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Development of a goal-directed movement intervention (GOAL) using a movement sensor for hospitalized patients: An intervention mapping approach

Objective: To describe the development of a goal-directed movement intervention in two medical wards, including recommendations for implementation and evaluation. Design: Implementation Research. Setting: Pulmonology and nephrology/gastroenterology wards of the University Medical Centre Utrecht, The Netherlands. Participants: Seven focus groups were executed including 28 nurses, 7 physical therapists and 15 medical specialists. Patients' perceptions were repeatedly assessed during the iterative steps of the intervention development. Intervention: Interventions were targeted to each ward's specific character, following an Intervention Mapping approach using literature and research meetings. Main measures: Intervention components were linked to Behavior Change Techniques and implementation strategies will be selected using the Expert Recommendation Implementing Change tool. Evaluation outcomes like number of patients using the movement intervention will be measured, based on the taxonomy of Proctor. Results: The developed intervention consists of: insight in patients movement behavior (monitoring & feedback), goal setting (goals & planning) and adjustments to the environment (associations & antecedents). The following implementation strategies are recommended: to conduct educational meetings, prepare & identify champions and audit & provide feedback. To measure service and client outcomes, the mean level of physical activity per ward can be evaluated and the Net Promoter Score can be used. Conclusion(s): This study shows the development of a goal-directed movement intervention aligned with the needs of healthcare professionals. This resulted in an intervention consisting of feedback & monitoring of movement behavior, goal setting and adjustments in the environment. Using a step-by-step iterative implementation model to guide development and implementation is recommended.

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Development of a goal-directed movement intervention (GOAL) using a movement sensor for hospitalized patients: An intervention mapping approach

Objective: To describe the development of a goal-directed movement intervention in two medical wards, including recommendations for implementation and evaluation. Design: Implementation Research. Setting: Pulmonology and nephrology/gastroenterology wards of the University Medical Centre Utrecht, The Netherlands. Participants: Seven focus groups were executed including 28 nurses, 7 physical therapists and 15 medical specialists. Patients' perceptions were repeatedly assessed during the iterative steps of the intervention development. Intervention: Interventions were targeted to each ward's specific character, following an Intervention Mapping approach using literature and research meetings. Main measures: Intervention components were linked to Behavior Change Techniques and implementation strategies will be selected using the Expert Recommendation Implementing Change tool. Evaluation outcomes like number of patients using the movement intervention will be measured, based on the taxonomy of Proctor. Results: The developed intervention consists of: insight in patients movement behavior (monitoring & feedback), goal setting (goals & planning) and adjustments to the environment (associations & antecedents). The following implementation strategies are recommended: to conduct educational meetings, prepare & identify champions and audit & provide feedback. To measure service and client outcomes, the mean level of physical activity per ward can be evaluated and the Net Promoter Score can be used. Conclusion(s): This study shows the development of a goal-directed movement intervention aligned with the needs of healthcare professionals. This resulted in an intervention consisting of feedback & monitoring of movement behavior, goal setting and adjustments in the environment. Using a step-by-step iterative implementation model to guide development and implementation is recommended.

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Translation of a tailored nutrition and resistance exercise intervention for elderly people to a real-life setting

