Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure
Open accessAbstract
Background:Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between individuals. Our goal is to investigate the relationships between BP and lifestyle factors and provide personalized and precise recommendations to improve BP, as opposed to the current practice of general lifestyle recommendations.Method:Our proposed system consists of automated data collection using home BP monitors and wearable activity trackers and feature engineering techniques to address time-series data and enhance interpretability. We propose Random Forest with Shapley-Value-based Feature Selection to offer personalized BP modeling and top lifestyle factor identification, and subsequent generation of precise recommendations based on the top factors.Result:In collaboration with UC San Diego Health and Altman Clinical and Translational Research Institute, we performed a clinical study, applying our system to 25 patients with elevated BP or stage I hypertension for three consecutive months. Our study results validate our system’s ability to provide accurate personalized BP models and identify the top features which can vary greatly between individuals. We also validate the effectiveness of personalized recommendations in a randomized controlled experiment. After receiving recommendations, the subjects in the experimental group decreased their BPs by 3.8 and 2.3 for systolic and diastolic BP, compared to the decrease of 0.3 and 0.9 for the subjects without recommendations.Conclusion:The study demonstrates the potential of using wearables and machine learning to develop personalized models and precise lifestyle recommendations to improve BP.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
All personalized intervention studies use predictive models based on multi-subject data.
“Yet, of note, all of these personalized intervention studies use predictive models based on multi-subject data.”
From Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study (2026) · cites this paper as [23]
The Results and Discussion, under “Experiment Setting,” says each person’s blood-pressure–lifestyle model was trained only on that person’s data. The Conclusion also describes personalized recommendations based on an individual model, making this study a counterexample to the claim that all such studies use models trained on multi-subject data. (LLM, conf=0.92)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredPersonalizing treatment across multiple fields of healthcare leads to higher treatment adherence and improved satisfaction.
“Studies across multiple fields of healthcare have shown that personalizing treatment leads to higher treatment adherence as well as improved satisfaction [20–23].”
From Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study (2026) · cites this paper as [23]
The Conclusion reports improved blood pressure after personalized lifestyle recommendations, not higher treatment adherence or satisfaction. The Results and Discussion discusses participant drop-off and engagement as limitations but does not report either claimed outcome. Searches for adherence and satisfaction found no supporting passage, and the study addresses blood pressure rather than multiple healthcare fields. (LLM, conf=0.75)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · cited 8× · see citing works
“To select the best ML methods for our task, we evaluate popular machine learning techniques, including Random Forest, Support Vector Machine [45], Gradient Boosting Decision Trees [46], LSTM [47], and ARIMA [10].”
MethodInfluentialAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 1× · see citing works
“001 and 20 as the learning rate and batch size with Adam optimizer [48].”
MethodAn Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · cited 2× · see citing works
“To select the best ML methods for our task, we evaluate popular machine learning techniques, including Random Forest, Support Vector Machine [45], Gradient Boosting Decision Trees [46], LSTM [47], and ARIMA [10].”
MethodA Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · cited 1× · see citing works
“we choose four representative feature selection methods, namely, Pearson Correlation-based Feature Selection [35], Information Gain-based Feature Selection [36], Random Forest Feature Importance (mean decrease impurity) [37], and Shapley Value Feature Importance [38], [39].”
MethodBook Review: Time Series Analysis: Forecasting and Control
Geurts, M. D., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,399 · see citing works
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623 · see citing works
Shapley, L. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511528446.003 · Cited by 10,698 · cited 2× · see citing works
“Shapley value, derived from Game Theory, assumes that each feature in a data sample is a ‘player’ in a game, and the prediction is the payout [11].”
MethodLeast Squares Support Vector Machine Classifiers
Suykens, J., & Vandewalle, J. (1999) · Neural Processing Letters · doi:10.1023/a:1018628609742 · Cited by 9,481 · cited 1× · see citing works
“To select the best ML methods for our task, we evaluate popular machine learning techniques, including Random Forest, Support Vector Machine [45], Gradient Boosting Decision Trees [46], LSTM [47], and ARIMA [10].”
