Name: Custom Smartphone Application to Guide Locomotor-Respiratory Coupling in the Field Using Step-Adaptive Breathing Sounds (Video) | JoVE Journal
Uploaded: 2024-09-27T13:10:00.000+00:00
Duration: 6 min 26 s
Description: Learn locomotor-respiratory coupling using the RunRhythm smartphone app with step-adaptive sound guidance. This method helps runners synchronize breathing with foot strikes to improve respiratory efficiency and running performance.

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This work was supported by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology under Contract No. 2021$$-$$0.641.557 and the federal state of Salzburg under the research program COMET-Competence Centers for Excellent Technologies-in the project Digital Motion in Sports, Fitness and Well-being (DiMo; Contract No. 872574).

This study was granted ethical approval by the Ethics Committee of the University of Salzburg (reference number: GZ 13/2021), and the participants gave their informed consent.
1. Getting started with RunRhythm
<ol>
	<li>Have the participants fill out a prequestionnaire to include demographic information, as well as running, sports, and breathing experience.</li>
	<li>Download the application to a functioning smartphone running Android 8.0 or later. 
	​NOTE: Closed user testing and application downloads are only accessible for selected users with Google Play accounts whose emails are added to the list of approved users. Testing is invite-only. Send access requests to the corresponding author.</li>
	<li>Confirm that the user has watched the tutorial before the first application use (Figure 1).</li>
</ol>
<img alt="RunRhythm app flowchart illustrating rhythm-guided running with breathing synchronization and control tips." class="xfigimg" src="/files/ftp_upload/66733/66733fig01.jpg" data-altupdatedat="1788304762000" data-alt="Figure 1"> 
Figure 1: App tutorial. RunRhythm provides an introductory tutorial upon its first opening, including details regarding locomotor-respiratory coupling, an animation showing how the application works, and tips for use. <a href="https://www.jove.com/files/ftp_upload/66733/66733fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Basic functionality
<ol>
	<li>Choose the desired soundscape using the horizontal toggle (Figure 2B). 
	NOTE: This can only be changed before the run.</li>
	<li>Change the desired LRC ratio (steps per inhale:steps per exhale) using the up or down toggles (Figure 2A); lock the toggle with the lock icon. When the screen is locked, use seek (<img alt="Transistor operation diagram, electronic circuit components, logic gate process, educational use." src="/files/ftp_upload/66733/66733eq01.jpg" style="margin: auto;" data-altupdatedat="1788304762000" data-alt="Equation 1">) from compatible headphones to change the ratio up and down. 
	NOTE: The ratio lock works to maintain a constant ratio difference when changing the ratio. For example, if 2:3 is locked, then toggling "up" will change the ratio to 3:4 (keeping 1 step more per exhale versus inhale).</li>
	<li>Change the temporality settings using the 3-position toggle on the interface (Figure 2C). When the screen is locked, use play and pause (<img alt="Play and pause button icons for audio or video controls." src="/files/ftp_upload/66733/66733eq02.jpg" style="margin: auto;" data-altupdatedat="1788304762000" data-alt="Equation 2">) from compatible headphones. 
	NOTE: Temporality can be toggled between full guidance (always on), medium guidance (1 min on/5 min off), and no guidance.</li>
	<li>Toggle the voiceover feature with the interface toggle at the bottom right (Figure 2D and 5A). </li>
	<li>Select start run to begin. When used for the first time, press allow to permit the device logging of movement and location.</li>
	<li>Complete the on-screen questionnaires before and after each run by pressing on the numbers corresponding to the current feeling experienced by the runner (Figure 3).</li>
</ol>
<img alt="Breathing app interface; rhythm ratio 3:4, soundscape selection, sound settings, audio toggle." class="xfigimg" src="/files/ftp_upload/66733/66733fig02v2.jpg" data-altupdatedat="1788304762000" data-alt="Figure 2"> 
Figure 2: Main app interface. (A) Locomotor-respiratory coupling ratio can be changed from the user interface with spinners that range from 2 to 9. Each value represents the number of steps per breath phase; i.e., 2:3 represents 2 steps per inhale: 3 steps per exhale. The lock icon can be used to fix the ratio difference; i.e., when locked at 2:3, shifting "up" changes the ratio to 3:4 (maintaining a difference of 1 step more per exhale). (B) Soundscape selection allows the user to choose from four predetermined sound layers: tribal, calming, electronic, and minimal. (C) Temporality toggle allows the user to choose from three predetermined settings for guidance frequency: full, medium, and off. (D) Voiceover toggle allows the user to turn voice cues on or off. <a href="https://www.jove.com/files/ftp_upload/66733/66733fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Exercise intensity self-assessment chart; fatigue, breathlessness, vitality ratings; mobile app interface." class="xfigimg" src="/files/ftp_upload/66733/66733fig03.jpg" data-altupdatedat="1788304762000" data-alt="Figure 3"> 
