A qualitative exploration of the collaborative working between palliative care and geriatric medicine : Barriers and facilitators from a European perspective

Background: With an increasing number of people dying in old age, collaboration between palliative care and geriatric medicine is increasingly being advocated in order to promote better health and health care for the increasing number of older people. The aim of this study is to identify barriers and facilitators and good practice examples of collaboration and integration between palliative care and geriatric medicine from a European perspective. Methods: Four semi-structured group interviews were undertaken with 32 participants from 18 countries worldwide. Participants were both clinicians (geriatricians, GPs, palliative care specialists) and academic researchers. The interviews were transcribed and independent analyses performed by two researchers who then reached consensus. Results: Limited knowledge and understanding of what the other discipline offers, a lack of common practice and a lack of communication between disciplines and settings were considered as barriers for collaboration between palliative care and geriatric medicine. Multidisciplinary team working, integration, strong leadership and recognition of both disciplines as specialties were considered as facilitators of collaborative working. Whilst there are instances of close clinical working between disciplines, examples of strategic collaboration in education and policy were more limited. Conclusions: Improving knowledge about its principles and acquainting basic palliative care skills appears mandatory for geriatricians and other health care professionals. In addition, establishing more academic chairs is seen as a priority in order to develop more education and development at the intersection of palliative care and geriatric medicine

In vivo imaging of pancreatic tumours and liver metastases using 7 Tesla MRI in a murine orthotopic pancreatic cancer model and a liver metastases model

<p>Abstract</p> <p>Background</p> <p>Pancreatic cancer is the fourth leading cause of tumour death in the western world. However, appropriate tumour models are scarce. Here we present a syngeneic murine pancreatic cancer model using 7 Tesla MRI and evaluate its clinical relevance and applicability.</p> <p>Methods</p> <p>6606PDA murine pancreatic cancer cells were orthotopically injected into the pancreatic head. Liver metastases were induced through splenic injection. Animals were analyzed by MRI three and five weeks following injection. Tumours were detected using T2-weighted high resolution sequences. Tumour volumes were determined by callipers and MRI. Liver metastases were analyzed using gadolinium-EOB-DTPA and T1-weighted 3D-Flash sequences. Tumour blood flow was measured using low molecular gadobutrol and high molecular gadolinium-DTPA.</p> <p>Results</p> <p>MRI handling and applicability was similar to human systems, resolution as low as 0.1 mm. After 5 weeks tumour volumes differed significantly (p < 0.01) when comparing calliper measurments (n = 5, mean 1065 mm³+/-243 mm³) with MRI (mean 918 mm³+/-193 mm³) with MRI being more precise. Histology (n = 5) confirmed MRI tumour measurements (mean size MRI 38.5 mm²+/-22.8 mm²versus 32.6 mm²+/-22.6 mm²(histology), p < 0,0004) with differences due to fixation and processing of specimens. After splenic injection all mice developed liver metastases with a mean of 8 metastases and a mean volume of 173.8 mm³+/-56.7 mm³after 5 weeks. Lymphnodes were also easily identified. Tumour accumulation of gadobutrol was significantly (p < 0.05) higher than gadolinium-DTPA. All imaging experiments could be done repeatedly to comply with the 3R-principle thus reducing the number of experimental animals.</p> <p>Conclusions</p> <p>This model permits monitoring of tumour growth and metastasis formation in longitudinal non-invasive high-resolution MR studies including using contrast agents comparable to human pancreatic cancer. This multidisciplinary environment enables radiologists, surgeons and physicians to further improve translational research and therapies of pancreatic cancer.</p

PerfectFit-project/virtual_coach_daily_step_goal_setting: Virtual coach for daily step goal setting

The code for the virtual coach that is created for the thesis project: Using Reinforcement Learning to Personalize Daily Step Goals for a Collaborative Dialogue with a Virtual Coach

Learning What to Attend to: Using bisimulation metrics to explore and improve upon what a deep reinforcement learning agent learns

