Bridging the Digital Divide in Smart Home Health Technologies for Older Adults: A Scoping Review of Barriers, Design Considerations, and Policy Implications

Abstract

The global shift toward an aging population presents significant challenges for healthcare systems. This is compounded by rising disability rates, fragmented care models that struggle to meet complex needs, and a digital divide caused by the emergence of smart technologies. This work aims to evaluate how the digital divide affects the adoption, usability, and perceived benefits of smart home technologies designed to support activities of daily living and health monitoring. The influences of socioeconomic status and geographic location (urban versus rural) on the digital divide are considered. Four databases (Web of Science™, Scopus^®, PubMed^®, and IEEE Xplore^®) were searched between 2014 and 2026, resulting in 71 studies that examined older adults, smart home technologies for daily living or health monitoring, and factors related to the digital divide. Findings were synthesized using the Technology Acceptance Model, Van Dijk’s Digital Divide Framework, and Health Behavior Models. Three key barriers were identified—economic, technical, and social—which disproportionately affected vulnerable groups. Smart Home Health Technologies (SH2Techs) present adoption challenges distinct from standalone devices because they require integrated infrastructure and sustained engagement. Limited research addressing the usability of non-clinical SH2Techs has identified the need for co-design, simplified interfaces, targeted training, and policy reforms to support equitable aging in place.

1. Introduction

The world is undergoing a change in demographics marked by a rapidly increasing population of older adults. This shift is in large part the result of advancements in public health, nutrition, and medical care; which have significantly extended life expectancy and improved the quality of life in later years [1]. However, this presents societies with significant challenges that strain healthcare infrastructures. Hospitals are facing escalating resource demands, access to primary care is often limited, and physician burnout is increasingly common and on the rise [2]. Compounding these systemic strains is the growing burden of age-related non-communicable diseases (NCDs) and mental health conditions, which demand comprehensive, integrated care models that address the full spectrum of physical and cognitive well-being in old age [3]. The World Health Organization, in its 2020 report, identifies that health systems are not yet adequately designed to meet the complex needs of older populations [4].

A critical flaw exacerbating this crisis is the prevailing healthcare paradigm’s reliance on reactive, disease-specific interventions rather than proactive, continuous care [5]. This fragmentation is illustrated by recent data, such as Canada’s 2022 Survey on Disability, which reported a rise in disability rates among older adults from 38% in 2017 to 40% in 2022 [6]. These statistics reveal deeper barriers to care, including income limitations, mobility restrictions, and the profound isolation faced by those in rural and remote communities [6]. As our analysis below suggests, these geographic and socioeconomic determinants of health are recurrent themes across global research and highlight a systemic failure to provide equitable care.

Amid these converging challenges, a new and critical barrier has emerged: the digital divide [7]. We define this divide as the systemic inequity in access to, meaningful usage of, and benefits derived from digital healthcare technologies and services experienced by older populations. Socioeconomic status and geographic location (urban versus rural) are significant determinants of an individual’s position within this divide, influencing every stage from initial access to effective utilization [7,8]. Figure 1 visualizes how these factors interact to provide a conceptual model of the digital divide in aging.

Figure 1. Conceptual model of the digital divide in aging.

In response, Digital Health Technologies (DHTs) have been recognized as a promising approach for supporting healthy adults by enabling continuous monitoring, promoting self-management, and facilitating care within the home environment [9]. Within the broad spectrum of DHTs, this review focuses specifically on Smart Home Health Technologies (SH2Techs).

For the purposes of this review, SH2Techs are defined as digital technologies that support health monitoring, disease self-management, activities of daily living (ADLs), rehabilitation, personal safety, or ageing in place within home or community environments. This definition is based on the intended health-related function of the technology rather than its physical form or technological platform. Consequently, this review on SH2Techs included home-based environmental monitoring technologies, ambient sensing systems, connected assistive devices, wearable sensors, smartphone applications, telehealth platforms, and other digitally enabled technologies when their primary purpose is to support non-clinical home-based health management, independent living, or aging in place. Conversely, technologies designed exclusively for clinician-directed diagnosis, hospital-based treatment, interventions requiring continuous professional supervision or general consumer smart devices without a health-related function were excluded.

Rather than classifying technologies according to device type alone, eligibility was determined by their intended purpose and context of use. Technologies were included when they supported non-clinical health management, routine daily functioning, or aging in place without requiring continuous operation by healthcare professionals. Consequently, telehealth platforms, wearable sensors, and mobile applications were considered eligible only when they formed part of a home-based health management strategy rather than solely facilitating clinician-directed diagnosis or treatment.

Compared with conventional telemedicine or standalone mobile health applications, SH2Techs are characterized by their integration within the home environment and their ability to provide continuous or longitudinal support through connected digital ecosystems. These systems generate information that can support proactive health management while enabling older adults to remain independent within their own homes and communities [10,11,12,13,14,15,16].

Despite this potential, the literature remains fragmented. Existing studies frequently use inconsistent terminology to describe similar technologies, thus making comparisons across studies difficult. Furthermore, relatively few reviews have synthesized how economic, technical, and social dimensions of the digital divide collectively influence the adoption and usability of SH2Tech. Although numerous studies have investigated digital health technologies for older adults, the existing evidence remains fragmented because similar technologies are described using inconsistent terminology and evaluated using diverse theoretical perspectives.

Also, the digital divide is increasingly recognized as a multidimensional phenomenon that extends beyond disparities in physical access to digital technologies. Van Dijk’s Digital Divide Framework conceptualizes the digital divide as comprising three interconnected dimensions: (i) material access, referring to the availability and affordability of digital devices and internet connectivity; (ii) skills access, encompassing digital literacy and the competencies required to effectively operate and navigate digital technologies; and (iii) usage access, reflecting the ability to meaningfully integrate digital technologies into everyday life to achieve beneficial health and social outcomes [8]. These dimensions are strongly influenced by socioeconomic status, educational attainment, geographic location, physical and cognitive functioning, and broader social determinants of health.

Consequently, there is limited synthesis examining how the economic, technical, and social dimensions of the digital divide collectively influence the adoption, usability, and sustained use of SH2Techs, particularly for non-clinical technologies intended to support independent living and aging in place. Furthermore, relatively few reviews have examined how intersecting factors including socioeconomic status, educational attainment, geographic location, digital literacy, and other social determinants of health combine to influence equitable access to SH2Techs among vulnerable older adult populations. Addressing these gaps forms the primary motivation for the present scoping review.

