A Biofeedback App to Instruct Abdominal Breathing (Breathing-Mentor): Pilot Experiment

Faust-Christmann, Corinna A.; Taetz, Bertram; Zolynski, Gregor; Zimmermann, Tobias; Bleser, Gabriele

doi:10.2196/13703

Cited by 15 publications

(15 citation statements)

References 34 publications

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“…The mHealth platform can enhance the delivery of exercise maneuvers through improvements in training accuracy, economic efficiency, safety continuity, and self-management compared with traditional clinician-to-client intervention [ 63 - 66 ]. In this study, we used a biofeedback system involving a mirror function (in effortful prolonged swallowing and effortful tongue rotation) and a real-time pitch graph (in effortful pitch glide), preventing improper or incorrect performance, which may counteract the purpose of the maneuvers and become entrenched [ 67 , 68 ].…”

Section: Discussionmentioning

confidence: 99%

Implementation of a Home-Based mHealth App Intervention Program With Human Mediation for Swallowing Tongue Pressure Strengthening Exercises in Older Adults: Longitudinal Observational Study

Kim¹,

Cho²,

Kim³

et al. 2020

JMIR Mhealth Uhealth

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Background Tongue pressure is an effective index of swallowing function, and it decreases with aging and disease progression. Previous research has shown beneficial effects of swallowing exercises combined with myofunctional tongue-strengthening therapy on tongue function. Tongue exercises delivered through mobile health (mHealth) technologies have the potential to advance health care in the digital age to be more efficient for people with limited resources, especially older adults. Objective The purpose of this study is to explore the immediate and long-term maintenance effects of an 8-week home-based mHealth app intervention with biweekly (ie, every 2 weeks) human mediation aimed at improving the swallowing tongue pressure in older adults. Methods We developed an mHealth app intervention that was used for 8 weeks (3 times/day, 5 days/week, for a total of 120 sessions) by 11 community-dwelling older adults (10 women; mean age 75.7 years) who complained of swallowing difficulties. The app included a swallowing monitoring and intervention protocol with 3 therapy maneuvers: effortful prolonged swallowing, effortful pitch glide, and effortful tongue rotation. The 8-week intervention was mediated by biweekly face-to-face meetings to monitor each participant’s progress and ability to implement the training sessions according to the given protocol. Preintervention and postintervention isometric and swallowing tongue pressures were measured using the Iowa Oral Performance Instrument. We also investigated the maintenance effects of the intervention on swallowing tongue pressure at 12 weeks postintervention. Results Of the 11 participants, 8 adhered to the home-based 8-week app therapy program with the optimal intervention dosage. At the main trial end point (ie, 8 weeks) of the intervention program, the participants demonstrated a significant increase in swallowing tongue pressure (median 17.5 kPa before the intervention and 26.5 kPa after the intervention; P=.046). However, long-term maintenance effects of the training program on swallowing tongue pressure at 12 weeks postintervention were not observed. Conclusions Swallowing tongue pressure is known to be closely related to dysphagia symptoms. This is the first study to demonstrate the effectiveness of the combined methods of effortful prolonged swallowing, effortful pitch glide, and effortful tongue rotation using mobile app training accompanied by biweekly human mediation in improving swallowing tongue pressure in older adults. The mHealth app is a promising platform that can be used to deliver effective and convenient therapeutic service to vulnerable older adults. To investigate the therapeutic efficacy with a larger sample size and observe the long-term effects of the intervention program, further studies are warranted. International Registered Report Identifier (IRRID) RR2-10.2196/19585

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Section: Discussionmentioning

confidence: 99%

Implementation of a Home-Based mHealth App Intervention Program With Human Mediation for Swallowing Tongue Pressure Strengthening Exercises in Older Adults: Longitudinal Observational Study

Kim¹,

Cho²,

Kim³

et al. 2020

JMIR Mhealth Uhealth

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“…In this study, respiration counts were estimated based on data from an accelerometer sensor in a smartphone. During recent advances in the mobile medical monitoring environment, a variety of smart devices have been developed [ 2 , 3 ]. There is also an increasing desire to manage health by measuring and analyzing data using such smart devices [ 1 , 5 ].…”

