Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Although the data from these trials have yet to be integrated into cardiovascular societies' guidelines, clinical use of AI-integrated wearable devices such as the smartwatches in the formal diagnosis and management of AF are currently being reviewed by multiple Canadian, European, and American cardiovascular societies.

“Although the data from these trials have yet to be integrated into cardiovascular societies' guidelines, clinical use of AI-integrated wearable devices such as the smartwatches in the formal diagnosis and management of AF are currently being reviewed by multiple Canadian, European, and American cardiovascular societies.”

Probably not a substantial claim: may not pass S3 (load-bearing).

RNNs often face challenges with long sequences.

“However, they often face challenges with long sequences.”

May combine two claims.

A new study published by our group has shown validity and feasibility of AF detection in elderly HF patients using the AW.

“A new study published by our group has shown validity and feasibility of AF detection in elderly HF patients using the AW.”

• cites [30] Heo, 2024, Feasibility of single-lead Apple watch electrocardiogram in atrial fibrillation detection among heart failure patients, JACC Adv sourceNot checked yet

  Not checked in the free run.

May combine two claims.

In the mSToPS study, ECG monitoring analyzed by a deep learning model led to a higher rate of new AF diagnosis than standard methods, and was associated with increased initiation of anticoagulation therapy and cardiology referral.

“This study showed that ECG monitoring analyzed by a deep learning model led to a higher rate of new AF diagnosis than standard methods, and was associated with increased initiation of anticoagulation therapy and cardiology referral.”

• cites [27] Steinhubl, 2018, Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial, JAMA sourceNot checked yet

  Not checked in the free run.

May combine two claims.

The algorithm's inputs included voltage data, aggregating information from patterns and identifying outputs of left ventricular systolic dysfunction, correctly identifying abnormalities in 90% of the ECGs analyzed.

“The algorithm's inputs included voltage data, aggregating information from patterns and identifying outputs of left ventricular systolic dysfunction, correctly identifying abnormalities in 90% of the ECGs analyzed.”

• cites [33] Khunte, 2023, Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices, NPJ Digit Med sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

CNNs use convolutional layers to capture spatial features from sequences.

“CNNs use convolutional layers to capture spatial features from sequences.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Transformers have revolutionized the field by using self-attention mechanisms to identify relationships across much longer sequences, significantly improving processing efficiency and model performance over earlier models.

“More recently, transformers have revolutionized the field by using self-attention mechanisms to identify relationships across much longer sequences, significantly improving processing efficiency and model performance over earlier models.23”

• cites [23] ZhangNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

The widespread use of wearable sensors and the lack of enhanced data aggregation capabilities pose new challenges in managing, interpreting, and integrating such data.

“The widespread use of wearable sensors and the lack of enhanced data aggregation capabilities pose new challenges in managing, interpreting, and integrating such data.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

AI's role will evolve to further enhance the work of clinicians by improving acumen and streamlined work flows that alleviate administrative burdens, thereby affording more time to dedicate to patient care.

“Yet, we stand at a pivotal moment in the evolution of AI because its role will evolve to further enhance the work of clinicians by improving acumen and streamlined work flows that alleviate administrative burdens, thereby affording more time to dedicate to patient care.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Although more potent than support vector machine or random forest in handling complex patterns, FCNNs still needed to explicitly account for the order of data points in time series, which is crucial for capturing temporal dependencies.

“Although more potent than support vector machine or random forest in handling complex patterns, FCNNs still needed to explicitly account for the order of data points in time series, which is crucial for capturing temporal dependencies (Fig. 1).”

May combine two claims.

The iCARE-AF study showed in patients with paroxysmal AF that on-demand use of anticoagulation on the basis of 1-lead ECG recordings is safe and feasible and can also help improve patient adherence to therapy.

“For instance the iContinuous Anticoagulation theRapy in patiEnts with Atrial Fibrillation (iCARE-AF) study, 28 showed in patients with paroxysmal AF that ondemand use of anticoagulation on the basis of 1-lead ECG recordings is safe and feasible and can also help improve patient adherence to therapy.”

