Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

BACKGROUND: A plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one's self-regulation over momentary eating impulses. OBJECTIVE: This study aims to examine the feasibility and effectiveness of a novel artificial intelligence-assisted weight management app in improving eating behaviors in a Southeast Asian cohort. METHODS: A single-group pretest-posttest study was conducted. Participants completed the 1-week run-in period of a 12-week app-based weight management program called the Eating Trigger-Response Inhibition Program (eTRIP). This self-monitoring system was built upon 3 main components, namely, (1) chatbot-based check-ins on eating lapse triggers, (2) food-based computer vision image recognition (system built based on local food items), and (3) automated time-based nudges and meal stopwatch. At every mealtime, participants were prompted to take a picture of their food items, which were identified by a computer vision image recognition technology, thereby triggering a set of chatbot-initiated questions on eating triggers such as who the users were eating with. Paired 2-sided t tests were used to compare the differences in the psychobehavioral constructs before and after the 7-day program, including overeating habits, snacking habits, consideration of future consequences, self-regulation of eating behaviors, anxiety, depression, and physical activity. Qualitative feedback were analyzed by content analysis according to 4 steps, namely, decontextualization, recontextualization, categorization, and compilation. RESULTS: The mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m2, and 92.60 (SD 18.24) cm, respectively. There were significant improvements in all the 7 psychobehavioral constructs, except for anxiety. After adjusting for multiple comparisons, statistically significant improvements were found for overeating habits (mean -0.32, SD 1.16; P<.001), snacking habits (mean -0.22, SD 1.12; P<.002), self-regulation of eating behavior (mean 0.08, SD 0.49; P=.007), depression (mean -0.12, SD 0.74; P=.007), and physical activity (mean 1288.60, SD 3055.20 metabolic equivalent task-min/day; P<.001). Forty-one participants reported skipping at least 1 meal (ie, breakfast, lunch, or dinner), summing to 578 (67.1%) of the 862 meals skipped. Of the 230 participants, 80 (34.8%) provided textual feedback that indicated satisfactory user experience with eTRIP. Four themes emerged, namely, (1) becoming more mindful of self-monitoring, (2) personalized reminders with prompts and chatbot, (3) food logging with image recognition, and (4) engaging with a simple, easy, and appealing user interface. The attrition rate was 8.4% (21/251). CONCLUSIONS: eTRIP is a feasible and effective weight management program to be tested in a larger population for its effectiveness and sustainability as a personalized weight management program for people with overweight and obesity. TRIAL REGISTRATION: ClinicalTrials.gov NCT04833803; https://classic.clinicaltrials.gov/ct2/show/NCT04833803.

Effects of probiotics, prebiotics and synbiotics on anthropometric, cardiometabolic and inflammatory markers: An umbrella review of meta-analyses.

BACKGROUND & AIMS: Though probiotics, prebiotics and synbiotics have been shown to confer health benefits, their effects on cardiometabolic risk factors remain unclear. Therefore, we conducted an umbrella review to examine their effectiveness on anthropometric, cardiometabolic and inflammatory markers. METHODS: We conducted an umbrella review on eligible systematic reviews with meta-analysis (SRMA) published from journals' inception till 13 January 2023 retrieved from seven electronic databases (CINAHL, EMBASE, ProQuest, PubMed, Scopus, The Cochrane Library, and Web of Science). Methodological quality was appraised using the Assessment of Multiple Systematic Reviews 2 (AMSTAR2) tool and certainty of evidence was graded into five classes. Random-effects meta-analyses were performed on outcome effect sizes at the SRMA and primary study levels. Extent of overlapping articles were evaluated using corrected cover area. RESULTS: 24 systematic reviews representing 265 unique studies, 1076 unique effect sizes and 25,973 subjects were included. Synbiotics were evidently more effective in improving weight (-1.91 kg, 95%CI -3.45 kg to -0.37 kg, p = 0.02), total cholesterol (-12.17 mg/dl, 95%CI -17.89 mg/dl to -6.46 mg/dl, p < 0.001), low-density lipoprotein (-12.26 mg/dl, 95%CI -18.27 mg/dl to -6.25 mg/dl, p < 0.01), waist circumference (-1.85 cm, 95%CI -2.77 cm to -0.94 cm, p < 0.01), and fasting plasma glucose (-9.68 mg/dl, 95%CI -16.18 mg/dl to -3.18 mg/dl, p < 0.01). Prebiotics were more effective in improving body mass index (-0.34 kg/m2, 95%CI -0.48 kg/m2 to -0.20 kg/m2, p < 0.01), and HOMA-IR (-0.92, 95%CI -1.91 to 0.07, p = 0.06). Probiotics were shown to be more effective in reducing diastolic blood pressure (-1.34 mmHg, 95%CI -2.14 mmHg to -0.55 mmHg, P < 0.01) improving insulin level change (-0.84 mIU/mL, 95%CI -1.27 mIU/mL to -0.41 mIU/mL, p < 0.01), and the percentage of body fat (-0.66%, 95%CI -0.70% to -0.61%, p < 0.01). For all outcomes, the credibility of evidence was classified as class IV. CONCLUSION: Pre-, pro-, and synbiotics can significantly enhance anthropometric indices, glucose and lipid profiles, blood pressure, and inflammatory markers in individuals confronting obesity. While suggesting their supplementation holds promise for this population, the true clinical impact hinges on tailoring these interventions to specific indications and customizing treatment strategies to align with individual patient needs.