MethodWhelton, P. K., Carey, R. M., Aronow, W. S., Casey, D. E., Collins, K. J., Dennison Himmelfarb, C., DePalma, S. M., Gidding, S., Jamerson, K. A., Jones, D. W., et al. (2018) · Hypertension · doi:10.1161/hyp.0000000000000065 · Cited by 7,391 · see citing works
17. A Value for n-Person Games
Shapley, L. S. (1953) · Contributions to the Theory of Games (AM-28), Volume II · doi:10.1515/9781400881970-018 · Cited by 6,950 · see citing works
Whelton, P. K., Carey, R. M., Aronow, W. S., Casey, D. E., Collins, K. J., Dennison Himmelfarb, C., DePalma, S. M., Gidding, S., Jamerson, K. A., Jones, D. W., et al. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.11.006 · Cited by 6,616 · see citing works
Sacks, F. M., Svetkey, L. P., Vollmer, W. M., Appel, L. J., Bray, G. A., Harsha, D., Obarzanek, E., Conlin, P. R., Miller, E. R., Simons-Morton, D. G., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200101043440101 · Cited by 5,638 · see citing works
THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED
WELCH, B. L. (1947) · Biometrika · doi:10.1093/biomet/34.1-2.28 · Cited by 4,135 · cited 3× · see citing works
“A two-sided Student’s t-test [49] is done to compare the reduction of mean BPs and max BPs for the two groups.”
MethodInfluentialAutomatic Time Series Forecasting: The forecast Package for R
Hyndman, R. J., & Khandakar, Y. (2008) · Journal of Statistical Software · doi:10.18637/jss.v027.i03 · Cited by 3,548 · cited 1× · see citing works
“Arima [44] to implement RFSV and other MLmodels.”
MethodClassification and regression trees
Loh, W. (2011) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.8 · Cited by 2,464 · see citing works
Do we need hundreds of classifiers to solve real world classification problems
Fernández-Delgado, M., Cernadas, E., Barro, S., & AmorimDinani (2014) · Journal of Machine Learning Research · doi:10.5555/2627435.2697065 · Cited by 2,131 · cited 1× · see citing works
“data where data is essentially in tabular form [31].”
BackgroundThe Generalization of `Student's' Problem when Several Different Population Variances are Involved
Welch, B. L. (1947) · Biometrika · doi:10.2307/2332510 · Cited by 1,946 · see citing works
Graph-based approach for airborne light detection and ranging segmentation
Vilariño, D. L., Cabaleiro, J. C., Sánchez, J. M., Rivera, F. F., & Pena, T. F. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015020 · Cited by 1,760 · see citing works
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Hall, M. A. (2000) · Cited by 1,713 · see citing works
Exercise Training for Blood Pressure: A Systematic Review and Meta‐analysis
Cornelissen, V. A., & Smart, N. A. (2013) · Journal of the American Heart Association · doi:10.1161/jaha.112.004473 · Cited by 1,683 · cited 2× · see citing works
“Although the effectiveness of lifestyle interventions on BP management has been proven in many studies [2]–[5], the insight on an individual level is absent.”
BackgroundConsistent Individualized Feature Attribution for Tree Ensembles
Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018) · arXiv · doi:10.48550/arxiv.1802.03888 · Cited by 1,430 · cited 3× · see citing works
“5, we illustrate the contribution from each feature to an increase (or decrease) in SBP prediction for two subjects using SHAP [41].”
MethodInfluentialAutomatic time series forecasting: the forecast package for RPreprint
Hyndman, R. J., & Khandakar, Y. (2017) · RePEc: Research Papers in Economics · doi:10.26180/5090230 · Cited by 1,011 · see citing works
Time Series Analysis Forecasting and Control.
Chakravarti, I. M., Box, G. E. P., & Jenkins, G. M. (1973) · Journal of the American Statistical Association · doi:10.2307/2284112 · Cited by 992 · see citing works
(2017) · PubMed · Cited by 961 · see citing works
Siu, A., Bibbins-Domingo, K., Grossman, D. C., Baumann, L., Davidson, K. W., Ebell, M., García, F. A. R., Gillman, M. W., Herzstein, J., Kemper, A. R., et al. (2015) · Annals of Internal Medicine · doi:10.7326/m15-2223 · Cited by 434 · see citing works
Sleep Duration and Cardiovascular Disease Risk
Covassin, N., & Singh, P. (2016) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2015.10.007 · Cited by 314 · see citing works
On the interpretability of machine learning-based model for predicting hypertension
Elshawi, R., Al-Mallah, M. H., & Sakr, S. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0874-0 · Cited by 274 · cited 2× · see citing works
“(1) Three HR zones (zone 1, 2, and 3) are defined as [27]:”
BackgroundMethodLong-term blood pressure prediction with deep recurrent neural networks
Su, P., Ding, X., Zhang, Y., Liu, J., Miao, F., & Zhao, N. (2018) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2018.8333434 · Cited by 216 · see citing works
A Feature Selection Method Based on Information Gain and Genetic Algorithm
Lei, S. (2012) · International Conference on Computer Science and Electronics Engineering (ICCSEE) · doi:10.1109/iccsee.2012.97 · Cited by 157 · cited 3× · see citing works
“Information Gain-based Feature Selection (IGFS), and Random Forest Feature Importance (RFFI).”
