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

[This corrects the article DOI: 10.2196/46036.].

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.

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.

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.

Effectiveness of interactive technology-assisted interventions on promoting healthy food choices: a scoping review and meta-analysis.

Making healthy food choices is crucial for health promotion and disease prevention. While there are an increasing number of technology-assisted interventions to promote healthy food choices, the underlying mechanism by which consumption behaviours and weight status change remains unclear. Our scoping review and meta-analysis of seventeen studies represents 3988 individuals with mean ages ranging from 19·2 to 54·2 years and mean BMI ranging from 24·5 kg/m2 to 35·6 kg/m2. Six main outcomes were identified namely weight, total calories, vegetables, fruits, healthy food, and fats and other food groups including sugar-sweetened beverages, saturated fats, snacks, wholegrains, Na, proteins, fibre, cholesterol, dairy products, carbohydrates, and takeout meals. Technology-assisted interventions were effective for weight loss (g = -0·29; 95 % CI -0·54, -0·04; I2 = 65·7 %, t = -2·83, P = 0·03) but not for promoting healthy food choices. This highlights the complexity in creating effective interactive technology-assisted interventions and understanding its mechanisms of influence and change. We also identified that there needs to be greater application of theory to inform the development of technology-assisted interventions in this area as new and improved interventions are being developed.

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.

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.

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.

Personal motivation, self-regulation barriers and strategies for weight loss in people with overweight and obesity: a thematic framework analysis.

OBJECTIVE: To explore motivations, self-regulation barriers and strategies in a multi-ethnic Southeast Asian population with overweight and obesity. DESIGN: Qualitative design using semi-structured face-to-face and videoconferencing interviews. Data were analysed using thematic framework analysis and constant comparison method. SETTING: Specialist weight management clinic. PARTICIPANTS: Twenty-two participants were purposively sampled from 13 April to 30 April 2021. Median age and BMI of the participants were 37·5 (interquartile range (IQR) = 13·3) and 39·2 kg/m2 (IQR = 6·1), respectively. And 31·8 % were men, majority had a high intention to adopt healthy eating behaviours (median = 6·5; IQR = 4·8-6·3) and 59 % of the participants had a medium level of self-regulation. RESULTS: Six themes and fifteen subthemes were derived. Participants were motivated to lose weight by the sense of responsibility as the family's pillar of support and to feel 'normal' again. We coupled self-regulation barriers with corresponding strategies to come up with four broad themes: habitual overconsumption - mindful self-discipline; proximity and convenience of food available - mental tenacity; momentary lack of motivation and sense of control - motivational boosters; and overeating triggers - removing triggers. We highlighted six unique overeating triggers namely: trigger activities (e.g. using social media); eating with family, friends and colleagues; provision of food by someone; emotions (e.g. feeling bored at home, sad and stressed); physiological condition (e.g. premenstrual syndrome); and the time of the day. CONCLUSIONS: Future weight management interventions should consider encompassing participant-led weight loss planning, motivation boosters and self-regulation skills to cope with momentary overeating triggers.

Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review.

BACKGROUND: Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. OBJECTIVE: This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. METHODS: A systematic scoping review was conducted according to the 5-stage framework by Arksey and O'Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science for studies that were published from inception until June 21, 2021. Articles that were not specific to AI, not research studies, and not written in English were omitted. RESULTS: Of the 3666 articles retrieved, 26 (0.71%) were eligible and included in this review. The mean age of the participants ranged from 30 to 72.6 years, the proportion of men ranged from 0% to 73.4%, and the sample sizes for primary studies ranged from 11 to 2780. The perceptions and needs of various populations in the use of AI were identified for general, primary, and community health care; chronic diseases self-management and self-diagnosis; mental health; and diagnostic procedures. The use of AI was perceived to be positive because of its availability, ease of use, and potential to improve efficiency and reduce the cost of health care service delivery. However, concerns were raised regarding the lack of trust in data privacy, patient safety, technological maturity, and the possibility of full automation. Suggestions for improving the adoption of AI in health care were highlighted: enhancing personalization and customizability; enhancing empathy and personification of AI-enabled chatbots and avatars; enhancing user experience, design, and interconnectedness with other devices; and educating the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study. CONCLUSIONS: The perceptions and needs of AI in its use in health care are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in health care. This would facilitate an increase in the effectiveness and efficiency of health care service delivery to improve patient outcomes and satisfaction.

Sustainability of Weight Loss Through Smartphone Apps: Systematic Review and Meta-analysis on Anthropometric, Metabolic, and Dietary Outcomes.