Nursing students' readiness towards the 'new normal' in clinical practice: a distributed cognition qualitative perspective.

BACKGROUND: Clinical practicums are a core component of baccalaureate nursing education. Following the coronavirus pandemic, there have been extensive changes in the workforce environment that may potentially affect nursing students' experience and readiness for clinical practicums. METHODS: A qualitative study was conducted to explore final-year nursing students' experiences and readiness for their final clinical practicum before becoming a registered nurse. A purposive sample of 24 final-year baccalaureate nursing students was included in this study. Individual semi-structured interviews were conducted face-to-face via Zoom. The data was analysed using an inductive thematic analysis approach. RESULTS: Three themes depicting students' experiences and clinical readiness were elucidated. The themes included: (1) Experiencing multiple concerns, (2) requiring a network of support, and (3) easing the transition to professional practice. Students considered the final clinical practicum as challenging and demanding which evoked numerous concerns. CONCLUSIONS: Considering the stress that final-year nursing students experience, it will be important to devise strategies ranging from personal, relational, and environmental protective factors to enable their successful transition and completion of clinical practice.

Public acceptance of using artificial intelligence-assisted weight management apps in high-income southeast Asian adults with overweight and obesity: a cross-sectional study.

Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results: 271 participant responses were analyzed, representing participants with a mean age of 31.56 ± 10.75 years, median (interquartile range) BMI, and waist circumference of 27.2 kg/m2 (24.2-28.4 kg/m2) and 86.4 (80.0-94.0) cm, respectively. In total, 188 (69.4%) participants intended to use AI-assisted weight loss apps. UTAUT2 explained 63.3% of the variance in our intention of the sample to use AI-assisted weight management apps with satisfactory model fit: CMIN/df = 1.932, GFI = 0.966, AGFI = 0.954, NFI = 0.909, CFI = 0.954, RMSEA = 0.059, SRMR = 0.050. Only performance expectancy, hedonic motivation, and the habit of using AI-assisted apps were significant predictors of intention. Comparison with existing literature revealed vast variabilities in the determinants of AI- and non-AI weight loss app acceptability in adults with and without overweight and obesity. UTAUT2 produced a good fit in explaining the acceptability of AI-assisted apps among a multi-ethnic, developed, southeast Asian sample with overweight and obesity. Conclusion: UTAUT2 model is recommended to guide the development of AI-assisted weight management apps among people with overweight and obesity.

Evaluating the online Resilience Skills Enhancement programme among undergraduate students: A double-blind parallel randomized controlled trial.

Resilience training has beneficial effects on the ability of undergraduate students to withstand adversity and stress. However, there are inconsistencies in the content and delivery approaches for resilience training. Given the increasing shifts towards computer-assisted instruction, there is a need to develop and evaluate innovative approaches for resilience training. This study aimed to examine the efficacy of two versions of the Resilience Skills Enhancement (RISE) programme. A randomized controlled trial was used to evaluate the effects of blended learning (BL) and a self-guided RISE programme on the resilience, social support, and learning outcomes of undergraduate students. One hundred and fourteen students were recruited and randomly allocated to receive either BL or the self-guided RISE programme. The within-group analyses indicated significant improvements in resilience scores for the BL (F = 37.74, p < 0.001) and self-guided groups (F = 10.16, p < 0.001) with moderate (d = 0.62, 95% CI: 0.25, 1.00) to large effect sizes (d = 1.11, 95% CI: 0.71, 1.50) respectively. There were also significant differences across the three time points for social support scores for the BL (F = 4.50, p < 0.05) and self-guided groups (F = 4.59, p < 0.05). Students in the BL group had considerable improvements in self-efficacy of learning and performance (F = 5.42, p < 0.01) and meta-cognitive self-regulation scores (F = 5.91, p < 0.01). In the between-group analyses, both BL and self-guided RISE were comparable for resilience, social support, and learning scores (p > 0.05). The study provided preliminary evidence that both modes, BL and self-guided RISE programme lead to positive effects on the resilience, social support, and learning scores of students.