MethodBlood Pressure Estimation Using Photoplethysmogram Signal and Its Morphological Features
Hasanzadeh, N., Ahmadi, M. M., & Mohammadzade, H. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2961411 · Cited by 144 · cited 1× · see citing works
“In [14]–[18], PPG signals were used to predict short-term BP with ensemble trees models [14]–[16] and neural-network-based models [17], [18].”
MethodExercise blood pressure: Clinical relevance and correct measurement
Sharman, J. E., & LaGerche, A. (2015) · Journal of Human Hypertension · doi:10.1038/jhh.2014.84 · Cited by 142 · cited 1× · see citing works
“stroke volume increase to meet the metabolic requirements of the muscles, which result in expansion of arteries and force exerted against the artery changes, which is translated into BP [21].”
BackgroundDeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
Ballinger, B., Hsieh, J., Singh, A., Sohoni, N., Wang, J., Tison, G., Marcus, G., Sanchez, J., Maguire, C., Olgin, J., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11891 · Cited by 142 · see citing works
Behnamian, A., Millard, K., Banks, S. N., White, L., Richardson, M. C., & Pasher, J. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2745049 · Cited by 135 · cited 1× · see citing works
“we choose four representative feature selection methods, namely, Pearson Correlation-based Feature Selection [35], Information Gain-based Feature Selection [36], Random Forest Feature Importance (mean decrease impurity) [37], and Shapley Value Feature Importance [38], [39].”
MethodLifestyle Medicine and the Management of Cardiovascular Disease
Doughty, K. N., Pilar, N. X. D., Audette, A., & Katz, D. L. (2017) · Current Cardiology Reports · doi:10.1007/s11886-017-0925-z · Cited by 104 · see citing works
Feature selection based on the Shapley value
Cohen, S. B., Ruppin, E., & Dror, G. (2005) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 103 · see citing works
McClung, H. L., Ptomey, L. T., Shook, R. P., Aggarwal, A., Gorczyca, A. M., Sazonov, E. E., Becofsky, K. M., Weiss, R., & Das, S. K. (2018) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2018.06.011 · Cited by 99 · cited 2× · see citing works
“Moreover, no widely adopted technology can assess dietary intake automatically and accurately [25].”
BackgroundMethodTravel Time Prediction Using a Seasonal Autoregressive Integrated Moving Average Time Series Model
Guin, A. (2006) · IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2006.1706789 · Cited by 92 · cited 1× · see citing works
“To cope with seasonality, the authors in [30] proposed Seasonal ARIMA (SARIMA).”
BackgroundHypertension and Hypothalamo-Pituitary-Adrenal Axis Hyperactivity Affect Frontal Lobe Integrity
Gold, S. M., Dziobek, I., Rogers, K., Bayoumy, A., McHugh, P. F., & Convit, A. J. (2005) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2004-2181 · Cited by 80 · cited 1× · see citing works
“Secondly, inadequate sleep, including issues of quantity and efficiency, also has a significant negative impact on BP, possibly by higher hypothalamic-pituitary-adrenal axis activation [23].”
BackgroundAn Empirical Study on Predicting Blood Pressure Using Classification and Regression Trees
Zhang, B., Wei, Z., Ren, J., Cheng, Y., & Zheng, Z. (2018) · IEEE Access · doi:10.1109/access.2017.2787980 · Cited by 72 · cited 1× · see citing works
“In [14]–[18], PPG signals were used to predict short-term BP with ensemble trees models [14]–[16] and neural-network-based models [17], [18].”
MethodCuffless Blood Pressure Estimation from only the Waveform of Photoplethysmography using CNN
Shimazaki, S., Kawanaka, H., Ishikawa, H., Inoue, K., & Oguri, K. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856706 · Cited by 67 · cited 1× · see citing works
“In [14]–[18], PPG signals were used to predict short-term BP with ensemble trees models [14]–[16] and neural-network-based models [17], [18].”
MethodContinuous Blood Pressure Estimation from PPG Signal
Slapničar, G., Luštrek, M., & Marinko, M. (2018) · Informatica · Cited by 59 · see citing works
Blood Pressure Prediction via Recurrent Models with Contextual Layer
Li, X., Wu, S., & Wang, L. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052604 · Cited by 58 · cited 2× · see citing works
“The authors in [20] proposed to predict BP using Long Short-Term Memory (LSTM) models [47] with additional contextual data (e.”
