This study presents a study protocol for a life course, institute-based standardized surveillance system to monitor overweight, obesity, and metabolic risk factors in India. The proposed system is designed as a continuous, periodic, and institution-anchored surveillance mechanism rather than a one-time survey or intervention trial. Its primary purpose is to generate timely, actionable data across key life stages to support early risk identification, preventive counseling, program monitoring, and policy feedback.

The protocol emphasizes routine surveillance embedded within existing institutional platforms, including kindergartens, Anganwadi centers, schools, colleges, primary health centers, and selected workplaces, to complement large-scale national surveys by providing higher-frequency, institute-level intelligence. The design is informed by principles of primary health care (PHC), including equity, community participation, intersectoral coordination, and appropriate use of technology.

The conceptual framework underpinning this surveillance protocol was developed through a structured synthesis of international surveillance models, relevant public health theories, and operational experience from ongoing implementation projects in India. Given the complex and coexisting forms of malnutrition observed across the Indian population, a multitheoretical and systems-oriented approach was adopted.

A targeted review of established international surveillance and cohort systems was undertaken to identify design features relevant to a life course, institute-based approach. The models reviewed included the Youth Risk Behavior Surveillance System (YRBS), the National Health and Nutrition Examination Survey (NHANES), the Environmental Influences on Child Health Outcomes (ECHO) program, the WHO European Childhood Obesity Surveillance Initiative, and the Global School Health Policies and Practices Survey [12-16].

These models informed key elements of the proposed protocol, including periodic data collection; standardized indicators; integration of anthropometric, behavioral, and biological measures; and the use of institutional platforms as units of observation and action. However, none of these systems individually addressed the dual requirements of life course coverage and routine institute-level surveillance within the Indian context, necessitating contextual adaptation and integration.

Five complementary theoretical perspectives were integrated to guide the design and operational logic of the surveillance system.

Nutrition transition theory provides a lens to understand the simultaneous rise in overweight and obesity alongside persistent undernutrition in India, driven by rapid dietary shifts, urbanization, and changing physical activity patterns [4,17]. Surveillance systems must therefore be capable of capturing both energy excess and nutrient deficiency within the same populations.

The life course perspective emphasizes that nutritional and behavioral exposures from preconception through young adulthood influence long-term metabolic risk. Surveillance across sequential life stages, including preconception, pregnancy, the first 1000 days, early childhood, school age, adolescence, and young adulthood, enables the early detection of risk trajectories and supports timely preventive action.

The social ecological model recognizes that health behaviors are shaped by interactions across individual, interpersonal, institutional, community, and policy levels [18]. Institute-based surveillance operates primarily at the institutional level, where schools, colleges, health facilities, and workplaces provide structured environments for measurement, feedback, and linkage to program action.

Behavior change communication models, including the health belief model and the social cognitive theory, highlight the role of awareness, perceived risk, and self-efficacy in behavior modification [19,20]. Surveillance systems that provide individualized and institution-level feedback, such as growth charts, dietary quality indicators, or physical activity summaries, can therefore function not only as monitoring tools but also as catalysts for behavioral change.

Finally, the surveillance-as-action paradigm reframes surveillance as an active public health function, in which data collection is explicitly linked to response, accountability, and program refinement [11]. Lessons from infectious disease surveillance systems, such as India’s Integrated Disease Surveillance Programme, demonstrate the feasibility of decentralized, real-time reporting mechanisms that inform timely action. Extending this paradigm to nutrition and NCD risk surveillance requires designing systems that are responsive, continuous, and locally actionable.

Integrating these theoretical perspectives yields a multidimensional framework for institute-based surveillance (Figure 1). The nutrition transition theory defines the evolving drivers of risk, the life course perspective identifies critical windows for monitoring, the social ecological model situates institutions as operational nodes, behavior change communication models ensure engagement through feedback, and the surveillance-as-action paradigm embeds responsiveness and accountability. Together, these elements provide a coherent foundation for designing a technology-enabled, life course surveillance system aligned with PHC principles.

The framework integrates 5 theoretical lenses (nutrition transition theory, life course perspective, social ecological model, behavior change communication models, and surveillance-as-action paradigm) within the 4 principles of PHC (equity, community participation, intersectoral coordination, and appropriate technology). Institutions, such as Anganwadi centers, schools, colleges, and primary health care facilities, serve as sentinel nodes for the periodic collection of anthropometric, dietary, biochemical, and behavioral indicators. The data are processed through hybrid OMR, digital, and IoT technologies, feeding upward into district and national dashboards, while feedback loops ensure local action and double-duty nutrition interventions across life stages.

