Efficacy of digital technology-based interventions for reducing caregiver burden and stress: a systematic review and meta-analysis

Abstract

Background:

Demographic aging and increasing dependency associated with chronic diseases have intensified the caregiving responsibilities of family members, often leading to significant burden and stress. Digital technology-based interventions have emerged as promising strategies to support family caregivers, yet their effectiveness remains inconsistent across studies.

Method:

A systematic review and meta-analysis was conducted following JBI methodology and PRISMA guidelines. Literature searches were performed in CINAHL Complete, MEDLINE Complete, Scopus, and Web of Science (August 2024, updated September 2025). Studies were included if they involved family caregivers aged ≥18 years supporting individuals with functional dependency, implemented technology-based interventions, and employed experimental designs. Two independent reviewers conducted screening, data extraction, and quality assessment. Meta-analyses were performed to calculate standardized effect sizes (Cohen's d) for caregiver burden, stress, and quality of life outcomes.

Results:

Sixteen studies comprising 2,716 caregivers were included, predominantly randomized controlled trials. Interventions utilized diverse digital modalities including mobile applications, websites, telemonitoring, and tele-coaching, with most delivered by nurses. Meta-analysis revealed significant short-term reductions in caregiver burden (d = −0.65, 95% CI: −1.00 to −0.30, p < 0.01) and stress (d = −0.62, 95% CI: −0.81 to −0.43, p < 0.01). However, heterogeneity was substantial for burden (I² = 75%) and effects on quality of life were non-significant with very high variability (I² = 92%). Long-term effectiveness could not be determined due to limited follow-up data.

Conclusion:

Digital technology-based interventions demonstrate moderate effectiveness in reducing caregiver burden and stress in the short term. However, considerable variability in outcomes suggests that effectiveness is influenced by intervention characteristics, delivery modalities, and contextual factors. Future research should focus to strengthen the consistency of the findings, including subgroup analyses by type of intervention and evaluation of their long-term effects.

Systematic Review Registration:

PROSPERO CRD42024574765.

1 Background

In Europe, the demographic shift characterised by a persistently low birth rates and increased life expectancy is leading to an ageing population (1). This transformation, combined with the increasing dependence associated with chronic diseases, poses considerable challenges to healthcare systems and families' social and economic structures (2, 3).

Family caregivers, often without formal healthcare training, face complex and demanding responsibilities previously undertaken by professionals, increasing the risk of burden and stress (4, 5). The literature shows that this experience can lead to adverse consequences for caregivers' well-being and quality of life (6–8). Therefore, promoting effective interventions that mitigate stress and burden, while enhancing the well-being of family caregivers is a public health priority.

In recent years, technology-based interventions have emerged as promising strategies to support caregivers. E-learning platforms, mobile applications, telemonitoring, and augmented reality offer innovative approaches that provide information, training, and ongoing, personalised support (9–11).

The evidence suggests that technology-based interventions can effectively reduce caregiver burden and stress (12–17). Recent systematic reviews with meta-analysis confirmed this effectiveness among informal carers of older adults (12, 17). The specific type of intervention appears to modulate this effect (12–17), underscoring the importance of selecting an appropriate delivery modality that accounts for the strengths and limitations of each strategy (12, 13, 15). Although the overall findings are promising, results across studies are not entirely consistent, indicating variability in outcomes depending on context, intervention type, and implementation (12, 17).

In this context, it is essential to critically synthesise the available evidence on the effectiveness of technology-based interventions for family caregivers. The present study aims to conduct a systematic review of the effectiveness of interventions that utilise technology-based approaches to reduce caregiver burden and stress or to enhance the quality of life and well-being of family caregivers, following the research question: Is there evidence that technology-based interventions decrease the burden of family caregivers? By analysing different technological approaches and related outcomes, this study seeks to identify promising practices and contribute to the development of more efficient and accessible strategies to support family caregivers.

