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Deep and slow abdominal breathing is an important skill for the management of stress and pain. However, despite multiple proofs on the effectiveness of biofeedback, most breathing apps remain limited to pacing specific breathing patterns, without sensor feedback on the actual breathing behavior.
To fill this gap, an app named
To reveal the possible effects of biofeedback, two versions of the mobile app were developed. Both contained the same visual instruction, but only the full version included additional biofeedback. In total, 40 untrained participants were randomly assigned to one of the two versions of the app. They had to follow the app’s instructions as closely as possible for 5 min.
The group with additional biofeedback showed an increased signal-to-noise ratio for instructed breathing frequency (0.1 Hz) compared with those using visual instruction without biofeedback (
This study supports the feasibility and usefulness of incorporating biofeedback in the Breathing-Mentor app to train abdominal breathing. Immediate effects on relaxation levels should, however, not be expected for untrained users.
Chronic stress has been identified as a critical factor that influences people’s physical and mental well-being [
Deep and slow diaphragmatic breathing can lead to a state of relaxation. Therefore, it is frequently taught as a basic strategy for the management of stress, anxiety, posttraumatic stress disorder [
Besides pacing, providing biofeedback is another approach for breathing trainings (eg, [
In this study, biofeedback refers to feedback about the movement of the abdomen during a breathing task. One example for this kind of biofeedback is the BellyBio Interactive Breathing app for iOS devices by RelaxLine. It uses the mobile phone’s built-in accelerometers to capture the abdominal breathing movements. For deep and slow breathing, the sound of the ocean is transformed to relaxing music. However, so far, no study has analyzed the effectiveness of such abdominal breathing feedback. Moreover, the app is recommended only for people who are already familiar with breathing exercises. Direct instructions should be used for novices instead [
The
To investigate the feasibility and usefulness of the additional biofeedback, a control version without biofeedback, that is, with visual instruction only, was implemented as well. For this purpose, we conducted a user study to reveal how people who are unfamiliar with breathing exercises deal with both versions of
The biofeedback signal is drawn over the sine wave (dark line, not present in the control version). It is obtained from the mobile phone’s accelerometers, given that the mobile phone is correctly positioned on the user. The latter is supported through an interactive calibration procedure. During the study, the mobile phone was fixed in a custom (three-dimensional [3D]-printed) frame, and the latter was fixed with an elastic band around the upper abdomen.
The Breathing-Mentor training user interface combines graphical (moving sine wave) and text instructions (inhale/exhale, counting from 1 to 4) for deep, slow abdominal breathing with biofeedback (dark line, not present in the control version).
The overall signal processing approach for transforming the accelerometer measurements into the breathing signal as visualized on the screen and used for data analysis is detailed below.
Accelerometers measure 3D linear acceleration, a combination of body acceleration and acceleration resulting from gravity, in the local sensor coordinate system. As the participants are stationary during breathing training, the acceleration resulting from gravity constitutes the major portion of the measurement. Moreover, in the training target pose, this component provides information about lateral and anterior and posterior tilt of the mobile phone with regard to the sagittal and transversal body plane, respectively (see
These assumptions were confirmed in pretests (1 min, 3 trials) with 5 persons already trained in abdominal breathing. For these trials, the frame including the mobile phone was positioned by the investigator (instructed by the algorithm developer) with its base above the center of the upper abdomen, so that the mobile phone’s long edge was approximately leveled, and the display was facing the person. The recorded accelerometer data from these trials were then used to obtain the major dispersion direction as the first principal component. A reference range (representing deep abdominal breathing) was extracted by projecting all recorded accelerometer vectors onto this principal component and calculating the average minimum and maximum values over the test persons.
