What initiatives, strategies, programs, innovations, or digital health policies are aimed at older adults or their caregivers?

The inclusion criteria were formulated based on the components of the Population, Concept, and Context (PCC) framework as outlined by the JBI [10]:

− Population: elderly or their caregivers (formal and informal). Elderly is defined as individuals at a stage of life characterized by biological, psychological, and social changes associated with aging. For this study, the population aged 60 years and older was considered, in accordance with the definition established by the World Health Organization [12].

Documents were excluded if they were interviews, opinion letters, or other types that did not contain their own methods and results; related exclusively at other age groups, surgical practices or pathology diagnosis; inquired exclusively into aspects of acceptability (i.e., the degree to which population or specific social groups accept the technology and the factors that increase or decrease the likelihood of their use); contained terms as ICT (which is not the subject of this review: intensive chemotherapy, intracranial tumors, Islamabad Capital Territory, among others); were clinical trial protocols, or only described the technology (or its development) without having been tested or used by elderly or caregivers.

Included terms like ICT (that, in our context, means Information and Communication Technology) with meanings unrelated to this review, such as “intensive chemotherapy,” “intracranial tumors,” or “Islamabad Capital Territory,” were excluded. This approach allowed to comprehensively capture studies aligned with our focus while filtering out unrelated documents using the same acronym.

The following biomedical data sources were consulted: PubMed (NCBI), CINAHL Plus with Full Text (EBSCO), Academic Search Ultimate (EBSCO), Cochrane Library (free access by Ministry of Health, Chile), Rehabilitation and Sports Medicine (EBSCO), Web of Science (Clarivate), Scielo.org and Emerald.

Also, grey literature (manuals, technical documents, scientific conference reports and conference presentations) was reviewed in World Health Organization and Pan American Health Organization.

Finally, a hand search was performed in OpenAire/Explore, Working with Older People, Smart Homecare Technology and TeleHealth, Journal of Assistive Technologies and Quality in Ageing and Older Adults and Google Scholar.

To identify relevant studies, a seasoned biomedical librarian (AJ) and four researchers specialized in public health and epidemiology (XM, MTA, MM, and MHA) conducted a comprehensive literature search following the stages recommended by the JBI [13].

Firstly, an initial pilot study search was conducted in PubMed (MHA and AJ) using keywords and Medical Subject Headings (MeSH) terms associated with “elderly,” “frail,” “senior,” “frail elderly,” “digital health,” “e-Health,” “telemedicine,” “telehealth,” “digital therapeutics,” “virtual medicine,” “information and communication technology,” “ICT,” “silver economy,” “mhealth,” “mobile application.” In this study, MeSH terms were not used in the final search as they did not effectively discriminate between relevant and non-relevant articles. Instead, free terms were utilized in the titles and abstracts of the documents, allowing for a more precise and targeted search. This approach detected a broader range of relevant studies and sought to ensure no pertinent research was overlooked. Using free terms refined the search criteria to better match the specific context and nuances of the research focus.

The search strategy was implemented across additional databases to ensure the results aligned consistently with the research question. Subsequently, the keywords identified from the relevant articles in the initial search were utilized to formulate a comprehensive and planned search strategy (Appendix search strategy).

A language filter was applied to include documents in English, French, Portuguese, and Spanish, and only studies in humans were included. There was no restriction by publication year. Finally, a review of the references of the included documents was conducted.

The search was conducted from 4 to 15 April 2022, for indexed literature, and 25 April to 26 May 2022, for grey literature and hand search. Reference management software was used to organize the reference database. Additionally, the search results were managed in Microsoft Excel v.2108, with separate sheets created for the search and review processes.

The titles and abstracts of the documents obtained from the identification phase were screened, selecting those to be reviewed in full text during the eligibility phase. Each document, at both stages, was reviewed by two independent researchers, and disagreements were resolved by a third reviewer, all from the research team (IM, CCL, AJ, CA, AO, PR, XM, MM, MHA, OU, TA, FS, and SE). Most of the reviewers have extensive experience in public health research, epidemiology, and literature reviews.

