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. 
	&#8203;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="Figure 1" class="xfigimg" src="/files/ftp_upload/66733/66733fig01.jpg" /> 
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.remotexs.ntu.edu.sg/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="Equation 1" src="/files/ftp_upload/66733/66733eq01.jpg" style="margin: auto;" />) 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 &#34;up&#34; 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="Equation 2" src="/files/ftp_upload/66733/66733eq02.jpg" style="margin: auto;" />) 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&#160;5A).&#160;</li>
	<li>Select&#160;start run&#160;to begin. When used for the first time, press&#160;allow&#160;to permit the device logging of movement and location.</li>
	<li>Complete the on-screen questionnaires before and after each run by&#160;pressing on the numbers&#160;corresponding to the current feeling experienced by the runner (Figure 3).</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66733/66733fig02v2.jpg" /> 
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 &#34;up&#34; 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)&#160;Voiceover toggle allows the user to turn voice cues on or off. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66733/66733fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66733/66733fig03.jpg" /> 
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.remotexs.ntu.edu.sg/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="Figure 4" class="xfigimg" src="/files/ftp_upload/66733/66733fig04.jpg" /> 
Figure 4: Backend settings. Backend settings include auto pause, a toggle for step detection, and identifier codes. (A)&#160;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.remotexs.ntu.edu.sg/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 &#34;on&#34; to log run&#160;distance, running speed, and route&#160;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 &#34;on&#34; 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 &#34;on&#34; 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&#160;guidance sounds. 
	NOTE: The application defaults to 120 as the SR at which &#34;running&#34; is detected and the sounds begin playing.</li>
	<li>Enable Breath Feedback: toggle &#34;on&#34; 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&#39;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="Figure 5" class="xfigimg" src="/files/ftp_upload/66733/66733fig05.jpg" /> 
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)&#160;After the run, a summary screen displays key metrics including total distance, average pace, and average step rate. <a href="https://www-jove-com.remotexs.ntu.edu.sg/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 Universit&#228;t Salzburg (EK-GZ 29/2023).
<ol>
	<li>Before discussing or instructing LRC with the app, perform an individual calibration to determine the runner&#39;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&#39;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&#39;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&#39;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&#39;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="Figure 6" class="xfigimg" src="/files/ftp_upload/66733/66733fig06.jpg" /> 
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.remotexs.ntu.edu.sg/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