MethodUsing a Random Forest to Inspire a Neural Network and Improving on It
Wang, S., Aggarwal, C. C., & Liu, H. (2017) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974973.1 · Cited by 58 · cited 1× · see citing works
“the underlying data sizes are small and no domain-specific insight can be used to design the architecture of the underlying neural network [32].”
BackgroundExercise and Dietary Influences on Arterial Stiffness in Cardiometabolic Disease
Sacre, J. W., Jennings, G., & Kingwell, B. A. (2014) · Hypertension · doi:10.1161/hypertensionaha.113.02277 · Cited by 51 · cited 1× · see citing works
“Although BP normally increases during physical activity, the inverse relationship between physical activity and BP has been shown in numerous observational studies and can be explained by the reduction of arterial stiffness through exercise [22].”
BackgroundPredicting Systolic Blood Pressure Using Machine Learning
Wu, T. H., Pang, G. K. H., & Kwong, E. W. Y. (2014) · International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs.2014.7069529 · Cited by 45 · cited 1× · see citing works
“RELATED WORK The authors in [13] predicted BP using demographic and contextual data (e.”
MethodGuthrie, N. L., Berman, M. A., Edwards, K. L., Appelbaum, K. J., Dey, S., Carpenter, J., Eisenberg, D. M., & Katz, D. L. (2019) · JMIR Cardio · doi:10.2196/13030 · Cited by 37 · cited 2× · see citing works
“In [28], a mobile app was designed to deliver behavioral recommendations on diet and exercise to manage hypertension.”
MethodSelf-citationDeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk PredictionPreprint
Ballinger, B. M., Hsieh, J. T., Singh, A. K., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.02511 · Cited by 29 · cited 1× · see citing works
“Based on heart rate and steps collected by wearables, the authors in [26] trained bidirectional LSTM models to diagnose various chronic diseases, including hypertension.”
BackgroundChiang, P.-H., & Dey, S. (2018) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2018.8531109 · Cited by 27 · cited 1× · see citing works
“In our preliminary work [7], we used Fitbit Charge HR and Omron Evolv to collect lifestyle and BP data, respectively, of 8 volunteers.”
MethodSelf-citationChiang, P.-H., & Dey, S. (2019) · IEEE Access · doi:10.1109/access.2019.2939218 · Cited by 21 · cited 1× · see citing works
“In [9], we proposed an onlineML technique to prioritize training samples based on the performance of prediction.”
MethodSelf-citationA Personalized Blood Pressure Prediction Model Using Recurrent Kernel Extreme Reservoir Machine
Abrar, S., Tahir, G. A., Kakudi, H. A., & Loo, C. K. (2019) · Lecture notes in networks and systems · doi:10.1007/978-3-030-12388-8_62 · Cited by 4 · cited 2× · see citing works
“However, the length of collected data in [19] was only a single day,”
BackgroundMethod
Flagged first, then by citations
Research works printed but not linked: 2
- [8]L. Breiman, “ Random forest,” Mach. Learn., vol. 45, no. 1, pp. 5– 32, 2001.
- [12]J. S. Alpert, “ Prevention, detection, evaluation, and management of high blood pressure in adults: Executive summary: A report of the American college of cardiology/American heart association task force on clinical practice guidelines,” J. Amer. College Cardiol., vol. 71, no. 19, pp. 2273– 2275, 2018. 29146535
Cited by
Sorted by citations
Wearable Devices: Implications for Precision Medicine and the Future of Health Care
Babu, M., Lautman, Z., Lin, X., Sobota, M. H. B., & Snyder, M. P. (2023) · Annual Review of Medicine · doi:10.1146/annurev-med-052422-020437 · Cited by 196 · see citing works
Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness
Oyebode, O., Fowles, J. R., Steeves, D., & Orji, R. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2089085 · Cited by 155 · see citing works
“%) focus on monitoring body functions (such as blood pressure (P. Chiang & Dey, 2019; P.-H. Chiang et al., 2021), blood glucose (W. Gu, 2017), postprandial glycemic response (Zeevi et al., 2015)) and health behaviours (Kang, 2021; Nosakhare & Picard, 2020) to determine health status and offer…”
BackgroundMethodInfluentialParvin, N., Joo, S., Jung, J. H., & Mandal, T. K. (2025) · Nanomaterials · doi:10.3390/nano15120895 · Cited by 130 · see citing works
“Deep learning models excel at detecting subtle pathological patterns, such as cardiac arrhythmias in ECG waveforms or early hypertension risk factors from blood pressure trends [122,123].”