Figure 3: Pre and post questionnaires. Identical questionnaires are presented at the start and end of every run. They must be answered to start or finish the run. <a href="https://www.jove.com/files/ftp_upload/66733/66733fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Backend settings
NOTE: Key parameters affecting application functionality can be changed by tapping on the three dots in the top right corner of the main interface. The default values reflect the recommended values but can be changed. This screen (Figure 4) contains the following settings:
<img alt="Mobile app settings screen; sound tempo slider; GPS, audio, and breath feedback toggles." class="xfigimg" src="/files/ftp_upload/66733/66733fig04.jpg" data-altupdatedat="1788304762000" data-alt="Figure 4"> 
Figure 4: Backend settings. Backend settings include auto pause, a toggle for step detection, and identifier codes. (A) Sound tempo threshold settings allow precise selection of step rate thresholds that bound generated sound guidance tempo. For example, selecting a lower threshold of 155 and an upper threshold of 180 ensures that the sound guidance will not deviate from the interval [155, 180], regardless of the actual SR detected. The default is [0, 200]. <a href="https://www.jove.com/files/ftp_upload/66733/66733fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>Toggle GPS and GPX "on" to log run distance, running speed, and route tracking. 
	NOTE: When on, it logs global positioning system (GPS) coordinates during the run from the factory output from the device. When off, it performs no logging.</li>
	<li>Auto Pause: toggle "on" for best performance. 
	NOTE: When on, it stops GPS logging when the detected speed is near 0. When off, it continues logging regardless of speed.</li>
	<li>Toggle headphone controls "on" when using headphones with music controls. 
	NOTE: When on, this feature allows for compatible headphones (wired or wireless, with controls) to change key parameters during the run (see step 2.4). When off, this step disables the headphone controls from changing the application parameters.</li>
	<li>System step detection: change this setting when the application behaves strangely; that is, if the sound tempo does not match the step tempo. 
	NOTE: When on, this feature utilizes the factory SR algorithm of the device manufacturer for all application functionalities. When off, it uses the custom algorithm by the application developer. Since devices have different factory SR detection performance based on manufacturer parameters such as chipset, this can be toggled to improve application performance.</li>
	<li>Sound tempo: set the sound tempo sliders to the desired SR thresholds (Figure 4A). 
	NOTE: Set to a specific window (i.e., 160-180) to create a lower and/or upper limit on the tempo of the generated sounds. The minimum lower = 0 and maximum upper = 200.</li>
	<li>Min. Run Steprate: change this threshold to any integer 0-200 to change the SR 
	threshold used to activate the LRC guidance sounds. 
	NOTE: The application defaults to 120 as the SR at which "running" is detected and the sounds begin playing.</li>
	<li>Enable Breath Feedback: toggle "on" for core application functionality. 
	NOTE: When on, normal application functionality can be expected with step and breath sounds during running. When off, this setting prevents the creation of any breath sounds. This is used for testing purposes, or when only step sounds are desired.</li>
	<li>Restart Intro: click text to replay the onboarding tutorial.</li>
	<li>Data Protection: click text to display the application manufacturer's data protection statement.</li>
	<li>Identifiers
	<ol>
		<li>Installation ID: record this value, which represents the unique identifier for this device and application build. It is reflected in the log files generated after each run.</li>
		<li>Version: record this value, which represents the application build version.</li>
		<li>Manufacturer/model: record this value, which represents the device identifier for make and model.</li>
	</ol>
	</li>
</ol>
4. Running with the app
<ol>
	<li>Start running!
	<ol>
		<li>First, when the step sounds activate; listen closely and step to the beat. Then, the breathing guidance will be activated; match inspiration and expiration precisely to the sound.</li>
	</ol>
	</li>
	<li>In-run interface: during the run, change the LRC ratio and temporality using the toggles on the on-screen interface (Figure 5A).</li>
	<li>Post-run report: press end run to stop the application. A summary screen displays key metrics including total distance, average pace, and average step rate (Figure 5B).</li>
</ol>