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 limitations38,39. Other populations such as obese individuals have even more potential benefits since they experience increased work of breathing and soft-tissue bouncing40,41,42; this dual problem may be addressed via LRC. LRC may help these individuals to run with less discomfort, and digital tools such as this application are accessible modes to deliver it to a larger number of such runners. It is plausible that LRC, and hence, this app could be additionally valuable in contexts that present additional respiratory challenges such as hypoxia, thoracic load carriage, and ultramarathon.
The app utilizes sound guidance to instruct LRC because most runners listen to audio while running43, rhythmic audio motivates and reduces fatigue during exercise31, and it is minimally invasive to other critical senses (i.e., vision). Laboratory studies on LRC have utilized sound cues with a metronome14, which does not consider the natural variability and complexity of gait dynamics during running. Another study used haptic guidance during field running; they reported low adherence to LRC instruction around 26%44. Considering the relatively higher adherence reported here (Figure 8 and Figure 9), it is suspected that auditory guidance is preferable.
While the app does detect and provide live feedback for SR, the BR guidance to specific LRC ratios is predetermined by the application settings. It neither senses nor adjusts based on the user&#39;s actual BR. While an individual calibration can be performed to approximate the appropriate LRC ratio choice (protocol section 7.1), without BR sensing, there is no mechanism to confirm adherence or adjust the instruction accordingly. The addition of a BR sensor would improve the application in these ways. For example, a recent review suggests that continuous feedback may have negative effects on learning and habituation45. Other strategies such as bandwidth (guidance only when the individual is outside of a predefined success threshold) or self-determined feedback may be most effective to support learning and self-efficacy45. Finally, the addition of a BR sensor could also provide feedback on phase coupling. Since this element of LRC is central to the visceral piston mechanism and resultant physiological benefits, it should be considered for future feedback applications.
The LRC individualization is essential to effective research applications. Performing this individualization ensures that LRC instruction does not substantially change the runner&#39;s BR from their natural one. Alternatively, if changing BR is desired, it enables the user to make changes more sensitive to their typical physiological state. This individual calibration should be done context-specific since BR is trait and state-dependent. For example, BR is typically elevated at higher relative exercise intensities20; hence, the natural LRC ratio may be different during long, slow duration running versus high-intensity threshold running. It may also be affected by factors like environment (indoor vs. outdoor, cold vs. hot), time of day, and biological sex46. Performing this calibration requires measurement systems that can estimate BR and SR synchronously. This can be done with standard sports science equipment such as spiroergometry and accelerometers. In the field, wearable sensors have demonstrated their potential to measure these parameters accurately36. BR and SR can be averaged over a given period to estimate the approximate LRC ratio. When appropriate, data may be trimmed to specific phases within the run since physiological demands (thus BR) and speed (thus SR) may vary. Consequently, the &#34;natural&#34; LRC ratio may vary within a run.
Specific elements of this application should be updated to improve user interaction. While some parameters can be changed with headphone controls, others (such as soundscape) cannot be. One might consider integrating gestures or smartwatch integration for easier interaction. In addition, the next iterations might consider some form of instruction &#34;reset&#34; to use when the runner loses their rhythm. Natural breathing interruptions such as coughing or swallowing, or simple distraction, might cause a runner to become unsynchronized with the guidance. Instead of holding the breath to wait for the next LRC cycle to come, a button, voice command, or sensor-detected event could be used to resynchronize the sound to the runner. Finally, since a majority of runners listen to music43, the app may detract from runners&#39; experience since they cannot listen to their favorite music and receive LRC guidance on &#34;full.&#34; Integrating LRC guidance into music itself might allow for an intuitive and natural experience to deliver breathing guidance and enjoy one&#39;s preferred music. This is not yet integrated into the application.
Some limitations to this method have already been identified in testing thus far. First, it is possible that the step sounds pull the runner away from their typical SR. While the step sonification only reflects the runner&#39;s real-time SR, the moving averaging implies that the sonified SR is initially lower than the runner&#39;s immediate SR. Thus, it may be valuable for the user to ignore the step sounds until they have established their own preferred SR for some seconds. Second, related to this previous point the step sounds are automatically played at the user&#39;s current SR, but they are not necessarily phase-synchronized with footstrike. Hence, it is possible that the user&#39;s SR matches the sonified SR but in antiphase coordination. This may result in undesirable LRC dynamics since the user may lose phase-synchronization of step and breath. This can be corrected with researcher/expert guidance observing the user with the audible guidance sounds. Next, the user selection of LRC ratios is desired for flexibility to diverse runners&#39; needs; however, it could lead to the accidental selection of undesirable ratios. For example, 2:2 may be an appropriate ratio for moderate and high-intensity running but may lead to hyperventilation at lower relative intensities. Highly unequal ratios (e.g., 3:6) may be useful for cueing long exhales, while the reverse (6:3) is undesirable for its excessively short exhale phase47. Finally, it is acknowledged that an application for only the Android platform is inherently limited since many mobile users prefer other platforms. Pilot testing revealed that the application performs as intended only on updated modern mobile devices.
We hope to see this application used in intervention studies of runners who might benefit most greatly from breathing guidance. Novices, females, adolescents, and those who experience exercise-induced breathlessness might experience the greatest benefits from LRC guidance8,48. Experimental methods might be enhanced with the use of the &#34;sound tempo&#34; feature to support desired pacing strategies. While untested thus far, other cyclic endurance activities (e.g., cycling, cross-country skiing) might benefit from LRC guidance such as that provided by the app. More work is needed to assess the suitability of this solution for such applications.