Anthropometric and cardiometabolic effects of polyphenols in people with overweight and obesity: an umbrella review.

CONTEXT: Polyphenols are plant-based compounds with potential anti-inflammatory, antioxidant, and anti-obesogenic properties. However, their effects on health outcomes remain unclear. OBJECTIVE: To evaluate the effects of polyphenols on anthropometric and cardiometabolic markers. DATA SOURCES: Six electronic databases-namely, EMBASE, CINAHL, PubMed, Scopus, The Cochrane Library (reviews only), and Web of Science-were searched for relevant systematic reviews with meta-analyses (SRMAs). DATA EXTRACTION: Three reviewers performed the data extraction via a data-extraction Microsoft Excel spreadsheet. DATA ANALYSIS: An umbrella review and meta-analysis of existing SRMAs was conducted. Eighteen SRMAs published from 2015 to 2023, representing 445 primary studies and 838 unique effect sizes, were identified. Meta-analyses were conducted using random-effects models with general inverse variance. Polyphenol-containing foods were found to significantly improve weight (-0.36 kg; 95% confidence interval [CI]: -0.62, 0.77 kg; P < 0.01, I2 = 64.9%), body mass index (-0.25 kg/m2; 95% CI: -0.34, -0.17 kg/m2; P < 0.001, I2 = 82.4%), waist circumference (-0.74 cm; 95% CI: -1.34, -0.15 cm; P < 0.01, I2 = 99.3%), low-density-lipoprotein cholesterol (-1.75 mg/dL; 95% CI: -2.56, -0.94; P < 0.001, I2 = 98.6%), total cholesterol (-1.23 mg/dL; 95% CI: -2.00, -0.46; P = 0.002, I2 = 94.6%), systolic blood pressure (-1.77 mmHg; 95% CI: -1.77, -0.93 mmHg; P < 0.001, I2 = 72.4%), diastolic blood pressure (-1.45 mmHg; 95% CI: -2.09, -0.80 mmHg; P < 0.001, I2 = 61.0%), fat percentage (-0.70%; 95% CI: -1.03, -0.36%; P < 0.001, I2 = 52.6%), fasting blood glucose (-0.18 mg/dL; 95% CI: -0.35, -0.01 mg/dL; P = 0.04, I2 = 62.0%), and C-reactive protein (CRP; including high-sensitivity-CRP [hs-CRP]) (-0.2972 mg/dL; 95% CI: -0.52, -0.08 mg/dL; P = 0.01, I2 = 87.9%). No significant changes were found for high-density-lipoprotein cholesterol (-0.12 mg/dL; 95% CI: -1.44, 0.69; P = 0.67, I2 = 89.4%) and triglycerides (-1.29 mg/dL; 95% CI: -2.74, 0.16; P = 0.08, I2 = 85.4%). Between-study heterogeneity could be explained by polyphenol subclass differences. CONCLUSION: The findings of this umbrella review support the beneficial effects of polyphenols on anthropometric and metabolic markers, but discretion is warranted to determine the clinical significance of the magnitude of the biomarker improvements. SYSTEMATIC REVIEW REGISTRATION: International Prospective Register of Systematic Reviews no. CRD42023420206.

Early prediction of ventilator-associated pneumonia with machine learning models: A systematic review and meta-analysis of prediction model performance✰.

BACKGROUND: Machine learning-based prediction models can catalog, classify, and correlate large amounts of multimodal data to aid clinicians at diagnostic, prognostic, and therapeutic levels. Early prediction of ventilator-associated pneumonia (VAP) may accelerate the diagnosis and guide preventive interventions. The performance of a variety of machine learning-based prediction models were analyzed among adults undergoing invasive mechanical ventilation. METHODS: This systematic review and meta-analysis was conducted in accordance with the Cochrane Collaboration. Machine learning-based prediction models were identified from a search of nine multi-disciplinary databases. Two authors independently selected and extracted data using predefined criteria and data extraction forms. The predictive performance, the interpretability, the technological readiness level, and the risk of bias of the included studies were evaluated. RESULTS: Final analysis included 10 static prediction models using supervised learning. The pooled area under the receiver operating characteristics curve, sensitivity, and specificity for VAP were 0.88 (95 % CI 0.82-0.94, I2 98.4 %), 0.72 (95 % CI 0.45-0.98, I2 97.4 %) and 0.90 (95 % CI 0.85-0.94, I2 97.9 %), respectively. All included studies had either a high or unclear risk of bias without significant improvements in applicability. The care-related risk factors for the best performing models were the duration of mechanical ventilation, the length of ICU stay, blood transfusion, nutrition strategy, and the presence of antibiotics. CONCLUSION: A variety of the prediction models, prediction intervals, and prediction windows were identified to facilitate timely diagnosis. In addition, care-related risk factors susceptible for preventive interventions were identified. In future, there is a need for dynamic machine learning models using time-depended predictors in conjunction with feature importance of the models to predict real-time risk of VAP and related outcomes to optimize bundled care.