• cites [28] Chen, 2020, A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation, Heart Rhythm sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Long-short-term memory networks were introduced to overcome these challenges, and they can learn long-term dependencies without the vanishing gradient problem.

“Long-short-term memory networks were introduced to overcome these challenges, and they can learn long-term dependencies without the vanishing gradient problem.”

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

Many current AI applications rely on antiquated labels to predict outcomes (eg, International Classification of Diseases, ninth revision codes) and primitive decision tools that hinder their ability to unlock new insights.

“Many current AI applications rely on antiquated labels to predict outcomes (eg, International Classification of Diseases, ninth revision codes) and primitive decision tools that hinder their ability to unlock new insights.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Because of their efficiency and capacity to 'understand' spatial correlations, CNNs are the most used algorithm in wearable devices.

“Because of their efficiency and capacity to”

Probably not a substantial claim: may not pass S4 (based).

Key barriers to the broader use of these devices is the need for rigourous evidence that unequivocally establishes their role in disease monitoring.

“Key barriers to the broader use of these devices is the need for rigourous evidence that unequivocally establishes their role in disease monitoring.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

Because clinicians routinely handle data from multiple sources and modalities, there is a pressing need to develop AI models that effectively integrate multimodal data.

“However, because clinicians routinely handle data from multiple sources and modalities, there is a pressing need to develop AI models that effectively integrate multimodal data.”

May combine two claims.

These challenges include the large volume of data generated by wearables, the complex temporal dynamics inherent in most wearable data streams, and the need to capture temporal dependencies.

“These challenges include the large volume of data generated by wearables, the complex temporal dynamics inherent in most wearable data streams, and the need to capture temporal dependencies.”

Classified as probably not a substantial claim.

These deep learning models demonstrate exceptional proficiency in decoding the intricate nonlinear relationships embedded within large quantities of data, thereby addressing these challenges.

“These models demonstrate exceptional proficiency in decoding the intricate nonlinear relationships embedded within large quantities of data, thereby addressing these challenges.”

May combine two claims.

Their noise-adapted model has an area under the receiver operating characteristic curve of 0.87, which is a robust performance compared with clinical ECGs.

“Their noise-adapted model has an area under the receiver operating characteristic curve of 0.87, which is a robust performance compared with clinical ECGs.”

• cites [33] Khunte, 2023, Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices, NPJ Digit Med sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

CNNs are suitable for segmenting and analyzing time-bound signal patterns and assessing wearable data as a 1-dimensional signal.

“They are suitable for segmenting and analyzing time-bound signal patterns and assessing wearable data as a 1-dimensional signal, like how they process images as 2-or 3-dimensional signals.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Currently, AI integration into medical care has been limited, with the progress mainly observed in the applications of medical imaging interpretation.

“Currently, AI integration into medical care has been limited, with the progress mainly observed in the applications of medical imaging interpretation.”

May combine two claims.

These findings highlight the potential for wearable devices but also underscore that these devices are underused among those with cardiovascular disease.

“These findings highlight the potential for wearable devices but also underscore that these devices are underused among those with cardiovascular disease.”

• AssumptionRests on an assumption: A recent survey showed that 49% of users reported using wearable devices every day and 82% of users expressed willingness to share their health data with clinicians.
• AssumptionRests on an assumption: Among individuals with or at risk of cardiovascular disease, fewer than 18% of those with established cardiovascular disease and 23% at risk reported using wearable devices, compared with 29% of the general population.
• AssumptionRests on an assumption: Sociodemographic factors such as older age, lower educational status, and lower household income were associated with a lower odds of wearable usage.

Probably not a substantial claim: may not pass S4 (based).

Wearable data present an ideal opportunity for deep learning models, incorporating diverse sensor parameters like cuffless blood pressure, heart rate, oxygen saturation, temperature, activity, 1-lead ECG, and blood glucose levels.

“Wearable data present an ideal opportunity for deep learning models, incorporating diverse sensor parameters like cuffless blood pressure, heart rate, oxygen saturation, temperature, activity, 1-lead ECG, and blood glucose levels.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

AI-driven insights will empower patients with greater understanding and involvement in their health care journey.

