A Systematic Review on Patient and Public Attitudes Toward Health Monitoring Technologies Across Countries

A Systematic Review on Patient and Public Attitudes Toward Health Monitoring Technologies Across Countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Systematic Review on Patient and Public Attitudes Toward Health Monitoring Technologies Across Countries Tiantian Chen, Ekaterina Hertog, Adam Mahdi, Samantha Vanderslott This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5076992/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jul, 2025 Read the published version in npj Digital Medicine → Version 1 posted 6 You are reading this latest preprint version Abstract The market for digital health monitoring is expanding rapidly, with technologies that track health information and provide access to medical data promising benefits for users, particularly in areas with limited healthcare resources. To understand user attitudes toward these technologies, we conducted a systematic review of literature with primary data about patient and public perspectives. We synthesized 562 studies (2000–2023) from PubMed, Embase, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus, including qualitative, quantitative, and mixed-methods research. We revealed a significant geographic bias, with most research concentrated in few countries, and identified access gaps in both Global South and Global North. While users generally showed positive attitudes toward health monitoring technologies, they expressed various concerns. We provide suggestions for future research to enhance the socially responsible integration of technology in healthcare. One important limitation of our approach is using English-language search terms. This potentially excluded relevant studies from underrepresented countries. Scientific community and society/Social sciences/Sociology Health sciences/Health care/Public health Health sciences/Health care/Quality of life Figures Figure 1 Figure 2 Introduction Health monitoring technologies are increasingly adopted in healthcare, a trend accelerated during the COVID-19 pandemic for more digital and remote modes of working fuelled by the need to reduce virus exposure among patients and healthcare professionals 1 . Techno-optimists who place strong faith in technological progress believe these technologies will improve the quality of data available to doctors, reduce the cost while increasing the efficiency of monitoring patients and recording their details, ultimately benefiting patients 2 . By removing geographical health barriers, digital health monitoring is seen as a way to improve healthcare access, especially in areas with limited healthcare resources, provided that the necessary digital infrastructure exists. Such benefits can only become reality if all the users are equally able and willing to adopt digital health monitoring effectively. This, however, cannot be taken for granted. The distribution and practices of health monitoring technologies predominantly assume users are white, well-paid, educated, cisgender, with good internet access, capable of managing their own healthcare, and live in high-income countries 3 . This leaves out many groups, such as carers who are not regarded as users in many studies and ethnic minorities whose needs are often neglected in technology development. Existing systematic reviews focus narrowly on specific users and technologies such as older adults’ attitudes toward smart home technologies 4 and users’ experience of wearable technologies (‘wearables’) 5 . This narrow focus makes reviews easier to conduct but limits understanding of how attitudes toward various monitoring technologies vary across different socioeconomic backgrounds and geographies. Understanding how the under-resourced communities and less advantaged individuals who potentially stand to benefit the most from such technologies, compare with more advantaged individuals is crucial if health monitoring technologies are to bring about greater equality in healthcare outcomes. Our systematic review assesses and synthesizes existing literature on patient and public attitudes toward health monitoring technologies, with a focus on identifying key factors that influence adoption across different socioeconomic and geographic groups. By summarizing the current knowledge on broadly defined health monitoring technologies, highlighting the gaps in the literature, and proposing directions for future research, our review seeks to provide insights into more equitable and public/patient-centred implementation of digital health monitoring. Results Characteristics of included studies 562 studies were included in this review. All the included studies were published between 2004 and 2023, with publications reaching their highest frequency in 2021—coinciding with the increased adoption of remote health monitoring during the COVID-19 pandemic 6 . Our sample included 240 quantitative studies, 201 qualitative studies, and 121 mixed methods studies. In our sample, 522 studies specified a single study location (16 articles did not specify the study location, and 24 studies included more than one study location. We excluded these articles in the analysis of geography to avoid confusion of numbers). Four countries accounted for over half of these single-location studies: the United States (153), the United Kingdom (67), Australia (32), and Germany (24) (see Fig. 1 ). Of the 546 studies that mentioned any location, 444 were conducted in the Global North. The Global South, particularly low-income countries, was severely underrepresented. Only five studies were conducted in low-income countries: Ethiopia, Sudan, and Uganda. Among 562 studies, 420 reviewed medical applications of health monitoring technologies, with diabetes (54), cardiology (39), and psychiatry (37) being the most frequently represented specialties. Smart home systems for older adults (71), activity trackers (36), and workplace well-being monitoring (11) are the most-mentioned non-medical applications in our sample. Studies in Global North covered more specialties than those in Global South. No studies on technologies for cancer treatment, smoking cessation, and alcohol addiction used samples from Global South, and only one study on respiratory diseases used a sample from Global South. Our ability to compare findings across demographic groups was constrained by limitations of the demographic data available in most studies in our sample. While most studies reported basic information like gender and age (see Table 1 ), few captured other key demographics such as ethnicity, education level, or place of residence. In analysing sampling methods, we distinguished between convenience sampling (nonprobability recruitment) and representative sampling (probability-based recruitment or a study mention being based on a representative sample). Although convenience sampling and smaller sample sizes (fewer than 50 participants) are standard in qualitative research, caution is warranted when applying statistical analysis to such samples. Among quantitative studies, only 47 (19.5%) used representative samples, and 78 (35.6%) had fewer than 50 participants. We also evaluated inclusion of economically disadvantaged, educationally disadvantaged, rural, or gender minority populations and noted a representation gap. For example, only 34 of the 122 studies on fitness trackers recruited participants from disadvantaged socio-economic backgrounds. Table 1 Number of articles reporting respondents’ gender, age, ethnicity, education, and rural/urban residence (2004–2023, n = 562) Demographic characteristics Number of studies mentioning (percentage) Sex/gender 447(79.5%) (9 articles specified sexual orientation) Age 464 (82.6%) Ethnicity 114 (20.3%) Educational background 154 (27.4%) Rural/urban residence/distance to hospitals 21 (3.7%) Income level 106 (18.9%) Among 562 studies, 504 focused on primary users (patients and public), five on secondary users (paid carers, parents, and family members), and 53 on both; the most commonly studied technology types were wearables, apps, and smart home systems.18 studies featured artificial intelligence (AI) technologies, with 15 in Global North and three in Global South. Notably, all studies from Global South originated in China. Thematic analysis We took an inductive approach to identify codes and group them into six themes reflecting public and patient perceptions of monitoring technologies: prior knowledge of monitoring technology, acceptability, usability, motivations and challenges, perceived benefits, and perceived risks. The themes introduced below are ordered in the way individuals engage with health monitoring technologies, from initial awareness to ongoing evaluation of benefits and risks. Due to the large number of studies in this review, we provide typical examples to illustrate these themes. Table 2 summarizes the key findings. We also present how codes and sub-codes were developed into the findings in Table 2 . The full list of themes as well as explanation of codes and sub-codes is available in Supplementary Table 6. Table 2 Summary of the key findings (2004–2023, n = 562) Theme Meaning Codes and sub-codes Number of articles with this theme Number of studies based in Global North Key findings about the theme Knowledge Awareness and familiarity Prior knowledge about health monitoring technologies Familiar, unfamiliar 59 44 62.7% studies reported unfamiliarity, while 37.3% studies reported familiarity The findings on the relationship between health risks and knowledge about technologies are contradictory. For example, a focus group study with 17 participants found patients’ anxiety about health issues motivated them to familiarize themselves with activity monitors. 15 At the same time, a community-based population survey with 317 respondents found that hypertension was not correlated with their use of wearable blood pressure technology. 16 Determinants of knowledge Socio-demographic factors, perceived usefulness 6 6 Acceptability Acceptance Acceptance of technologies Mixed, low interest/negative, positive 294 233 88.1% found positive attitudes to technologies, 8.8% found negative or low interest in them, and 3.1% found mixed Some found a correlation between respondents’ socio-economic background characteristics and acceptability, but others did not. Determinants of acceptance Correlation or no correlation with socio-demographic factors, self-control, appearance, external factors, technology difference, desire, location, knowledge, relationship with others, perceived ease of use, perceived usefulness, security 136 100 Usability User experience User experience of technologies Ease of use, comfortable, appealing appearance, durability, language, self-application, technical difficulties 335 141 Of 270 articles examining perceived ease of use, 214 found users regarded technologies easy to use. Of 99 articles examining perceived comfort, 83 reported comfortable to use. Of 90 studies about opinions on device appearance, 39 studies found devices visually appealing. Perceived usability problems varied with respondents’ socio-demographic characteristics. For example, interviews with 20 pregnant women in their second trimester found a finger ring to monitor sleep/stress data might be too big for women. 45 Interviews with seven patients found old people felt difficult to seal an oxygen device. 46 Improvement of usability Information storage, eliminate Ads, offline activity, more inclusive, Context of use, justification, comfort, wording, loss, unobtrusiveness, functionality, location, gamification, tailored feedback, data interpretation, appearance features 231 197 Motivations and barriers Motivations Factors motivating or preventing informants from using technologies Perceived usefulness, perceived ease of use, social relationship, competition, curiosity, anxiety, pleasure, fun/coolness, brand, external environment, giving back 163 136 Anxiety about health risks and perceived usefulness to mitigate risks were major motivations for use of tracking devices. Barriers Unfit body shape, not inclusive, usability issue, no correlation, not convinced, the foreign body, lack of autonomy, lack of collaboration, knowledge, forget, incompatible with other devices, access issues, cost (time, money, etc), functionality, interfere current life, replace existing care, no need, embarrassment, data sharing concern, distrust 187 153 Affordability is a direct barrier preventing people from using monitoring technologies. Certain groups are digitally excluded. For example, interviews and workshops with the queer community indicate that gender options for wearables lack queer option. 5 9 Another focus group study found age prevents the elderly from using wireless healthcare sensors independently. 2 7 How to motivate Compatibility, reduce cost, award/stimulus, promote understanding, one-to-one basis/build relationship 34 28 The most frequently mentioned incentive for technology adoption is a trusting relationship between manufacturers and potential users. Perceived benefits Perceived advantages of technologies Technology development, individualized treatment, contribute to research/social good, promote policy/infrastructure, improve privacy, supplement/replace routine test, security, good relationship, legitimize their symptoms, behavior change, awareness, reduction burden, improving their adherence, support, improve confidence/accomplishment, self-control 284 250 Increasing health consciousness, changing behaviors, and improving social relationships are three major perceived benefits. Perceived risks Concerns about technologies Impact professionalism, dehumanizing care, decrease opportunity, bias Accuracy, surveillance, burden, reduced autonomy, adverse events, privacy, malfunction 213 194 Three major concerns are privacy, accuracy, and feeling burdened. Certain groups were more concerned about risks of monitoring technologies. For example, a survey of 241 women using fertility trackers found women trying to conceive were the most skeptical about the accuracy of fertility apps. 69 Research also indicates that women and those with university education were more perceptive to privacy risks. 