Abbaoui, W., Retal, S., Bhiri, B. E., Kharmoum, N., & Ziti, S. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101475 · Cited by 92 · see citing works
Smart Consumer Wearables as Digital Diagnostic Tools: A Review
Chakrabarti, S., Biswas, N., Jones, L., Kesari, S., & Ashili, S. (2022) · Diagnostics · doi:10.3390/diagnostics12092110 · Cited by 68 · see citing works
“Applying random forest and ARIMA on blood pressure data has identified a personalized dependence of blood pressure on other lifestyle factors [112].”
MethodYuan, H., Yu, K., Xie, F., Liu, M., & Sun, S. (2024) · Medicine Advances · doi:10.1002/med4.75 · Cited by 61 · see citing works
Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review
Olyanasab, A., & Annabestani, M. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14020203 · Cited by 60 · see citing works
“Furthermore, the system used Shapley values to identify lifestyle factors that contribute to high blood pressure [19].”
MethodCuffless Blood Pressure in clinical practice: challenges, opportunities and current limits.
Henry, B., Merz, M., Hoang, H., Abdulkarim, G., Wosik, J., & Schoettker, P. (2024) · Blood Pressure · doi:10.1080/08037051.2024.2304190 · Cited by 54 · see citing works
“Deep learning techniques utilize advanced machine learning methods, showing promising performance in various medical fields, including BP estimation [56, 57].”
Martindale, A. P. L., Llewellyn, C. D., de Visser, R. O., Ng, B., Ngai, V., Kale, A. U., di Ruffano, L. F., Golub, R. M., Collins, G. S., Moher, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45355-3 · Cited by 58 · see citing works
Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review
Dlima, S. D., Shevade, S., Menezes, S. R., & Ganju, A. (2022) · JMIR Bioinformatics and Biotechnology · doi:10.2196/39618 · Cited by 53 · see citing works
“…[40,60] Energy expenditure [39,52] Liebowitz Social Anxiety Scale [40] Blood glucose levels [38] Screen on-and-off status [40,60] Sleep duration [19, 26, 39, 48, 49, 53, 56, 60] Generalized Anxiety Disorder 7-Item Scale [40] N/A a Ambient audio [40] Sleep efficiency [19,48,49,53,56] Patient…”
BackgroundMethodInfluentialHealth Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review
Cai, Y., Yu, F., Kumar, M., Gladney, R., & Mostafa, J. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192215115 · Cited by 48 · see citing works
An IoT-Based Framework for Personalized Health Assessment and Recommendations Using Machine Learning
Jagatheesaperumal, S. K., Rajkumar, S., Suresh, J. V., Gumaei, A., Alhakbani, N., Uddin, M. Z., & Hassan, M. M. (2023) · Mathematics · doi:10.3390/math11122758 · Cited by 44 · see citing works
Abdelaal, Y., Aupetit, M. J. M., Baggag, A., & Al‐Thani, D. (2024) · Journal of Medical Internet Research · doi:10.2196/53863 · Cited by 45 · see citing works
“The remaining studies focused on patients with stage-1 hypertension [38,39], which is when the blood pressure of an individual ranges from 130 to 139 mm Hg systolic or 80 to 89 mm Hg diastolic most of the time.”
InfluentialQin, K., Huang, W. X., Zhang, T., & Tang, S. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10353-8 · Cited by 41 · see citing works
Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care
Rakers, M. M., van Buchem, M. M., Kucenko, S., de Hond, A., Kant, I. M. J., van Smeden, M., Moons, K. G. M., Leeuwenberg, A., Chavannes, N. H., Villalobos-Quesada, M. J., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.32990 · Cited by 36 · see citing works
Li, Z., He, B., Li, Y., Liu, B.-F., Zhang, G., Liu, S., Hu, Y., & Li, Y. (2025) · ACS Nano · doi:10.1021/acsnano.5c04337 · Cited by 34 · see citing works
Recommender Systems Applications: Data Sources, Features, and Challenges
Alfaifi, Y. H. (2024) · Information · doi:10.3390/info15100660 · Cited by 24 · see citing works
Visual analysis of machine learning methods in the field of ergonomics — Based on Cite Space V
Zhang, M., Li, H., & Tian, S. (2022) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2022.103395 · Cited by 23 · see citing works
Kang, Y., Peng, L., Guo, J. M., Lu, Y., Yang, Y., Fan, B., & Pu, B. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3334373 · Cited by 24 · see citing works
“Recent research [11] applied machine learning to wearable data in both clinical and real-world settings.”
BackgroundPersonalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals
Nan, J., Herbert, M. S., Purpura, S., Henneken, A. N., Ramanathan, D., & Mishra, J. (2024) · Sensors · doi:10.3390/s24082640 · Cited by 22 · see citing works
“…based on a person’s attributes, however, can be much easier to sustain and yield higher satisfaction as seen in studies that have used personalized technologies in diverse cases of diabetes and hypertension management, amblyopic eye vision, and text communication for hospital patients [19–22].”
