METHODS: The South East Asia Community Observatory (SEACO) is a dynamic prospective community cohort. We contacted a random sample of 1007 adults (18+) who had previously provided PA data in 2018. We asked about PA during the MCO (March-May 2020) and at the time of interview (June 2020).
RESULTS: During the MCO, PA reduced by a mean of 6.7 hours/week (95% confidence interval (CI) = 5.3, 8.0) compared to 2018, with the largest reductions among those in employment. By June, PA was 3.4 hours/week (95% CI = 2.0, 4.8) less than 2018, leaving 34% of adults currently inactive (20% in 2018). Reductions in occupational PA were not replaced with active travel or activity at home. Despite these observed reductions, most participants did not think the MCO had affected their PA.
CONCLUSIONS: Movement restrictions are associated with lower PA lasting beyond the period of strict restrictions; such longer-term reductions in PA may have a detrimental impact on health. Future MCOs should encourage people to be active, but may additionally need targeted messaging for those who don't necessarily realise they are at risk. In particular, policies developed in more affluent countries may not easily translate to LMICs.
METHODS: The International Society of Global Health (ISoGH) used the Child Health and Nutrition Research Initiative (CHNRI) method to identify research priorities for future pandemic preparedness. Eighty experts in global health, translational and clinical research identified 163 research ideas, of which 42 experts then scored based on five pre-defined criteria. We calculated intermediate criterion-specific scores and overall research priority scores from the mean of individual scores for each research idea. We used a bootstrap (n = 1000) to compute the 95% confidence intervals.
RESULTS: Key priorities included strengthening health systems, rapid vaccine and treatment production, improving international cooperation, and enhancing surveillance efficiency. Other priorities included learning from the coronavirus disease 2019 (COVID-19) pandemic, managing supply chains, identifying planning gaps, and promoting equitable interventions. We compared this CHNRI-based outcome with the 14 research priorities generated and ranked by ChatGPT, encountering both striking similarities and clear differences.
CONCLUSIONS: Priority setting processes based on human crowdsourcing - such as the CHNRI method - and the output provided by ChatGPT are both valuable, as they complement and strengthen each other. The priorities identified by ChatGPT were more grounded in theory, while those identified by CHNRI were guided by recent practical experiences. Addressing these priorities, along with improvements in health planning, equitable community-based interventions, and the capacity of primary health care, is vital for better pandemic preparedness and response in many settings.
METHODS: We performed a bibliometric analysis of Web of Science Core Collection for all years to determine the number of studies performed in each country that used an inventory or a questionnaire on aggression, anxiety, depression, borderline personality, narcissism, self-harm, shame, or childhood trauma. We conducted a simple observational analysis of distributions by countries to derive the main overall conclusions, assisted by ChatGPT to test its ability to summarise and interpret this type of information. We also carried out a study in Croatia to examine some psychometric properties of five commonly used questionnaires, using Cronbach's α coefficient and zero-order correlations.
RESULTS: We observed a concentration of research activity in a few high-income countries, primarily the United States and several European nations, suggesting a robust research infrastructure and a strong emphasis on studying psychological and psychiatric states within their population. In contrast, low- and middle-income countries were notably under-represented in research on psychological and psychiatric states, although the gap seems to be closing in some countries. Turkey, Iran, Brazil, South Africa, Mexico, India, Malaysia and Pakistan have been consistently contributing an increasing number of studies and catching up with the most research-intensive high-income countries. The national case study in Croatia confirmed adequate psychometric properties of the most frequently used questionnaires.
CONCLUSIONS: Addressing research gaps in low- and middle-income countries is crucial, because relying solely on research from high-income countries may not fully capture the nuances of psychological and psychiatric states within diverse populations. To bridge this gap, it is essential to prioritise mental health research in low-resource settings, provide training and resources to local researchers, and establish international collaborations. Such efforts can lead to the development of culturally valid questionnaires, an improved understanding of psychological and psychiatric states in diverse contexts, and the creation of effective interventions to promote mental well-being on a global scale.
Methods: We adapted the Child Health and Nutrition Research Initiative (CHNRI) methodology to identify global COPD research priorities.
