AI and Chatbot-Supported Interventions for Physical Activity and Obesity-Related Lifestyle Behaviors: Scoping Review With Attention to Family Involvement

Introduction

Childhood physical inactivity (PA) and obesity are persistent, high-impact challenges across the life course and often cluster within families through shared environments, routines, and resources [1]. World Health Organization (WHO) guidelines recommend that children and adolescents accumulate an average of at least 60 minutes per day of moderate-to-vigorous PA, while also reducing sedentary time [2]. Yet recent global surveillance indicates that PA levels remain low across childhood and adolescence. Global Matrix 4.0 data from 57 countries suggest that only about 27%-33% of children and adolescents aged 5-17 years meet recommended activity levels [3], and the WHO reports that 81% of adolescents aged 11-17 years are insufficiently active worldwide [4].

Pediatric overweight and obesity also represent a growing global public health challenge. In 2022, more than 390 million children and adolescents aged 5-19 years worldwide were living with overweight, including more than 160 million living with obesity. The prevalence of overweight, including obesity, among children and adolescents aged 5‐19 years increased from 8% in 1990 to 20% in 2022, while the prevalence of obesity alone increased from 2% to 8% [5]. Important regional differences are also evident. Latin America and the Caribbean, the Middle East and North Africa, and North America have among the highest prevalence of overweight, while the largest numbers of affected children and adolescents are concentrated in East Asia and the Pacific, Latin America and the Caribbean, and South Asia [6]. These patterns reflect broader behavioral, environmental, and structural determinants of health and reinforce the need for accessible and scalable approaches to support healthy behaviors among children, adolescents, and families [1].

Families play a central role in shaping children’s health behaviors [7]. Caregivers influence opportunities for PA through role modeling, encouragement, co-participation, transportation, and the organization of routines [8]. Therefore, family-based approaches are widely recognized as an important strategy for supporting healthy lifestyle behaviors and pediatric obesity prevention and treatment [7]. At the same time, participation in intensive family-based programs can be difficult to sustain because of barriers such as cost, scheduling demands, transportation, workforce limitations, and unequal access to care [7]. These challenges are particularly relevant for underserved families and have increased interest in more flexible and scalable approaches to family-centered behavior change support.

Digital health and mHealth have rapidly expanded as a delivery channel for behavior change interventions, supported by widespread mobile device access and increasing interest from health systems in scalable, low-cost approaches [9,10]. Within this broader digital health landscape, conversational tools such as chatbots and virtual agents represent one emerging delivery format that may support health promotion through interactive communication, tailored prompts, and ongoing engagement [9,10]. However, for pediatric PA and relevant lifestyle behaviors, the literature remains scattered across diverse populations, platforms, intervention designs, and outcomes.

AI has further expanded the potential of digital health interventions for children and families by enabling more tailored, interactive, and responsive (ie, real-time) support [11,12]. In contrast to static digital content, AI-enabled systems such as chatbots and conversational agents may help deliver intervention components based on user input, preferences, or real-time data [13]. In pediatric lifestyle interventions, these features may be especially relevant because children’s PA and related behaviors are shaped by changing routines, developmental needs, and family context [12]. However, the evidence base remains fragmented across platforms, target behaviors, and outcome measurement strategies [14].

Despite growing interest in AI-enabled digital health tools, few studies have synthesized the current evidence on AI and chatbot-supported interventions for pediatric PA and obesity-related lifestyle behaviors while examining the extent and nature of family involvement [10,12,13]. Previous reviews have examined conversational agents in health care broadly, chatbot-supported lifestyle interventions across age groups, or the feasibility and acceptability of chatbots for nutrition and PA promotion among adolescents [10,12,13]. However, these reviews often aggregate adult and pediatric populations, emphasize individual-level interventions, or examine digital health more broadly without distinguishing AI-driven systems from traditional mobile apps [10,12]. Moreover, family engagement within digital interventions is inconsistently conceptualized and operationalized, ranging from passive caregiver monitoring to active, dyadic, or multigenerational participation [12]. Questions also remain regarding cultural responsiveness, equity considerations, accessibility for underserved families, and safeguards related to data privacy, algorithmic bias, transparency, and accountability [12]. To address these gaps, the present review focuses specifically on AI and chatbot-supported interventions for pediatric PA and related lifestyle behaviors, with particular attention to family engagement, technology characteristics, cultural tailoring, and implementation gaps.

