Accelerometer‐based heart rate adjustment for ambulatory stress research
Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additi...
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| Published in | Psychophysiology Vol. 62; no. 1; pp. e14721 - n/a |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Blackwell Publishing Ltd
01.01.2025
John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment (n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task‐baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between‐subject correlations of aHR with task‐baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within‐subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes.
Conventional accelerometer‐based additional heart rate focuses solely on concurrent movement intensity to predict heart rate. This study introduces a novel method to correct heart rate not only for ongoing physical activity but also for postural changes, type of physical activity, and past physical activity. This approach enhances the ability to differentiate between the impact of physical and mental demands on heart rate in daily life. |
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| AbstractList | Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment (n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task‐baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between‐subject correlations of aHR with task‐baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within‐subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes.
Conventional accelerometer‐based additional heart rate focuses solely on concurrent movement intensity to predict heart rate. This study introduces a novel method to correct heart rate not only for ongoing physical activity but also for postural changes, type of physical activity, and past physical activity. This approach enhances the ability to differentiate between the impact of physical and mental demands on heart rate in daily life. Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment (n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task‐baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between‐subject correlations of aHR with task‐baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within‐subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes. Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment ( n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R 2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task‐baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between‐subject correlations of aHR with task‐baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within‐subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes. Conventional accelerometer‐based additional heart rate focuses solely on concurrent movement intensity to predict heart rate. This study introduces a novel method to correct heart rate not only for ongoing physical activity but also for postural changes, type of physical activity, and past physical activity. This approach enhances the ability to differentiate between the impact of physical and mental demands on heart rate in daily life. Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment (n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task-baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between-subject correlations of aHR with task-baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within-subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes.Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment (n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task-baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between-subject correlations of aHR with task-baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within-subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes. Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment (n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task-baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between-subject correlations of aHR with task-baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within-subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes. Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment ( n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R 2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task‐baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between‐subject correlations of aHR with task‐baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within‐subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes. Conventional accelerometer‐based additional heart rate focuses solely on concurrent movement intensity to predict heart rate. This study introduces a novel method to correct heart rate not only for ongoing physical activity but also for postural changes, type of physical activity, and past physical activity. This approach enhances the ability to differentiate between the impact of physical and mental demands on heart rate in daily life. |
| Author | Noordzij, Matthijs L. Geus, Eco J. C. Gevonden, Martin J. Ven, Sjors R. B. |
| AuthorAffiliation | 1 Department of Biological Psychology Vrije Universiteit Amsterdam The Netherlands 2 Department of Psychology, Health and Technology University of Twente Enschede The Netherlands |
| AuthorAffiliation_xml | – name: 2 Department of Psychology, Health and Technology University of Twente Enschede The Netherlands – name: 1 Department of Biological Psychology Vrije Universiteit Amsterdam The Netherlands |
| Author_xml | – sequence: 1 givenname: Sjors R. B. orcidid: 0000-0003-0910-7704 surname: Ven fullname: Ven, Sjors R. B. email: s.r.b.van.de.ven@vu.nl organization: Vrije Universiteit – sequence: 2 givenname: Martin J. orcidid: 0000-0001-7867-1443 surname: Gevonden fullname: Gevonden, Martin J. organization: Vrije Universiteit – sequence: 3 givenname: Matthijs L. orcidid: 0000-0002-5013-9225 surname: Noordzij fullname: Noordzij, Matthijs L. organization: University of Twente – sequence: 4 givenname: Eco J. C. orcidid: 0000-0001-6022-2666 surname: Geus fullname: Geus, Eco J. C. organization: Vrije Universiteit |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39562517$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1109_SR_2024_3513953 crossref_primary_10_1111_psyp_70021 |
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| Snippet | Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic... |
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| SubjectTerms | accelerometer Accelerometry - methods additional heart rate Adult ambulatory assessment autonomic nervous system Exercise - physiology Female Heart rate Heart Rate - physiology Humans Male Original Physical activity Posture Posture - physiology Social interactions stress Stress response Stress, Psychological - diagnosis Stress, Psychological - physiopathology Young Adult |
| Title | Accelerometer‐based heart rate adjustment for ambulatory stress research |
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