Diagnostic Test Accuracy of artificial intelligence-assisted detection of acute coronary syndrome: A systematic review and meta-analysis.

BACKGROUND: Artificial intelligence (AI) has potential uses in healthcare including the detection of health conditions and prediction of health outcomes. Past systematic reviews had reviewed the accuracy of artificial neural networks (ANN) on Electrocardiogram (ECG) readings but that of other AI models on other Acute Coronary Syndrome (ACS) detection tools remains unclear. METHODS: Nine electronic databases were searched from 2012 to 31 August 2022 including grey literature search and hand searching of references of included articles. Risk of bias was assessed by two independent reviewers using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Test characteristics namely true positives, false positives, true negatives, and false negatives were extracted from all included articles into a 2x2 table. Study-specific estimates of sensitivity and specificity were pooled using hierarchical summary receiver operating characteristic (HSROC) model and displayed using a forest plot and HSROC curve. RESULTS: 66 studies were included in the review. A total of 518,931 patients were included whose mean ages varied from 32.62 to 70 years old. In 66 studies, the sensitivity and specificity of AI-based detection for ACS screening ranged from 64 % to 100 % and 65 %-100 %, respectively. The overall quality of evidence was low due to the inclusion of case-control studies. CONCLUSION: Results of the study inform the potential of using AI-assisted ACS detection for accurate diagnosis and prompt treatment for ACS. Adherence to the Standards for Reporting of Diagnostic Accuracy (STARD) guideline and having more cohort studies for future Diagnostic Test Accuracy (DTA) studies are necessary to improve the quality of evidence of AI-based detection of ACS.

Effectiveness of resilience interventions among cancer patients - A systematic review, meta-analysis, and meta-regression of randomised controlled trials.

PURPOSE: This systematic review, meta-analysis and meta-regression aimed to (1) evaluate the effects of resilience interventions on cancer patients' resilience and posttraumatic growth and (2) identify essential contents and features of resilience interventions. METHODS: A systematic review, meta-analysis, and meta-regression analyses were conducted. Published and unpublished randomised controlled trials evaluating the effects of resilience interventions among cancer patients were retrieved from nine databases, trial registries, and grey literature. The mean and standard deviation scores were used to compute the effect sizes. RESULTS: 23 randomised controlled trials comprising 3287 cancer patients were included. The random effects model found that resilience interventions had beneficial impacts on patients' resilience, posttraumatic growth, quality of life, anxiety, and depressive symptoms with moderate to large effects. The subgroup analyses concluded that theoretically guided interventions that adopted synchronous communication delivered physically had greater effect sizes. Interventions comprising skills that promote patients' cognitive flexibility, self-efficacy, self-esteem, self-regulation, and coping had greater effect in comparison with interventions lacking these components. The meta-regression analyses revealed that sample size has a significant effect on posttraumatic growth scores. More well-designed trials are needed to confirm the effects of resilience interventions. CONCLUSIONS: There is merit in utilizing resilience interventions to improve cancer patients' resilience and psychological well-being. Resilience interventions should be incorporated into the routine care for cancer patients and survivors.

The global prevalence of overweight and obesity among nurses: A systematic review and meta-analyses.