92 Of the 59 articles on patients’ or general public’s prior knowledge of monitoring technologies , 37 found most respondents were unfamiliar with the technology under scrutiny, while 22 found most respondents reported familiarity. Six studies analyzed factors influencing knowledge about technologies, four revealing correlations with participants’ socio-economic characteristics like age and gender. For instance, a study found that patients over 65, who could benefit most from tablet monitoring, were least familiar with the technology 7 . The findings on the associations between health risks and technology knowledge are inconsistent. One focus group study with 17 older patients found anxiety about health issues increased familiarity with activity monitors 8 , while a survey with 317 patients found no correlation between known hypertension and knowledge about wearable blood pressure technology 9 . Of 294 articles studying acceptability of health monitoring technologies, 259 found positive attitudes to technologies, 26 found negative or low interest in them, and 9 documented mixed attitudes. 56 out of the 294 studies containing this theme used samples from Global South and virtually all of these studies recorded positive attitudes. All but 31 studies documenting negative or mixed attitudes used samples from Global North 10 . 136 articles analyzed factors influencing acceptability. 65 found correlations with socio-economic characteristics, showing that women, educated respondents, and high earners were more receptive to such technologies 11 , 12 . Six studies found no such correlation. For example, a surveyed sample of 60 senior citizens found age did not influence their acceptance of Ambient Intelligence, an app monitoring home environment 13 . Remaining papers did not focus on socio-economic characteristics but rather considered other factors. Perceived technology usefulness and ease of use were uniformly positively related to acceptability in 40 and 20 papers respectively 14 , 15 . 17 studies associated awareness of risks with increased acceptability 16 , while 15 found no link between prior technology knowledge and acceptability 17 . Two studies linked respondents’ personality traits with technology adoption. For example, interviews with 22 Parkinson’s patients found that those with a positive outlook were more likely to wear sensors 18 . 16 studies found fewer social relations increased acceptability. For example, research has indicated that those with pets 19 or with good social bonds 20 were less likely to accept monitoring technologies. Other influential external factors included usage scenario 21 and device appearance 22 . For example, a test with 20 dementia patients revealed they were less satisfied with wearable GPS devices at home 20 . Of 270 articles examining perceived usability , in 214 studies most users found the technologies tested easy to use. For some patients and elderly people, ease of use meant independence in applying devices, which was highly valued by both carers and patients, especially after activities like showering 23 , 24 . Usability complaints included complex operation manuals, data loss, connection failures, false alarms, lack of waterproofing, susceptibility to damage, excessive advertisements, and short battery life 25 , 26 . Suggestions for improvement included clear data interpretation, visualization, and comparison features 27 , 28 . Some patients required manufacturer to justify the usefulness of function, as indicated in interviews from kidney transplant patients 29 . Some users requested less paternalistic language 30 and proposed gamifying devices to increase engagement 31 . Some respondents requested offline activities, such as a dementia education workshop for carers of dementia patients to supplement wearable devices 32 . Regarding comfort, 83 out of 99 studies reported that monitoring technologies were comfortable to use, with suggestions to secure device attachment and reduce skin irritation 29 . In terms of device appearance, 51 of 90 studies noted issues with size, color, and style 33 , while 39 found devices visually appealing 34 . Users preferred non-obtrusive designs, as seen with diabetes patients who concealed glucose monitoring devices under clothing 35 . Some devices were criticized for medicalizing or stigmatizing users, which caused social pressure, such as children concealing bedwetting alarms 36 . 50 studies revealed that perceived usability problems varied with respondents’ socio-demographic characteristics, calling for tailored improvements. For instance, interviews with pregnant women found a sleep/stress monitoring ring too large 37 , and interviews with older patients found they struggled with sealing oxygen devices 38 . Individual health conditions also influenced preferences: elderly individuals with poor eyesight valued visible fall detectors 21 , brain injury patients favored voice recognition for health diaries 27 , and migraine sufferers preferred dim screens 39 . Some comparative studies indicated potential cultural differences in user evaluations; for example, one study found Arab users were more likely to focus on physiological measurements of fitness trackers compared to non-Arab users who focused more on goal achievement 10 . 163 studies examined motivations of using health monitoring technologies. Among them, 129 studies identified anxiety over health risks and the perceived usefulness of tracking devices to mitigate these risks as key motivations for technology adoption 13 , 35 . For instance, patients with peripheral arterial disease used disease detectors due to concerns about limb loss 35 . Other intrinsic motivations included being able to share achievements with peers 40 and curiosity about personal health metrics 41 . Social influences were significant extrinsic motivators, with recommendations from family members 25 or healthcare providers driving adoption 38 . Peer competition also spurred usage, particularly in fitness tracking 25 . Brand appeal 41 and contributing to scientific research 42 were additional factors. Of 187 articles identifying challenges to using monitoring technologies, affordability was a major challenge in 72 studies. For example, older adults in Ireland reported that inadequate insurance coverage prevented them from purchasing activity trackers 43 . Lack of interest was noted in 58 studies, often due to unawareness of health risks 40 or scepticism about technology benefits 34 . For example, carers of adults with developmental disabilities refused a smart home system for lacking essential human interaction functionality 44 . Users also lost interest if they were the only person in the community to use technologies 43 or if they felt uncomfortable with being watched 45 . With lack of interest, people tended to forget to use monitoring technology 46 . User skills and knowledge gaps were highlighted in 56 articles, with users finding learning new devices burdensome and disruptive to daily routines 18 . Compatibility issues with current mobile phones or hospital devices 47 , lack of preferred functions 34 , and technical usability concerns like discomfort or poor fit 35 were also reported challenges. For example, some elderly users found that hip protectors for detecting falls did not fit with other assistive equipment or failed to work when falls occurred onto knees 32 . Additionally, discomfort with implanted monitors was likened to feeling foreign to the body 48 . Regulations also posed challenges. For instance, school mobile phone policies hindered students’ use of glucose monitors 49 , and lack of government endorsement of AI significantly hindered the widespread adoption of AI-enabled wearable medical devices in China 50 . Furthermore, certain populations are digitally excluded. For example, interviews with the queer community indicated that gender options for wearables are binary 51 . In a focus group study, elderly people reported difficulty understanding health monitors 20 . Trusting relationships between manufacturers and users, fostered by communication efforts such as providing user support, emerged as critical for technology adoption 52 . For example, one qualitative study found community education facilitated acceptance of bednet use monitors in Uganda 53 . Family members 54 and doctors 55 also played an essential role in facilitating adoption. Other suggested strategies to enhance adoption included improving device compatibility, offering monetary incentives like cost reduction or insurance reimbursement 55 , and non-monetary incentives like positive verbal reinforcement praising users for using monitoring products, gamification of medicare, and peer competition 56 . Among 284 studies on perceived benefits , three major advantages were increased health consciousness (146 studies), behavior change (49), and improved social relationships (29). For example, users reported they could track effects of medications 27 , stages of disease 57 , and body functions such as basal body temperature and cervical mucus 58 . They increased activity levels 33 , stopped smoking 46 , and took medicines more punctually 59 . Users also reported monitoring technologies facilitated greater interaction with healthcare professionals, particularly during COVID-19 60,61 . Additionally, 24 studies found users felt supported and empowered by these technologies. For example, contact tracing apps in the US during the pandemic gave users hope that their life would soon be normalized 62 . Users also reported increased confidence through greater control over their lives. With smart home systems detecting falls, for example, elderly people felt they could live independently 14 , and their family members felt relieved of caregiver burden 63 . Both patients 30 and caregivers 63 reported improved healthcare efficiency, such as reduced hospital visits with monitoring technologies. Moreover, they reported satisfaction with clinical experience due to personalized care 64 , better data storage 65 , and accurate treatment 66 . For example, a qualitative study found patients with depression felt gamified data collection more enjoyable 67 . Other reported benefits included enhanced treatment methods, research advancements, and digital infrastructure development in rural areas 68 , 69 . 213 studies mentioned perceived risks . Notably, some benefits of monitoring technologies were also perceived as risks. Privacy was a major concern in 131 studies, with worries about non-transparent data collection and misuse of personal data by caregivers, manufacturers, insurance companies, or criminals 70 , 71 . In 58 studies, users experienced various types of burdens with monitoring technologies. First, they mentioned data obsession, where over-reliance on technology may undermine autonomy 67 or result in over-treatment 72 . For example, patients with depression in focus groups reported sleep loss from constant mood tracking 66 . Second, caregivers felt overwhelmed by the additional work of reviewing monitor results, especially during COVID-19 73 . Third, users were worried about bias and stigmas produced by health monitoring. For example, a survey with 245 older adults showed they felt sad about their age when using contactless monitoring 54 . People were also concerned about health data being used to discriminate in insurance pricing and employment 74 . 51 studies highlighted concerns about technology errors, such as inaccurate sensor data affecting health decisions 67 , lost connections 75 , device crashes 76 , and malware infections 16 . Beyond privacy infringement, burdens, and technology errors, monitoring technologies were seen to dehumanize healthcare by reducing the direct human contact that is expected of healthcare. For instance, carers in a retirement home faced residents’ doubts about their professionalism when using mobile devices for alarms 77 . Many patients preferred face-to-face communication 70 . These technologies could also decrease communication with family 74 and healthcare providers, as seen in a study with 19 patients where a smart health platform reduced referrals to private practices 78 . Aside from social and emotional risks, research participants noted physical risks such as skin rash 79 and radiation from wireless technology 80 . Socio-demographic factors influenced concerns about technology risks. For instance, a survey of 241 women found women trying to conceive were skeptical about fertility app accuracy 58 . Research also indicated women and those with higher education levels particularly worried about privacy 81 . Discussion Research on health monitoring technologies has accelerated alongside their increasing adoption in medical and consumer settings. This review analyzed existing studies, examining their geographic scope, sampling methods, user demographics, and types of technologies deployed. We synthesized current understanding of patient and public attitudes through six key themes: knowledge, acceptability, usability, motivations and challenges, perceived benefits, and perceived risks. Looking at our core themes first, we found that while many patients and members of the public were unfamiliar with existing health monitoring technologies, research documented generally positive attitudes towards them. In studies where users tested health monitoring technologies, most users found them easy to use, and patients and their carers highly valued the ability to utilise devices independently. In many studies, familiarity, acceptability, and perceived usability of monitoring technologies varied by socio-demographic characteristics. Certain populations, such as older adults 28 , or queer people 51 , felt digitally excluded. This finding highlights how the digital health divide extends beyond unequal access to technologies. Even