2 Methods

2.1 Design

The proposed systematic review will be conducted in accordance with JBI methodology for systematic reviews of effectiveness (18) and reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (19). The review protocol was registered in the PROSPERO database (https://www.crd.york.ac.uk/PROSPERO/view/CRD42024574765).

Table 1 summarises the inclusion criteria for this review following the PICOD acronym. Additionally, studies had to be published in Portuguese, English, or Spanish, be available in full text and were not subject to any publication date restrictions.

2.2 Search methods

The search and identification of studies in the databases took place in August 2024 and an updating September 2025. The literature search was conducted in the following databases: CINAHL Complete, MEDLINE Complete (via EBSCOhost), Scopus, and Web of Science. Search terms were derived from the elements of the PICOD framework. In databases using controlled vocabularies (CINAHL and MEDLINE), Medical Subject Headings (MeSH) and CINAHL Headings were employed to increase specificity. The search strategies were adapted to the requirements of each database and could be consulted in supplementary material.

2.3 Data extraction

The results from the bibliographic search were imported into Rayyan (20) to remove duplicates and conduct an initial screening of titles and abstracts based on predefined inclusion criteria. Two authors independently analysed and extracted the data, resolving disagreements through discussion or, if necessary, with a third reviewer, following JBI methodological recommendations (18). Selected studies then underwent full-text review. A data extraction form in Microsoft Excel was used to collect information on study title, authors/year, country, objectives, sample, design, instruments, intervention type or content, effectiveness results, effect size, and study limitations.

2.4 Quality assessment

The methodological quality of the included studies was assessed using the JBI Critical Appraisal Checklist for Randomized Controlled Trials (21) and the Checklist for Quasi-Experimental Studies (22). Two independent reviewers conducted the quality assessments for ensure the methodological quality of the articles. Potential disagreements were solved by a third reviewer.

2.5 Statistical analysis

The meta-analysis was conducted using IBM SPSS (version 30.0). Studies providing sufficient data, including means, standard deviations, and sample sizes, were included. Extracted data also encompassed effect sizes or sufficient information to calculate them; when such data were unavailable, the study authors were contacted. As the included studies employed different caregiver burden and stress scales, standardised effect sizes (Cohen's d) with 95% confidence intervals were calculated to enable comparability across studies. Negative values indicated a reduction in caregiver burden or stress in the intervention group compared with controls or baseline measurements. Heterogeneity was assessed using Cochran's Q test and the I² statistic. A random-effects model was applied to account for potential heterogeneity across studies. Forest plots were generated to visualise study weights and the consistency of effects. Sensitivity analyses were conducted by excluding studies at high risk of bias. The findings of studies that were not comparable and could not be included in the statistical pooling were presented narratively.

3 Results

3.1 Search results

A total of 2,405 articles were identified. After excluding duplicates, the remaining 2,084 records were screened by title and abstract. Finally, 54 full-text reports for further assessment were retrieved. After evaluating their eligibility, 16 studies met the inclusion criteria. A flow diagram is presented in Figure 1 (19).

Figure 1

3.2 Study characteristics

The characteristics of the included studies and their participant populations are summarised in Table 2. Of the 16 included studies, most of them were published in the last five years, were conducted in the USA and employed RCT designs.

3.3 Characteristics of the participants

The studies comprised 2,716 caregivers of patients with a variety of diseases or health conditions (Table 2). Some studies were targeted at caregivers of patients with dementia (23–25), chronic heart failure (26–28), chronic health conditions (29, 30), traumatic brain injury (31), major depressive disorders (32), multiple sclerosis (33, 34), cancer (35), physical and mental disabilities (36, 37) elderly (38), and military with post-traumatic injuries (36). Sample sizes of each study ranged from 27 to 780 participants. Most of the caregivers were spouses or partners (23–28, 31–36), adult children caring for their parents (29, 30, 38) and parents caring for their children (37).