The average accelerometer vector was also used as reference vector for aiding a repeatable positioning of the custom frame on the participants of the study and thus improving the validity and reliability of the extracted breathing signal. For this, the app provided an interactive calibration procedure with traffic light feedback on the angle deviations between the currently measured accelerometer vector and the reference vector in the xy-plane and in the xz-plane (green: <5°, orange: <15°, red: otherwise; see
Positioning of the smartphone on the upper abdomen and interactive calibration procedure with traffic light feedback, aiding a repeatable positioning during the study. The yellow and cyan rectangles indicate the sagittal and transversal body planes, respectively. The coordinate system denotes the sensor coordinate frame, in which the accelerometer measurements are given. In the user interface, the half circle refers to the alignment in the smartphone’s xy-plane and the rectangle refers to the alignment in the xz-plane. For a successful alignment (through manually adjusting the position of the custom frame on the upper abdomen), both marks were required to be in the green area for five seconds.
To investigate the feasibility and usefulness of the additional biofeedback from the user perspective, we conducted a user study to reveal how people who are unfamiliar with breathing exercises deal with
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the local Ethics Committee of the Department for Social Sciences. All participants (see Participants section) gave their informed consent for inclusion in the beginning. They were randomly assigned to the experimental group (EG) with biofeedback or the control group (CG) without biofeedback. In the beginning, previous experience with breathing exercises for relaxation was screened as described by Chittaro and Sioni [
The 3 measurement blocks are described in
Description of the 3 measurement blocks.
Block sequence | Verbal instruction | Screen content | Control questions directly after the block |
Baseline | Please breathe as slowly and deeply as possible with the abdomen. | Blank screen. The word |
The instruction was easy to follow (1=totally disagree—5=totally agree) |
Training | Please follow the instructions on the screen as closely as possible while breathing with the abdomen. | Interface of Breathing-Mentor (see |
Questionnaire on the app’s effectiveness (1=totally disagree—5=totally agree) |
Post | Please breathe as slowly and deeply as possible with the abdomen. | Blank screen. The word |
The instruction was easy to follow (1=totally disagree—5=totally agree) |
A total of 40 participants took part in the pilot study. One person from the CG was excluded owing to a chronic respiratory disease, resulting in a final sample size of 39. The mean age was 26.51 years (range 20-42 years, SD 4.41 years). Groups did not differ significantly with regard to age (
As no special breathing frequency was instructed in the baseline block, the power of all slow breathing–related frequency bands (0.055-0.195 Hz, width 0.01 Hz each) was compared between both groups in a variance analysis with repeated measurements to check for systematic differences between the groups. Neither systematic group effects nor interaction of group with frequency bands was expected for the baseline block.
For the training block, the following 2 measures of the objective breathing behavior comparable with the study of Chittaro and Sioni [
The first measure, the spectral power in the recommended frequency band (0.09-0.11 Hz), indicates how intensely the respiratory act is performed for the recommended frequency band. The second measure, respiratory signal-to-noise ratio (SNR), describes the ratio between the power of the recommended breathing frequency band (0.09-0.11 Hz) and the power in the entire breathing spectrum (excluding the band of the recommended frequency, the 0-0.05 Hz band to remove low-frequency fluctuations, and the direct current offset; see [
Both, the spectral power in the recommended frequency band as well as the respiratory SNR are expected to increase in both groups for the training block owing to the visual instruction for the 0.1-Hz breathing rate, compared with the baseline condition. If the additional biofeedback actually enhances performance during the breathing exercise, there should be a main effect of group for both dependent measures. The additional within-subject factor
To test whether a single 5-min training session is already enough to cause changes in the abdominal breathing patterns toward the requested breathing frequency (0.1 Hz), the spectral power in the recommended frequency and the SNR of the postmeasurement block were compared in both groups with the baseline in 2 variance analyses with repeated measures.