BackgroundExercise, cancer, and the cardiovascular system: clinical effects and mechanistic insights
Wernhart, S., & Rassaf, T. (2024) · Basic Research in Cardiology · doi:10.1007/s00395-024-01034-4 · Cited by 22 · see citing works
“A combination of wearables and artificial intelligence-based approaches can be regarded promising to increase cancer detection [40, 108], treatment [221], as well as an improvement of lifestyle interventions [30] and early recognition of cardiac disease and heart failure [13, 56].”
Artificial intelligence and digital twins for the personalised prediction of hypertension risk
Naik, A., Nalepa, J., Wijata, A. M., Mahon, J., Mistry, D., Knowles, A. T., Dawson, E. A., Lip, G. Y. H., Olier, I., & Ortega‐Martorell, S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110718 · Cited by 18 · see citing works
Manga, S., Muthavarapu, N., Redij, R., Baraskar, B., Kaur, A., Gaddam, S., Gopalakrishnan, K., Shinde, R., Rajagopal, A., Samaddar, P., et al. (2023) · Sensors · doi:10.3390/s23125744 · Cited by 18 · see citing works
“[173] Random forest Personalised lifestyle recommendations; applying these recommendations showed SBP decreased by 3.”
MethodSelf-citationRecent developments in machine learning modeling methods for hypertension treatment
Kohjitani, H., Koshimizu, H., Nakamura, K., & Okuno, Y. (2024) · Hypertension Research · doi:10.1038/s41440-023-01547-w · Cited by 18 · see citing works
Trends and Gaps in Digital Precision Hypertension Management: Scoping Review
Clifford, N., Tunis, R., Ariyo, A., Yu, H., Rhee, H., & Radhakrishnan, K. (2024) · Journal of Medical Internet Research · doi:10.2196/59841 · Cited by 16 · see citing works
“In addition, the scope of each study varies; some studies aim to improve BP prediction accuracy, while others focus on optimizing treatment pathways [64], integrating lifestyle data [59], or providing real-time [58] and personalized health care recommendations [59].”
Artificial Intelligence to Improve Blood Pressure Control: A State-of-the-Art Review
Karnik, A., & Yang, E. (2025) · American Journal of Hypertension · doi:10.1093/ajh/hpaf035 · Cited by 16 · see citing works
Lu, J. K., Sijm, M., Janssens, G. E., Goh, J., & Maier, A. B. (2023) · GeroScience · doi:10.1007/s11357-023-00815-4 · Cited by 17 · see citing works
“Of the 236 observational and 38 interventional studies, two articles reported an observational study followed by an interventional study [13, 14].”
BackgroundLeitner, J., Chiang, P.-H., Khan, B., & Dey, S. (2022) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh55609.2022.00030 · Cited by 12 · see citing works
“25 of the 38 patients in our cohort are from our previous work [7].”
BackgroundMethodResultInfluentialSelf-citationHsieh, W.-H., Ku, C., Hwang, H. P.-C., Tsai, M.-J., & Chen, Z.-Z. (2023) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2023.3234207 · Cited by 11 · see citing works
“[20] investigated the relationships between blood pressure and lifestyle factors using blood pressure monitors and wearable activity trackers.”
MethodCoppens, I., Pessemier, T. D., & Martens, L. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09407-z · Cited by 11 · see citing works
A systematic review on artificial intelligence approaches for smart health devices
Aversano, L., Iammarino, M., Mancino, I., & Montano, D. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2232 · Cited by 11 · see citing works
“…( Cheikhrouhou et al., 2021 ), diabetes ( Ramazi et al., 2021 ) and cancer ( Masood et al., 2018 ), (ii) LSTM when it comes to heart problems ( Chiang, Wong & Dey, 2021 ), covid ( Neog, Dutta & Medhi, 2022 ), mental health ( Jung et al., 2021 ), lung disease diseases ( Schoun et al., 2018 )…”
Propositions for Smart Healthcare Systems
Mittal, P., Dhir, S., & Hasteer, N. (2022) · IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) · doi:10.1109/soli57430.2022.10294726 · Cited by 8 · see citing works
“This system will monitor and store patients’ health data and will also suggest the best medical treatment [44], [45].”
BackgroundHe, F., Wu, Y., Yang, J., Chen, K., Xie, J., Tuersun, Y., Li, L., Wu, F., Kan, Y., Deng, Y., et al. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08126-8 · Cited by 8 · see citing works
“Smart home can relieve stress [6], alleviate loneliness [7], increase security [8], enhance ease of movement [9], improve quality of life [10], and improve blood pressure [11].”
