Promoting Physical Activity Through Conversational Agents: Mixed-Method Systematic Review (Preprint)

2020 ◽  
Author(s):  
Tiffany Christina Luo ◽  
Adrian Aguilera ◽  
Courtney Lyles ◽  
Caroline Astrid Figueroa
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Mixed Methods ◽  
Language Processing ◽  
Mixed Method ◽  
Health Behavior Change ◽  
Open Science ◽  
Virtual Agents ◽  
Conversational Agents ◽  
Privacy Measures

BACKGROUND Regular physical activity is crucial to wellbeing, but healthy habits are difficult to create and maintain. Interventions delivered via conversational agents (eg, chatbots or virtual agents) are a novel and potentially accessible way to promote physical activity. Thus, it is important to understand the evolving landscape of research utilizing conversational agents. OBJECTIVE This mixed-method systematic review aimed to 1) summarize the usability and effectiveness of conversational agents in promoting physical activity, 2) describe common theories and intervention components utilized, 3) identify areas for further development, and 4) make recommendations for conversational agents targeting health behavior change. METHODS We conducted a mixed-method systematic review. We searched 7 electronic databases (PsycINFO, PubMed, Embase, CINAHL, ACM Digital Library, Scopus, and Web of Science) for quantitative, qualitative, and mixed methods studies that conveyed primary research on automated conversational agents designed to increase physical activity. Two reviewers independently screened studies and assessed methodological quality using the Mixed Methods Appraisal Tool (MMAT). Data on intervention impact and effectiveness, treatment characteristics, and challenges were extracted and analyzed using parallel-results convergent synthesis and narrative summary. RESULTS In total, 255 studies were identified, 20 of which met our inclusion criteria. Overall, conversational agents had moderate usability and feasibility and were effective in promoting physical activity. However, quality of evidence varied. Common challenges facing interventions were repetitive program content, high attrition, technical issues, safety, and privacy. CONCLUSIONS Conversational agents hold promise for physical activity interventions. However, there is a lack of rigorous research on long-term intervention effectiveness and patient safety. Future interventions should be based in evidence-informed theories and treatment approaches, and they should address users’ desires for program variety, safety and privacy measures, natural language processing, and delivery via mobile devices. CLINICALTRIAL The protocol for this systematic review was registered in the Open Science Framework (OSF) registries (osf.io/p4v6y).

scholarly journals Promoting Physical Activity Through Conversational Agents: Mixed Methods Systematic Review (Preprint)

10.2196/25486 ◽  
2020 ◽  
Author(s):  
Tiffany Christina Luo ◽  
Adrian Aguilera ◽  
Courtney Lyles ◽  
Caroline Astrid Figueroa
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Mixed Methods ◽  
Conversational Agents

scholarly journals The effect of social media interventions on physical activity and dietary behaviours in young people and adults: a systematic review

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Victoria A. Goodyear ◽  
Grace Wood ◽  
Bethany Skinner ◽  
Janice L. Thompson
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Social Media ◽  
Body Composition ◽  
Body Weight ◽  
Mixed Methods ◽  
Controlled Trials ◽  
Activity Levels ◽  
Physical Activity Levels ◽  
Media Interventions

Abstract Background The objectives of this systematic review were to update the evidence base on social media interventions for physical activity and diet since 2014, analyse the characteristics of interventions that resulted in changes to physical activity and diet-related behaviours, and assess differences in outcomes across different population groups. Methods A systematic search of the literature was conducted across 5 databases (Medline, Embase, EBSCO Education, Wiley and Scopus) using key words related to social media, physical activity, diet, and age. The inclusion criteria were: participants age 13+ years in the general population; an intervention that used commercial social media platform(s); outcomes related to changes to diet/eating or physical activity behaviours; and quantitative, qualitative and mixed methods studies. Quality appraisal tools that aligned with the study designs were used. A mixed methods approach was used to analyse and synthesise all evidence. Results Eighteen studies were included: randomised control trials (n = 4), non-controlled trials (n = 3), mixed methods studies (n = 3), non-randomised controlled trials (n = 5) and cross-sectional studies (n = 3). The target population of most studies was young female adults (aged 18–35) attending college/university. The interventions reported on positive changes to physical activity and diet-related behaviours through increases in physical activity levels and modifications to food intake, body composition and/or body weight. The use of Facebook, Facebook groups and the accessibility of information and interaction were the main characteristics of social media interventions. Studies also reported on Instagram, Reddit, WeChat and Twitter and the use of photo sharing and editing, groups and sub-groups and gamification. Conclusions Social media interventions can positively change physical activity and diet-related behaviours, via increases in physical activity levels, healthy modifications to food intake, and beneficial changes to body composition or body weight. New evidence is provided on the contemporary uses of social media (e.g. gamification, multi-model application, image sharing/editing, group chats) that can be used by policy makers, professionals, organisations and/or researchers to inform the design of future social media interventions. This study had some limitations that mainly relate to variation in study design, over-reliance of self-reported measures and sample characteristics, that prevented comparative analysis. Registration number: PROPSERO;CRD42020210806.