We analyze the internal representations that deep Reinforcement Learning (RL) agents form of their environments and whether these representations correspond to what such agents should ideally learn. The purpose of this comparison is both a better understanding of why certain algorithms or network architectures perform better than others and the development of methods that specifically target discrepancies between what is and what should be learned. The concept of ideal representation we utilize is based on stochastic bisimulation and bisimulation metrics, which are measures of whether and to which degree states are behaviorally similar, respectively. Learning an internal representation in which states are equivalent if and only if they are bisimilar and in which distances between non-equivalent states are proportional to how behaviorally similar the states are has several desirable theoretical properties. Yet, we show empirically that the extent to which such a representation is learned in practice depends on several factors and that a precise such representation is not created in any case. We further provide experimental results that suggest that learning a representation that is close to this target internal state representation during training may improve upon the learning speed and consistency, and doing so by the end of training upon generalization.Computer Science | Data Science and Technolog

Addressing people’s current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active: Data and analysis code

 This is the data and analysis code underlying the paper "Addressing people’s current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active" by Nele Albers, Mark A. Neerincx, and Willem-Paul Brinkman. This paper proposes a Reinforcement Learning (RL)-algorithm for persuading people in the context of a virtual coach for quitting smoking and becoming more physically active. Study The paper is based on a longitudinal study on the crowdsourcing platform Prolific run between 20 May 2021 and 30 June 2021. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 1523).   In this study, smokers who were contemplating or preparing to quit smoking interacted with the text-based virtual coach Sam in up to five conversational sessions. In each session, participants were assigned a new preparatory activity for quitting smoking, such as thinking of and writing down reasons for quitting smoking. Since becoming more physically active may make it easier to quit smoking, half of the activities addressed becoming more physically active. The virtual coach chose from five persuasion types to persuade people to do their activity. In the first two sessions, the persuasion type was chosen uniformly at random; in the last three sessions, the persuasion type was determined by a persuasion algorithm. In the next session, participants were asked to indicate the effort they spent on their activity, which served as basis for the reward signal for the persuasion algorithm. The study was pre-registered in the Open Science Framework (OSF): https://osf.io/k2uac. This pre-registration describes the study design, measures, etc. Note that the data we provide here is only a part of the data collected in the study, namely, the data related to studying the effectiveness of the persuasion algorithm.  Pointers to further resources: Data on the acceptance of the virtual coach can be found here: https://doi.org/10.4121/19934783.v1. Data on users' needs for a digital smoking cessation application can be found here: https://doi.org/10.4121/20284131.v2. Data on users' action plans for doing the activities (n = 469) and free-text responses to reflective questions about the activities (n = 2026) is available here: https://doi.org/10.4121/21905271.v1. The implementation of the virtual coach Sam is available here: https://doi.org/10.5281/zenodo.6319356.  The formulations for the 24 preparatory activities used in the study can be found in the supplementary material of the paper (S8 Appendix). Data We collected four main types of data: Perceived motivational impact and effort. The perceived motivational impact of the conversational sessions and the effort spent on the activities were used to evaluate the effectiveness of the persuasion algorithm. Both were measured during the conversational sessions. Involvement in the activities. We used people's involvement in their activities for an exploratory subgroup analysis comparing the algorithm effectiveness for people with low and high involvement. User characteristics (e.g., age, gender, Big-Five personality, quitter self-identity). This data was collected by means of questionnaires and from participants' Prolific profiles. RL-samples (states, actions, rewards). This data was collected from the conversational sessions. The actions were the five persuasion types (e.g., consensus, action planning, no persuasion), and the reward was based on the effort. Please consult the "Data"-folder for more information on the data we collected.</p

Addressing people’s current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active: Data and analysis code