For single comparisons,
The screening for previous experience with breathing exercises for relaxation revealed no systematic differences between groups (see
For the baseline block, there was a main effect of frequency bands with more power for frequency bands near the normal breathing rate (0.2 Hz; see
Screening of previous experience with breathing exercises for the control group and experimental group. Absolute frequency of yes and no answers, chi-square values, and
Item | Control group (yes/no) | Experimental group (yes/no) | Chi-square ( |
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Do you know the difference between abdominal and thoracic breathing? | 15/4 | 16/4 | 0.01 (1) | .94 |
Do you know that abdominal breathing is used in the context of relaxing exercises? | 13/6 | 13/7 | 0.05 (1) | .82 |
Do you use breathing exercises for relaxation? | 4/15 | 5/15 | 0.08 (1) | .77 |
Do you meditate regularly? (at least once a month) | 2/17 | 4/16 | 0.67 (1) | .41 |
Mean powers of frequency bands in the baseline block for both groups. Error bars indicate standard error of the mean.
Results of the analysis of variance for the power of frequency bands in the baseline block.
Factor | ||
Frequency bands | 32.21 (13,481) | <.001 |
Group | 0.36 (1,37) | .56 |
Frequency bands × group | 1.36 (13,481) | .18 |
In both groups, there was an increase of the spectral power in the recommended frequency (CG:
To reveal changes over time, 5 time blocks of 1 min each were included as repeated measurements variable to investigate group differences in breathing performance.
For the spectral power in the recommended frequency, the analysis of variance revealed neither significant main effects nor an interaction between group and time (see
For the SNR, there was a main effect of time with decreased SNR during the first minute compared with the second minute (CG:
Comparisons for the subjective ratings of the 2 versions of the app are provided in
Means and SDs of power of the requested frequency band (0.09-0.11 Hz) and the signal-to-noise ratio in both groups for the 3 measurement blocks.
Statistical value | Power: control group | Power: experimental group | Signal-to-noise ratio: control group | Signal-to-noise ratio: experimental group | |
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Mean | 0.0000041 | 0.0000036 | 0.31 | 0.64 |
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SD | 0.0000067 | 0.0000063 | 0.57 | 1.93 |
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Mean | 0.0000234 | 0.0000277 | 9.17 | 12.18 |
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SD | 0.0000243 | 0.0000171 | 6.50 | 12.20 |
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Mean | 0.0000065 | 0.0000100 | 0.73 | 1.28 |
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SD | 0.0000112 | 0.0000159 | 1.29 | 2.59 |
Results of the analysis of variance for the power of the recommended frequency in the training block.
Factor | ||
Group | 0.27 (1,37) | .61 |
Time | 1.30 (4,148) | .27 |
Group×time | 1.01 (4,148) | .41 |
Results of the analysis of variance for the signal-to-noise ratio for the recommended frequency in the training block.
Factor | ||
Group | 4.18 (1,37) | .048 |
Time | 3.75 (4,148) | .006 |
Group × time | 0.78 (4,148) | .54 |
Group comparisons of signal to noise ratio (SNR) in the training block over time. SNR increases after the first minute in both groups. The analysis of variance reveals significant group differences but no interaction with time. Error bars indicate standard errors of the mean.
Group comparisons of the subjective app ratings [
Item (1=strongly disagree, 5=strongly agree) | Mean (SD) CGa | Mean (SD) EGb | ||
The app facilitates relaxation. | 3.58 (1.12) | 2.90 (1.17) | 1.85 (37) | .07 |
The app is pleasant to use. | 4.00 (0.94) | 3.70 (0.98) | 0.97 (37) | .34 |
It is easy to follow the app instructions. | 4.74 (0.56) | 4.15 (1.14) | 2.06 (37) | .049 |
The app effectively teaches how to breathe. | 4.37 (1.07) | 4.25 (0.91) | 0.37 (37) | .71 |
The app is effective in reducing stress. | 3.74 (1.10) | 2.90 (1.12) | 2.36 (37) | .02 |
The app is effective in increasing attention to breathing. | 4.63 (0.76) | 4.55 (0.69) | 0.35 (37) | .73 |
aCG: control group.
bEG: experimental group.
