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

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.

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.

Strategies to Improve Physical Activity and Nutrition Behaviours in Children and Adolescents: A Review

Despite widespread acknowledgement of the multifarious health benefits of physical activity (PA), including prevention and control of obesity, an overwhelming majority of children and adolescents are not sufficiently active to realise such benefits. Concurrently, young people are significantly impacted by the rapid global rise of sedentarism, and suboptimal dietary patterns during key phases of development. Regrettably, the cumulative effects of unhealthy behaviours during the growing years predisposes young people to the early stages of several chronic conditions, including obesity. Clear and consistent approaches are urgently needed to improve eating and activity behaviours of children and adolescents. Based on existing evidence of "best bets" to prevent and control obesity and its comorbidities, we present a set of non-negotiable strategies as a 'road map' to achieving prevention and improving the health of children and adolescents.

Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of Artificial Intelligence, focused on the management of patients with obesity.

Methods

The perspectives of the authors were augmented by scientific support from published citations and integrated with information derived from search engines (i.e., Chrome by Google, Inc) and chatbots (i.e., Chat Generative Pretrained Transformer or Chat GPT).

Results

Artificial Intelligence (AI) is the technologic acquisition of knowledge and skill by a nonhuman device, that after being initially programmed, has varying degrees of operations autonomous from direct human control, and that performs adaptive output tasks based upon data input learnings. AI has applications regarding medical research, medical practice, and applications relevant to the management of patients with obesity. Chatbots may be useful to obesity medicine clinicians as a source of clinical/scientific information, helpful in writings and publications, as well as beneficial in drafting office or institutional Policies and Procedures and Standard Operating Procedures. AI may facilitate interactive programming related to analyses of body composition imaging, behavior coaching, personal nutritional intervention & physical activity recommendations, predictive modeling to identify patients at risk for obesity-related complications, and aid clinicians in precision medicine. AI can enhance educational programming, such as personalized learning, virtual reality, and intelligent tutoring systems. AI may help augment in-person office operations and telemedicine (e.g., scheduling and remote monitoring of patients). Finally, AI may help identify patterns in datasets related to a medical practice or institution that may be used to assess population health and value-based care delivery (i.e., analytics related to electronic health records).

Conclusions

AI is contributing to both an evolution and revolution in medical care, including the management of patients with obesity. Challenges of Artificial Intelligence include ethical and legal concerns (e.g., privacy and security), accuracy and reliability, and the potential perpetuation of pervasive systemic biases.

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.

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

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.

What Intervention Elements Drive Weight Loss in Blended-Care Behavior Change Interventions? A Real-World Data Analysis with 25,706 Patients

Background: Blended-care behavior change interventions (BBCI) are a combination of digital care and coaching by health care professionals (HCP), which are proven effective for weight loss. However, it remains unclear what specific elements of BBCI drive weight loss. Objectives: This study aims to identify the distinct impact of HCP-elements (coaching) and digital elements (self-monitoring, self-management, and education) for weight loss in BBCI. Methods: Long-term data from 25,706 patients treated at a digital behavior change provider were analyzed retrospectively using a ridge regression model to predict weight loss at 3, 6, and 12 months. Results: Overall relative weight loss was −1.63 kg at 1 month, −3.61 kg at 3 months, −5.28 kg at 6 months, and −6.55 kg at 12 months. The four factors of BBCI analyzed here (coaching, self-monitoring, self-management, and education) predict weight loss with varying accuracy and degree. Coaching, self-monitoring, and self-management are positively correlated with weight losses at 3 and 6 months. Learn time (i.e., self-guided education) is clearly associated with a higher degree of weight loss. Number of appointments outside of app coaching with a dietitian (coach) was negatively associated with weight loss. Conclusions: The results testify to the efficacy of BBCI for weight loss-with particular positive associations per time point-and add to a growing body of research that characterizes the distinct impact of intervention elements in real-world settings, aiming to inform the design of future interventions for weight management.

Mitigating Issues With/of/for True Personalization

A common but false perception persists about the level and type of personalization in the offerings of contemporary software, information systems, and services, known as Personalization Myopia: this involves a tendency for researchers to think that there are many more personalized services than there genuinely are, for the general audience to think that they are offered personalized services when they really are not, and for practitioners to have a mistaken idea of what makes a service personalized. And yet in an era, which mashes up large amounts of data, business analytics, deep learning, and persuasive systems, true personalization is a most promising approach for innovating and developing new types of systems and services—including support for behavior change. The potential of true personalization is elaborated in this article, especially with regards to persuasive software features and the oft-neglected fact that users change over time.

The Use of Artificial Intelligence-Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations

Background

Overweight and obesity have now reached a state of a pandemic despite the clinical and commercial programs available. Artificial intelligence (AI) chatbots have a strong potential in optimizing such programs for weight loss.