“Additionally, AI-driven insights will empower patients with greater understanding and involvement in their health care journey.”

May combine two claims.

These traditional methods handled structured data well and performed adequately even with relatively small data sets.

“These methods handled structured data well and performed adequately even with relatively small data sets.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Recurrent neural networks (RNNs) were developed to model time-series data, to explicitly address the limitations of FCNNs.

“As the field progressed, recurrent neural networks (RNNs) were developed to model time-series data, to explicitly address the limitations of FCNNs.22”

• cites [22] LaiNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable).

Addressing these challenges necessitates multimodal AI solutions that capture the complexity of health and disease.

“Addressing these challenges necessitates multimodal AI solutions that capture the complexity of health and disease.18”

• cites [18] Acosta, 2022, Multimodal biomedical AI, Nat Med sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Deep learning pipelines were later introduced to wearables, and their success in various time-series forecasting tasks offers a solution for addressing wearable data challenges.

“Deep learning pipelines were later introduced to wearables, and their success in various time-series forecasting tasks offers a solution for addressing wearable data challenges.21”

• cites [21] Lim, 2021, Time-series forecasting with deep learning: a survey, Philos Trans A Math Phys Eng SciNot checked yet

  Not checked in the free run.

May combine two claims.

Fully connected neural networks (FCNNs) marked a significant shift by using deep learning for wearables' data, enhancing the ability to model nonlinear relationships within larger data sets.

“Fully connected neural networks (FCNNs) marked a significant shift by using deep learning for wearables' data, enhancing the ability to model nonlinear relationships within larger data sets.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

RNNs have successfully integrated large quantities of wearable data, processing sequences by maintaining a hidden state that captures information about previous data points, thus preserving temporal dependencies.

“RNNs have successfully integrated large quantities of wearable data, processing sequences by maintaining a hidden state that captures information about previous data points, thus preserving temporal dependencies.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Transformers in wearables signify a move toward models that require substantial data sets but offer remarkable accuracy and speed.

“Transformers in wearables signify a move toward models that require substantial data sets but offer remarkable accuracy and speed.”

May combine two claims.

Wearable technology provides a unique opportunity to improve symptom management and reduce hospitalization, narrowing the equity gap in cardiac care access by facilitating the diagnosis, prognosis, and remote monitoring.

“Wearable technology provides a unique opportunity to improve symptom management and reduce hospitalization, narrowing the equity gap in cardiac care access by facilitating the diagnosis, prognosis, and remote monitoring.32”

• cites [32] Shumba, 2023, Wearable technologies and AI at the far edge for chronic heart failure prevention and management: a systematic review and prospects, Sensors (Basel) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

The Amazfit Health Band has a sensitivity of 96%, specificity of 98%, and 97% accuracy for the detection of AF.

“The Amazfit Health Band (Zepp Health, Hefei, China), which records PPG and 1-lead ECG data, has a sensitivity 96%, specificity of 98%, and 97% accuracy for the detection of AF. 28”

• cites [28] Chen, 2020, A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation, Heart Rhythm sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based).

At this time, there are no established clinical standards of care for wearable sensors.

“At this time, there are no established clinical standards of care for wearable sensors.”

Classified as probably not a substantial claim. May combine two claims.

Without consistent data, the effect and validity might be limited, and particularly affect those with unequal access to digital care and thus worsen health disparities.

“Without consistent data, the effect and validity might be limited, and particularly affect those with unequal access to digital care and thus worsen health disparities.”

May combine two claims.

This might significantly affect the course of HF management in Black patients and lead to suboptimal care.

“This might significantly affect the course of HF management in Black patients and lead to suboptimal care.49”

• cites [49] Ketcham, 2024, Racial bias in pulse oximetry measurement: considerations in patients with heart failure, Circ Cardiovasc Qual Outcomes sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable).

The successful adoption of such technology hinges on its ability to facilitate better decision-making leading to improved patient outcomes and experiences, rather than merely increasing the volume of data the clinician receives.

