Understanding the effectiveness and design of parent-oriented mobile health interventions: a systematic review and narrative synthesis

Abstract

Background

Parents of children with a health condition experience high levels of distress which can have long-term impact on the child and parent. Dyadic interventions have the potential to decrease this distress, however several barriers to access including time constraints have been reported. Mobile health (mHealth) interventions can address several of these barriers.

Goal

The goal of this systematic review was to review and synthesize the literature examining the effects of parent-oriented mHealth interventions and their content and design.

Methods

We searched PubMed/MEDLINE, Embase, PsycINFO, CINAHL and Cochrane Central databases from January 2013 to 2023 using a search strategy based on telemedicine and parents/caregivers. Included studies were randomized controlled trials assessing the effect of parent-oriented mHealth interventions on child and parent health. The Cochrane risk-of-bias tool was used to assess for bias in studies. Trial details and design and content features of interventions were extracted. Outcomes were organized using the Van Houtven’s Framework for Informal Caregiver Interventions. Results are presented narratively.

Results

Fifty papers pertaining to 49 unique studies met our inclusion criteria. More than half of the studies scored high-risk for bias. Interventions targeted a wide range of pediatric conditions. Intervention type included texting (n = 17) and investigator-developed mobile applications (n = 16). Interventions significantly improved parent psychological health and child health outcomes. Key intervention features and design included the use/application of codesign and a theory-driven intervention.

Conclusion

Parent-oriented mHealth interventions identified in this review significantly improved both parent and child health outcomes. Therefore, these interventions have the potential to support parents outside of a clinical setting.

Background

Parents of children with physical or mental health conditions or disabilities are often expected to take on several roles while caring for their child. These complex roles include being a proxy medical-decision maker, advocate, care coordinator, and provider of direct patient care, responsible for medication administration and assistance with activities of daily living [1, 2]. Parents of children with serious or chronic illnesses, such as cystic fibrosis, diabetes, and cancer, experience significantly higher levels of parenting stress compared to parents of healthy children [3]. Studies show that up to 40% of parents of children with chronic conditions report clinically significant stress, with 38% experiencing moderate to severe anxiety and 26% facing moderate to severe depression [4]. This ongoing stress can severely affect parents'overall quality of life, with up to 45% of parents at risk for a decline in health-related quality of life [5].

A parent’s psychological health has been shown to impact their child’s physical and psychological well-being, including levels of anxiety [1, 6, 7]. Further, studies have found a significant positive association between children’s psychological health and overall family relationships including family cohesion and conflict [8]. This connection between parent and child health underscores why family-centered care models have become integral to pediatric medicine [9]. Parents have reported that having access to resources such as emotional support and information related to clinical knowledge and skills is essential for enhancing their caregiving ability [10,11,12,13]. Despite this known connection, parents of ill children have reported barriers to accessing supportive interventions. These barriers stem from a variety of issues including a lack of evidence-based interventions [14,15,16], limited staff knowledge regarding the delivery of psychosocial interventions [17, 18], and the inability to attend in-person support sessions due to child treatment and other family demands [19].

Mobile health interventions (mHealth), including digital applications, texting with clinicians and automated text-based prompts, have the potential to address several known barriers to parent-oriented interventions [20]. This terminology can be traced back to the pioneering work of Istepanian et al. in 2003, [21,22,23] who first defined it as emerging mobile computing, medical sensor and communication technologies for healthcare. The field expanded in 2007 with the introduction of the first generation of smartphones [21,22,23]. In 2011, the World Health Organization (WHO) stated that mHealth has the potential to transform health service delivery globally. The development and growth of these interventions were further accelerated during the COVID- 19 pandemic, due to the need for social distancing and lockdowns [21,22,23]. Together, interest in these interventions has increased, in part due to their ability to provide enhanced access to personalized support, allowing users to receive assistance in real time and in various non-clinical environments in response to changes in health status or behaviors [20, 24].