with seemingly equal access, benefits may still not be equally distributed. To maximise the benefits of these technologies, manufacturers and policymakers need to consider groups that would most benefit, such as older adults, and eliminate barriers to adoption. Sociality influences the adoption of these technologies 19 , 20 . Individuals with richer social connections may be less interested in adopting these technologies initially, but family, friends, and effective communication efforts by manufacturers could promote knowledge about these technologies and encourage use. Several properties of health monitoring technologies identified as bringing benefits to their users have also been flagged as potential sources of risk. Respondents reported behavior changes but also feared over-reliance on technology and over-treatment 27 , 33 , 46 , 57 , 58 , 59 , 67 , 72 . While some experienced more interaction with healthcare professionals, others worried about reduced communication with family and clinicians 33 , 72 , 74 , 77 , 78 . Some praised these technologies for personalized care, but there were also concerned about the misuse of personal data 64 , 65 , 66 , 70 , 71 . In the cases, where the same characteristics of technologies could bring benefits to some users but result in negative unintended consequences for others, the potential benefits and risks should be carefully considered before these technologies are widely adopted. Health monitoring devices, like other digital technologies that collect personal information, raise privacy concerns. Perceived privacy risks underscore a divide between users and the companies handling their data. This review found that users feared data misuse by companies, necessitating robust policies and guidelines for inclusivity and trustworthiness 70 , 71 . While research indicates the need for comprehensive policies to build trust and ensure inclusive practices, regulatory approaches vary significantly across nations. For example, the US classifies monitoring technologies as either medical or non-medical devices, with medical devices regulated by the 21st Century Cures Act 82 . In contrast, Japan classifies devices by risk level, requiring approval for higher risk devices 83 . Studying contrasting regulatory models could inform the development of more effective global standards for responsible health data monitoring. In all, our review identified several knowledge gaps. Ensuring high-quality samples is challenging and costly, leading many studies to rely on small, convenience samples that limit generalizability. Many studies provide limited demographic details for their samples, with few studies specifying participants’ ethnicity, income, education, or urban/rural locations. These practices complicate interpretation of the findings, especially when they are contradictory. For example, some studies show socio-economic factors affect technology acceptability, while others do not 11 , 12 , 13 . Some documented contradictions might result from different methodologies and research contexts. More details about sampling strategies in future research would inform an understanding of applicability and generalizability of research findings and to help build a fuller picture of attitudes to health monitoring in different populations and across different contexts. Future research would also benefit from open research approaches, including pre-registering designs, hypotheses, methods, and analyses to guide confirmatory tests 84 . Additionally, researchers should share research materials, anonymized data, and analysis codes to facilitate examination and reproduction of findings. These practices improve the efficiency and accuracy of science, such that the growing literature on digital monitoring technologies can ultimately provide clearer answers about where evidence is strong, and where evidence is limited, or applicable only to certain populations. Such processes are especially important in the healthcare and technology space, where hype and fear-tactics surrounding technology innovation and public health crisis too often drive policy. Virtually all the knowledge about attitudes to monitoring technologies comes from Global North, especially from Anglophone countries. People from Global South are under-represented, despite Global South being home to the majority of the world’s population. Monitoring technologies are often used very differently in Global North and Global South. To give one example, fertility apps are used to enhance pregnancy chances in Global North and to limit reproduction opportunities in Global South 85 . A few comparative studies also suggest cultural differences in attitudes to technologies 10 . This means that we cannot extrapolate findings from research based on people living in Global North to the rest of the world. Health monitoring could improve healthcare access in areas with limited resources, but current recruitment strategies under-represent those who might benefit most. Research with more diverse samples is necessary to understand how under-represented populations interact with health monitoring technologies. In addition to identifying sampling gaps, our analysis revealed significant gaps in health monitoring technologies across both Global South and Global North regions. First, in the Global South, we identified a critical mismatch between available health technologies and actual disease burden. While these regions face a dual challenge of managing infectious diseases (like malaria and HIV/AIDS) while confronting rising rates of non-communicable diseases (such as cancer and respiratory illnesses), few studies address the latter. The ongoing epidemiological transition driven by urbanization and lifestyle changes in Global South underscores the need for technological developments tailored to these contexts 86 , 87 . Second, we found that AI-powered technologies like smart home systems and robots are predominantly studied in Global North (15 studies), with only three such studies emerging from Global South. This disparity reflects broader challenges in the Global South, including underdeveloped digital infrastructure, limited data models, and insufficient financial investment schemes 88 . As AI technology continues to advance, this gap risks further widening in resource-constrained regions. Third, Access challenges persist even in the Global North, where many individuals struggle with financial constraints, technological literacy, and reliable internet connectivity 22 , 34 , 43 . Current research has primarily focused on building trust to increase technology adoption 52 , 53 . However, there is an urgent need for studies examining how underserved communities in the Global North can overcome practical barriers, particularly regarding affordability and device compatibility. While this review identified key aspects of the digital divide in health monitoring technologies, it has limitations, that warrant discussion. First, our predominantly English-language search methodology captured only seven non-English studies, potentially excluding crucial research from underrepresented regions and limiting our understanding of the North-South divide. Future research would benefit from incorporating non-English search terms and multilingual databases to improve inclusivity. Second, while we distinguished between primary and secondary technology users, our analysis of caregivers’ perspectives was limited. Some studies revealed caregivers' concerns about dehumanized care, which contrasts with common assumptions about job displacement through digitization. A more thorough examination of caregivers' viewpoints is needed in future reviews. Third, we identified complex patterns in health monitoring technology adoption. Notably, technology hesitancy and refusal were predominantly reported in Global North countries, with 31 of 35 studies describing negative or mixed attitudes originating from this region. We also found some studies identified correlation between respondents’ socio-economic characteristics and acceptability, while some did not. These patterns may be due to varying methodologies and research contexts. Given the limited number of studies to draw on, we refrained from drawing definitive conclusions. A detailed discussion of these findings is available in Supplementary Table 8 and Supplementary Note 2 due to space constraints. Further research is needed to explore and address these patterns more comprehensively. Finally, our brief analysis of conflicts of interest in studies revealed mixed impacts. While industry funding enabled research in specialized areas like tetraplegia and idiopathic scoliosis, studies with declared conflicts of interest tended to report more favourable outcomes compared to studies without such conflicts. Further research should examine how corporate influence, and financial interests affect research findings in this field. In conclusion, we systematically reviewed patient and public attitudes toward health monitoring technologies and found that participants are generally positive about adopting these technologies as part of their care and find these technologies useful. At the same time the knowledge about health monitoring technologies is limited. There is a need to balance carefully the perceived risks and benefits of these technologies, and obstacles to adoption affect some populations more than others. Our findings particularly highlight a critical need for research to address significant knowledge gaps regarding these technologies in regions outside the Global North. Methods Search strategy and inclusion criteria In this review, we included peer-reviewed study and pre-prints with primary data about patients’ and public attitudes toward health monitoring technology, published between 2000 and 2023, which provided enough data for thematic analysis. Where there were multiple reports of the same study (e.g., a pre-print followed by a peer-reviewed research publication), we included only the peer-reviewed publication. The early 2000s marked significant advancements in health monitoring technologies, including the development of wearables, the widespread adoption of smartphones, and progress in health data analytics, which have profoundly shaped the landscape of health monitoring 89 . This time period is crucial for understanding the rise and growth of health monitoring technologies, making it an essential focus for this review. The review’s end date is 2023, aligning with the year the search was conducted to ensure the inclusion of the most current and relevant studies. We excluded commentaries, conference abstracts, and reviews lacking detailed information to explain their conclusions. Although we did not apply language filters, our search was conducted in English, since a majority of studies were published in English 90 . Besides English studies, we found two Chinese, three French, and two Arabic studies, which we accessed using Google Translator. We defined health monitoring technologies as digital products that collect human health-related data without direct intervention of healthcare providers, therefore putting the onus on usage by individual patients and (where also specified) their carers. Our definition of health was broad. We included studies focusing on both clinical measurements (like blood pressure and heart rhythm) and other factors that influence health (such as physical activity and mood). We excluded technologies that require direct healthcare provider participation, such as telehealth and videoconferencing systems. We excluded population-level disease surveillance systems, focusing instead on individual user attitudes. We defined attitudes broadly to include technology acceptance, prior knowledge, user experience, motivations, and perceived benefits and risks. Our definition of users encompassed patients, members of the general public, and both formal and informal caregivers who use monitoring technologies. We adopted the concept of primary (patients and members of the public targeted by technologies) and secondary users (carers) to highlight carers’ unique needs 91 . We excluded studies where the users were professionals, such as doctors, nurses, industry representatives, or policymakers. The review was registered with PROSPERO before data extraction (CRD42023446772). Since studies on health monitoring technologies are interdisciplinary, to minimize bias, we searched multiple databases on August 1, 2023: PubMed, Embase, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus. The selected databases are well-established for their comprehensive coverage of health, technology, and multidisciplinary research. Specifically, PubMed and Embase are essential resources for accessing medical and clinical studies, while IEEE Xplore and the ACM Digital Library provide critical insights into the engineering and computational dimensions of health technologies. Web of Science and Scopus offer broad multidisciplinary coverage, facilitating the inclusion of research spanning diverse fields. These databases are also recognized for their extensive international reach, supporting the inclusion of studies from various regions. Despite these databases’ broad coverage, we acknowledge that some regional studies may not be included due to database indexing limitations. We conducted a title and abstract search of literature published between January 1, 2000, and July 31, 2023. We consulted MeSH to identify search terms, but it did not provide specific terms for monitoring technologies. We therefore adopted a multiple-synonym search strategy (see Supplementary Table 1) to maximize the number of usable studies. We imported the bibliographic search results into Rayyan 92 , a systematic review management platform, and eliminated duplicate entries. Two authors (TC and SV) independently screened study titles and abstracts. We defined our inclusion and exclusion criteria using the Population, Intervention, Comparison, Outcomes and Study (PICOS) framework to ensure transparency