Given the rapid evolution of AI technologies and the heterogeneity of study designs, including pilot feasibility trials, randomized controlled trials, and early-stage implementation studies, a scoping review is warranted. Thus, the objective of this scoping review was to map and characterize AI and chatbot-supported interventions targeting pediatric PA and obesity-related lifestyle behaviors. Priority areas of focus are (1) technology type and AI features, (2) delivery platforms and intervention components, (3) definition and operationalization of family engagement, (4) cultural tailoring and bilingual delivery strategies, and (5) the characteristics of the existing evidence base, including study designs, outcomes, and gaps to inform future research and equitable implementation.

Methods

Study Design

A formal review protocol was not prospectively registered. Before screening began, the research team developed the review objectives, eligibility criteria, search strategy, screening procedures, and standardized data-extraction form. The complete database search strategies are provided in Multimedia Appendix 1, and the completed PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist is provided in Checklist 1 [15]. The review aimed to map and synthesize the existing literature on AI and chatbot-supported interventions targeting pediatric PA and obesity-related lifestyle behaviors, with particular attention to the extent and nature of family involvement.

Search Strategy

Information Sources

A comprehensive literature search was conducted across 7 electronic databases, including PubMed, Web of Science, APA PsycINFO, Academic Search Complete, CINAHL Ultimate, IEEE Xplore, and Scopus. The final search was performed on February 8, 2026. Searches were limited to English-language peer-reviewed publications published within the past 10 years. This timeframe was selected to capture contemporary AI-enabled and chatbot-supported technologies relevant to current practice, given the rapid evolution of conversational-agent platforms, natural language processing, adaptive personalization, wearable integration, and generative AI. Earlier digital interventions may be less comparable with the systems currently used in pediatric health promotion. The search was limited to peer-reviewed publications indexed in the selected databases. A formal gray-literature search and supplementary backward or forward citation tracking were not conducted.

Search Terms

Search strategies were developed to capture three primary domains: (1) AI-enabled conversational technologies, (2) PA and obesity-related outcomes, and (3) child and family populations. Terms for AI technologies included chatbot*, chat bot*, conversational agent*, virtual agent*, relational agent*, virtual assistant*, AI coach*, ChatGPT (OpenAI), large language model*, LLM*, and generative AI. Health-related terms included physical activity*, exercise*, obese*, obesity, overweight, weight management, obesity prevention, sedentary, and screen. Population terms included child*, adolescent*, youth, teen*, pediatric*, family*, parent*, caregiver*, family-based, and parent-based. Database-specific syntax adjustments were applied as needed, including title and abstract field restrictions and wildcard limits. The detailed search history was provided in supplementary material (Multimedia Appendix 1).

Review articles, systematic reviews, scoping reviews, and meta-analyses were excluded during the search process where possible using database filters.

Eligibility Criteria

Eligibility criteria were guided by the population, intervention, comparator, outcomes, and study design (PICOS) framework. Studies were included if they: (P) involved children or adolescents aged 0‐18 years or parents, caregivers, or families supporting the health behaviors of a child or adolescent within this age range; (I) evaluated an AI-enabled chatbot, conversational agent, or related digital system designed to support PA or obesity-related lifestyle behaviors; (C) included any comparator, such as usual care or an alternative intervention, or no comparator; (O) reported outcomes related to PA or exercise, sedentary behavior or screen time, obesity, overweight, or weight management; and (S) reported empirical quantitative, qualitative, or mixed methods findings.

Studies were eligible if they involved children or adolescents or parents, caregivers, or families supporting the health behaviors of children or adolescents. Direct parent or caregiver involvement was not required because family involvement was examined as an analytic dimension rather than an eligibility criterion.

Studies were excluded if they described general digital health tools without an automated conversational, chatbot, AI-enabled, adaptive, or personalized support component, were purely technical development papers without behavioral outcomes, or were nonempirical publications such as editorials, commentaries, protocols, or review articles. Only studies published in English were included.