The protocol design was further refined through practical insights from initial implementation phases of intramural projects funded by the Indian Council of Medical Research. These experiences informed decisions related to indicator selection, technology mix, data flow architecture, quality assurance mechanisms, and ethical safeguards.

No new human participant data were collected for the preparation of this study. Ethical approvals for the referenced implementation projects were obtained in accordance with the Indian Council of Medical Research guidelines.

India has generated substantial evidence on population nutrition and health status through large-scale surveys, such as the NNFHS, the CNNS, the National NCD Monitoring Survey, and WHO STEPS. These surveys have produced nationally representative estimates of anthropometry, dietary patterns, and behavioral risk factors and remain indispensable for benchmarking and policy planning [2,6,21,22]. However, their episodic nature—typically conducted at intervals of 5 years or longer—creates substantial temporal gaps, limiting their usefulness for continuous monitoring, institutional feedback, and timely preventive action.

In addition, existing program platforms, such as the RBSK and the RKSK, provide important opportunities for screening and counseling among children and adolescents. Nevertheless, the data generated through these platforms are largely clinical, fragmented across sectors, and seldom integrated into longitudinal nutrition or metabolic risk surveillance systems [23]. As a result, despite a strong foundation for population-level assessment, India currently lacks a routine, institute-anchored surveillance mechanism that connects educational and health institutions as recurring observation points within communities.

While national surveys, such as the NFHS and the CNNS, are invaluable for estimating prevalence and trends at state and national levels, they are not designed to support routine institutional feedback, early detection of emerging risk patterns, or rapid program adjustment. Large sampling frames and complex field operations constrain their applicability for local decision-making, and delays between data collection, analysis, and dissemination frequently exceed 2 to 3 years [9]. Furthermore, many surveys prioritize women of reproductive age and children under 5 years, resulting in the relative underrepresentation of adolescents (10‐19 y) and young adults (20‐24 y)—life stages that are critical for the consolidation of dietary, physical activity, and other behavioral risk factors influencing long-term NCD risk [24].

By contrast, infectious disease surveillance systems, such as India’s Integrated Disease Surveillance Programme, demonstrate that continuous, institution-based data flows are operationally feasible and can support early detection and timely response [11]. Applying similar surveillance logic to nutrition and metabolic risk factors offers an opportunity to address a major systems gap in NCD prevention.

Institutions—including kindergartens, Anganwadi centers, schools, colleges, primary health centers, and selected workplaces—represent stable environments with defined populations, established governance structures, and recurring service or academic calendars. Anchoring surveillance within these institutions offers several operational advantages.

First, sustainability is enhanced by aligning data collection with routine institutional cycles, reducing dependence on externally funded survey rounds. Second, continuity is facilitated through repeated measurements within the same institutions, enabling longitudinal assessment of trends and early identification of deviations in anthropometry, dietary quality, or metabolic risk markers. Third, inclusivity is strengthened by integrating both public and private institutions, improving representation across socioeconomic groups. Finally, actionability is increased, as institution-level data can directly inform local health promotion activities, school meal planning, counseling services, and community interventions.

In educational settings, surveillance may use OMR-based paper tools—similar in principle to systems, such as the Youth Risk Behavior Surveillance System—allowing standardized data collection in contexts with limited digital infrastructure [25]. In health care institutions and workplaces, digital or IoT-enabled platforms can be used where feasible to support real-time data capture and integration.

An institute-based surveillance system is inherently life course oriented. During adolescence and young adulthood, schools and colleges provide access to large populations for monitoring body mass index, dietary patterns, physical activity, and other behavioral risk factors. In the preconception and pregnancy periods, primary health centers and Anganwadi centers serve as key surveillance nodes for indicators, such as maternal nutritional status, gestational weight gain, anemia, and gestational diabetes. For infants and young children, growth monitoring and infant and young child feeding indicators can be integrated within existing Integrated Child Development Services (ICDS) and POSHAN 2.0 platforms [26].

This institutional continuum enables the systematic documentation of nutritional exposures across successive life stages and facilitates linkage between early-life risk factors and later adolescent or adult outcomes. By embedding surveillance within institutions that individuals routinely access, the system supports repeated observation without imposing additional survey burden.

The proposed protocol emphasizes standardized anthropometric and behavioral indicators, uniform data formats, and interoperable digital dashboards across institutions. The data are envisioned to flow upward from classrooms and clinics to district and state repositories, creating a distributed yet integrated surveillance architecture. Periodic, nonstigmatizing feedback reports at institutional and district levels are intended to transform surveillance from a descriptive exercise into an actionable public health function, supporting double-duty actions that address both undernutrition and overweight.

