Bibi, A., Lin, M., Zhang, X. C., & Margraf, J. (2020). Psychometric properties and measurement invariance of Depression, Anxiety and Stress Scales (DASS-21) across cultures. International Journal of Psychology : Journal International De Psychologie,
55(6), 916–925. https://doi.org/10.1002/ijop.12671
BioSign GmbH (Ed.). (2023). Documentation for HRV-Scanner. https://www.biosign.de/download_HRVScanner/HRV-Scanner%20Dokumentation%20Part%201%20EN.pdf
Chung, A. H., Gevirtz, R. N., Gharbo, R. S., Thiam, M. A., & Ginsberg, J. P. J. (2021). Pilot Study on Reducing Symptoms of Anxiety with a Heart Rate Variability Biofeedback Wearable and Remote Stress Management Coach. Applied Psychophysiology and Biofeedback,
46(4), 347–358. https://doi.org/10.1007/s10484-021-09519-x
Da Silva, V. P., Oliveira, N. A. de, Silveira, H., Mello, R. G. T., & Deslandes, A. C. (2015). Heart rate variability indexes as a marker of chronic adaptation in athletes: A systematic review. Annals of Noninvasive Electrocardiology,
20(2), 108–118. https://doi.org/10.1111/anec.12237
Delli Pizzi, S., Chiacchiaretta, P., Mantini, D., Bubbico, G., Edden, R. A., Onofrj, M., Ferretti, A., & Bonanni, L. (2017). Gaba content within medial prefrontal cortex predicts the variability of fronto-limbic effective connectivity. Brain Structure & Function,
222(7), 3217–3229. https://doi.org/10.1007/s00429-017-1399-x
Economides, M., Lehrer, P., Ranta, K., Nazander, A., Hilgert, O., Raevuori, A., Gevirtz, R., Khazan, I., & Forman-Hoffman, V. L. (2020). Feasibility and Efficacy of the Addition of Heart Rate Variability Biofeedback to a Remote Digital Health Intervention for Depression. Applied Psychophysiology and Biofeedback,
45(2), 75–86. https://doi.org/10.1007/s10484-020-09458-z
Fan, J., Gu, X., Guise, K. G., Liu, X., Fossella, J., Wang, H., & Posner, M. I. (2009). Testing the behavioral interaction and integration of attentional networks.
Brain and Cognition,
70(2), 209–220. https://doi.org/10.1016/j.bandc.2009.02.002
Greene, D. J., Barnea, A., Herzberg, K., Rassis, A., Neta, M., Raz, A., & Zaidel, E. (2008). Measuring attention in the hemispheres: The lateralized attention network test (LANT). Brain and Cognition,
66(1), 21–31. https://doi.org/10.1016/j.bandc.2007.05.003
Guede-Fernández, F., Ferrer-Mileo, V., Mateu-Mateus, M., Ramos-Castro, J., García-González, M. Á., & Fernández-Chimeno, M. (2020). A photoplethysmography smartphone-based method for heart rate variability assessment: device model and breathing influences. Biomedical Signal Processing and Control,
57, 101717. https://doi.org/10.1016/j.bspc.2019.101717
Heiss, S., Vaschillo, B., Vaschillo, E. G., Timko, C. A., & Hormes, J. M. (2021). Heart rate variability as a biobehavioral marker of diverse psychopathologies: A review and argument for an "ideal range". Neuroscience and Biobehavioral Reviews,
121, 144–155. https://doi.org/10.1016/j.neubiorev.2020.12.004
Johnson, D., Deterding, S., Kuhn, K.‑A., Staneva, A., Stoyanov, S., & Hides, L. (2016). Gamification for health and wellbeing: A systematic review of the literature. Internet Interventions,
6, 89–106. https://doi.org/10.1016/j.invent.2016.10.002
Jose, A., Nayak, S., Rajesh, A., Kamath, N., & Nalini, M. (2022). Impact of relaxation therapy on premenstrual symptoms: A systematic review. Journal of Education and Health Promotion,
11(1), 401. https://doi.org/10.4103/jehp.jehp_586_22
Laborde, S., Ackermann, S., Borges, U., D'Agostini, M., Giraudier, M., Iskra, M., Mosley, E., Ottaviani, C., Salvotti, C., Schmaußer, M., Szeska, C., van Diest, I., Ventura-Bort, C., Voigt, L., Wendt, J., & Weymar, M. (2023). Leveraging Vagally Mediated Heart Rate Variability as an Actionable, Noninvasive Biomarker for Self-Regulation: Assessment, Intervention, and Evaluation. Policy Insights from the Behavioral and Brain Sciences,
10(2), 212–220. https://doi.org/10.1177/23727322231196789
Laborde, S., Allen, M. S., Borges, U., Dosseville, F., Hosang, T., Iskra, M., Mosley, E., Salvotti, C., Spolverato, L., Zammit, N., & Javelle, F. (2022). Effects of voluntary slow breathing on heart rate and heart rate variability: A systematic review and a meta-analysis.
