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G Ravindran KK, Della Monica C, Atzori G, Lambert D, Hassanin H, Revell V, Dijk DJ. Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study. JMIR Mhealth Uhealth 2024; 12:e53643. [PMID: 39190477 PMCID: PMC11387924 DOI: 10.2196/53643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 05/13/2024] [Accepted: 06/25/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations. OBJECTIVE We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting. METHODS Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA. RESULTS All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001). CONCLUSIONS Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.3390/clockssleep6010010.
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Affiliation(s)
- Kiran K G Ravindran
- Surrey Sleep Research Centre, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, United Kingdom, and the University of Surrey, Guildford, London, United Kingdom
| | - Ciro Della Monica
- Surrey Sleep Research Centre, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, United Kingdom, and the University of Surrey, Guildford, London, United Kingdom
| | - Giuseppe Atzori
- Surrey Sleep Research Centre, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, United Kingdom, and the University of Surrey, Guildford, London, United Kingdom
| | - Damion Lambert
- Surrey Sleep Research Centre, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, United Kingdom, and the University of Surrey, Guildford, London, United Kingdom
| | - Hana Hassanin
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, United Kingdom, and the University of Surrey, Guildford, London, United Kingdom
- Surrey Clinical Research Facility, University of Surrey, Guildford, United Kingdom
- NIHR Royal Surrey Clinical Research Facility, Guildford, United Kingdom
| | - Victoria Revell
- Surrey Sleep Research Centre, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, United Kingdom, and the University of Surrey, Guildford, London, United Kingdom
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, United Kingdom, and the University of Surrey, Guildford, London, United Kingdom
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Rothschild JA, Stewart T, Kilding AE, Plews DJ. Predicting daily recovery during long-term endurance training using machine learning analysis. Eur J Appl Physiol 2024:10.1007/s00421-024-05530-2. [PMID: 38900201 DOI: 10.1007/s00421-024-05530-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 06/14/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE The aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, sleep, HRV, and subjective well-being measures. METHODS Self-selected nutrition intake, exercise training, sleep habits, HRV, and subjective well-being of 43 endurance athletes ranging from professional to recreationally trained were monitored daily for 12 weeks (3572 days of tracking). Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model. The model performance was compared with a baseline intercept-only model. RESULTS Prediction error (root mean square error [RMSE]) was lower than baseline for the group models (11.8 vs. 14.1 and 0.22 vs. 0.29 for AM PRS and HRV change, respectively). At the individual level, prediction accuracy outperformed the baseline model but varied greatly across participants (RMSE range 5.5-23.6 and 0.05-0.44 for AM PRS and HRV change, respectively). CONCLUSION At the group level, daily recovery measures can be predicted based on commonly measured variables, with a small subset of variables providing most of the predictive power. However, at the individual level, the key variables may vary, and additional data may be needed to improve the prediction accuracy.
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Affiliation(s)
- Jeffrey A Rothschild
- Sports Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, Auckland, New Zealand.
- High Performance Sport New Zealand, Auckland, New Zealand.
| | - Tom Stewart
- Sports Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, Auckland, New Zealand
| | - Andrew E Kilding
- Sports Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, Auckland, New Zealand
| | - Daniel J Plews
- Sports Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, Auckland, New Zealand
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Mishica C, Kyröläinen H, Taskinen S, Hynynen E, Nummela A, Holmberg HC, Linnamo V. Associations between objective measures of performance-related characteristics and perceived stress in young cross-country skiers during pre-season training. J Sports Sci 2024:1-10. [PMID: 38247021 DOI: 10.1080/02640414.2024.2304499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024]
Abstract
Monitoring performance-related characteristics of athletes can reveal changes that facilitate training adaptations. Here, we examine the relationships between submaximal running, maximal jump performance (CMJ), concentrations of blood lactate, sleep duration (SD) and latency (SL), and perceived stress (PSS) in junior cross-country skiers during pre-season training. These parameters were monitored in 15 male and 14 females (17 ± 1 years) for the 12-weeks prior to the competition season, and the data was analysed using linear and mixed-effect models. An increase in SD exerted a decrease in both PSS (B = -2.79, p ≤ 0.01) and blood lactate concentrations during submaximal running (B = -0.623, p ≤ 0.05). In addition, there was a negative relationship between SL and CMJ (B = -0.09, p = 0.08). Compared to males, females exhibited higher PSS scores and little or no change in performance-related tests. A significant interaction between time and sex was present in CMJ with males displaying an effect of time on CMJ performance. For all athletes, lower PSS appeared to be associated with longer overnight sleep. Since the females experienced higher levels of stress, monitoring of their PSS might be beneficial. These findings have implications for the preparation of young athletes' competition season.