Full text was searched through open access, by requesting assistance from the Biomedical Library of our university or asking directly from the authors. The entire selection process was diagrammed in a flow chart identifying each stage (identification, screening, and eligibility) (Figure 1).

Data extraction from the selected studies followed the methodology outlined by Peters et al. [13]. The research team initially piloted and implemented the extraction process, testing the designed matrix and refining it based on received feedback. This approach facilitated the standardization of criteria for the final data extraction. Upon completion, six researchers (CCL, CA, AO, XM, IM, MHA) reviewed this phase, validating the information extracted from the documents and adjusting as needed.

Data were compiled in a single spreadsheet (Supplementary Material), containing the articles’ main characteristics, along with specific aspects of the review question. Data extraction categories included a detailed overview of each study’s bibliographic information, context, objectives, research design, outcomes, and key findings related to this scoping review [10]. To classify digital health interventions, the WHO’s classification of interventions, services, and applications in health was used [14], and was considered those aimed at person and healthcare providers. Three stages were considered for the level of technological development: conceptualization and design (focus on the generation and initial formulation of the intervention, ranging from the initial idea to the design of a functional prototype), testing and validation (includes pilot tests and clinical evaluations aimed at testing the intervention in controlled settings or with small groups to validate its efficacy, usability, and safety), and implementation and scaling (cover both small and large-scale implementation, commercialization, and post-implementation evaluation, focusing on bringing the intervention into wider use and evaluating its impact and effectiveness in real-world, long-term settings) [15].

Data from the selected studies and documents were synthesized by four researchers (CCL, CA, XM, MHA) through an iterative process, according to the research question.

A total of 4,401 records were identified, obtaining 3,471 (78.8%) after eliminating duplicates. Those 3,471 were reviewed in title and abstract (screening). Of them, 853 (24.6%) were selected to continue with the full-text review (eligibility). Finally, 421 (49.4%) documents were included in the analysis and came from: indexed databases 288 (68.4%); grey literature 7 (1.7%); hand search 7 (1.7%) and references review 119 (28.3%) [3, 4, 16–434].

When categorized according to indexed articles or grey literature, 97.6% (n = 411) and 2.4% (n = 10) were identified, respectively. Documents published from 1988 to 2022 (the year the review was in progress), with almost 90% of the publications since 2008 (Table 1).

For the 411 indexed articles, the sex of the corresponding author and the study design were recorded. Grey literature was excluded from this analysis due to the nature of the authorships and the type of scientific communication. In this regard, it was found that 47.3% (n = 194) of the corresponding authors were female. About the study design, 51.1% (n = 210) were clinical trials or experimental and 20.2% (n = 83) were observational (Table 1).

The interventions were primarily aimed at the elderly population (93.3%, n = 393), followed by healthcare professionals (48.0%, n = 202), and caregivers (33.0%, n = 139). Among the interventions focused on caregivers, the most studied group were family or informal caregivers (52.5%, n = 73) (Table 2).

Twenty documents focused exclusively on interventions for healthcare professionals. These interventions included teleophthalmology, emergency triage, cognitive assessments and development of technologies for care and tele-rehabilitation systems. Other areas addressed include reducing emergency admissions, using wearable devices for health monitoring, supporting caregivers with digital tools, and adapting tele-neuropsychology for the COVID-19 pandemic.

Only five documents focused specifically on caregivers. These studies examined the effectiveness of a platform to enhance caregiver competence, satisfaction, and coping abilities, identified mobile apps for caregivers, and outlined best practices from successful interventions. They also analyzed psychosocial interventions using technology, examined web-based interventions, focusing on their development, delivery, and impact on caregiver health outcomes. Additionally, they explored how technology can improve caregivers’ quality of life.

Chronic diseases were the most studied condition, accounting for 51.8% (n = 218) of the total, with hypertension, diabetes, obesity, chronic obstructive pulmonary disease, chronic pain, and joint issues being the most common. Fragility-related issues, including low grip strength, falls, and balance disorders, were the focus of 27.1% (n = 114) of studies. Neurological conditions, such as Alzheimer’s and dementia were examined in 24.0% (n = 101) of the cases. Mental health including depression, anxiety, social isolation, and loneliness comprised 17.8% (n = 75). Studies on healthy ageing and quality of life made up 13.1% (n = 55) of the total. Less than 2% of the studies focused on sensory impairment and infectious disease, respectively (Table 2).