“However, the successful adoption of such technology hinges on its ability to facilitate better decision-making leading to improved patient outcomes and experiences, rather than merely increasing the volume of data the clinician receives.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Jain et al. proposed a framework of 6 equitable AI algorithm principles to maximize performance while minimizing bias.

“Recently, Jain et al. have proposed a framework of 6 equitable AI algorithm principles to maximize performance while minimizing bias.”

• cites [51]Not checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: Bias in AI is a significant source of concern in machine learning and AI-generated technology.
• AssumptionRests on an assumption: These biases usually reflect widespread societal views about race, gender, biological sex, and ethnicity.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Clinicians report concern with regard to patient data privacy and potential legal liability with the use of new digital programs.

“Furthermore, clinicians report concern with regard to patient data privacy and potential legal liability with the use of new digital programs.”

• cites [61] Cowie, 2022, The digital future is now, JACC Heart Fail sourceNot checked yet

  Not checked in the free run.

• cites [62] Han, 2021, Changes in the digital health landscape in cardiac electrophysiology: a pre-and peri-pandemic COVID-19 era survey, Cardiovasc Digit Health J sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Cuffless PPG wearable wristbands using features such as pulse transit time have been shown to accurately measure continuous blood pressure and screen for hypertension using AI.

“Cuffless PPG wearable wristbands using features such as pulse transit time have been shown to accurately measure continuous blood pressure and screen for hypertension using AI.39”

• cites [39] Kuwabara, 2019, Validation of two watch-type wearable blood pressure monitors according to the ANSI/AAMI/ISO81060-2:2013 guidelines: Omron HEM-6410T-ZM and HEM-6410T-ZL, J Clin Hypertens (Greenwich) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based).

Data privacy, operability, and integrity present challenges to scaling wearable devices in clinical medicine.

“However data privacy, operability, and integrity present challenges to scaling wearable devices in clinical medicine.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Next-generation cybersecurity tools such as blockchain have been proposed as a means for secure storage and prevention of data breaches.

“Nextgeneration cybersecurity tools such as block chain have been proposed as a means for secure storage and prevention of such data breaches.56”

• cites [56] Cohen, 2019, Big data, big tech, and protecting patient privacy, JAMA sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: Sensitive patient data via wearable technology is subject to breaches.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

In some patients, navigating wearable devices, particularly in patients with cognitive, visual, or hearing impairments, lack of non-English resources, cost of the device and internet access, and a complex interface can limit usability and compliance.

“Furthermore, in some patients, navigating wearable devices, particularly in patients with cognitive, visual, or hearing impairments, lack of non-English resources, cost of the device and internet access, and a complex interface can limit usability and compliance.”

Probably not a substantial claim: may not pass S3 (load-bearing).

In Europe, the General Data Protection Regulation states that everyone has the right to control and protect their own personal data.

“In Europe, the General Data Protection Regulation states that everyone has the right to control and protect their own personal data”

• cites [58] Sarpatwari, 2017, Recalibrating privacy protections to promote patient engagement, N Engl J Med sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: Sensitive patient data via wearable technology is subject to breaches.

May combine two claims.

A successful V3 process might be challenging because each component of the framework might be built by a different company and most applications might not be broadly transferable to different populations.

“A successful V3 (ie, does the tool measure what it claims to measure?Is the measurement appropriate for the target population?)process might be challenging because each component of the framework might be built by a different company and most applications might not be broadly transferable to different populations (ie, AW parameters to detect oxygen consumption were derived from healthy populations”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

From the clinician's perspective, wearable data offers potentially invaluable insights into risk-modifying factors, medication adherence, and disease progression that is independent of patients' self-reports.

“From the clinician's perspective, wearable data offers potentially invaluable insights into risk-modifying factors, medication adherence, and disease progression that is independent of patients' self-reports.”

May combine two claims.

In one study, 72% of patients were willing to share their health data with their physician, but fewer were willing to share with their health insurer or manufacturing companies (53%).

“In one study, patients who were willing to share their health data with their physician (72%) but fewer with their health insurer or manufacturing companies (53%).57”

• cites [57] Hasselgren, 2020, Blockchain in healthcare and health sciences-a scoping review, Int J Med Inform sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: Sensitive patient data via wearable technology is subject to breaches.

