Application-Based Interventions for Family Caregivers of Older Adults: Scoping Review

Introduction

With the aging global population, the increasing number of individuals with chronic health conditions is placing a growing demand on family caregivers for assistance [1,2]. Family caregivers, typically family members or close friends, provide essential care to older adults with chronic conditions, managing activities of daily living and instrumental activities of daily living, including medication management, transportation, and meal preparation [3]. However, caregiver burden often results in negative outcomes such as stress, worse mental health, and reduced well-being [4-6]. Caregivers also face higher risks of social isolation and loneliness, issues that were further exacerbated during the COVID-19 pandemic [7-9]. Despite their central role, caregivers frequently neglect their own health and well-being, underscoring the need for interventions that support both the caregiver and care recipient [10-12].

In this context, behavioral application-based interventions delivered via mobile phones, tablets, or the web have emerged as a powerful tool for enhancing caregiver support. Applications allow caregivers to access support, resources, and health management tools, regardless of the intensity or demands of their caregiving role [13-17]. This flexibility is particularly valuable for caregivers who struggle to seek support due to time constraints or geographical isolation [18,19]. Tablet, internet, and smartphone use has become prevalent across all age groups, including older adults, many of whom rely on smartphones as their primary or only digital device [20]. The widespread adoption of smartphones and use of web and mobile applications offer unique opportunities to address caregivers’ needs in ways that traditional, nonvirtual services cannot [14,21,22]. Behavioral application-based interventions for caregivers could also broaden opportunities to improve psychosocial outcomes, such as reducing stress, improving social connectedness, and promoting well-being [14,23-25].

Despite growing interest in application-based interventions, evidence on their effectiveness and usability remains limited. Many caregivers are older adults themselves, and the usability of these applications is critical, especially for those experiencing age-related changes that affect their interaction with technology [26-28]. As the population of older adults grows, the need for usable, accessible applications to support caregivers in managing their own well-being while caring for others becomes increasingly urgent.

Previous systematic reviews have explored the use of digital health technologies for caregivers, with findings indicating that technology positively enhances self-efficacy, strain, and mastery [24,29,30]. However, many of these reviews focused broadly on mobile interventions [30,31] or specific chronic conditions [14,30], such as dementia or cancer, without thoroughly examining the features (eg, education, stress management, and peer support) that make mobile apps particularly useful or effective for caregivers. Furthermore, past reviews often evaluated existing apps available commercially in app stores (eg, Google Play Store and iOS App Store) rather than in the context of a behavioral intervention study without specificity regarding how these apps influence caregivers’ well-being [30,31]. The importance of investigating mechanisms is underscored by the National Institutes of Health (NIH) [32].

This scoping review aims to address these gaps by focusing on application-based interventions for family caregivers of older adults with chronic conditions [33]. It will build upon existing reviews that have explored the broader role of technology in caregiving but have not fully examined application-based behavioral interventions. By summarizing current research on application designs, features, and their impact on caregiver outcomes, the results of this review will inform future application development and research, ultimately aiming to improve the well-being of family caregivers in an aging society.

The primary objective of this scoping review is to identify and describe application-based interventions (web, mobile, and tablet) designed to support family caregivers of older adults with chronic conditions. Specifically, this review will examine the designs (eg, user-centered approach and theory-based) and features (eg, education, stress management, and peer support) of application-based interventions (mobile, web, or tablet) for family caregivers of older adults, as well as their impact on key caregiver outcomes (eg, well-being, social support, and self-efficacy).

Methods

Design

This scoping review is reported using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and is guided by the Joanna Briggs Institute methodology [34], which uses the population, concept, and context framework to define the scope of the review, eligibility criteria, and search strategy. The population is family caregivers of individuals with the following chronic diseases identified by the Centers for Disease Control and Prevention (CDC) to be prevalent among older adults in the United States [33]: heart disease, cancer, chronic lung disease, stroke, Alzheimer disease or related dementias (ADRD; including mild cognitive impairment), diabetes, and chronic kidney disease. The concept focuses on application-based interventions, including mobile, web, and tablet applications designed to support caregivers. The context encompasses any care environment or setting in which caregiving takes place, without limitations on geographic region or health care system.