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Affiliation(s)
- Christina Mishica
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Heikki Kyröläinen
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Sara Taskinen
- Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland
| | - Esa Hynynen
- Finnish Institute of High-Performance Sport KIHU, Jyväskylä, Finland
| | - Ari Nummela
- Finnish Institute of High-Performance Sport KIHU, Jyväskylä, Finland
| | | | - Vesa Linnamo
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
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Delling AC, Jakobsmeyer R, Coenen J, Christiansen N, Reinsberger C. Home-Based Measurements of Nocturnal Cardiac Parasympathetic Activity in Athletes during Return to Sport after Sport-Related Concussion. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094190. [PMID: 37177393 PMCID: PMC10181314 DOI: 10.3390/s23094190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023]
Abstract
Sport-related concussions (SRC) are characterized by impaired autonomic control. Heart rate variability (HRV) offers easily obtainable diagnostic approaches to SRC-associated dysautonomia, but studies investigating HRV during sleep, a crucial time for post-traumatic cerebral regeneration, are relatively sparse. The aim of this study was to assess nocturnal HRV in athletes during their return to sports (RTS) after SRC in their home environment using wireless wrist sensors (E4, Empatica, Milan, Italy) and to explore possible relations with clinical concussion-associated sleep symptoms. Eighteen SRC athletes wore a wrist sensor obtaining photoplethysmographic data at night during RTS as well as one night after full clinical recovery post RTS (>3 weeks). Nocturnal heart rate and parasympathetic activity of HRV (RMSSD) were calculated and compared using the Mann-Whitney U Test to values of eighteen; matched by sex, age, sport, and expertise, control athletes underwent the identical protocol. During RTS, nocturnal RMSSD of SRC athletes (Mdn = 77.74 ms) showed a trend compared to controls (Mdn = 95.68 ms, p = 0.021, r = -0.382, p adjusted using false discovery rate = 0.126) and positively correlated to "drowsiness" (r = 0.523, p = 0.023, p adjusted = 0.046). Post RTS, no differences in RMSSD between groups were detected. The presented findings in nocturnal cardiac parasympathetic activity during nights of RTS in SRC athletes might be a result of concussion, although its relation to recovery still needs to be elucidated. Utilization of wireless sensors and wearable technologies in home-based settings offer a possibility to obtain helpful objective data in the management of SRC.
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Affiliation(s)
- Anne Carina Delling
- Institute of Sports Medicine, Department of Exercise and Health, Paderborn University, 33098 Paderborn, Germany
| | - Rasmus Jakobsmeyer
- Institute of Sports Medicine, Department of Exercise and Health, Paderborn University, 33098 Paderborn, Germany
| | - Jessica Coenen
- Institute of Sports Medicine, Department of Exercise and Health, Paderborn University, 33098 Paderborn, Germany
| | - Nele Christiansen
- Institute of Sports Medicine, Department of Exercise and Health, Paderborn University, 33098 Paderborn, Germany
| | - Claus Reinsberger
- Institute of Sports Medicine, Department of Exercise and Health, Paderborn University, 33098 Paderborn, Germany
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
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Lundstrom CJ, Foreman NA, Biltz G. Practices and Applications of Heart Rate Variability Monitoring in Endurance Athletes. Int J Sports Med 2023; 44:9-19. [PMID: 35853460 DOI: 10.1055/a-1864-9726] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Heart rate variability reflects fluctuations in the changes in consecutive heartbeats, providing insight into cardiac autonomic function and overall physiological state. Endurance athletes typically demonstrate better cardiac autonomic function than non-athletes, with lower resting heart rates and greater variability. The availability and use of heart rate variability metrics has increased in the broader population and may be particularly useful to endurance athletes. The purpose of this review is to characterize current practices and applications of heart rate variability analysis in endurance athletes. Important considerations for heart rate variability analysis will be discussed, including analysis techniques, monitoring tools, the importance of stationarity of data, body position, timing and duration of the recording window, average heart rate, and sex and age differences. Key factors affecting resting heart rate variability will be discussed, including exercise intensity, duration, modality, overall training load, and lifestyle factors. Training applications will be explored, including heart rate variability-guided training and the identification and monitoring of maladaptive states such as overtraining. Lastly, we will examine some alternative uses of heart rate variability, including during exercise, post-exercise, and for physiological forecasting and predicting performance.
