A novel approach to assess dynamic treatment regimes embedded in a SMART with an ordinal outcome.

Sequential multiple assignment randomized trials (SMARTs) are used to construct data-driven optimal intervention strategies for subjects based on their intervention and covariate histories in different branches of health and behavioral sciences where a sequence of interventions is given to a participant. Sequential intervention strategies are often called dynamic treatment regimes (DTR). In the existing literature, the majority of the analysis methodologies for SMART data assume a continuous primary outcome. However, ordinal outcomes are also quite common in clinical practice. In this work, first, we introduce the notion of generalized odds ratio ( G O R $$ GOR $$ ) to compare two DTRs embedded in a SMART with an ordinal outcome and discuss some combinatorial properties of this measure. Next, we propose a likelihood-based approach to estimate G O R $$ GOR $$ from SMART data, and derive the asymptotic properties of its estimate. We discuss alternative ways to estimate G O R $$ GOR $$ using concordant-discordant pairs and two-sample U $$ U $$ -statistic. We derive the required sample size formula for designing SMARTs with ordinal outcomes based on G O R $$ GOR $$ . A simulation study shows the performance of the estimated G O R $$ GOR $$ in terms of the estimated power corresponding to the derived sample size. The methodology is applied to analyze data from the SMART+ study, conducted in the UK, to improve carbohydrate periodization behavior in athletes using a menu planner mobile application, Hexis Performance. A freely available Shiny web app using R is provided to make the proposed methodology accessible to other researchers and practitioners.

Effect of feeding regimen on circadian activity rhythms of food anticipatory by ghrelin hormone in a pig model.

Increasingly diverse meal patterns affect the internal body clock. Ghrelin secretion is closely associated with the anticipation of a regularly scheduled mealtime, leading ghrelin to be a putative candidate for food-related entraining signals that drive activity rhythms. Here, growing pigs with different meal frequencies were used to construct an irregular eating pattern model. We found that irregular eating patterns changed central ghrelin levels of pigs, affected the circadian entrainment and circadian rhythm pathways in hypothalamus tissue, and altered the daily behavior and food anticipatory activity (FAA). To determine whether ghrelin exerts an effect, growing pigs were intravenously injected with ghrelin antagonist [D-Lys3]-GHRP-6 for 7 days. We showed here that [D-Lys3]-GHRP-6 administration decreased locomotor activity of growing pigs in the 4-h window preceding onset of food availability. In addition, we also confirmed that the direct role of ghrelin in molecular mechanism of regulating clock genes expression via calcium mobilization through intracellular PKC/PLC and AC/PKA pathways in vitro. Collectively, irregular eating patterns affect the central circadian system by ghrelin, supporting ghrelin as a temporal messenger of food-entrainment in hypothalamic circadian functions.

Evolution of China's NOx emission control strategy during 2005∼2020 over coal-fired power plants: A satellite-based assessment.

Since especially the 12th Five-Year Plan (2011-2015), China has made great efforts to reverse the increasing trend of NOx emissions through end-of-pipe measures. With the Ozone Monitoring Instrument (OMI) level 2 swath product of tropospheric NO2, this study explores the temporal-spatial patterns of NOx concentrations over China's coal-fired power plants from 2005 to 2020 and investigates the evolution of its control strategy. The nationwide deployment of flue-gas denitration facilities was a critical measure to mitigate NOx emissions from coal-fired power plants, while this study externally assesses the implementation gap of their operation. Our results illustrate that, besides the impacts of economic cycles, China's control strategy experienced a dramatic transformation from an ad hoc campaign style for meeting short-term temporary targets to more sustainable, technology- and governance-centered institutional arrangements for ensuring long-term fundamental solutions. Furthermore, the satellite-based assessment may provide not only ex post evaluation, but also in-time and independent data for more effective and efficient environmental compliance monitoring.

Evaluation of Combinations of Nudging, Pricing, and Labeling Strategies to Improve Diet Quality: A Virtual Grocery Store Experiment Employing a Multiphase Optimization Strategy.