and methodological rigor. Table 3 summarizes the criteria used to screen studies for inclusion. The authors then independently reviewed the full texts of potentially eligible studies against exclusion criteria. TC and SV discussed and resolved any discrepancies between their assessments (see Supplementary Table 2) before finalizing the list of included literature. For example, we excluded an article about an automated call monitoring intervention for older wheelchair users 93 . Though automated calls monitor patients’ health information, we decided to exclude this article because it did not specify how automated calls would be answered or whether these calls were embedded in a device. Figure 2 summarizes the exclusion process. Table 3 The PICOS framework for inclusion and exclusion criteria PICOS element Inclusion criteria Exclusion criteria Population Patients, the general public, or formal/informal caregivers who use health monitoring technologies Healthcare professionals, developers, industry representatives, or policymakers Intervention Studies involving the use or experience of health monitoring technologies (e.g., wearables, sensors) Studies not involving health monitoring technologies (eg. Population-level disease surveillance systems) Comparator N/A N/A Outcomes Attitudes, perceptions, motivations, barriers, or experiences of users Studies only reporting technical performance without user perspectives Study design Empirical studies using primary data (quantitative, qualitative, or mixed methods) published between 2000 and 2023. Both peer-reviewed study and pre-prints were included. Reviews, commentaries, protocols, editorials, studies not using primary data, studies published out of the time frame Data analysis Our systematic review identified 670 studies that met the inclusion criteria and passed quality assessment using the Mixed Methods Appraisal Tool 94 (MMAT; see Supplementary Table 3). The Mixed Methods Appraisal Tool assesses study internal validity. TC and SV independently assessed each study. Our assessment focused several key aspects: the clarity of the research questions and the alignment between research questions and methodology, how representative the study participants are of the target population, and the transparency of data collection procedures. We excluded 108 studies that reported potential or actual conflicts of interest to mitigate potential biases that may have affected the internal validity of the studies (see Supplementary Table 4). Of the remaining 562 studies included in our synthesis, 348 explicitly declared no conflicts of interest, while 214 provided no conflict-of-interest information (see Fig. 2 ). For comparative purposes, we separately summarized the 108 excluded studies with declared conflicts in Supplementary Note 1. While we acknowledge the extensive literature examining conflicts of interest in research 95 , analyzing this body of work fell outside the scope of our systematic review. Our analysis focused solely on conflicts of interest as defined by journal publication requirements. We piloted an Excel data extraction sheet (see Supplementary Table 5) using five randomly selected articles. TC and SV independently extracted publication year, study design, study location, technology type, target users, sample characteristics, sampling strategies, and attitude results. Due to the number of studies included, we only used information made available in the studies rather than contacting authors to obtain any information that was not included. We conducted a numeric analysis to calculate the number of studies by various categories such as publication year and study design and a thematic analysis of attitude results using Virginia Braun and Victoria Clarke’s six-stage thematic analysis protocol 96 . We did not use meta-analysis because we included heterogeneous study designs. TC then thematically coded the results on attitudes using MAXQDA, merging similar codes and identifying the most significant or frequent initial qualitative analysis codes which could be used to develop themes. Initial coding was discussed with EH, and the codebook was modified (see Supplementary Table 6). Finally, to infer analytical themes, TC organized the codes according to different aspects of users’ attitudes such as acceptability and usability. Analytical themes were refined through discussion among all authors. Declarations Data availability Data supporting this study are available within the main article and Supplementary Table 9. Code availability The codebook is available in Supplementary Table 6. Acknowledgments This research was supported by the John Fell Oxford University Press Research Fund (project number: 0012751; PI Adam Mahdi) and a UK Research and Innovation grant (grant number ES/T007265/1; PI Ekaterina Hertog). The funders were not involved in the research. Author Contributions TC: Conceptualization, Methodology, Screening, Data Collection, Writing-Original Draft, Writing-Review & Editing SV: Conceptualization, Methodology, Screening, Data Collection, Writing-Review & Editing, Supervision EH: Conceptualization, Methodology, Writing-Review & Editing, Supervision, Funding Acquisition AM: Conceptualization, Visualization, Supervision, Funding Acquisition Each author has read and approved the final manuscript. 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Conflict of interest and medicine: Knowledge, practices, and mobilizations (Routledge,2021). Braun, V. and Clarke, V. Using thematic analysis in psychology. Qualitative Research in Psychology. 3, 77–101; https://doi.org/10.1191/1478088706qp063oa (2006) Additional Declarations No competing interests reported. Supplementary Files supplementaryinformation.pdf Cite Share Download PDF Status: Published Journal Publication published 12 Jul, 2025 Read the published version in npj Digital Medicine → Version 1 posted Editorial decision: Accepted 30 May, 2025 Reviews received at journal 28 May, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers invited by journal 25 Apr, 2025 Submission checks completed at journal 21 Apr, 2025 First submitted to journal 17 Apr, 2025 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. 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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-5076992","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447994592,"identity":"466063fc-f081-4734-b053-752e0dacf807","order_by":0,"name":"Tiantian Chen","email":"","orcid":"","institution":"Global Health 50/50","correspondingAuthor":false,"prefix":"","firstName":"Tiantian","middleName":"","lastName":"Chen","suffix":""},{"id":447994593,"identity":"4e28c3e4-9cd8-40ad-ba36-89391a66b7c0","order_by":1,"name":"Ekaterina Hertog","email":"data:image/png;base64,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","orcid":"","institution":"University of Oxford","correspondingAuthor":true,"prefix":"","firstName":"Ekaterina","middleName":"","lastName":"Hertog","suffix":""},{"id":447994596,"identity":"37e9a7b7-6528-46f1-aa19-3b953407f805","order_by":2,"name":"Adam Mahdi","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Mahdi","suffix":""},{"id":447994599,"identity":"71c6b94c-239f-4010-a275-778913653756","order_by":3,"name":"Samantha Vanderslott","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"","lastName":"Vanderslott","suffix":""}],"badges":[],"createdAt":"2024-09-12 10:40:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5076992/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5076992/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41746-025-01762-4","type":"published","date":"2025-07-12T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81708432,"identity":"ac2f8e3f-82a9-4e14-8e22-00933c36c7d2","added_by":"auto","created_at":"2025-04-30 14:08:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2382917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of studies and World Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe maps illustrate that highly studied regions have smaller populations, whereas densely populated areas are underrepresented in studies about health monitoring technologies. \u003cstrong\u003ea \u003c/strong\u003eNumber of studies on health monitoring technologies included in our sample by study location where we identified single study location (2004-2023, n=522). \u003cstrong\u003eb \u003c/strong\u003eWorld Population Distribution. We use the population data from World Population Prospect 2024. See https://population.un.org/wpp/ for more information.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5076992/v1/c41bcd2e4289b360309a04b0.png"},{"id":81707691,"identity":"fc640f52-4eed-488f-83db-502c9641f9a6","added_by":"auto","created_at":"2025-04-30 14:00:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":396419,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Chart.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis flow chart illustrates the study selection process for this systematic review.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5076992/v1/c968287cbce84cdd295892e6.png"},{"id":86700097,"identity":"819b06c0-d99b-4177-b1a3-9382a37771a8","added_by":"auto","created_at":"2025-07-14 16:11:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3869772,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5076992/v1/fdb082a6-c0bd-4e3e-a020-7b7c7d4aaedc.pdf"},{"id":81707705,"identity":"7aa51426-06bb-4147-8cae-70808121df28","added_by":"auto","created_at":"2025-04-30 14:00:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3690693,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5076992/v1/dbbdefe54d0e1d3d8b7941bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Systematic Review on Patient and Public Attitudes Toward Health Monitoring Technologies Across Countries","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealth monitoring technologies are increasingly adopted in healthcare, a trend accelerated during the COVID-19 pandemic for more digital and remote modes of working fuelled by the need to reduce virus exposure among patients and healthcare professionals\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Techno-optimists who place strong faith in technological progress believe these technologies will improve the quality of data available to doctors, reduce the cost while increasing the efficiency of monitoring patients and recording their details, ultimately benefiting patients\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. By removing geographical health barriers, digital health monitoring is seen as a way to improve healthcare access, especially in areas with limited healthcare resources, provided that the necessary digital infrastructure exists.\u003c/p\u003e \u003cp\u003eSuch benefits can only become reality if all the users are equally able and willing to adopt digital health monitoring effectively. This, however, cannot be taken for granted. The distribution and practices of health monitoring technologies predominantly assume users are white, well-paid, educated, cisgender, with good internet access, capable of managing their own healthcare, and live in high-income countries\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This leaves out many groups, such as carers who are not regarded as users in many studies and ethnic minorities whose needs are often neglected in technology development.\u003c/p\u003e \u003cp\u003eExisting systematic reviews focus narrowly on specific users and technologies such as older adults\u0026rsquo; attitudes toward smart home technologies\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and users\u0026rsquo; experience of wearable technologies (\u0026lsquo;wearables\u0026rsquo;)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This narrow focus makes reviews easier to conduct but limits understanding of how attitudes toward various monitoring technologies vary across different socioeconomic backgrounds and geographies. Understanding how the under-resourced communities and less advantaged individuals who potentially stand to benefit the most from such technologies, compare with more advantaged individuals is crucial if health monitoring technologies are to bring about greater equality in healthcare outcomes. Our systematic review assesses and synthesizes existing literature on patient and public attitudes toward health monitoring technologies, with a focus on identifying key factors that influence adoption across different socioeconomic and geographic groups. By summarizing the current knowledge on broadly defined health monitoring technologies, highlighting the gaps in the literature, and proposing directions for future research, our review seeks to provide insights into more equitable and public/patient-centred implementation of digital health monitoring.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eCharacteristics of included studies\u003c/p\u003e \u003cp\u003e562 studies were included in this review. All the included studies were published between 2004 and 2023, with publications reaching their highest frequency in 2021\u0026mdash;coinciding with the increased adoption of remote health monitoring during the COVID-19 pandemic\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Our sample included 240 quantitative studies, 201 qualitative studies, and 121 mixed methods studies.\u003c/p\u003e \u003cp\u003eIn our sample, 522 studies specified a single study location (16 articles did not specify the study location, and 24 studies included more than one study location. We excluded these articles in the analysis of geography to avoid confusion of numbers). Four countries accounted for over half of these single-location studies: the United States (153), the United Kingdom (67), Australia (32), and Germany (24) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the 546 studies that mentioned any location, 444 were conducted in the Global North. The Global South, particularly low-income countries, was severely underrepresented. Only five studies were conducted in low-income countries: Ethiopia, Sudan, and Uganda.