The Value of Tracking Data on the Behavior of Patients Who Have Undergone Bariatric Surgery:Explorative Study

Background: To maintain the benefits of a bariatric procedure, patients have to change their lifestyle permanently. This happens within a context of coresponsibilities of health care professionals and their social support system. However, most interventions are focused on the patient as an individual. In this explorative pilot study, behavioral, contextual, and experiential data were gathered to obtain insight on coresponsibility. Objective: The aim of this study is to explore the use of trackers by patients who have undergone bariatric surgery in a data-enabled design approach. Methods: Behavioral and contextual data on the households of patients who have undergone bariatric surgery were explored using a smartphone with an interactive user interface (UI), weight scale, activity bracelet, smart socket, accelerometer motion sensor, and event button to find examples of opportunities for future interventions. Results: A total of 6 households were monitored. Approximately 483,000 data points were collected, and the participants engaged in 1483 conversations with the system. Examples were found using different combinations of data types, which provided the obesity team a better understanding of patient behaviors and their support system, such as a referral to a family coach instead of a dietician. Another finding regarding the partners was, for example, that the conversational UI system facilitated discussion about the support structure by asking for awareness. Conclusions: An intelligent system using a combination of quantitative data gathered by data tracking products in the home environment and qualitative data gathered by app-enhanced short conversations, as well as face-to-face interviews, is useful for an improved understanding of coresponsibilities in the households of patients who have undergone bariatric surgery. The examples found in this explorative study so far encourage research in this field.</p

The Value of Tracking Data on the Behavior of Patients Who Have Undergone Bariatric Surgery:Explorative Study

Background: To maintain the benefits of a bariatric procedure, patients have to change their lifestyle permanently. This happens within a context of coresponsibilities of health care professionals and their social support system. However, most interventions are focused on the patient as an individual. In this explorative pilot study, behavioral, contextual, and experiential data were gathered to obtain insight on coresponsibility. Objective: The aim of this study is to explore the use of trackers by patients who have undergone bariatric surgery in a data-enabled design approach. Methods: Behavioral and contextual data on the households of patients who have undergone bariatric surgery were explored using a smartphone with an interactive user interface (UI), weight scale, activity bracelet, smart socket, accelerometer motion sensor, and event button to find examples of opportunities for future interventions. Results: A total of 6 households were monitored. Approximately 483,000 data points were collected, and the participants engaged in 1483 conversations with the system. Examples were found using different combinations of data types, which provided the obesity team a better understanding of patient behaviors and their support system, such as a referral to a family coach instead of a dietician. Another finding regarding the partners was, for example, that the conversational UI system facilitated discussion about the support structure by asking for awareness. Conclusions: An intelligent system using a combination of quantitative data gathered by data tracking products in the home environment and qualitative data gathered by app-enhanced short conversations, as well as face-to-face interviews, is useful for an improved understanding of coresponsibilities in the households of patients who have undergone bariatric surgery. The examples found in this explorative study so far encourage research in this field.</p

ExperimentSuite: A tool enabling the Data-Driven Design Process

The development of smart connected prototypes is becoming part of the designers' toolbox. These connected prototypes allow collecting data about the way people use them. There is an opportunity to examine how is data collection beneficial already during the design process of a connected product. The way to test the proposition of a connected prototype is by testing it with users. Therefore, companies setup home placement tests by multidisciplinary teams. The current home placement tests lack a tool that enables real time data collection, and real-time two way communication between the designer team and the participants of the home placement test. This thesis proposes a home placement test management tool. Twelve main features are made possible by the proposed home placement test management tool during the design process of a connected product. These twelve features are introduced in three related areas. Firstly, it is discussed how the tool improves knowledge transfer between the participants of the home placement test, and among the creators of the home placement test. Secondly, it is discussed what enhancements does data collection and access to data bring to the design process. Thirdly, it is discussed how to design services for inter-connected products. The findings of this thesis are based on a six months internship at Philips Design. The aim of the internship was creating a home placement test of a connected product that collects data about the product use by the participants. The work presented in this thesis is evaluated and examined based on a case study from the industry