The surveillance protocol adopts a life course approach, structured around institutions that serve as fixed and recurring community anchors, including Anganwadi centers, schools, colleges, workplaces, and primary health care facilities (Table 1). These institutions function as sentinel surveillance units, periodically capturing nutrition-related and NCD-related risk indicators and generating data for both local action and higher-level aggregation. The system design aligns with World Health Organization recommendations for public health surveillance systems to be continuous, simple, flexible, acceptable, and action-oriented [16,22].

Surveillance windows are defined across the continuum from preconception to young adulthood, consistent with the first 1000 days framework and the adolescent-youth continuum [22,27]. Stratification by life stage enables the identification of critical periods for early risk detection, counseling, and preventive intervention.

This institution-anchored stratification supports the continuity of surveillance across life stages while leveraging existing human resources, such as teachers, auxiliary nurse midwives, and accredited social health activists, thereby minimizing additional operational burden.

To ensure comparability across institutions and over time, the protocol proposes a standardized core indicator set, supplemented by context-specific expanded indicators where resources permit. Indicators are organized into four primary domains.

The surveillance system uses a hybrid technology model designed to accommodate India’s heterogeneous digital infrastructure.

Ethical safeguards are integral to the acceptability and sustainability of the surveillance system. Key provisions include informed consent and assent procedures, particularly for minors; confidentiality through anonymization and restricted access to identifiable data; and delivery of feedback using nonstigmatizing language to minimize the risk of labeling or harm [32]. Data governance will be overseen by institutional ethics committees and aligned with the Digital Personal Data Protection Act, 2023 [33]. An explicit equity lens will guide implementation to ensure the inclusion of low-resource institutions and marginalized populations.

The surveillance workflow is designed as a station-based model aligned with the academic or service delivery schedule of the institution. On a typical school surveillance day, students are sequentially routed through 4 stations: registration and unique identifier assignment; anthropometric measurements; self-administered or interviewer-assisted behavioral and dietary assessment; and, where applicable, biochemical assessment.

Each station is managed by trained personnel, including teachers or institutional staff, project technical staff, and health functionaries. Anthropometric measurements are conducted using standardized protocols, and the average time per student is estimated at 5 to 7 minutes. A team of 4 to 6 trained personnel can complete measurements for approximately 120 to 150 students per day, depending on the institutional schedule and the availability of space.

Surveillance periodicity varies by life stage and institutional platform, with annual or biennial rounds in schools and colleges, and more frequent measurements integrated within routine PHC contacts for maternal and early childhood platforms.

Data captured through OMR-based tools or digital devices undergo field-level validation, including completeness checks and basic plausibility screening. Digitized data are transmitted through secure, encrypted channels to a central server. In settings with limited internet connectivity, offline data storage with periodic batch synchronization is used.

Each participant is assigned a unique surveillance identifier to enable longitudinal linkage while maintaining confidentiality. Role-based access controls define permissions for data entry, validation, analysis, and dashboard visualization.

Automated dashboards generate institution-level summaries, which are shared with authorized stakeholders. Individual-level reports are accessible only to designated health personnel for counseling and referral.

Data retention follows institutional and national data governance policies, with deidentified datasets used for analysis and reporting.

All measurement devices are calibrated at predefined intervals. A minimum of 5% of anthropometric measurements are repeated for intraobserver and interobserver reliability assessment. Automated data checks flag biologically implausible values and missing fields at the point of digitization.

Quality indicators for each surveillance round include coverage, completeness of core variables, the proportion of implausible values, and turnaround time from data collection to feedback generation.

The surveillance system will be implemented in a phased manner, beginning with sentinel institutions in Hyderabad and the Medchal–Malkajgiri districts of Telangana. A pilot evaluation will assess feasibility and system performance using predefined indicators, including institutional coverage, proportion of complete records, turnaround time for feedback reports, stakeholder acceptability, and the proportion of identified high-risk individuals linked to counseling or referral services.

The findings from the pilot phase will be used to refine workflows, training modules, and technology architecture prior to scale-up.

The increasing prevalence of overweight, obesity, and other forms of malnutrition among Indian children, adolescents, and young adults underscores the need for surveillance systems that are continuous, standardized, and embedded within routine institutional settings. While national surveys remain essential for population-level benchmarking, their episodic design limits their ability to support early detection, local decision-making, and preventive action. The institute-based surveillance protocol presented in this paper addresses this gap by proposing a life course–oriented, institution-anchored system that links data generation directly to action across education, health, and nutrition platforms.

Anchoring surveillance within institutions, such as Anganwadi centers, schools, colleges, primary health centers, and workplaces, offers several advantages over traditional survey-based approaches. First, repeated measurements aligned with institutional calendars enable timely monitoring of trends, allowing emerging risks to be identified before they consolidate into established disease patterns. Second, the protocol spans critical life stages, from preconception and the first 1000 days through adolescence and young adulthood, supporting a life course perspective that is essential for interrupting intergenerational cycles of malnutrition and metabolic risk [24,27].

