Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment
Abstract Background Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for...
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12916-024-03252-y |
| _version_ | 1797276516640358400 |
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| author | Yuri G. Rykov Michael D. Patterson Bikram A. Gangwar Syaheed B. Jabar Jacklyn Leonardo Kok Pin Ng Nagaendran Kandiah |
| author_facet | Yuri G. Rykov Michael D. Patterson Bikram A. Gangwar Syaheed B. Jabar Jacklyn Leonardo Kok Pin Ng Nagaendran Kandiah |
| author_sort | Yuri G. Rykov |
| collection | DOAJ |
| description | Abstract Background Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI. Methods We used the dataset collected from a 10-week single-arm clinical trial in older adults (50–70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors’ data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using “leave-one-subject-out” and “leave-one-interval-out” cross-validation. Results The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61. Conclusions Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI. |
| first_indexed | 2024-03-07T15:29:18Z |
| format | Article |
| id | doaj.art-ff3b6bcf1ee443d29df89fb657e6766c |
| institution | Directory Open Access Journal |
| issn | 1741-7015 |
| language | English |
| last_indexed | 2024-03-07T15:29:18Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj.art-ff3b6bcf1ee443d29df89fb657e6766c2024-03-05T16:32:24ZengBMCBMC Medicine1741-70152024-01-0122111410.1186/s12916-024-03252-yPredicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairmentYuri G. Rykov0Michael D. Patterson1Bikram A. Gangwar2Syaheed B. Jabar3Jacklyn Leonardo4Kok Pin Ng5Nagaendran Kandiah6Neuroglee TherapeuticsNeuroglee TherapeuticsNeuroglee TherapeuticsNeuroglee TherapeuticsDementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological UniversityDepartment of Neurology, National Neuroscience InstituteDementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological UniversityAbstract Background Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI. Methods We used the dataset collected from a 10-week single-arm clinical trial in older adults (50–70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors’ data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using “leave-one-subject-out” and “leave-one-interval-out” cross-validation. Results The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61. Conclusions Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.https://doi.org/10.1186/s12916-024-03252-yDigital physiological featuresDigital biomarkersWearable sensor dataMild cognitive impairmentRemote patient monitoringMachine learning |
| spellingShingle | Yuri G. Rykov Michael D. Patterson Bikram A. Gangwar Syaheed B. Jabar Jacklyn Leonardo Kok Pin Ng Nagaendran Kandiah Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment BMC Medicine Digital physiological features Digital biomarkers Wearable sensor data Mild cognitive impairment Remote patient monitoring Machine learning |
| title | Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment |
| title_full | Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment |
| title_fullStr | Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment |
| title_full_unstemmed | Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment |
| title_short | Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment |
| title_sort | predicting cognitive scores from wearable based digital physiological features using machine learning data from a clinical trial in mild cognitive impairment |
| topic | Digital physiological features Digital biomarkers Wearable sensor data Mild cognitive impairment Remote patient monitoring Machine learning |
| url | https://doi.org/10.1186/s12916-024-03252-y |
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