Teaching Data-Enabled Design: Student-led Data Collection in Design Education

[EN] Designers are increasingly engaging with data practices in their day-to-day work as design practice becomes increasingly connected. To facilitate this practice, design education needs to evolve and embrace data design. In this paper we introduce a Master-level elective course around the Data-enabled Design methodology. This challenge-based learning activity aims to teach how to use data as a creative material in addressing a real-life design challenge from selected industrial partners. In this article we demonstrate how our master students learn how to prototype, conduct data-enabled interviews, adapt their prototypes, and introduce design interventions via a multi-step approach, leveraging their growing knowledge and skills around contextual data. We share how we use a strong collaboration with our industrial partners and a predefined data infrastructure to help our students  use data for sharing valid research findings and presenting experiential interventions.Noortman, R.; Lovei, P.; Funk, M. (2022). Teaching Data-Enabled Design: Student-led Data Collection in Design Education. En 8th International Conference on Higher Education Advances (HEAd'22). Editorial Universitat Politècnica de València. 223-230. https://doi.org/10.4995/HEAd22.2022.1458322323

Engaging senior adults with technology for behavior change

Amidst today’s ever-expanding waistlines there is a clear need to investigate technology’s potential to support behavior change and stimulate increased physical activity. Physical activity has also been shown to increase the independence and well-being of older adults, yet an important segment of this community is often excluded from the necessary in-context research due to the barriers they face to technology acceptance. Currently, there is limited knowledge on how to overcome these barriers to participation. We created a specific Product Service System that supports older adults to engage with the proposed technological interventions to enable important in-context behavior change research. Our approach converges knowledge from the domains of living laboratories, co-design, and existing experience of design research with older adults. From our experiences with this Product Service System, we provide guidelines to support other researchers setting-up a living laboratory study with older adults to explore technology’s potential to motivate behavior change

Identifying a Motivational Profile for Older Adults Towards Increased Physical Activity

Personalizing behavior change (BC) strategies to motivate increased physical activity is especially important for the diverse older adult population. However, there is a lack of knowledge about how to profile older users to most effectively personalize BC solutions. Self-awareness and social awareness are BC strategies commonly used in commercially available applications to promote physical activities. Through a randomized controlled trial (N = 53), we studied the effect of some personal factors on the physical activity of older adults under these two strategies. For this purpose, each BC strategy was implemented in a mobile application. Based on the statistical analysis of the measured step data and the collected questionnaire data, we identified a list of personal factors to personalize each BC strategy towards improved physical activity. Hereby we suggest how to create effective motivational profiles and provide design recommendations to personalize these BC strategies toward increased physical activity for older users

Engaging senior adults with technology for behavior change

Amidst today’s ever-expanding waistlines there is a clear need to investigate technology’s potential to support behavior change and stimulate increased physical activity. Physical activity has also been shown to increase the independence and well-being of older adults, yet an important segment of this community is often excluded from the necessary in-context research due to the barriers they face to technology acceptance. Currently, there is limited knowledge on how to overcome these barriers to participation. We created a specific Product Service System that supports older adults to engage with the proposed technological interventions to enable important in-context behavior change research. Our approach converges knowledge from the domains of living laboratories, co-design, and existing experience of design research with older adults. From our experiences with this Product Service System, we provide guidelines to support other researchers setting-up a living laboratory study with older adults to explore technology’s potential to motivate behavior change