A medical librarian (AH) searched on December 20, 2022, to identify studies focusing on application-based interventions for family caregivers of older adults. The search was conducted in the following databases: PsycINFO (EBSCO), MEDLINE ALL (Ovid), Embase (Ovid), Web of Science Core Collection (Editions=A&HCI, BKCI-SSH, BKCI-S, CCR-EXPANDED, ESCI, IC, CPCI-SSH, CPCI-S, SCI-EXPANDED, SSCI) (Clarivate), ACM Guide to Computing Literature (ACM Digital Library), and Engineering Village (Elsevier). Publication dates ranged from 2007, the year the first iPhone was released, to the date the search was conducted (2022). The search strategy consisted of selecting appropriate keywords such as “family caregiver” and “mhealth,” combined with a “double-NOT” strategy to exclude records that were pediatric-only. Search term examples include Medical Subject Headings such as “mobile applications” and “caregivers,” and keywords such as “Android,” “iPhone,” “care partner,” and “carer.” The full search strategy used for all databases is included in Multimedia Appendix 1. The current review protocol was registered and published on the Open Science Framework [35].

Selection Criteria

Included papers consisted of (1) published and peer-reviewed intervention studies, feasibility studies, pilot studies, randomized clinical trials (RCTs), or clinical trials, with English full texts available; (2) application-based interventions (mobile, tablet, or web) for family caregivers of older adults with one or more of the chronic diseases listed above; (3) applications designed and developed for family caregivers; and (4) papers reporting outcomes (eg, usability and well-being).

Papers were excluded based on the following criteria: (1) the paper is a review, perspective, opinion, conference abstract, commentary, book, book chapter, workshop, presentation, protocol paper, thesis, dissertation, or paper without outcome measures; (2) text-messaging or telephone-based interventions; (3) papers that focused on non-application–based interventions (eg, patient portals, virtual reality [VR], smart home, and wearables); (4) interventions targeting populations other than caregivers (eg, care recipients and health care professionals); (5) application interventions that used general web platforms accessible by the general population (eg, social media and Zoom [Zoom Video Communications]); (6) studies that used an application prototype (eg, mock-ups) rather than a functional application; (7) interventions consisting solely of internet forums or discussion boards; and (8) studies that did not specify care recipients.

Selection of Studies

All retrieved studies were screened using the Covidence (Veritas Health Innovation Ltd) systematic review software developed by Veritas Health Innovation Ltd. First, duplicate titles and abstracts were identified and removed automatically using Covidence (AH). Then, four researchers (XYL, FF, ESC, and SC) applied the selection criteria and independently screened titles and abstracts. Full texts were then independently screened by 5 researchers (XYL, FF, RZ, KH, and SC) to ensure they met the eligibility criteria. Each title, abstract, and full text was reviewed by 2 researchers, and disagreements were resolved by a third researcher and through consensus meetings. Regular team meetings were conducted throughout the screening process to clarify questions or uncertainties.

Data Extraction and Analysis

Upon completion of full-text screening, data from included papers were extracted into a predesigned Microsoft Excel spreadsheet. The extracted fields included (1) study information (title, author, DOI, year of publication, and country of origin); (2) research questions and aims; (3) study characteristics (qualitative, quantitative, or mixed methods); (4) participant characteristics (number, condition, inclusion and exclusion criteria, relationship to the care recipient, and demographic characteristics of the caregiver and the care recipient); (5) recruitment strategy (where, how, attrition, and target recruitment goal); (6) intervention (type, duration, participants per condition, setting, treatment fidelity, and frequency of nonapplication components); (7) training (how and who trained participants); (8) application characteristics (type, features, application use frequency, recommended and actual application use, and satisfaction); (9) primary and secondary outcome measures; and (10) quantitative and qualitative results.

Five authors (XYL, FF, ESC, KH, and RZ) independently extracted data from included papers using predefined and standardized templates. The authors extracted and discussed 3 studies together before beginning the data extraction process to ensure proper and standardized extraction. Regular meetings were conducted throughout the extraction process to clarify questions or uncertainties. Moreover, 10% of papers were randomly selected for duplicate extraction to ensure reliability.