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

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 participant increased LRC &#34;attachment&#34; (a quantification of adherence to whole-integer LRC ratios) from 7.9% to 84.1% (figure) at a ratio of 2:3 steps per inhale:exhale (5:1 steps per breath) (Figure 7). The overall group change in entrainment increased from 26.3 &#177; 10.7% (control) to 69.9 &#177; 20.0% (with LRC guidance).
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/66733/66733fig07.jpg" /> 
Figure 7: Example coupling data. Example of LRC prevalence in one participant during a 30 min easy run (A) without and (B) with app guidance. The left y-axis (blue) displays LRC ratio (steps per breath), and highlighted sections in black correspond to a period of LRC &#34;attachment&#34; &#62; 30 s. The right y-axis displays the discrete relative phase of steps within the breath cycle, where 0&#176; corresponds to inspiration. During the attachment periods, note the parallel lines in the discrete relative phase reflect consistent phase coupling of breath to step. (A) In the control run without guidance, note there was only a brief period (~8% attached) of LRC to a ratio of 2:2 (4 steps per breath). (B) In the run with app guidance, a ratio of 2:3 (5 steps per breath) was instructed; note the substantial, long periods of LRC to the instructed ratio (~84% attached). Abbreviation: LRC = locomotor-respiratory coupling. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66733/66733fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In a pilot intervention study, four novice runners ran for 4 weeks with the application (total 10 runs) and instructions to test various LRC ratios. They completed pre and post tests indoors (incremental treadmill test to exhaustion) and outdoors (self-paced 30 min run) to determine how their LRC and other running characteristics changed with the intervention and application use. More specifically, the pre and post tests were completed without LRC guidance or instruction to observe the natural, habituated LRC behavior before and after the intervention. We found marked differences in LRC behavior after the intervention, including not only an increase in overall LRC but also utilization of various ratios and a general reduction in exercise BR. For example, one participant increased LRC from 39.7% to 53.4% after the intervention (Figure 8). Notably, while in the pretest, they performed LRC exclusively at ratios of 2:2 and 2:1 near maximum intensity, in the posttest they used LRC ratios 3:4 and 3:3 early and throughout the middle of the test. Not only were they coupling more, but they were utilizing different ratios at different times, resulting in lower average BR and also increased ventilatory efficiency (VE/VCO2 slope). Other participants had similar changes in LRC (+40% outdoors, +20% indoors), and the one participant in the control condition (no LRC guidance) saw no change (&#60;25% overall, considered nonsignificant). There were some individual trends in subjective vitality response to exercise pre vs. post-intervention showing favorable increases in vitality with LRC (Figure 9).
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/66733/66733fig08.jpg" /> 
Figure 8: Example coupling data 2. Example of LRC prevalence in one participant during a maximal incremental treadmill test to exhaustion (A) before and (B) after a 4 week intervention running with the app. The left y-axis (blue) displays the LRC ratio (steps per breath), and highlighted sections in black correspond to a period of LRC &#34;attachment&#34; &#62; 30 s. The right y-axis displays the discrete relative phase of steps within the breath cycle, where 0&#176; corresponds to inspiration. During the attachment periods, note the parallel lines in the discrete relative phase reflect consistent phase coupling of breath to step. (A) In the pre-test before the intervention, note the two periods (~40% attached) of LRC to a ratio of 2:2 (4 steps per breath) and 2:1 (3 steps per breath) near the end of the test. (B) In the post-test after the intervention, not only were there longer periods of LRC (~53% attached), but longer ratios were used (4:4, 3:4, and 3:3), and more LRC was performed earlier in the test at lower relative exercise intensities. Abbreviation: LRC = locomotor-respiratory coupling. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66733/66733fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/66733/66733fig09.jpg" /> 
Figure 9: Individual subjective vitality scores recorded throughout the indoor running test pre and post-intervention. P01 and p03 received LRC guidance, while p04 was in the control group. The other participant (p02) has faulty questionnaire data (not shown in the figure). Note the diverging scores for p01 and p03, where the post test trends show increasing vitality after the run. Abbreviation: LRC = locomotor-respiratory coupling. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66733/66733fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Author Spotlight: Exploring Breathing Techniques and Digital Solutions for Enhancing Running Performance

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.