BACKGROUND: Combining increased dietary protein intake and resistance exercise training for elderly people is a promising strategy to prevent or counteract the loss of muscle mass and decrease the risk of disabilities. Using findings from controlled interventions in a real-life setting requires adaptations to the intervention and working procedures of healthcare professionals (HCPs). The aim of this study is to adapt an efficacious intervention for elderly people to a real-life setting (phase one) and test the feasibility and potential impact of this prototype intervention in practice in a pilot study (phase two).METHODS: The Intervention Mapping approach was used to guide the adaptation in phase one. Qualitative data were collected from the original researchers, target group, and HCPs, and information was used to decide whether and how specified intervention elements needed to be adapted. In phase two, a one-group pre-test post-test pilot study was conducted (n = 25 community-dwelling elderly), to elicit further improvements to the prototype intervention. The evaluation included participant questionnaires and measurements at baseline (T0) and follow-up (T1), registration forms, interviews, and focus group discussions (T1). Qualitative data for both phases were analysed using an inductive approach. Outcome measures included physical functioning, strength, body composition, and dietary intake. Change in outcomes was assessed using Wilcoxon signed-rank tests.RESULTS: The most important adaptations to the original intervention were the design of HCP training and extending the original protein supplementation with a broader nutrition programme aimed at increasing protein intake, facilitated by a dietician. Although the prototype intervention was appreciated by participants and professionals, and perceived applicable for implementation, the pilot study process evaluation resulted in further adaptations, mostly concerning recruitment, training session guidance, and the nutrition programme. Pilot study outcome measures showed significant improvements in muscle strength and functioning, but no change in lean body mass.CONCLUSION: The combined nutrition and exercise intervention was successfully adapted to the real-life setting and seems to have included the most important effective intervention elements. After adaptation of the intervention using insights from the pilot study, a larger, controlled trial should be conducted to assess cost-effectiveness.TRIAL REGISTRATION: Trial registration number: ClinicalTrials.gov NL51834.081.14 (April 22, 2015).

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Translation of a tailored nutrition and resistance exercise intervention for elderly people to a real-life setting

BACKGROUND: Combining increased dietary protein intake and resistance exercise training for elderly people is a promising strategy to prevent or counteract the loss of muscle mass and decrease the risk of disabilities. Using findings from controlled interventions in a real-life setting requires adaptations to the intervention and working procedures of healthcare professionals (HCPs). The aim of this study is to adapt an efficacious intervention for elderly people to a real-life setting (phase one) and test the feasibility and potential impact of this prototype intervention in practice in a pilot study (phase two).METHODS: The Intervention Mapping approach was used to guide the adaptation in phase one. Qualitative data were collected from the original researchers, target group, and HCPs, and information was used to decide whether and how specified intervention elements needed to be adapted. In phase two, a one-group pre-test post-test pilot study was conducted (n = 25 community-dwelling elderly), to elicit further improvements to the prototype intervention. The evaluation included participant questionnaires and measurements at baseline (T0) and follow-up (T1), registration forms, interviews, and focus group discussions (T1). Qualitative data for both phases were analysed using an inductive approach. Outcome measures included physical functioning, strength, body composition, and dietary intake. Change in outcomes was assessed using Wilcoxon signed-rank tests.RESULTS: The most important adaptations to the original intervention were the design of HCP training and extending the original protein supplementation with a broader nutrition programme aimed at increasing protein intake, facilitated by a dietician. Although the prototype intervention was appreciated by participants and professionals, and perceived applicable for implementation, the pilot study process evaluation resulted in further adaptations, mostly concerning recruitment, training session guidance, and the nutrition programme. Pilot study outcome measures showed significant improvements in muscle strength and functioning, but no change in lean body mass.CONCLUSION: The combined nutrition and exercise intervention was successfully adapted to the real-life setting and seems to have included the most important effective intervention elements. After adaptation of the intervention using insights from the pilot study, a larger, controlled trial should be conducted to assess cost-effectiveness.TRIAL REGISTRATION: Trial registration number: ClinicalTrials.gov NL51834.081.14 (April 22, 2015).