The development and design of mHealth interventions is a complex process, and the lack of involvement of intervention users (or end-users) such as patients and families can limit intervention effectiveness, integration into practice and sustainability [25,26,27]. Co-design of mHealth interventions, in which a diverse range of partners participate in the design and development process [25], is one method to address this issue [28]. However, little is known about the extent to which co-design has been used to guide the development of parent mHealth intervention and its impact.

To date, several reviews have explored the effectiveness and design of mHealth interventions in adults with various health conditions, including dementia and frailty, as well as their family caregivers [29,30,31]. In pediatrics, one review and meta-analysis focused on pediatric-oriented mHealth interventions found that parent involvement in mHealth interventions led to effect sizes larger than those without parental inovlvement [19]. Other reviews in pediatrics have concentrated on specific acute or chronic conditions [32, 33], have not provided important information regarding design and development [34], shown heterogenous effectiveness results [35] or are not recent [36]. Given the lessons that can be learned across different conditions and the continued exponential growth of mHealth, an updated review addressing each of these gaps is necessary.

Our overarching goal was to synthesize the literature examining the effects of parent-oriented mHealth interventions; as well as the content and design of such interventions. Our specific objectives were to describe: (1) the impact of parent-oriented mHealth interventions on parent health outcomes compared to a control group; (2) the impact of parent-oriented mHealth interventions on child health outcomes compared to a control group; (3) the design, content, and functionality of the identified parent-oriented mHealth interventions; and (4) evaluate the quality of these studies.

Materials and methods

Study design, literature search and study selection

A systematic review was conducted. Our reporting is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Appendix A) [37]. The review is registered on PROSPERO (ID# CRD42023404861). We searched PubMed/MEDLINE, Embase, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, and Cochrane Central databases on January 26, 2023, with the assistance of a research librarian. Our search was limited to studies published from 2013 onward. The search strategy, developed using synonyms for telemedicine and parents, is presented in Appendix B.

Using the Population, Intervention, Comparison Outcomes and Study (PICOS) [38] design as a guide, our inclusion criteria were as follows:

• Population: Parents included any family members providing a significant amount of childcare to support a child’s health and well-being. Children of these parents had to have a chronic or acute physical or mental health condition, or neurodevelopmental, intellectual or developmental disability. Chronic conditions were defined as lasting more than three months or occurred three times or more within one year, requiring ongoing medical attention or limiting activities of daily living [39]. Acute conditions were those with sudden onset, involving a short course of treatment (less than three months), and where a return to baseline was likely (e.g. acute bronchitis) [40].

• Intervention: Studies were included if they assessed the efficacy of parent-oriented mHealth interventions aimed at improving child or parental physical, psychological or developmental health. These interventions had to have been accessible through a mobile electronic device, including smartphones or tablets with interactive cellular communication capability [19, 29, 30]. Parents had to be among the users of the intervention to qualify for inclusion.

• Comparison: Control groups included usual care, no treatment, waitlist, or an active intervention.

• Outcomes: Parent outcomes included any observer or self-reported measure related to their ability to care for their child and their own psychological or physical health. Child health outcomes included any observer or self-reported measures related to the physical, psychosocial, or developmental health of the child.

• Study design: To be included studies had to be a randomized controlled trial of any size. We excluded dissertations, abstracts, and studies not published in English.

Perfect agreement on the application of eligibility criteria was achieved through two pilot tests involving 200 randomly selected abstracts, assessed by two independent coders (A.K. and I.Z.). After removing duplicates, all titles and abstracts, as well as full text articles, were screened in Covidence by two independent reviewers (A.K. and I.Z.). Any discrepancies were resolved by a third reviewer (P.P.).

Data extraction procedures

A code book was developed by two authors (A.K. and I.Z.) to guide the extraction of information regarding the study, child, parent, and intervention. The information extracted about the intervention was adapted from the Template for Intervention Description and Replication checklist [41]. Data from a random sample of 10% of identified studies were extracted in duplicate by both authors (A.K. and I.Z.), achieving 100% agreement. Data from the remaining studies were extracted by one author (A.K.) and checked for accuracy by a secondary author (I.Z.) Any identified disagreements were resolved through discussion until full agreement was reached.