BACKGROUND: Evidence on the long-term effects of weight management smartphone apps on various weight-related outcomes remains scarce. OBJECTIVE: In this review, we aimed to examine the effects of smartphone apps on anthropometric, metabolic, and dietary outcomes at various time points. METHODS: Articles published from database inception to March 10, 2022 were searched, from 7 databases (Embase, CINAHL, PubMed, PsycINFO, Cochrane Library, Scopus, and Web of Science) using forward and backward citation tracking. All randomized controlled trials that reported weight change as an outcome in adults with overweight and obesity were included. We performed separate meta-analyses using random effects models for weight, waist circumference, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, blood glucose level, blood pressure, and total energy intake per day. Methodological quality was assessed using the Cochrane Risk of Bias tool. RESULTS: Based on our meta-analyses, weight loss was sustained between 3 and 12 months, with a peak of 2.18 kg at 3 months that tapered down to 1.63 kg at 12 months. We did not find significant benefits of weight loss on the secondary outcomes examined, except for a slight improvement in systolic blood pressure at 3 months. Most of the included studies covered app-based interventions that comprised of components beyond food logging, such as real-time diet and exercise self-monitoring, personalized and remote progress tracking, timely feedback provision, smart devices that synchronized activity and weight data to smartphones, and libraries of diet and physical activity ideas. CONCLUSIONS: Smartphone weight loss apps are effective in initiating and sustaining weight loss between 3 and 12 months, but their effects are minimal in their current states. Future studies could consider the various aspects of the socioecological model. Conversational and dialectic components that simulate health coaches could be useful to enhance user engagement and outcome effectiveness. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42022329197; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=329197.

Weight-loss interventions for improving emotional eating among adults with high body mass index: A systematic review with meta-analysis and meta-regression.

OBJECTIVES: To evaluate the effectiveness of weight-loss interventions on emotional eating among adults with high body mass index (BMI). METHODS: A systematic review, meta-analysis and meta-regression were performed on randomized controlled trials published from inception until 19 March 2021. RESULTS: Thirty-one studies were included, representing 1203 participants with mean ages ranging from 21.8 to 57.3 years old and BMI 27.2-43.5 kg/m2 . We found small-to-medium interventional effects on emotional eating (n = 18; Hedges' g = 0.22; p = 0.01, I2  = 61.7%), uncontrolled eating (n = 16; Hedges' g = 0.46; p < 0.001, I2  = 71.6%) and cognitive restraint (n = 18; Hedges' g = 0.42; p < 0.001, I2  = 75.8%). Small-to-medium interventional effects were only found for emotional eating (n = 8; Hedges' g = 0.45; p = 0.02, I2  = 74.3%) 3-month post-intervention, and on BMI (n = 4; Hedges' g = 0.43; p < 0.05, I2  = 33.4%) and weight (n = 6; Hedges' g = 0.36; p < 0.01, I2  < 10.4%) 12-month post-intervention. Age, male proportion, baseline BMI, attrition rate and intervention length were not significant moderators of the heterogeneity between studies. CONCLUSION: Interventions improved emotional eating and weight loss along a year-long trajectory.

The role of artificial intelligence in enhancing clinical nursing care: A scoping review.

AIM: To present an overview of how artificial intelligence has been used to improve clinical nursing care. BACKGROUND: Artificial intelligence has been reshaping the healthcare industry but little is known about its applicability in enhancing nursing care. EVALUATION: A scoping review was conducted. Seven electronic databases (CINAHL, Cochrane Library, EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science) were searched from 1 January 2010 till 20 December 2020. Grey literature and reference lists of included articles were also searched. KEY ISSUES: Thirty-seven studies encapsulating the use of artificial intelligence in improving clinical nursing care were included in this review. Six use cases were identified - documentation, formulating nursing diagnoses, formulating nursing care plans, patient monitoring, patient care prediction such as falls prediction (most common) and wound management. Various techniques of machine learning and classification were used for predictive analyses and to improve nurses' preparedness and management of patients' conditions CONCLUSION: This review highlighted the potential of artificial intelligence in improving the quality of nursing care. However, more randomized controlled trials in real-life healthcare settings should be conducted to enhance the rigor of evidence. IMPLICATIONS FOR NURSING MANAGEMENT: Education in the application of artificial intelligence should be promoted to empower nurses to lead technological transformations and not passively trail behind others.

Generation Z undergraduate students' resilience during the COVID-19 pandemic: a qualitative study.