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong 562 studies, 420 reviewed medical applications of health monitoring technologies, with diabetes (54), cardiology (39), and psychiatry (37) being the most frequently represented specialties. Smart home systems for older adults (71), activity trackers (36), and workplace well-being monitoring (11) are the most-mentioned non-medical applications in our sample. Studies in Global North covered more specialties than those in Global South. No studies on technologies for cancer treatment, smoking cessation, and alcohol addiction used samples from Global South, and only one study on respiratory diseases used a sample from Global South.\u003c/p\u003e \u003cp\u003eOur ability to compare findings across demographic groups was constrained by limitations of the demographic data available in most studies in our sample. While most studies reported basic information like gender and age (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), few captured other key demographics such as ethnicity, education level, or place of residence. In analysing sampling methods, we distinguished between convenience sampling (nonprobability recruitment) and representative sampling (probability-based recruitment or a study mention being based on a representative sample). Although convenience sampling and smaller sample sizes (fewer than 50 participants) are standard in qualitative research, caution is warranted when applying statistical analysis to such samples. Among quantitative studies, only 47 (19.5%) used representative samples, and 78 (35.6%) had fewer than 50 participants. We also evaluated inclusion of economically disadvantaged, educationally disadvantaged, rural, or gender minority populations and noted a representation gap. For example, only 34 of the 122 studies on fitness trackers recruited participants from disadvantaged socio-economic backgrounds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of articles reporting respondents\u0026rsquo; gender, age, ethnicity, education, and rural/urban residence (2004\u0026ndash;2023, n\u0026thinsp;=\u0026thinsp;562)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of studies mentioning (percentage)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex/gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e447(79.5%) (9 articles specified sexual orientation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e464 (82.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural/urban residence/distance to hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong 562 studies, 504 focused on primary users (patients and public), five on secondary users (paid carers, parents, and family members), and 53 on both; the most commonly studied technology types were wearables, apps, and smart home systems.18 studies featured artificial intelligence (AI) technologies, with 15 in Global North and three in Global South. Notably, all studies from Global South originated in China.\u003c/p\u003e \u003cp\u003eThematic analysis\u003c/p\u003e \u003cp\u003eWe took an inductive approach to identify codes and group them into six themes reflecting public and patient perceptions of monitoring technologies: prior knowledge of monitoring technology, acceptability, usability, motivations and challenges, perceived benefits, and perceived risks. The themes introduced below are ordered in the way individuals engage with health monitoring technologies, from initial awareness to ongoing evaluation of benefits and risks. Due to the large number of studies in this review, we provide typical examples to illustrate these themes. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the key findings. We also present how codes and sub-codes were developed into the findings in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The full list of themes as well as explanation of codes and sub-codes is available in Supplementary Table\u0026nbsp;6.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the key findings (2004\u0026ndash;2023, n\u0026thinsp;=\u0026thinsp;562)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeaning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCodes and sub-codes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of articles with this theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of studies based in Global North\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKey findings about the theme\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwareness and familiarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrior knowledge about health monitoring technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFamiliar, unfamiliar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e62.7% studies reported unfamiliarity, while 37.3% studies reported familiarity \u003c/p\u003e \u003cp\u003eThe findings on the relationship between health risks and knowledge about technologies are contradictory. For example, a focus group study with 17 participants found patients\u0026rsquo; anxiety about health issues motivated them to familiarize themselves with activity monitors.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e At the same time, a community-based population survey with 317 respondents found that hypertension was not correlated with their use of wearable blood pressure technology.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeterminants of knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSocio-demographic factors, perceived usefulness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAcceptability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcceptance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAcceptance of technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixed, low interest/negative, positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e88.1% found positive attitudes to technologies, 8.8% found negative or low interest in them, and 3.1% found mixed\u003c/p\u003e \u003cp\u003eSome found a correlation between respondents\u0026rsquo; socio-economic background characteristics and acceptability, but others did not.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeterminants of acceptance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrelation or no correlation with socio-demographic factors, self-control, appearance, external factors, technology difference, desire, location, knowledge, relationship with others, perceived ease of use, perceived usefulness, security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUsability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUser experience of technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEase of use, comfortable, appealing appearance, durability, language, self-application, technical difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOf 270 articles examining perceived ease of use, 214 found users regarded technologies easy to use. Of 99 articles examining perceived comfort, 83 reported comfortable to use. Of 90 studies about opinions on device appearance, 39 studies found devices visually appealing.\u003c/p\u003e \u003cp\u003ePerceived usability problems varied with respondents\u0026rsquo; socio-demographic characteristics. For example, interviews with 20 pregnant women in their second trimester found a finger ring to monitor sleep/stress data might be too big for women.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e Interviews with seven patients found old people felt difficult to seal an oxygen device.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImprovement of usability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInformation storage, eliminate Ads, offline activity, more inclusive, Context of use, justification, comfort, wording, loss, unobtrusiveness, functionality, location, gamification, tailored feedback, data interpretation, appearance features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMotivations and barriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMotivations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactors motivating or preventing informants from using technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerceived usefulness, perceived ease of use, social relationship, competition, curiosity, anxiety, pleasure, fun/coolness, brand, external environment, giving back\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAnxiety about health risks and perceived usefulness to mitigate risks were major motivations for use of tracking devices.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnfit body shape, not inclusive, usability issue, no correlation, not convinced, the foreign body, lack of autonomy, lack of collaboration, knowledge, forget, incompatible with other devices, access issues, cost (time, money, etc), functionality, interfere current life, replace existing care, no need, embarrassment, data sharing concern, distrust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAffordability is a direct barrier preventing people from using monitoring technologies. \u003c/p\u003e \u003cp\u003eCertain groups are digitally excluded. For example, interviews and workshops with the queer community indicate that gender options for wearables lack queer option.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e9\u003c/sup\u003e Another focus group study found age prevents the elderly from using wireless healthcare sensors independently.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow to motivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCompatibility, reduce cost, award/stimulus, promote understanding, one-to-one basis/build relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe most frequently mentioned incentive for technology adoption is a trusting relationship between manufacturers and potential users.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePerceived benefits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived advantages of technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTechnology development, individualized treatment, contribute to research/social good, promote policy/infrastructure, improve privacy, supplement/replace routine test, security, good relationship, legitimize their symptoms, behavior change, awareness, reduction burden, improving their adherence, support, improve confidence/accomplishment, self-control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIncreasing health consciousness, changing behaviors, and improving social relationships are three major perceived benefits.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePerceived risks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConcerns about technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImpact professionalism, dehumanizing care, decrease opportunity, bias Accuracy, surveillance, burden, reduced autonomy, adverse events, privacy, malfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThree major concerns are privacy, accuracy, and feeling burdened. \u003c/p\u003e \u003cp\u003eCertain groups were more concerned about risks of monitoring technologies. For example, a survey of 241 women using fertility trackers found women trying to conceive were the most skeptical about the accuracy of fertility apps.\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eResearch also indicates that women and those with university education were more perceptive to privacy risks.\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOf the 59 articles on patients\u0026rsquo; or general public\u0026rsquo;s prior \u003cem\u003eknowledge of monitoring technologies\u003c/em\u003e, 37 found most respondents were unfamiliar with the technology under scrutiny, while 22 found most respondents reported familiarity. Six studies analyzed factors influencing knowledge about technologies, four revealing correlations with participants\u0026rsquo; socio-economic characteristics like age and gender. For instance, a study found that patients over 65, who could benefit most from tablet monitoring, were least familiar with the technology\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The findings on the associations between health risks and technology knowledge are inconsistent. One focus group study with 17 older patients found anxiety about health issues increased familiarity with activity monitors\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, while a survey with 317 patients found no correlation between known hypertension and knowledge about wearable blood pressure technology\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOf 294 articles studying \u003cem\u003eacceptability\u003c/em\u003e of health monitoring technologies, 259 found positive attitudes to technologies, 26 found negative or low interest in them, and 9 documented mixed attitudes. 56 out of the 294 studies containing this theme used samples from Global South and virtually all of these studies recorded positive attitudes. All but 31 studies documenting negative or mixed attitudes used samples from Global North\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e136 articles analyzed factors influencing acceptability. 65 found correlations with socio-economic characteristics, showing that women, educated respondents, and high earners were more receptive to such technologies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Six studies found no such correlation. For example, a surveyed sample of 60 senior citizens found age did not influence their acceptance of Ambient Intelligence, an app monitoring home environment\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Remaining papers did not focus on socio-economic characteristics but rather considered other factors. Perceived technology usefulness and ease of use were uniformly positively related to acceptability in 40 and 20 papers respectively\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. 