These themes inform our core research questions:

• Primary Question: How does the digital divide impact the adoption, usability, and perceived benefits of smart home technologies designed to support activities of daily living and basic monitoring among older adults?
• Secondary Question: How do socioeconomic status (SES) and geographic location (urban vs. rural) influence the digital divide, and subsequently, the access and usage of smart home technologies for activities of daily living and health monitoring among older adults?

To interpret our findings regarding the relationship between older adults and SH2Techs within the digital divide, we employ an integrated theoretical framework combining three complementary models:

• The Technology Acceptance Model (TAM) [17]: This model focuses on users’ perceptions of a technology’s Usefulness (PU—perceived usefulness) and Ease of Use (PEOU—perceived ease of use) as primary determinants of their intention to adopt and use it. This lens is crucial for dissecting usability and perception-based barriers at the individual level.
• Van Dijk’s Digital Divide Framework [8]: This framework provides a structural, societal-level view to analyze disparities in material access (devices, connectivity), skills (digital literacy), and usage (the effective application of technology for beneficial outcomes). This directly addresses the access, skills, and usage gaps central to our research questions.
• The Health Belief Model (HBM) [18] and the Theory of Planned Behavior (TPB) [19]: These models examine the individual beliefs, attitudes, perceived benefits and barriers, self-efficacy, and social influences that shape health-related behaviours, including the adoption of technology for health management. That is, they provide useful frameworks for understanding factors influencing technology adoption among older adults.

While each model offers valuable independent insights, their integration allows us to construct a more robust and multi-dimensional analytical framework [9,17,18,19]. We acknowledge the inherent overlaps—for instance, PEOU from TAM aligns closely with the Skills Divide in van Dijk’s model and the concept of Self-Efficacy in the HBM and TPB.

2. Methods

2.1. Eligibility

The study eligibility criteria were structured using the PICOTS framework (Population, Intervention, Comparator, Outcomes, Time, Study Design) to ensure a systematic and transparent selection process. This scoping review was conducted and reported in accordance with the Preferred Reporting Items for Scoping Reviews (PRISMA-ScR) guidelines. The review methodology, screening process, data extraction procedures, and reporting structure were developed to align with PRISMA-ScR recommendations to ensure transparency, reproducibility, and methodological rigor.

2.1.1. Inclusion Criteria

Inclusion criteria were defined using the PICOTS framework.

The participants/population were included if they were defined as “older adults”, “seniors”, “elderly”, “aging population”, “older people”, or “geriatric”, community-dwelling individuals, and dementia patients. The Interventions/Exposures included were healthcare or healthcare technology aspects, such as those related to healthy aging and aging-in-place supports. Types of Studies to be included were those published from 2014 onwards to capture the evolution of modern smart home ecosystems. For the purposes of this review, non-clinical SH2Techs were defined as digital technologies intended to support health monitoring, health self-management, activities of daily living (ADLs), personal safety, rehabilitation, or independent living within home or community environments without requiring continuous operation or supervision by healthcare professionals. Eligible technologies included ambient sensing systems, home-based monitoring technologies, connected assistive devices, wearable technologies, smartphone applications, and telehealth platforms when they primarily supported independent health management and ageing in place. Technologies designed exclusively for clinician-directed diagnosis, acute treatment, hospital-based care, intensive medical monitoring, or interventions requiring ongoing professional oversight were excluded. Given the review’s exploratory nature, a formal comparator was not required.

2.1.2. Exclusion Criteria

Exclusion criteria were defined using the PICOTS framework.

Conversely, studies were excluded if they focused on younger populations, individuals in palliative care, or those with specific psychiatric conditions (e.g., schizophrenia, bipolar disorder) or residing in assisted living facilities, as these contexts involve distinct care paradigms and technology requirements. We excluded technologies unrelated to healthcare or aging-in-place, such as those designed for entertainment or general home management without a health component. To maintain focus on user-managed technologies, we excluded clinical or long-term care devices requiring professional operation (e.g., ventilators, dialysis equipment, unless directly relevant to daily, non-clinical activities) and studies centered on hospital, hospice, or long-term care settings. Furthermore, we excluded articles published before 2014, conference papers to ensure quality and depth, and non-English publications.

2.2. Search Strategy

To identify all relevant literature, a comprehensive search was executed on 30 June 2026, across four major electronic databases: Web of Science™, Scopus^®, PubMed^®, and IEEE Xplore^®. The search strategy was designed to be sensitive and encompass the core concepts of the review. A master search string was developed and then tailored to the specific syntax of each database. The core query combined key terms using Boolean operators: (“digital divide” OR “digital inclusion” OR “technology gap” OR “accessibility gap”) AND (“healthcare” OR “technology” OR “eHealth” OR “telemedicine” OR “digital health” OR “health technology” OR “telehealth”) AND (“older adults” OR “seniors” OR “elderly” OR “aging population” OR “older people” OR “geriatric”). The search strategy intentionally prioritized sensitivity over specificity because terminology describing SH2Techs varies substantially across engineering, health, and social science disciplines. Many studies investigating home-based monitoring systems connected assistive technologies, aging-in-place interventions, wearable technologies, and telehealth-supported self-management do not explicitly use terms such as “smart home”, “ambient assisted living”, or “Internet of Things” despite meeting our operational definition of SH2Techs. Consequently, broader digital health terminology was used during database searching, while the operational definition of SH2Techs was applied during title/abstract screening and full-text eligibility assessment to ensure that only studies meeting the predefined scope were ultimately included.

The process was conducted in accordance with best practices for systematic evidence synthesis, as outlined in the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines [20]. Consistent with the objectives of the PRISMA Extension for Scoping Reviews (PRISMA-ScR), this review was designed to systematically map the breadth and characteristics of the available evidence rather than evaluate the effectiveness of interventions or estimate pooled outcomes. Consequently, a formal methodological quality assessment or risk-of-bias appraisal of the included studies was not performed. This approach aligns with established guidance for scoping reviews, which emphasises evidence mapping, identification of research gaps, and characterisation of the existing literature over critical appraisal of individual studies.”

A completed PRISMA-ScR checklist is provided in the Supplementary Materials, and the study selection process is summarized in the PRISMA flow diagram (Figure 2). The complete database-specific search strategies for PubMed, Scopus, Web of Science, and IEEE Xplore are provided in the Supplementary Materials to facilitate transparency and reproducibility.

Figure 2. PRISMA flowchart of the selection process.

Protocol Registration

This review protocol was registered through the Open Science Framework (OSF) prior to completion of the screening and data extraction phases. The registered protocol is available at https://doi.org/10.17605/OSF.IO/HU724.