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Human-Centered AI for Dementia Care: Using Reinforcement Learning for Personalized Interventions Support in Eating and Drinking Scenarios

For people with early-dementia (PwD), it can be challenging to remember to eat and drink regularly and maintain a healthy independent living. Existing intelligent home technologies primarily focus on activity recognition but lack adaptive support. This research addresses this gap by developing an AI system inspired by the Just-in-Time Adaptive Intervention (JITAI) concept. It adapts to individual behaviors and provides personalized interventions within the home environment, reminding and encouraging PwD to manage their eating and drinking routines. Considering the cognitive impairment of PwD, we design a human-centered AI system based on healthcare theories and caregivers’ insights. It employs reinforcement learning (RL) techniques to deliver personalized interventions. To avoid overwhelming interaction with PwD, we develop an RL-based simulation protocol. This allows us to evaluate different RL algorithms in various simulation scenarios, not only finding the most effective and efficient approach but also validating the robustness of our system before implementation in real-world human experiments. The simulation experimental results demonstrate the promising potential of the adaptive RL for building a human-centered AI system with perceived expressions of empathy to improve dementia care. To further evaluate the system, we plan to conduct real-world user studies.

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Human-Centered AI for Dementia Care: Using Reinforcement Learning for Personalized Interventions Support in Eating and Drinking Scenarios

For people with early-dementia (PwD), it can be challenging to remember to eat and drink regularly and maintain a healthy independent living. Existing intelligent home technologies primarily focus on activity recognition but lack adaptive support. This research addresses this gap by developing an AI system inspired by the Just-in-Time Adaptive Intervention (JITAI) concept. It adapts to individual behaviors and provides personalized interventions within the home environment, reminding and encouraging PwD to manage their eating and drinking routines. Considering the cognitive impairment of PwD, we design a human-centered AI system based on healthcare theories and caregivers’ insights. It employs reinforcement learning (RL) techniques to deliver personalized interventions. To avoid overwhelming interaction with PwD, we develop an RL-based simulation protocol. This allows us to evaluate different RL algorithms in various simulation scenarios, not only finding the most effective and efficient approach but also validating the robustness of our system before implementation in real-world human experiments. The simulation experimental results demonstrate the promising potential of the adaptive RL for building a human-centered AI system with perceived expressions of empathy to improve dementia care. To further evaluate the system, we plan to conduct real-world user studies.

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Fit for play

Abstract: Sedentary behaviour in children, four years of age and older, has increased over the last decades. These children become physically less skilled, which demotivates them for regular sports activities. They become susceptible to health risks such as obesity and have a heightened chance to develop depression and a lower self-esteem. Sports professionals acknowledge that these children in time become unable to keep up with the sports education pace, leaving them prone to social exclusion as well.Exergames seem promising in their potential to increase the amount and quality of physical exercise in this group. Moreover, they offer strategies to motivate children to a more active and healthier lifestyle. However, some issues remain unclear regarding their applicability and individual fittingness. For one thing sports professionals have little to no experience using exergames in physical education, let alone understand which games could be appropriate to structurally activate said children. In addition, existing exergames regularly lack a suitable degree of adaptivity regarding what a child is physically capable of, which psychological needs should be addressed, and to what inactive children find appealing in terms of gameplay.The aim of our research project is to build a first prototype of an adaptive platform for exergames to motivate inactive children to structurally engage in physical exercise more, and better. The participative design method we used in our preliminary qualitative research led to a better understanding of the barriers to move and the psychological needs children have when it comes to physical exercise. We made a first global list of requirements for the adaptive platform and an overview of necessary design directions.Future pursuits in this project include a participative design research study amongst both children and sports professionals, and a thorough review of the literature and state of the art knowledge. We will use this knowledge to create a first prototype of an adaptive platform in collaboration with a serious game company and an organisation of sport professionals. After user testing we will use the evaluation findings as a baseline for future measurements regarding the adaptation of suggested exergames and to formalize and disseminate found design guidelines.