Probably not a substantial claim: may not pass S3 (load-bearing).

According to a survey of health care professionals, lack of sufficient clinical infrastructure and personnel is the primary barrier to the use of health digital tools.

“According to a survey of health care professionals, lack of sufficient clinical infrastructure and personnel is the primary barrier to the use of health digital tools.”

• cites [60] Gawałko, 2021, The European TeleCheck-AF project on remote app-based management of atrial fibrillation during the COVID-19 pandemic: centre and patient experiences, Europace sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Wearable monitoring devices capable of estimating HRV show promise and might identify those who benefit from cardiac resynchronization therapy.

“Wearable monitoring devices capable of estimating HRV show promise and might identify those who benefit from cardiac resynchronization therapy.34,35”

• cites [34] Nolan, 1998, Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-heart), Circulation sourceNot checked yet

  Not checked in the free run.

• cites [35] Sherazi, 2015, Prognostic significance of heart rate variability among patients treated with cardiac resynchronization therapy: MADIT-CRT (Multicenter Automatic Defibrillator Implantation Trial-Cardiac Resynchronization Therapy), JACC Clin Electrophysiol sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The stated benefits of using such devices include fewer in-person visits to the hospital, reassurance and peace of mind, and more consistent follow-ups.

“The stated benefits of using such devices include fewer in-person visits to the hospital, reassurance and peace of mind, and more consistent follow-ups.”

• cites [59] Leclercq, 2022, Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: proceedings of the European Society of Cardiology cardiovascular round table, Europace sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

The lack of transparency in AI algorithms is another concern among clinicians, because it can be challenging to decipher the meaning of data and the sources of information derived from patient presentations.

“The lack of transparency in AI algorithms is another concern among clinicians, because it can be challenging to decipher the meaning of data and the sources of information derived from patient presentations.”

• cites [61] Cowie, 2022, The digital future is now, JACC Heart Fail sourceNot checked yet

  Not checked in the free run.

• cites [62] Han, 2021, Changes in the digital health landscape in cardiac electrophysiology: a pre-and peri-pandemic COVID-19 era survey, Cardiovasc Digit Health J sourceNot checked yet

  Not checked in the free run.

May combine two claims.

The ZOLL uCor patch equipped with ECG monitor and radiofrequency sensor has the ability to measure pulmonary fluid content and is currently being evaluated in a clinical trial for its ability to predict congestion.

“For instance, the ZOLL uCor patch (ZOLL Medical Corp, Pittsburgh, PA) equipped with ECG monitor and radiofrequency sensor has the ability to measure pulmonary fluid content and is currently being evaluated in a clinical trial for its ability to predict congestion.37”

• cites [37] Singhal, 2020, The role of wearables in heart failure, Curr Heart Fail Rep sourceNot checked yet

  Not checked in the free run.

May combine two claims.

One such trial used the REMOTE-CR platform and showed improved activity level with cost effectiveness compared with centre-based programs.

“One such trial used the REMOTE-CR (Android), a real-time remote rehabilitation platform that includes the use of a chestworn sensor and showed improved activity level with cost effectiveness compared with centre-based programs.43”

• cites [43] Abraham, 2011, Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: a randomised controlled trial, Lancet sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Even after clinical validation of data, data storage practices can play a significant role in quality and reliability of data.

“Even after clinical validation of data, data storage practices can play a significant role in quality and reliability of data.”

May combine two claims.

Because unequal access to care means less money is spent for Black patients, this introduces bias when using health care spending as a means to estimate care needs.

“Because unequal access to care means less money is spent for Black patients, this introduces bias when using health care spending as a means to estimate care needs.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

Regulatory hurdles for accessing proprietary archives could widen gaps and lead to inequity in medical research and development.

“Regulatory hurdles for accessing proprietary archives could also widen the gaps and lead to inequity in medical research and development.”

May combine two claims.

A meta-analysis of 9 trials that evaluated wearable monitors of physical activity in patients with cardiovascular disease showed that exercise prescription and monitoring via a wearable device was superior to no device, and increased adherence to rehabilitation plans.