BACKGROUND: Several studies have reported the prevalence of overweight and obesity in various countries but the global prevalence of nurses with overweight and obesity remains unclear. A consolidation of figures globally can help stakeholders worldwide improve workforce development and healthcare service delivery. OBJECTIVE: To investigate the global prevalence of overweight and obesity among nurses. DESIGN: Systematic review with meta-analysis. SETTING: 29 different countries across the WHO-classified geographical region. PARTICIPANTS: Nurses. METHODS: Eight electronic databases were searched for articles published from inception to January 2023. Two independent reviewers performed the article screening, methodological appraisal and data extraction. Methodological appraisal was conducted using Newcastle-Ottawa Scale (NOS). Inter-rater agreement was measured using Cohen's Kappa. Meta-analyses were conducted to pool the effect sizes on overweight, obesity and waist circumference using random effects model and adjusted using generalised linear mixed models and Hartung-Knapp method. Logit transformation was employed to stabilise the prevalence variance. Subgroup analyses were performed based on methodological quality and geographical regions. Heterogeneity was assessed using the I2 statistic. RESULTS: Among 10,587 studies, 83 studies representing 158,775 nurses across 29 countries were included. Based on BMI, the global prevalence of overweight and obesity were 31.2% (n = 55, 95% CI: 29%-33.5%; p < .01) and 16.3% (n = 76, 95% CI: 13.7%-19.3%, p < .01), respectively. Subgroup analyses indicated that the highest prevalence of overweight was in Eastern Mediterranean (n = 9, 37.2%, 95% CI: 33.1%-41.4%) and that of obesity was in South-East Asia (n = 5, 26.4%, 95% CI: 5.3%-69.9%). NOS classification, NOS scores, sample size and the year of data collected were not significant moderators. CONCLUSIONS: This review indicated the global prevalence of overweight and obesity among nurses along with the differences between regions. Healthcare organisations and policymakers should appreciate this increased risk and improve working conditions and environments for nurses to better maintain their metabolic health. PATIENT OR PUBLIC CONTRIBUTION: Not applicable as this is a systematic review. REGISTRATION: PROSPERO (ref: CRD42023403785) https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=403785. TWEETABLE ABSTRACT: High prevalence of overweight and obesity among nurses worldwide.

Efficacy and safety of tirzepatide for treatment of overweight or obesity. A systematic review and meta-analysis.

BACKGROUND: Recent studies suggest that tirzepatide, a dual glucose-dependent insulinotropic-peptide (GIP) and glucagon-like peptide-1 receptor agonist (GLP-1 RA), has significant weight loss effects. This systematic review and meta-analysis aims to assess the efficacy and safety of tirzepatide for weight loss in patients with overweight or obesity. METHODS: Medline, Embase and Cochrane CENTRAL were searched for randomized controlled trials (RCTs) on tirzepatide's weight loss efficacy for these patients. A single arm meta-analysis of proportions estimated primary outcomes, ≥5%, ≥10%, and ≥15% weight loss, and adverse events (AEs); while meta-analysis of means estimated secondary outcomes. Comparative meta-analysis was conducted between tirzepatide and control arms where mean differences and odds ratios were estimated for continuous and dichotomous outcomes respectively. RESULTS: RCTs included in this study revealed that among 5800 patients, 78.22% (95% CI: 72.15% to 83.73%), 55.60% (95% CI: 46.54% to 64.47%), 32.28% (95% CI: 23.17% to 42.12%) achieved ≥5%, ≥10%, and ≥15% weight loss, respectively. Tirzepatide 5 mg demonstrated weight loss superiority relative to placebo (MD: -12.47 kg, 95% CI: -13.94 kg to -11.00 kg) and semaglutide (n = 1409, MD: -1.90 kg, 95% CI: -2.97 kg to -0.83 kg) with dose-dependent increase for 10 mg and 15 mg doses. The comparison between tirzepatide and semaglutide was examined in the SURPASS-2 trial that was included in this systematic review. For AEs, there was increase odds of experiencing gastrointestinal AEs with tirzepatide compared to placebo, but no significant difference with semaglutide. CONCLUSION: Tirzepatide has significant potential as a weight loss drug in patients with overweight and obesity, with little increase in AEs compared to other weight loss drugs. With its ability to concurrently target multiple aspects of metabolic syndrome, it should be considered as the next helm of weight loss therapies.

Effects of Plant-Based Diets on Anthropometric and Cardiometabolic Markers in Adults: An Umbrella Review.