<img alt="Running app interface showing steps, breath rate, and distance metrics for performance tracking." class="xfigimg" src="/files/ftp_upload/66733/66733fig05.jpg" data-altupdatedat="1788304762000" data-alt="Figure 5"> 
Figure 5: In and post-run interface. (A) During the run, a simplified interface is available that allows the user to change key parameters including locomotor-respiratory coupling ratio and temporality. It also displays the current running pace and step rate. (B) After the run, a summary screen displays key metrics including total distance, average pace, and average step rate. <a href="https://www.jove.com/files/ftp_upload/66733/66733fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
5. Research with the app
NOTE: The above functionalities were developed to maximize user experience and enable research studies of LRC across diverse contexts. The steps given below describe how to integrate the app into a study from initial familiarization to post run application logs. These study methods have been reviewed and approved for experimentation with humans by the ethics committee of Paris Lodron Universität Salzburg (EK-GZ 29/2023).
<ol>
	<li>Before discussing or instructing LRC with the app, perform an individual calibration to determine the runner's natural LRC ratio. Choose a running task that is representative of the activity and study context and record SR and BR during the trial. Calculate the nearest whole-integer LRC ratio from their quotient (SR/BR = LRC ratio; i.e., 160/30 = 5.3 = recommended ratio 5 steps per breath).</li>
	<li>Place the phone in a clothes pocket, waist pack, or armband that is firmly against the body.</li>
	<li>Use headphones during outdoor running to improve the delivery of the sound instructions and enable headphone controls. Alternatively, use a Bluetooth speaker during familiarization with a researcher to follow LRC Instructions together.</li>
	<li>Familiarize the runner with LRC and the app. 
	NOTE: While the app should not require familiarization with a researcher, for research purposes, it may be helpful for a researcher to explain LRC, the application, its purpose, and demonstrate its functionality. LRC should be introduced using the verbal cues of Coates and Kowalchik35.
	<ol>
		<li>Begin with rhythmic foot tapping while seated or supine, and instructions to breathe at a given ratio (usually 2:2) in sync with the feet.</li>
		<li>Repeat while walking.</li>
		<li>Repeat while running in place.</li>
		<li>Introduce the application and briefly describe the user interface.</li>
		<li>Fasten the device to the runner's body according to the above recommendations (section 7.2).</li>
		<li>Click start run and run in place.</li>
		<li>Encourage the runner to keep stepping at their own pace while the sounds adjust to their SR.</li>
		<li>Verbally confirm that the breathing sounds are also heard.</li>
		<li>Instruct the runner to breathe according to the sounds.</li>
		<li>Continue stepping and breathing with the sounds for at least 30-60 s.</li>
		<li>Repeat the process with different application settings (i.e., LRC ratios, temporality, voiceover) to confirm understanding.</li>
		<li>Have the researcher and runner run together playing the guidance sounds over a Bluetooth speaker to reinforce LRC fundamentals (Figure 6). 
		NOTE: The sounds trigger on as the application detects a running SR. The step sounds will automatically match the user's SR with some delay.</li>
	</ol>
	</li>
	<li>Use a minimally-invasive sensor setup that allows for step and breath detection to confirm the runner's adherence to the LRC instructions. 
	NOTE: For example, the Hexoskin smart shirt can accurately measure steps and breath onsets during running with minimal interference to the runner36.</li>
	<li>Temporality setting: use the medium guidance in combination with other music applications, allowing for the user's music to be played during the 5 min periods when the application guidance is silent. 
	NOTE: The no guidance setting still logs questionnaires, GPS, and SR, which may be helpful for some research purposes.</li>
	<li>Logs: download log files to access timestamped data of the run and application parameters. 
	NOTE: .csv log files record key run metadata and every interaction with the app, including: timestamped footstrikes, chosen LRC ratio and changes, and questionnaire responses. They are stored on GDPR-compliant servers and titled with the app unique ID, date, and time.</li>
</ol>
<img alt="Two people jogging on a snowy path, dressed in winter attire, emphasizing exercise in cold weather." class="xfigimg" src="/files/ftp_upload/66733/66733fig06.jpg" data-altupdatedat="1788304762000" data-alt="Figure 6"> 
Figure 6: Researcher familiarization. In research contexts, familiarization by the primary investigator is recommended to ensure a conceptual understanding of locomotor-respiratory coupling and proper use of the application. <a href="https://www.jove.com/files/ftp_upload/66733/66733fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Enhancing Running Performance and Comfort Using RunRhythm