2.3. Screening

Title and abstract screening, followed by full-text eligibility assessment, was undertaken independently by two reviewers using predefined inclusion and exclusion criteria. Disagreements were resolved through discussion and consensus, with unresolved differences adjudicated by senior members of the review team. Because the review protocol was designed to achieve consensus through independent review and structured adjudication rather than formal reliability testing, inter-rater agreement statistics (e.g., Cohen’s kappa) were not calculated. Methodological consistency was maintained through reviewer training, standardized screening procedures, pilot testing of the eligibility criteria, independent review, and consensus-based decision-making throughout the review process.

Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia. Available at www.covidence.org.) was used for screening and data management. Title and abstract screening were independently performed by OG and JG. Studies considered potentially eligible proceeded to full-text review, which was also independently conducted by OG and JG. Disagreements during title/abstract screening were resolved by HW, while disagreements during full-text eligibility assessment were resolved by HW and QF through discussion until consensus was achieved. Data extraction was completed independently by OG (28 studies) and JG (19 studies) using a standardized extraction form developed in Covidence before being consolidated for analysis.

2.4. Extraction & Analysis

A standardized data extraction form was developed and piloted prior to full data extraction to ensure consistency across reviewers. For each included study, the following information was extracted: publication characteristics, study design, country of origin, participant demographics, healthcare context, technology type, study objectives, outcomes, digital divide factors, technology acceptance constructs, implementation barriers and facilitators, privacy and security considerations, training and support requirements, and key findings relevant to SH2Tech adoption.

Data extraction was completed in Covidence. OG completed extraction for 28 studies, and JG completed extraction for 19 studies. Following extraction, the .csv file was exported, cleaned, and synthesized by OG. The cleaned extracted data captured the following fields: Study ID, Title, Intervention, Country (first author), Institution, Technology type, Healthcare Context, Year, Study Type, Definition of Digital Divide, Study Design, Sampling Method, Total Sample Size, Mean Age, Health & Cognitive Status, Technology Familiarity, Type of Technology Studied, Access Barriers—Economic, Access Barriers—Technical, Access Barriers—Social, Training or Support Provided, Technology Adoption Rate, Self-Reported Ease of Use, Willingness to Use Technology, Privacy Concerns, Healthcare Self-Management Improvement, Key Findings/Themes, Review Paper (Yes/No, Type), eHealth/mHealth Mentioned (Yes/No), Literacy & Adherence Mentioned (Yes/No), Telemedicine/Telehealth Mentioned (Yes/No). Table 1 summarises the identified barriers, with articles grouped by their reported study design to distinguish focused and reproducible systematic reviews, broad evidence-mapping scoping reviews, interpretive narrative reviews, and general literature reviews without a specified systematic or scoping methodology.

Following extraction, an iterative thematic synthesis was undertaken. Codes were initially generated inductively from the extracted data and subsequently organized according to the Technology Acceptance Model (TAM) [17], Van Dijk’s Digital Divide Framework [8], and Health Behavior Models [18,19]. Themes were refined through repeated comparison across studies to identify recurring patterns relating to technology acceptance, digital inequalities, implementation barriers, and strategies to promote equitable adoption of SH2Techs. Consistent with the objectives of a scoping review and PRISMA-ScR guidance, a formal methodological quality appraisal or risk-of-bias assessment was not performed, as the purpose of this review was to comprehensively map the available evidence rather than evaluate the effectiveness of individual interventions.

2.5. Theoretical Framework

To comprehensively analyze the adoption of smart home health technologies, we constructed a theoretical framework integrating three complementary models. This integrated approach allows us to examine the technological, socioeconomic, and individual behavioral pillars critical to stakeholder engagement. The framework combines the Technology Acceptance Model (TAM) for assessing usability and perception of technology; the Digital Divide Framework for highlighting access, skills, and usage disparities in digital health adoption; and Health Behavior Models such as the Health Promotion Model (HPM) and Theory of Planned Behavior (TPB) for understanding personal motivators and barriers. These models were systematically mapped to specific criteria during our data extraction process. Also, the goal of this combined framework is to address the technological, socioeconomic, and individual engagement pillars of our stakeholders with smart home technologies.

From TAM, we linked Perceived Usefulness (PU) to metrics like Healthcare Self-Management Benefits and Willingness to Use Technology; Perceived Ease of Use (PEOU) to Self-Reported Ease of Use and Training or Support Provided; and Adoption Rate & Technology Familiarity was mapped to the Type of Technology Studied, and the Interaction Experience.

The Digital Divide Framework informed our analysis of structural inequalities: the Access Divide was mapped to Economic, Technical and Social Barriers; the Skills Divide to Digital Literacy Familiarity, and Training Needs; and the Usage Divide to metrics like eHealth/mHealth Adoption, Telemedicine Uptake, and Longitudinal Support Needs.

Finally, Health Behavior Models provided the lens for individual engagement, mapping Perceived Benefits to Healthcare Self-Management and Digital Health Engagement data; Perceived Barriers to Privacy Concerns, Social and Economic Barriers; Self-Efficacy and Social to Personalized Training Needs and Institutional Support.

We explicitly acknowledge the conceptual overlaps between these models; for instance, TAM’s “ease of use,” the Digital Divide’s “skills,” and Health Behavior’s “self-efficacy” all converge on the user’s capability. Rather than a limitation, this intersection strengthens our analysis by providing multiple perspectives on a single issue, the implications of which we will explore in detail in the discussion.

3. Results

3.1. Overview of Findings

Using our methods 2059 articles were found using our search strategy. 766 duplicates were removed. In the title and abstract screening, 877 articles were excluded. From the full text screening, 47 articles were accepted based on our inclusion and exclusion criteria and then moved to the text extraction stage. The full details can be seen in Figure 2.

The included studies represented a diverse range of research methodologies, including systematic reviews, scoping reviews, narrative reviews, literature reviews, qualitative studies, mixed-methods investigations, survey-based studies, randomized controlled trials, pilot implementation studies, and observational research (Table 1). Review articles primarily synthesized existing evidence regarding barriers, facilitators, and implementation strategies, whereas original empirical studies generated primary evidence relating to technology acceptance, usability, digital literacy, socioeconomic influences, and adoption of Smart Home Health Technologies.

Table 1. Summary of the barriers to access from the reviewed articles.