Neuroscience and Biobehavioral Reviews,
138, 104711. https://doi.org/10.1016/j.neubiorev.2022.104711
Laborde, S., Allen, M. S., Borges, U., Iskra, M., Zammit, N., You, M., Hosang, T., Mosley, E., & Dosseville, F. (2022). Psychophysiological effects of slow-paced breathing at six cycles per minute with or without heart rate variability biofeedback. Psychophysiology,
59(1), e13952. https://doi.org/10.1111/psyp.13952
Lehrer, P. M., Kaur, K., Sharma, A., Shah, K., Huseby, R., Bhavsar, J., & Zhang, Y. (2020). Heart Rate Variability Biofeedback Improves Emotional and Physical Health and Performance: A Systematic Review and Meta Analysis. Applied Psychophysiology and Biofeedback,
45(3), 109–129. https://doi.org/10.1007/s10484-020-09466-z
Lehrer, P. M., Vaschillo, E., Vaschillo, B., Lu, S.‑E., Scardella, A., Siddique, M., & Habib, R. H. (2004). Biofeedback treatment for asthma. Chest,
126(2), 352–361. https://doi.org/10.1378/chest.126.2.352
Matsumoto, T., Ushiroyama, T., Kimura, T., Hayashi, T., & Moritani, T. (2007). Altered autonomic nervous system activity as a potential etiological factor of premenstrual syndrome and premenstrual dysphoric disorder. BioPsychoSocial Medicine,
1, 24. https://doi.org/10.1186/1751-0759-1-24
Minen, M. T., Corner, S., Berk, T., Levitan, V., Friedman, S., Adhikari, S., & Seng, E. B. (2021). Heartrate variability biofeedback for migraine using a smartphone application and sensor: A randomized controlled trial. General Hospital Psychiatry,
69, 41–49. https://doi.org/10.1016/j.genhosppsych.2020.12.008
Penttilä, J., Helminen, A., Jartti, T., Kuusela, T., Huikuri, H. V., Tulppo, M. P., Coffeng, R., & Scheinin, H. (2001). Time domain, geometrical and frequency domain analysis of cardiac vagal outflow: Effects of various respiratory patterns. Clinical Physiology (Oxford, England),
21(3), 365–376. https://doi.org/10.1046/j.1365-2281.2001.00337.x
Pizzoli, S. F. M., Marzorati, C., Gatti, D., Monzani, D., Mazzocco, K., & Pravettoni, G. (2021). A meta-analysis on heart rate variability biofeedback and depressive symptoms. Scientific Reports,
11(1), 6650. https://doi.org/10.1038/s41598-021-86149-7
Quintana, D. S., Elvsåshagen, T., Zak, N., Norbom, L. B., Pedersen, P. Ø., Quraishi, S. H., Bjørnerud, A., Malt, U. F., Groote, I. R., Kaufmann, T., Andreassen, O. A., & Westlye, L. T. (2017). Diurnal Variation and Twenty-Four Hour Sleep Deprivation Do Not Alter Supine Heart Rate Variability in Healthy Male Young Adults. PloS One,
12(2), e0170921. https://doi.org/10.1371/journal.pone.0170921
Richter, P., Werner, J., Heerlein, A., Kraus, A., & Sauer, H. (1998). On the validity of the Beck Depression Inventory. A review.