“Furthermore, a meta-analysis of 9 trials that evaluated wearable monitors of physical activity in patients with cardiovascular disease showed that exercise prescription and monitoring via a wearable device was superior to no device, and increased adherence to rehabilitation plans.44”

• cites [44] Hannan, 2019, Impact of wearable physical activity monitoring devices with exercise prescription or advice in the maintenance phase of cardiac rehabilitation: systematic review and meta-analysis, BMC Sport Sci Med Rehabil sourceNot checked yet

  Not checked in the free run.

May combine two claims.

In the MiCORE study, a 43% reduction in 30-day rehospitalization and significant health-related cost reductions were reported.

“In their study, a 43% reduction in 30-day rehospitalization and significant health-related cost reductions were reported.41”

• cites [41] Marvel, 2021, Digital health intervention in acute myocardial infarction, Circ Cardiovasc Qual Outcomes sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Deidentification of data is a possibility to protect data, however, because of the extent of available data for each user, they can theoretically be reidentified.

“Deidentification of data is a possibility to protect data, however, because of the extent of available data for each user, they can theoretically be reidentified.55”

• cites [55] Sharon, 2019, Introduction to the special theme: the expansion of the health data ecosystem – rethinking data ethics and governance, Sage JNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: Sensitive patient data via wearable technology is subject to breaches.

Probably not a substantial claim: may not pass S3 (load-bearing).

Approximately two-thirds of the group were interested in continuing using the mobile-based monitoring system in the future for quality of life benefits.

“Approximately two-thirds of the group were interested in continuing using the mobile-based monitoring system in the future for quality of life benefits.59”

• cites [59] Leclercq, 2022, Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: proceedings of the European Society of Cardiology cardiovascular round table, Europace sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based).

AI algorithms allow for multimodal analysis of data from various sensors and wearable technologies alongside patient's electronic medical records, biochemical, and imaging findings.

“AI algorithms allow for multimodal analysis of data from various sensors and wearable technologies alongside patient's electronic medical records, biochemical, and imaging findings.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

Accurate real-time continuous monitoring of health data could pave the way for enhancing prevention and management of cardiovascular diseases such as arrhythmias, HF, hypertension, and improved adherence to cardiac rehabilitation programs.

“Accurate real-time continuous monitoring of health data could pave the way for enhancing prevention and management of cardiovascular diseases such as arrhythmias, HF, hypertension, and improved adherence to cardiac rehabilitation programs.”

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

By harnessing real-time patient-generated data obtained from free-living environments, health care providers can gain valuable insight into individuals' cardiovascular health and deliver personalized care interventions.

“By harnessing real-time patient-generated data obtained from free-living environments, health care providers can gain valuable insight into individuals' cardiovascular health and deliver personalized care interventions.”

Probably not a substantial claim: may not pass S4 (based).

Wearable devices have shown potential application in screening and monitoring of cardiovascular diseases.

“Wearable devices have shown potential application in screening and monitoring of cardiovascular diseases as discussed previously.”

Probably not a substantial claim: may not pass S4 (based).

This transition presents unique challenges such as ensuring accuracy and validity of biosensors embedded within these devices.

“This transition presents unique challenges such as ensuring accuracy and validity of biosensors embedded within these devices.”

Probably not a substantial claim: may not pass S4 (based).

Application of AI has helped play a key role in optimizing wearable technology performance and integration in the cardiovascular medicine and clinical setting.

“Application of AI has helped play a key role in optimizing wearable technology performance and integration in the cardiovascular medicine and clinical setting.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Some clinicians perceive AI-guided care as diminishing the role of human intelligence and expertise.

“Some clinicians perceive AI-guided care as diminishing the role of human intelligence and expertise.”

• cites [61] Cowie, 2022, The digital future is now, JACC Heart Fail sourceNot checked yet

  Not checked in the free run.

• cites [62] Han, 2021, Changes in the digital health landscape in cardiac electrophysiology: a pre-and peri-pandemic COVID-19 era survey, Cardiovasc Digit Health J sourceNot checked yet

  Not checked in the free run.