Author & Year	Economic Barriers	Technical Barriers	Social Barriers
Systematic Reviews
Watkins 2014 [21]	Socioeconomic status	Technological literacy	N/A
Mawela 2020 [22]	N/A	Privacy concerns	N/A
Tian 2024 [11]	Cost, affordability	Usability	Social acceptance
Scoping Reviews
Ji 2022 [23]	N/A	Low responsiveness to technological changes	Privacy concerns, autonomy issues
De Santis 2023 [24]	Socioeconomic status	Digital competence	Social isolation, access to digital technologies
Fowe 2023 [25]	Cost, affordability	Usability, device comfort and aesthetics	Adherence, attitudinal barriers
Narrative Reviews
Shen 2017 [26]	Cost, reimbursement	Privacy, data volume	Rapid change
Satariano 2014 [27]	Cost, reimbursement	Technological literacy	Ergonomic issues, educational barriers
Pech 2021 [28]	Cost, reimbursement	Technological challenges	Generational digital divide, acceptability factors
Literature Reviews
Dahlke 2020 [29]	Cost	Technological literacy	Sociocultural factors
da Silva 2024 [30]	N/A	Digital inclusion	N/A
Gao 2024 [31]	Cost	Technological literacy	Social participation
Mixed and Qualitative Approaches
Holthe 2024 [12]	Cost of digital tools, access to internet	Digital literacy	Social inclusion
Schütz 2022 [32]	Socioeconomic status	Complexity of remote health monitoring, digital divide	N/A
Rogers 2017 [33]	Cost, access to technology infrastructure	Technological limitations	Social isolation, lack of technology integration
Ma 2023 [34]	Cost, accessibility	Technological acceptance	Social participation barriers, lack of social support
Chen 2024 [35]	Cost	Data privacy	Sociocultural exclusion
Abdusalomov 2024 [36]	Accessibility	N/A	N/A
Kokorelias 2024 [37]	Cost, accessibility	Trust, privacy	Social care, social connections
Baldissera 2024 [38]	Cost, access to technology	Technological infrastructure	Social isolation
Arieli 2023 [39]	Cost	Technological literacy, usability issues	Social isolation, lack of engagement
Fournier 2021 [40]	Cost	Technology gaps, integration challenges	Social isolation
Saibene 2020 [41]	Cost, socioeconomic status	Technological acceptance	Social acceptance
Cosco 2019 [42]	Accessibility	Technological literacy	Lack of engagement
Macis 2016 [43]	Accessibility	Platform hyper-specialization	Social participation
Hakobyan 2014 [44]	N/A	Limited smartphone ownership among older adults	Technological acceptance
Corti 2023 [45]	Cost, financial barriers	Technological skills, usability	Generational divide, lack of social support
Al-Tamimi 2025 [46]	Affordability, cost of purchase and maintenance	Usability, learning difficulties, troubleshooting burden, interoperability, privacy concerns	Ageism, technology anxiety, loss-of-independence concerns
Bao 2026 [47]	Financial constraints, affordability concerns	Digital health literacy, cognitive/memory barriers, multilingual support, data security	Insufficient family/peer support
Chen Y. 2025 [48]	Care and equipment costs, financial burden	Privacy concerns, complex interfaces	Social isolation, family conflicts
Gupta 2025 [49]	Socioeconomic disparities	Low digital literacy, small interfaces, insufficient training	Aging/dependency stigma, autonomy concerns, gendered and urban–rural inequities
Li 2025 [50]	N/A	Operational complexity, non-age-friendly interface design	Technology anxiety, preference for familiar
Meixia 2026 [51]	Device/service fees	Platform fragmentation, low digital literacy	Distrust, privacy concerns, loneliness
Mu 2026 [52]	Entry-cost barriers, regional resource disparities	Low digital literacy and self-efficacy, non-age-friendly design, platform interoperability, data security challenges	Peer support, family engagement
Rafi 2026 [53]	High device/service cost, affordability gaps, rural-urban resource disparity, need for subsidized access	Low digital literacy, internet connectivity, app instability/usability failures	Lack of trust in AI decisions, preference for human contact, privacy/cybersecurity and regulatory-gap concerns, reliance on intergenerational support structures
Zhang 2026 [54]	Rural resource constraints; limited availability of sustained technical and community support; unequal capacity of households and local services to maintain digital technologies	Skills divide, including “not knowing how to use” and “not using well”; operational difficulty; limited digital literacy; inability to troubleshoot; ineffective instruction; technology abandonment over time	Motivation divide, including unwillingness and fear of use; support divide, including “no one teaches” and “cannot teach effectively”; reliance on family, community workers and providers; sociocultural influences on willingness and continued use; persistence divide from initial access to eventual abandonment
Zhang 2025 [55]	Resource constraints in nursing homes, insufficient staffing and technical-support resources, uneven access to smart-care resources	Insufficient digital skills, complex interfaces/operations, lack of training and technical support, age/education/functional limitations, inadequate data-security systems	Preference for familiar traditional care, low perceived usefulness, technology anxiety, privacy and data-misuse concerns, risk of further marginalizing less digitally proficient residents
Zhao 2026 [56]	High equipment cost (34.15%), institutional financial burden, limited digital-aging resources, insufficient government financial support	Low information-technology proficiency (48.78%), lack of product knowledge (46.34%), safety concerns (31.71%), low education, declining cognitive/physical ability, poor age-appropriateness (14.63%), caregiver skill limitations	Lack of family support (24.39%), resistance to smart eldercare, unfamiliarity and fear of digital systems, reliance on traditional devices, limited institutional and multi-stakeholder support
Zhu 2026 [57]	Not directly examined; cost and product-value concerns discussed contextually	Lower internet literacy, complex/multifunctional interfaces, setup and troubleshooting difficulties, repeated logins/password burden, poor system reliability, unreasonable/English-only information formats	Inertia and preference for traditional controls, dependence on children for support, perceived privacy/security risks, fear of scams and cloud surveillance, product-fit uncertainty
Survey-Based Studies
Naszay 2018 [58]	Cost	N/A	N/A
Wei 2023 [59]	Socioeconomic status	Technology literacy	Family support
Yoon 2020 [60]	Socioeconomic status	Technology literacy	Race/ethnicity
Fowe 2022 [61]	Cost	Technology attitudes, device proficiency, privacy concerns	Reluctance to adopt new technologies
Mao 2022 [62]	Cost, economic factors	Technology complexity (for medication management)	N/A
Cao 2023 [63]	Cost, economic factors	Technological accessibility	Social support
O’Connell 2018 [64]	Cost	Assistance needed to use technology	Geographic isolation
Cao 2022 [65]	N/A	Digital access and use, digital literacy	N/A
Chen D. 2025 [66]	Device/technology purchase costs	Lack of digital literacy, perceived complexity, technological anxiety	Gender inequality in career/economic/family position, gendered caregiving roles, cohort effects
Chi 2026 [67]	Socioeconomic status	Lack of internet access, low digital literacy	Reduced human interaction
Dong 2026 [68]	SES, affordability	Digital engagement, low technology familiarity	Cultural reluctance for family support
Huang 2025 [69]	Income, device/broadband affordability	Lower education-linked digital literacy and skills	Preference for traditional health information sources
Jaana 2025 [70]	Lower income	Limited awareness and digital engagement	Rural residence, perceived usefulness gaps, provincial disparities in service availability
Lin 2025 [71]	N/A	Low technology self-efficacy and confidence, privacy concerns	Internalized ageism, lack of training and technical support
Luan 2026 [72]	N/A	Operational complexity, non-age-friendly interfaces, privacy/data-protection concerns	Technological anxiety, weakened family/caregiver support, loneliness
Pan 2025 [73]	Device pricing/affordability	Lower digital health literacy, usability and design complexity	Generational resistance/unfamiliarity, aesthetic and comfort preferences, privacy/data-protection concerns
Xu 2026 [74]	N/A	Perceived complexity	Technology anxiety
Population-based Studies
Frishammar 2023 [75]	Cost	Lack of trust, technology anxiety	Negative attitudes towards technology use
Cheng 2023 [76]	Cost	Digital literacy	Social isolation, social support
Peng 2024 [77]	N/A	Homebound status	Mobility challenges
Niemiec 2023 [78]	Cost, socioeconomic status	N/A	Social participation, instrumental activities of daily engagement
Tural 2021 [79]	Perceived affordability	N/A	Technology skepticism, privacy concerns, return on investment
Arthanat 2021 [80]	Access to information communication technology resources	Digital literacy	Social environment for learning
Liu 2020 [81]	Socioeconomic status	Technological health literacy	Lower educational attainment, lower health status
Brunzini 2023 [82]	Cost, accessibility	Digital usability	Social support
Kohn 2024 [83]	Cost, resource intensity	Technology access, comfort using technology	Social engagement
Forbes 2024 [84]	N/A	Technological design factors	Sociocultural factors in design
da Silva Sobrinho 2024 [85]	N/A	Installation challenges, difficulties with web-based searches	Social interactions
Kuoremäki 2014 [86]	N/A	Technological acceptance	Social interactions
Bao 2025 [87]	N/A	Complex interfaces, privacy concerns, low digital proficiency	Cultural and linguistic mismatch, perceived irrelevance
Chokphukhiao 2026 [88]	Device cost/affordability, low socioeconomic status	Digital literacy/skills gap, lack of digital equipment, limited rural internet, complex interface	Fear of online fraud/distrust, reliance on traditional information channels
Song 2026 [89]	Low income	Low digital literacy and usability capacity	Social isolation

