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

Abstract 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: 66 out of 5573 potential studies were included, representing more than 2,031 participants. Three tenets of self-regulation were identified - self-monitoring (n=66, 100%), optimization 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 recognizing 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 personalized nudges/prompts. Only six studies reported average weight losses (2.4% to 4.7%) of which two were statistically significant. Conclusion: The use of AI for weight loss is still undeveloped. Based on this study findings, we proposed a framework on the applicability of AI for weight loss but cautioned its contingency upon engagement and contextualisation.

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Artificial intelligence based prediction models: sales forecasting application in automotive aftermarket

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219187 ◽

2021 ◽

pp. 1-13

Author(s):

Mert Girayhan Türkbayrağí ◽

Elif Dogu ◽

Y. Esra Albayrak

Keyword(s):

Artificial Intelligence ◽

Computational Intelligence ◽

Prediction Models ◽

Predictive Analytics ◽

Demand Characteristic ◽

Product Portfolio ◽

Ann Model ◽

Nonlinear Relationships ◽

Relationship Of ◽

Automotive Aftermarket

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance.

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Contextual, Real-Time Micro-Nudges Improve Eating Mindfulness and are Associated with Weight Loss (Preprint)

10.2196/preprints.14497 ◽

2019 ◽

Author(s):

Katherine Nameth ◽

Lisa C Offringa ◽

Katelijn Vleugels ◽

Christopher Gardner ◽

Dena Bravata

Keyword(s):

Weight Loss ◽

Behavior Change ◽

Real Time ◽

Average Weight ◽

Mindful Eating ◽

Dominant Hand ◽

Obese People ◽

Novel Technology ◽

Management Interventions ◽

Drinking Behaviors

BACKGROUND Most obesity management interventions do not achieve sustained behavior change and, thus, do not result in long term weight loss. A promising approach to weight loss involves mindful eating coaching, which increases awareness of internal cues including hunger and satiety. The purpose of this study was to evaluate the use of a novel technology that promotes mindful eating and drinking behaviors by providing contextual, real-time micro-nudges on wrist-worn wearable devices. OBJECTIVE Evaluate the use of a novel technology, promoting mindful eating and drinking behaviors using contextual micro-nudges on a wrist-worn device, and assess how it facilitates behavior change and weight loss. METHODS Participants used the mindful eating technology for 5 weeks. The primary outcomes of interest collected at the end of the intervention were user acceptability and engagement. Secondary outcomes collected before and at the end of the intervention were mindfulness while eating, consumptions behaviors, and weight loss. RESULTS 17 overweight and obese people completed the intervention. They found the technology to be highly acceptable: 75% reported that using the wearable on their dominant hand felt natural; 88% found it convenient to keep their phone nearby and use the wearable all day; 75% did not find the wearable’s vibrations (haptic) associated with the micro-nudges to be disruptive to the meal experience; 88% enjoyed having the Klue metrics visible on their wearable at all times. On average, the duration of the intervention was 34.2 days (SD 1.2). On average, participants used the app for all but 1.8 (SD 2.0) days, 13.0 (SD 1.0) hours per day when active, received to 27.2 micro-nudges (notifications) per day and engaged in 13.9 daily interactive coaching moments. Moreover, 94% of participants significantly improved their scores on the validated Mindful Eating Questionnaire (P=.001). Similarly, 94% of participants reported improving at least one consumption behavior during the study and 77% reported improving three or more consumption behaviors (e.g., eating more mindfully, remaining well hydrated). Average weight loss was 1.3kg (SD 2.3, P=0.03). CONCLUSIONS The novel technology evaluated in this study provided real-time micro-nudges on a wrist-worn wearable that were acceptable to users and used frequently. Technologies such as these that interact with the user in-the-moment as behaviors are developing may lead to sustained engagement and could have a significant role in increasing mindful eating and producing positive behavior changes associated with successful weight loss. CLINICALTRIAL Stanford e-Protocol #39068