17 studies associated awareness of risks with increased acceptability\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, while 15 found no link between prior technology knowledge and acceptability\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Two studies linked respondents\u0026rsquo; personality traits with technology adoption. For example, interviews with 22 Parkinson\u0026rsquo;s patients found that those with a positive outlook were more likely to wear sensors\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. 16 studies found fewer social relations increased acceptability. For example, research has indicated that those with pets\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e or with good social bonds\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e were less likely to accept monitoring technologies. Other influential external factors included usage scenario \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and device appearance\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. For example, a test with 20 dementia patients revealed they were less satisfied with wearable GPS devices at home\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOf 270 articles examining perceived \u003cem\u003eusability\u003c/em\u003e, in 214 studies most users found the technologies tested easy to use. For some patients and elderly people, ease of use meant independence in applying devices, which was highly valued by both carers and patients, especially after activities like showering\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Usability complaints included complex operation manuals, data loss, connection failures, false alarms, lack of waterproofing, susceptibility to damage, excessive advertisements, and short battery life\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Suggestions for improvement included clear data interpretation, visualization, and comparison features\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Some patients required manufacturer to justify the usefulness of function, as indicated in interviews from kidney transplant patients\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Some users requested less paternalistic language\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and proposed gamifying devices to increase engagement\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Some respondents requested offline activities, such as a dementia education workshop for carers of dementia patients to supplement wearable devices\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegarding comfort, 83 out of 99 studies reported that monitoring technologies were comfortable to use, with suggestions to secure device attachment and reduce skin irritation\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In terms of device appearance, 51 of 90 studies noted issues with size, color, and style\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, while 39 found devices visually appealing\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Users preferred non-obtrusive designs, as seen with diabetes patients who concealed glucose monitoring devices under clothing\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Some devices were criticized for medicalizing or stigmatizing users, which caused social pressure, such as children concealing bedwetting alarms\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e50 studies revealed that perceived usability problems varied with respondents\u0026rsquo; socio-demographic characteristics, calling for tailored improvements. For instance, interviews with pregnant women found a sleep/stress monitoring ring too large\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, and interviews with older patients found they struggled with sealing oxygen devices\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Individual health conditions also influenced preferences: elderly individuals with poor eyesight valued visible fall detectors\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, brain injury patients favored voice recognition for health diaries\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and migraine sufferers preferred dim screens\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Some comparative studies indicated potential cultural differences in user evaluations; for example, one study found Arab users were more likely to focus on physiological measurements of fitness trackers compared to non-Arab users who focused more on goal achievement\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e163 studies examined \u003cem\u003emotivations\u003c/em\u003e of using health monitoring technologies. Among them, 129 studies identified anxiety over health risks and the perceived usefulness of tracking devices to mitigate these risks as key motivations for technology adoption\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. For instance, patients with peripheral arterial disease used disease detectors due to concerns about limb loss\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Other intrinsic motivations included being able to share achievements with peers\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and curiosity about personal health metrics\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Social influences were significant extrinsic motivators, with recommendations from family members \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e or healthcare providers driving adoption\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Peer competition also spurred usage, particularly in fitness tracking\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Brand appeal\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and contributing to scientific research\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e were additional factors.\u003c/p\u003e \u003cp\u003eOf 187 articles identifying \u003cem\u003echallenges\u003c/em\u003e to using monitoring technologies, affordability was a major challenge in 72 studies. For example, older adults in Ireland reported that inadequate insurance coverage prevented them from purchasing activity trackers\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Lack of interest was noted in 58 studies, often due to unawareness of health risks\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e or scepticism about technology benefits\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. For example, carers of adults with developmental disabilities refused a smart home system for lacking essential human interaction functionality\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Users also lost interest if they were the only person in the community to use technologies\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e or if they felt uncomfortable with being watched\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. With lack of interest, people tended to forget to use monitoring technology\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. User skills and knowledge gaps were highlighted in 56 articles, with users finding learning new devices burdensome and disruptive to daily routines\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCompatibility issues with current mobile phones or hospital devices\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, lack of preferred functions\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and technical usability concerns like discomfort or poor fit\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e were also reported challenges. For example, some elderly users found that hip protectors for detecting falls did not fit with other assistive equipment or failed to work when falls occurred onto knees\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Additionally, discomfort with implanted monitors was likened to feeling foreign to the body\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Regulations also posed challenges. For instance, school mobile phone policies hindered students\u0026rsquo; use of glucose monitors\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, and lack of government endorsement of AI significantly hindered the widespread adoption of AI-enabled wearable medical devices in China\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, certain populations are digitally excluded. For example, interviews with the queer community indicated that gender options for wearables are binary\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In a focus group study, elderly people reported difficulty understanding health monitors\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Trusting relationships between manufacturers and users, fostered by communication efforts such as providing user support, emerged as critical for technology adoption\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. For example, one qualitative study found community education facilitated acceptance of bednet use monitors in Uganda\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Family members\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and doctors\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e also played an essential role in facilitating adoption. Other suggested strategies to enhance adoption included improving device compatibility, offering monetary incentives like cost reduction or insurance reimbursement\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, and non-monetary incentives like positive verbal reinforcement praising users for using monitoring products, gamification of medicare, and peer competition\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong 284 studies on \u003cem\u003eperceived benefits\u003c/em\u003e, three major advantages were increased health consciousness (146 studies), behavior change (49), and improved social relationships (29). For example, users reported they could track effects of medications\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, stages of disease\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, and body functions such as basal body temperature and cervical mucus \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. They increased activity levels\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, stopped smoking\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, and took medicines more punctually\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Users also reported monitoring technologies facilitated greater interaction with healthcare professionals, particularly during COVID-19\u003csup\u003e60,61\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, 24 studies found users felt supported and empowered by these technologies. For example, contact tracing apps in the US during the pandemic gave users hope that their life would soon be normalized\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Users also reported increased confidence through greater control over their lives. With smart home systems detecting falls, for example, elderly people felt they could live independently\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and their family members felt relieved of caregiver burden\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Both patients\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and caregivers\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e reported improved healthcare efficiency, such as reduced hospital visits with monitoring technologies. Moreover, they reported satisfaction with clinical experience due to personalized care\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, better data storage\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003c/sup\u003e and accurate treatment\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. For example, a qualitative study found patients with depression felt gamified data collection more enjoyable\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Other reported benefits included enhanced treatment methods, research advancements, and digital infrastructure development in rural areas\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e213 studies mentioned \u003cem\u003eperceived risks\u003c/em\u003e. Notably, some benefits of monitoring technologies were also perceived as risks. Privacy was a major concern in 131 studies, with worries about non-transparent data collection and misuse of personal data by caregivers, manufacturers, insurance companies, or criminals \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. In 58 studies, users experienced various types of burdens with monitoring technologies. First, they mentioned data obsession, where over-reliance on technology may undermine autonomy\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e or result in over-treatment\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. For example, patients with depression in focus groups reported sleep loss from constant mood tracking\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Second, caregivers felt overwhelmed by the additional work of reviewing monitor results, especially during COVID-19\u003csup\u003e73\u003c/sup\u003e. Third, users were worried about bias and stigmas produced by health monitoring. For example, a survey with 245 older adults showed they felt sad about their age when using contactless monitoring\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. People were also concerned about health data being used to discriminate in insurance pricing and employment\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. 