Study Selection

All retrieved records were imported into Rayyan (Qatar Computing Research Institute), a web-based systematic review management platform [16]. Rayyan was used to manage records, identify potential duplicates, and support blinded title and abstract screening. Potential duplicates identified through bibliographic metadata were manually reviewed and confirmed before removal. Final eligibility decisions were made by the reviewers rather than by the software. Title and abstract screening were conducted independently by 2 reviewers (DP and SA), who were blinded to each other’s decisions during the initial screening phase. At this stage, records were excluded if they were clearly unrelated to pediatric, adolescent, or family populations; did not involve AI-enabled chatbots, conversational agents, or related automated digital systems; did not address PA, obesity, sedentary behavior, weight management, or related lifestyle behaviors; or were clearly nonempirical publications. Records were retained for full-text review when eligibility could not be determined from the title and abstract alone. Full-text screening was then conducted independently by 2 reviewers (DP and SA) using the complete eligibility criteria. At this stage, specific reasons for exclusion were recorded. Articles were excluded during full-text review if they did not report PA or related lifestyle outcomes, did not include children, adolescents, parents, caregivers, or families, did not include an AI-enabled or chatbot-supported intervention component, or provided insufficient information to determine eligibility. Disagreements were resolved through discussion, and unresolved disagreements were adjudicated by a third reviewer (QJ). Interrater reliability was assessed using Cohen kappa (κ=0.81).

Data Extraction and Synthesis

Data were extracted using a standardized form developed for this review. Extracted variables included publication and study characteristics (author, year, country, design, and sample size); participant characteristics (target population, age, sex, and family involvement); intervention characteristics (intervention type, delivery platform, duration, comparator, wearable or sensor use, and behavior change components); AI-related features (AI or automation role, intervention system or platform, reported AI technique or automation logic, input data type, and AI tasks or outputs); theoretical foundations; and reported outcomes, including feasibility, acceptability, usability, engagement, PA, and related lifestyle outcomes, secondary outcomes, and key findings. Extracted data were synthesized narratively to characterize the scope of the evidence and identify patterns across study design, intervention delivery, AI functionality, family engagement, and outcomes.

Critical Appraisal

A formal critical appraisal or methodological quality assessment was not conducted. The purpose of this scoping review was to map the characteristics, applications, and gaps within an emerging and heterogeneous evidence base rather than to assess the certainty of evidence or estimate pooled intervention effects.

Results

Overview

Figure 1 presents the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. The initial database search identified 2730 records. After removing 363 duplicate records, 2367 unique articles remained for title and abstract screening. Of these, 2231 were excluded because they were clearly outside the review scope. A total of 136 full-text articles were assessed for eligibility. During full-text review, 122 articles were excluded for the following primary reasons: no PA or related lifestyle outcome, not child-, adolescent-, parent-, caregiver-, or family-focused, no AI-enabled or chatbot-supported component, or insufficient information to determine eligibility. Ultimately, 14 studies met the inclusion criteria and were included in the final sample (Figure 1).

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Study Overview

A total of 14 original studies were included in this scoping review [17-30] (Table 1). The studies were published between 2021 and 2026, with 12 of the 14 published in 2023 or later. The 14 studies were conducted across 10 countries. The United States and South Korea were the most frequently represented settings (n=3 each), followed by one study each from Turkey, Spain, Italy, the United Kingdom, Belgium, Switzerland, Greece, and Canada. Although the included studies were geographically diverse, all were conducted in middle- or high-income contexts.

Among included studies, 3 studies were randomized controlled trials [19,24,26], one was quasi-randomized [25], one was a pre-post feasibility study with a comparison group [17], and 3 were single-arm or uncontrolled pilot or pre-post studies [18,22,27]. The remaining 6 were noneffectiveness-focused designs, including one qualitative adaptation study [30], one co-design mixed methods study [23], 2 development or usability studies [20,28], one cross-sectional survey [21], and one autoethnographic feasibility or development paper [29]. Sample sizes ranged from 9 to 900 participants, with most studies enrolling relatively small samples; only one large, randomized trial included more than 500 participants [19].

Participant and Family Characteristics

The 14 studies targeted a broad range of pediatric populations, including infants, school-aged children, and adolescents (Table 2). Most studies focused on school-aged children or adolescents, whereas one large trial focused on infants from birth to 24 months [19]. Several studies targeted specific clinical or high-need populations, including youth with autism spectrum disorder (n=2) [17,30], children or adolescents with obesity or overweight (n=5) [18,22,24,26,29], childhood or adolescent cancer survivors (n=1) [21], and adolescents facing barriers to PA or living in under-resourced settings (n=2) [23,27].