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<li>De Ruiter, C. J., Van Oeveren, B., Francke, A., Zijlstra, P., Van Dieen, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Running+speed+can+be+predicted+from+foot+contact+time+during+outdoor+over+ground+running%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">Running speed can be predicted from foot contact time during outdoor over ground running.</a> PLoS One. 11 (9), e0163023(2016).</li>
<li>Adams, D., Pozzi, F., Willy, R. W., Carrol, A., Zeni, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Altering+cadence+or+vertical+oscillation+during+running%3A+Effects+on+running+related+injury+factors%5BTitle%5D)+AND+%22Int+J+Sports+Phys+Ther%22%5BJournal%5D)">Altering cadence or vertical oscillation during running: Effects on running related injury factors.</a> Int J Sports Phys Ther. 13 (4), 633-642 (2018).</li>
<li>Bood, R. J., Nijssen, M., Van Der Kamp, J., Roerdink, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+power+of+auditory-motor+synchronization+in+sports%3A+Enhancing+running+performance+by+coupling+cadence+with+the+right+beats%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">The power of auditory-motor synchronization in sports: Enhancing running performance by coupling cadence with the right beats.</a> PLoS One. 8 (8), e70758(2013).</li>
<li>Harbour, E., Van Rheden, V., Schwameder, H., Finkenzeller, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Step-adaptive+sound+guidance+enhances+locomotor-respiratory+coupling+in+novice+female+runners%3A+A+proof-of-concept+study%5BTitle%5D)+AND+%22Front+Sports+Act+Living%22%5BJournal%5D)">Step-adaptive sound guidance enhances locomotor-respiratory coupling in novice female runners: A proof-of-concept study.</a> Front Sports Act Living. 5, 1112663(2023).</li>
<li>Van Rheden, V., Harbour, E., Finkenzeller, T., Meschtscherjakov, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Into+the+rhythm%3A+Evaluating+breathing+instruction+sound+experiences+on+the+run+with+novice+female+runners%5BTitle%5D)+AND+%22Multimodal+Technol+Interact%22%5BJournal%5D)">Into the rhythm: Evaluating breathing instruction sound experiences on the run with novice female runners.</a> Multimodal Technol Interact. 8 (4), 25(2024).</li>
<li>Buchner, L., Amesberger, G., Finkenzeller, T., Moore, S. R., Wurth, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+modified+german+subjective+vitality+scale+%28svs-gm%29%3A+Psychometric+properties+and+application+in+daily+life%5BTitle%5D)+AND+%22Front+Psychol%22%5BJournal%5D)">The modified german subjective vitality scale (svs-gm): Psychometric properties and application in daily life.</a> Front Psychol. 13, 948906(2022).</li>
<li>Coates, B., Kowalchik, C. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aCoates%2C+B+%22Runner%27s+world+running+on+air%3A+The+revolutionary+way+to+run+better+by+breathing+smarter%22">Runner's world running on air: The revolutionary way to run better by breathing smarter</a>. , Rodale Books. (2013).</li>
<li>Harbour, E., Schwameder, H. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aHarbour%2C+E+%22Measuring+rhythmic+synchrony%3A+A+practical+guide%22">Measuring rhythmic synchrony: A practical guide</a>. , (2022).</li>
<li>Bramble, D. M., Carrier, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Running+and+breathing+in+mammals%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Running and breathing in mammals.</a> Science. 219 (4582), 251-256 (1983).</li>