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Weight Loss in a Digital Diabetes Prevention Program Powered by Artificial Intelligence: Retrospective Cohort Study (Preprint)

10.2196/preprints.28687 ◽

2021 ◽

Author(s):

Sarah A Graham ◽

Jonathan H Hori ◽

Fjori Shemaj ◽

Natalie Stein ◽

OraLee H Branch

Keyword(s):

Artificial Intelligence ◽

Weight Loss ◽

Prevention Program ◽

Diabetes Prevention ◽

Diabetes Prevention Program ◽

Average Weight ◽

User Characteristics ◽

Educational Mission ◽

Effective Manner ◽

Control And Prevention

BACKGROUND The National Diabetes Prevention Program (DPP), governed by the Centers for Disease Control and Prevention (CDC), reduces the incidence of diabetes and diabetes-associated medical costs. Typically, providing this program is staffing-intensive, limiting the ability to scale the DPP and keep pace with the growing incidence of prediabetes. OBJECTIVE We investigated the average weight loss of users of a program called Lark DPP that has full CDC recognition and is powered by conversational artificial intelligence (AI). METHODS We analyzed weight loss of 674 users who met CDC qualifications (completed ≥3 lessons in months 1-6 with ≥9 months between first and last lessons). In addition to the weight loss expected from the CDC curriculum, we investigated whether user characteristics and engagement with AI coaching increased the likelihood of achieving the CDC’s benchmark of ≥5% weight loss at 12 months using logistic regression. RESULTS We observed that 279 users met CDC qualifications and achieved an average of 5.2% (SE=.4) weight loss at 12 months (46% achieved ≥5%). CDC qualifiers completed an average of 20.7 (SE=.4) of 26 available educational missions/lessons. The number of weeks with >2 weigh-ins (standardized coefficient β=.39; P<.001); days with an exchange with the AI coach (β=.24; P=.016); and days since last coaching exchange at final weigh-in (β=-.45; P<.001) were significantly associated with the likelihood of achieving ≥5% weight loss. CONCLUSIONS The Lark DPP resulted in weight loss and sustained engagement for 12 months that was equal to or greater than in-person or hybrid-digital DPPs. Beyond the association between educational mission completion and weight loss, the synchronous personalized feedback and exchanges with the AI coach increased the likelihood of achieving ≥5% weight loss. An AI-powered program is one method to deliver DPPs in a scalable and resource-effective manner to keep pace with the prediabetes epidemic.

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Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review (Preprint)

10.2196/preprints.23939 ◽

2020 ◽

Author(s):

Christine Buchanan ◽

M Lyndsay Howitt ◽

Rita Wilson ◽

Richard G Booth ◽

Tracie Risling ◽

...

Keyword(s):

Artificial Intelligence ◽

Health System ◽

Scoping Review ◽

Predictive Analytics ◽

Digital Health ◽

Shared Responsibility ◽

Compassionate Care ◽

Health Technologies ◽

Emerging Trends ◽

Research Questions

BACKGROUND Artificial intelligence (AI) is set to transform the health system, yet little research to date has explored its influence on nurses—the largest group of health professionals. Furthermore, there has been little discussion on how AI will influence the experience of person-centered compassionate care for patients, families, and caregivers. OBJECTIVE This review aims to summarize the extant literature on the emerging trends in health technologies powered by AI and their implications on the following domains of nursing: administration, clinical practice, policy, and research. This review summarizes the findings from 3 research questions, examining how these emerging trends might influence the roles and functions of nurses and compassionate nursing care over the next 10 years and beyond. METHODS Using an established scoping review methodology, MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Center, Scopus, Web of Science, and ProQuest databases were searched. In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI. Data were charted using structured forms and narratively summarized. RESULTS A total of 131 articles were retrieved from the scoping review for the 3 research questions that were the focus of this manuscript (118 from database sources and 13 from targeted websites). Emerging AI technologies discussed in the review included predictive analytics, smart homes, virtual health care assistants, and robots. The results indicated that AI has already begun to influence nursing roles, workflows, and the nurse-patient relationship. In general, robots are not viewed as replacements for nurses. There is a consensus that health technologies powered by AI may have the potential to enhance nursing practice. Consequently, nurses must proactively define how person-centered compassionate care will be preserved in the age of AI. CONCLUSIONS Nurses have a shared responsibility to influence decisions related to the integration of AI into the health system and to ensure that this change is introduced in a way that is ethical and aligns with core nursing values such as compassionate care. Furthermore, nurses must advocate for patient and nursing involvement in all aspects of the design, implementation, and evaluation of these technologies. INTERNATIONAL REGISTERED REPORT RR2-10.2196/17490