Objective

This study aimed to review AI chatbot use cases for weight loss and to identify the essential components for prolonging user engagement.

Methods

A scoping review was conducted using the 5-stage framework by Arksey and O’Malley. Articles were searched across nine electronic databases (ACM Digital Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science) until July 9, 2021. Gray literature, reference lists, and Google Scholar were also searched.

Results

A total of 23 studies with 2231 participants were included and evaluated in this review. Most studies (8/23, 35%) focused on using AI chatbots to promote both a healthy diet and exercise, 13% (3/23) of the studies used AI chatbots solely for lifestyle data collection and obesity risk assessment whereas only 4% (1/23) of the studies focused on promoting a combination of a healthy diet, exercise, and stress management. In total, 48% (11/23) of the studies used only text-based AI chatbots, 52% (12/23) operationalized AI chatbots through smartphones, and 39% (9/23) integrated data collected through fitness wearables or Internet of Things appliances. The core functions of AI chatbots were to provide personalized recommendations (20/23, 87%), motivational messages (18/23, 78%), gamification (6/23, 26%), and emotional support (6/23, 26%). Study participants who experienced speech- and augmented reality–based chatbot interactions in addition to text-based chatbot interactions reported higher user engagement because of the convenience of hands-free interactions. Enabling conversations through multiple platforms (eg, SMS text messaging, Slack, Telegram, Signal, WhatsApp, or Facebook Messenger) and devices (eg, laptops, Google Home, and Amazon Alexa) was reported to increase user engagement. The human semblance of chatbots through verbal and nonverbal cues improved user engagement through interactivity and empathy. Other techniques used in text-based chatbots included personally and culturally appropriate colloquial tones and content; emojis that emulate human emotional expressions; positively framed words; citations of credible information sources; personification; validation; and the provision of real-time, fast, and reliable recommendations. Prevailing issues included privacy; accountability; user burden; and interoperability with other databases, third-party applications, social media platforms, devices, and appliances.

Conclusions

AI chatbots should be designed to be human-like, personalized, contextualized, immersive, and enjoyable to enhance user experience, engagement, behavior change, and weight loss. These require the integration of health metrics (eg, based on self-reports and wearable trackers), personality and preferences (eg, based on goal achievements), circumstantial behaviors (eg, trigger-based overconsumption), and emotional states (eg, chatbot conversations and wearable stress detectors) to deliver personalized and effective recommendations for weight loss.

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.

Effect of health literacy and shared decision-making on choice of weight-loss plan among overweight or obese participants receiving a prototype artificial intelligence robot intervention facilitating weight-loss management decisions

Implementation of artificial intelligence (AI) in medical decision-making is still in early development. We developed an AI robot intervention prototype with a health literacy-friendly interface that uses interactive voice response (IVR) surveying to assist in decision-making for weight loss. The weight-specific health literacy instrument (WSHLI) and Shared Decision-Making Questionnaire (SDMQ) were used to measure factors influencing weight-loss decisions. Factors associated with participants choosing to lose weight were analyzed using logistic regression, and factors influencing the selection of specific weight-loss plans were examined with one-way analysis of variance. Our study recruited 144 overweight or obese adults (69.4% women, 58.3% with body mass index (BMI) ≥ 24). After interacting with the AI robot, 78% of the study population made the decision to lose weight. SDMQ score was a significant factor positively influencing the decision for weight-loss (odds ratio [OR]: 2.16, 95% confidence interval [CI]: 1.09–4.29, p = 0.027). Individuals who selected self-monitored lifestyle modification (mean ± SD: 11.52 ± 1.95) had significantly higher health literacy than those who selected dietician-assisted plan (9.92 ± 2.30) and physician-guided treatment (9.60 ± 1.52) (both p = 0.001). The study results demonstrated that our prototype AI robot can effectively encourage individuals to make decisions regarding weight management and that both WSHLI and SDMQ scores affect the choice of weight-loss plans.

The Potential Role of Digital Health in Obesity Care

Obesity is a complex, multi-factorial, chronic condition which increases the risk of a wide range of diseases including type 2 diabetes mellitus, cardiovascular disease and certain cancers. The prevalence of obesity continues to rise and this places a huge economic burden on the healthcare system. Existing approaches to obesity treatment tend to focus on individual responsibility and diet and exercise, failing to recognise the complexity of the condition and the need for a whole-system approach. A new approach is needed that recognises the complexity of obesity and provides patient-centred, multidisciplinary care which more closely meets the needs of each individual with obesity. This review will discuss the role that digital health could play in this new approach and the challenges of ensuring equitable access to digital health for obesity care. Existing technologies, such as telehealth and mobile health apps and wearable devices, offer emerging opportunities to improve access to obesity care and enhance the quality, efficiency and cost-effectiveness of weight management interventions and long-term patient support. Future application of machine learning and artificial intelligence to obesity care could see interventions become increasingly automated and personalised.