<li>Abu-Hasan, M., Tannous, B., Weinberger, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Exercise-induced+dyspnea+in+children+and+adolescents%3A+If+not+asthma+then+what%5BTitle%5D)+AND+%22Ann+Allergy+Asthma+Immunol%22%5BJournal%5D)">Exercise-induced dyspnea in children and adolescents: If not asthma then what.</a> Ann Allergy Asthma Immunol. 94 (3), 366-371 (2005).</li>
<li>Weinberger, M., Abu-Hasan, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Perceptions+and+pathophysiology+of+dyspnea+and+exercise+intolerance%5BTitle%5D)+AND+%22Pediatr+Clin+North+Am%22%5BJournal%5D)">Perceptions and pathophysiology of dyspnea and exercise intolerance.</a> Pediatr Clin North Am. 56 (1), 33-48 (2009).</li>
<li>Dreher, M., Kabitz, H. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Impact+of+obesity+on+exercise+performance+and+pulmonary+rehabilitation%5BTitle%5D)+AND+%22Respirol%22%5BJournal%5D)">Impact of obesity on exercise performance and pulmonary rehabilitation.</a> Respirol. 17 (6), 899-907 (2012).</li>
<li>Ekström, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Obesity+is+a+major+contributing+cause+of+breathlessness+in+the+population%5BTitle%5D)+AND+%22Respirology%22%5BJournal%5D)">Obesity is a major contributing cause of breathlessness in the population.</a> Respirology. 28 (4), 303-304 (2023).</li>
<li>Hansen, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Exercise+tolerance+in+obese+vs.+Lean+adolescents%3A+A+systematic+review+and+meta-analysis%5BTitle%5D)+AND+%22Obes+Rev%22%5BJournal%5D)">Exercise tolerance in obese vs. Lean adolescents: A systematic review and meta-analysis.</a> Obes Rev. 15 (11), 894-904 (2014).</li>
<li>Statista. What running related activities do you use your phone/app on phone for. , 
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<li>Van Hooren, B., Goudsmit, J., Restrepo, J., Vos, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Real-time+feedback+by+wearables+in+running%3A+Current+approaches%2C+challenges+and+suggestions+for+improvements%5BTitle%5D)+AND+%22J+Sports+Sci%22%5BJournal%5D)">Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements.</a> J Sports Sci. 38 (2), 214-230 (2020).</li>
<li>Tipton, M. J., Harper, A., Paton, J. F., Costello, J. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+human+ventilatory+response+to+stress%3A+Rate+or+depth%5BTitle%5D)+AND+%22Physiol+J%22%5BJournal%5D)">The human ventilatory response to stress: Rate or depth.</a> Physiol J. 595 (17), 5729-5752 (2017).</li>
<li>Laborde, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Slow-paced+breathing%3A+Influence+of+inhalation%2Fexhalation+ratio+and+of+respiratory+pauses+on+cardiac+vagal+activity%5BTitle%5D)+AND+%22Sustainability%22%5BJournal%5D)">Slow-paced breathing: Influence of inhalation/exhalation ratio and of respiratory pauses on cardiac vagal activity.</a> Sustainability. 13 (14), 7775(2021).</li>
<li>Allado, E., Poussel, M., Hily, O., Chenuel, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+interest+of+rehabilitation+of+respiratory+disorders+in+athletes%3A+Myth+or+reality%5BTitle%5D)+AND+%22Ann+Phys+Rehabil+Med%22%5BJournal%5D)">The interest of rehabilitation of respiratory disorders in athletes: Myth or reality.</a> Ann Phys Rehabil Med. 65 (4), 101461(2022).</li>
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Ulf Jensen was employed by Adidas AG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