 This is the data and analysis code underlying the paper "Addressing people’s current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active" by Nele Albers, Mark A. Neerincx, and Willem-Paul Brinkman. This paper proposes a Reinforcement Learning (RL)-algorithm for persuading people in the context of a virtual coach for quitting smoking and becoming more physically active. Study The paper is based on a longitudinal study on the crowdsourcing platform Prolific run between 20 May 2021 and 30 June 2021. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 1523).   In this study, smokers who were contemplating or preparing to quit smoking interacted with the text-based virtual coach Sam in up to five conversational sessions. In each session, participants were assigned a new preparatory activity for quitting smoking, such as thinking of and writing down reasons for quitting smoking. Since becoming more physically active may make it easier to quit smoking, half of the activities addressed becoming more physically active. The virtual coach chose from five persuasion types to persuade people to do their activity. In the first two sessions, the persuasion type was chosen uniformly at random; in the last three sessions, the persuasion type was determined by a persuasion algorithm. In the next session, participants were asked to indicate the effort they spent on their activity, which served as basis for the reward signal for the persuasion algorithm. The study was pre-registered in the Open Science Framework (OSF): https://osf.io/k2uac. This pre-registration describes the study design, measures, etc. Note that the data we provide here is only a part of the data collected in the study, namely, the data related to studying the effectiveness of the persuasion algorithm.  Pointers to further resources: Data on the acceptance of the virtual coach can be found here: https://doi.org/10.4121/19934783.v1. Data on users' needs for a digital smoking cessation application can be found here: https://doi.org/10.4121/20284131.v2. Data on users' action plans for doing the activities (n = 469) and free-text responses to reflective questions about the activities (n = 2026) is available here: https://doi.org/10.4121/21905271.v1. The implementation of the virtual coach Sam is available here: https://doi.org/10.5281/zenodo.6319356.  The formulations for the 24 preparatory activities used in the study can be found in the supplementary material of the paper (S8 Appendix). Data We collected four main types of data: Perceived motivational impact and effort. The perceived motivational impact of the conversational sessions and the effort spent on the activities were used to evaluate the effectiveness of the persuasion algorithm. Both were measured during the conversational sessions. Involvement in the activities. We used people's involvement in their activities for an exploratory subgroup analysis comparing the algorithm effectiveness for people with low and high involvement. User characteristics (e.g., age, gender, Big-Five personality, quitter self-identity). This data was collected by means of questionnaires and from participants' Prolific profiles. RL-samples (states, actions, rewards). This data was collected from the conversational sessions. The actions were the five persuasion types (e.g., consensus, action planning, no persuasion), and the reward was based on the effort. Please consult the "Data"-folder for more information on the data we collected.</p

Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior - Data, Analysis Code and Appendix

This repository contains the data, analysis code, and appendix of the paper "Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior" by Nele Albers, Mark A. Neerincx, and Willem-Paul Brinkman, published in Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023). Data The paper is based on data collected during a study on the online crowdsourcing platform Prolific run between 20 May 2021 and 30 June 2021. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 1523). In this study, smokers who were contemplating or preparing to quit smoking interacted with the text-based virtual coach Sam in up to five conversational sessions. In each session, participants were assigned a new preparatory activity for quitting smoking, such as thinking of and writing down reasons for quitting smoking. Since becoming more physically active may make it easier to quit smoking, half of the activities addressed becoming more physically active. The virtual coach chose from five persuasive strategies to persuade people to do their activity. In the first two sessions, the persuasive strategy was chosen uniformly at random; in the last three sessions, the persuasive strategy was determined by a persuasion algorithm that differed between four conditions. In the next session, participants were asked to indicate the effort they spent on their activity, which served as basis for the reward signal for the persuasion algorithm.  The study was pre-registered in the Open Science Framework (OSF): https://osf.io/k2uac. This pre-registration describes the study design, measures, etc. Note that the data we provide here is only a part of the data collected in the study, namely, the data related to studying the prediction of behavior (i.e., the effort people spent on their activities) based on user states and characteristics. Analysis Code Our analysis can be reproduced using Docker and Jupyter Notebook. We provide instructions for this in the README-files accompanying our analysis code. Appendix We also provide the Appendix of our paper, which contains more information on the virtual coach (including the conversation structure and preparatory activities), persuasion algorithm, data collection, optimal and worst policies computed for research questions Q3 and Q4, and the weighting of samples based on similarity for research question Q6. Regarding the preparatory activities, note that there were two different formulations: one for during the session, and one for the reminder message people received on Prolific.The former asked people to do the activity "after this session" and told people that they would receive the video link in the Prolific reminder message in case the activity involved watching a video; the latter asked people to do the activity "before the next session" in sessions 1-4 and contained the video link in case the activity involved watching a video. All activity formulations can be found together with the virtual coach code: https://github.com/PerfectFit-project/virtual_coach_rl_persuasion_algorithm/blob/main/Activities.csv. Custom action code further modifies the reminder message activity formulation for session 5, which is the last session (https://github.com/PerfectFit-project/virtual_coach_rl_persuasion_algorithm/blob/main/actions/actions.py). Further Resources Here are some pointers to further resources: Data on the acceptance of the virtual coach can be found here: https://doi.org/10.4121/19934783.v1. Data on users' needs for a digital smoking cessation application can be found here: https://doi.org/10.4121/20284131.v2. Data on users' action plans for doing the activities (n = 469) and free-text responses to reflective questions about the activities (n = 2026) is available here: https://doi.org/10.4121/21905271.v1. The implementation of the virtual coach Sam is available here: https://doi.org/10.5281/zenodo.6319356.  Journal paper describing the persuasion algorithm and analyzing its effectiveness: https://doi.org/10.1371/journal.pone.0277295. If you have questions about the data, analysis code, or appendix, please contact Nele Albers ([email protected]). </p