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. ...

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

Nanyang Technological University

Research

JoVE Journal

Biology

317 Views.  Paris Lodron University of Salzburg. 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.

Video: Author Spotlight: Exploring Breathing Techniques and Digital Solutions for Enhancing Running Performance

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Behavior

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

Positron emission tomography (PET) combined with X-ray computed tomography (CT) is an important molecular imaging platform that is required for accurate diagnosis and clinical staging of a variety of diseases. The advantage of PET imaging is the ability to visualize and quantify a myriad of biological processes in vivo with high sensitivity and accuracy. However, there are multiple factors that determine image quality and quantitative accuracy of PET images. One of the foremost factors influencing image quality in PET imaging of the thorax and upper abdomen is respiratory motion, resulting in respiration-induced motion blurring of anatomical structures. Correction of these artefacts is required for providing optimal image quality and quantitative accuracy of PET images.
Several respiratory gating techniques have been developed, typically relying on acquisition of a respiratory signal simultaneously with PET data. Based on the respiratory signal acquired, PET data is selected for reconstruction of a motion-free image. Although these methods have been shown to effectively remove respiratory motion artefacts from PET images, the performance is dependent on the quality of the respiratory signal being acquired. In this study, the use of an amplitude-based optimal respiratory gating (ORG) algorithm is discussed. In contrast to many other respiratory gating algorithms, ORG permits the user to have control over image quality versus the amount of rejected motion in the reconstructed PET images. This is achieved by calculating an optimal amplitude range based on the acquired surrogate signal and a user-specified duty cycle (the percentage of PET data used for image reconstruction). The optimal amplitude range is defined as the smallest amplitude range still containing the amount of PET data required for image reconstruction. It was shown that ORG results in effective removal of respiration-induced image blurring in PET imaging of the thorax and upper abdomen, resulting in improved image quality and quantitative accuracy.

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

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.

Positron emission tomography (PET) combined with X-ray computed tomography (CT) is an important molecular imaging platform that is required for accurate ...

Cancer Research

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

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the Healthy Landscape and Healthy People Lab, National Taiwan University (HLHP-NTU), on smartphones and smartwatches to collect data on users' real-time psychological and physiological responses and environmental information. A flexible and integrated research method was proposed because it can be difficult to measure multi-dimensional aspects of personal data in on-site studies in landscape and outdoor recreation research. An on-site study conducted in 2020 at the National Taiwan University campus was used as an application example. A dataset of 385 participants was used after excluding invalid samples. During the experiment, participants were asked to walk around campus for 30 min when their heart rate and psychological-scale items were measured, together with several environmental metrics. This work aimed to provide a possible solution to help on-site studies track real-time human responses that match ambient factors. Due to the app's flexibility, its use on wearable devices shows excellent potential for multidisciplinary research studies.

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.

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the ...

Environment

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

The fields that develop methods for sensory substitution and sensory augmentation have aimed to control external goals using signals from the central nervous systems (CNS). Less frequent however, are protocols that update external signals self-generated by interactive bodies in motion. There is a paucity of methods that combine the body-heart-brain biorhythms of one moving agent to steer those of another moving agent during dyadic exchange. Part of the challenge to accomplish such a feat has been the complexity of the setup using multimodal bio-signals with different physical units, disparate time scales and variable sampling frequencies.
In recent years, the advent of wearable bio-sensors that can non-invasively harness multiple signals in tandem, has opened the possibility to re-parameterize and update the peripheral signals of interacting dyads, in addition to improving brain- and/or body-machine interfaces. Here we present a co-adaptive interface that updates efferent somatic-motor output (including kinematics and heart rate) using biosensors; parameterizes the stochastic bio-signals, sonifies this output, and feeds it back in re-parameterized form as visuo/audio-kinesthetic reafferent input. We illustrate the methods using two types of interactions, one involving two humans and another involving a human and its avatar interacting in near real time. We discuss the new methods in the context of possible new ways to measure the influences of external input on internal somatic-sensory-motor control.