Note: Review types were classified according to the terminology used by the original authors and their reported methods. Systematic reviews used prespecified and reproducible methods to answer focused questions. Scoping reviews systematically mapped the breadth, characteristics, and gaps of broader evidence. Narrative reviews provided interpretive syntheses without fully reproducible search and selection methods. Literature reviews provided general overviews but lacked sufficient methodological detail to be classified as systematic, scoping, or narrative reviews.

Qualitative investigations predominantly explored older adults’ lived experiences, perceptions of usability, trust, privacy concerns, caregiver support, and social influences affecting SH2Tech adoption. In contrast, quantitative studies primarily examined measurable outcomes including technology acceptance, utilization patterns, digital literacy, health outcomes, and associations between demographic characteristics and technology adoption. Mixed-methods studies provided complementary evidence by integrating quantitative measures with qualitative insights into user experiences and implementation challenges.

The 71 included studies consistently identified economic, technical, and social barriers affecting the adoption of Smart Home Health Technologies among older adults. Across the included literature, economic barriers were the most frequently reported, followed by technical barriers related to usability and digital literacy, and social barriers related to isolation and support. Table 1 summarizes the barriers identified across the included studies. Most observational studies included in this review were conducted in North America, Europe, and China, with comparatively limited representation from low- and middle- income countries. This geographical distribution reflects the current concentration of SH2Tech research within economically developed settings and highlights an important evidence gap regarding implementation within resource-constrained environments We identified a significant gap in the literature, with research specifically focused on non-clinical SH2Techs for older adults being scarce compared to the more established fields of telemedicine or mobile health apps. This is compounded by a lack of standardized terminology; the absence of a universally accepted definition for “smart home technologies” complicates the identification and synthesis of relevant studies.

The high-level summary of the barriers to access studied is listed in Table 1 below. Our findings reveal that older adults face interconnected barriers to SH2Tech adoption, which we have categorized into economic, technical, and social domains. These domains align precisely with van Dijk’s digital divide framework of access, skills, and usage [8]. Economic barriers, the most frequently cited obstacle, directly limit material access to devices and connectivity. Technical barriers, including poor usability and low digital literacy, reflect a profound skills gap. Social barriers, such as a lack of support or negative attitudes from family and peers, hinder meaningful usage. These challenges are not isolated. In fact, nearly every study in our review reported at least one such barrier, that is, they are pervasive and deeply interrelated, resulting in a compounding effect that limits adoption.

Despite these challenges, literature offers promising strategies to mitigate the divide. A growing body of evidence indicates that acceptance can be significantly improved through personalized user training, age-friendly design principles that prioritize simplicity, and, most importantly, inclusive co-design processes that actively involve older adults in the technology development lifecycle [90,91]. We observed that improving the perceived usefulness and ease of use, which are core constructs of the Technology Acceptance Model, is directly linked to greater engagement and sustained use among older adults [92]. However, fundamental hurdles remain. Economic constraints are a persistent, systemic barrier, and pervasive concerns over privacy and data security continue to erode the trust essential for widespread adoption [93].

The following sections detail these findings. We first examine the specific barriers and user needs (Section 3.2), examine the vulnerability of low-income and rural older adults (Section 3.3), then analyze the factors influencing technology acceptance and training (Section 3.4), and finally, we consolidate evidence-based design recommendations to guide future, more equitable SH2Tech development (Section 3.5).

3.2. Digital Divide in Healthcare—A Multifaceted Examination of Barriers and Needs for Older Adults

The integration of digital health technologies into the lives of older adults is not merely a technical challenge, but a complex socio-economic issue. This section synthesizes the predominant barriers—economic, technical, and social—that collectively create a significant digital divide and hinder the effective adoption of SH2Techs.