Other conditions were accounted for in 5.0% (n = 21) of the studies and were related to medication problems, acute illnesses, nutrition and dietary status, specific pathologies, medical and hospital care, health awareness and education.

The studied countries classified by income and region according to the World Bank [435], are presented in Table 2. Of the total of 421 records, it was possible to classify 378 documents (89.8%), the rest did not specify countries (1.0%, n = 4) or referred to the world level (9.3%, n = 39).

When analyzing income, 80.0% (n = 337) were from high-income countries (HIC), 6.7% (n = 28) from upper-middle-income economies (UMIC), 1.0% (n = 4) from lower-middle-income (LMIC), and only 0.2% (n = 1) from low-income countries (LIC). A 1.9% (n = 8) were a combination of high/upper-middle and high/upper-middle/lower middle.

The most studied regions, accounting for 62.5%, with Europe and Central Asia (35.4%, n = 149), and North America (27.1%, n = 114), followed by East Asia and Pacific (18.5%, n = 78).

It was also studied whether the technology was applied at home, in care centers and residences for the elderly, healthcare facilities or other places. It was found that 76,0% (n = 320) declared a home context, 28.5% (n = 120) care centers and residences for the elderly, 26.4% (n = 111) healthcare facilities and 3.1% (n = 13) others (public transportation, hospital at home, community centers, university, outpatient rehabilitation sports club, welfare centers and a facility designed to research providing an authentic home environment).

Of those who studied a healthcare facility (n = 111), it was found that the level of healthcare corresponded to 64.0% (n = 71) primary, 55.0% (n = 61) secondary and 47.7% (n = 53) tertiary.

Despite the diverse methodologies and study designs in the reviewed documents, the reported results related to the use of healthcare technologies were predominantly positive (86.9%, n = 366). Additionally, 11% (n = 47) reported no differences or partially positive results, and only 1.9% (n = 8) negative results (Table 2).

When analyzing the level of technological development, 97.4% (n = 410) of the documents indicated some level. Specifically, 18.8% (n = 79) were at the conceptualization and design stage, 69.1% (n = 291) were in testing and validation, and 25.9% (n = 109) were in the implementation and scalability stage. Documents reporting multiple levels of development were mostly reviews (Table 2).

In relation to the health objective sought by the technological tool, it was found that 62.9% (n = 265) addressed monitoring/follow-up; 47.7% (n = 201) prevention; 47.5% (n = 221) treatment; 20.4% (n = 86) promotion; 19.0% (n = 80) diagnosis, and 16.6% (n = 70) rehabilitation (Table 2).

By classifying digital health interventions, services, and applications in health, of the 421 documents, 61.5% (n = 259) focused on targeted communication to persons; 45.1% (n = 190) on personal health tracking; 23.0% (n = 97) on demand communication with persons; 19.2% (n = 81) on person-to-person communication; and 9.0% (n = 38) on other categories. This last category includes digital therapeutics based on virtual technology and games, home-based exercise and rehabilitation programs, guided and personalized therapies, interventions based on robotics, emotional support and communication, and medical devices and telemedicine applications. Of those interventions aimed at healthcare providers, 56.8% (n = 239) were related to telemedicine; 47.5% (n = 200) to healthcare provider decision support; 26.8% (n = 113) to prescription and medication management; 18.5% (n = 78) to referral coordination and 3.1% (n = 13) to other classifications. The latter category considered healthcare provider training, healthcare provider communication, person-centered health records, laboratory and diagnostics imaging management, scheduling and activity planning for healthcare providers, person-centered health records, professional advisory roles (Table 2).

The type of technology related to hardware and software was analyzed. Regarding hardware, 51.3% (n = 216) referred to telephone (cell phone, smart phone, landline phone); 39.4% (n = 166) to desktop or laptop computer; similar proportions (close to 20%) were found for wearables (wrist-worn devices or step counters); Tablet and sensors and positioning system. Less than 15% was found for video game consoles (exergames, balance board, dance mat), interactive TV, robots (social robots, robotic rollators, assistive telepresence robot), and radio RX/TX with interaction. Telehealth devices (with or without health measurement) were found on 11.9% (n = 50). On the other category was found communication and recording devices, cameras, emergency and alarm systems, reminders, storage and playback devices (Table 2).