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The importance of ensuring artificial intelligence and machine learning can be understood at the human level

European Journal of Public Health ◽

10.1093/eurpub/ckz185.259 ◽

2019 ◽

Vol 29 (Supplement_4) ◽

Author(s):

S Ram

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Large Scale ◽

Clinical Decision Making ◽

Prediction Models ◽

Predictive Analytics ◽

Black Box ◽

User Engagement ◽

Box Models ◽

Black Box Models

Abstract With rapid developments in big data technology and the prevalence of large-scale datasets from diverse sources, the healthcare predictive analytics (HPA) field is witnessing a dramatic surge in interest. In healthcare, it is not only important to provide accurate predictions, but also critical to provide reliable explanations to the underlying black-box models making the predictions. Such explanations can play a crucial role in not only supporting clinical decision-making but also facilitating user engagement and patient safety. If users and decision makers do not have faith in the HPA model, it is highly likely that they will reject its use. Furthermore, it is extremely risky to blindly accept and apply the results derived from black-box models, which might lead to undesirable consequences or life-threatening outcomes in domains with high stakes such as healthcare. As machine learning and artificial intelligence systems are becoming more capable and ubiquitous, explainable artificial intelligence and machine learning interpretability are garnering significant attention among practitioners and researchers. The introduction of policies such as the General Data Protection Regulation (GDPR), has amplified the need for ensuring human interpretability of prediction models. In this talk I will discuss methods and applications for developing local as well as global explanations from machine learning and the value they can provide for healthcare prediction.

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Gender Differences in Internal Beliefs About Weight and Negative Attitudes Towards Self and Others

Psychology of Women Quarterly ◽

10.1111/j.1471-6402.1997.tb00132.x ◽

1997 ◽

Vol 21 (4) ◽

pp. 581-593 ◽

Cited By ~ 33

Author(s):

Marika Tiggemann ◽

Esther D. Rothblum

Keyword(s):

Weight Loss ◽

Locus Of Control ◽

Self Esteem ◽

Social Consequences ◽

Self Control ◽

Control Beliefs ◽

Average Weight ◽

Obese People ◽

Weight Locus Of Control ◽

People’S Attitudes

Previous research has found that people with an internal weight locus of control (beliefs in self-control over weight) are more likely to join and stay in weight-loss programs and have higher self-esteem than those who have an external locus of control (e.g., belief that weight is due to luck, genes). There has been no research on how weight locus of control affects the self-esteem of people who are not average weight or not satisfied with their weight. The present study predicted that for people who are overweight, weight locus of control would be negatively related to self-esteem. The results confirmed this interaction between weight locus of control and weight on self-esteem for women, but not for men. The second prediction was that internal weight locus of control would have negative social consequences in terms of greater negative stereotyping of obese people, and this was also confirmed for women. Because weight loss is rarely permanent, it would seem important to change people's attitudes about the lack of control that they (and others) have over body weight.

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Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

JMIR Nursing ◽

10.2196/23939 ◽

2020 ◽

Vol 3 (1) ◽

pp. e23939

Author(s):

Christine Buchanan ◽

M Lyndsay Howitt ◽

Rita Wilson ◽

Richard G Booth ◽

Tracie Risling ◽

...

