Job rotation is an organisational strategy widely used in human-based production lines with the aim of preventing work-related musculoskeletal disorders (WMSDs). These work environments are characterised by the presence of a high repetition of movements, which is a major risk factor associated with WMSDs. This article presents a genetic algorithm to obtain rotation schedules aimed at preventing WMSDs in such environments. To do this, it combines the effectiveness of genetic algorithms optimisation with the ability to evaluate the presence of risk by repeated movements by following the OCRA ergonomic assessment method. The proposed algorithm can design solutions in which workers will switch jobs with high repeatability of movements with other less demanding jobs that support their recovery. In addition, these solutions are able to diversify the tasks performed by workers during the day, consider their disabilities and comply with restrictions arising from the work organisation.

Background: Obesity and overweight are a serious health problem worldwide with multiple and connected causes. Simultaneously, chatbots are becoming increasingly popular as a way to interact with users in mobile health apps.Objective: This study reports the user-centered design and feasibility study of a chatbot to collect linked data to support the study of individual and social overweight and obesity causes in populations.Methods: We first studied the users’ needs and gathered users’ graphical preferences through an open survey on 52 wireframes designed by 150 design students; it also included questions about sociodemographics, diet and activity habits, the need for overweight and obesity apps, and desired functionality. We also interviewed an expert panel. We then designed and developed a chatbot. Finally, we conducted a pilot study to test feasibility.Results: We collected 452 answers to the survey and interviewed 4 specialists. Based on this research, we developed a Telegram chatbot named Wakamola structured in six sections: personal, diet, physical activity, social network, user's status score, and project information. We defined a user's status score as a normalized sum (0-100) of scores about diet (frequency of eating 50 foods), physical activity, BMI, and social network. We performed a pilot to evaluate the chatbot implementation among 85 healthy volunteers. Of 74 participants who completed all sections, we found 8 underweight people (11%), 5 overweight people (7%), and no obesity cases. The mean BMI was 21.4 kg/m2 (normal weight). The most consumed foods were olive oil, milk and derivatives, cereals, vegetables, and fruits. People walked 10 minutes on 5.8 days per week, slept 7.02 hours per day, and were sitting 30.57 hours per week. Moreover, we were able to create a social network with 74 users, 178 relations, and 12 communities.Conclusions: The Telegram chatbot Wakamola is a feasible tool to collect data from a population about sociodemographics, diet patterns, physical activity, BMI, and specific diseases. Besides, the chatbot allows the connection of users in a social network to study overweight and obesity causes from both individual and social perspectives.

Quality of life (QoL) indicators are now being adopted as clinical outcomes in clinical trials on cancer treatments. Technology-free daily monitoring of patients is complicated, time-consuming and expensive due to the need for vast amounts of resources and personnel. The alternative method of using the patients’ own phones could reduce the burden of continuous monitoring of cancer patients in clinical trials. This paper proposes monitoring the patients’ QoL by gathering data from their own phones. We considered that the continuous multiparametric acquisition of movement, location, phone calls, conversations and data use could be employed to simultaneously monitor their physical, psychological, social and environmental aspects. An open access phone app was developed (Human Dynamics Reporting Service (HDRS)) to implement this approach. We here propose a novel mapping between the standardized QoL items for these patients, the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and define HDRS monitoring indicators. A pilot study with university volunteers verified the plausibility of detecting human activity indicators directly related to QoL.

Job rotation is an organizational strategy based on the systematic exchange of workers between jobs in a planned manner according to specific criteria. This study presents the GS-Rot method, a method based on Game Theory, in order to design job rotation schedules by considering not only workers’ job preferences, but also the competencies required for different jobs. With this approach, we promote workers’ active participation in the design of the rotation plan. It also let us deal with restrictions in assigning workers to job positions according to their disabilities (temporal or permanent). The GS-Rot method has been implemented online and applied to a case in a work environment characterized by the presence of a high repetition of movements, which is a significant risk factor associated with work-related musculoskeletal disorders (WMSDs). A total of 17 workstations and 17 workers were involved in the rotation, four of them with physical/psychological limitations. Feasible job rotation schedules were obtained in a short time (average time 27.4 milliseconds). The results indicate that in the rotations driven by preference priorities, almost all the workers (94.11%) were assigned to one of their top five preferences. Likewise, 48.52% of job positions were assigned to workers in their top five of their competence lists. When jobs were assigned according to competence, 58.82% of workers got an assignment among their top five competence lists. Furthermore, 55.87% of the workers achieved jobs in their top five preferences. In both rotation scenarios, the workers varied performed jobs, and fatigue accumulation was balanced among them. The GS-Rot method achieved feasible and uniform solutions regarding the workers’ exposure to job repetitiveness.

The objective of this study was to assess the feasibility of using a user-centered chatbotfor collecting linked data to study overweight and obesity causes ina target population. In total 980 people participated in the feasibility study organized in three studies: (1) within a group of university students (88 participants), (2) in a small town (422 participants), and (3) within a university community (470 participants). We gathered self-reported data through the Wakamola chatbot regarding participants diet, physical activity, social network, living area, obesity-associated diseases, and sociodemographic data. For each study, we calculated the mean Body Mass Index (BMI) and number of people in each BMI level. Also, we defined and calculated scores (1–100 scale) regarding global health, BMI, alimentation, physical activity and social network. Moreover, we graphically represented obesity risk for living areas and the social network with nodes colored by BMI. Students group results: Mean BMI 21.37 (SD 2.57) (normal weight), 8 people underweight, 5 overweight, 0 obesity, global health status 78.21, alimentation 63.64, physical activity 65.08 and social 26.54, 3 areas with mean BMI level of obesity, 17 with overweight level. Small town´s study results: Mean BMI 25.66 (SD 4.29) (overweight), 2 people underweight, 63 overweight, 26 obesity, global health status 69.42, alimentation 64.60, physical activity 60.61 and social 1.14, 1 area with mean BMI in normal weight; University´s study results: Mean BMI 23.63 (SD 3.7) (normal weight), 22 people underweight, 86 overweight, 28 obesity, global health status 81.03, alimentation 81.84, physical activity 70.01 and social 1.47, 3 areas in obesity level, 19 in overweight level. Wakamola is a health care chatbot useful to collect relevant data from populations in the risk of overweight and obesity. Besides, the chatbot provides individual self-assessment of BMI and general status regarding the style of living. Moreover, Wakamola connects users in a social network to help the study of O&O´s causes from an individual, social and socio-economic perspective.

This work aimed to study the effect of confinement on weight and lifestyle using the Wakamola chatbot to collect data from 739 adults divided into two groups (341 case-control, 398 confinement). Nutrition score (0–100 scale) improved for men (medians 81.77–82.29, p < 0.05), with no difference for women (medians 82.29 in both cases). Both genders reduced the consumption of sweetmeats and sugared drinks (p < 0.01); men increased their consumption of vegetables, salad, and legumes (p < 0.01). Both genders reduced their physical activity score (men 100–40.14, p < 0.01, women 80.42–36.12, p < 0.01). Women sat less hours/week, men’s medians 28.81–28.27, women’s medians 35.97–23.33, p = 0.03. Both genders slept longer (hours/day), men 7–7.5, women 7–8 (p < 0.01) (medians). Their overall health score was significantly reduced (men 85.06–74.05, p < 0.01, women 84.47–72.42, p < 0.01), with no significant weight difference in either gender. Wakamola helped to contact participants and confirm changes in their lifestyle during confinement.