BACKGROUND: Several intervention strategies have been shown to improve diet quality. However, there is limited evidence on the increase in effectiveness that may be achieved through select combinations of these strategies. PURPOSE: This study aimed to identify an effective multicomponent intervention to improve diet quality of a grocery basket by applying a Multiphase Optimization Strategy framework and testing various combinations of four promising strategies using a fully functional web-based grocery store: (i) front-of-pack food labels and real-time feedback of the healthiness of the shoppers' grocery basket, (ii) a tax, (iii) ordering products by a nutritional quality score, and (iv) healthier substitute offers. METHODS: We conducted a hypothetical shopping study (N = 756) with a randomized full factorial design (16 conditions) to estimate main and interaction effects of the four interventions. RESULTS: The "food labels & real-time feedback" and "ordering" strategies had significantly positive main effects on overall diet quality of the shopping basket (both at p < .001). We found no effects on diet quality for the "tax" and "healthier substitute offers." None of the two-way interaction effects for different strategies on overall diet quality and nutrients were significant. CONCLUSIONS: Having "food labels & real-time feedback" and "ordering" simultaneously seemed to be more effective at improving diet quality, compared to having only one of these interventions. These results suggest that a combination of food labels with real-time feedback and ordering interventions can be part of a promising multicomponent strategy to improve diet quality in online shopping platforms. TRIAL REGISTRATION: ClinicalTrials.gov NCT04632212.

Incentives for Uptake of and Adherence to Outpatient Stroke Rehabilitation Services: A 3-Arm Randomized Controlled Trial.

OBJECTIVE: To determine if rehabilitation uptake and adherence can be increased by providing coordinated transportation (increased convenience) and eliminating out-of-pocket costs (reduced expense). DESIGN: Three-arm randomized controlled trial. SETTING: Stroke units of 2 Singapore tertiary hospitals. PARTICIPANTS: Singaporeans or permanent residents 21 years or older who were diagnosed as having stroke and were discharged home with physician's recommendation to continue outpatient rehabilitation (N=266). INTERVENTIONS: A Transportation Incentives arm (T), which provides free transportation services, a Transportation & Sessions Incentives arm (T&S), offering free transportation and prescribed stroke rehabilitation sessions, and a control arm, Education (E), consisting of a stroke rehabilitation educational program. MAIN OUTCOME MEASURES: The primary study outcome was uptake of outpatient rehabilitation services (ORS) among patients poststroke and key predefined secondary outcomes being number of sessions attended and adherence to prescribed sessions. RESULTS: Uptake rate of ORS was 73.0% for E (confidence interval [CI], 63.8%-82.3%), 81.8% for T (CI, 73.8%-89.8%), and 84.3% for T&S (CI, 76.7%-91.8%). Differences of T and T&S vs E were not statistically significant (P=.22 and P=.10, respectively). However, average number of rehabilitation sessions attended were significantly higher in both intervention arms: 5.50±7.65 for T and 7.51±9.52 for T&S vs 3.26±4.22 for control arm (E) (T vs E: P=.017; T&S vs E: P<.001). Kaplan-Meier analysis indicated that persistence was higher for T&S compared with E (P=.029). CONCLUSIONS: This study has demonstrated a possibility in increasing the uptake of and persistence to stroke ORS with free transportation and sessions. Incentivizing survivors of stroke to take up ORS is a new strategy worthy of further exploration for future policy change in financing ORS or other long-term care services.

Sequential Multiple Assignment Randomized Trial (SMART) to identify optimal sequences of telemedicine interventions for improving initiation of insulin therapy: A simulation study.

BACKGROUND: To examine the value of a Sequential Multiple Assignment Randomized Trial (SMART) design compared to a conventional randomized control trial (RCT) for telemedicine strategies to support titration of insulin therapy for Type 2 Diabetes Mellitus (T2DM) patients new to insulin. METHODS: Microsimulation models were created in R using a synthetic sample based on primary data from 63 subjects enrolled in a pilot study of a smartphone application (App), Diabetes Pal compared to a nurse-based telemedicine strategy (Nurse). For comparability, the SMART and an RCT design were constructed to allow comparison of four (embedded) adaptive interventions (AIs). RESULTS: In the base case scenario, the SMART has similar overall mean expected HbA1c and cost per subject compared with RCT, for sample size of n = 100 over 10,000 simulations. SMART has lower (better) standard deviations of the mean expected HbA1c per AI, and higher efficiency of choosing the correct AI across various sample sizes. The differences between SMART and RCT become apparent as sample size decreases. For both trial designs, the threshold value at which a subject was deemed to have been responsive at an intermediate point in the trial had an optimal choice (i.e., the sensitivity curve had a U-shape). SMART design dominates the RCT, in the overall mean HbA1c (lower value) when the threshold value is close to optimal. CONCLUSIONS: SMART is suited to evaluating the efficacy of different sequences of treatment options, in addition to the advantage of providing information on optimal treatment sequences.