To test whether a single 5-min training session is already enough to evoke changes in the abdominal breathing patterns toward the requested breathing frequency (0.1 Hz), we compared the baseline and the postmeasurement block with regard to the spectral power of this frequency in both groups. There were no main effect of measurement block, no effect of group, and no interaction between block and group (see
Comparable results were found for the SNR. There was no main effect of measurement block, no main effect of group, and no interaction between group and block (see
Results of the analysis of variance for the power of the recommended frequency in the postmeasurement block.
Factor | ||
Group | 0.49 (1,37) | .49 |
Block | 3.60 (1,37) | .07 |
Group × block | 0.58 (1,37) | .45 |
Results of the analysis of variance for the signal-to-noise ratio for the recommended frequency in the postmeasurement block.
Factor | ||
Group | 1.33 (1,37) | .26 |
Block | 1.60 (1,37) | .21 |
Group × block | 0.07 (1,37) | .80 |
The
The baseline block revealed that both groups were comparable before the breathing training regarding their ability to breathe deeply and slowly with the abdomen. Breathing frequencies near the normal breathing frequency (0.2 Hz) were more prominent in both groups compared with slower frequencies. This shows that participants were rather novices for slow abdominal breathing exercises. This finding agrees with the results from the questionnaire on previous experience with breathing exercises for relaxation purposes. Although most participants were aware that abdominal breathing can be used for relaxation exercises, only few participants actually reported practicing such exercises. Thus, the participants were representative of users who could benefit from a training app for diaphragmatic breathing [
Indeed, both versions of Breathing-Mentor (visual instruction only and visual instruction with additional biofeedback) enabled the users to realize the requested breathing frequency of 0.1 Hz more accurately compared with the baseline, as reflected by the spectral power and the SNR. This was expected, as both conditions include the wave-based visual instruction, which has already been shown to be very effective for mobile breathing training [
The main research question of this study was, how additional biofeedback in a mobile app, as implemented in Breathing-Mentor (see the Methods section for details), influences people’s ability to follow the visual breathing instruction and their subjective usage experience.
Although the spectral power of the desired frequency band did not result in significant group differences, the SNR was higher for the biofeedback training group (see the Results section for details). This means that abdominal breathing at the desired frequency was not more prominent compared with the CG without biofeedback, but the occurrence of undesired frequency bands was reduced for the biofeedback group, resulting in enhanced SNR values. These findings support the effectiveness of the additional biofeedback on breathing behavior.
This benefit in performance was, however, combined with lower subjective ratings regarding the effectiveness of the app to reduce stress and ease with which app instructions could be followed for the biofeedback training. This result could be a consequence of increased cognitive workload and attention resources that are required to interpret and modulate the biofeedback graph [
To summarize, Breathing-Mentor seems to be a useful tool to teach specific abdominal breathing patterns. An immediate improvement of the user’s relaxation state should, however, not be expected, especially for persons who are inexperienced with breathing tasks. With further experience, tools such as the BellyBio Interactive Breathing app might be more useful, as the auditory feedback allows to close the eyes and to focus more intensively on the body, which are facilitating factors for deep relaxation [
Finally, the frame that is used to hold the mobile phone at a stable position is 1 limitation factor. Although there were no user complaints concerning the usability of this approach, the correct positioning of the mobile phone was guaranteed by the calibrating procedure and the principal investigator in this study. Other fixing solutions should be considered for everyday use.
In summary, it should be noted that participants were rapidly able to adjust their breathing pattern to the instruction (within 1 min). This result supports the feasibility and usefulness of biofeedback in mobile breathing apps based on the mobile phone’s accelerometers, especially for people who are unfamiliar with breathing techniques. Immediate effects on the user’s relaxation state should, however, not be expected.
control group
experimental group
signal-to-noise ratio
The authors would like to thank Sabrina Defren and Nils Beese for supporting the data collection. The junior research group wearHEALTH is funded by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF, reference number: 16SV7115).
None declared.