importance of parallel investments in workforce development and robust clinical trials to validate new applications.\u003c/p\u003e\n\u003cp\u003eThe gender gap in familiarity, where men were significantly more likely to report prior knowledge or use of spatial computing, echoes wider digital confidence disparities (17). Addressing this imbalance may require more inclusive design, gender-sensitive training resources and targeted community engagement. While age was not statistically significant, the trend of declining familiarity among older adults deserves attention, especially since this demographic often has the most to gain from digital interventions supporting independent living and chronic disease management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations of this study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo our knowledge, our study is among the first UK-based investigations to systematically examine public and healthcare professional perceptions of spatial computing specifically in relation to self-care and primary care contexts. The survey instrument was theoretically grounded, drawing on established public health and self-care frameworks, including the Seven Pillars of Self-Care, and was refined through pilot testing to ensure clarity and usability. The inclusion of both community-dwelling adults and healthcare professionals, alongside detailed demographic data, enabled exploration of equity-relevant differences in familiarity and perceived utility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalytically, the use of non-parametric, rank-based methods allowed appropriate comparison of perceived benefits and barriers across multiple self-care domains while respecting the ordinal nature of Likert-scale data.\u003c/p\u003e\n\u003cp\u003eThe principal limitation of this study was that it employed convenience sampling via an online survey platform, which necessarily favours individuals with internet access, higher digital literacy and greater engagement with emerging technologies. As such, the sample cannot be considered representative of the UK population, and groups most at risk of digital exclusion, including some older adults, individuals from socioeconomically deprived backgrounds and those with disabilities, are likely under-represented. Findings should therefore be interpreted as reflecting the views of a digitally engaged segment of the public rather than population-level attitudes.\u003c/p\u003e\n\u003cp\u003eSecond, familiarity, attitudes and perceived benefits were self-reported and may be subject to recall bias or social desirability bias. Reported familiarity does not necessarily equate to sustained or competent use, particularly given that regular use of spatial computing devices was uncommon in this sample. Third, although statistically significant associations were observed between familiarity and demographic variables such as gender, age and ethnicity, some subgroup sizes, particularly within ethnic minority categories and among healthcare professionals, were small. These analyses were exploratory and unadjusted for multiple testing; accordingly, observed associations should be treated as hypothesis-generating rather than confirmatory.\u003c/p\u003e\n\u003cp\u003eFinally, the cross-sectional design precludes any inference about causality or temporal change. Perceptions captured at a single time point may evolve rapidly as spatial computing technologies mature, costs change and evidence accumulates. Longitudinal and mixed-methods studies, as well as purposefully stratified samples, will be essential to assess how familiarity, acceptability and equity implications develop over time and in real-world implementation settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for practice, policy and research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of this study have several important implications. First, they highlight the growing public receptivity to immersive technologies in healthcare, particularly in domains such as mental wellbeing, physical activity and health education. Policymakers and digital health developers should capitalise on this receptivity by prioritising co-designed applications that reflect user preferences and address their concerns especially regarding privacy, usability and equity.\u003c/p\u003e\n\u003cp\u003eSecond, integration into self-care and primary care pathways will require technical innovation coupled to robust evidence of impact. Trials evaluating cost-effectiveness, clinical outcomes and behavioural changes resulting from spatial computing interventions are urgently needed. Future studies should also explore long-term engagement and potential harms, including over-reliance, cybersickness, or exacerbation of digital exclusion.\u003c/p\u003e\n\u003cp\u003eThird, our data highlight the need for targeted digital literacy initiatives, particularly for women and older adults who appear less familiar with these technologies. Community-based workshops, primary care integration pilots and educational campaigns could help narrow this gap and promote equitable uptake.\u003c/p\u003e\n\u003cp\u003eFinally, the ethical and regulatory context must evolve in tandem. Concerns around data privacy, algorithmic bias, consent and equitable access remain salient. As spatial computing platforms become increasingly intertwined with AI decision-support systems, safeguards must be established to ensure transparency, accountability and trustworthiness. National frameworks for immersive health technologies, analogous to those developed for telehealth and mobile apps, will be essential to guide ethical deployment.\u003c/p\u003e\n\u003cp\u003eBy providing the first UK-wide snapshot of public and professional perceptions of spatial computing framed explicitly through a self-care lens, this study establishes a baseline against which future implementation trials, policy evaluations and equity-focused deployment strategies can be assessed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSpatial computing represents a promising frontier in the evolution of digital health, with strong perceived value for self-care, patient education and public health engagement. This study offers timely and actionable insights into how the UK public perceives these tools, highlighting optimism for immersive applications that support mental health, health literacy and behavioural change. At the same time, it highlights important barriers, including cost, training gaps and digital confidence disparities, that must be addressed to ensure equitable adoption. As spatial computing matures, its integration into mainstream healthcare must be guided by inclusive design, rigorous evaluation and robust governance.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eAR\u003c/strong\u003e – Augmented reality\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCHERRIES\u003c/strong\u003e – Checklist for Reporting Results of Internet E-Surveys\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEHR\u003c/strong\u003e – Electronic health record\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGP\u003c/strong\u003e – General practitioner\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHCP\u003c/strong\u003e – Healthcare professional\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eICREC\u003c/strong\u003e – Imperial College Research Ethics Committee\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eKAPB\u003c/strong\u003e – Knowledge, attitudes, perceptions and behaviours\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMR\u003c/strong\u003e – Mixed reality\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNHS\u003c/strong\u003e – National Health Service\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNIHR\u003c/strong\u003e – National Institute for Health and Care Research\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePIS\u003c/strong\u003e – Participant information sheet\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSCARU\u003c/strong\u003e – Self-Care Academic Research Unit\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eUK\u003c/strong\u003e – United Kingdom\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eVR\u003c/strong\u003e – Virtual reality\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Imperial College London Research Ethics Committee (reference number:\u0026nbsp;21IC7375). All participants were provided with a Participant Information Sheet and gave informed consent electronically before taking part in the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDe-identified data supporting the findings of this study are available from the corresponding author on reasonable request. The data are not publicly available due to ethical restrictions related to participant confidentiality and consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.