BackgroundActive workstations have been proposed as a feasible approach for reducing occupational sedentary time. This study used a network meta-analysis (NMA) to assess and compare the overall efficacy of active workstation interventions according to type and concomitant strategy for reducing work-specific sitting time in office workers.MethodsPubMed, Web of Science, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) databases were searched from database inception until May 2022 to obtain randomized controlled trials (RCTs) assessing the efficacy of active workstations with or without concomitant strategies for reducing occupational sedentary time in office workers. The risk of bias of the RCTs included in this study was assessed according to the Cochrane Handbook. An NMA with STATA 15.1 was used to construct a network diagram, league figures, and the final surface under the cumulative ranking curve (SUCRA) values. The certainty of evidence was assessed using the grading of recommendations, assessment, development, and evaluation (GRADE) approach.ResultsA total of 23 eligible studies including eight different types of interventions with 1428 office workers were included. NMA results showed that compared to a typical desk, multicomponent intervention (standardized mean difference (SMD) = - 1.50; 95% confidence interval (CI) - 2.17, - 0.82; SUCRA = 72.4%), sit-stand workstation + promotion (Reminders of rest breaks, posture variation, or incidental office activity) (SMD = - 1.49; 95%CI - 2.42, - 0.55; SUCRA = 71.0%), treadmill workstation + promotion (SMD = - 1.29; 95%CI - 2.51, - 0.07; SUCRA = 61.6%), and sit-stand workstation (SMD = - 1.10, 95%CI - 1.64, - 0.56; SUCRA = 50.2%) were effective in reducing occupational sedentary time for office workers.ConclusionsMulticomponent intervention, sit-stand workstation + promotion, treadmill workstation + promotion, and sit-stand workstation appear to be effective in reducing work-specific sedentary time for office workers. Furthermore, multicomponent interventions and active workstations + promotion better reduced work-specific sedentary time than active workstation alone. However, the overall certainty of the evidence was low.

Background: Sedentary time in workplaces has been linked to increased risks of chronic occupational diseases, obesity, and overall mortality. Currently, there is a burgeoning research interest in the implementation of multicomponent interventions aimed at decreasing sedentary time among office workers, which encompass a comprehensive amalgamation of individual, organizational, and environmental strategies. Objective: This meta-analysis aims at evaluating the effectiveness of multicomponent interventions to mitigate occupational sedentary behavior at work compared with no intervention. Methods: PubMed, Web of Science, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) databases were searched from database inception until March 2023 to obtain randomized controlled trials (RCTs) assessing the efficacy of multicomponent interventions on occupational sedentary behavior among office-based workers. Two reviewers independently extracted the data and assessed the risk of bias by using the Cochrane Collaboration's risk of bias tool. The average intervention effect on sedentary time was calculated using Stata 15.1. Mean differences (MDs) with 95% CIs were used to calculate the continuous variables. Subgroup analyses were performed to determine whether sit-stand workstation, feedback, and prompt elements played an important role in multicomponent interventions. Further, the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) system was used to evaluate the certainty of evidence. Results: A total of 11 RCTs involving 1894 patients were included in the analysis. Five studies were rated as low risk of bias, 2 as unclear risk of bias, and 4 as high risk. The meta-analysis results showed that compared with no intervention, multicomponent interventions significantly reduced occupational sitting time (MD=-52.25 min/8-h workday, 95% CI -73.06 to -31.44; P<.001) and occupational prolonged sitting time (MD=-32.63 min/8-h workday, 95% CI -51.93 to -13.33; P=.001) and increased occupational standing time (MD=44.30 min/8-h workday, 95% CI 23.11-65.48; P<.001), whereas no significant differences were found in occupational stepping time (P=.06). The results of subgroup analysis showed that compared with multicomponent interventions without installment of sit-stand workstations, multicomponent interventions with sit-stand workstation installment showed better effects for reducing occupational sitting time (MD=-71.95 min/8-h workday, 95% CI -92.94 to -51.15), increasing occupational standing time (MD=66.56 min/8-h workday, 95% CI 43.45-89.67), and reducing occupational prolonged sitting time (MD=-47.05 min/8-h workday, 95% CI -73.66 to -20.43). The GRADE evidence summary showed that all 4 outcomes were rated as moderate certainty. Conclusions: Multicomponent interventions, particularly those incorporating sit-stand workstations for all participants, are effective at reducing workplace sedentary time. However, given their cost, further research is needed to understand the effectiveness of low-cost/no-cost multicomponent interventions.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

BSCKGROUND: Sedentary behavior (SB) negatively impact health and is highly prevalent in the population. Digital behavior change interventions (DBCIs) have been developed to modify behaviors such as SB by technologies. However, it is unknown which behavior change techniques (BCTs) are most frequently employed in SB as well as the effect associated with DBCIs in this field. The aim of this systematic review was: (a) to evaluate the BCT most frequently employed in digital health including all technologies available and interventions aimed at increasing physical activity (PA), reducing sedentary time, and improving adherence to exercise in the clinical population, and (b) to review the effect associated with DBCIs in this field. METHODS: The database used was Medline, as well as Scopus, Scielo, and Google Scholar. For the search strategy, we considered versions of behavior/behavioral, mHealth/eHealth/telemedicine/serious game/gamification. The terms related to PA and SB were included, the criteria for inclusion were randomized clinical trials (RCTs), adults, intervention based on digital media, and outcome variable lifestyle modification; a last 5 years filter was included. Michie's Taxonomy was used to identify BCTs. The study was registered under the number PROSPERO CRD42019138681. RESULTS: Eighteen RCTs were included in the present systematic review, 5 of them healthy adults, and 13 of them with some illness. Studies included 2298 sedentary individuals who were followed up for 5 weeks-3 years. The most used BCTs were goal setting, problem solving, review outcomes/goals, feedback on behavior and outcomes of behavior, self-monitoring of behavior, social support, information about health consequences, and behavior practice/rehearsal. The effect associated with DBCIs showed improvements, among several related to PA and physiologic self-reported and anthropometric outcomes. CONCLUSION: The BCTs most used in digital health to change outcomes related to SB were goals and planning, feedback and monitoring, social support, natural consequences, repetition, and substitution. Besides these findings, DBCIs are influenced by several factors like the type of intervention, patients' preferences and values, or the number of BCTs employed. More research is needed to determine with precision which DBCIs or BCTs are the most effective to reduce SB in the clinical population.