BackgroundCorsi, A., de Souza, F. F., Pagani, R., & Kovaleski, J. L. (2022) · Smart Health · doi:10.1016/j.smhl.2022.100310 · Cited by 8 · see citing works
Artificial intelligence in primary care
Sherbini, A. E., Glicksberg, B. S., & Krittanawong, C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00039-5 · Cited by 9 · see citing works
Towett, G., Snead, R. S., Marczika, J., Ambalavanan, R., Mbogori-Kairichi, M., & Malioukis, A. (2025) · Digital Health · doi:10.1177/20552076251406654 · Cited by 10 · see citing works
Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension
Varzideh, F., Mone, P., Kansakar, U., Pande, S., Jankauskas, S. S., & Santulli, G. (2026) · Hypertension · doi:10.1161/hypertensionaha.126.26094 · Cited by 6 · see citing works
Maeda, T., Sakamoto, Y., Hosoki, S., Satoh, A., Koyoshi, R., Yamashita, S., & Arima, H. (2024) · Hypertension Research · doi:10.1038/s41440-024-01771-y · Cited by 5 · see citing works
“The timing of outcome measures was the number of days: 14 [10], 30 [8, 9], 60 [9], 70 or longer [10], 90 [9, 11] and 180 [7, 11].”
InfluentialThe rise of large language models in wearable health sensing
Farrahi, V. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2025.12.003 · Cited by 5 · see citing works
Smyth, B. (2025) · Trans. Recomm. Syst. · doi:10.1145/3742442 · Cited by 6 · see citing works
“The advent of wearable technologies has created new opportunities for RS to support people in the physical world [83–87].”
Digital Ayurveda: Harnessing Technology for Holistic Care
Sharma, S. (2025) · Journal of Ayurveda · doi:10.4103/joa.joa_386_25 · Cited by 3 · see citing works
Personalization in the metaverse: A survey on recommender systems across immersive technologies
Bouayad, A., Akdim, I., Illi, C., Moudakir, F. Z., Mekouar, L., & Iraqi, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131614 · Cited by 3 · see citing works
Forecasting Hypotension During Haemodialysis via Data Analysis and Machine Learning Algorithms
Ramathilagam, A., Gopikannan, P., Shamili, N., & Saravanakumar, R. (2025) · International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS) · doi:10.1109/icc-robins64345.2025.11086330 · Cited by 3 · see citing works
“The system generates tailored advice on activity, sleep, and habits, offering a data-driven approach to hypertension prevention and control [10].”
A review of AI-based health prediction using apple watch and fitbit data
Khan, N., Deshpande, N. M., Singh, P., & Kadam, P. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07783-8 · Cited by 5 · see citing works
“…and fitbit data Data analysis with ANOVA; LSTM and ANN models Increase in types of activities analyzed; validation with varied datasets needed [19, 20] BP and lifestyle data from galaxy watch, PMData RF, CART, SVM, GBDT, LSTM, ARIMA; random forest, bayesian neural networks Larger datasets…”
Afanasieva, T. V., & Zamashkin, I. S. (2024) · Russian Journal of Preventive Medicine · doi:10.17116/profmed2024270617 · Cited by 2 · see citing works
Wan, C., Xie, C., Liu, L., Wu, D., & Li, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889293 · Cited by 2 · see citing works
“To address this, personalized BP estimation has gained increasing attention [7], [8].”
Nan, J., Purpura, S., Jaiswal, S., Afshar, H., Maric, V., Manchanda, J. K., Taylor, C. T., Ramanathan, D., & Mishra, J. (2026) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-026-00062-3 · Cited by 1 · see citing works
“Studies across multiple fi elds of healthcare have shown that personalizing treatment leads to higher treatment adherence as well as improved satisfaction [20 – 23].”