After extraction was completed, descriptive quantitative analyses were conducted to summarize the frequency and distribution of application interventions across various caregiver populations, study designs, and outcomes. Thematic analyses were also conducted, in which an initial inductive approach was used to identify themes that were validated and revisited until finalized. All discrepancies were discussed among the authors. The full review process is illustrated in the flowchart in Multimedia Appendix 2.

Ethical Considerations

Ethical approval was not required for this study as it is a scoping review of existing literature and does not involve human participants, animals, or the collection of original primary data.

Results

Characteristics

Figure 1 shows the full PRISMA flow diagram outlining the study selection process. The database search yielded 9482 studies, in which 3211 duplicates were identified and removed, resulting in 6271 studies for screening. A total of 5981 studies were excluded during title and abstract screening, resulting in 290 full-text papers that were assessed for eligibility. Of those, 241 papers were excluded, which resulted in 49 studies included in this review (Figure 1). Studies were published between 2009 and 2022; 55% (27/49) studies were conducted in North America (United States and Canada), 39% (19/49) in Europe (Netherlands, Germany, Italy, Sweden, France, United Kingdom, Austria, Denmark, Poland, and Spain), 4% (2/49) in Asia (China and South Korea), and 2% (1/49) in Australia. The majority of studies (73%, 36/49) targeted family caregivers of people with ADRD, with 12% (6/49) and 8% (4/49) targeting cancer and stroke, respectively. A few studies (6%, 3/49) included family caregivers of older adults with various chronic conditions rather than a single condition (Table 1).

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Final studies included 21 quantitative, 5 qualitative, and 23 mixed methods studies with various designs: 25 RCTs, 15 feasibility studies, 5 pre-post intervention studies, and 4 usability studies. Intervention durations ranged from 1 session lasting 15‐30 minutes to 2 years. The majority of interventions were home-based; 5 were delivered virtually, 2 in clinical settings (eg, medical center), and 1 was not reported. A few studies involved interventionists, often clinical psychologists, nurses, or social workers, who received specialized training to deliver the interventions along with an app. About half of all studies (n=27) reported that participants were trained on how to use the app by research assistants or staff via in-person sessions, phone calls, or online methods. Only 20% (10/49) of studies reported fidelity measures, including regular check-ins and adherence assessments to ensure protocol compliance.

Study Participants

Common eligibility criteria across studies included primary caregivers who were adults (typically aged >18 years) caring for a person with dementia or another chronic condition (eg, cancer). Most studies required caregivers to have access to the internet and a device (computer, smartphone, or tablet; n=30). Language proficiency was also a common criterion, with caregivers needing to read and write in the language of the study (n=22), such as English, German, or Chinese. Many studies excluded caregivers with mental health conditions or cognitive impairment (n=11). Recruitment methods varied, with caregivers recruited through health care settings such as memory clinics and hospitals (n=20), community organizations such as Alzheimer’s Association and support groups (n=31), online platforms including websites and social media (n=17), and print media such as flyers and newspaper ads (n=18).

Demographic characteristics typically included middle-aged to older adults with a mean age of around 60 years. Caregivers were predominantly female (70%‐80%) and had varied educational backgrounds, with many having at least some college education. Employment status was mixed, with some caregivers employed full-time, part-time, or retired. Care recipients were generally older adults, aged >70 years, with a slight majority being female. The relationship between caregivers and care recipients was primarily spousal or adult children caring for their parents. Sample sizes varied widely, ranging from small pilot studies with about 10‐30 participants (n=8) to larger trials with >100 participants (n=9). Many studies did not report their target recruitment goals (n=36) or attrition rate (n=22). Among those that reported these outcomes, most met their target recruitment (n=8), whereas some did not (n=5). Attrition rates also varied, with some studies reporting high retention (80%‐90%; n=6) and others reporting significant dropout rates (20%‐30%; n=8) (Table 1).

Application Characteristics

The dataset includes 44 unique applications designed to support caregivers of individuals with various chronic conditions. These applications are categorized as web-based (n=30), mobile (n=12), and tablet (n=2) platforms. The design of these applications often followed adaptations of in-person or previously developed intervention (n=9), a user-centered approach (n=9), and theory-based design (n=3). Other design approaches involve iterative development with user feedback and collaboration with health care professionals (n=3).