Risk of bias

Using the Cochrane Risk-Of-Bias tool for randomized trials (ROB2) two reviewers (A.K. and E.M.) rated a random sample of 20% of studies in duplicate, achieving 80% agreement across all ROB2 domains [42]. The remaining studies were assessed individually, and any questions related to bias assessment were discussed as a group. Images for ROB2 were created using the Risk-of-bias VISualization tool [43].

Outcomes and data synthesis

A narrative synthesis, tabulation, and descriptive analysis of the items extracted from the studies were conducted. To synthesize the cumulative impact of parent-oriented mHealth interventions, parent and child health outcomes were included in the narrative synthesis only if they were reported in two or more studies, with data on the remaining outcomes presented in tabular form.

The significant impact of the intervention on outcomes was determined based on a reported statistical difference of p < 0.05 between groups-over time, between groups, or a within a group from pre- to post-test. Data related to intervention content (type of intervention, frequency of use) and design features (co-design processes and theoretical frameworks utilized) were extracted and included in the narrative synthesis.

Interventions and parent and child health outcomes were organized using the framework developed by Van Houtven and colleagues (2011) for informal caregiver interventions [12]. This framework, designed for primary caregivers of adult patients, highlights that most caregiver-oriented interventions aim to improve or address four major categories pertaining to caregiving: (1) clinical knowledge, (2) psychological skills, (3) support seeking, and (4) quantity of caregiving (i.e. number of caregiving hours per week) [12]. Interventions were categorized according to this framework. Further, as outlined by the framework, parent and child health outcomes were classified as: (1) psychological health, (2) physical health, (3) healthcare utilization, and (4) economic status (i.e., changes in costs of health care services) [12].

Psychological health is defined as a dynamic state of internal equilibrium that enables individuals to use their abilities in harmony with the universal values of society, encompassing basic cognitive and social skills, as well as the ability to cope and function in social roles [44]. Parent psychological outcomes were further categorized as non-social, social and caregiving related, while child psychological outcomes were delineated into self-management related and non-social outcomes [12].

Results

Our search identified 10,035 titles and abstracts. After excluding 2850 duplicates, 7185 titles and abstracts were screened, and 108 full articles assessed for eligibility. Following screening, 50 articles pertaining to 49 unique studies were included (PRISMA diagram in Fig. 1).

Fig. 1

PRISMA flowchart of studies. This flowchart shows the number of records identified from the search (10,035), the number of records excluded based on title and abstract (7077), and the number of studies excluded based on the full article review (55), and the reason for exclusions. Fifty research articles (49 studies) were included in this analysis

Characteristics and participant traits

Table 1 summarizes the characteristics of the studies included in this research. The studies were published between 2014 and 2023, across 13 different countries, with the highest number published in 2017 (10 studies, 20%), 2018 (10 studies, 20%), and 2022 (8 studies, 16%) (see Fig. 2). The majority of the studies originated from the United States (26 studies, 53%) and China (8 studies, 16%).

Table 1 Study and participant demographics

Fig. 2

Number of studies and intervention type per year: This bar graph shows the number of studies (y-axis) published per year (x-axis). Each bar is broken down and colour coded according to intervention type

Out of the 49 studies, 19 (39%) were pilot or feasibility randomized controlled trials (RCTs), while 30 (61%) were full RCTs. The control groups used in these studies included usual care or wait-list control (22 studies, 45%) and active education interventions (27 studies, 55%). Studies in which usual care or waitlist control was used as a control group produced 27 statistically significant results (of 57 outcomes measured; 47%); while studies which used an active alternate intervention produced 23 of 55 (42%).

Sample sizes varied widely, ranging from 16 to 1,677 participants, with a mean of 155 and a standard deviation of 266. The most frequently studied child health condition was type 1 diabetes (8 studies, 16%), followed by autism spectrum disorder (ASD) (5 studies, 10%), asthma (5 studies, 10%), cancer (4 studies, 8%), obesity (4 studies, 8%), and heart disease (4 studies, 8%). Additionally, six studies (12%) focused on acute conditions, including post-operative management and recovery following hospital stays.

