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      A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss


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          This systematic review aimed to evaluate AI chatbot characteristics, functions, and core conversational capacities and investigate whether AI chatbot interventions were effective in changing physical activity, healthy eating, weight management behaviors, and other related health outcomes.


          In collaboration with a medical librarian, six electronic bibliographic databases (PubMed, EMBASE, ACM Digital Library, Web of Science, PsycINFO, and IEEE) were searched to identify relevant studies. Only randomized controlled trials or quasi-experimental studies were included. Studies were screened by two independent reviewers, and any discrepancy was resolved by a third reviewer. The National Institutes of Health quality assessment tools were used to assess risk of bias in individual studies. We applied the AI Chatbot Behavior Change Model to characterize components of chatbot interventions, including chatbot characteristics, persuasive and relational capacity, and evaluation of outcomes.


          The database search retrieved 1692 citations, and 9 studies met the inclusion criteria. Of the 9 studies, 4 were randomized controlled trials and 5 were quasi-experimental studies. Five out of the seven studies suggest chatbot interventions are promising strategies in increasing physical activity. In contrast, the number of studies focusing on changing diet and weight status was limited. Outcome assessments, however, were reported inconsistently across the studies. Eighty-nine and thirty-three percent of the studies specified a name and gender (i.e., woman) of the chatbot, respectively. Over half (56%) of the studies used a constrained chatbot (i.e., rule-based), while the remaining studies used unconstrained chatbots that resemble human-to-human communication.


          Chatbots may improve physical activity, but we were not able to make definitive conclusions regarding the efficacy of chatbot interventions on physical activity, diet, and weight management/loss. Application of AI chatbots is an emerging field of research in lifestyle modification programs and is expected to grow exponentially. Thus, standardization of designing and reporting chatbot interventions is warranted in the near future.

          Systematic review registration

          International Prospective Register of Systematic Reviews (PROSPERO): CRD42020216761.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12966-021-01224-6.

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          Most cited references42

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          Utilization of the PICO framework to improve searching PubMed for clinical questions

          Background Supporting 21st century health care and the practice of evidence-based medicine (EBM) requires ubiquitous access to clinical information and to knowledge-based resources to answer clinical questions. Many questions go unanswered, however, due to lack of skills in formulating questions, crafting effective search strategies, and accessing databases to identify best levels of evidence. Methods This randomized trial was designed as a pilot study to measure the relevancy of search results using three different interfaces for the PubMed search system. Two of the search interfaces utilized a specific framework called PICO, which was designed to focus clinical questions and to prompt for publication type or type of question asked. The third interface was the standard PubMed interface readily available on the Web. Study subjects were recruited from interns and residents on an inpatient general medicine rotation at an academic medical center in the US. Thirty-one subjects were randomized to one of the three interfaces, given 3 clinical questions, and asked to search PubMed for a set of relevant articles that would provide an answer for each question. The success of the search results was determined by a precision score, which compared the number of relevant or gold standard articles retrieved in a result set to the total number of articles retrieved in that set. Results Participants using the PICO templates (Protocol A or Protocol B) had higher precision scores for each question than the participants who used Protocol C, the standard PubMed Web interface. (Question 1: A = 35%, B = 28%, C = 20%; Question 2: A = 5%, B = 6%, C = 4%; Question 3: A = 1%, B = 0%, C = 0%) 95% confidence intervals were calculated for the precision for each question using a lower boundary of zero. However, the 95% confidence limits were overlapping, suggesting no statistical difference between the groups. Conclusion Due to the small number of searches for each arm, this pilot study could not demonstrate a statistically significant difference between the search protocols. However there was a trend towards higher precision that needs to be investigated in a larger study to determine if PICO can improve the relevancy of search results.
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            The inevitable application of big data to health care.

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              NCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4

              The third UN High-Level Meeting on Non-Communicable Diseases (NCDs) on Sept 27, 2018, will review national and global progress towards the prevention and control of NCDs, and provide an opportunity to renew, reinforce, and enhance commitments to reduce their burden. NCD Countdown 2030 is an independent collaboration to inform policies that aim to reduce the worldwide burden of NCDs, and to ensure accountability towards this aim. In 2016, an estimated 40·5 million (71%) of the 56·9 million worldwide deaths were from NCDs. Of these, an estimated 1·7 million (4% of NCD deaths) occurred in people younger than 30 years of age, 15·2 million (38%) in people aged between 30 years and 70 years, and 23·6 million (58%) in people aged 70 years and older. An estimated 32·2 million NCD deaths (80%) were due to cancers, cardiovascular diseases, chronic respiratory diseases, and diabetes, and another 8·3 million (20%) were from other NCDs. Women in 164 (88%) and men in 165 (89%) of 186 countries and territories had a higher probability of dying before 70 years of age from an NCD than from communicable, maternal, perinatal, and nutritional conditions combined. Globally, the lowest risks of NCD mortality in 2016 were seen in high-income countries in Asia-Pacific, western Europe, and Australasia, and in Canada. The highest risks of dying from NCDs were observed in low-income and middle-income countries, especially in sub-Saharan Africa, and, for men, in central Asia and eastern Europe. Sustainable Development Goal (SDG) target 3.4-a one-third reduction, relative to 2015 levels, in the probability of dying between 30 years and 70 years of age from cancers, cardiovascular diseases, chronic respiratory diseases, and diabetes by 2030-will be achieved in 35 countries (19%) for women, and 30 (16%) for men, if these countries maintain or surpass their 2010-2016 rate of decline in NCD mortality. Most of these are high-income countries with already-low NCD mortality, and countries in central and eastern Europe. An additional 50 (27%) countries for women and 35 (19%) for men are projected to achieve such a reduction in the subsequent decade, and thus, with slight acceleration of decline, could meet the 2030 target. 86 (46%) countries for women and 97 (52%) for men need implementation of policies that substantially increase the rates of decline. Mortality from the four NCDs included in SDG target 3.4 has stagnated or increased since 2010 among women in 15 (8%) countries and men in 24 (13%) countries. NCDs and age groups other than those included in the SDG target 3.4 are responsible for a higher risk of death in low-income and middle-income countries than in high-income countries. Substantial reduction of NCD mortality requires policies that considerably reduce tobacco and alcohol use and blood pressure, and equitable access to efficacious and high-quality preventive and curative care for acute and chronic NCDs.

                Author and article information

                Int J Behav Nutr Phys Act
                Int J Behav Nutr Phys Act
                The International Journal of Behavioral Nutrition and Physical Activity
                BioMed Central (London )
                11 December 2021
                11 December 2021
                : 18
                : 160
                [1 ]GRID grid.27860.3b, ISNI 0000 0004 1936 9684, Department of Communication, , University of California Davis, ; Davis, USA
                [2 ]GRID grid.27860.3b, ISNI 0000 0004 1936 9684, Department of Public Health Sciences, , University of California Davis, ; Davis, USA
                [3 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Education and Research Services, , University of California, San Francisco (UCSF) Library, UCSF, ; San Francisco, USA
                [4 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Physiological Nursing, , UCSF, ; San Francisco, USA
                Author information
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                : 31 May 2021
                : 10 November 2021
                Custom metadata
                © The Author(s) 2021

                Nutrition & Dietetics
                artificial intelligence,chatbot,conversational agent,physical activity,weight loss,weight maintenance,diet,nutrition,sedentary behavior,systematic review


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