Catching Patient’s Attention at the Right Time to Help Them Undergo Behavioural Change: Stress Classification Experiment from Blood Volume Pulse

Lisowska, Aneta; Wilk, Szymon; Peleg, Mor

doi:10.1007/978-3-030-77211-6_8

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…We considered using the patient's internal context such as stress [43] and cognitive load [67], derived from data captured by the watch, alongside external context information such as location and time of the day for learning the best time to intervene. Given that prior to the intervention we had no data about patients' responsiveness in varying contexts, we conducted an experiment on simulated data [68] to evaluate different ML approaches to notification schedule personalisation and found adaptive supervised learning approaches to be more effective than reinforcement learning methods.…”

Section: Develop Personalisation Methodsmentioning

confidence: 99%

“…Consumer-grade smartwatches are frequently equipped with a photoplethysmogram (PPG) sensor that could capture blood volume pulse (BVP) signal [42]. Therefore, based on experiments on a public dataset and a literature review, we hypothesized that we could detect when a patient performed deep breathing exercise from the changes to their BVP [43]. Nevertheless, during initial evaluation of the device we found that the ASUS API exposes only extracted heart rate variability features rather than the raw PPG signal.…”

Section: Select Bci Supporting Technologiesmentioning

confidence: 99%

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What do you need to consider when designing mobile health intervention?

Lisowska¹,

Wilk²,

Peleg³

2022

Preprint

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Designing theory-driven digital health interventions is a challenging task that needs support. We created a guide for the incomers in the field on how to design digital health interventions with case studies from the Cancer Better Life Experience (CAPABLE) European project. The guide explains how behaviour change theories can inform customisation and personalisation of the intervention. The proposed SATO (ideaS expAnded wiTh bciO) design workflow is based on the IDEAS (Integrate, Design, Assess, and Share) framework and is aligned with the Behaviour Change Intervention Ontology (BCIO). We provide a checklist of the activities that should be performed during intervention planning as well as app design templates which bundle together relevant behaviour change techniques. In the process of creating this guide, we found the necessity to extend the BCIO to support the scenarios of multiple clinical goals in the same application. The extension utilizes existing classes and properties where possible.

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Section: Develop Personalisation Methodsmentioning

confidence: 99%

Section: Select Bci Supporting Technologiesmentioning

confidence: 99%

What do you need to consider when designing mobile health intervention?

Lisowska¹,

Wilk²,

Peleg³

2022

Preprint

Self Cite

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“…Stress patterns in connection with social support networks of hospice care were shared by Guo et al [ 20 ]. Patient stress was classified with experiments from blood volume pulse by Lisowska et al [ 21 ]. The stress level with related aspects in cancer patients was discussed by Durangi et al [ 22 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Stress Estimation Model for the Sustainable Health of Cancer Patients

Adeel

Mehmood

Ullah

2022

Computational and Mathematical Methods in Medicine

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Good health is the most important and very necessary characteristic for stress-free, skillful, and hardworking people with a cooperative environment to create a sustainable society. Validating two algorithms, namely, sequential minimal optimization for regression (SMOreg) using vector machine and linear regression (LR) and using their predicted cancer patients’ cases, this study presents a patient’s stress estimation model (PSEM) to forecast their families’ stress for patients’ sustainable health and better care with early management by under-study cancer hospitals. The year-wise predictions (1998-2010) by LR and SMOreg are verified by comparing with observed values. The statistical difference between the predictions (2021-2030) by these models is analyzed using a statistical t -test. From the data of 217067 patients, patients’ stress-impacting factors are extracted to be used in the proposed PSEM. By considering the total population of under-study areas and getting the predicted population (2021-2030) of each area, the proposed PSEM forecasts overall stress for expected cancer patients (2021-2030). Root mean square error (RMSE) (1076.15.46) for LR is less than RSME for SMOreg (1223.75); hence, LR remains better than SMOreg in forecasting (2011-2020). There is no significant statistical difference between values (2021-2030) predicted by LR and SMOreg ( p value = 0.767 > 0.05 ). The average stress for a family member of a cancer patient is 72.71%. It is concluded that under-study areas face a minimum of 2.18% stress, on average 30.98% stress, and a maximum of 94.81% overall stress because of 179561 expected cancer patients of all major types from 2021 to 2030.

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“…The methods that assess stress levels by analysing physical reactions typically focus on observing changes in behaviour, body gestures [16], speech [17], eye activity [18][19][20] or facial expression [21,22]. On the other hand, methods from the second group measure the reactions not visible from the outside, i.e., reactions such as changes in cortisol level [21], heart activity [23], brain activity [24][25][26][27][28][29][30][31][32], muscle activity [33,34], blood activity [35,36], respiratory response [37] or sweating response [38,39].…”

Section: Introductionmentioning

confidence: 99%

Stressors Length and the Habituation Effect—An EEG Study

Rejer

Wacewicz

Schab

et al. 2022

Sensors

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The research described in this paper aimed to determine whether people respond differently to short and long stimuli and whether stress stimuli repeated over time evoke a habituation effect. To meet this goal, we performed a cognitive experiment with eight subjects. During this experiment, the subjects were presented with two trays of stress-inducing stimuli (different in length) interlaced with the main tasks. The mean beta power calculated from the EEG signal recorded from the two prefrontal electrodes (Fp1 and Fp2) was used as a stress index. The main results are as follows: (i) we confirmed the previous finding that beta power assessed from the EEG signal recorded from prefrontal electrodes is significantly higher for the STRESS condition compared to NON-STRESS condition; (ii) we found a significant difference in beta power between STRESS conditions that differed in length—the beta power was four times higher for short, compared to long, stress-inducing stimuli; (iii) we did not find enough evidence to confirm (or reject) the hypothesis that stress stimuli repeated over time evoke the habituation effect; although the general trends aggregated over subjects and stressors were negative, their slopes were not statistically significant; moreover, there was no agreement among subjects with respect to the slope of individual trends.

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Catching Patient’s Attention at the Right Time to Help Them Undergo Behavioural Change: Stress Classification Experiment from Blood Volume Pulse

Cited by 8 publications

References 18 publications

What do you need to consider when designing mobile health intervention?

What do you need to consider when designing mobile health intervention?

Stress Estimation Model for the Sustainable Health of Cancer Patients

Stressors Length and the Habituation Effect—An EEG Study

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