Sample size calculation based on precision for pilot sequential multiple assignment randomized trial (SMART).

The sequential multiple assignment randomized trial (SMART) is a design used to develop dynamic treatment regimes (DTRs). Given that DTRs are generally less well researched, pilot SMART studies are often necessary. One challenge in pilot SMART is to determine the sample size such that it is small yet meaningfully informative for future full-fledged SMART. Here, we develop a precision-based approach, where the calculated sample size confines the marginal mean outcome of a DTR within a prespecified margin of error. The sample size calculations will be presented for two-stage SMARTs, and for various common outcome types.

A flexible micro-randomized trial design and sample size considerations.

Technological advancements have made it possible to deliver mobile health interventions to individuals. A novel framework that has emerged from such advancements is the just-in-time adaptive intervention, which aims to suggest the right support to the individuals when their needs arise. The micro-randomized trial design has been proposed recently to test the proximal effects of the components of these just-in-time adaptive interventions. However, the extant micro-randomized trial framework only considers components with a fixed number of categories added at the beginning of the study. We propose a more flexible micro-randomized trial design which allows addition of more categories to the components during the study. Note that the number and timing of the categories added during the study need to be fixed initially. The proposed design is motivated by collaboration on the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation study, which learns to deliver effective text messages to encourage physical activity among patients with diabetes and depression. We developed a new test statistic and the corresponding sample size calculator for the flexible micro-randomized trial using an approach similar to the generalized estimating equation for longitudinal data. Simulation studies were conducted to evaluate the sample size calculators and an R shiny application for the calculators was developed.

Inhibition of ghrelin activity by the receptor antagonist [D-Lys3]-GHRP-6 enhances hepatic fatty acid oxidation and gluconeogenesis in a growing pig model.

Despite its central role in regulating energy intake and metabolism, ghrelin is little understood when it comes to its effects on hepatic lipid and glucose metabolism. Growing pigs were intravenously injected with ghrelin receptor antagonist [D-Lys3]-GHRP-6 (DLys; 6 mg/kg body weight) for seven days to determine whether ghrelin plays a role in glucose and lipid metabolism. DLys treatment significantly reduced body weight gain and adipose histopathology found that DLys treatment dramatically reduced adipocyte size. DLys treatment significantly increased serum NEFA and insulin levels, hepatic glucose level and HOMA-IR, and significantly decreased serum TBA level of growing pigs after fasting. Moreover, DLys treatment changed the dynamics of serum metabolic parameters, including glucose, NEFA, TBA, insulin, GH, leptin, and cortisol. Liver transcriptome showed that DLys treatment affected the metabolism-related pathways. Compared with the control group, adipose tissue lipolysis (the adipose triglyceride lipase level was significantly increased), hepatic gluconeogenesis (the G6PC protein level was significantly increased) and fatty acid oxidation (the CPT1A protein level was significantly increased) were promoted in the DLys group. DLys treatment expanded degrees of oxidative phosphorylation in the liver, coming about in a higher NAD+ /NADH proportion and enactment of the SIRT1 signaling pathway. Additionally, the liver protein levels of the DLys group were significantly higher than those of the control group for GHSR, PPAR alpha, and PGC-1. To summarize, inhibition of ghrelin activity can significantly affect metabolism and alter energy levels by enhancing fat mobilization, hepatic fatty acid oxidation and gluconeogenesis without affecting fatty acid uptake and synthesis in the liver.

COVID-19 may have increased global support for universal health coverage: multi-country observational study.