Results: 62 experts contributed 230 research ideas, which were scored by 34 researchers according to six pre-defined criteria: answerability, effectiveness, feasibility, deliverability, burden reduction, and equity. The top-ranked research priority was the need for new effective strategies to support smoking cessation. Of the top 20 overall research priorities, six were focused on feasible and cost-effective pulmonary rehabilitation delivery and access, particularly in primary/community care and low-resource settings. Three of the top 10 overall priorities called for research on improved screening and accurate diagnostic methods for COPD in low-resource primary care settings. Further ideas that drew support involved a better understanding of risk factors for COPD, development of effective training programmes for health workers and physicians in low resource settings, and evaluation of novel interventions to encourage physical activity.
Conclusions: The experts agreed that the most pressing feasible research questions to address in the next decade for COPD reduction were on prevention, diagnosis and rehabilitation of COPD, especially in low resource settings. The largest gains should be expected in low- and middle-income countries (LMIC) settings, as the large majority of COPD deaths occur in those settings. Research priorities identified by this systematic international process should inform and motivate policymakers, funders, and researchers to support and conduct research to reduce the global burden of COPD.
Methods: We administered relevant translations of the BOLD-1 questionnaire with additional questions from ECRHS-II, performed spirometry and arranged specialist clinical review for a sub-group to confirm the diagnosis. Using random sampling, we piloted a community-based survey at five sites in four LMICs and noted any practical barriers to conducting the survey. Three clinicians independently used information from questionnaires, spirometry and specialist reviews, and reached consensus on a clinical diagnosis. We used lasso regression to identify variables that predicted the clinical diagnoses and attempted to develop an algorithm for detecting asthma and COPD.
Results: Of 508 participants, 55.9% reported one or more chronic respiratory symptoms. The prevalence of asthma was 16.3%; COPD 4.5%; and 'other chronic respiratory disease' 3.0%. Based on consensus categorisation (n = 483 complete records), "Wheezing in last 12 months" and "Waking up with a feeling of tightness" were the strongest predictors for asthma. For COPD, age and spirometry results were the strongest predictors. Practical challenges included logistics (participant recruitment; researcher safety); misinterpretation of questions due to local dialects; and assuring quality spirometry in the field.
Conclusion: Detecting asthma in population surveys relies on symptoms and history. In contrast, spirometry and age were the best predictors of COPD. Logistical, language and spirometry-related challenges need to be addressed.
Methods: The search was conducted across three databases: PubMed, CINAHL and Emerald using four key concepts: 'health', 'index', 'context', 'develop', which was supplemented with Google searching and reference scanning. A researcher screened the titles, abstracts and subsequently full texts and confirmed the findings with the research team at each stage. Data charting was performed according to the included publications and identified indices. The collation was performed by describing the indices and made observation on its development method using a priori framework consist of four processes: underpinning theory, model or framework; data selection and processing; formation of index; testing of index.
Results: Twenty-six publications describing population health indices were included, and 27 indices were identified. These indices covered the following health topics: overall health outcomes (n = 15), outcomes for specific health topics (n = 4), diseases outcome (n = 6), assist health resource allocation for priority minority subgroup or geographic area (n = 4), quality of health or health care (n = 2). Twenty-one indices measure health for general populations while six measure defined subpopulations. Fourteen of the indices reported at least one of the development processes according to the a priori framework: underpinning theory, model or framework (n = 7); data selection and processing (n = 8); formation of index (n = 12); testing of index (n = 9).
Conclusions: Few population health indices measure specific health topics or health of specific sub-population. There is also a lack of usage of theories, models or framework in developing these indices. Efforts to develop a guideline is proposed on how population health indices can be developed systematically and rigorously to ensure validity and comprehensive assessment of the indices.
METHODS: We adopted the Joanna Briggs Institute's scoping review protocol and followed the Cochrane Rapid Review method to accelerate the review process, using the Implementation and Operation of Mobile Health projects framework and The Extended Technology Acceptance Model of Mobile Telephony to categorise the results. We conducted the review in four stages: (1) establishing value, (2) identifying digital health policy, (3) searching for evidence of infrastructure, design, and end-user adoption, (4) local input to interpret relevance and adoption factors. We used open-source national/international statistics such as the World Health Organization, International Telecommunication Union, Groupe Speciale Mobile, and local news/articles/government statistics to scope the current status, and systematically searched five databases for locally relevant exemplars.