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Fit for play

Abstract: Sedentary behaviour in children, four years of age and older, has increased over the last decades. These children become physically less skilled, which demotivates them for regular sports activities. They become susceptible to health risks such as obesity and have a heightened chance to develop depression and a lower self-esteem. Sports professionals acknowledge that these children in time become unable to keep up with the sports education pace, leaving them prone to social exclusion as well.Exergames seem promising in their potential to increase the amount and quality of physical exercise in this group. Moreover, they offer strategies to motivate children to a more active and healthier lifestyle. However, some issues remain unclear regarding their applicability and individual fittingness. For one thing sports professionals have little to no experience using exergames in physical education, let alone understand which games could be appropriate to structurally activate said children. In addition, existing exergames regularly lack a suitable degree of adaptivity regarding what a child is physically capable of, which psychological needs should be addressed, and to what inactive children find appealing in terms of gameplay.The aim of our research project is to build a first prototype of an adaptive platform for exergames to motivate inactive children to structurally engage in physical exercise more, and better. The participative design method we used in our preliminary qualitative research led to a better understanding of the barriers to move and the psychological needs children have when it comes to physical exercise. We made a first global list of requirements for the adaptive platform and an overview of necessary design directions.Future pursuits in this project include a participative design research study amongst both children and sports professionals, and a thorough review of the literature and state of the art knowledge. We will use this knowledge to create a first prototype of an adaptive platform in collaboration with a serious game company and an organisation of sport professionals. After user testing we will use the evaluation findings as a baseline for future measurements regarding the adaptation of suggested exergames and to formalize and disseminate found design guidelines.

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Employable through social media

This longitudinal, quantitative study contributes to the debate on technology-based professional development by examining the extent to which a learning (LinkedIn) intervention in a university setting affects an individual’s social media use for professional development, and the extent to which this relates to self-reported employability. In addition, we investigated how this relationship is moderated by an individual’s motivation to communicate through social media (LinkedIn). Based on social capital theory and the conservation of resources theory, we developed a set of hypotheses that were tested based on longitudinal data collected from university employees (N = 101) in middle- and high-level jobs. First, in line with our expectations, social media use for professional development was significantly higher after the learning intervention than before. Second, partially in line with our expectations, social media use for professional development was positively related with the employability dimension anticipation and optimization. Third, contrary to our expectations, motivation to communicate through social media (LinkedIn) did not have a moderating role in this relationship. We concluded that the learning intervention has the potential to foster social media use for professional development, and in turn, can contribute to individuals’ human capital in terms of their employability. Hence, the intervention that forms the core of this empirical research can be a sustainable and promising human resource management (HRM) practice that fits the human capital agenda.

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Development of a goal-directed movement intervention (GOAL) using a movement sensor for hospitalized patients: An intervention mapping approach

Development of a goal-directed movement intervention (GOAL) using a movement sensor for hospitalized patients: An intervention mapping approach

Translation of a tailored nutrition and resistance exercise intervention for elderly people to a real-life setting

Translation of a tailored nutrition and resistance exercise intervention for elderly people to a real-life setting

Human-Centered AI for Dementia Care: Using Reinforcement Learning for Personalized Interventions Support in Eating and Drinking Scenarios

Human-Centered AI for Dementia Care: Using Reinforcement Learning for Personalized Interventions Support in Eating and Drinking Scenarios

Fit for play

Fit for play

Employable through social media

Projecten 1

Wie zorgt? De Ontwikkeling van een Intelligente Zorgomgeving die Zorgprofessionals Ondersteunt

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Development of a goal-directed movement intervention (GOAL) using a movement sensor for hospitalized patients: An intervention mapping approach

Development of a goal-directed movement intervention (GOAL) using a movement sensor for hospitalized patients: An intervention mapping approach

Translation of a tailored nutrition and resistance exercise intervention for elderly people to a real-life setting

Translation of a tailored nutrition and resistance exercise intervention for elderly people to a real-life setting

Human-Centered AI for Dementia Care: Using Reinforcement Learning for Personalized Interventions Support in Eating and Drinking Scenarios

Human-Centered AI for Dementia Care: Using Reinforcement Learning for Personalized Interventions Support in Eating and Drinking Scenarios

Fit for play

Fit for play

Employable through social media

Projecten 1

Wie zorgt? De Ontwikkeling van een Intelligente Zorgomgeving die Zorgprofessionals Ondersteunt