Characteristics of parent-oriented interventions and their development

Table 2 outlines the characteristics of the interventions, the outcomes assessed, and the measurement tools used. The main objectives of these interventions were as follows:

• Improving or managing child health: 34 out of 49 studies (69%)

• Improving or managing parent health: 11 out of 49 studies (22%)

• Improving or managing both child and parent health: 4 out of 49 studies (8%)

Table 2 Interventional features and design

In terms of technology, 15 studies (31%) evaluated new apps specifically designed for health management, while 18 studies (37%) utilized text messaging. Additionally, 13 studies (26%) employed existing apps like WhatsApp or WeChat, and 3 studies (6%) used a combination of methods. Participants engaged with the interventions at different frequencies: daily (16 studies; 33%), weekly (10 studies; 20%), 2–3 times a week (7 studies; 14%), or as needed (6 studies; 12%).

Twelve articles (24%) discussed a theoretical framework or model that informed the content or structure of the interventions. These included Bandura’s self-efficacy theory (2 studies; 17%), psychological models of stress and coping (1 study; 12%), and resilience-based frameworks (1 study; 12%).

Nineteen studies (39%) reported that interventions were co-designed with various stakeholders: parents (4 studies; 21%), parents and their children (3 studies; 16%), healthcare professionals (2 studies; 11%), and community members (2 studies; 11%). The most common co-design methods included establishing advisory boards with patients, parents, or community partners (4 studies; 21%), conducting focus groups with end-users to develop content for prototypes (3 studies; 16%), usability testing of prototypes (2 studies; 11%), and conducting surveys to assess the needs of parents and children (1 study; 5%).

Interventional target

Thirty studies (61%) aimed to enhance parents'clinical skills and knowledge. Of these, 22 studies (45%) focused on improving parents'abilities to manage their child's condition, while 8 studies (16%) targeted increasing parents'understanding of the disease and its expected clinical course.

Seventeen studies (35%) aimed to boost parents'psychological skills, specifically in two areas: self-efficacy for performing tasks related to their child's care (6 studies) and coping strategies for managing the demands of caring for a child with a health condition (11 studies).

Additionally, three studies (6%) sought to provide social support to parents, but this support was secondary to the primary goals of improving clinical or psychological skills. Notably, no interventions aimed at reducing the overall caregiving burden, such as the number of tasks or hours spent providing care [12].

Parent health outcomes

The parent health outcomes identified were exclusively related to psychological health. No studies examining physical health, economic factors, or healthcare utilization outcomes were identified (see Table 3).

Table 3 mHealth intervention impact on parental health outcomes

Parent psychological health

Non-social psychological outcomes

Depression scores showed significant improvement in four out of seven studies (57%) that assessed this outcome [57, 62, 72, 92], using a variety of validated tools. mHealth interventions significantly reduced stress compared to the control group in three out of six studies (50%) [57, 67, 76]. However, in one study (17%), the control group, which received an in-person version of the intervention, reported a significantly greater decrease in stress than the intervention group [87]. Parent anxiety scores were measured in six studies, and significant improvements related to the intervention were observed in four of these studies (67%) [55, 62, 92, 94].

Resilience showed significant improvement in two out of three studies (67%) that measured this construct [72, 76]. Quality of Life (QoL) was assessed in seven studies, with significant improvements observed in three of these studies (43%) [61, 62, 92].

Social psychological outcomes

The quality of a parent's relationship with their family or ill child was measured in three studies. Of these, two studies (67%) reported significant improvements in the intervention group [59, 68]. These studies measured the outcome using a subscale of the Parenting and Family Adjustment Scale [133] and the child-parent relationship scale [151].

Caregiving related psychological outcomes

Parents'ability to manage their child's condition was assessed six times across four studies, showing significant improvement from baseline in two of the six measurements (33%). Specifically, improvements were noted in parents'confidence in managing their child's arthritis [78] symptoms and their reported ability to manage their child’s chronic kidney disease [84].