Introduction: The multiple risks generated by the COVID-19 pandemic intensified the debate about healthcare access and coverage. Whether the burden of disease caused by the coronavirus outbreak changed public opinion about healthcare provision remains unclear. In this study, it was specifically examined if the pandemic changed support for governmental intervention in healthcare as a proxy to support for universal health coverage (UHC). It also examined which psychological factors related to the socioeconomic interdependence exposed by the pandemic may be associated with a potential change. Methods: Online survey data was collected over 18 months (from March 2020 to August 2021) across 73 countries, containing various social attitudes and risk perceptions related to COVID-19. This was a convenience sample composed of voluntary participants (N = 3,176; age 18 years and above). Results: The results show that support for government intervention in healthcare increased across geographical regions, age groups, and gender groups (an average increase of 39%), more than the support for government intervention in other social welfare issues. Factors related to socioeconomic interdependence predicted increased support for government intervention in healthcare, namely, social solidarity (ß = 0.14, p < 0.0001), and risk to economic livelihood (ß = 0.09, p < 0.0001). Trust in the government to deal with COVID-19 decreased over time, and this negative trajectory predicted a demand for better future government intervention in healthcare (ß = -0.10, p = 0.0003). Conclusion: The COVID-19 pandemic may have been a potential turning point in the global public support for UHC, as evidenced by a higher level of consensus that governments should be guarantors of healthcare.

Antagonization of Ghrelin Suppresses Muscle Protein Deposition by Altering Gut Microbiota and Serum Amino Acid Composition in a Pig Model.

Ghrelin is an appetite-stimulating hormone that can increase food intake and has been reported to prevent muscle loss; however, the mechanism is not yet fully understood. In this study, [D-Lys3]-GHRP-6 (GHRP) was used to investigate the effects of the antagonization of ghrelin on muscle protein deposition, eating patterns and gut microbiota in a pig model. We found that the growth performance and muscle fiber cross-sectional area of pigs treated with GHRP were significantly reduced compared with the control (CON) group. Moreover, the levels of serum isoleucine, methionine, arginine and tyrosine in the GHRP group were lower than that of the CON group. The abundance of acetate-producing bacteria (Oscillospiraceae UCG-005, Parabacteroides and Oscillospiraceae NK4A214 group) and acetate concentration in the colons of pigs treated with GHRP were significantly reduced. In addition, the injection of GHRP down-regulated the mRNA expression of MCT-1 and mTOR, and it up-regulated the mRNA expression of HDAC1, FOXO1 and Beclin-1. In summary, the antagonization of ghrelin reduced the concentration of important signal molecules (Arg, Met and Ile) that activate the mTOR pathway, concurrently reduce the concentration of HDAC inhibitors (acetate), promote autophagy and finally reduce protein deposition in muscles.

Effects of lactic acid bacteria-fermented formula milk supplementation on ileal microbiota, transcriptomic profile, and mucosal immunity in weaned piglets.

BACKGROUND: Lactic acid bacteria (LAB) participating in milk fermentation naturally release and enrich the fermented dairy product with a broad range of bioactive metabolites, which has numerous roles in the intestinal health-promoting of the consumer. However, information is lacking regarding the application prospect of LAB fermented milk in the animal industry. This study investigated the effects of lactic acid bacteria-fermented formula milk (LFM) on the growth performance, intestinal immunity, microbiota composition, and transcriptomic responses in weaned piglets. A total of 24 male weaned piglets were randomly divided into the control (CON) and LFM groups. Each group consisted of 6 replicates (cages) with 2 piglets per cage. Each piglet in the LFM group were supplemented with 80 mL LFM three times a day, while the CON group was treated with the same amount of drinking water. RESULTS: LFM significantly increased the average daily gain of piglets over the entire 14 d (P < 0.01) and the average daily feed intake from 7 to 14 d (P < 0.05). Compared to the CON group, ileal goblet cell count, villus-crypt ratio, sIgA, and lactate concentrations in the LFM group were significantly increased (P < 0.05). Transcriptomic analysis of ileal mucosa identified 487 differentially expressed genes (DEGs) between two groups. Especially, DEGs involved in the intestinal immune network for IgA production pathways, such as polymeric immunoglobulin receptor (PIGR), were significantly up-regulated (P < 0.01) by LFM supplementation. Moreover, trefoil factor 2 (TFF2) in the LFM group, one of the DEGs involved in the secretory function of goblet cells, was also significantly up-regulated (P < 0.01). Sequencing of the 16S rRNA gene of microbiota demonstrated that LFM led to selective enrichment of lactate-producing and short-chain fatty acid (SCFA)-producing bacteria in the ileum, such as an increase in the relative abundance of Enterococcus (P = 0.09) and Acetitomaculum (P < 0.05). CONCLUSIONS: LFM can improve intestinal health and immune tolerance, thus enhancing the growth performance of weaned piglets. The changes in microbiota and metabolites induced by LFM might mediate the regulation of the secretory function of goblet cells.