We conducted an umbrella review to consolidate the evidence of adopting plant-based diets on anthropometric and cardiometabolic outcomes. Six electronic databases (CINAHL, EMBASE, PubMed, Scopus, the Cochrane Library, and Web of Science) were searched for systematic reviews with meta-analysis (SRMAs) published from each journal's inception until 1 October 2022. Effect sizes from SRMAs and primary studies were pooled separately using random effects models. Overlapping primary studies were removed for primary studies' analyses. Seven SRMAs representing 51 primary studies were included, suggesting significant benefits of plant-based diets on weight (-2.09 kg, 95% CI: -3.56, -0.62 kg, p = 0.01, I2 = 95.6%), body mass index (-0.95 kg/m2, 95% CI: -1.26, -0.63 kg/m2, p = 0.002; I2 = 45.1%), waist circumference (-2.20 cm, 95% CI: -0.08, 0.00 cm, p = 0.04; I2 = 88.4%), fasting blood glucose (-0.11 mmol/L, 95% CI: -0.13, -0.09 mmol/L, p < 0.001, I2 = 18.2%), and low-density lipoprotein cholesterol (-0.31 mmol/L, 95% CI: -0.41, -0.20 mmol/L, p < 0.001, I2 = 65.6%). Changes in high-density lipoprotein cholesterol, triglycerides, and blood pressure were not statistically significant. Generally, plant-based diets were recommended to improve anthropometry, lipid profile, and glucose metabolism. However, findings should be interpreted with caution, because most of the reviews were rated to be of low credibility of evidence and were largely based on Western eating habits and traditions, which may limit the generalizability of findings.

Effectiveness of Combined Health Coaching and Self-Monitoring Apps on Weight-Related Outcomes in People With Overweight and Obesity: Systematic Review and Meta-analysis.

BACKGROUND: Self-monitoring smartphone apps and health coaching have both individually been shown to improve weight-related outcomes, but their combined effects remain unclear. OBJECTIVE: This study aims to examine the effectiveness of combining self-monitoring apps with health coaching on anthropometric, cardiometabolic, and lifestyle outcomes in people with overweight and obesity. METHODS: Relevant articles published from inception till June 9, 2022, were searched through 8 databases (Embase, CINAHL, PubMed, PsycINFO, Scopus, The Cochrane Library, and Web of Science). Effect sizes were pooled using random-effects models. Behavioral strategies used were coded using the behavior change techniques taxonomy V1. RESULTS: A total of 14 articles were included, representing 2478 participants with a mean age of 39.1 years and a BMI of 31.8 kg/m2. Using combined intervention significantly improved weight loss by 2.15 kg (95% CI -3.17 kg to -1.12 kg; P<.001; I2=60.3%), waist circumference by 2.48 cm (95% CI -3.51 cm to -1.44 cm; P<.001; I2=29%), triglyceride by 0.22 mg/dL (95% CI -0.33 mg/dL to 0.11 mg/dL; P=.008; I2=0%), glycated hemoglobin by 0.12% (95% CI -0.21 to -0.02; P=.03; I2=0%), and total calorie consumption per day by 128.30 kcal (95% CI -182.67 kcal to -73.94 kcal; P=.003; I2=0%) kcal, but not BMI, blood pressure, body fat percentage, cholesterol, and physical activity. Combined interventional effectiveness was superior to receiving usual care and apps for waist circumference but only superior to usual care for weight loss. CONCLUSIONS: Combined intervention could improve weight-related outcomes, but more research is needed to examine its added benefits to using an app. TRIAL REGISTRATION: PROSPERO CRD42022345133; https://tinyurl.com/2zxfdpay.

Socio-Demographic, Behavioral and Psychological Factors Associated with High BMI among Adults in a Southeast Asian Multi-Ethnic Society: A Structural Equation Model.

While various influencing factors of overweight and obesity have been identified, the underlying mechanism remains unclear. We examined the relationships among sociodemographic, behavioral, and psychological factors on anthropometry in a multi-ethnic population with overweight and obesity. Participants (N = 251) were recruited from January to October 2022. Mean age and self-reported BMI were 31.7 ± 10.1 years and 29.2 ± 7.2 kg/m2. Participants were mostly female (52.4%) and overweight (58.2%). Multivariate multiple regression was performed using maximum likelihood estimation. Body mass index was associated with waist circumference, age, sex, race, marital status, education level, residential region, overeating habit, immediate thinking, self-regulation, and physical activity, but not anxiety, depression, or the intention to change eating habits. Final model indicated good fit: χ2 (30, N = 250) = 33.5, p = 0.32, CFI = 0.993, TLI = 0.988, RMSEA = 0.022, and SRMR = 0.041. Direct effects were found between BMI and overeating (ß = 0.10, p = 0.004), race (ß = -0.82, p < 0.001), marital status (ß = -0.42, p = 0.001), and education level (ß = -0.28, p = 0.019). Crisps (68.8%), cake (66.8%) and chocolate (65.6%) were identified as the most tempting foods. Immediate thinking indirectly increased overeating habits through poor self-regulation, although sociodemographic characteristics better predicted anthropometry than psycho-behavioral constructs.