Causal Inference Based Hierarchical Regression Model for Cuffless Blood Pressure Estimation
Song, X., Zan, X., Fu, Z., & Ding, X. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782557 · Cited by 1 · see citing works
Cotte, F., Lourenço, F. D., Pereira, M. P., Kini, N., Schmude, M., Pimenta, A., Lemesiou, A., Gilbert, S., Mehrali, T., Monteiro, M. S., et al. (2026) · NEJM AI · doi:10.1056/aioa2500833 · Cited by 1 · see citing works
Cotte, F., Lourenço, F. D., Pereira, M. P., Kini, N., Schmude, M., Pimenta, A., Lemesiou, A., Gilbert, S., Mehrali, T., Monteiro, M. S., et al. (2025) · medRxiv · doi:10.1101/2025.07.24.25332138 · Cited by 1 · see citing works
DECODIFICAÇÃO E SISTEMA IHM DE PROCESSAMENTO DE BIOSSINAIS UTILIZANDO MICROCONTROLADORES
BOTTON, L. F. D. S. D. (2023) · doi:10.17771/pucrio.acad.65481
He, F., Wu, Y., Yang, J., Chen, K., Xie, J., Tuersun, Y., Li, L., Wu, F., Kan, Y., Deng, Y., et al. (2022) · Figshare · doi:10.6084/m9.figshare.20056128
Song, X., & Ding, X. (2024) · Connected Health And Telemedicine · doi:10.20517/chatmed.2023.30
Akoosh, L. M. S., Siddiqui, F., Zafar, S., Naaz, S., & Alam, M. A. (2024) · Journal of Natural Remedies · doi:10.18311/jnr/2024/44045
Patient-oriented systems for digital prevention of chronic non-communicable diseases
Afanasieva, T. V., & Zamashkin, I. S. (2025) · Health Care of the Russian Federation · doi:10.47470/0044-197x-2025-69-3-289-294
Computational Offloading for Wearables-Based 6G Fitness Applications
Khanh, Q. V., Anh, D. V., Ban, N. T., Hue, C. T. M., & Quý, N. M. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3611885
“To address this, Chiang et al. [26] proposed a wearables-based solution and artificial intelligence (AI) techniques to give lifestyle recommendations to improve the blood pressure index.”
An extensive analysis of artificial intelligence and internet of things for modern healthcare realm
Ngoc, A. P. T., & Singh, K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36434-1.00013-6
He, F., Wu, Y., Yang, J., Chen, K., Xie, J., Tuersun, Y., Li, L., Wu, F., Kan, Y., Deng, Y., et al. (2022) · Figshare · doi:10.6084/m9.figshare.20056125
Arima, H., Itoh, H., Ohkubo, T., Ohya, Y., Kabayama, M., Kishi, T., Hirawa, N., Miura, K., Miura, S.-I., Yamamoto, K., et al. (2026) · Hypertension Research · doi:10.1038/s41440-025-02509-0
da Silva, J. H. B., Germano-Soares, A. H., de Oliveira, T. V., Silva, L. C. B., Filho, V. C. B., Silva, T. C. D. A., & Tassitano, R. M. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106370 · Cited by 1 · see citing works
Personalized Healthcare and Precision Medicine: Scoping Review with ☸SAIMSARA
SAIMSARA (2026) · SAIMSARA Journal · doi:10.62487/saimsaraec3e56f2
Machine learning classifier based on printed organic electronics
Sadeghi, M., Rabbani, R., Moin, A., Eminoğlu, B., Ono, S., Toor, A., Khan, Y., Rabaey, J., & Arias, A. C. (2026) · Matter & Light · doi:10.1016/j.matlit.2026.100064
Dickerson, D. M., Lim, K., Tourje, C. D., Naidu, R., Cianni, L., & Kibler, A. B. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1887889
“Nan et al. developed personalized ML models to predict wellbeing and empathy from smartwatch and ecological momentary assessment data (20), while Chiang et al. used wearable and behavioral data to generate personalized lifestyle recommendations for blood-pressure improvement (21).”
Artificial Intelligence for Hypertension Risk Prediction and Management: A Systematic Review
Oyshi, F. K., Mahzabin, N., Ayman, N., Mazumder, U. H., Rahman, M. A., & Hasan, M. (2026) · Engineering Reports · doi:10.1002/eng2.70997
Sciepuro, R., Czarnowska, M., & Czarnowska, N. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.3(51).2026.6352
- Authors
- Chiang, P.-H., Wong, M., & Dey, S.
- Affiliations
- University of California San Diego (US)
- UC San Diego Health System (US)
- Corresponding author
- Chiang, Po-Han
- Venue
- IEEE Journal of Translational Engineering in Health and Medicine
- Published
- 2021
- Volume
- 9
- Pages
- 1-13
- ISSN
- 2168-2372 (ISSN-L)
- PMC
- PMC8577573
- OpenAlex
- W3184579817
- Cited by
- 64
- References
- 53
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 19 · 2023: 8 · 2022: 11
- FWCI
- 6.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Journal of Translational Engineering in Health and Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Blood Pressure and Hypertension Studies
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- blood pressure, hypertension, lifestyle factors, personalized recommendation, wearable activity trackers, home blood pressure monitoring, Shapley value, feature selection, random forest, time series, physical activity, sleep, systolic and diastolic blood pressure
- MeSH terms
- Machine Learning, Wearable Electronic Devices, Blood Pressure, Humans, Life Style, Sphygmomanometers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