Ten [17-21,25,26,28-30] of the 14 studies included a parent, caregiver, or family component in the target population or intervention design, such as parent-child dyads, family-based obesity management, or studies collecting both child and caregiver perspectives. The remaining 4 [22-24,27] were primarily youth-focused, although family influence was acknowledged as an important contextual factor. The degree of family involvement varied considerably, ranging from minimal parental support or consent to parent-mediated delivery, co-participation in activities, shared monitoring, and parent-facing intervention interfaces.

Reporting of demographic and contextual characteristics was inconsistent. Child age was available in all 14 studies. While parents’ age was reported in only 4 studies [17,19-21]. Female participation or sex distribution was reported in 11 studies [17-24,26,27,29]. Among studies reporting child sex distribution, the proportion of female participants ranged from 16.7% to 65.0% [20,24]. Language or linguistic context could be identified in all 14 studies. However, cultural tailoring was reported in only one study [19], and 4 additional studies [20,21,23,30] described some form of contextual, linguistic, developmental, or population-specific tailoring.

Intervention Design and Delivery Characteristics

Intervention design and delivery characteristics were presented in Table 3. Mobile app– or smartphone-based delivery was the most common format, appearing in 9 studies [17-19,22,24-27,29]. Six studies [19,20,23,28-30] incorporated a clearly web-based component, such as a dashboard, web platform, online interface, or browser-based system. Other delivery modes included chatbot-supported platforms, WhatsApp (Meta)-supported programming, SMS text messaging or responsive text messaging, serious games, metaverse-supported systems, and hybrid platforms integrating child-, parent-, and clinician-facing tools.

Nine studies [17-19,22,24-27,29] reported a defined intervention or exposure period, with durations ranging from 2 weeks to 24 months. Four studies [17,24,25,27] had relatively short intervention periods of 2-4 weeks, 3 [22,26,29] lasted approximately 8-12 weeks, and 2 [18,19] extended beyond 5 months, including one 24-month randomized trial and one 12-month obesity management program. The remaining 5 studies [20,21,23,28,30] were qualitative, developmental, survey-based, or case-based and therefore did not involve a conventional intervention period.

Most interventions were multicomponent and incorporated established behavior change strategies. Self-monitoring or tracking components were present in 9 studies [18-22,25-27,29], personalized feedback or adaptive content in 10 studies [17-20,23,25-27,29,30], and education or informational support in 9 studies [18,19,21-23,25-27,30]. Goal setting or challenge-based components were identified in 7 studies [19,22,25-28,30], gamification or reward-related elements in 7 studies [17,20,22,24-26,29], and social support, co-participation, or human-supported features in 8 studies [17-20,23,25,29,30]. Reminders or prompts were described as a distinct feature in 4 studies [17,24,26,27]. Although all studies were relevant to PA, only 5 [17,23,24,27,28] focused primarily on PA or exercise promotion; the remaining 9 [18-22,25,26,29,30] embedded PA within broader obesity-management, lifestyle, rehabilitation, or healthy living interventions.

Wearable devices, sensor-based inputs, or objective digital monitoring were reported in 6 studies [18,24,25,27-29]. These included Fitbit (Google LLC) devices, accelerometers, computer vision–based gesture recognition, smartphone- or app-linked monitoring, and planned smartwatch integration. However, most studies still relied partly or primarily on self-reported behavior, questionnaires, or user-entered information.

AI Features and Functional Characteristics

The included studies varied substantially in the sophistication and reporting of their AI and automation features (Table 4). To improve clarity, we distinguished the named intervention system or platform from the reported AI technique or automation logic. Three studies [17,27,28] used generative AI or large language model–based approaches directly in intervention delivery or content development. Several studies used scripted, predefined, or automated chatbots and tailoring functions based on structured pathways, branching logic, or automated rules [19,22,25,26,30]. Moore et al [23] described natural language understanding for intent recognition and dialogue routing. The study by de Arriba Muñoz et al [18] used a hybrid personalization engine, Zarkogianni et al [29] used a machine learning–based recommendation system, Oh et al [24] used computer vision and deep learning for gesture recognition, and Kim et al [20] described a broader integrated chatbot-supported digital health ecosystem.

Across studies, the most common role of AI was to support personalization and automated interaction rather than diagnosis or prediction. Tailored feedback, adaptive support, or personalized recommendations were described in 10 studies [17-20,22,23,25-27,29]. Chatbot-based or conversational coaching features were present in 8 studies [17,21-23,25-27,29], activity generation or exercise recommendation in 4 studies [17,24,28,29], and monitoring or self-management support in 6 studies [18,19,22,25,26,29].