Resilience has been documented as an essential component in managing stress. However, understanding how undergraduate students with different sociodemographic characteristics perceive resilience remains understudied. This study aimed to explore how undergraduate students in one university define and build resilience during the COVID-19 pandemic. Students' perception and preferences for receiving resilience training were additionally solicited. A descriptive qualitative cross-sectional study was conducted. Twenty-seven students were interviewed using a semi-structured interview guide via Skype instant messaging. The thematic analysis generated five themes: resilience as enduring and withstanding; the building blocks of resilience; resilience: learning or earning; pedagogical considerations for resilience training; and a blended platform for resilience training. Participants described resilience as an enduring and withstanding trait essential for university students. Resilience can be built from intrinsic and extrinsic factors. Intrinsic factors that enhanced resilience included desire to succeed and motivation. Extrinsic factors were relational in nature, and friends, family, teachers, and religion were found to boost resilience. Students had several recommendations in designing resilience training, and they recommended the use of a blended platform. Further, students suggested the use of videos, narratives from resilient individuals, and using reflective practice as a pedagogy in resilience training. Future resilience training should consist of personal and interpersonal factors and should be introduced early during the academic term of students' university life. As the COVID-19 pandemic compounds an already challenging academic climate, this study lends it findings to expand the resilience literature and develop future resilience training.

The potential of artificial intelligence in enhancing adult weight loss: a scoping review.

OBJECTIVE: To present an overview of how artificial intelligence (AI) could be used to regulate eating and dietary behaviours, exercise behaviours and weight loss. DESIGN: A scoping review of global literature published from inception to 15 December 2020 was conducted according to Arksey and O'Malley's five-step framework. Eight databases (CINAHL, Cochrane-Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus and Web of Science) were searched. Included studies were independently screened for eligibility by two reviewers with good interrater reliability (k = 0·96). RESULTS: Sixty-six out of 5573 potential studies were included, representing more than 2031 participants. Three tenets of self-regulation were identified - self-monitoring (n 66, 100 %), optimisation of goal setting (n 10, 15·2 %) and self-control (n 10, 15·2 %). Articles were also categorised into three AI applications, namely machine perception (n 50), predictive analytics only (n 6) and real-time analytics with personalised micro-interventions (n 10). Machine perception focused on recognising food items, eating behaviours, physical activities and estimating energy balance. Predictive analytics focused on predicting weight loss, intervention adherence, dietary lapses and emotional eating. Studies on the last theme focused on evaluating AI-assisted weight management interventions that instantaneously collected behavioural data, optimised prediction models for behavioural lapse events and enhance behavioural self-control through adaptive and personalised nudges/prompts. Only six studies reported average weight losses (2·4-4·7 %) of which two were statistically significant. CONCLUSION: The use of AI for weight loss is still undeveloped. Based on the current study findings, we proposed a framework on the applicability of AI for weight loss but cautioned its contingency upon engagement and contextualisation.

Relationship between self-care adherence, time perspective, readiness to change and executive function in patients with heart failure.

This study examined the relationship between self-care adherence, time perspective (TP), readiness to change (RTC) and executive function in heart failure (HF) self-care. 147 heart failure patients completed questionnaires on self-care, TP, RTC; and cognitive tasks that reflect working memory and inhibition. Positive correlation was found between self-care, future-oriented TP (r = 0.362, P = 0.01), RTC (r = 0.184, P = 0.05) and working memory (r = 0.174, P = 0.01). Mediation analysis elucidated the indirect effect of RTC on self-care through TP (B = 1.205, bias-corrected bootstrapped at 95% confidence interval 0.532, 2.145) explaining 62.0% of the total effect. Working memory did not moderate this relationship and inhibition did not predict self-care. Self-care scores were lower than cut-off of 70 (mean = 51.2, standard deviation = 17.2). Age (r = - 0.220), staying alone (r = - 0.270) income < 1000 (r = - 0.270) and not having formal education (r = - 0.165) were correlated with TP. Healthcare professionals could motivate HF patients to perform regular self-care behaviours by tailoring interventions according to their TP and RTC.

Leisure-Time Physical Activity and Depressive Symptoms Among Patients With Coronary Heart Disease: The Mediating Role of Physical Activity Self-Efficacy.