This methodology presents one of the first evidence-based, field-ready digital tools for instructing LRC to runners. Early results suggest that it is effective not only in quickly learning and adhering to LRC but can also be taught over time and retained. While LRC may emerge naturally with increased running experience, novices are less likely to perform it37. Coincidentally, they are especially likely to experience barriers to participation that include poor fitness and respiratory limitations<su...

Running is perhaps the most widely popular form of exercise in part due to its accessibility and range of physical and mental health benefits1,2. Nonetheless, many aspiring runners struggle to start or maintain running habits. This could be due to breathing difficulties, which affect an estimated 20-40% of runners3,4,5. Reducing exercise-induced breathlessness is theoretically possible with the use of specific breathing techniques, but the exact methods, risks, and benefits of doing so are unclear. While improving fitness and/or slow-paced breathing at rest may alleviate respiratory discomfort during exercise6,7, these solutions take weeks or months to realize benefits. Some research has speculated that the direct implementation of breathing techniques during exercise may be more effective at producing acute benefits8, although such investigations are limited. Digital tools that enable individualized instruction might be needed to perform such studies in an effective intervention format.
Locomotor-respiratory coupling (LRC) is a synchronization phenomenon in which breathing and movement are frequency- and/or phase-synchronized. In specific exercises such as running, LRC indicates a whole-integer ratio between breathing (BR) and step rate (SR), as well as phase locking of footstrike to breath onset (i.e., stepping exactly on inspiration). LRC can be expressed volitionally or automatically and may emerge as a learned behavior with exercise training9. Humans naturally synchronize gait with interfering auditory noises (including breathing) perhaps to reduce auditory stimulation, which suggests LRC has evolutionary phenomenology10. Some reports indicate that LRC benefits movement economy and performance, and reduces breathlessness11,12,13,14,15. Some studies reported negligible benefits16,17,18. Any physiological benefits could be related to &#34;step-driven flows&#34;: each footstrike creates downward momentum of the abdominal contents (the &#34;visceral piston&#34;), which when synchronized with the onset of inhale or exhale can be additive to ventilation.
Daley et al.19 measured ventilatory flow and impact forces during treadmill running and speculated that step-driven flows can contribute up to 10-12% to total ventilation. They also reported that it could speed up ventilatory transitions. Other neuromechanical mechanisms may interact with this phenomenon9. While the visceral piston is a result of precise phase coupling, frequency coupling may be independently valuable, especially to the novice runner. BR is closely related to effort across different exercise intensities20. As SR is generally stable and associated with running speed21, LRC may support self-awareness and enable easy pacing of BR and, thus, intensity throughout the run. Finally, LRC at uneven ratios (e.g., 5:1 steps per breath) could help to prevent exercise-related transient abdominal pain (&#34;side stitch&#34;). A majority of runners experience this temporary, but distracting and painful symptom each year22, often leading to their need to stop running. One theory of side stitch etiology is that repeated breathing on the same side footstrike may irritate the phrenic nerve. Thus, it might be avoided by LRC at uneven ratios, which leads to breathing on alternate legs.
Few reports have discussed how to support runners in performing LRC. At least two studies have exhibited biofeedback-style methods14,23 while many utilized simple verbal coaching24,25. While these methods have shown promise in stimulating LRC acutely, they are highly standardized and require specialized equipment. As such, they are likely not suitable for field applications, nor are they accessible to most runners. Regardless, sound guidance is a natural choice since humans intuitively synchronize movement to predictable auditory events (metronome or music)26. Applications should thus carefully consider sound tempo and structure in the context of motor learning. While simple, constant-tempo audio is predictable and efficacious for stimulating entrainment, it contradicts the naturally nonlinear behavior of step and respiratory rhythms in healthy runners27,28. Changing a runner&#39;s preferred SR might reduce running economy 29 or could modify injury risk factors30. Thus, sound instructions should be continuously adapted in real time to follow the runner&#39;s SR31.
We recently introduced a concept that integrates the above recommendations into a simple, user-friendly, custom smartphone application32. The first iteration allows for the selection of a single LRC ratio to be instructed throughout the run. The phone&#39;s stock SR algorithm is leveraged to provide real-time SR information to the application. Then, step-synchronized sounds are produced indicating when the runner should exhale and inhale: a high-pitched tone for steps during inhalation, and a low-pitched tone during expiration. Prescribed LRC ratios were derived from a control visit without breathing instruction. We found a large increase in LRC from 26.3 &#177; 10.7% to 69.9 &#177; 20.0% of the run with the application instruction during outdoor submaximal running. Limitations noted with the protocol and application include extensive familiarization required, limited sample size, and constant sound instruction. Hence, a new version of this application was developed to improve user experience and enable more broad testing and experimentation in field exercise. This application is titled RunRhythm since its intended purpose is to support runners in finding and maintaining a rhythm during running. It will be referred to hereafter as the app.