Input data were most commonly participant- or caregiver-entered, including text prompts, questionnaires, goal selections, behavior logs, profile information, or interaction history. Six studies [18,24,25,27-29] incorporated wearable, sensor, image, or other objectively tracked data as part of the AI-supported system. Technical reporting of AI methods was often limited. Seven studies [20,21,23,27-30] provided only limited, conceptual, or partial descriptions of AI logic or architecture, whereas 6 [17-19,22,24,25] provided at least moderate detail about system workflow, personalization logic, or model type. One study [21] did not evaluate a live AI system but instead assessed attitudes toward a proposed future chatbot.

Theory, Implementation, and Outcome Findings

Theoretical frameworks were identified in most studies (Table 5), including Social Cognitive Theory [19]; the capability, opportunity, motivation, and behavior (COM-B) model; the Theoretical Domains Framework [23]; the Health Action Process Approach; the Elaboration Likelihood Model; Persuasive Systems Design [25]; Self-Determination Theory [27,29]; the Health Promotion Model; the Technology Acceptance Model [21], and the Multi-Process Action Control (M-PAC) model [28]. Other studies referenced broader approaches such as cognitive-behavioral principles, family-centered care, or user-centered design, whereas Aydemir [17] and Oh et al [24] did not report an explicit theoretical framework.

Outcome assessment approaches varied across studies (Table 5). Six studies [18,19,24,25,27,29] included an objective or performance-based PA-related measure, such as accelerometer-assessed activity, Fitbit-derived metrics, step counts, calorie expenditure, physical fitness testing, or device-recorded movement patterns. Four studies [17,21,23,26] relied primarily on self-report or parent-report PA-related measures, including exercise questionnaires, activity hours, or perceived confidence and motivation for PA. The remaining 4 studies [20,22,28,30] did not directly assess intervention-related PA outcomes.

The studies frequently examined outcomes beyond PA alone. Anthropometric or weight-related outcomes, including BMI, BMI z score, body fat, waist circumference, or obesity-related indicators, were reported in 6 studies [18,19,22,24,26,29]. Diet- or nutrition-related outcomes were reported in 4 studies [18,19,21,29], screen time or sedentary behavior in 3 studies [19,25,27], and sleep-related outcomes in 3 studies [19,21,25]. Psychosocial, motivational, quality-of-life, and usability-related outcomes were also commonly reported, suggesting that many AI-supported family-based interventions were conceptualized as broader healthy lifestyle or obesity-management programs rather than narrowly focused PA interventions.

Among the 8 studies that reported direct PA-, exercise-, movement-, or fitness-related outcomes, 4 [17,23-25] reported significant improvements in PA participation, exercise performance, movement-related behavior, or fitness-related indicators. Two studies [26,27] reported little or no statistically significant improvement in PA despite favorable findings for engagement or other health outcomes. One study [18] collected wearable-based PA data but reported stronger clinical obesity-related effects than PA-specific effects, and one additional study [19] described PA-related findings as part of a broader multibehavior framework without demonstrating a clearly significant PA effect.

All 14 original studies reported at least one feasibility-, acceptability-, usability-, or engagement-related outcome (Table 5). Five studies [20,21,23,28,30] were primarily qualitative, developmental, survey-based, or case-based in design and therefore emphasized user preferences, accessibility needs, perceived usefulness, desired features, implementation barriers, or design priorities rather than behavioral efficacy. These studies generally reported favorable perceptions of AI-supported tools, particularly when interventions were viewed as personalized, interactive, and supportive of family routines or youth autonomy.

Among the intervention-oriented studies, quantitative implementation indicators were commonly reported. Examples included high parent satisfaction and 92% participation in WhatsApp discussions in one parent-mediated ChatGPT study [17]; a System Usability Scale score of 72 in one obesity-management intervention [18]; 86.3% retention at 24 months in the large primary care trial; 85% friendliness, 100% perceived utility, and 69% acceptability in one pediatric obesity app pilot [22]; 61 total chatbot conversations and 73% definite intention to use in one conversational-agent co-design study [23]; average daily app usage of 71.5% in one obesity management trial [26]; and a mean of 8.9 (SD 7.6) behavior entries, 30 reflections, and 33.5 AI feedback interactions in one school-based chatbot study [27]. One quasi-randomized trial reported substantial nonuse attrition, with 18% of participants never using the app and 30% stopping use by week 2 [25].