We present protocols and methods of analyses to build co-adaptive interfaces that stream, parameterize, analyze, and modify human body and heart signals in close-loop. This setup interfaces signals derived from the peripheral and central nervous systems of the person with external sensory inputs to help track biophysical change.

The fields that develop methods for sensory substitution and sensory augmentation have aimed to control external goals using signals from the central ...

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

There is increasing interest in the use of the forced oscillation technique (FOT) or oscillometry to characterize respiratory mechanics in healthy and diseased individuals. FOT, a complementary method to traditional pulmonary function testing, utilizes a range of oscillatory frequencies superimposed on tidal breathing to measure the functional relationship between airway pressure and flow. This passive assessment provides an estimate of respiratory system resistance (Rrs) and reactance (Xrs) that reflect airway caliber and energy storage and dissipation, respectively. Despite the recent increase in popularity and updated Technical Standards, clinical adoption has been slow which relates, in part, to the lack of standardization regarding the acquisition and reporting of FOT data. The goal of this article is to address the lack of standardization across laboratories by providing a comprehensive written protocol for FOT and an accompanying video. To illustrate that this protocol can be utilized irrespective of a particular device, three separate FOT devices have been employed in the case examples and video demonstration. This effort is intended to standardize the use and interpretation of FOT, provide practical suggestions, as well as highlight future questions that need to be addressed.

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.

There is increasing interest in the use of the forced oscillation technique (FOT) or oscillometry to characterize respiratory mechanics in healthy and ...

Medicine

Conducting Respiratory Oscillometry in an Outpatient Setting

Respiratory oscillometry is a different modality of pulmonary function testing that is increasingly used in a clinical and research setting to provide information regarding lung mechanics. Respiratory oscillometry is conducted through three acceptable measurements of tidal breathing and can be performed with minimal contraindications. Young children and patients who cannot perform spirometry due to cognitive or physical impairment can usually complete oscillometry. The main advantages of respiratory oscillometry are that it requires minimal patient cooperation and is more sensitive in detecting changes in small airways than conventional pulmonary function tests. Commercial devices are now available. Updated technical guidelines, standard operating protocols, and quality control/assurance guidelines have recently been published. Reference values are also available. 
We conducted oscillometry test audits before and after implementing a formal respiratory oscillometry training program and standard operating protocol. We observed improvement in the quality of tests completed, with a significant increase in the number of acceptable and reproducible measurements. 
The current paper outlines and demonstrates a standard operating protocol to conduct respiratory oscillometry in an outpatient setting. We highlight the key steps to ensuring acceptable and reproducible quality measurements according to the recommended European Respiratory Society (ERS) guidelines, as quality control is critical to measurement accuracies. Potential problems and pitfalls are also discussed with suggestions to resolve technical errors.

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

Respiratory oscillometry is a different modality of pulmonary function testing that is increasingly used in a clinical and research setting to provide ...

Oxygenation-sensitive Cardiac MRI with Vasoactive Breathing Maneuvers for the Non-invasive Assessment of Coronary Microvascular Dysfunction