Economic Barriers—The Fundamental Hurdle of Affordability: Economic constraints emerged as the most pervasive and fundamental barrier to access. A substantial majority of the reviewed studies highlighted the high cost of devices and ongoing internet connectivity as a primary obstacle [11,12,21,24,25,26,27,28,29,31,32,33,34,35,36,37,38,39,40,41,42,43,45,58,59,60,61,62,63,64,75,76,78,79,80,81,82,83]. The concern over affordability was explicitly mentioned twenty-six times across the literature, demonstrating its critical role. This challenge is compounded by a lack of reimbursement models for digital health tools within both public and private insurance schemes which thereby places the full financial burden on the older adult [11,12,25,26,27,28,29,31,33,34,35,37,38,39,40,41,45,58,61,62,63,64,75,76,78,82,83]. For instance, well-designed reimbursement models can inform policy frameworks that promote equitable access to emerging health technologies by enabling incremental adoption, such as through user trial periods or phased implementation strategies [11]. This approach can ease the financial burden associated with limited funding or infrastructure to allow users and care providers to evaluate and integrate smart home health technologies without the immediate cost of full-scale purchase or deployment. Without targeted policy interventions to address cost, reimbursement, and accessibility [11,25,33,34,36,37,38,42,43,79,80,82] SH2Techs risk becoming cost prohibited.

Technical Barriers—The Challenges of Usability, Literacy, and Trust: Even when economic hurdles are overcome, significant technical barriers impede effective usage. Older adults frequently encounter usability issues, system complexity, and low digital literacy, which directly impact their ability and willingness to use SH2Techs [11,12,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,37,38,39,40,41,42,43,44,45,59,60,61,62,63,64,65,75,76,77,80,81,82,83,84,85,86]. Digital literacy, defined as the ability to understand, use, and critically evaluate digital platforms for health decision-making [39], is a critical prerequisite that many older adults lack [12,21,27,29,31,39,42,59,60,76,80,81]. In fact, since aging is associated with changes in cognitive and motor abilities, digital literacy must be explicitly considered in technology design. Furthermore, privacy and data security concerns, explicitly identified in nineteen papers, erode the trust essential for adopting technologies that continuously monitor behavior and health [22,26,35,37,61]. Users expressed apprehension about how their sensitive health data is collected, stored, and shared [61]. When combined with device complexity and compatibility issues, these factors can make technology feel intimidating and inaccessible, leading to abandonment [32,43,62].

Social Barriers—The Crucial Role of Support and Context: The adoption of technology is deeply embedded in a social context. Social barriers, particularly the lack of social support and engagement challenges, were prominent themes, highlighted in 32 studies [12,33,34,35,37,38,39,40,41,43,45,47,48,52,53,54,56,57,63,67,68,71,72,76,78,80,82,83,84,85,86,89]. The absence of a supportive network, including family, friends, or caregivers, to assist with setup, troubleshooting, and encouragement, makes it profoundly difficult for older adults to engage with new technologies. On the other hand, positive social influences and training are significant predictors of technology adoption, demonstrating that the social environment is as important as the technology itself.

Key Adoption Factors and Methodological Approaches: Despite these barriers, literature identifies key factors that can facilitate adoption. Personal motivation and cognitive capacity are significant intrinsic predictors of technology use [12,34,36,63,77]. Furthermore, technologies that align with older adults’ design preferences such as prioritizing simplicity and intuitiveness, see higher adoption rates [25,34,42,62,63]. Crucially, there is a demonstrable link between the adoption of well-designed SH2Techs and positive health outcomes, including improved healthcare self-management [24,59,80]. This relationship is often mediated by the technology’s perceived ease of use and usefulness, core constructs of the Technology Acceptance Model [17]. The evidence base for these findings was derived from a mixed-methods approach. The reviewed studies employed a range of methodologies, with 27 utilizing qualitative or mixed methods [12,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57], 17 relying on survey-based data [58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74], and 15 employing population-based methods [75,76,77,78,79,80,81,82,83,84,85,86,87,88,89].

3.3. Vulnerability of Low-Income and Rural Older Adults

Evidence synthesised across the included studies consistently demonstrated that older adults living in low-income households and rural communities experience disproportionately greater barriers to adopting Smart Home Health Technologies. These disparities reflect the cumulative effects of economic disadvantage, limited digital infrastructure, lower levels of digital literacy, reduced access to technical support, and fewer opportunities for technology training. Rather than representing isolated barriers, these challenges frequently interact to reinforce existing social and health inequities.

Several studies further demonstrated that these disadvantages intersect with broader social determinants of health, including educational attainment, race and ethnicity, disability, social isolation, and healthcare accessibility. Consequently, the digital divide among vulnerable older adults should be understood as a multidimensional and intersectional phenomenon rather than a consequence of limited technology access alone [11,21,24,27,32,59,60,62,63,64,76,78,80,81,82].

3.4. Technology Acceptance & Training Needs

3.4.1. Driving Adoption Through Perceived Usefulness and Healthcare Benefits

The successful adoption of SH2Techs by older adults is profoundly influenced by their Perceived Usefulness, a core construct of the Technology Acceptance Model [17]. When older adults discern a direct, tangible benefit to their health and independence, they are significantly more likely to embrace and consistently use these technologies [59,60]. Applications that deliver concrete healthcare benefits, such as medication adherence reminders and automated fall detection systems, are highly valued as they directly support self-management and provide a critical sense of security [12,28,58,61]. The utility is further magnified in technologies like wearable sensors for tracking vital signs, which facilitate real-time health monitoring [24]. By providing timely data that can be shared with healthcare providers, these tools bridge the gap between home and clinic, actively supporting chronic disease management and potentially improving long-term health outcomes [83]. Crucially, adoption is not merely about the tool itself, but its personal relevance. Sustained use is significantly higher when technology is personalized to meet individual needs, and coupled with tailored training, which directly improves user satisfaction and long-term engagement [36,80].

3.4.2. Overcoming Barriers via Ease of Use and Effective Training

Conversely, a primary deterrent to adoption is poor Perceived Ease of Use. Overly complex designs with confusing navigation and multi-step processes present significant cognitive and physical barriers, leading to low uptake and frequent abandonment [25,58,80]. To counter this, technology design must prioritize intuitive interfaces, simple navigation, and features like large text to accommodate varying levels of digital literacy and visual acuity [39,62]. Importantly, designing for age-related changes in perceptual, cognitive, and motor abilities is not an optional addition, but a fundamental requirement for usability.

Effective training strategies are equally critical. Research indicates that short, repeated training sessions are far more effective for knowledge retention and skill-building than single, lengthy tutorials [25,59]. The human element is highly beneficial; one-on-one support is vital for overcoming initial anxiety and building confidence [12,24]. Furthermore, involving family members in the training process can foster a supportive learning environment and improve digital literacy [61,80]. The most effective way to ensure usability is to involve the end-user directly in the design process. Co-design methodologies, which actively engage older adults in creating and refining technologies, consistently lead to more satisfying, usable, and ultimately successful products [44].