Discussion

Principal Findings

This scoping review explored the emerging evidence on AI and chatbot-supported interventions targeting PA and obesity-related lifestyle behaviors among children and adolescents, with attention to family involvement. Across the 14 studies included, the evidence indicates that AI-supported interventions are being used in increasingly diverse ways to support PA, obesity management, and related health behaviors in pediatric populations, but the literature remains varied in terms of intervention design, AI capabilities, family involvement, and outcome measurement.

Complex AI Features Across Studies

Across the included studies, AI features span a continuum of complexity, from rule-based scripted chatbots and automated tailoring systems (eg, structured coaching or tailored text messaging) to hybrid systems that incorporated limited natural language understanding or personalization. AI was most commonly used to support personalization, user interaction, and ongoing behavioral support, rather than for diagnostic, predictive, or decision-making purposes. Many studies provided only high-level or conceptual descriptions of system functionality, with limited detail on model architecture, training data, validation procedures, or performance across subgroups. This lack of transparency makes it difficult to assess reproducibility, evaluate potential bias, or determine whether AI-driven personalization meaningfully contributes to outcomes beyond standard digital tailoring approaches. These gaps are particularly important given the increasing integration of more complex AI systems, which introduce additional concerns related to accuracy, safety, and algorithmic bias. Future research should adopt AI-specific reporting standards and explicitly document how AI outputs are validated, monitored, and updated over time across diverse populations.

Delivery Platforms and Intervention Components

Regarding the delivery platform and intervention components, most interventions embed common behavior change functions, including self-monitoring, goal setting, education, feedback, and rewards, within various platforms. These platforms range from low-burden text messaging and web dashboards to multiapp family–clinician ecosystems, wearable-linked systems, higher-complexity immersive environments, and sensor-rich exercise apps. Engagement strategies also include gamification, relational dialogue, narrative content, and school- or family-embedded routines. Within this structure, AI primarily serves as a delivery and personalization mechanism that shaped how these components were implemented. AI enabled more dynamic forms of support, such as routine coaches, on-demand assistants, and interactive educational or motivational agents [23,25,28]. In this sense, AI modifies the timing, intensity, and responsiveness of intervention delivery rather than fundamentally changing its core behavioral content.

Extent and Operationalization of Family Engagement

A central contribution to this review is the characterization of family involvement, which was important but inconsistently operationalized. In some studies, parents were critical to implementation, such as delivering activities at home, receiving caregiver education, or interacting with the digital system directly [17-19,29]. In others, youth were the primary users and family influence was acknowledged but not fully integrated into the intervention structure [23,24,27]. This variability suggests that the field has not yet converged on a clear definition of what constitutes a “family-based” AI intervention. Importantly, most studies do not measure family engagement systematically enough to test whether caregiver involvement mediates or moderates intervention effects. More precise conceptualization of whether caregivers are intended as co-users, facilitators, role models, or targets of behavior change may improve both intervention design and interpretation of outcomes.

Limited Cultural Tailoring and Bilingual Intervention

With respect to cultural tailoring and bilingual delivery, evidence remains limited. One large randomized clinical trial embedded bilingual delivery (English/Spanish) and cultural appropriateness processes in a racially and ethnically diverse population [19]. Although all studies could be situated within identifiable linguistic or contextual settings, specific cultural adaptation was rare, and the evidence base was entirely in middle- and high-income countries. This limits the transferability of current findings to lower-resource contexts and to culturally diverse family populations whose routines, values, digital access, and barriers to health behavior change may differ substantially. The lack of consistent reporting on cultural tailoring, bilingual adaptation, and structural access issues also limits understanding of how well current interventions align with the realities of underserved families. These limitations introduce broader concerns in digital health that technologies may inadvertently reinforce inequities if they are developed primarily in well-resourced settings without sufficient adaptation for different social and cultural contexts [31].

Exploratory Feasibility and Efficacy Findings

Consistent with the aims of this review, PA emerged as the most prominent target behavior, either as a primary intervention focus or as part of broader multibehavior lifestyle and obesity-management programs. Several studies focused directly on PA promotion, home exercise, or movement-related confidence and performance [17,23,24,27], whereas others addressed PA within broader interventions targeting obesity prevention, rehabilitation, or healthy lifestyle routines [18,19,25,26,29]. This pattern likely reflects both the importance of PA in pediatric health promotion and the broader reality that children’s movement behaviors are closely associated with diet, sleep, sedentary behavior, and family routines. Rather than treating PA as an isolated outcome, many of the included studies positioned it within a more comprehensive family lifestyle framework.