Psychopathology,
31(3), 160–168. https://doi.org/10.1159/000066239
Schmalenberger, K. M., Eisenlohr-Moul, T. A., Würth, L., Schneider, E., Thayer, J. F., Ditzen, B., & Jarczok, M. N. (2019). A Systematic Review and Meta-Analysis of Within-Person Changes in Cardiac Vagal Activity across the Menstrual Cycle: Implications for Female Health and Future Studies.
Journal of Clinical Medicine,
8(11). https://doi.org/10.3390/jcm8111946
Schuman, D. L., Lawrence, K. A., Boggero, I., Naegele, P., Ginsberg, J. P., Casto, A., & Moser, D. K. (2023). A Pilot Study of a Three-Session Heart Rate Variability Biofeedback Intervention for Veterans with Posttraumatic Stress Disorder. Applied Psychophysiology and Biofeedback,
48(1), 51–65. https://doi.org/10.1007/s10484-022-09565-z
Schumann, A., Helbing, N., Rieger, K., Suttkus, S., & Bär, K.‑J. (2022). Depressive rumination and heart rate variability: A pilot study on the effect of biofeedback on rumination and its physiological concomitants. Frontiers in Psychiatry,
13, 961294. https://doi.org/10.3389/fpsyt.2022.961294
Schumann, A., La Cruz, F. de, Köhler, S., Brotte, L., & Bär, K.‑J. (2021). The Influence of Heart Rate Variability Biofeedback on Cardiac Regulation and Functional Brain Connectivity.
Frontiers in Neuroscience,
15, 691988. https://doi.org/10.3389/fnins.2021.691988
Sørensen, L., Wass, S., Osnes, B., Schanche, E., Adolfsdottir, S., Svendsen, J. L., Visted, E., Eilertsen, T., Jensen, D. A., Nordby, H., Fasmer, O. B., Binder, P.‑E., Koenig, J., & Sonuga-Barke, E. (2019). A psychophysiological investigation of the interplay between orienting and executive control during stimulus conflict: A heart rate variability study.
Physiology & Behavior,
211, 112657. https://doi.org/10.1016/j.physbeh.2019.112657
Thayer, J. F., Hansen, A. L., Saus-Rose, E., & Johnsen, B. H. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: The neurovisceral integration perspective on self-regulation, adaptation, and health.
Annals of Behavioral Medicine : A Publication of the Society of Behavioral Medicine,
37(2), 141–153. https://doi.org/10.1007/s12160-009-9101-z
Tschudin, S., Bertea, P. C., & Zemp, E. (2010). Prevalence and predictors of premenstrual syndrome and premenstrual dysphoric disorder in a population-based sample.