Setting Physical Activity Goals with a Virtual Coach: Data and Analysis Code

This is the data and analysis code underlying the paper "Setting Physical Activity Goals with a Virtual Coach: Vicarious Experiences, Personalization and Acceptance" by Nele Albers, Beyza Hizli, Bouke L. Scheltinga, Eline Meijer, and Willem-Paul Brinkman. The paper examines the use of personalized vicarious experiences in a goal-setting dialog for physical activity with a virtual coach. Study The paper is based on the study conducted in March 2022 for the publicly available Master's thesis by Beyza Hizli (http://resolver.tudelft.nl/uuid:b7225a91-6ae8-4a32-8441-38fb7ff74b4c). In this study, 39 fluently English-speaking adults set a running or walking goal with the text-based virtual coach Jody.  Participants thereby received either two of the three overall most motivating vicarious experiences (i.e., "generic" experiences) or "personalized" vicarious experiences chosen based on a linear regression model. During the dialog, participants wrote what they could take away from the examples from other people that they saw. After the dialog, participants further provided information regarding their self-efficacy for the type of goal they set (i.e., running or walking), ratings of examples on perceived motivational impact, and a free-text response about what they found motivating about the examples they saw. The study was pre-registered in the Open Science Framework (OSF): https://osf.io/4duwh.  Links to further resources: The implementation of the virtual coach Jody is available here: https://doi.org/10.5281/zenodo.6647381. The 72 examples from other people used in the study can be found in the dataset accompanying the Master's thesis by Beyza Hizli: https://doi.org/10.4121/20047328.v1. A video of part of the dialog with the virtual coach is available here: https://youtu.be/7WJy7-H3QEQ. Data We collected data in three separate studies: Study to collect the vicarious experiences (part A). We gathered the vicarious experiences together with data on individual characteristics of the participants (e.g., age, stage of change for becoming physically active). We collected vicarious experiences from 72 people. Study to rate the vicarious experiences on perceived motivational impact and similarity (part B). We asked 36 individuals to each rate 18 vicarious experiences on perceived motivational impact and similarity to the person in the experience. We further collected data on individual characteristics of these 36 participants. Study to evaluate the goal-setting dialog and the vicarious experiences therein (part C). 39 participants interacted with the text-based virtual coach Jody to set a running or walking goal. Participants saw either personalized or generic examples from other people. We also collected data on individual characteristics of these 39 participants. Please refer to the OSF pre-registration for more information on the data we collected. In addition, we describe our measures in detail in a separate sheet in each data file in the "Data"-folder. Note that we do not provide all data here: we describe in our README-files which data needs to be downloaded from the repository accompanying the Master's thesis by Beyza Hizli to reproduce some of our analyses (https://doi.org/10.4121/20047328.v1). If you have any questions, please contact Nele Albers ([email protected]). </p