In all studies, most participants were female except one (27). Caregivers' educational attainment ranged from secondary level to graduate level.

3.4 Characteristics of the interventions

The key characteristics and content of the interventions are summarised and presented in Table 3.

3.4.1 Intervention components, educational content, and delivery approaches

Except one article (29) all the others were focussed on training essential skills related to patient care. Additionally, some studies incorporated topics like stress management (23, 24, 28, 30–32, 35–37), problem-solving techniques (36), sleep hygiene (28, 32), communication strategies (32, 36), relaxation techniques (24, 28, 37), sharing experiences among peers (38), self-care (23, 28, 33, 36), resources for caregivers (23, 25, 30, 33, 38), positive mental health techniques (29, 30), and cognitive restructuring techniques (37). Caregivers had access to interactive resources and tools, allowing them to practice caregiving strategies in real-life scenarios while being provided support in dealing with negative emotions and developing self-care practices. Programmes also included personalised coaching and follow-up sessions, which helped address their specific challenges and encouraged peer interaction to foster shared learning and support. Table 3 summarises the content of the interventions. The interventions employed diverse digital delivery modes and learning modalities (Figure 2).

Figure 2

Several studies supplemented digital components with telephone follow-ups (25, 26, 31, 33, 36), online chat discussions (30, 32, 38), and tele-coaching (23, 25, 28, 33, 36).

The educational resources included flip charts, newsletters, written materials, audio recordings, videos, images, reflective exercises, lectures, and home practice activities. In four studies, the intervention was entirely self-managed without direct interaction with health professionals (27, 29, 35, 37). Each study employed a unique combination of these approaches (Table 3).

3.4.2 Programme duration, frequency, intervenors, and control conditions

Most studies involved weekly interventions, with only one study including daily (29) or alternate-day interventions (27). One study mentioned 24 h nursing support (26). The overall duration of the intervention programmes ranged from one to six months (Table 3).

Of the interventions, six were delivered solely by nurses (28–32, 34). Others were facilitated by nurses and physicians (26, 27), psychologists (24, 38), social workers (33), research teams (35, 37), care teams navigators (25), coaches (36), and facilitators (23).

In three studies, the caregivers in the control group were placed on a waitlist and received the intervention following its delivery to the experimental group (23, 24, 35). In the other ten studies, the control groups received standard or usual care (25–32, 34). In one study, the control group was exposed to a different app that monitored their stress levels and well-being (37), while others provided website access without any coaching sessions (33) or online chat room (38). One study did not have a control group (36).

3.5 Caregivers' outcomes and measurements

The most frequently assessed outcome was caregiver burden. Table 4 summarises the outcome variables and measurements used across all the 16 studies. Several measures were used to assess caregiver stress, burden, quality of life, and well-being. Notably, the Zarit Burden Interview (ZBI) and the Caregiver Burden Inventory (CBI) were commonly employed. Quality of life was measured by the WHOQOL-BREF and SF36, and its mental health dimension was assessed through the Positive Mental Health Questionnaire. Stress levels were assessed using one subscale of the Depression Anxiety Stress Scale (DASS) and the Perceived Stress Scale (PSS). Well-being was assessed in only two studies using different instruments.

3.6 Effects of intervention by technologies

The efficacy of the interventions is also presented in Table 4. Among the thirteen studies that assessed caregiver burden, only three did not report statistically significant results (23, 24, 35). Of the six studies that assessed stress, only one reported no statistically significant results associated with the intervention (38). Fewer studies examined the impact of interventions on overall quality of life (28, 32) mental health (29, 30), and well-being (24, 37), which limits the ability to draw conclusions regarding these variables. Nevertheless, Minaei-Moghadam et al. (32) reported a significant improvement in quality-of-life scores for the intervention group compared to the control group. Three studies reported improvements in mental health in caregivers receiving interventions (28–30), although no statistically significant differences were observed in physical health (28). Fuller-Tyszkiewicz et al. (37) documented a statistically significant decline in subjective well-being among participants in the control group, a trend not observed in the intervention group. Similarly, Meichsner et al. (24) reported significant improvements in emotional well-being over time, with most participants showing positive changes.