51 studies highlighted concerns about technology errors, such as inaccurate sensor data affecting health decisions\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, lost connections\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e, device crashes\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, and malware infections\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBeyond privacy infringement, burdens, and technology errors, monitoring technologies were seen to dehumanize healthcare by reducing the direct human contact that is expected of healthcare. For instance, carers in a retirement home faced residents\u0026rsquo; doubts about their professionalism when using mobile devices for alarms\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Many patients preferred face-to-face communication\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. These technologies could also decrease communication with family\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e and healthcare providers, as seen in a study with 19 patients where a smart health platform reduced referrals to private practices\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Aside from social and emotional risks, research participants noted physical risks such as skin rash\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e and radiation from wireless technology \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSocio-demographic factors influenced concerns about technology risks. For instance, a survey of 241 women found women trying to conceive were skeptical about fertility app accuracy\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Research also indicated women and those with higher education levels particularly worried about privacy\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eResearch on health monitoring technologies has accelerated alongside their increasing adoption in medical and consumer settings. This review analyzed existing studies, examining their geographic scope, sampling methods, user demographics, and types of technologies deployed. We synthesized current understanding of patient and public attitudes through six key themes: knowledge, acceptability, usability, motivations and challenges, perceived benefits, and perceived risks. Looking at our core themes first, we found that while many patients and members of the public were unfamiliar with existing health monitoring technologies, research documented generally positive attitudes towards them. In studies where users tested health monitoring technologies, most users found them easy to use, and patients and their carers highly valued the ability to utilise devices independently. In many studies, familiarity, acceptability, and perceived usability of monitoring technologies varied by socio-demographic characteristics. Certain populations, such as older adults \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, or queer people\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e, felt digitally excluded. This finding highlights how the digital health divide extends beyond unequal access to technologies. Even with seemingly equal access, benefits may still not be equally distributed. To maximise the benefits of these technologies, manufacturers and policymakers need to consider groups that would most benefit, such as older adults, and eliminate barriers to adoption. Sociality influences the adoption of these technologies\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Individuals with richer social connections may be less interested in adopting these technologies initially, but family, friends, and effective communication efforts by manufacturers could promote knowledge about these technologies and encourage use.\u003c/p\u003e \u003cp\u003eSeveral properties of health monitoring technologies identified as bringing benefits to their users have also been flagged as potential sources of risk. Respondents reported behavior changes but also feared over-reliance on technology and over-treatment\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. While some experienced more interaction with healthcare professionals, others worried about reduced communication with family and clinicians\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Some praised these technologies for personalized care, but there were also concerned about the misuse of personal data\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. In the cases, where the same characteristics of technologies could bring benefits to some users but result in negative unintended consequences for others, the potential benefits and risks should be carefully considered before these technologies are widely adopted.\u003c/p\u003e \u003cp\u003eHealth monitoring devices, like other digital technologies that collect personal information, raise privacy concerns. Perceived privacy risks underscore a divide between users and the companies handling their data. This review found that users feared data misuse by companies, necessitating robust policies and guidelines for inclusivity and trustworthiness\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. While research indicates the need for comprehensive policies to build trust and ensure inclusive practices, regulatory approaches vary significantly across nations. For example, the US classifies monitoring technologies as either medical or non-medical devices, with medical devices regulated by the 21st Century Cures Act\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. In contrast, Japan classifies devices by risk level, requiring approval for higher risk devices\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Studying contrasting regulatory models could inform the development of more effective global standards for responsible health data monitoring.\u003c/p\u003e \u003cp\u003eIn all, our review identified several knowledge gaps. Ensuring high-quality samples is challenging and costly, leading many studies to rely on small, convenience samples that limit generalizability. Many studies provide limited demographic details for their samples, with few studies specifying participants\u0026rsquo; ethnicity, income, education, or urban/rural locations. These practices complicate interpretation of the findings, especially when they are contradictory. For example, some studies show socio-economic factors affect technology acceptability, while others do not\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Some documented contradictions might result from different methodologies and research contexts. More details about sampling strategies in future research would inform an understanding of applicability and generalizability of research findings and to help build a fuller picture of attitudes to health monitoring in different populations and across different contexts. Future research would also benefit from open research approaches, including pre-registering designs, hypotheses, methods, and analyses to guide confirmatory tests\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Additionally, researchers should share research materials, anonymized data, and analysis codes to facilitate examination and reproduction of findings. These practices improve the efficiency and accuracy of science, such that the growing literature on digital monitoring technologies can ultimately provide clearer answers about where evidence is strong, and where evidence is limited, or applicable only to certain populations. Such processes are especially important in the healthcare and technology space, where hype and fear-tactics surrounding technology innovation and public health crisis too often drive policy.\u003c/p\u003e \u003cp\u003eVirtually all the knowledge about attitudes to monitoring technologies comes from Global North, especially from Anglophone countries. People from Global South are under-represented, despite Global South being home to the majority of the world\u0026rsquo;s population. Monitoring technologies are often used very differently in Global North and Global South. To give one example, fertility apps are used to enhance pregnancy chances in Global North and to limit reproduction opportunities in Global South\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. A few comparative studies also suggest cultural differences in attitudes to technologies\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This means that we cannot extrapolate findings from research based on people living in Global North to the rest of the world. Health monitoring could improve healthcare access in areas with limited resources, but current recruitment strategies under-represent those who might benefit most. Research with more diverse samples is necessary to understand how under-represented populations interact with health monitoring technologies.\u003c/p\u003e \u003cp\u003eIn addition to identifying sampling gaps, our analysis revealed significant gaps in health monitoring technologies across both Global South and Global North regions. First, in the Global South, we identified a critical mismatch between available health technologies and actual disease burden. While these regions face a dual challenge of managing infectious diseases (like malaria and HIV/AIDS) while confronting rising rates of non-communicable diseases (such as cancer and respiratory illnesses), few studies address the latter. The ongoing epidemiological transition driven by urbanization and lifestyle changes in Global South underscores the need for technological developments tailored to these contexts\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. Second, we found that AI-powered technologies like smart home systems and robots are predominantly studied in Global North (15 studies), with only three such studies emerging from Global South. This disparity reflects broader challenges in the Global South, including underdeveloped digital infrastructure, limited data models, and insufficient financial investment schemes\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. As AI technology continues to advance, this gap risks further widening in resource-constrained regions. Third, Access challenges persist even in the Global North, where many individuals struggle with financial constraints, technological literacy, and reliable internet connectivity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Current research has primarily focused on building trust to increase technology adoption\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. However, there is an urgent need for studies examining how underserved communities in the Global North can overcome practical barriers, particularly regarding affordability and device compatibility.\u003c/p\u003e \u003cp\u003eWhile this review identified key aspects of the digital divide in health monitoring technologies, it has limitations, that warrant discussion. First, our predominantly English-language search methodology captured only seven non-English studies, potentially excluding crucial research from underrepresented regions and limiting our understanding of the North-South divide. Future research would benefit from incorporating non-English search terms and multilingual databases to improve inclusivity. Second, while we distinguished between primary and secondary technology users, our analysis of caregivers\u0026rsquo; perspectives was limited. Some studies revealed caregivers' concerns about dehumanized care, which contrasts with common assumptions about job displacement through digitization. A more thorough examination of caregivers' viewpoints is needed in future reviews. Third, we identified complex patterns in health monitoring technology adoption. Notably, technology hesitancy and refusal were predominantly reported in Global North countries, with 31 of 35 studies describing negative or mixed attitudes originating from this region. We also found some studies identified correlation between respondents\u0026rsquo; socio-economic characteristics and acceptability, while some did not. These patterns may be due to varying methodologies and research contexts. Given the limited number of studies to draw on, we refrained from drawing definitive conclusions. A detailed discussion of these findings is available in Supplementary Table\u0026nbsp;8 and Supplementary Note 2 due to space constraints. Further research is needed to explore and address these patterns more comprehensively. Finally, our brief analysis of conflicts of interest in studies revealed mixed impacts. While industry funding enabled research in specialized areas like tetraplegia and idiopathic scoliosis, studies with declared conflicts of interest tended to report more favourable outcomes compared to studies without such conflicts. Further research should examine how corporate influence, and financial interests affect research findings in this field.\u003c/p\u003e \u003cp\u003eIn conclusion, we systematically reviewed patient and public attitudes toward health monitoring technologies and found that participants are generally positive about adopting these technologies as part of their care and find these technologies useful. At the same time the knowledge about health monitoring technologies is limited. There is a need to balance carefully the perceived risks and benefits of these technologies, and obstacles to adoption affect some populations more than others. Our findings particularly highlight a critical need for research to address significant knowledge gaps regarding these technologies in regions outside the Global North.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSearch strategy and inclusion criteria\u003c/p\u003e \u003cp\u003e In this review, we included peer-reviewed study and pre-prints with primary data about patients\u0026rsquo; and public attitudes toward health monitoring technology, published between 2000 and 2023, which provided enough data for thematic analysis. Where there were multiple reports of the same study (e.g., a pre-print followed by a peer-reviewed research publication), we included only the peer-reviewed publication. The early 2000s marked significant advancements in health monitoring technologies, including the development of wearables, the widespread adoption of smartphones, and progress in health data analytics, which have profoundly shaped the landscape of health monitoring\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. This time period is crucial for understanding the rise and growth of health monitoring technologies, making it an essential focus for this review. The review\u0026rsquo;s end date is 2023, aligning with the year the search was conducted to ensure the inclusion of the most current and relevant studies. We excluded commentaries, conference abstracts, and reviews lacking detailed information to explain their conclusions. Although we did not apply language filters, our search was conducted in English, since a majority of studies were published in English\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Besides English studies, we found two Chinese, three French, and two Arabic studies, which we accessed using Google Translator.