Archives of Women's Mental Health,
13(6), 485–494. https://doi.org/10.1007/s00737-010-0165-3
van Dijk, W., Huizink, A. C., Oosterman, M., Lemmers-Jansen, I. L. J., & Vente, W. de (2023). Validation of Photoplethysmography Using a Mobile Phone Application for the Assessment of Heart Rate Variability in the Context of Heart Rate Variability-Biofeedback. Psychosomatic Medicine,
85(7), 568–576. https://doi.org/10.1097/psy.0000000000001236
Yuda, E., Shibata, M., Ogata, Y., Ueda, N., Yambe, T., Yoshizawa, M., & Hayano, J. (2020). Pulse rate variability: A new biomarker, not a surrogate for heart rate variability. Journal of Physiological Anthropology,
39(1), 21. https://doi.org/10.1186/s40101-020-00233-x
Zahn, D., Adams, J., Krohn, J., Wenzel, M., Mann, C. G., Gomille, L. K., Jacobi-Scherbening, V., & Kubiak, T. (2016). Heart rate variability and self-control--A meta-analysis. Biological Psychology,
115, 9–26. https://doi.org/10.1016/j.biopsycho.2015.12.007
Table 1. Sample description
| Group waitlist (n = 13) | Group only intervention (n = 14) |
| Mean ± SD | Range | Mean ± SD | Range |
Age | 25.3 ± 7.0 | 18-37 | 22.6 ± 2.8 | 19-27 |
BMI in kg/m2 | 22.9 ± 4.5 | 18.3-32.2 | 22.9 ± 3.6 | 18.4-29.4 |
Gender | 12f, 1m | 14f |
Active menstrual cycle | 11 yes, 2 no | 14 yes |
HC usage | 36.4% | 42.9% |
PAF sum | 54.7 ± 13.4 | 23-70 | 57.4 ± 20.2 | 19-93 |
BDI-II sum | 12.9 ± 7.2 | 0-27 | 13.4 ± 8.0 | 2-30 |
Inclusion criterium | 4 BDI, 4 PAF, 5 both | 4 BDI, 4 PAF, 6 both |
DASS sum | 34.1 ± 7,1 | 27-54 | 40.5 ± 8.3 | 25-50 |
RMSSD in ms | 37.0 ± 10.9 | 19.6-51.5 | 41.9 ± 30.1 | 18.7-128.9 |
ANT-R Executive in ms | 148.1 ± 56.8 | 76.7-291.3 | 129.2 ± 39.1 | 80.8-194.9 |
ANT-R Orienting in ms | 85.0 ± 37.3 | 36.3-149.6 | 120.6 ± 35.1 | 46.7-183.8 |
Note. The DASS, RMSSD, and ANT-R values were obtained during the first laboratory session of each participant. All other values were assessed during the screening. SD – standard deviation; BMI – body-mass-index; HC – hormonal contraceptives; PAF – premenstrual assessment form; BDI-II – Becks Depression Inventory; DASS – depression anxiety stress scales; RMSSD – root mean square of successive differences; ANT-R – revised attention network test.
Table 2. Results of a linear mixed model predicting premenstrual symptoms
| value PAF20 item |
Predictors | Estimates | CI | p |
(Intercept) | 2.92 | 2.44 – 3.40 | <0.001 |
group [W] | -0.28 | -0.50 – -0.07 | 0.010 |
time | -0.30 | -0.54 – -0.07 | 0.012 |
scale [psy] | 1.08 | 0.61 – 1.56 | <0.001 |
group [W] * time | 0.40 | 0.03 – 0.78 | 0.034 |
(group [I] * time) * scale [psy] | -0.36 | -0.65 – -0.06 | 0.017 |
(group [W] * time )* scale [psy] | 0.13 | -0.26 – 0.51 | 0.520 |
Random Effects |
σ2 | 1.40 |
τ00 participant | 0.58 |
τ00 item | 0.24 |
ICC | 0.37 |
N participant | 24 |
N item | 20 |
Observations | 1300 |
Marginal R2 / Conditional R2 | 0.116 / 0.442 |
Note. The random effect structure includes participant intercepts and item intercepts. PAF20 – premenstrual assessment form short version; group – treatment (biofeedback vs. waitlist); W – waitlist; I – intervention (biofeedback); psy – psychological symptoms.
Table 3. Post-Hoc Tukey effects of time*treatment*scale interaction term predicting premenstrual symptoms
| Scale | Diff | p |
Waitlist | Physio | 0.06 | .54 |
Psych | 0.14 | .11 |
Intervention | Physio | -0.19 | .012 |
Psych | -0.42 | <.0001 |
Note. Diff – standardized pre-post treatment difference; psych – psychological symptoms; physio – physiological symptoms.