Oxygenation-sensitive cardiac magnetic resonance imaging (OS-CMR) is a diagnostic technique that uses the inherent paramagnetic properties of deoxyhemoglobin as an endogenous source of tissue contrast. Used in combination with standardized vasoactive breathing maneuvers (hyperventilation and apnea) as a potent non-pharmacologic vasomotor stimulus, OS-CMR can monitor changes in myocardial oxygenation. Quantifying such changes during the cardiac cycle and throughout vasoactive maneuvers can provide markers for coronary macro- and microvascular function and thereby circumvent the need for any extrinsic, intravenous contrast or pharmacologic stress agents.
OS-CMR uses the well-known sensitivity of T2*-weighted images to blood oxygenation. Oxygenation-sensitive images can be acquired on any cardiac MRI scanner using a modified standard clinical steady-state free precession (SSFP) cine sequence, making this technique vendor-agnostic and easily implemented. As a vasoactive breathing maneuver, we apply a 4-min breathing protocol of 120 s of free breathing, 60 s of paced hyperventilation, followed by an expiratory breath-hold of at least 30 s. The regional and global response of myocardial tissue oxygenation to this maneuver can be assessed by tracking the signal intensity change. The change over the initial 30 s of the post-hyperventilation breath-hold, referred to as the breathing-induced myocardial oxygenation reserve (B-MORE) has been studied in healthy people and various pathologies. A detailed protocol for performing oxygen-sensitive CMR scans with vasoactive maneuvers is provided.
As demonstrated in patients with microvascular dysfunction in yet incompletely understood conditions, such as inducible ischemia with no obstructive coronary artery stenosis (INOCA), heart failure with preserved ejection fraction (HFpEF), or microvascular dysfunction after heart transplantation, this approach provides unique, clinically important, and complementary information on coronary vascular function.

The assessment of microvascular function by oxygenation-sensitive cardiac magnetic resonance imaging in combination with vasoactive breathing maneuvers is unique in its ability to assess rapid dynamic changes in myocardial oxygenation in vivo and, thus, may serve as a critically important diagnostic technique for coronary vascular function.

Oxygenation-sensitive cardiac magnetic resonance imaging (OS-CMR) is a diagnostic technique that uses the inherent paramagnetic properties of ...

Effect of Odor on Brain Excitability Assessed by a Breath-Synchronized Olfactometer

Combining a Breath-Synchronized Olfactometer with Brain Simulation to Study the Impact of Odors on Corticospinal Excitability and Effective Connectivity

It is widely accepted that olfactory stimulation elicits motor behaviors, such as approaching pleasant odorants and avoiding unpleasant ones, in animals and humans. Recently, studies using electroencephalography and transcranial magnetic stimulation (TMS) have demonstrated a strong link between processing in the olfactory system and activity in the motor cortex in humans. To better understand the interactions between the olfactory and the motor systems and to overcome some of the previous methodological limitations, we developed a new method combining an olfactometer that synchronizes the random order presentation of odorants with different hedonic values and the TMS (single- and dual-coil) triggering with nasal breathing phases. This method allows probing the modulations of corticospinal excitability and effective ipsilateral connectivity between the dorsolateral prefrontal cortex and the primary motor cortex that could occur during pleasant and unpleasant odor perception. The application of this method will allow for objectively discriminating the pleasantness value of an odorant in a given participant, indicating the biological impact of the odorant on brain effective connectivity and excitability. In addition, this could pave the way for clinical investigations in patients with neurological or neuropsychiatric disorders who may exhibit&#160;odor hedonic alterations and maladaptive approach-avoidance behaviors.

Author Spotlight: Exploring Olfactory Influences on Corticospinal Excitability - Insights and Innovations in Neurological Research 

This paper describes using a breath-synchronized olfactometer to trigger single- and dual-coil transcranial magnetic stimulation (TMS) during odorant presentation synchronized to human nasal breathing. This combination allows us to objectively investigate how pleasant and unpleasant odors impact corticospinal excitability and brain-effective connectivity in a given individual.

It is widely accepted that olfactory stimulation elicits motor behaviors, such as approaching pleasant odorants and avoiding unpleasant ones, in animals ...