\u003c/p\u003e \u003cp\u003eWe defined health monitoring technologies as digital products that collect human health-related data without direct intervention of healthcare providers, therefore putting the onus on usage by individual patients and (where also specified) their carers. Our definition of health was broad. We included studies focusing on both clinical measurements (like blood pressure and heart rhythm) and other factors that influence health (such as physical activity and mood). We excluded technologies that require direct healthcare provider participation, such as telehealth and videoconferencing systems.\u003c/p\u003e \u003cp\u003eWe excluded population-level disease surveillance systems, focusing instead on individual user attitudes. We defined attitudes broadly to include technology acceptance, prior knowledge, user experience, motivations, and perceived benefits and risks. Our definition of users encompassed patients, members of the general public, and both formal and informal caregivers who use monitoring technologies. We adopted the concept of primary (patients and members of the public targeted by technologies) and secondary users (carers) to highlight carers\u0026rsquo; unique needs\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. We excluded studies where the users were professionals, such as doctors, nurses, industry representatives, or policymakers.\u003c/p\u003e \u003cp\u003e The review was registered with PROSPERO before data extraction (CRD42023446772). Since studies on health monitoring technologies are interdisciplinary, to minimize bias, we searched multiple databases on August 1, 2023: PubMed, Embase, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus. The selected databases are well-established for their comprehensive coverage of health, technology, and multidisciplinary research. Specifically, PubMed and Embase are essential resources for accessing medical and clinical studies, while IEEE Xplore and the ACM Digital Library provide critical insights into the engineering and computational dimensions of health technologies. Web of Science and Scopus offer broad multidisciplinary coverage, facilitating the inclusion of research spanning diverse fields. These databases are also recognized for their extensive international reach, supporting the inclusion of studies from various regions. Despite these databases\u0026rsquo; broad coverage, we acknowledge that some regional studies may not be included due to database indexing limitations. We conducted a title and abstract search of literature published between January 1, 2000, and July 31, 2023. We consulted MeSH to identify search terms, but it did not provide specific terms for monitoring technologies. We therefore adopted a multiple-synonym search strategy (see Supplementary Table\u0026nbsp;1) to maximize the number of usable studies.\u003c/p\u003e \u003cp\u003eWe imported the bibliographic search results into Rayyan\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e, a systematic review management platform, and eliminated duplicate entries. Two authors (TC and SV) independently screened study titles and abstracts. We defined our inclusion and exclusion criteria using the Population, Intervention, Comparison, Outcomes and Study (PICOS) framework to ensure transparency and methodological rigor. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the criteria used to screen studies for inclusion. The authors then independently reviewed the full texts of potentially eligible studies against exclusion criteria. TC and SV discussed and resolved any discrepancies between their assessments (see Supplementary Table\u0026nbsp;2) before finalizing the list of included literature. For example, we excluded an article about an automated call monitoring intervention for older wheelchair users\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. Though automated calls monitor patients\u0026rsquo; health information, we decided to exclude this article because it did not specify how automated calls would be answered or whether these calls were embedded in a device. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the exclusion process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe PICOS framework for inclusion and exclusion criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePICOS element\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclusion criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients, the general public, or formal/informal caregivers who use health monitoring technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthcare professionals, developers, industry representatives, or policymakers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies involving the use or experience of health monitoring technologies (e.g., wearables, sensors)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies not involving health monitoring technologies (eg. Population-level disease surveillance systems)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitudes, perceptions, motivations, barriers, or experiences of users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies only reporting technical performance without user perspectives\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmpirical studies using primary data (quantitative, qualitative, or mixed methods) published between 2000 and 2023. Both peer-reviewed study and pre-prints were included.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReviews, commentaries, protocols, editorials, studies not using primary data, studies published out of the time frame\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eOur systematic review identified 670 studies that met the inclusion criteria and passed quality assessment using the Mixed Methods Appraisal Tool\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e (MMAT; see Supplementary Table\u0026nbsp;3). The Mixed Methods Appraisal Tool assesses study internal validity. TC and SV independently assessed each study. Our assessment focused several key aspects: the clarity of the research questions and the alignment between research questions and methodology, how representative the study participants are of the target population, and the transparency of data collection procedures. We excluded 108 studies that reported potential or actual conflicts of interest to mitigate potential biases that may have affected the internal validity of the studies (see Supplementary Table\u0026nbsp;4). Of the remaining 562 studies included in our synthesis, 348 explicitly declared no conflicts of interest, while 214 provided no conflict-of-interest information (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For comparative purposes, we separately summarized the 108 excluded studies with declared conflicts in Supplementary Note 1. While we acknowledge the extensive literature examining conflicts of interest in research\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e, analyzing this body of work fell outside the scope of our systematic review. Our analysis focused solely on conflicts of interest as defined by journal publication requirements.\u003c/p\u003e \u003cp\u003eWe piloted an Excel data extraction sheet (see Supplementary Table\u0026nbsp;5) using five randomly selected articles. TC and SV independently extracted publication year, study design, study location, technology type, target users, sample characteristics, sampling strategies, and attitude results. Due to the number of studies included, we only used information made available in the studies rather than contacting authors to obtain any information that was not included.\u003c/p\u003e \u003cp\u003eWe conducted a numeric analysis to calculate the number of studies by various categories such as publication year and study design and a thematic analysis of attitude results using Virginia Braun and Victoria Clarke\u0026rsquo;s six-stage thematic analysis protocol\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. We did not use meta-analysis because we included heterogeneous study designs. TC then thematically coded the results on attitudes using MAXQDA, merging similar codes and identifying the most significant or frequent initial qualitative analysis codes which could be used to develop themes. Initial coding was discussed with EH, and the codebook was modified (see Supplementary Table\u0026nbsp;6). Finally, to infer analytical themes, TC organized the codes according to different aspects of users\u0026rsquo; attitudes such as acceptability and usability. Analytical themes were refined through discussion among all authors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting this study are available within the main article and Supplementary Table 9.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe codebook is available in Supplementary Table 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the John Fell Oxford University Press Research Fund (project number: 0012751; PI Adam Mahdi) and a UK Research and Innovation grant (grant number ES/T007265/1; PI Ekaterina Hertog). The funders were not involved in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTC: Conceptualization, Methodology, Screening, Data Collection, Writing-Original Draft, Writing-Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eSV: Conceptualization, Methodology, Screening, Data Collection, Writing-Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eEH: Conceptualization, Methodology, Writing-Review \u0026amp; Editing, Supervision, Funding Acquisition\u003c/p\u003e\n\u003cp\u003eAM: Conceptualization, Visualization, Supervision, Funding Acquisition\u003c/p\u003e\n\u003cp\u003eEach author has read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWittbold, K.A., Carroll, C., Iansiti, M., Zhang, H.M. and Landman, A.B. \u003cem\u003eHow Hospitals Are Using AI to Battle Covid-19\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hbr.org/2020/04/how-hospitals-are-using-ai-to-battle-covid-19\u003c/span\u003e\u003cspan address=\"https://hbr.org/2020/04/how-hospitals-are-using-ai-to-battle-covid-19\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez, J. 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Qualitative Research in Psychology. 3, 77\u0026ndash;101; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1191/1478088706qp063oa\u003c/span\u003e\u003cspan address=\"10.1191/1478088706qp063oa\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5076992/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5076992/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe market for digital health monitoring is expanding rapidly, with technologies that track health information and provide access to medical data promising benefits for users, particularly in areas with limited healthcare resources. To understand user attitudes toward these technologies, we conducted a systematic review of literature with primary data about patient and public perspectives. We synthesized 562 studies (2000\u0026ndash;2023) from PubMed, Embase, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus, including qualitative, quantitative, and mixed-methods research. We revealed a significant geographic bias, with most research concentrated in few countries, and identified access gaps in both Global South and Global North. While users generally showed positive attitudes toward health monitoring technologies, they expressed various concerns. We provide suggestions for future research to enhance the socially responsible integration of technology in healthcare. One important limitation of our approach is using English-language search terms. This potentially excluded relevant studies from underrepresented countries.\u003c/p\u003e","manuscriptTitle":"A Systematic Review on Patient and Public Attitudes Toward Health Monitoring Technologies Across Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 14:00:27","doi":"10.21203/rs.3.rs-5076992/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-05-30T09:46:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-29T03:31:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190800070496867777351157571721048289345","date":"2025-05-29T03:30:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-25T11:57:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-21T14:16:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Digital Medicine","date":"2025-04-17T16:44:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f9e783b-c708-41b4-bf82-70e52eac6286","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47684285,"name":"Scientific community and society/Social sciences/Sociology"},{"id":47684286,"name":"Health sciences/Health care/Public health"},{"id":47684287,"name":"Health sciences/Health care/Quality of life"}],"tags":[],"updatedAt":"2025-07-14T16:09:09+00:00","versionOfRecord":{"articleIdentity":"rs-5076992","link":"https://doi.org/10.1038/s41746-025-01762-4","journal":{"identity":"npj-digital-medicine","isVorOnly":false,"title":"npj Digital Medicine"},"publishedOn":"2025-07-12 15:57:42","publishedOnDateReadable":"July 12th, 2025"},"versionCreatedAt":"2025-04-30 14:00:27","video":"","vorDoi":"10.1038/s41746-025-01762-4","vorDoiUrl":"https://doi.org/10.1038/s41746-025-01762-4","workflowStages":[]},"version":"v1","identity":"rs-5076992","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5076992","identity":"rs-5076992","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}