scholarly journals The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (Preprint)

2020 ◽  
Author(s):  
Madison Milne-Ives ◽  
Caroline de Cock ◽  
Ernest Lim ◽  
Melissa Harper Shehadeh ◽  
Nick de Pennington ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Health Care ◽  
Language Processing ◽  
Health Care Services ◽  
Interactive Voice Response ◽  
Future Research ◽  
Conversational Agents ◽  
Privacy And Security ◽  
Care Services

BACKGROUND The high demand for health care services and the growing capability of artificial intelligence have led to the development of conversational agents designed to support a variety of health-related activities, including behavior change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase the accessibility to health care services for the public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in health care is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption. OBJECTIVE This systematic review aims to assess the effectiveness and usability of conversational agents in health care and identify the elements that users like and dislike to inform future research and development of these agents. METHODS PubMed, Medline (Ovid), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and the Association for Computing Machinery Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in health care. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another. RESULTS A total of 31 studies were selected and included a variety of conversational agents, including 14 chatbots (2 of which were voice chatbots), 6 embodied conversational agents (3 of which were interactive voice response calls, virtual patients, and speech recognition screening systems), 1 contextual question-answering agent, and 1 voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback. CONCLUSIONS The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents. INTERNATIONAL REGISTERED REPORT RR2-10.2196/16934

scholarly journals The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review

10.2196/20346 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e20346
Author(s):  
Madison Milne-Ives ◽  
Caroline de Cock ◽  
Ernest Lim ◽  
Melissa Harper Shehadeh ◽  
Nick de Pennington ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Health Care ◽  
Language Processing ◽  
Health Care Services ◽  
Interactive Voice Response ◽  
Future Research ◽  
Conversational Agents ◽  
Privacy And Security ◽  
Care Services

Background The high demand for health care services and the growing capability of artificial intelligence have led to the development of conversational agents designed to support a variety of health-related activities, including behavior change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase the accessibility to health care services for the public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in health care is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption. Objective This systematic review aims to assess the effectiveness and usability of conversational agents in health care and identify the elements that users like and dislike to inform future research and development of these agents. Methods PubMed, Medline (Ovid), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and the Association for Computing Machinery Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in health care. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another. Results A total of 31 studies were selected and included a variety of conversational agents, including 14 chatbots (2 of which were voice chatbots), 6 embodied conversational agents (3 of which were interactive voice response calls, virtual patients, and speech recognition screening systems), 1 contextual question-answering agent, and 1 voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback. Conclusions The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents. International Registered Report Identifier (IRRID) RR2-10.2196/16934

Predictors of Success of Workplace Physical Activity Interventions: A Systematic Review

2019 ◽  
Vol 16 (8) ◽  
pp. 647-656 ◽  
Author(s):  
Samuel D. Muir ◽  
Sandun S.M. Silva ◽  
Mulu A. Woldegiorgis ◽  
Hayley Rider ◽  
Denny Meyer ◽  
...  
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Health Behavior ◽  
Behavior Change ◽  
Health Behavior Change ◽  
Demographic Variables ◽  
Predictors Of Success ◽  
Physically Active ◽  
Physical Activity Interventions ◽  
Effective Interventions

Background: Despite holding great potential for addressing concerns regarding public health, recent systematic reviews have found effect sizes for interventions targeting physical activity to be small. Before interventions can be improved, the factors influencing outcomes must be identified. This systematic review aimed to identify predictors of success, measured in terms of engagement (eg, involvement duration) and health behavior change (eg, increased step counts), of workplace interventions targeting physical activity. Methods: A structured search of 3 databases (PubMed, PsycINFO, and Web of Science) was conducted to identify articles published between January 2000 and April 2017. For inclusion, articles needed to test a workplace intervention targeting physical activity and perform a quantitative analysis, identifying predictors of engagement or health behavior change. Results: Twenty-two studies were identified for review (median quality score = 70%). Demographic variables (eg, gender, age) were inconsistent predictors of success. However, employees in better health and physically active at baseline were found to have a greater likelihood of success. Conclusions: It appears that achieving successful results among employees at high risk of poor health outcomes remains a significant challenge for interventions. It is hoped that program developers can use this information to create effective interventions particularly for more sedentary employees.

scholarly journals Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint

10.2196/22845 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e22845 ◽  
Author(s):  
Jingwen Zhang ◽  
Yoo Jung Oh ◽  
Patrick Lange ◽  
Zhou Yu ◽  
Yoshimi Fukuoka
Keyword(s):  
Physical Activity ◽  
Artificial Intelligence ◽  
Behavior Change ◽  
Language Processing ◽  
Lifestyle Modification ◽  
Health Behavior Change ◽  
Healthy Diet ◽  
Ethical Principles ◽  
Change Model ◽  
Research Experience

Background Chatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet. Objective The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations. Methods We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model. Results Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper. Conclusions As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot’s relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.

Sign in / Sign up

Export Citation Format

Share Document