The primary functions of these applications include education (n=39), social support (n=19), and self-management tools (n=21). Common components across applications included informational resources (n=39), interactive services such as forums and chat (n=22), and personalized feedback or coaching from researchers or interventionists (eg, coaches and health care professionals; n=10).

Many applications provided educational content for caregivers, including a range of informational resources (n=37) to support chronic condition management and caregiving strategies. Structured modules further enhanced this learning experience by covering caregiving-related topics such as symptom management and communication skills. Practical tools were another important aspect of these applications, with features such as calendars and schedules helping caregivers organize tasks, appointments, and daily activities. Additionally, some applications included resource libraries designed for easy access to papers, videos, and external links for caregiving support.

Interactive services (n=22) were another key feature of these applications, allowing caregivers to connect with others through forums, chat rooms, and social networks. These platforms provided a space for caregivers to share experiences, offer support, and build community. In addition to forums, many applications include private messaging features for more personal communication with other caregivers or health care professionals. Customization and personalization options, including reminder notifications or email alerts, made the applications more responsive to individual caregiver needs.

Support tools in these applications (n=19) often incorporated interventionist-guided approaches to provide step-by-step assistance based on cognitive-behavioral principles or problem-solving techniques. Caregivers also received regular personalized feedback and coaching from interventionists to help monitor progress and adjust strategies as needed. Tracking and monitoring tools were also common (n=21), with applications offering progress tracking features such as daily assessments (eg, ecological momentary assessments) to regularly track mood, stress, and other key metrics.

Only about half of all studies (n=27) reported that participants were trained on how to use the app by research personnel (eg, in-person: n=10, phone calls: n=3, or online: n=6). Participants were typically trained through initial orientation sessions. App use was typically measured through log data (n=24) or self-reported usage (n=5), but many studies did not report app use (n=24). Actual app use during the intervention varied, with 5 studies showing consistent engagement and 11 studies reporting drop-offs in usage over time. Data on user satisfaction were reported in about half of all studies (n=24), with 16 studies reporting moderate to high satisfaction among caregivers.

Intervention Effects on Study Outcomes

Interventions with a comparison group (n=25) followed a waitlist model or received standard care. Control group participants typically had access to basic resources, such as printed materials or standard educational content, but did not receive the more comprehensive features or digital tools provided to the treatment group.

The primary and secondary outcomes across all studies predominantly focused on caregiver well-being and health (eg, depression and stress; n=33), burden (n=16), self-efficacy and competence (n=16), feasibility and acceptability (n=32), social support (n=9), quality of life (n=4), care recipient’s symptoms (n=2), coping strategies (n=4), and intervention feedback (n=21).

Overall, the intervention effects on outcomes were generally positive. Many studies reported improvements in primary and secondary outcomes (eg, caregiver well-being, reduced caregiver burden, and enhanced mental health) across time or condition (n=39). For example, caregivers experienced lower levels of depression and anxiety and increased self-efficacy [41]. Usability and acceptability were commonly assessed (n=25), with caregivers often discussing ease of use and satisfaction. Qualitative results showed that a prevalent reason for app usage was the emotional impact of caregiving (n=8), where caregivers shared their experiences of stress, guilt, and isolation. Support and communication also emerged as significant themes regarding why and what caregivers need in terms of support, highlighting the importance of social support networks and effective communication with health care providers. Additional themes included practical caregiving challenges (n=8) and the need for personalized tools (n=2). However, 11 studies reported declining engagement over time, indicating the need for ongoing support and engagement strategies. Moreover, 10 studies showed null effects for outcomes across time or condition, which warrants further discussion (eg, possibility of app content or design failing to meet user needs, inadequate user training, or low engagement) [43,45,57,64,67-69,77,78,84].

Discussion

Principal Findings

This review synthesized 49 studies evaluating application-based interventions for family caregivers of chronically ill older adults (eg, ADRD, cancer, and stroke). These studies primarily examined the impact of applications on outcomes, including caregiver well-being, burden, and mental health. While most interventions were perceived as beneficial for enhancing well-being and social support, several design and research limitations were noted. In the following “Discussion” section, we discuss how these applications support caregivers and identify design and evaluation challenges to guide future research.