The purpose of this report is to introduce a new digital tool and methodological approach that enables intuitive and field-ready LRC guidance for research studies involving experienced or aspiring runners. The app is a research-grade application in beta testing for Android devices. The core functionalities of the application are SR detection and LRC guidance. When running is detected, breathing sounds are created according to the selected settings in the user interface. The application calculates SR from the phone accelerometer using one of two algorithms: either the factory SR algorithm implemented by the device manufacturer or a custom SR algorithm created by the application manufacturer. Both algorithms produce a constant livestream of SR, which is then smoothed on a moving average according to an adaptive window. The window size is dynamic to balance reactivity and outlier smoothing. The result is a constantly updated value of live SR.
Since the app calculates SR from the device movement, the placement of the phone on the body is of utmost importance. Most stock SR algorithms are position-agnostic, and therefore, can be placed on any part of the body during running to produce accurate SR values. The custom algorithm implemented here also behaves as such. However, a firm placement closer to the center of mass may improve the stability of the SR detection, and as a result, the sound quality produced by the app. Pilot testing shows that placements with 1-dimensional oscillation (i.e., vertically up and down such as in a chest pocket or waist pack) may perform better than those with 2-dimensional movement (i.e., swinging such as in a thigh pocket or armband).
SR data are fed to an integrated sound engine (see the Table of Materials). Step sounds are played only if the system detects SR &#62; 0. When the SR is above a preset threshold (determined in backend settings [protocol section 3.6]; i.e., 120), the application understands that the user is running and triggers the start of breathing guidance sounds. Then, this live SR value is used to set the tempo of the step and breathing guidance sounds as long as a &#34;running&#34; SR value is maintained. When SR &#62; threshold, the sounds generated match the tempo of SR by default. The exception is when the backend setting &#34;sound tempo&#34; is altered (determined in backend settings [protocol section 3.5]). For example, with a selected upper limit of 180, even if the runner begins running at a higher SR of 185, the sound tempo will not exceed 180. When they lower their SR down to 175, the sounds will lower to 175, continuously adjusting within the preset limits. As described in protocol step 3.5, these sliders allow the user or researcher to set limits on the minimum and maximum sound tempo (bpm). The app allows different LRC ratios (steps:breath) to be selected before or changed during the run. The number of steps per breath phase can be changed from 2 to 9; i.e., a 2:3 ratio reflects 2 steps per inhale and 3 steps per exhale.
Different &#34;soundscapes&#34; were designed to provide a pleasant audio experience to more runners with diverse music tastes based on user feedback and early in-lab experiments33. They have different sounds mapped to the real-time step rate, the instructed breathing phases, and background ambient noise. Step sounds are simple beats that play at the tempo of each footstrike (i.e., right and left steps). Breath sounds integrate several sonic elements and play at a much slower tempo depending on the chosen LRC ratio. The soundscapes available are tribal: organic and instrumental with sharp breathing transitions and step sounds; calming: light and ocean-inspired with smooth transitions and step sounds; energizing: electronic and driving with sharp transitions and step sounds; minimal: simple and smooth with only breath sounds (no step sounds).
The voiceover feature adds simple voice cues corresponding to best-practice research findings regarding LRC familiarization. It provides a series of instructions at the start of the run and then every 5 min after that. First, it states the selected LRC ratio. Then, it states the intended breathing phase in sync with the sound cues for the first three breath cycles. Then it reminds the user, &#34;find your step rate, and step to the beat.&#34; For each run, a pre and post run questionnaire is integrated to add subjective feeling data to each run. The subjective vitality short scale34 asks a single item regarding the runner&#39;s feeling from 0 to 10. A 0-10 rating of the fatigue scale asks the user to rate their current state of fatigue. Finally, a 0-10 scale rates the degree of breathlessness currently experienced. All of these scales are asked before and after each run. Only after the run, the user is asked to rate their experience of the run&#39;s intensity (i.e., light, medium, high, intervals). Users can change the LRC ratio and temporality during the run using the on-screen interface or headphone controls. This may help users feel agency during the run and enables the exploration of personal suitability. In addition, the ratio may need to be quickly changed in response to running events (e.g., hills, fatigue). This protocol includes a description of how to run with the app and later recommendations for its use within research protocols of various types (i.e., indoor, outdoor, interventional, cross-sectional).

