A personalized sensor support tool for the training of mindful walking

Pryss, Rüdiger; Reichert, Manfred; John, Dennis; Frank, Julian; Schlee, Winfried; Probst, Thomas

doi:10.1109/bsn.2018.8329672

Cited by 13 publications

(6 citation statements)

References 23 publications

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“…Especially in healthcare and medicine, mobile apps are a basis for new data sources and medical insights [2]- [4]. In addition to the sophisticated collection possibilities of data, mobile apps can be utilized to guide and inform patients about health conditions and questions as well as health-related day-by-day issues [5], [6]. Interestingly, both in research and industry, less efforts have been undertaken to evaluate the quality and evidence of mobile health apps.…”

Section: Introductionmentioning

confidence: 99%

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

Stach

Probst

Meßner

et al. 2020

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)

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The utilization of mobile technology in the field of medicine and healthcare has become a decisive aspect. The entire field is denoted as mobile health (mHealth). For mHealth, the development and use of mobile applications are crucial. The purposes and goals of mHealth apps, in turn, are manifold. As a consequence, a plethora of mHealth apps can be found in the app stores. Interestingly, for patients, users, and health care providers that consider to use mHealth apps one aspect has been less pursued so far: Systematic and standardized ways that help about the quality of an app or its medical evidence are mainly missing. The Mobile App Rating Scale (MARS) is a standardized instrument that aims at the systematic and comparable evaluation of the quality of mobile health apps as well as categorizing their goals and functions. It comprises 23 items, which are utilized to calculate a rating scale. Having MARS in mind, a database was developed that is called Mobile Health App Database (MHAD). The latter offers technical features to systematically utilize the MARS for researchers as well as clinicians and end-users that (i) want to evaluate apps as well as (ii) want an interactive and easy-to-use web interface that shows the results of the rating procedure. MHAD comprises a rating platform that supports the conduction of MARS ratings and their release process. With the information platform, a web application was developed that prepares the data stored in the rating platform for being freely viewed and studied by users, patients, and health care providers. The goal of MHAD constitutes to be an open science repository that encourages researchers to release their MARS ratings to a broader audience. Such repositories become more and more important in many fields, especially in the field of mHealth.

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Section: Introductionmentioning

confidence: 99%

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

Stach

Probst

Meßner

et al. 2020

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)

Self Cite

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“…Being more aware of stressors in daily life might help individuals with high perceived stress reactivity to adapt their stress responses in the long run [29]. Thus, linking TYS to ecological momentary interventions (EMIs) [30] such as mobile apps for training mindfulness might be a fruitful avenue for future stress research [31][32][33][34].…”

Section: Principal Findingsmentioning

confidence: 99%

Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study

Pryss¹,

John²,

Schlee³

et al. 2019

JMIR Mhealth Uhealth

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Background The mobile phone app, TrackYourStress (TYS), is a new crowdsensing mobile health platform for ecological momentary assessments of perceived stress levels. Objective In this pilot study, we aimed to investigate the time trend of stress levels while using TYS for the entire population being studied and whether the individuals’ perceived stress reactivity moderates stress level changes while using TYS. Methods Using TYS, stress levels were measured repeatedly with the 4-item version of the Perceived Stress Scale (PSS-4), and perceived stress reactivity was measured once with the Perceived Stress Reactivity Scale (PSRS). A total of 78 nonclinical participants, who provided 1 PSRS assessment and at least 4 repeated PSS-4 measurements, were included in this pilot study. Linear multilevel models were used to analyze the time trend of stress levels and interactions with perceived stress reactivity. Results Across the whole sample, stress levels did not change while using TYS (P=.83). Except for one subscale of the PSRS, interindividual differences in perceived stress reactivity did not influence the trajectories of stress levels. However, participants with higher scores on the PSRS subscale reactivity to failure showed a stronger increase of stress levels while using TYS than participants with lower scores (P=.04). Conclusions TYS tracks the stress levels in daily life, and most of the results showed that stress levels do not change while using TYS. Controlled trials are necessary to evaluate whether it is specifically TYS or any other influence that worsens the stress levels of participants with higher reactivity to failure.

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“…Ambient Walk is designed to use audio-visual interaction as an interventional medium that provides novel means to foster mindfulness and relaxation. A similar mobile application has been proposed in [88]. This mobile tool supports mindful walking to reduce stress and to target such diseases as diabetes or depression.…”

Section: Emotions As Part Of Aal In Mobilitymentioning

confidence: 99%

Towards Truly Affective AAL Systems

Pudāne

Petroviča

Lavendelis

et al. 2019

Lecture Notes in Computer Science

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Affective computing is a growing field of artificial intelligence. It focuses on models and strategies for detecting, obtaining, and expressing various affective states, including emotions, moods, and personality related attributes. The techniques and models developed in affective computing are applicable to various affective contexts, including Ambient Assisted Living. One of the hypotheses for the origin of emotion is that the primary purpose was to regulate social interactions. Since one of the crucial characteristics of Ambient Assisted Living systems is supporting social contact, it is unthinkable to build such systems without considering emotions. Moreover, the emotional capacity needed for Ambient Assisted Living systems exceeds simple user emotion detection and showing emotion expressions of the system. In addition, emotion generation and emotion mapping on rational thinking and behavior of a system should be considered. The chapter discusses the need and requirements for these processes in the context of various application domains of Ambient Assisted Living, i.e., healthcare, mobility, education, and social interaction.

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A personalized sensor support tool for the training of mindful walking

Cited by 13 publications

References 23 publications

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study

Towards Truly Affective AAL Systems

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