Application-Based Intervention Support for Caregivers

The majority of included studies aimed to provide caregivers with education, emotional support, and self-management tools. ADRD caregivers, in particular, benefited from applications that offered resources for managing caregiving tasks and emotional well-being. Caregivers appreciated interventions that enhanced their access to information and social support, especially during stressful caregiving periods. However, some caregivers reported dissatisfaction with applications that were too complex or poorly aligned with their needs, highlighting the importance of user-centered design. In line with broader caregiving research, these findings underscore how psychosocial factors—such as caregivers’ perceived social support, resilience, and sense of coherence—shape their ability to benefit from digital tools and influence the extent to which applications can meaningfully support their emotional well-being [22,60,78,84].

For cancer and stroke caregivers, the feedback was similar. Applications focused on information delivery and psychosocial support were generally well received, but challenges arose when the app content was too general or failed to provide disease-specific advice. Across all caregiver groups, there was a demand for more personalized, adaptable interventions that could cater to the evolving needs of caregivers and care recipients as their conditions progressed. These needs are also influenced by well-established psychosocial influences on caregiver stress and coping, including role overload, work-related strain, and the extent to which caregivers have access to emotional and informational resources [39,43,58,76,77,80].

While many interventions aimed to reduce negative emotional states, they did not adequately address broader aspects of well-being, such as promoting caregivers’ own self-care practices. Additionally, many interventions focused on reducing stress and burden without a parallel effort to enhance positive emotions or resilience, which could result in caregivers feeling only marginally supported. This gap is notable given evidence that resilience, emotional regulation, and prosocial coping behaviors are key determinants of caregiver health and may moderate the impact of app interventions [24,85]. There is a need for more holistic intervention approaches, promoting not just stress relief but also strategies to enhance self-care, emotional fulfillment, and resilience [25]. This highlights the need for application-based interventions to shift from solely focusing on minimizing negative outcomes to actively promoting well-being. Integrating established psychosocial constructs—and drawing on validated measures such as the Zarit Burden Interview, resilience scales, and sense-of-coherence instruments—may help future app designs more effectively target mechanisms that drive meaningful change for caregivers [5,6,36,38,46,86].

Design and Evaluation Considerations

A significant issue identified in many studies was the lack of clarity regarding which components of multifaceted interventions were responsible for specific outcomes. For instance, some applications aimed to increase both information access and social support, but studies often failed to specify which intervention component influenced outcomes such as well-being or burden. This lack of specificity makes it challenging to identify the mechanisms through which the app intervention impacts outcomes, a key step in developing targeted behavioral interventions [32].

In some cases, negative or null findings were reported without clear explanations. Exploring potential reasons for these outcomes is crucial, including the possibility of app content or design failing to meet user needs, inadequate user training, or low engagement [29]. Additionally, negative or null findings were frequently underreported, making it challenging to understand why some interventions failed to produce the desired effects, which could be a result of publication bias. Unintended consequences, such as caregivers feeling overwhelmed by app content or difficulty integrating app use into their daily routines, were rarely explored but warrant attention in future studies. Future research should also explore potential unintended consequences or reasons for lack of effects, such as inadequate app design, caregiver disengagement, or issues with the intervention’s delivery.

Another issue relates to studies that claimed to use mixed methods designs. While both qualitative and quantitative data were collected, these data were not integrated or triangulated to provide a comprehensive understanding of the intervention’s effects, which may limit the study’s implications.

Research Considerations

Many studies in this review lacked consistent reporting on key aspects of application-based interventions, such as usage, participant training procedures, and fidelity measures. For instance, of 49 studies, only half (n=27) reported whether participants received any training on using the applications. This omission is particularly problematic given that technology proficiency can vary widely among caregivers, especially older adults. Past research on technology training and proficiency highlights the importance of offering caregivers adequate guidance on digital tools [87], yet many studies failed to measure or report this. Studies that did not measure participants’ baseline technology skills may have missed critical factors influencing application engagement and outcomes. Additionally, the lack of data on app satisfaction and usage makes it difficult to assess how well the applications were received and whether caregivers used them as intended. This lack of information limits the ability to assess intervention fidelity (only 20% of studies reported any fidelity measures) and their real-world applicability.