Effects of digital health interventions on the psychological outcomes of perinatal women: umbrella review of systematic reviews and meta-analyses.

ABSTRACTEvidence about the effects of digital health interventions (DHIs) on the psychological outcomes of perinatal women is increasing but remains inconsistent. An umbrella review was conducted to (1) assess the effect of DHIs on depressive, anxiety and stress symptoms and (2) compare the effects of DHIs on different digital platforms and population natures. Ten databases were searched from inception until December 23, 2022. The Hartung-Knapp-Sidik-Jonkman random-effects meta-analyses were utilised. Methodological quality was evaluated using the Assessment of Multiple Systematic Reviews 2 (AMSTAR-2). Twenty-four systematic reviews with 41 meta-analyses involving 45,509 perinatal women from 264 primary studies were included. The credibility of the evidence of meta-analyses was rated as highly suggestive (4.88%), suggestive (26.83%), weak (51.22%) or non-significant (17.07%) according to AMSTAR-2. Our findings suggest that DHIs are beneficial for reducing stress symptoms. However, conflicting effects were found on anxiety symptoms. Subgroup and meta-regression analyses suggested that DHIs effectively improve depressive symptoms in postnatal women, and DHIs using the website platform are highly effective in stress reduction. DHIs can be implemented adjuvant to usual obstetric care to improve depressive and stress symptoms. Additional well-designed RCTs with long-term follow-up are warranted.

Effectiveness of acceptance and commitment therapy on weight, eating behaviours and psychological outcomes: a systematic review and meta-analysis.

PURPOSE: To examine the effectiveness of ACT on weight (body mass index and body mass), eating behaviours (binge eating, emotional eating, external eating and restraint eating), and psychological outcomes (quality of life [QoL], depression, psychological flexibility, and weight stigma) among adults with overweight and obesity. METHODS: Seven electronic databases (CINAHL, EMBASE, PubMed, PsycInfo Scopus, The Cochrane Library, and Web of Science) were searched from inception through 17 June 2022. 13 studies and 48 unique effect sizes were analyzed using random-effects models. Pooled effect estimates were calculated using weighted mean differences (WMD) and standardized mean differences expressed in Hedges' g (g). Heterogeneity was assessed using Q-statistics and interpreted using I2. RESULTS: ACT was found to be effective in improving weight loss in terms of BMI (k = 6, WMD = - .50, 95% CI = - .90; - .11, t = - 3.25, p = .20, I2 = .0%), psychological flexibility and weight-related stigma. However, non-significant changes were found for body mass (k = 4, WMD = - 0.33 95% CI = - 1.53; 0.87, t = - .88, p = .44, I2 = .0%), binge eating (k = 4, g = - .34, 95% CI = - 1.31; 0.62, t = - 1.13, p = .34, I2 = 71.1%), emotional eating (k = 6, g = - .20, 95% CI = - 0.54; 0.15, t = - 1.47, p = .20, I2 = 45.0%), external eating (k = 5, g = - .40, 95% CI = 0.96; 0.16, t = - 1.99, p = .12, I2 = 81.8%), restraint eating (k = 3, g = .22 95% CI = - 0.57; 1.01, t = 1.19, p = .36, I2 = 69.1%), QoL (k = 3, g = .01, 95% CI = - 1.51; 1.52, t = .02, p = .99, I2 = 90.2%) and depression (k = 3, g = - .55, 95% CI = - 1.78; 0.67, t = - 1.94, p = .19, I2 = 79.9%). CONCLUSION: ACT could be effective in improving weight loss but more studies are needed to ascertain its effectiveness and the underlying mechanism by which the various components influence weight-related outcomes. LEVEL OF EVIDENCE: Evidence obtained from a systematic review and meta-analysis of existing empirical studies.

Experiences of simulation-based learning among undergraduate nursing students: A systematic review and meta-synthesis.

OBJECTIVES: We aimed to examine the experiences of simulation-based learning (SBL) among undergraduate nursing students. DESIGN: We conducted a systematic review and meta-synthesis on the experiences among undergraduate nursing studies who received SBL. DATA SOURCES: We searched through six databases namely PubMed, CINAHL, Scopus, Web of Science, Embase, ProQuest, for qualitative studies published from January 2011 to January 2022. REVIEW METHODS: A meta-synthesis was conducted according to the three-stage framework outlined by Thomas and Harden (2008). Critical appraisal was performed using the Critical Appraisal Skills Program (CASP) checklist. A standardised data extraction form was developed with reference from JBI Qualitative Assessment and Review Instrument Data Extraction Tools for Qualitative Research (JBI-QARI) checklist for data extraction. RESULTS: Fifteen studies were included, and four themes emerged from the synthesis: (1) acquiring knowledge and skills through SBL; (2) positive experiences of using high-fidelity simulation (HFS) and virtual reality simulation (VRS) methods; (3) challenges encountered while using SBL methods and (4) drawing parallels between simulation and real clinical settings. CONCLUSION: SBL allowed undergraduate nursing students to gain knowledge, acquire skills and have a positive SBL experiences. However, the provision of innovative strategies and resources for nursing students to overcome SBL-based challenges are still needed.

