Toward Collective Intelligence for Fighting Obesity

Addo, Ivor D.; Ahamed, Sheikh Iqbal; Chu, William Cheng‐Chung

doi:10.1109/compsac.2013.109

Cited by 11 publications

(13 citation statements)

References 17 publications

(19 reference statements)

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“…Most (8/23, 35%) of the studies focused on promoting a healthy diet and exercise, whereas only 4% (1/23) studies focused on a healthy diet, exercise, and stress management ( Figure 2 and Multimedia Appendix 4 ). In all, 11 out of the 14 (79%) planned or trialed experimental studies [ 26 , 29 - 33 , 35 - 37 , 40 , 43 - 45 ] reported program durations that ranged from 1 hour to 12 months [ 30 - 32 ]. Only 1 study mentioned the intention of comparing algorithms to yield accurate behavioral predictions [ 43 ].…”

Section: Resultsmentioning

confidence: 99%

“…In all, 11 out of the 14 (79%) planned or trialed experimental studies [ 26 , 29 - 33 , 35 - 37 , 40 , 43 - 45 ] reported program durations that ranged from 1 hour to 12 months [ 30 - 32 ]. Only 1 study mentioned the intention of comparing algorithms to yield accurate behavioral predictions [ 43 ]. In total, 12 studies mentioned the use of a behavior change framework to guide AI chatbot development ( Multimedia Appendix 4 ).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Chew¹

2022

JMIR Med Inform

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Chew¹

2022

JMIR Med Inform

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“…Another work in which collective intelligence was applied focusing on one of the most illnesses problems of the current society is obesity. Starting from the Human-Robot Interaction research, it is possible to develop a system that employs machine learning algorithms in order to feature a humanoid robot Health Coach that helps to reduce childhood obesity [1]. Other example of this kind of application is chronic disease with elderly patients [18].…”

Section: Use and Application Of Collective Intelligence In Ehealthmentioning

confidence: 99%

“…No one can dispute the fact that it is almost impossible to avoid these deaths, but a high number of lives could be saved if some measures would be adopted. One of the most important factors during these emergencies is the fast information exchange and the use of collaborative techniques 1 Research partially supported by the Spanish project DArDOS (TIN2015-65845-C3-1-R) and the Comunidad de Madrid project SICOMORo-CM (S2013/ICE-3006). The first author is supported by a Universidad Complutense de Madrid -Santander Universidades grant.…”

Section: Introductionmentioning

confidence: 99%

Collective intelligence and databases in eHealth: A survey1

Camacho

Merayo

Núñez

2017

IFS

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Healthcare is one of the most important concerns of society, being extremely relevant the accuracy and quality of their services. Basically, eHealth can be considered as the area where electronic processes and communications are used to improve the quality of medical assistance. Despite the relative maturity of the field, recalls and problems related to medical devices, applications and services are still very frequent. Therefore, it is necessary to improve and expand current research to provide advances that can be transmitted to the society. As an initial step, it is essential to have a good understanding of the current state-of-the-art of eHealth. The main goal of this paper is to identify the most relevant and recent work on eHealth but, due to the immensity of the field and the scope of the journal, we will concentrate on the use of two specific technologies: databases and collective intelligence. In addition to review the main concepts related to eHealth and the most influent academic papers, we will describe projects which are essential for the correct performance of many healthcare services. As a result of our study, we reached some interesting conclusions that might be useful for future projects and we encourage to apply them in order to avoid some of the problems that we have found in existing projects.

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“…The scope of activity monitoring facilitated by the Kinect sensor includes the participant's time spent walking around the house, indoor localization, screen capture and timestamps for medicine consumption, time spent watching TV, interactions with the Humanoid companion, etc. [12].…”

Section: B Multimodal Health Monitoring In Elderly Carementioning

confidence: 99%

Reference Architectures for Privacy Preservation in Cloud-based IoT Applications

Addo¹,

Ahamed²,

Yau³

et al. 2014

STSC

Self Cite

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As the promise of the Internet of Things (IoT) materializes in our everyday lives, we are often challenged with a number of concerns regarding the efficacy of the current data privacy solutions that support the pervasive components at play in IoT. The privacy and security concerns surrounding IoT applications often manifests themselves as a threat to end-user adoption and negatively impacts trust among end-users. In this paper, we present a reference software architectures for building cloud-enabled IoT applications in support of collaborative pervasive systems aimed at achieving trustworthiness among end-users in IoT scenarios. We describe a case study that leverages this reference architecture to protect sensitive user data in IoT application implementation. We then evaluate the response data from our end-user survey. In addition we present a Secure, Private and Trustworthy protocol (named SPTP) that was prototyped for addressing critical security, privacy and trust concerns surrounding mobile, pervasive and cloud services in Collective Intelligence (CI) scenarios. We present our evaluation criteria for the proposed protocol, our results and future work.

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Toward Collective Intelligence for Fighting Obesity

Cited by 11 publications

References 17 publications

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

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

Collective intelligence and databases in eHealth: A survey1

Reference Architectures for Privacy Preservation in Cloud-based IoT Applications

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