Section: Discussionmentioning

confidence: 99%

“…Owing to the rapid recent development of mobile medical monitoring, an increasing number of people desire to manage their health through health care services in daily life. To provide information for health management, it is important to monitor biosignals constantly [ 1 , 2 ]. Among biosignals, respiration is the easiest to measure and provides various types of information about health management based on a number of respiration parameters and respiration patterns [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…To provide information for health management, it is important to monitor biosignals constantly [ 1 , 2 ]. Among biosignals, respiration is the easiest to measure and provides various types of information about health management based on a number of respiration parameters and respiration patterns [ 2 ]. Respiration data collected in daily life can be used as a health care index involving factors such as feedback for sleep apnea syndrome and other issues [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

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Respiration Rate Estimation Based on Independent Component Analysis of Accelerometer Data: Pilot Single-Arm Intervention Study

Lee¹,

Yoo²

2020

JMIR Mhealth Uhealth

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Background As the mobile environment has developed recently, there have been studies on continuous respiration monitoring. However, it is not easy for general users to access the sensors typically used to measure respiration. There is also random noise caused by various environmental variables when respiration is measured using noncontact methods in a mobile environment. Objective In this study, we aimed to estimate the respiration rate using an accelerometer sensor in a smartphone. Methods First, data were acquired from an accelerometer sensor by a smartphone, which can easily be accessed by the general public. Second, an independent component was extracted to calibrate the three-axis accelerometer. Lastly, the respiration rate was estimated using quefrency selection reflecting the harmonic component because respiration has regular patterns. Results From April 2018, we enrolled 30 male participants. When the independent component and quefrency selection were used to estimate the respiration rate, the correlation with respiration acquired from a chest belt was 0.7. The statistical results of the Wilcoxon signed-rank test were used to determine whether the differences in the respiration counts acquired from the chest belt and from the accelerometer sensor were significant. The P value of the difference in the respiration counts acquired from the two sensors was .27, which was not significant. This indicates that the number of respiration counts measured using the accelerometer sensor was not different from that measured using the chest belt. The Bland-Altman results indicated that the mean difference was 0.43, with less than one breath per minute, and that the respiration rate was at the 95% limits of agreement. Conclusions There was no relevant difference in the respiration rate measured using a chest belt and that measured using an accelerometer sensor. The accelerometer sensor approach could solve the problems related to the inconvenience of chest belt attachment and the settings. It could be used to detect sleep apnea through constant respiration rate estimation in an internet-of-things environment.

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“…Dysfunctional breathing patterns affect 9.5% of the general population [ 7 ], increasing to 30% in the asthma population and 75% in the anxiety population [ 8 ]. It is possible to manipulate breathing patterns during wakefulness using exercises that target the biochemistry, biomechanics and frequency [ 9 , 10 , 11 ]. In this way, the breath can be “trained” to restore nasal breathing, improve diaphragm function, slow the respiratory rate and increase tolerance to changes in arterial carbon dioxide (CO 2 ) pressure.…”

Section: Introductionmentioning

confidence: 99%

Breathing Re-Education and Phenotypes of Sleep Apnea: A Review

McKeown

O’Connor-Reina

Plaza

2021

JCM

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Four phenotypes of obstructive sleep apnea hypopnea syndrome (OSAHS) have been identified. Only one of these is anatomical. As such, anatomically based treatments for OSAHS may not fully resolve the condition. Equally, compliance and uptake of gold-standard treatments is inadequate. This has led to interest in novel therapies that provide the basis for personalized treatment protocols. This review examines each of the four phenotypes of OSAHS and explores how these could be targeted using breathing re-education from three dimensions of functional breathing: biochemical, biomechanical and resonant frequency. Breathing re-education and myofunctional therapy may be helpful for patients across all four phenotypes of OSAHS. More research is urgently needed to investigate the therapeutic benefits of restoring nasal breathing and functional breathing patterns across all three dimensions in order to provide a treatment approach that is tailored to the individual patient.

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A Biofeedback App to Instruct Abdominal Breathing (Breathing-Mentor): Pilot Experiment

Cited by 15 publications

References 34 publications

Implementation of a Home-Based mHealth App Intervention Program With Human Mediation for Swallowing Tongue Pressure Strengthening Exercises in Older Adults: Longitudinal Observational Study

Implementation of a Home-Based mHealth App Intervention Program With Human Mediation for Swallowing Tongue Pressure Strengthening Exercises in Older Adults: Longitudinal Observational Study

Respiration Rate Estimation Based on Independent Component Analysis of Accelerometer Data: Pilot Single-Arm Intervention Study

Breathing Re-Education and Phenotypes of Sleep Apnea: A Review

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