Keyword(s):

Artificial Intelligence ◽

Health System ◽

Scoping Review ◽

Predictive Analytics ◽

Digital Health ◽

Shared Responsibility ◽

Compassionate Care ◽

Health Technologies ◽

Emerging Trends ◽

Research Questions

Background Artificial intelligence (AI) is set to transform the health system, yet little research to date has explored its influence on nurses—the largest group of health professionals. Furthermore, there has been little discussion on how AI will influence the experience of person-centered compassionate care for patients, families, and caregivers. Objective This review aims to summarize the extant literature on the emerging trends in health technologies powered by AI and their implications on the following domains of nursing: administration, clinical practice, policy, and research. This review summarizes the findings from 3 research questions, examining how these emerging trends might influence the roles and functions of nurses and compassionate nursing care over the next 10 years and beyond. Methods Using an established scoping review methodology, MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Center, Scopus, Web of Science, and ProQuest databases were searched. In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI. Data were charted using structured forms and narratively summarized. Results A total of 131 articles were retrieved from the scoping review for the 3 research questions that were the focus of this manuscript (118 from database sources and 13 from targeted websites). Emerging AI technologies discussed in the review included predictive analytics, smart homes, virtual health care assistants, and robots. The results indicated that AI has already begun to influence nursing roles, workflows, and the nurse-patient relationship. In general, robots are not viewed as replacements for nurses. There is a consensus that health technologies powered by AI may have the potential to enhance nursing practice. Consequently, nurses must proactively define how person-centered compassionate care will be preserved in the age of AI. Conclusions Nurses have a shared responsibility to influence decisions related to the integration of AI into the health system and to ensure that this change is introduced in a way that is ethical and aligns with core nursing values such as compassionate care. Furthermore, nurses must advocate for patient and nursing involvement in all aspects of the design, implementation, and evaluation of these technologies. International Registered Report Identifier (IRRID) RR2-10.2196/17490

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Parasympathetic Concomitants of Habitual, Spontaneous, and Instructed Emotional Suppression

Journal of Psychophysiology ◽

10.1027/0269-8803/a000171 ◽

2017 ◽

Vol 31 (2) ◽

pp. 78-89 ◽

Cited By ~ 2

Author(s):

Asmir Gračanin ◽

Igor Kardum ◽

Jasna Hudek-Knežević

Keyword(s):

Negative Emotion ◽

Self Regulation ◽

Experimental Manipulation ◽

Task Demands ◽

Self Control ◽

Integration Model ◽

Sinus Arrhythmia ◽

Emotional Suppression ◽

Emotion Experience ◽

Similar Relation

Abstract. The neurovisceral integration model proposes that different forms of self-regulation, including the emotional suppression, are characterized by the activation of neural network whose workings are also reflected in respiratory sinus arrhythmia (RSA). However, most of the previous studies failed to observe theoretically expected increases in RSA during emotional suppression. Even when such effects were observed, it was not clear whether they resulted from specific task demands, a decrease in muscle activity, or they were the consequence of more specific self-control processes. We investigated the relation between habitual or trait-like suppression, spontaneous, and instructed suppression with changes in RSA during negative emotion experience. A modest positive correlation between spontaneous situational and habitual suppression was observed across two experimental tasks. Furthermore, the results showed greater RSA increase among participants who experienced higher negative affect (NA) increase and reported higher spontaneous suppression than among those with higher NA increase and lower spontaneous suppression. Importantly, this effect was independent from the habitual suppression and observable facial expressions. The results of the additional task based on experimental manipulation, rather than spontaneous use of situational suppression, indicated a similar relation between suppression and RSA. Our results consistently demonstrate that emotional suppression, especially its self-regulation component, is followed by the increase in parasympathetic activity.

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