A pilot sequential multiple assignment randomized trial (SMART) protocol for developing an adaptive coaching intervention around a mobile application for athletes to improve carbohydrate periodization behavior.

BACKGROUND: It has recently been identified that manipulating carbohydrate availability around exercise activity can enhance training-induced metabolic adaptations. Despite this approach being accepted in the athletic populations, athletes do not systematically follow the guidelines. Digital environments appear to allow nutritionists to deliver this intervention at scale, reducing expensive human coaching time. Yet, digitally delivered dietary behavior change interventions for athletes and the coaching strategy to support them are still novel concepts within sports nutrition. METHODS/DESIGN: We aim to recruit 900 athletes across the UK. 500 athletes will be recruited to test the feasibility of a novel menu planner mobile application with coaching for 6 weeks. 250 athletes with pre-existing nutritionist support will also be recruited as control. We will then conduct a 4-week pilot sequential multiple assignment randomized trial (SMART) with an additional 150 athletes. In the SMART, athletes will be given the application and additional coaching according to their engagement responses. The primary outcomes are the mobile application and coach uptake, retention, engagement, and success in attaining carbohydrate periodization behavior. Secondary outcomes are changes in goal, weight, carbohydrate periodization self-efficacy, and beliefs about consequences. Due to the high attrition nature of digital interventions, all quantitative analyses will be carried out based on both the intention-to-treat and per-protocol principles. DISCUSSION: This study will be the first to investigate improving carbohydrate periodization using a digital approach and tailored coaching strategies under this context. Foundational evidence from this study will provide insights into the feasibility of the digital approach.

Assessing the sweet sorghum-based ethanol potential on saline-alkali land with DSSAT model and LCA approach.

BACKGROUND: The key problem of non-grain energy plants' scale development is how to estimate the potential of GHG emission reduction accurately and scientifically. This study presents a method coupled DSSAT (the Decision Support System for Agrotechnology Transfer) and the life cycle assessment (LCA) method to simulate the spatial distribution of sweet sorghum-based ethanol production potential on saline-alkali land. The GHG (greenhouse gas) emission mitigation and net energy gains of the whole life of sweet sorghum-based ethanol production were then analyzed. RESULTS: The results of the case study in Dongying, Shandong Province, China showed that developing sweet sorghum-based ethanol on saline-alkali land had GHG emission mitigation and energy potentials. The LC-GHG emission mitigation potential of saline-alkali land in Dongying was estimated at 63.9 thousand t CO2 eq, equivalent to the carbon emission of 43.4 Kt gasoline. The LC-NEG potential was predicted at 5.02 PJ, equivalent to the caloric value of 109 Kt gasoline. On average, LC-GHG emission mitigation and LC-NEG were predicted at 55.09 kg CO2 eq/t ethanol and 4.33 MJ/kg ethanol, respectively. CONCLUSIONS: The question of how to evaluate the potential of sweet sorghum-based ethanol development scientifically was solved primarily in this paper. The results will provide an important theoretical support for planning the bioenergy crops on saline-alkali land and develop the fuel ethanol industry.

mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study.

INTRODUCTION: Depression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycaemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individual's behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention. METHODS AND ANALYSIS: In a three-arm randomised controlled trial, we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18-75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up. ETHICS AND DISSEMINATION: The Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our user-designed methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings. TRIAL REGISTRATION NUMBER: NCT03490253; pre-results.

Spatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China.

Epidemiological studies conducted around the world have reported that the under-five mortality rate (U5MR) is closely associated with income and educational attainment. However, geographic elements should also remain a major concern in further improving child health issues, since they often play an important role in the survival environment. This study was undertaken to investigate the relationship between the U5MR, geographic, and socioeconomic factors, and to explore the associated spatial variance of the relationship in China using the geographically weighted regression (GWR) model. The results indicate that the space pattern of a high U5MR had been narrowed notably during the period from 2001 to 2010. Nighttime lights (NL) and the digital elevation model (DEM) both have obvious influences on the U5MR, with the NL having a negative impact and DEM having a positive impact. Additionally, the relationship between the NL and DEM varied over space in China. Moreover, the relevance between U5MR and DEM was narrowed in 2010 compared to 2001, which indicates that the development of economic and medical standards can overcome geographical limits.