The reviewed studies also indicate that AI-supported interventions were generally more consistent in demonstrating feasibility, acceptability, and engagement than in behavioral efficacy. These findings align with previous youth-focused syntheses emphasizing that chatbots and AI-supported tools are often perceived as appealing and acceptable when they are interactive, personalized, and low burden [12]. However, the current review also found that favorable usability does not necessarily translate into sustained engagement or robust changes in PA participation, exercise performance, calorie expenditure, cardiopulmonary function, or confidence and motivation for PA [17,23-25], and some broader obesity-management or prevention interventions also showed favorable changes in clinical or anthropometric outcomes [18,19,29]. At the same time, findings were not uniform across studies. Tan et al [27] did not detect significant changes in PA, and Stasinaki et al [26] did not show clear superiority of the conversational-agent intervention over the comparison condition for BMI-SD score (BMI-SDS). Moreover, many included studies were feasibility, pilot, or developmental studies rather than adequately powered effectiveness trials. These findings suggest that while AI-supported interventions show promise, the current evidence remains stronger for feasibility and acceptability than for clinically meaningful and sustained improvements in PA and related outcomes.

Implications for Practice and Research

This review highlights several implications for intervention design, implementation, and future research. In practice, AI-supported family interventions may be most useful when integrated into broader family-based, school-based, or clinical programs rather than positioned as isolated tools. Their strengths appear to lie in delivering tailored education, self-monitoring support, reminders, coaching, and feedback in ways that are flexible, scalable, and responsive to daily family routines. However, the findings also indicate that intervention success likely depends on more than the presence of AI alone. Additional attention to developmental appropriateness, caregiver roles, response quality, burden of use, and contextual relevance will be essential to sustaining engagement and producing meaningful behavior change.

Future research and program development should prioritize family-centered co-design, more rigorous comparative and longitudinal study designs, and greater consistency and transparency in reporting AI features, personalization logic, and behavior change techniques. More culturally and linguistically responsive adaptation is also needed, particularly for diverse and underserved family populations. Additional work should examine implementation outcomes such as reach, adoption, maintenance, scalability, and cost, alongside behavioral and clinical outcomes. Emerging approaches such as generative AI, wearable-integrated personalization, and adaptive feedback systems may offer added value, but their safety, accuracy, transparency, and practical benefit require more careful evaluation in pediatric settings. Continued interdisciplinary collaboration will play an important role in ensuring that AI-supported family interventions are not only innovative but also ethical, accessible, and equitable.

Limitations

Several limitations should be acknowledged. First, the number of included studies was small, and the evidence base was highly heterogeneous. Further, the included studies varied widely in population, intervention modality, duration, AI sophistication, and measured outcomes, which limits comparability. In addition, many studies were pilots, feasibility studies, or usability-focused investigations rather than adequately powered trials. Further, reporting of demographic, contextual, and technical AI details was often incomplete, making it difficult to assess generalizability or identify active intervention ingredients. The search was limited to English-language publications; therefore, relevant studies published in other languages may not have been captured, which may have introduced selection bias. Further, the search was limited to peer-reviewed publications indexed in the selected databases and did not include a formal gray-literature search or supplementary citation tracking. Therefore, relevant evidence reported in theses, reports, conference materials, policy documents, unpublished sources, or publications not indexed in the selected databases may not have been captured. A formal critical appraisal or methodological quality assessment was not conducted; therefore, the methodological rigor of the included studies was not evaluated.

Conclusion

In conclusion, this scoping review found that AI and chatbot-supported interventions for pediatric PA and obesity-related lifestyle behaviors are a rapidly emerging but still early-stage field. The existing literature suggests that these interventions are generally feasible, acceptable, and promising as scalable tools for supporting healthy behavior change among children and families. However, evidence for effectiveness, particularly for PA outcomes, remains mixed and limited by methodological heterogeneity, short follow-up, and inconsistent outcomes. The review also highlights important gaps in family-based design, cultural tailoring, equity, and evaluation of advanced AI capabilities. Future research should prioritize rigorous trials, clearer conceptualization of family engagement, objective outcome measurement, and culturally responsive development to ensure that AI-supported interventions are not only innovative but also effective, equitable, and meaningful for diverse families.