In terms of software, it was found that the most widely used was videoconference platforms with 36.6% (n = 153); followed by text/audio messaging with 26.1% (n = 110); digital health portals with 21.6% (n = 91), and less than 20% each of the following: health and fitness apps, video games, email, and digital community or groups. In the other category was found records and data management software or platforms interactive and cognitive assistants telephone services websites and online platforms, exercise and educational content (Table 2).

In relation to the health objective and the condition under study, for chronic diseases, the most prevalent objective was treatment (76.1%), for fragility was prevention (63.2%), whereas for infectious diseases, it is monitoring/follow-up (83.3%). Meanwhile, prevention and monitoring/follow-up highlight their primary objective in the case of healthy aging and quality of life (69.1% and 60.0%, respectively). The category named other is not described due to the diversity of situations. Figure 2.

When the year of publication and type of device studied were analyzed (Figure 3), it was found that over the years, there is a noticeable increase in the use of telephones, especially in the recent period from 2020 to 2022, suggesting a massive adoption of this technology in the analyzed studies (mostly driven by cell phone and smartphone). The use of desktop or laptop computers remains constant over the years, with significant peaks around 2011 and 2022. Activity monitors and sensors show an increasing trend from 2010 onwards, indicating a rise in the analysis of these technologies, particularly in the years 2015, 2017, and 2021. There is a sustained use of telehealth devices over the years, with a notable increase in 2022, possibly reflecting the response to the COVID-19 pandemic and the need for remote health solutions.

Figure 4 illustrates the utilization of various technological devices across different health objectives, including promotion, prevention, diagnosis, treatment, monitoring/follow-up, and rehabilitation. Telephones and computers are the most used devices for all health objectives analyzed. However, in the case of promotion, video game consoles (31.4%) and wearables (27.9%) also have a significant presence. For prevention and diagnosis, sensors and positioning systems (32.8% and 28.8%, respectively) and wearables (26.4% and 28.8%, respectively) are also prominent. In the case of therapy or treatment, Tablets rank third (26.0%). For monitoring/follow-up, wearable activity monitors (31.3%) and sensors and positioning systems (25.3%) are also considered important, and finally, for rehabilitation, interactive TV (22.9%) is significant. Overall, the figure highlights the diverse roles of these devices in healthcare, with some being more versatile and widely used across multiple functions.

The frequency and distribution of technological devices across multiple diseases, highlights the prominent role of telephones and computers. The data reveals that these devices are extensively used to address chronic diseases (62.8% and 42.2%, respectively), fragility (46.5% and 43.0%) and mental health issues (64.0% and 46.7%). Infectious diseases show same proportion for telephones, computers and Tablets (50.0%, each one). Additionally, telephones show the highest usage for sensory impairments (87.5%). Tablets, wearables and sensors also play a vital role, particularly in neurological conditions (30.7%, 20.8%, and 21.8%) and healthy aging (20.0%, 20.0%, and 25.5%).

Increasing integration of technology in older adults’ lives, along with their increasing proficiency, is driving a significant rise in digital health interventions. Despite this growth, further research in middle- and low-income countries, for caregivers and evaluating effectiveness and feasibility of these technological interventions is needed. The future of digital health for elderly population, caregivers and healthcare professionals presents challenges that must be addressed to realize its full potential. One major issue is accessibility, as these technologies evolve it will be essential ensuring that they are user-friendly and inclusive for all older adults, regardless of their technical skills. Data privacy and security also become critical concerns as more personal health information is shared through digital platforms, demanding robust protections to build and maintain trust among users. Additionally, healthcare providers will face the challenge of integrating digital health tools effectively within traditional care settings, which may require new training and adaptation to evolving workflows. Finally, supporting caregivers and family members in adopting these technologies could be complex, as they often have diverse needs and limited time. Addressing these challenges will be key to harnessing the power of digital health to improve care and quality of life for aging populations.