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Systematic review and meta-analysis of the effectiveness of chatbots on lifestyle behaviours

Singh, Ben, Olds, Timothy, Brinsley, Jacinta, Dumuid, Dot, Virgara, Rosa, Matricciani, Lisa, Watson, Amanda, Szeto, Kimberley, Eglitis, Emily, Miatke, Aaron, Simpson, Catherine E. M., Vandelanotte, Corneel, Maher, Carol·2023·npj Digital Medicine
DOI 10.1038/s41746-023-00856-1PMC PMC10290125
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Abstract

Chatbots (also known as conversational agents and virtual assistants) offer the potential to deliver healthcare in an efficient, appealing and personalised manner. The purpose of this systematic review and meta-analysis was to evaluate the efficacy of chatbot interventions designed to improve physical activity, diet and sleep. Electronic databases were searched for randomised and non-randomised controlled trials, and pre-post trials that evaluated chatbot interventions targeting physical activity, diet and/or sleep, published before 1 September 2022. Outcomes were total physical activity, steps, moderate-to-vigorous physical activity (MVPA), fruit and vegetable consumption, sleep quality and sleep duration. Standardised mean differences (SMD) were calculated to compare intervention effects. Subgroup analyses were conducted to assess chatbot type, intervention type, duration, output and use of artificial intelligence. Risk of bias was assessed using the Effective Public Health Practice Project Quality Assessment tool. Nineteen trials were included. Sample sizes ranged between 25–958, and mean participant age ranged between 9–71 years. Most interventions ( n = 15, 79%) targeted physical activity, and most trials had a low-quality rating ( n = 14, 74%). Meta-analysis results showed significant effects (all p < 0.05) of chatbots for increasing total physical activity (SMD = 0.28 [95% CI = 0.16, 0.40]), daily steps (SMD = 0.28 [95% CI = 0.17, 0.39]), MVPA (SMD = 0.53 [95% CI = 0.24, 0.83]), fruit and vegetable consumption (SMD = 0.59 [95% CI = 0.25, 0.93]), sleep duration (SMD = 0.44 [95% CI = 0.32, 0.55]) and sleep quality (SMD = 0.50 [95% CI = 0.09, 0.90]). Subgroup analyses showed that text-based, and artificial intelligence chatbots were more efficacious than speech/voice chatbots for fruit and vegetable consumption, and multicomponent interventions were more efficacious than chatbot-only interventions for sleep duration and sleep quality (all p < 0.05). Findings from this systematic review and meta-analysis indicate that chatbot interventions are efficacious for increasing physical activity, fruit and vegetable consumption, sleep duration and sleep quality. Chatbot interventions were efficacious across a range of populations and age groups, with both short- and longer-term interventions, and chatbot only and multicomponent interventions being efficacious.

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Research works printed but not linked: 47