Improving adult eating behaviours by manipulating time perspective: a systematic review and meta-analysis.

There is a growing body of research on improving health behaviours through future thinking but that in improving eating behaviour remains unclear. Hence, we conducted a systematic review and meta-analysis on the effectiveness of time perspective manipulation in improving adult eating behaviours. Articles published from inception through 17 March 2022 were retrieved from eight databases (CINAHL, Embase, ProQuest, PsycINFO, PubMed, Scopus, The Cochrane library, and Web of Science). Meta-analysis was conducted using random effects models with effect sizes reported in Hedges' g. Sixteen experimental studies were included, representing 1,914 participants with mean ages ranging from 20.5 to 44.1 years old and mean baseline BMI of 22.0 kg/m2 to 37.2 kg/m2. The interventions included were episodic future thinking (EFT; n = 12), message framing (n = 2), and varying inter-meal intervals (IMI; n = 2) in improving food choices, food intake and portion size. Two studies reported significant interventional effectiveness for domain-specific EFT while all studies on message framing and IMI reported significant interventional effectiveness. Message framing seemed to be more effective when both temporal distance and valence frames were manipulated. However, the pooled effect sizes of all the interventions did not result in a significant interventional effect (n = 5; g = -0.49; 95% confidence interval (CI) = -1.44, 0.47; p = 0.23; I2=87.2%). More rigorous research is needed to ascertain the effectiveness of manipulating time perspective in improving eating behaviours before such interventions are more widely used.

Essential elements of weight loss apps for a multi-ethnic population with high BMI: a qualitative study with practical recommendations.

Smartphone weight loss apps are constantly being developed but the essential elements needed by a multi-ethnic population with overweight and obesity remains unclear. Purpose: To explore the perceptions of an Asian multi-ethnic population with overweight and obesity on the essential elements of weight loss apps. Twenty two participants were purposively sampled from a specialist weight management clinic in Singapore from 13 April to 30 April 2021. Recorded interviews were conducted using face-to-face and videoconferencing modalities. Data saturation was reached at the 18th participant. Data analysis was performed using inductive content analysis with constant comparison between and within transcripts. Findings: Three themes and eight subthemes on the essential app components emerged-(a) comprehensive and flexible calorie counters; (b) holistic, gradual and individualized behavior change recommendations tailored for people with overweight and obesity, and (c) just-in-time reminders of future consequences. There was a need to incorporate flexible options for food logging; break down general recommendations into small steps towards sustainable changes; tailor app contents for people with overweight and obesity; and evoke one's considerations of future consequences. Future weight loss apps should be designed to meet the needs of those with overweight and obesity, the very population that needs assistance with weight loss. Future apps could consider leveraging the capacity of artificial intelligence to provide personalized weight management in terms of sustaining self-regulation behaviors, optimizing goal-setting and providing personalized and timely recommendations for weight loss.

Weight loss smartphone apps are ubiquitous but the essential elements needed and preferred by people with overweight and obesity remains unclear. Therefore, we conducted a qualitative study to explore the perceptions and needs of this population in a weight loss app to prolong app engagement and enhance weight loss success. 22 participants were interviewed through face-to-face or videoconferencing sessions during the peak of the COVID-19 pandemic. Interview transcripts were analyzed within and between participants to develop codes, subthemes and themes that represent the participants' perceptions and needs in weight loss apps. Participants expressed the need for (a) comprehensive and flexible calorie counters; (b) holistic, gradual, and individualized behavior change recommendations tailored for people with overweight and obesity; and (c) just-in-time reminders of future consequences. Our findings showed a need to incorporate flexible options for food logging; break down general recommendations into small steps towards sustainable changes; tailor app contents for people with overweight and obesity; and evoke one's considerations of future consequences. Future apps could consider leveraging the capacity of artificial intelligence to provide personalized weight management in terms of sustaining self-regulation behaviors, optimizing goal-setting and providing personalized and timely recommendations for weight loss.