3.5. Key Design Recommendations for Bridging the Digital Divide for Older Adults

Synthesizing the evidence on barriers and facilitators, our scoping review culminates in five key, interconnected design and implementation recommendations to ensure SH2Techs genuinely meet the needs of older adults. These strategies are crucial for moving from theoretical potential to practical adoption, in order to effectively bridge the digital divide.

• Prioritize accessibility and usability through intuitive design: Technology interfaces must be simplified, with large, high-contrast text, unambiguous icons, and clear, jargon-free instructions. The primary goal is to reduce cognitive load, which can be achieved by minimizing the number of steps required for common tasks. As established by the CREATE team [94] designing for age-related changes in perceptual and cognitive abilities is a foundational requirement, not an afterthought [7]. In our review, devices that embodied these principles were consistently better received by users [58,62,79].
• Implement tailored training and sustained support systems: Effective training must be aligned with the learning patterns of older adults. This involves personalized, one-on-one sessions that proceed at the user’s pace, employ simple language, and use practical, real-life demonstrations. Short, repeated training sessions are far more effective for knowledge retention and building confidence than a single, lengthy orientation [25,29]. Involving family members or peers creates a supportive learning environment and provides a readily available resource for troubleshooting, thereby fostering the self-efficacy necessary for independent use [61,80]. Studies confirm that such supportive training is directly linked to sustained technology use [12,24,80].
• Build trust by embedding privacy and security by design: To alleviate significant user concerns, systems must incorporate transparent data practices and easy-to-understand privacy controls that allow users to manage their information. Adopting the formal principles of Privacy by Design, which mandate embedding privacy protections directly into the system architecture, is essential for reassuring users that their sensitive health data is secure [95]. This includes clear communication about compliance with data protection standards like HIPAA (US) or GDPR (European) [59].
• Embrace co-design to directly engage the end-user: Involving older adults as active partners in the design and testing phases is a proven methodology for preemptively identifying usability issues. This participatory approach leads to more satisfying, relevant, and effective products, as the technology is refined based on real-world feedback rather than assumptions [44].
• Address affordability through innovative policy and funding: Given that cost is a dominant barrier, strategic models are needed to reduce the financial burden. This includes advocating for public subsidies, insurance reimbursement for prescribed devices, and exploring leasing options. As several researchers have emphasized, establishing sustainable reimbursement and funding models is a critical systemic step to enable the equitable adoption of SH2Techs among older adults, particularly those in lower-income or underserved groups. Without stable payment pathways and supportive policy frameworks, the scalability and long-term integration of these technologies into health and social care systems remain limited [11,96,97].

4. Discussion

4.1. Interconnected Economic, Technical, and Social Barriers

Our scoping review elucidates that the challenges hindering the adoption of smart home health technologies among older adults are not isolated obstacles but are deeply interconnected. This systemic view reveals that the digital divide is a dynamic outcome of interacting with economic, technical, and social factors, demanding a holistic, system-level understanding of technology adoption. As visualized in Figure 3, these domains amplify one another:

Figure 3. Conceptual summary of the interconnected major economic, technical, and social barriers influencing equitable SH2Tech adoption among older adults, and the corresponding design, training, and policy considerations identified across the included studies.

• Economic constraints (device/internet costs, lack of reimbursement [11,28]) directly limit access to technology.
• This scarcity exacerbates technical literacy gaps—without devices, digital literacy skills cannot develop [21,59].
• Both these factors fuel social exclusion, as limited access reduces opportunities for peer learning and support [12,34].

This pattern was consistently observed across the literature. For instance, device subsidies unaccompanied by tailored training fail to build the necessary skills for usage, while simplified interfaces remain irrelevant without affordable internet connectivity or trust in the system’s data security. As Peek et al. [98] note in their systematic review, factors influencing technology acceptance are multifaceted and interdependent, requiring a correspondingly integrated approach to solutions.

Figure 3 uses a nested schematic to represent the overlapping and interdependent nature of the economic, technical and social barrier domains. These domains should not be interpreted as following a fixed linear or causal sequence. Their relationships may be reciprocal and context-dependent; therefore, directional arrows were not used.

4.2. Integrating Theoretical Frameworks to Interpret Adoption Barriers

To fully decode the complexity of these adoption barriers, we applied an integrated theoretical lens combining the Technology Acceptance Model (TAM) [17], van Dijk’s Digital Divide framework [8], and Health Behavior Theories such as the Health Belief Model (HBM) and Theory of Planned Behavior (TPB) [18,19]. This synthesis was instrumental in explaining the profound impact of certain barriers. For example, the construct of Perceived Ease of Use (PEOU) in TAM directly mirrors van Dijk’s “skills divide” and aligns with the concept of self-efficacy in health behavior models. This theoretical overlap explains why older adults, even with financial access, often abandon devices with complex interfaces—a failure in PEOU directly impacts their self-efficacy.

Similarly, Perceived Usefulness (PU) in TAM converges with van Dijk’s “motivational access” and the outcome expectations of the HBM. This convergence clarifies why technologies that fail to demonstrate clear, tangible health benefits see low uptake, even among users who are financially secure and technically capable [63].

4.3. The Critical Social Dimension Gap

While technical usability and economic affordability often dominate intervention strategies, our review identifies the social dimension as a critical yet frequently overlooked determinant of sustained adoption. For example, privacy and data security concerns emerged as pivotal underexplored social barriers [22,37]. Several studies reported that sustained engagement with Smart Home Health Technologies may decline over time owing to factors including usability challenges, changing user needs, limited technical support, privacy concerns, and reduced perceived benefit. These findings highlight the importance of designing technologies that remain adaptable, user-centred, and responsive to the evolving needs of older adults throughout long-term use [75]. Crucially, these social dimensions interact with technical design while stigma decreases with aesthetically discreet, consumer-oriented devices [25].

4.4. Towards Holistic Solutions

Given this complex interplay, effective solutions must address multiple factors simultaneously and be integrated. To address this, we propose a three-pillar strategy:

Context-Embedded Design: SH2Techs must be co-designed with diverse older adults, moving beyond mere usability testing to actively involving them in the design process. This ensures technologies address real-world needs and preferences.

Community-Integrated Implementation: Training and support must leverage existing social infrastructures, such as senior centers and faith groups, and incorporate multigenerational support from family or peers.

Policy-Enabled Access: Financial solutions must be comprehensive. This involves combining device subsidies with broadband expansion initiatives and public funding for digital literacy programs, particularly targeting rural and low-income groups. This integrated approach acknowledges that economic access, technical usability, and social embeddedness are equally important and mutually reinforcing for achieving meaningful and sustained adoption.