Personalizing motivational strategies

Design for behaviour change (BC) has the potential to motivate older\u3cbr/\u3eadults to increase their physical activity and enjoy the health benefits\u3cbr/\u3ehereof. Despite this potential, there is a lack of knowledge about how\u3cbr/\u3eto profile users, to most effectively personalize strategies, which is\u3cbr/\u3edetrimental to the overall effectiveness of these BC solutions. Thus we\u3cbr/\u3econducted a random control trial in which the effects of two BC\u3cbr/\u3estrategies, implemented in two otherwise similar mobile applications,\u3cbr/\u3eare compared. From the statistical analysis of the measured step data\u3cbr/\u3eand the collected survey data, we were able to create motivation\u3cbr/\u3eprofiles for BC by triangulating an individual’s contextual, behavioural\u3cbr/\u3eand psychological factors. Here we share an overview of our approach.\u3cbr/\u3eIn this way we aim to inform designers doing important work in the field\u3cbr/\u3eof BC toward increased physical activity

The Value of Tracking Data on the Behavior of Patients Who Have Undergone Bariatric Surgery: Explorative Study

BackgroundTo maintain the benefits of a bariatric procedure, patients have to change their lifestyle permanently. This happens within a context of coresponsibilities of health care professionals and their social support system. However, most interventions are focused on the patient as an individual. In this explorative pilot study, behavioral, contextual, and experiential data were gathered to obtain insight on coresponsibility. ObjectiveThe aim of this study is to explore the use of trackers by patients who have undergone bariatric surgery in a data-enabled design approach. MethodsBehavioral and contextual data on the households of patients who have undergone bariatric surgery were explored using a smartphone with an interactive user interface (UI), weight scale, activity bracelet, smart socket, accelerometer motion sensor, and event button to find examples of opportunities for future interventions. ResultsA total of 6 households were monitored. Approximately 483,000 data points were collected, and the participants engaged in 1483 conversations with the system. Examples were found using different combinations of data types, which provided the obesity team a better understanding of patient behaviors and their support system, such as a referral to a family coach instead of a dietician. Another finding regarding the partners was, for example, that the conversational UI system facilitated discussion about the support structure by asking for awareness. ConclusionsAn intelligent system using a combination of quantitative data gathered by data tracking products in the home environment and qualitative data gathered by app-enhanced short conversations, as well as face-to-face interviews, is useful for an improved understanding of coresponsibilities in the households of patients who have undergone bariatric surgery. The examples found in this explorative study so far encourage research in this field

How did you sleep?: Exploring by-proxy sleep assessment in a field study setup

Objectives/Introduction: Sleep is crucial for both mental and physical health, and sleep disorders can pose a severe burden on health‐related quality of life. Importantly, family members may be affected by each other's sleep and wake behavior. Finally, there may be differences in sleep perception between family members. As a tool for future studies, we designed a concept for ‘By‐Proxy Sleep Assessment’, namely, the rating of one's partner's or children's sleep quality. This might provide a useful additional sleep quality measure, especially when assessing people with difficulties reporting their own sleep, such as children and people with intellectual disabilities or dementia. Methods: We used data from the FieldLab study, created to collect data on sleep and sleep related behaviors in five households, using an IoT ecosystem to combine subjective and objective information from connected objects measuring variables such as sleep, physical activity and environmental factors. A chat application enabled communication between researchers and participants and qualitative data was obtained through means of questionnaires and scheduled chatbot messages. Participants were asked to rate subjective sleep quality as well as the sleep quality of their partners and children on a scale of 0 to 10 each morning. Results: A total of 143 nights of partner By‐Proxy Sleep Assessment were collected from three couples and 40 nights of By‐Proxy Sleep Assessment for children from two couples. Subject‐proxy differences were averaged over the study period and varied between −0.38 ± 1.32 (−3 ‐ +2) and 1.23 ± 1.30 (−1 ‐ +3) for partners and −0.11 ± 0.97 (−3 ‐ +2) and 0.61 ± 1.50 (−1 ‐ +4) for children/parents. Preliminary analysis of IoT data and qualitative measurements through the chatbot revealed that both internal (e.g. migraines) and external factors (e.g. room temperature) contribute to discrepancies in sleep quality assessment by a proxy. Conclusions: Experience sampling studies can offer a new perspective on sleep quality and sleep perception. Although the By‐Proxy Sleep Assessment ratings already correlated rather well with the subjects’ own ratings, the IoT data may aid in improving the reliability of this approach