Table 4. Results of a linear mixed model predicting depressive symptoms
| value BDI item |
Predictors | Estimates | CI | p |
(Intercept) | 1.69 | 0.54 – 0.83 | <0.001 |
group [W] | -0.09 | -0.18 – 0.00 | 0.054 |
time | -0.17 | -0.24 – -0.10 | <0.001 |
group [W] * time | 0.21 | 0.09 – 0.33 | 0.001 |
Random Effects |
σ2 | 0.34 |
τ00 participant | 0.10 |
τ00 item | 0.03 |
ICC | 0.27 |
N participant | 27 |
N item | 21 |
Observations | 1617 |
Marginal R2 / Conditional R2 | 0.010 / 0.281 |
Note. The random effect structure includes participant intercepts and item intercepts. BDI – Beck’s Depression Inventory II; group – treatment (biofeedback vs. waitlist); W – waitlist.
Table 5. Results of a linear mixed model predicting stress/anxiety symptoms
| value DASS item |
Predictors | Estimates | CI | p |
(Intercept) | 1.12 | 0.64 – 1.60 | <0.001 |
time | -0.14 | -0.22 – -0.07 | <0.001 |
group [W] | -0.11 | -0.20 – -0.01 | 0.023 |
scale [D] | 0.11 | -0.19 – 0.40 | 0.466 |
scale [S] | 0.44 | 0.15 – 0.74 | 0.003 |
rmssd | 0.14 | 0.03 – 0.26 | 0.015 |
time * group [W] | 0.21 | 0.09 – 0.34 | 0.001 |
Random Effects |
σ2 | 0.35 |
τ00 participant | 0.14 |
τ00 item | 0.07 |
ICC | 0.39 |
N participant | 27 |
N item | 21 |
Observations | 1617 |
Marginal R2 / Conditional R2 | 0.074 / 0.432 |
Note. The random effect structure includes participant intercepts and item intercepts. DASS – Depression Anxiety and Stress Scale; group – treatment (biofeedback vs. waitlist); W – waitlist; D – depression scale; S – stress scale; RMSSD – root mean square of successive differences.
Table 6. Results of a linear mixed model predicting vagally mediated heart rate variability
| log(RMSSD in ms) |
Predictors | Estimates | CI | p |
(Intercept) | 3.53 | 3.32 – 3.75 | <0.001 |
group [W] | 0.07 | -0.15 – 0.29 | 0.546 |
time | 0.06 | -0.12 – 0.23 | 0.509 |
group [W] * time | -0.13 | -0.42 – 0.16 | 0.381 |
Random Effects |
σ2 | 0.08 |
τ00 participant | 0.21 |
ICC | 0.71 |
N participant | 25 |
Observations | 71 |
Marginal R2 / Conditional R2 | 0.003 / 0.711 |
Note. The random effect structure includes participant intercepts. RMSSD – root mean square of successive differences; group – treatment (biofeedback vs. waitlist); W – waitlist.
Table 7. Results of a linear mixed model predicting attentional control
| Reaction time |
Predictors | Estimates | CI | p |
(Intercept) | 600.31 | 574.20 – 626.41 | <0.001 |
time | -37.63 | -49.22 – -26.05 | <0.001 |
group [W] | 39.00 | 31.02 – 46.99 | <0.001 |
flanker [incongruent] | 130.14 | 123.20 – 137.08 | <0.001 |
cue [valid] | -98.44 | -106.51 – -90.37 | <0.001 |
time × group [I] × cue [invalid] | 45.56 | 28.15 – 62.97 | <0.001 |
time × group [W] ×cue [invalid] | -0.22 | -16.33 – 15.89 | 0.978 |
time × group [I] × cue [valid] | 41.09 | 27.57 – 54.61 | <0.001 |
time × group [I] × flanker [incongruent] | -26.57 | -37.90 – -15.25 | <0.001 |
time × group [W] × flanker [incongruent] | -9.44 | -23.31 – 4.43 | 0.182 |
Random Effects |
σ2 | 11369.21 |
τ00 participant | 4325.41 |
ICC | 0.28 |
N participant | 27 |
Observations | 7040 |
Marginal R2 / Conditional R2 | 0.267 / 0.469 |
Note. The model predicts trial-based prediction times of the revised attention network test. The random effect structure includes participant intercepts. Group – treatment (biofeedback vs. waitlist); W – waitlist; I – intervention (biofeedback).