Public Health Foundation of India

This article explores the potential of visual and creative methods to examine how transformation is understood and experienced by coastal communities living along the coast at four sites in India and Bangladesh, at the front line of climate change and related uncertainties. The visual methods with the locals unpacked that bottom-up knowledge involves slow, deliberate and mundane processes of transformation entwined in the resilience of the ‘below’, while recognising confusion and tension at multiple levels within the community around the differential impacts of climate change. The research findings indicate that transformation is a complex process that entails structural changes at the personal, cultural and collective levels to address climate change within a given context. The authors’ collective understanding, through the visual methods, is that climate solutions are often top-down, with little representation of bottom-up perspectives, which risks further marginalising vulnerable groups. An engaged process of knowledge co-production has the potential to support broader actors’ engagement and, eventually, bring about change. The article underscores the need for inclusive, community-driven strategies that address both ecological and social dimensions. Transformation from below may happen through co-mixing of real-world and theoretical knowledge, continued engagement and ensuring that communities can transfer the practical realisations gained through participatory action research into daily practices. The problems need to be framed in ways that are seen as relevant and appropriate to the experiential knowledge and lived experiences, which helps reflect on alternative frames for marginal systems and provides moments of opportunity and policy windows.

Heart failure (HF) is a chronic and progressive cardiovascular condition associated with significant morbidity, mortality and healthcare burden. Increasing evidence points to a critical role of gut dysbiosis and the gut–heart–brain axis in HF pathophysiology. Altered gut microbiota may influence systemic inflammation, neurohormonal activity and cardiac function through gut-derived metabolites such as trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs). Yoga-based cardiac rehabilitation (Yoga-CaRe) is a cost-effective intervention that has been shown to improve quality of life, exercise capacity and cardiovascular outcomes in cardiac patients. However, the mechanism underlying its benefits remains unclear. Furthermore, its effect on gut microbiota diversity and the downstream impact on the gut–heart–brain axis in HF remains largely unexplored. This study outlines a prospective, randomised, open-label, blinded-endpoint trial investigating the effects of a 12-week Yoga-CaRe intervention versus enhanced standard care in 60 HF patients with reduced ejection fraction. Participants will be randomly assigned in a 1:1 ratio to either the Yoga-CaRe or the control group. The Yoga-CaRe group will participate in 20 supervised yoga sessions, complemented by guided daily home practice, while the control group will receive enhanced standard care. The trial will assess changes in gut microbiota composition, levels of gut-derived metabolites (TMAO and SCFAs), inflammatory biomarkers (TNF-α and high-sensitivity C reactive protein), heart rate variability, 6 min walk test (6MWT) and echocardiography. Biological samples and clinical data will be analysed using integrated bioinformatics and statistical approaches to evaluate intervention efficacy and identify potential mechanistic pathways. The YoGH-Biome study has received ethical clearance from the Institutional Ethics Committee of the SDM College of Medical Sciences and Hospital, India (SDMIEC/2025/1073). It is registered with the Clinical Trials Registry of India. Study results will be disseminated via scientific publications, conferences and stakeholder forums to inform integrative strategies for HF management. Trial registration number: CTRI/2023/12/060757.

The South-East Asia Region (SEAR) faces a growing burden of non-communicable diseases (NCDs), shaped in part by complex commercial determinants of health (CDoH). This analysis considers how aggressive marketing, policy interference and addictive product design by the tobacco, alcohol and ultra-processed food (UPF) industries contribute to this burden. SEAR’s distinctive demographic, cultural and economic conditions create both vulnerabilities and opportunities. Rapid urbanisation, high population density, rising disposable incomes and uneven policy enforcement create environments in which commercial actors can expand market reach and influence consumption patterns. These industries frequently target youth and lower socioeconomic groups through tailored marketing, sponsorships, digital engagement and strategic product placement. Cultural norms further shape consumption in SEAR, including the longstanding use of smokeless tobacco, socially embedded alcohol consumption in several countries and the growing incorporation of UPFs into daily diets. These patterns are strengthened by expanding digital and e-commerce ecosystems that increase exposure, accessibility and the normalisation of health-harming products across diverse populations. Despite challenges, the region can address CDoH by adapting evidence-based strategies like marketing restrictions, excise taxes on sugar-sweetened beverages and safeguards against industry interference to local policies. Strengthening regulatory frameworks, enforcing comprehensive marketing restrictions and adopting WHO ‘best buy’ interventions are critical steps. In parallel, international cooperation and capacity building in low- and middle-income countries are essential, as is the engagement of civil society and academia to enhance accountability and support effective policy implementation. Collectively, these strategies can help SEAR accelerate progress in reducing NCD risks and improving population health.

The public health employment landscape is evolving due to technological advancements and complex global challenges. This study analyses employment trends, essential competencies and the role of emerging technologies through responses from 211 stakeholders, including professionals, employers and academic faculty. Findings highlight a growing demand for multidisciplinary skills, such as project management, data analysis, advocacy and proficiency, in technologies like artificial intelligence (AI), geographic information systems (GIS) and telemedicine. Emerging tools, such as blockchain, big data analytics and mHealth, are reshaping workforce needs. Despite the expanding scope of public health careers, significant gaps remain in curricula, particularly in practical and tech-oriented training. To address these, there is a need for competency-based education and experiential learning to prepare graduates for real-world demands. Building an adaptable workforce equipped with updated training and a solid competency framework is essential for the sector’s future success. The integration of modern technologies and crossdisciplinary skills is key to meeting public health challenges effectively.

BackgroundGait assessment is an important tool for evaluating health risks in older adults but remains underused in low-resource settings. We explored the feasibility of using a low-cost, simple walking protocol with smartphone video capture to extract health-related gait signals by classifying sex and age. Sex and age are fundamental biological factors linked to most health- and aging-related outcomes. Establishing baseline classification performance provides justification for future exploration of more complex health-related conditions using this protocol. ObjectiveThis study aimed to assess whether pose parameters derived from smartphone-based gait videos can be used by machine learning models to classify age and sex. MethodsA cross-sectional study was conducted with 155 participants (Thailand: n=59, 38.1%; India: n=96, 61.9%). Participants performed a simple walking protocol while being recorded using smartphones. Pose estimation was conducted using the MediaPipe algorithm to extract 109 features related to joint distances, angles, and walking speed. For feasibility assessment, we calculated the proportion of recordings for which pose estimation could be extracted. Elastic-net logistic regression and histogram-based gradient boosting classifiers were used for analysis. Model performance was evaluated using 5-fold cross-validation. Outcomes were sex (male vs female) and age group (aged<65 vs ≥65 y). ResultsPose parameters were successfully extracted from 145 (93.5%) of the 155 video recordings. Among the 145 participants, 94 (64.8%) were female, and 55 (37.9%) were aged 65 years or older. The 2 analytic models demonstrated comparable performance. Sex classification achieved a maximum mean area under the receiver operating characteristic curve of approximately 0.90 (SD 0.06), whereas age classification achieved a maximum mean area under the receiver operating characteristic curve of approximately 0.70 (SD 0.09). Classification performance was primarily influenced by the number of features used, clothing characteristics, and the quality of pose estimation. ConclusionsThis simple smartphone-based gait assessment protocol was able to extract meaningful pose parameters and classify biological features (age and sex). Further studies are warranted to evaluate its potential utility for disease screening, risk stratification, and longitudinal health monitoring.