Moreover, adapting in-person behavioral interventions to app formats was common but not always well documented. When delivered via digital platforms, behavioral interventions must adhere to principles such as those outlined in the NIH Stage Model for Behavioral Intervention Development [32], which emphasizes the importance of specifying mechanisms, conducting feasibility studies, and ensuring intervention fidelity. Similarly, recruitment goals and attrition rates were often missing, particularly in feasibility studies and RCTs, which should adhere to standards such as CONSORT (Consolidated Standards of Reporting Trials) for transparent reporting [88]. However, many studies in this review did not clearly align their design with this model, resulting in unclear stages of intervention development and evaluation, where some studies attempted to combine intervention development and RCT phases without clearly defining how these stages were managed.

Limitations of Current Studies

Several limitations were identified. First, sample sizes were small and geographically limited, reducing the generalizability of findings. The studies predominantly involved caregivers from North America and Europe, with minimal representation from Asia and other regions. This limits the cultural generalizability of the findings, as caregiving practices, expectations of family responsibility, and help-seeking behaviors vary across cultural contexts. Additionally, the feasibility and effectiveness of application-based interventions may differ in settings where access to smartphones, internet connectivity, digital literacy, or technological infrastructure is limited [8]. Additionally, marginalized groups and long-distance caregivers were underrepresented, given limited participant diversity in race, ethnicity, and socioeconomic status. Further, many studies did not account for disease stage or care recipient etiologies, which could have provided more nuanced insights into the effectiveness of these applications for different caregiver populations.

Research design weaknesses were also prevalent. Many studies lacked robust control groups, relied on small sample sizes, or used short follow-up periods, limiting the strength and stability of the reported findings. In several studies, the intervention development process was not clearly described (eg, how the app was developed or adapted to digital formats), and reporting was inconsistent regarding levels of rigor, evaluation procedures, and adherence monitoring. These methodological limitations make it difficult to draw firm conclusions about the effectiveness or sustainability of application-based interventions for caregivers. Moreover, few studies examined how or why the interventions had any effects. Mediation or moderation analyses were rarely conducted, making it challenging to identify which components—such as educational content, stress-management tools, peer support features, or mood and symptom tracking—were most influential in improving caregiver outcomes. As a result, the mechanisms of change underlying application-based interventions remain unclear. To advance understanding of how app interventions influence caregiver outcomes, future studies should incorporate mediation and moderation analyses to clarify the mechanisms of change. Such work can help isolate whether specific features drive improvements in caregiver well-being. Finally, many interventions were prototypes or research-only platforms that were not publicly accessible, highlighting challenges related to scalability, implementation, and long-term integration into real-world caregiving contexts.

Limitations of the Current Review

This scoping review has several limitations. First, as a common limitation of scoping reviews [89], we did not include a critical appraisal of the methodological quality or risk of bias in the included studies, which may limit the validity of our findings. Additionally, the database search was conducted in December 2022; thus, any relevant studies published after this date were not included in the screening process. ChatGPT was released in November 2022 and significantly accelerated the development of artificial intelligence (AI)-based applications. With the increasing interest in AI, future reviews can consider examining the application of AI to promote caregivers’ health and well-being. Finally, we did not assess cost-effectiveness or broader health care system implications, which are important considerations for future research.

Conclusions

This review highlights the growing potential of application-based interventions to support family caregivers of older adults, particularly those caring for individuals with ADRD, cancer, and stroke. While many interventions are well-received and demonstrate positive effects on caregiver well-being, there are significant gaps in research design, evaluation, and reporting. Future studies should prioritize greater integration of self-care practices, clarity in intervention components, rigorous adherence to behavioral intervention frameworks, including the NIH Stage Model, and more diverse participant samples. Improved reporting on app usage, satisfaction, and recruitment outcomes is essential for advancing the field and ensuring these digital tools meet the complex needs of caregivers. Moreover, as technology evolves, ongoing research is needed to explore how emerging innovations can further enhance well-being. Addressing these gaps will be crucial for developing effective, scalable, and accessible digital interventions for caregivers across diverse populations.