Third, institution-level data enhance local relevance and usability, enabling context-specific responses such as school meal modifications, targeted nutrition counseling, or physical activity promotion. Fourth, integration across platforms, such as RBSK, RKSK, POSHAN 2.0, and PM POSHAN, enables double-duty actions that simultaneously address undernutrition and overweight, aligning with global recommendations for integrated malnutrition prevention [34]. Finally, the hybrid use of OMR-based paper tools and digital or IoT-enabled technologies supports cost-efficient scalability while remaining sensitive to infrastructural diversity and ethical considerations [31].

International experience demonstrates the feasibility and utility of institution-based and school-linked surveillance systems for child and adolescent health. Systems, such as the Youth Risk Behavior Surveillance System in the United States and the WHO European Childhood Obesity Surveillance Initiative, illustrate how standardized, low-burden data collection in schools can generate actionable insights at scale [25,43]. The NHANES and the ECHO further demonstrate the value of integrating behavioral, anthropometric, and biological measures within continuous or longitudinal frameworks [13,14].

Compared with these systems, the proposed institute-based model is distinguished by its explicit life course coverage, its integration of both public and private institutions, and its alignment with existing national programs rather than stand-alone surveillance infrastructure (Table 1). By combining behavioral, anthropometric, and selected biochemical indicators within a unified framework, the protocol adapts global best practices to India’s epidemiological and institutional context while retaining international comparability.

Despite its conceptual and operational strengths, the implementation of the proposed surveillance system may encounter several challenges. Capacity constraints related to the training and supervision of teachers, frontline health workers, and institutional staff may affect the consistency of data collection. Ensuring data quality and standardization across diverse settings will require robust calibration protocols, periodic validation, and automated data checks. Ethical concerns, including the protection of minors’ data, informed consent, and the prevention of stigma, necessitate strong governance mechanisms and careful design of feedback processes [32].

Interoperability across existing program databases and the inclusion of private institutions will require sustained intersectoral coordination and alignment of data standards. Long-term sustainability will depend on policy commitment and financing mechanisms that transition surveillance from time-limited projects to routine institutional practice. These challenges are not unique to this protocol and can be mitigated through phased implementation, pilot testing within sentinel sites, and integration with digital public infrastructure and existing program workflows.

By shifting nutrition and metabolic risk monitoring from episodic surveys to continuous, institution-anchored surveillance, the proposed protocol offers a pathway to strengthen preventive public health practice in India. The system is designed to generate timely, disaggregated evidence that can inform program refinement, resource allocation, and local action while maintaining alignment with national and global nutrition and NCD goals. If implemented at scale, this approach has the potential to transform how malnutrition in all its forms is monitored and addressed across the life course. The use of structured institutional feedback reports ensures that surveillance outputs are directly translated into decentralized preventive action within schools, Anganwadi centers, and PHC systems.

India is at a critical juncture in its nutrition transition, with undernutrition, overweight, obesity, and diet-related NCDs coexisting within the same populations and life stages. Although national surveys continue to provide essential population-level estimates, their episodic design limits their capacity to support timely, local, and life course–specific preventive action. The institute-based standardized surveillance protocol presented in this paper offers a structured approach to addressing this gap.

By embedding surveillance within routine institutional settings—Anganwadi centers, schools, colleges, primary health centers, and workplaces—and aligning it with existing national programs, the proposed system reorients nutrition and metabolic risk monitoring from episodic assessment to continuous prevention. The use of a hybrid technology model combining OMR-based paper tools, digital platforms, and IoT-enabled devices enables scalability while remaining sensitive to infrastructural diversity and ethical considerations. Importantly, the protocol operationalizes the core principles of PHC—equity, community participation, intersectoral coordination, and appropriate technology—ensuring that surveillance functions as a public good rather than a purely technical exercise.

International experience from systems, such as YRBS, NHANES, ECHO, Global School Health Policies and Practices Survey, and the WHO European Childhood Obesity Surveillance Initiative, demonstrates that institutionalized surveillance is both feasible and impactful when integrated into education and health systems. Adapted to India’s context, the proposed life course surveillance model has the potential to generate timely, disaggregated evidence that informs double-duty actions, strengthens program convergence, and supports national commitments to the SDGs, particularly SDG 2 (Zero Hunger) and SDG 3 (Good Health and Well-being).

Reframing surveillance as a participatory, institute-anchored process transforms data collection into a continuous cycle of learning, accountability, and prevention. If implemented and sustained, this approach can contribute meaningfully to strengthening India’s public health response to malnutrition in all its forms across the life course.