Setting Physical Activity Goals with a Virtual Coach: Data and Analysis Code

This is the data and analysis code underlying the paper "Setting Physical Activity Goals with a Virtual Coach: Vicarious Experiences, Personalization and Acceptance" by Nele Albers, Beyza Hizli, Bouke L. Scheltinga, Eline Meijer, and Willem-Paul Brinkman. The paper examines the use of personalized vicarious experiences in a goal-setting dialog for physical activity with a virtual coach. Study The paper is based on the study conducted in March 2022 for the publicly available Master's thesis by Beyza Hizli (http://resolver.tudelft.nl/uuid:b7225a91-6ae8-4a32-8441-38fb7ff74b4c). In this study, 39 fluently English-speaking adults set a running or walking goal with the text-based virtual coach Jody.  Participants thereby received either two of the three overall most motivating vicarious experiences (i.e., "generic" experiences) or "personalized" vicarious experiences chosen based on a linear regression model. During the dialog, participants wrote what they could take away from the examples from other people that they saw. After the dialog, participants further provided information regarding their self-efficacy for the type of goal they set (i.e., running or walking), ratings of examples on perceived motivational impact, and a free-text response about what they found motivating about the examples they saw. The study was pre-registered in the Open Science Framework (OSF): https://osf.io/4duwh.  Links to further resources: The implementation of the virtual coach Jody is available here: https://doi.org/10.5281/zenodo.6647381. The 72 examples from other people used in the study can be found in the dataset accompanying the Master's thesis by Beyza Hizli: https://doi.org/10.4121/20047328.v1. A video of part of the dialog with the virtual coach is available here: https://youtu.be/7WJy7-H3QEQ. Data We collected data in three separate studies: Study to collect the vicarious experiences (part A). We gathered the vicarious experiences together with data on individual characteristics of the participants (e.g., age, stage of change for becoming physically active). We collected vicarious experiences from 72 people. Study to rate the vicarious experiences on perceived motivational impact and similarity (part B). We asked 36 individuals to each rate 18 vicarious experiences on perceived motivational impact and similarity to the person in the experience. We further collected data on individual characteristics of these 36 participants. Study to evaluate the goal-setting dialog and the vicarious experiences therein (part C). 39 participants interacted with the text-based virtual coach Jody to set a running or walking goal. Participants saw either personalized or generic examples from other people. We also collected data on individual characteristics of these 39 participants. Please refer to the OSF pre-registration for more information on the data we collected. In addition, we describe our measures in detail in a separate sheet in each data file in the "Data"-folder. Note that we do not provide all data here: we describe in our README-files which data needs to be downloaded from the repository accompanying the Master's thesis by Beyza Hizli to reproduce some of our analyses (https://doi.org/10.4121/20047328.v1). If you have any questions, please contact Nele Albers ([email protected]). </p

Setting Physical Activity Goals with a Virtual Coach: Data and Analysis Code

This is the data and analysis code underlying the paper "Setting Physical Activity Goals with a Virtual Coach: Vicarious Experiences, Personalization and Acceptance" by Nele Albers, Beyza Hizli, Bouke L. Scheltinga, Eline Meijer, and Willem-Paul Brinkman. The paper examines the use of personalized vicarious experiences in a goal-setting dialog for physical activity with a virtual coach. Study The paper is based on the study conducted in March 2022 for the publicly available Master's thesis by Beyza Hizli (http://resolver.tudelft.nl/uuid:b7225a91-6ae8-4a32-8441-38fb7ff74b4c). In this study, 39 fluently English-speaking adults set a running or walking goal with the text-based virtual coach Jody.  Participants thereby received either two of the three overall most motivating vicarious experiences (i.e., "generic" experiences) or "personalized" vicarious experiences chosen based on a linear regression model. During the dialog, participants wrote what they could take away from the examples from other people that they saw. After the dialog, participants further provided information regarding their self-efficacy for the type of goal they set (i.e., running or walking), ratings of examples on perceived motivational impact, and a free-text response about what they found motivating about the examples they saw. The study was pre-registered in the Open Science Framework (OSF): https://osf.io/4duwh.  The implementation of the virtual coach Jody is available here: https://doi.org/10.5281/zenodo.6647381. The 72 examples from other people used in the study can be found in the dataset accompanying the Master's thesis by Beyza Hizli: https://doi.org/10.4121/20047328.v1. A video of part of the dialog with the virtual coach is available here: https://youtu.be/7WJy7-H3QEQ. Data We collected data on three separate studies: Study to collect the vicarious experiences (part A). We gathered the vicarious experiences together with data on individual characteristics of the participants (e.g., age, stage of change for becoming physically active). We collected vicarious experiences from 72 people. Study to rate the vicarious experiences on perceived motivational impact and similarity (part B). We asked 36 individuals to each rate 18 vicarious experiences on perceived motivational impact and similarity to the person in the experience. We further collected data on individual characteristics of these 36 participants. Study to evaluate the goal-setting dialog and the vicarious experiences therein (part C). 39 participants interacted with the text-based virtual coach Jody to set a running or walking goal. Participants saw either personalized or generic examples from other people. We also collected data on individual characteristics of these 39 participants. Please refer to the OSF pre-registration for more information on the data we collected. In addition, we describe our measures in detail in a separate sheet in each data file in the "Data"-folder. Note that we do not provide all data here: we describe in our README-files which data needs to be downloaded from the repository accompanying the Master's thesis by Beyza Hizli to reproduce some of our analyses (https://doi.org/10.4121/20047328.v1). If you have any questions, please contact Nele Albers ([email protected]). </p