Neuroscience

Dynamic Lung Ventilation Assessment with 3D Phase Resolved Functional Lung MRI

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Pulmonary magnetic resonance imaging (MRI) offers a variety of radiation-free techniques tailored to assess regional lung ventilation or its surrogates. These techniques encompass direct measurements, exemplified by hyperpolarized gas MRI and fluorinated gas MRI, as well as indirect measurements facilitated by oxygen-enhanced MRI and proton-based Fourier decomposition (FD) MRI. In recent times, there has been substantial progress in the field of FD MRI, which involved improving spatial/temporal resolution, refining sequence design and postprocessing, and developing a comprehensive whole-lung approach.
The two-dimensional (2D) phase-resolved functional lung (PREFUL) MRI stands out as an FD-based approach developed for the comprehensive assessment of regional ventilation and perfusion dynamics, all within a single MR acquisition. Recently, a new advancement has been made with the development of 3D PREFUL to assess dynamic ventilation of the entire lung using 8 min exam with a self-gated sequence.
The 3D PREFUL acquisition involves employing a stack-of-stars spoiled-gradient-echo sequence with a golden angle increment. Following the compressed sensing image reconstruction of approximately 40 breathing phases, all the reconstructed respiratory-resolved images undergo registration onto a fixed breathing phase. Subsequently, the ventilation parameters are extracted from the registered images.
In a study cohort comprising healthy volunteers and patients with chronic obstructive pulmonary disease, the 3D PREFUL ventilation parameters demonstrated strong correlations with measurements obtained from pulmonary function tests. Additionally, the interscan repeatability of the 3D PREFUL technique was deemed to be acceptable, indicating its reliability for repeated assessments of the same individuals.
In summary, 3D PREFUL ventilation MRI provides a whole lung coverage and captures ventilation dynamics with enhanced spatial resolution compared to 2D PREFUL. 3D PREFUL technique offers a cost-effective alternative to hyperpolarized 129Xe MRI, making it an attractive option for patient-friendly evaluation of pulmonary ventilation.

Three-dimensional (3D) phase-resolved functional lung (PREFUL) is a functional magnetic resonance imaging (MRI) technique that allows for quantification of regional ventilation of the whole human lung volume, using tidal breathing and contrast-agent free acquisition for 8 min. Here, we present an MR protocol to collect and analyze 3D PREFUL imaging data.

Pulmonary magnetic resonance imaging (MRI) offers a variety of radiation-free techniques tailored to assess regional lung ventilation or its surrogates. ...

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Evaluating respiratory drive presents challenges due to the obtrusiveness and impracticality of current methods like functional magnetic resonance imaging (fMRI). Electromyography (EMG) offers a surrogate measure of respiratory drive to the muscles, allowing the determination of both the magnitude and timing of muscle activation. The magnitude reflects the level of muscle activation, while the timing indicates the onset and offset of muscle activity relative to specific events, such as inspiratory flow and activation of other muscles. These metrics are critical for understanding respiratory coordination and control, especially under varying loads or in the presence of respiratory pathophysiology. This study outlines a protocol for acquiring and analyzing respiratory muscle EMG signals in healthy adults and patients with respiratory health conditions. Ethical approval was obtained for the studies, which included participant preparation, electrode placement, signal acquisition, preprocessing, and postprocessing. Key steps involve cleaning the skin, locating muscles via palpation and ultrasound, and applying electrodes to minimize electrocardiography (ECG) contamination. Data is acquired at a high sampling rate and gain, with synchronized ECG and respiratory flow recordings. Preprocessing includes filtering and transforming the EMG signal, while postprocessing involves calculating onset and offset differences relative to the inspiratory flow. Representative data from a healthy male participant performing incremental inspiratory threshold loading (ITL) illustrate the protocol's application. Results showed earlier activation and prolonged duration of extradiaphragmatic muscles under higher loads, correlating with increased EMG magnitude. This protocol facilitates a detailed assessment of respiratory muscle activation, providing insights into both normal and pathophysiologic motor control strategies.

Here, we describe a protocol to record and analyze respiratory electromyography (EMG) signals. It includes the anatomic references for placing the EMG electrodes over several respiratory muscles, removing electrocardiographic noise from the EMG signals, and acquiring the EMG root mean square (RMS) and onset timing of activity.

Evaluating respiratory drive presents challenges due to the obtrusiveness and impracticality of current methods like functional magnetic resonance imaging ...