3.7 Risk of bias

The bias analysis was performed separately for RCT and quasi- experimental studies.

Table 5 presents a methodological quality analysis of the selected RCTs. The studies exhibit overall strong methodological quality, with scores ranging from 7/13 to 11/13 for RCTs. All these studies employed true randomization, ensuring equitable distribution of participants and consistency in outcome measurement and statistical analysis. Notwithstanding these results, the analysis had an important limitation because of the lack of allocation concealment and blinding, particularly among participants, intervention providers, and outcome assessors. These aspects increased the risk of performance and detection bias, potentially impacting the internal validity of the study findings. Furthermore, some RCTs did not fully report participant follow-up, potentially affecting the reliability of their results. The studies that scored the highest (28, 30, 38) demonstrated greater adherence to RCT design principles while some studies with lower scores (24, 35) lacked key methodological weaknesses.

Table 6 presents a methodological quality analysis of the two included experimental studies that were not RCTs. Overall, the studies demonstrated moderate to strong methodological foundations, with scores of 6/9 and 9/9. Both quasi-experimental studies clearly established causal relationships between variables, ensuring the direction of the effect was well defined. However, Easom et al. (36) lacked a control group, limiting its ability to establish causality, and variations in treatment conditions introduced potential confounders.

3.8 Effects of digital technology-based interventions

In the meta-analysis, caregiver burden was evaluated as an outcome at both 3-month and 6-month follow-up, whereas stress was assessed only at 3-month follow-up.

3.8.1 Burden at <3 months

The meta-analysis initially included nine studies. Using a random-effects model, the overall effect size was Cohen's d = −1.02 (95% CI: −1.80 to −0.24; p = 0.01). Three studies reported no statistically significant differences in burden (23, 24, 35). This indicates a significant negative effect of large magnitude. However, heterogeneity across studies was extremely high [Q(8) = 93.78, p < 0.001; I² = 95%], suggesting that much of the observed variation was due to substantial differences between studies rather than chance. Inspection of the forest plot revealed that the study by Ganefianty et al. (31) reported an unusually large effect size (d = −4.09), which could be considered an outlier. To assess the robustness of the findings, a sensitivity analysis was conducted excluding Ganefianty et al. In this revised analysis, based on eight studies (Figure 3), the overall effect remained significant, with Cohen's d = −0.65 (95% CI: −1.00 to −0.30; p < 0.01). Although the effect size was reduced, it still indicated a moderate negative impact. Heterogeneity decreased [Q(7) = 25.69, p < 0.01; I² = 75%] but remained substantial, suggesting that the outlier had considerably inflated inconsistency among the results. Overall, both the initial and sensitivity analyses demonstrate a significant negative effect. The exclusion of the outlier produced a more conservative yet robust estimate, strengthening the reliability of the findings.

Figure 3

3.8.2 Burden at 6 months

Only two studies (23, 24) reported caregiver burden at six-month follow-up. Given the limited number of studies, we did not conduct a meta-analysis. Hepburn et al. (23) found a small, non-significant reduction in burden (Cohen's d = −0.13), while Meichsner et al. (24) reported a non-significant increase (Cohen's d = 0.53). The conflicting direction of effects and small number of studies preclude definitive conclusions about six-month effectiveness.

3.8.3 Stress at <3 months

Four studies provided sufficient data to assess the effectiveness of technology-based interventions in reducing caregiver stress (Figure 4). The pooled effect size was Cohen's d = −0.62 (95% CI:−0.81 to −0.43, p < 0.01), indicating a moderate and statistically significant reduction in stress favouring the intervention.