<table><tbody><tr><td>Android smartphone</td><td>Samsung or Google</td><td></td><td>Minimum Android 8.0 required for application functionality</td></tr><tr><td>FMOD engine&amp;nbsp;</td><td>Firelight Technologies Pty Ltd</td><td></td><td>Sound engine</td></tr><tr><td>Hexoskin smart shirt&amp;nbsp;</td><td>Carr&amp;eacute; Technologies</td><td></td><td>Wearable sensor shirt</td></tr><tr><td>RunRhythm application for Android&amp;nbsp;</td><td>adidas GmbH and abios GmbH</td><td></td><td></td></tr></tbody></table>

custom smartphone application to guide locomotor-respiratory coupling in the field using step-adaptive breathing sounds

Custom Smartphone Application to Guide Locomotor-Respiratory Coupling in the Field Using Step-Adaptive Breathing Sounds

While running is amongst the most popular activities for competition and leisure, an estimated 20-40% of runners may suffer from respiratory limitations. Some of these runners may benefit from breathing techniques to improve performance or alleviate respiratory discomfort. One such technique is locomotor-respiratory coupling (LRC), a frequency and phase synchronization of breath to step. Studies have demonstrated that LRC may benefit ventilatory efficiency via "step-driven flows," and some experts have argued it could be used for pacing exercise or increasing positive emotional states. Nevertheless, it may be difficult to perform without coaching or guidance. Here we propose RunRhythm, a custom smartphone application to deliver step-synchronized sound guidance for LRC. This concept builds on previous evidence that sound guidance can be effective and integrates features to maximize adherence and individualization. Preliminary results show that this application is a promising and efficacious method suitable for research on LRC in field exercise. Recommendations for use and further development are discussed to further develop this concept for the benefit of a wider population.

The app is the second iteration of this application designed for the purpose of supporting LRC and delivering an audio breathing guidance experience. Numerous pilot studies and one journal publication have been performed supporting its efficacy and confirming positive user experience. In a cross-sectional study investigating the acute effects of LRC instruction (mentioned in the Introduction), it was found that running with guidance greatly increased LRC in 17 novice runners32. For example, one pa...

Locomotor-respiratory coupling (LRC) is potentially advantageous for runners but may be difficult to perform. We introduce a custom solution implemented on a smartphone to individualize and guide runners toward LRC.

While running is amongst the most popular activities for competition and leisure, an estimated 20-40% of runners may suffer from respiratory limitations. ...

custom-smartphone-application-to-guide-locomotor-respiratory-coupling

Research

JoVE Journal

Biology

The present protocol provides a detailed step-by-step description of the procedures required to execute measurements of respiratory system mechanics as well as the assessment of airway responsiveness to inhaled methacholine in mice using the forced oscillation technique (flexiVent; SCIREQ Inc, Montreal, Qc, Canada).

Evaluation of Respiratory System Mechanics in Mice using the Forced Oscillation Technique

Amplitude-based optimal respiratory gating (ORG) effectively removes respiratory-induced motion blurring from clinical 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images. Correction of FDG-PET images for these respiratory motion artefacts improves image quality, diagnostic and quantitative accuracy. Removal of respiratory motion artefacts is important for adequate clinical management of patients using PET.

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm

As the use of forced oscillation technique (FOT) is increasingly utilized to characterize respiratory mechanics, there is a need to standardize methods with respect to nascent technical guidelines and various manufacturer's recommendations. A detailed protocol is provided including FOT assessment and interpretation for two cases to facilitate the standardization of methods.

Employing the Forced Oscillation Technique for the Assessment of Respiratory Mechanics in Adults

The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

We demonstrate a standard operating protocol to conduct respiratory oscillometry, highlighting key quality control and assurance procedures.

Conducting Respiratory Oscillometry in an Outpatient Setting

This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and specificity.

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

This study effectively accomplished the automated classification of two distinct categories by acquiring cough sound data from patients diagnosed with chronic obstructive pulmonary disease (COPD) and respiratory tract infections (RTI), utilizing an integration of speech signal processing techniques and machine learning algorithms.

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

M3-BREATHE is a mobile health (mHealth) system that integrates wearable sensors to monitor respiration, behaviour, and environmental exposures for personalized treatment and health evaluation in the real world.

Real-World M3-BREATHE: Toward Multimodal Mobile Monitoring of Behaviour, Respiration, and Exposures for Treatment and Health Evaluation

A simple procedure of performing custom microRNA microarray experiments is described. The steps include isolating RNA, labeling RNA and reference DNA, hybridizing the samples to microarrays, scanning the microarrays, quantifying and analyzing hybridization signals.

Performing Custom MicroRNA Microarray Experiments

To study the relationship between protein homeostasis, stress and aging, we monitored changes in protein folding by following protein dysfunction, protein localization in the cell and protein stability at the organismal, cellular and protein levels, using the genetically tractable metazoan Caenorhabditis elegans as a model system.

Using Caenorhabditis elegans as a Model System to Study Protein Homeostasis in a Multicellular Organism

Learn locomotor-respiratory coupling using the RunRhythm smartphone app with step-adaptive sound guidance. This method helps runners synchronize breathing with foot strikes to improve respiratory efficiency and running performance.

Custom Smartphone Application to Guide Locomotor-Respiratory Coupling in the Field Using Step-Adaptive Breathing Sounds

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