4.5. Imperatives for Future Research

To continue addressing gaps identified in this review, new research should measure the evolving dynamics of how older adults use technology. Intersectional longitudinal studies can do this—tracking how adoption barriers and technology needs evolve over time across diverse populations, for example, varying by income, race, geography, and disability status. Cross-sectional data often masks important shifts driven by changing physical, cognitive, or social circumstances. Understanding these evolving dynamics is key to designing adaptive SH2Tech systems. Ultimately, the adoption of digital health technologies by older adults should not be conceptualized as a single moment of uptake but as an ongoing process of engagement, embedded within broader social, economic, and institutional contexts. Only by simultaneously addressing access, usability, and support can smart home technologies truly fulfill their promise of supporting independence and quality of life for older adults.

Unlike previous reviews that focused primarily on individual digital health technologies such as telemedicine, wearable devices, or smart home systems, this review integrates evidence across multiple Smart Home Health Technology modalities using complementary theoretical perspectives from technology acceptance, the digital divide, and health behavior. This broader synthesis highlights how economic, technical, and social barriers interact simultaneously to influence equitable adoption among older adults and provides practical implications for future technology design, implementation, and policy. The recommendations presented throughout this Discussion represent the authors’ synthesis and interpretation of the evidence identified across the included studies rather than direct conclusions from any single study. They are intended to inform future research, technology development, and policy aimed at improving the equitable adoption and implementation of SH2Tech for older adults.

4.6. Key Limitations

This scoping review identifies four critical limitations that constrain the current evidence base on Smart Home Health Technologies for older adults.

First, a pronounced scarcity of longitudinal research hinders our understanding of long-term effectiveness. While many studies capture initial adoption, few track sustained engagement or health outcomes over time beyond the initial adoption period [11,32]. This gap makes it difficult to assess the durability of these interventions or to understand how barriers and facilitators evolve as users age and their health needs change. Consequently, we lack crucial data on aging in place with technology lifecycle, leaving developers and clinicians without guidance on how to adapt systems for progressive needs.

Second, the literature demonstrates a significant lack of intersectional analysis. Older adults from marginalized groups, including rural residents, low-income individuals, racial/ethnic minorities, and those with disabilities, are severely underrepresented. Data are rarely disaggregated by these intersecting factors, obscuring how multiple forms of disadvantage compound to create unique barriers to access [64,81]. As initially articulated by Crenshaw [99] an intersectional lens is essential to uncover how systems of power and identity create layered inequalities. The current homogenous participant pools render the evidence base inadequate for informing equitable, real-world implementation. Future research must adopt such frameworks to fully capture the nuanced reality of digital health disparities and prevent the perpetuation of embedded inequities.

Third, a Western-centric and English-language publication bias skews the evidence base. Our reliance on databases like Web of Science and Scopus likely overlooks innovations and context-specific findings from the Global South, where innovative, low-cost solutions may be emerging. This parochialism limits the generalizability of current models and fails to capture diverse socio-cultural perspectives on aging, family support, and technology acceptance. The predominance of studies from high-income countries should also be considered when interpreting the findings. Differences in digital infrastructure, healthcare systems, socioeconomic conditions, and cultural contexts may limit the transferability of these findings to low- and middle-income countries. Broadening future searches to include regional databases (e.g., SciELO, CNKI) and grey literature from bodies like the World Health Organization and the International Telecommunication Union is not merely an academic exercise but a necessity for enriching the field with globally relevant insights. The search strategy also used broad health terminology rather than explicitly including all smart home-specific terms. Consequently, some studies indexed exclusively under terms such as ‘smart home’, ‘ambient assisted living’, ‘ambient intelligence’, or ‘Internet of Things’ may not have been retrieved. Although title/abstract and full-text screening were conducted independently by two reviewers, a formal measure of inter-rater agreement, such as Cohen’s kappa, was not prospectively calculated. Consensus-based adjudication supported consistent application of the eligibility criteria but did not provide a quantitative estimate of reviewer agreement.

Fourth, a methodological over-reliance on cross-sectional designs provides only a static snapshot of adoption. The field lacks the longitudinal and mixed-methods studies needed to track the dynamic process of how user needs, preferences, and technological integration shift across the aging trajectory. Incorporating these approaches is critical to moving beyond the question of if technologies are adopted to understanding how they are sustained, adapted, or abandoned within the daily lives of older adults. Collectively, these limitations restrict the generalizability of our findings and highlight an urgent need for more inclusive, long-term, and methodologically diverse research to build a truly robust and applicable knowledge base.

5. Conclusions

In conclusion, this review establishes that the digital divide in SH2Techs adoption for older adults is perpetuated by a self-reinforcing cycle of economic, technical, and social barriers. Economic constraints, such as high device costs and a lack of reimbursement models, systematically exclude lower-income and rural populations, turning technology from a form of support into a source of financial stress. Technical barriers, including poor usability, complex interfaces, and legitimate privacy concerns, inhibit confident engagement, especially for those with limited digital literacy, leading to frustration and abandonment. Social factors, such as isolation, stigma, and insufficient support networks, further drive the disengagement and abandonment of potentially beneficial technologies, underscoring that adoption is a socially embedded process, not merely a technical one.

To dismantle this cycle and bridge the divide, a coordinated, multi-stakeholder approach is essential. This demands a shift from siloed solutions to integrated ecosystems of support:

• Designers and engineers must move beyond one-size-fits-all solutions to prioritize intuitive, inclusive interfaces that accommodate diverse abilities and embed privacy-by-design principles aligned with standards like ISO 27701 [100] to build the essential trust that is the bedrock of sustained use.
• Policymakers are urged to fund critical infrastructure like rural broadband expansion and develop innovative reimbursement mechanisms for non-clinical home-based monitoring, as advocated in reports by bodies like the World Health Organization, thereby treating digital access as a social determinant of health.
• Healthcare providers and community organizations must implement personalized, scaffolded training programs that leverage social reinforcement from family and peers to build lasting self-efficacy, transforming technology from a daunting obstacle into an empowering tool.

SH2Techs offer transformative potential to enable early health interventions, preserve autonomy, and enhance well-being, ultimately supporting the profound human desire to age with dignity in one’s own community. Realizing this potential, however, demands an equity-centered reorientation of both research and practice. By championing co-design with diverse older adults, adopting intersectional evaluation frameworks, and pursuing rigorous longitudinal studies, we can transform SH2Techs from niche innovations into universally accessible tools. Ultimately, only through such comprehensive and inclusive strategies can these technologies fulfill their promise as essential enablers of dignity, autonomy, and health for all older adults, regardless of their socioeconomic status, ability, or background.