  1. Kohl, H. et al. The pandemic of physical inactivity: global action for public health. Lancet 380, 294–305 (2012). · DOI · Paper page
  2. Stamatakis, E. et al. Is the time right for quantitative public health guidelines on sitting? A narrative review of sedentary behaviour research paradigms and findings. Br. J. Sports Med. 53, 377 (2019). · DOI · Paper page
  3. Endalifer, M. L. & Diress, G. Epidemiology, predisposing factors, biomarkers, and prevention mechanism of obesity: a systematic review. J. Obes. 2020, 6134362 (2020). · DOI
  4. Conn, V., Hafdahl, A. & Mehr, D. Interventions to increase physical activity among healthy adults: meta-analysis of outcomes. Am. J. Public Health 101, 751–758 (2011). · DOI
  5. Kang, M., Marshall, S. J., Barreira, T. & Lee, J. Effect of pedometer-based physical activity interventions: a meta-analysis. Res Q Exerc Sport. 80, 648–655 (2009).
  6. Chase, J. Interventions to increase physical activity among older adults: a meta-analysis. Gerontologist 55, 706–718 (2015). · DOI
  7. Bull, E., Dombrowski, S., McCleary, N. & Johnston, M. Are interventions for low-income groups effective in changing healthy eating, physical activity and smoking behaviours? A systematic review and meta-analysis. BMJ Open. 4, e006046 (2014). · DOI
  8. Delgado-Noguera, M., Tort, S., Martínez-Zapata, M. & Bonfill, X. Primary school interventions to promote fruit and vegetable consumption: A systematic review and meta-analysis. Prev. Med. 53, 3–9 (2011). · DOI
  9. Murawski, B., Wade, L., Plotnikoff, R., Lubans, D. & Duncan, M. A systematic review and meta-analysis of cognitive and behavioral interventions to improve sleep health in adults without sleep disorders. Sleep. Med. Rev. 40, 160–169 (2018). · DOI
  10. Sadeghi-Bazargani, H., Tabrizi, J. S. & Azami-Aghdash, S. Barriers to evidence-based medicine: a systematic review. J. Evaluation Clin. Pract. 20, 793–802 (2014). · DOI
  11. Kelly, S. et al. Barriers and facilitators to the uptake and maintenance of healthy behaviours by people at mid-life: a rapid systematic review. PLoS One 11, e0145074 (2016). · DOI
  12. Müller, A. M., Alley, S., Schoeppe, S. & Vandelanotte, C. The effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries: a systematic review. Int. J. Behav. Nutr. Phys. Act. 13, 109 (2016). · DOI · Paper page
  13. Arroyo, A. C. & Zawadzki, M. J. The implementation of behavior change techniques in mhealth apps for sleep: systematic review. JMIR Mhealth Uhealth. 10, e33527 (2022). · DOI
  14. Fiedler, J., Eckert, T., Wunsch, K. & Woll, A. Key facets to build up eHealth and mHealth interventions to enhance physical activity, sedentary behavior and nutrition in healthy subjects – an umbrella review. BMC Public Health 20, 1605 (2020). · DOI
  15. McCarroll, R., Eyles, H. & Ni Mhurchu, C. Effectiveness of mobile health (mHealth) interventions for promoting healthy eating in adults: A systematic review. Preventive Med. 105, 156–168 (2017). · DOI
  16. Provoost, S., Lau, H., Ruwaard, J. & Riper, H. Embodied conversational agents in clinical psychology: a scoping review. J. Med Internet Res. 19, e151 (2017). · DOI · Paper page
  17. Oh, Y., Zhang, J., Fang, M. & Fukuoka, Y. A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. Int. J. Behav. Nutr. Phys. Act. 18, 160 (2021). · DOI · Paper page
  18. Bickmore, T., Schulman, D. & Sidner, C. Automated interventions for multiple health behaviors using conversational agents. Patient Educ. Couns. 92, 142–148 (2013). · DOI · Paper page
  19. Dhinagaran, D. et al. Conversational agent for healthy lifestyle behavior change: web-based feasibility study. JMIR Form. Res. 5, e27956 (2021). · DOI
  20. Friederichs, S., Bolman, C., Oenema, A., Guyaux, J. & Lechner, L. Motivational interviewing in a Web-based physical activity intervention with an avatar: randomized controlled trial. J. Med. Internet Res. 16, e48 (2014). · DOI · Paper page
  21. Philip, P. et al. Efficacy of a smartphone-based virtual companion to treat insomniac complaints in the general population: sleep diary monitoring versus an internet autonomous intervention. J. Clin. Med. 11, 4387 (2020). · DOI
  22. Carfora, V., Bertolotti, M. & Catellani, P. Informational and emotional daily messages to reduce red and processed meat consumption. Appetite 141, 104331 (2019). · DOI
  23. Cushing, C. et al. Adaptive mHealth intervention for adolescent physical activity promotion. J. Pediatr. Psychol. 46, 536–546 (2021). · DOI
  24. Carlin, A., Logue, C., Flynn, J., Murphy, M. & Gallagher, A. Development and feasibility of a family-based health behavior intervention using intelligent personal assistants: randomized controlled trial. JMIR Form. Res. 5, e17501 (2021). · DOI
  25. De-Jongh González, O. et al. The Aim2Be mHealth intervention for children with overweight or obesity and their parents: person-centered analyses to uncover digital phenotypes. J. Med. Internet Res. 24, e35285 (2022). · DOI