Seventeen studies examined parents'satisfaction with the intervention, using various methods, including qualitative interviews (2 studies), the Client Satisfaction Questionnaire (3 studies) [131], and investigator-developed surveys (10 studies). All studies reported high satisfaction with the mHealth intervention, except for one study where only 37% of parents found the application somewhat useful, compared to 70% who endorsed the utility of the control treatment [70]. Additionally, one study reported no difference in satisfaction between the mHealth and control groups [77].

Child health outcomes

Child health outcomes included physical health related to diabetes, cardiovascular issues, asthma, and neurodevelopmental disorders, as well as healthcare utilization, psychological health, and disease management (see Table 4).

Table 4 mHealth Intervention impact on child health outcomes

Child physical and neurodevelopmental health

Diabetes

Diabetes-related health was assessed in seven studies, focusing on glycemic control as measured by Hemoglobin A1C (HbA1 C) levels. Two studies (29%) reported significant improvements in glycemic control in the mHealth group [61, 62]. However, in one study (14%), the significant improvement favored the control group [47].

Cardiovascular health

The most commonly assessed cardiovascular health outcomes were body mass index (BMI), evaluated in four studies, and weight, assessed in two studies. A significant improvement in BMI, favoring the use of mHealth interventions, was observed in one out of the four studies (25%) [65].

Asthma

Asthma control was evaluated in three studies using either spirometry or the Child Asthma Control Test [186]. Significant improvement was observed in one of the studies (33%) [52].

Neurodevelopmental disorders

The neurodevelopmental disorders identified in the studies included autism spectrum disorder (ASD) (3 studies), attention deficit hyperactivity disorder (ADHD) (1 study), and multiple neurodevelopmental disorders (1 study). Children's behaviors associated with these disorders were measured seven times across four studies, focusing on behavioral and emotional problems [59], pro-social behaviors [59, 63], and adaptive behaviors [89]. Of these seven assessments, significant improvements from baseline were observed in five (71%) instances [57, 59, 63].

The impact of mHealth interventions on the severity of ASD [57] and ADHD [88] symptoms was evaluated in one study each, with both studies reporting significant positive improvements.

Healthcare resources

Healthcare resource usage was assessed in terms of hospitalizations (4 studies) and emergency department visits (5 studies). Among the studies that evaluated hospitalizations, one study (25%) found that hospitalizations were significantly lower in the intervention group [62]. In another study (25%), significantly fewer infants in the control group were readmitted to the hospital compared to those in the intervention group [50].

Child psychological

Self-management related psychological outcomes

Children's ability or confidence in managing their health conditions was assessed across 17 outcomes in 9 studies. These included management behaviors (7 outcomes), self-efficacy (4 outcomes), self-management (4 outcomes), and perceived burden of disease-related problems (2 outcomes). Significant differences in self-management activities and health knowledge favoring the intervention group were noted in three instances across two studies (19%) [61, 69].

Adherence to treatment was evaluated in 21 instances across 14 studies, with significant improvements in the intervention group observed in three studies (14%) [60, 71, 74]. In one additional study, no significant difference was found overall; however, an analysis of intervention engagement revealed that participants who engaged more with the intervention showed significant improvements in medication adherence [91].

Non-social psychological outcomes

Pediatric quality of life (QoL) was assessed eleven times across ten studies, focusing on either disease-specific QoL or generic health-related QoL. Of these measurements, significant improvements from baseline in the intervention group were observed in two instances (17%) [48, 69].

Outcome effects of mhealth intervention features and design

Table 5 presents the outcome results based on the features and design modalities of mHealth interventions. In the 19 studies that reported co-designing interventions, a total of 58 parent and child health outcomes were assessed, with statistically significant improvements observed in 20 outcomes (34%).

Table 5 Trends towards significant differences per mHealth intervention feature and design

Among the 12 (24%) studies that employed a theory-driven intervention, 37 health outcomes for children and parents were measured, with 18 outcomes (49%) showing significant improvements in the parent-oriented mHealth group. In studies where participants were required to engage with the intervention at-least daily, 18 out of 40 assessed outcomes (45%) showed significant improvement. Similarly, in studies that required at least weekly (and less frequently than daily) interaction with the intervention, 19 out of 49 outcomes (39%) demonstrated significant improvement.