BACKGROUND: Despite the positive effect of physical activity on reducing depressive symptoms among patients with coronary heart disease (CHD), the effect of physical activity on depressive symptoms is poorly understood. AIMS: To examine the mediating role of physical activity self-efficacy in the relationship between leisure-time physical activity and depressive symptoms in CHD patients. METHODS: This was a secondary data analysis study. A total of 593 CHD patients were included. Data on leisure-time physical activity, physical activity self-efficacy, and depressive symptoms were collected by validated questionnaires. Sociodemographic and clinical data were collected via patient interviews and medical records reviewing. The approach of Baron and Kenny was adopted to examine the mediating effect of physical activity self-efficacy on the association between leisure-time physical activity and depressive symptoms. RESULTS: On average, participants aged 56.9 (± 12.5) years old, with 66% male. Statistical analyses showed that leisure-time physical activity was significantly associated with depressive symptoms (ß = -0.041, p = .040) and physical activity self-efficacy (ß = 0.197, p = .001), and physical activity self-efficacy was significantly associated with depressive symptoms (ß = -0.223, p = .001) after adjusting for leisure-time physical activity. The indirect effect of leisure-time physical activity on depressive symptoms through physical activity self-efficacy was also significant (ß = -0.044, 95% confidence interval: -0.064, -0.027), suggesting a mediating role of physical activity self-efficacy on the relationship between leisure-time physical activity and depressive symptoms. LINKING EVIDENCE TO ACTION: Leisure-time physical activity is associated with reduced depressive symptoms among CHD patients, and this association is mediated by physical activity self-efficacy. This mediating model has important clinical implications, where integrating physical activity that is enjoyable and pleasant, and self-efficacy building elements in physical activity regimens should be considered, so as to improve psychological outcomes among CHD patients.

Motivation, Challenges and Self-Regulation in Heart Failure Self-Care: a Theory-Driven Qualitative Study.

BACKGROUND: Self-care behaviours are crucial in reducing chronic heart failure (HF) morbidity and mortality but performance remains poor worldwide. This study draws on Temporal Self-regulation Theory (TST) to explore participants' motivations, challenges and personalised self-regulation strategies to enhance self-care. METHOD: Seventeen HF patients were purposively sampled and recruited from outpatient and inpatient settings at a Singaporean tertiary hospital from December 2017 to March 2018. Unstructured face-to-face interviews were conducted. Data were analysed using thematic analysis with constant comparison. RESULTS: Five themes emerged. Self-care motivations were (1) consideration of family's future and (2) consideration of own past, while demotivation was (3) fatalistic consideration of own future. Barriers of behaviour change were (4) difficulty adopting physical activity and (5) difficulty deviating from personal dietary habits and sociocultural dietary norms. Personalised strategies to overcome these challenges were described in the 12 subthemes that emerged. Themes were well-fitted into the TST-(1-3) corresponded to time perspective, (4-5) corresponds to behaviour prepotency and the subthemes corresponded to self-regulatory capacity. Motivation could be enhanced by stimulating considerations of one's past regrets, family's future well-being and real-life success stories to instil hope. Clinicians and case managers could enhance self-regulation by empowering patients with tactical and situational skills to develop personalised plans to improve lifestyle habits and strategies to resist temptations. CONCLUSION: Future person-centred self-care interventions could be tailored according to the study findings. Better self-care could improve patient outcomes, reduce rehospitalisation and alleviate global healthcare burden. Findings could be generalised to healthy populations as primary prevention.

The suitability of motivational interviewing versus cognitive behavioural interventions on improving self-care in patients with heart failure: A literature review and discussion paper.

BACKGROUND: Chronic heart failure remains a major public health concern due to its high prevalence and disease burden. Although self-care has been advocated as the sustainable solution, it remains inadequate. Recent studies have shown the potential of integrating structured counselling elements into traditional educational programs to enhance self-care but the optimal counselling method remains unclear. AIM: To compare the applicability of cognitive behavioural interventions and motivational interviewing on improving self-care behaviours in patients with chronic heart failure. METHOD: A systematic three-step search strategy was used to identify studies that incorporated cognitive behavioural interventions and/or motivational interviewing to improve heart failure self-care. Quantitative and qualitative trial studies that met the inclusion criteria were appraised using the Joanna Brigg's Institute criteria. RESULTS: Motivational interviewing showed higher potential in improving HF self-care behaviours, but sustainability remains unclear. Cognitive behavioural interventions only showed effectiveness when applied to patients with comorbid depressive symptoms. Statistically significant results were only elucidated upon statistical adjustments and examination of behaviours individually. Potential effective components of CBI include setting up environmental reminders, addressing misconceptions and skills-training while that of MI was the communication style. CONCLUSION: MI and CBI could be used synergistically by extracting their key effective components to strengthen the intention-behaviour link in improving HF self-care behaviours. MI could be used to enhance the intention to change by evoking ambivalence and change talk. CBI could be used to enhance problem-solving skills and set environmental reminders to strengthen the translation of intention to behaviour.

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.