Figure 4

Individual study effects ranged from d = −0.43 to d = −0.80, with all confidence intervals excluding zero, demonstrating consistency in the direction of effect across studies. Heterogeneity was low [I² = 15%; Q(3) = 3.33, p = 0.34], suggesting minimal variability between studies and supporting the reliability of the pooled estimate.

3.8.4 Quality of life and well-being

Six studies assessed quality of life and/or well-being as outcomes (24, 28–30, 32, 37). Two studies (24, 30) did not report sufficient statistical data for inclusion in the meta-analysis, limiting the pooled analysis to four studies.

The meta-analysis (Figure 5) revealed a small, non-significant overall effect favouring the intervention (Cohen's d = 0.40, 95% CI: −0.28 to 1.07, p = 0.25). Notably, heterogeneity was very high [I² = 92%; Q(3) = 25.17, p < 0.01], indicating substantial and statistically significant variability across studies. This heterogeneity is reflected in the divergent findings: while Riegel et al. (28) found a moderate improvement in quality of life (d = 0.48 for mental health SF36 subscale), Minaei-Moghadam et al. (32) reported a high improvements in quality of life (d = 1.36), Fuller-Tyszkiewicz et al. (37) found a small, non-significant effect (d = 0.19), and Ferré-Grau et al. (29) reported a small effect in the opposite direction (d = −0.36).

Figure 5

The marked heterogeneity and inconsistent direction of effects preclude a definitive conclusion about the effectiveness of technology-based interventions on caregiver quality of life. These divergent results may reflect important differences in intervention characteristics (e.g., type, intensity, delivery mode), caregiver populations, disease stages, or outcome measurement instruments, suggesting that effectiveness may be context-dependent and warranting further investigation through subgroup analyses or qualitative synthesis.

4 Discussion

This systematic review and meta-analysis aimed to assess how effective digital technology-based interventions are at reducing the burden and stress experienced by family caregivers. Our findings suggest that these interventions, especially those focused on psychoeducation, show promise. However, their effectiveness is shaped by several factors that need closer examination.

Our review of 16 studies highlighted a variety of technological approaches. These included mobile applications, websites, and tele-coaching, used to support caregivers looking after individuals with various health conditions such as dementia, heart failure, and cancer. Most caregivers in these studies were women, typically partners or daughters of the care recipients.

The meta-analysis showed a significant overall short-term effect (less than 3 months) in reducing caregiver burden (Cohen's d = −0.65) and stress (d = −0.62). The impact on stress was consistent across studies, with low variability. However, the effect on caregiver burden showed considerable variability (I² = 75%), even after removing an outlier study.

This suggests that the effectiveness of these interventions may vary considerably depending on the context and research methods employed. In contrast, no statistically significant effect was observed on caregivers' quality of life. This outcome was characterised by very high variability (I² = 92%) and inconsistent findings across studies. Firm conclusions regarding long-term effectiveness could not be drawn due to the limited number of studies with six-month follow-ups and their conflicting results.

Our findings largely align with existing literature, which supports the effectiveness of technology-based interventions in reducing caregiver burden (13–17, 41).

A recent systematic review further reinforces this, providing a moderate estimate of effect for short-term reductions in burden and stress (17).

However, our analysis highlights the complexity behind these effects. The high variability observed in caregiver burden suggests that the effectiveness of interventions is not uniform. Factors such as the intervention's delivery method (synchronous vs. asynchronous), intensity, duration, and the type of professional support can all influence the outcomes. This point has also been raised by other researchers (15, 42, 43). Our observation that synchronous approaches might be better suited for psychological outcomes, such as stress, while asynchronous methods could be more useful for self-care and disease management, adds an important nuance to this discussion.

Furthermore, the lack of a clear effect on quality of life, which is often a secondary outcome in studies, contrasts with improvements seen in related areas like self-efficacy and reduced depressive symptoms global (16, 41, 44). This suggests that the impact of these interventions might be more specific rather than broad.