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Affiliation(s)
| | - Nicholas A Foreman
- School of Kinesiology, University of Minnesota Twin Cities, Minneapolis, United States
| | - George Biltz
- School of Kinesiology, University of Minnesota Twin Cities, Minneapolis, United States
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NUUTTILA OLLIPEKKA, NUMMELA ARI, KORHONEN ELISA, HÄKKINEN KEIJO, KYRÖLÄINEN HEIKKI. Individualized Endurance Training Based on Recovery and Training Status in Recreational Runners. Med Sci Sports Exerc 2022; 54:1690-1701. [PMID: 35975912 PMCID: PMC9473708 DOI: 10.1249/mss.0000000000002968] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
PURPOSE Long-term development of endurance performance requires a proper balance between strain and recovery. Because responses and adaptations to training are highly individual, this study examined whether individually adjusted endurance training based on recovery and training status would lead to greater adaptations compared with a predefined program. METHODS Recreational runners were divided into predefined (PD; n = 14) or individualized (IND; n = 16) training groups. In IND, the training load was decreased, maintained, or increased twice a week based on nocturnal heart rate variability, perceived recovery, and heart rate-running speed index. Both groups performed 3-wk preparatory, 6-wk volume, and 6-wk interval periods. Incremental treadmill tests and 10-km running tests were performed before the preparatory period ( T0 ) and after the preparatory ( T1 ), volume ( T2 ), and interval ( T3 ) periods. The magnitude of training adaptations was defined based on the coefficient of variation between T0 and T1 tests (high >2×, low <0.5×). RESULTS Both groups improved ( P < 0.01) their maximal treadmill speed and 10-km time from T1 to T3 . The change in the 10-km time was greater in IND compared with PD (-6.2% ± 2.8% vs -2.9% ± 2.4%, P = 0.002). In addition, IND had more high responders (50% vs 29%) and fewer low responders (0% vs 21%) compared with PD in the change of maximal treadmill speed and 10-km performance (81% vs 23% and 13% vs 23%), respectively. CONCLUSIONS PD and IND induced positive training adaptations, but the individualized training seemed more beneficial in endurance performance. Moreover, IND increased the likelihood of high response and decreased the occurrence of low response to endurance training.
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Affiliation(s)
- OLLI-PEKKA NUUTTILA
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, FINLAND
| | - ARI NUMMELA
- Finnish Institute of High Performance Sport KIHU, Jyväskylä, FINLAND
| | - ELISA KORHONEN
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, FINLAND
| | - KEIJO HÄKKINEN
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, FINLAND
| | - HEIKKI KYRÖLÄINEN
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, FINLAND
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Reliability and Sensitivity of Nocturnal Heart Rate and Heart-Rate Variability in Monitoring Individual Responses to Training Load. Int J Sports Physiol Perform 2022; 17:1296-1303. [PMID: 35894977 DOI: 10.1123/ijspp.2022-0145] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/11/2022] [Accepted: 05/20/2022] [Indexed: 11/18/2022]
Abstract
PURPOSE To assess the reliability of nocturnal heart rate (HR) and HR variability (HRV) and to analyze the sensitivity of these markers to maximal endurance exercise. METHODS Recreational runners recorded nocturnal HR and HRV on nights after 2 identical low-intensity training sessions (n = 15) and on nights before and after a 3000-m running test (n = 23). Average HR, the natural logarithm of the root mean square of successive differences (LnRMSSD), and the natural logarithm of the high-frequency power (LnHF) were analyzed from a full night (FULL), a 4-hour (4H) segment starting 30 minutes after going to sleep, and morning value (MOR) based on the endpoint of the linear fit through all 5-minute averages during the night. Differences between the nights were analyzed with a general linear model, and intraclass correlation coefficient (ICC) was used for internight reliability assessments. RESULTS All indices were similar between the nights followed by low-intensity training sessions. A very high ICC (P < .001) was observed in all analysis segments with a range of .97 to .98 for HR, .92 to .97 for LnRMSSD, and .91 to .96 for LnHF. HR increased (P < .001), whereas LnRMSSD (P < .01) and LnHF (P < .05) decreased after the 3000-m test compared with previous night only in 4H and FULL. Increments in HR (P < .01) and decrements in LnRMSSD (P < .05) were greater in 4H compared with FULL and MOR. CONCLUSIONS Nocturnal HR and HRV indices are highly reliable. Demanding maximal exercise increases HR and decreases HRV most systematically in 4H and FULL segments.
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