RESULTS: We found 118 studies (2015-2021) and 114 supplementary online news articles and national statistics. Digital health policy was available in all countries, but scarce skilled labour, lack of legislation/interoperability support, and interrupted electricity and internet services were limitations. Older patients, women and those living in rural areas were least likely to have access to ICT infrastructure. Renewable energy has potential in enabling digital health care. Low usage mobile data and voice service packages are relatively affordable options for mHealth in the five countries.
CONCLUSIONS: Effective implementation of digital health technologies requires a supportive policy, stable electricity infrastructures, affordable mobile internet service, and good understanding of the socio-economic context in order to tailor the intervention such that it functional, accessible, feasible, user-friendly and trusted by the target users. We suggest a checklist of contextual factors that developers of digital health initiatives in LMICs should consider at an early stage in the development process.
METHODS: An international cross-sectional study was conducted in 30 countries across six World Health Organization regions from July 2020 to August 2021, with 16 512 adults self-reporting changes in 18 lifestyle factors and 13 interim health outcomes since the pandemic.
RESULTS: Three networks were computed and tested. The central variables decided by the expected influence centrality were consumption of fruits and vegetables (centrality = 0.98) jointly with less sugary drinks (centrality = 0.93) in the lifestyles network; and quality of life (centrality = 1.00) co-dominant (centrality = 1.00) with less emotional distress in the interim health outcomes network. The overall amount of exercise had the highest bridge expected influence centrality in the bridge network (centrality = 0.51). No significant differences were found in the network global strength or the centrality of the aforementioned key variables within each network between males and females or health workers and non-health workers (all P-values >0.05 after Holm-Bonferroni correction).
CONCLUSIONS: Consumption of fruits and vegetables, sugary drinks, quality of life, emotional distress, and the overall amount of exercise are key intervention components for improving overall lifestyle, overall health and overall health via lifestyle in the general population, respectively. Although modifications are needed for all aspects of lifestyle and interim health outcomes, a larger allocation of resources and more intensive interventions were recommended for these key variables to produce the most cost-effective improvements in lifestyles and health, regardless of gender or occupation.
METHODS: We surveyed 16 512 adults from July 2020 to August 2021 in 30 territories. Participants self-reported their medical histories and the perceived impact of COVID-19 on 18 lifestyle factors and 13 health outcomes. For each disease subgroup, we generated lifestyle, health outcome, and bridge networks. Variables with the highest centrality indices in each were identified central or bridge. We validated these networks using nonparametric and case-dropping subset bootstrapping and confirmed central and bridge variables' significantly higher indices through a centrality difference test.
FINDINGS: Among the 48 networks, 44 were validated (all correlation-stability coefficients >0.25). Six central lifestyle factors were identified: less consumption of snacks (for the chronic disease: anxiety), less sugary drinks (cancer, gastric ulcer, hypertension, insomnia, and pre-diabetes), less smoking tobacco (chronic obstructive pulmonary disease), frequency of exercise (depression and fatty liver disease), duration of exercise (irritable bowel syndrome), and overall amount of exercise (autoimmune disease, diabetes, eczema, heart attack, and high cholesterol). Two central health outcomes emerged: less emotional distress (chronic obstructive pulmonary disease, eczema, fatty liver disease, gastric ulcer, heart attack, high cholesterol, hypertension, insomnia, and pre-diabetes) and quality of life (anxiety, autoimmune disease, cancer, depression, diabetes, and irritable bowel syndrome). Four bridge lifestyles were identified: consumption of fruits and vegetables (diabetes, high cholesterol, hypertension, and insomnia), less duration of sitting (eczema, fatty liver disease, and heart attack), frequency of exercise (autoimmune disease, depression, and heart attack), and overall amount of exercise (anxiety, gastric ulcer, and insomnia). The centrality difference test showed the central and bridge variables had significantly higher centrality indices than others in their networks (P