4.1 Strengths and limitations

One of the main strengths of this study is its rigorous methodology, which followed JBI and PRISMA guidelines, and the meta-analysis that allowed us to quantify the effectiveness of the interventions. However, several limitations should be considered when interpreting the results.

The primary limitation is the high methodological variability among the included studies. Interventions differed considerably in terms of technological approach, frequency, duration, and content. Additionally, a wide range of tools were used to measure the same outcomes, making direct comparisons and generalisation of results difficult. The nature of the control groups also varied, from usual care to waiting lists or partial access to the intervention, which could have influenced the size of the observed effects.

Another significant limitation is the lack of long-term evaluation. Most studies assessed effects only at the end of the intervention, and the scarcity of follow-up data prevented us from determining if the benefits lasted over time. This is a critical gap, as sustained effects are essential for adherence and the lasting impact of such programmes.

Regarding the methodological quality of the primary studies, our analysis of bias risk revealed important weaknesses. Specifically, most randomised controlled trials lacked proper allocation concealment and blinding of participants, professionals, and outcome assessors. These factors increase the risk of performance and detection bias, respectively, potentially compromising the internal validity of the results. Finally, the exclusion of some studies from the meta-analysis due to insufficient statistical data (means and standard deviations) might have limited the precision of our effect estimates.

4.2 Clinical implications

The findings of this study have direct implications for clinical practice, particularly for nurses, who were most frequently involved in delivering these interventions (45–48). The evidence shows that digital interventions can be an effective tool for reducing caregiver burden and stress in the short term. Therefore, healthcare professionals should consider integrating these technologies as a complement to usual care, offering more accessible and continuous support.

The choice of technological modality requires careful consideration. Our results suggest that the approach (synchronous, asynchronous, or mixed) can influence outcomes, with real-time interaction (synchronous) potentially being more beneficial for emotional and psychological support (25, 28, 33, 36). Interventions should be tailored to the specific needs of the caregiver, taking into account their goals, digital literacy, and the condition of the person being cared for (17). The central role of nurses and multidisciplinary teams, identified in the studies, highlights the importance of a collaborative and holistic approach to supporting caregivers, using technology to extend the reach and effectiveness of professional care.

4.3 Implications for future research

The limitations identified in this review point to several directions for future research. Firstly, it is crucial to conduct studies with longer follow-up periods to assess how sustainable the effects of interventions are in the medium and long term. While short-term effectiveness has been demonstrated, it is unclear whether the benefits persist over time. Secondly, the high variability in outcomes, especially for caregiver burden and quality of life, highlights the need to investigate the most effective “active ingredients’ of these interventions. Future studies should compare different modalities (e.g., synchronous vs. asynchronous), durations, and intensities of intervention to determine which combinations produce the best results for different caregiver profiles.

To allow for more robust comparisons between studies and more precise meta-analyses, greater standardisation of outcome assessment tools is essential. The use of a core outcome set for caregiver burden, stress, and quality of life would be a significant advancement for the field. Additionally, the methodological quality of clinical trials needs improvement, with particular attention to the proper implementation and reporting of allocation concealment and blinding, to minimise bias risks. Finally, given the inconclusive results, caregivers' quality of life and well-being deserve to be investigated as primary outcomes in future studies.

5 Conclusions

In conclusion, this systematic review and meta-analysis confirms the potential of digital technology-based interventions as an effective strategy to reduce family caregiver burden and stress in the short term. However, the effectiveness of these interventions is not universal; it is influenced by various methodological and contextual factors, leading to considerable variability in effects, particularly concerning caregiver burden and quality of life. The long-term sustainability of these benefits remains an open question. For the full potential of these technologies to be realised, future research should focus on optimising intervention protocols, improving the methodological quality of studies, and standardising outcomes. This will enable the development of more robust, personalised, and lasting support strategies for family caregivers.

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