Text messaging and novel mobile applications developed by the research teams for health management significantly improved 30% (11 out of 37) of the assessed outcomes across 18 studies, and 28% (10 out of 36) across 15 studies, respectively. In contrast, previously developed applications that were adapted for health management showed significant improvements in 76% (28 out of 37) of parent and child outcomes across 13 studies.

Risk of bias assessment

According to the Cochrane ROB2 tool, 26 studies (53%) were assessed as having a high risk of bias, 16 studies (33%) had some concerns, and 7 studies (14%) had low concerns (see Fig. 3a). The bias domain with the highest risk across the studies was deviations from the intended interventions, affecting 16 out of 49 studies (33%). In contrast, the domain with the lowest risk was the randomization process, which was properly implemented in 41 out of 49 studies (84%) (see Fig. 3b).

Fig. 3

Risk of bias assessment. Individual bias assessment (a) and (b) overall summary for included randomized controlled trials assessed using the Cochrane collaboration tool

Discussion

We synthesized the literature examining the effectiveness of parent-oriented mHealth interventions on parent and child health outcomes and identified key content and design features that may have contributed to their effectiveness. We identified 49 studies, most frequently published in developed countries, including the United States (26 studies), with more than half scoring high-risk for bias. Overall, the identified interventions were found to be highly acceptable to parents and improved parent psychological health, with the largest impact observed in non-social psychological outcomes including a 57% improvement in depression, a 67% improvement in anxiety, and a 67% improvement in resilience.

Early evidence also suggests the utility of these interventions in improving child health outcomes, particularly regarding neurodevelopmental health outcomes, in which measured outcomes showed a 71% significant improvement. Interventions frequently connected with participants used texting or novel applications daily or weekly but were infrequently underpinned by theoretical health behaviour frameworks or developed through end-user co-designed. Evaluated interventions that involved daily engagement (45% improvement) or weekly engagement (39%), used co-design development techniques (34%), were theory-driven (49%), and included mobile applications (76% for previously established and 28% for novel) demonstrated strong effectiveness in improving both parent and child health outcomes.

Our finding that parent-oriented mHealth interventions are more common and beneficial in the context of chronic childhood conditions compared to acute ones may reflect the prolonged and complex care required from parents in chronic situations [199]. Interventions applied in the context of neurodevelopmental disorders, including ASD, cardiovascular disease and cancer, had the largest impact on both parent and child health outcomes. Parents of children with these conditions often experience a significant subjective burden, characterized by intense physical, emotional, social, and financial stress, which is linked to the extensive hours and demanding care tasks these conditions require [200,201,202]. It is important to further explore whether these types of interventions increase the burden on parents and families of children with chronic conditions, as well as on the healthcare system, in terms of cost and time. While previous reviews have shown that these interventions are relatively low-cost [203], more research is needed in pediatrics to assess their cost-effectiveness compared to usual care. Additionally, further work is required to determine if mHealth interventions place additional strain and expectations on parents of sick children and on healthcare providers who are already overburdened [204, 205]. Interestingly, in some studies, control groups showed significantly better outcomes than the intervention groups. This highlights the need for careful adaptation of interventions when transitioning to mobile-based formats, as well as the need to consider the therapeutic benefit of integrating person-based care into digital technology design [206].

Interventions aimed at enhancing caregiving capacity and support may be particularly effective for these parents [200,201,202]. However, demographics information about the parents, aside from sex, was inconsistently reported, making it challenging to determine who would benefit most from these interventions. However, most parents identified as females, specifically mothers. The lack of engagement of fathers in parent-oriented interventions has been previously noted and is linked to beliefs about gender roles regarding caregiving, lack of relevant interventions, and limited awareness of available interventions [207].

Less than 20% of the identified interventions aimed to enhance parental psychological skills, despite evidence showing positive impacts on parental psychological health, including a 67% improvement in reported anxiety and a 57% improvement in depression. This lack of focus on the well-being of parents has been noted previously and should be a key research direction in the field [208,209,210]. Our review demonstrates that when mHealth applications do consider parent psychological health in their design, they positively impact both caregiver and child outcomes. Such interventions align with the family-centered care model integral to pediatrics and address an expressed need among parent caregivers of children with chronic conditions [200, 211].