  26. Wright, J. et al. Randomized trial of a family-based, automated, conversational obesity treatment program for underserved populations. Obesity 21, E369–E378 (2013). · DOI
  27. To, Q., Green, C. & Vandelanotte, C. feasibility, usability, and effectiveness of a machine learning-based physical activity chatbot: quasi-experimental study. JMIR Mhealth Uhealth. 9, e28577 (2021). · DOI · Paper page
  28. King, A. et al. Ongoing physical activity advice by humans versus computers: the community health advice by telephone (CHAT) trial. Health Psychol. 26, 718–727 (2021). · DOI
  29. Hassoon, A. et al. Randomized trial of two artificial intelligence coaching interventions to increase physical activity in cancer survivors. NPJ Digit. Med. 4, 168 (2021). · DOI
  30. Watson, A., Bickmore, T., Cange, A., Kulshreshtha, A. & Kvedar, J. An internet-based virtual coach to promote physical activity adherence in overweight adults: randomized controlled trial. J. Med. Internet Res. 14, e1 (2012). · DOI · Paper page
  31. Lorenz, N., Heim, E., Roetger, A., Birrer, E. & Maercker, A. Randomized controlled trial to test the efficacy of an unguided online intervention with automated feedback for the treatment of insomnia. Behav. Cogn. Psychother. 47, 287–302 (2019). · DOI
  32. Robert, C. et al. Effectiveness of eHealth nutritional interventions for middle-aged and older adults: systematic review and meta-analysis. J. Med. Internet Res. 23, e15649 (2021). · DOI
  33. Champion, K. et al. Effectiveness of school-based eHealth interventions to prevent multiple lifestyle risk behaviours among adolescents: a systematic review and meta-analysis. Lancet Digital Health 1, e206–e221 (2019). · DOI · Paper page
  34. Schroé, H. et al. Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention. Int. J. Behav. Nutr. Phys. Act. 17, 27 (2020). · DOI
  35. Awoke, M. et al. Behaviour change techniques in weight gain prevention interventions in adults of reproductive age: meta-analysis and meta-regression. Nutrients 14, 209 (2022). · DOI
  36. Noordman, J., van der Weijden, T. & van Dulmen, S. Communication-related behavior change techniques used in face-to-face lifestyle interventions in primary care: A systematic review of the literature. Patient Educ. Couns. 89, 227–244 (2012). · DOI
  37. Xu, L., Sanders, L., Li, K. & Chow, J. Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review. JMIR Cancer 7, e27850 (2021). · DOI · Paper page
  38. van Kessel, R., Wong, B. L. H., Clemens, T. & Brand, H. Digital health literacy as a super determinant of health: more than simply the sum of its parts. Internet Interv. 27, 100500 (2022). · DOI · Paper page
  39. Chew, H. The use of artificial intelligence-based conversational agents (chatbots) for weight loss: scoping review and practical recommendations. JMIR Med. Inform. 10, e32578 (2022). · DOI
  40. Hartman, S. J. et al. Fitbit use and activity levels from intervention to 2 years after: secondary analysis of a randomized controlled trial. JMIR Mhealth Uhealth. 10, e37086 (2022). · DOI
  41. Shin, D. The perception of humanness in conversational journalism: an algorithmic information-processing perspective. N. Media Soc. 24, 26 (2021).
  42. McCoy, T. H. & Perlis, R. H. Temporal Trends and Characteristics of Reportable Health Data Breaches, 2010-2017. JAMA 320, 1282–1284 (2018). · DOI · Paper page
  43. Rosenfeld, L., Torous, J. & Vahia, I. V. Data Security and Privacy in Apps for Dementia: An Analysis of Existing Privacy Policies. Am. J. Geriatr. Psychiatry 25, 873–877 (2017). · DOI · Paper page
  44. Koppel, R. & Kreda, D. Health care information technology vendors’ “hold harmless” clause: implications for patients and clinicians. JAMA 301, 1276–1278 (2009). · DOI · Paper page
  45. Goodman, K. W. et al. AMIA Board of Directors. Challenges in ethics, safety, best practices, and oversight regarding HIT vendors, their customers, and patients: a report of an AMIA special task force. J. Am. Med. Inform. Assoc. 18, 77–81 (2011). · DOI · Paper page
  46. Hozo, S., Djulbegovic, B. & Hozo, I. Estimating the mean and variance from the median, range, and the size of a sample. BMC Med. Res. Methodol. 5, 13 (2005). · DOI · Paper page
  47. Cohen, J. Statistical Power Analysis for the Behavioral Sciences (2nd ed.). (1988).

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Metadata

Authors
Singh, B., Olds, T., Brinsley, J., Dumuid, D., Virgara, R., Matricciani, L., Watson, A., Szeto, K., Eglitis, E., Miatke, A., Simpson, C. E. M., Vandelanotte, C., & Maher, C.
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2023-06-23
Volume
6
Issue
1
ISSN
2398-6352
DOI
10.1038/s41746-023-00856-1
PMC
PMC10290125
Cited by
224
References
25
Language
en
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