\u0026nbsp;Austen El-Osta is supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration (ARC) Northwest London. The views expressed are those of the authors and not necessarily those of the NHS or the NIHR or the Department of Health and Social Care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAE-O conceived the study, led the study design, oversaw data collection, conducted the analysis and drafted the manuscript. CSQ, AA, MA and SA contributed to survey development, interpretation of findings and critical revision of the manuscript.\u003cbr\u003e\u0026nbsp;All authors reviewed and approved the final manuscript and agree to be accountable for all aspects of the work. AE-O is the guarantor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all participants who took part in the survey. The authors also acknowledge the support of the Self-Care Academic Research Unit (SCARU), Imperial College London.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and public involvement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients and members of the public were not involved in the design, conduct, reporting or dissemination plans of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTwitter:\u0026nbsp;\u003c/strong\u003e@austenelosta @ImperialSCARU\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe King\u0026rsquo;s Fund (2024) Public satisfaction with the NHS and social care in 2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNHS. 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JMIR XR Spat Comput (JMXR) 1(1):e59409\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOfcom (2023) Adults\u0026rsquo; Media Use and Attitudes report 2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNICE (2022) Evidence standards framework for digital health technologies\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnsworth H, Dillon B, Collinson L, Powell H, Salmon M, Oladapo T et al (2021) The NICE Evidence Standards Framework for digital health and care technologies \u0026ndash; Developing and maintaining an innovative evidence framework with global impact. Digit HEALTH 7:20552076211018617\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eISF. International Self-care Forum Homepage (2025) [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://isfglobal.org/\u003c/span\u003e\u003cspan address=\"https://isfglobal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Osta A (2019) The Self-Care Matrix: a unifying framework for self-care. Int J Self Help Self Care 10:38\u0026ndash;56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Osta A, Sasco ER, Barbanti E, Webber I, Alaa A, Karki M et al (2023) Tools for measuring individual self-care capability: a scoping review. 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Nat Med 25(1):44\u0026ndash;56\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Imperial College London","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spatial computing, Virtual reality, Augmented reality, Mixed reality, Digital health, Self-care, Health literacy, Primary care, Patient education, Adoption barriers","lastPublishedDoi":"10.21203/rs.3.rs-8720861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8720861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess public and healthcare professional knowledge, attitudes, perceptions and behaviours regarding spatial computing technologies (virtual, augmented and mixed reality) in healthcare, with a focus on perceived benefits for self-care and barriers to adoption in primary care and community settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCross-sectional online survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUK-wide, web-based survey conducted between January 2025 and August 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCommunity-dwelling adults aged ≥ 18 years residing in the UK, including healthcare professionals. A total of 405 respondents completed the survey; 41 were healthcare professionals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterventions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo intervention was delivered. Participants completed a structured questionnaire assessing familiarity with spatial computing, perceived utility across self-care domains aligned to the Seven Pillars of Self-Care and perceived barriers to adoption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary and Secondary Outcome Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary outcomes were self-reported familiarity with spatial computing technologies and perceived benefit across self-care domains. Secondary outcomes included perceived barriers to adoption and associations between demographic characteristics and familiarity. Analyses used descriptive statistics and exploratory inferential tests (χ², Fisher’s exact, Friedman and Wilcoxon signed-rank tests with Bonferroni correction).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost respondents (71.4%) reported familiarity with spatial computing technologies, although regular use was uncommon (4.7%). Oculus Quest (57.5%) and Apple Vision Pro (46.9%) were the most recognised platforms. Participants perceived strong potential for supporting health literacy, mental wellbeing and physical activity, particularly through guided mindfulness, avatar-led exercise and immersive patient education. Perceived benefit was lower for medication management and dietary guidance. Familiarity was statistically associated with gender (p \u0026lt; 0.001), age (p = 0.002) and ethnicity (p = 0.006), with higher awareness among men, younger adults and some minority ethnic groups. The most frequently cited barriers to adoption were high cost (56.5% rating as critical), lack of training (67.4% rating 4–5) and data privacy concerns (63.5%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpatial computing is viewed positively by the public and healthcare professionals as a tool to support self-care and aspects of healthcare delivery, particularly health literacy, mental wellbeing and physical activity. However, high cost, training gaps and privacy concerns remain substantial barriers. Targeted investment in evidence generation, workforce training and inclusive governance will be necessary to support equitable and responsible implementation.\u003c/p\u003e","manuscriptTitle":"Public perceptions of spatial computing in health: Opportunities and barriers for supporting self-care and wellbeing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 10:41:05","doi":"10.21203/rs.3.rs-8720861/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c21953ee-b873-41ab-99d8-e037407d5342","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61898615,"name":"Health Policy"},{"id":61898616,"name":"Preventive Medicine"}],"tags":[],"updatedAt":"2026-01-29T10:41:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 10:41:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8720861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8720861","identity":"rs-8720861","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}