Although some evaluated interventions resulted in significant positive child health outcomes, this was not consistent across all studies. Challenges related to collecting health-related subjective ratings from younger children may partly explain this discrepancy [212]. Many identified studies relied on parent proxy-reported outcomes, which may not accurately reflect the child health status, particularly concerning psychological health outcomes [213]. Additionally, longer intervention periods may be necessary to improve various child objective and functional health outcomes such as HbA1C or blood pressure, as well as to enhance parental caregiving self-efficacy, and, consequently, child health [214].

Interventions that incorporate co-design methodologies in their development (34% improvement), are theory-driven (49%), and include more frequent interaction with users (45%) appear to be effective. Our findings suggest that co-design practices appear useful for developing more widely utilized digital interventions for parents [215, 216]. Although this approach has been inconsistently applied, the importance of grounding intervention features and function in behavioral change frameworks or models to enhance impact has been previously demonstrated and is reflected in our findings [217]. However, while we report on whether or not studied interventions were based on a theoretical framework, we cannot comment on the extent to which interventions correctly applied the framework’s tenets in their design. Due to the complex and multi-component nature of mHealth interventions, comparisons between those that include these elements and those that do not are challenging [218]. Future research should focus on identifying the features that are most effective in different patient settings and age group—a goal that could be achieved through co-design efforts involving parents, pediatric patients, and clinicians.

Questions remain around the methods for successfully implementing parent–child mHealth interventions into clinical practice. In particular, the lack of digital inclusion—encompassing access to and the relevance of digital technologies for individuals or groups—limits many populations’ capacity to engage with and potentially benefit from these care models [219]. Ensuring that digital health interventions are designed equitably is critical to minimizing the digital healthcare divide. Frameworks such as the eHealth Literacy Framework may inform the design and implementation of digital interventions, improving their applicability across target populations [220]. Particular considerations, including those related to literacy and user experience norms, are essential when developing interventions for pediatric patients and their parents. One option to address this issue is to develop applications that include specific profiles for both parent and child users.

Other concerns include how data from these interventions can be effectively integrated into child electronic health records, particularly when outcomes are parent-proxy reported due to the child’s age or illness, or when the data pertain to parental health status rather than that of the child [208]. Additionally, given the family and treatment related demands, engaging parents of children with chronic conditions in consistent and longitudinal use of interventions poses a barrier to implementation [221, 222]. Engagement strategies such, as gamifying interventions and utilizing push notifications, have been suggested to improve retention in mHealth studies [223].

Limitations

The studies identified in this review are not without limitations. Several studies exhibited a high risk-of-bias due to a lack of participant blinding. Although it is challenging to blind participants without an active control group, future studies could blind outcome assessors and analysts. Additionally, most studies were published in high income countries. Given the pressing need to increase access to high quality child healthcare in lower income countries, future research should focus on evaluating mHealth interventions in these regions.

This review also has limitations. Due to the wide variety of health conditions and types of interventions, a meta-analysis was not feasible. Furthermore, we only included RCTs, so we cannot comment on the results of mHealth evaluations using different study designs. In addition, to capture the full extent of mHealth interventions in the literature, we included pilot or feasibility studies that were not powered for statistical significance. Finally, studies not published in English were excluded, which may limit our understanding of these interventions in other cultural contexts.

Conclusions

Overall, parent-oriented mHealth interventions appear to improve parent psychological health and may positively affect child health. Given these encouraging findings and the widespread accessibility of mobile digital devices, mHealth interventions could significantly enhance the quality of family-centered pediatric healthcare. Intervention functionalities and design features, such as co-design and the use of health behaviour theoretical frameworks, may be valuable in amplifying the impacts of developed mHealth applications. Further research is needed to elucidate when and how to apply these technologies most effectively within pediatric care. Taken together, parent-oriented mHealth interventions represent a promising tool for improving outcomes for both parents and their children, facilitating family-centered care.