An mHealth Lifestyle Intervention Service for Improving Blood Pressure using Machine Learning and IoMTs

Leitner, Jared; Chiang, Po-Han; Khan, Brian; Dey, Sujit

doi:10.1109/icdh55609.2022.00030

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…The diet questions are tailored to measure information relevant to hypertension, including alcohol, red meat, fruits or vegetables, and salt consumption [ 25 ]. The details of the questionnaire are described in our previous study [ 23 ]. In addition, we asked participants to complete a study experience survey that asked them to rate the difficulty level of completing the study tasks, how useful they found the recommendations, and their experience using the app.…”

Section: Methodsmentioning

confidence: 99%

“…The objective of these precise lifestyle recommendations is to encourage participants to concentrate on 1 aspect of their lifestyle at a time, focusing on the factor that has the greatest association with their BP based on the underlying relationship between their BP and lifestyle factors. We describe the AI-based intervention platform in more detail in our previous study [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

“…While the platform autonomously engages patients, it is clinician supervised and notifies clinicians of critical BP readings. In our previous study [ 23 ], we enrolled 38 participants who were prehypertensive or had stage-1 hypertension (SBP between 120 and 139 mm Hg or DBP between 80 and 89 mm Hg) and demonstrated that 75% of the participants receiving the intervention were able to achieve a controlled BP (<130/80 mm Hg) after 16 weeks of engagement. However, the limitations of the previous study [ 23 ] are as follows: (1) the participants were not provided with an interactive mobile app for the delivery of our precise lifestyle recommendations, (2) the small number of participants did not enable rigorous evaluation, and (3) the study did not consider patients with stage-2 hypertension who can potentially benefit more from lifestyle management.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial

Leitner,

Chiang,

Agnihotri

et al. 2024

JMIR Cardio

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Background Home blood pressure (BP) monitoring with lifestyle coaching is effective in managing hypertension and reducing cardiovascular risk. However, traditional manual lifestyle coaching models significantly limit availability due to high operating costs and personnel requirements. Furthermore, the lack of patient lifestyle monitoring and clinician time constraints can prevent personalized coaching on lifestyle modifications. Objective This study assesses the effectiveness of a fully digital, autonomous, and artificial intelligence (AI)–based lifestyle coaching program on achieving BP control among adults with hypertension. Methods Participants were enrolled in a single-arm nonrandomized trial in which they received a BP monitor and wearable activity tracker. Data were collected from these devices and a questionnaire mobile app, which were used to train personalized machine learning models that enabled precision lifestyle coaching delivered to participants via SMS text messaging and a mobile app. The primary outcomes included (1) the changes in systolic and diastolic BP from baseline to 12 and 24 weeks and (2) the percentage change of participants in the controlled, stage-1, and stage-2 hypertension categories from baseline to 12 and 24 weeks. Secondary outcomes included (1) the participant engagement rate as measured by data collection consistency and (2) the number of manual clinician outreaches. Results In total, 141 participants were monitored over 24 weeks. At 12 weeks, systolic and diastolic BP decreased by 5.6 mm Hg (95% CI −7.1 to −4.2; P<.001) and 3.8 mm Hg (95% CI −4.7 to −2.8; P<.001), respectively. Particularly, for participants starting with stage-2 hypertension, systolic and diastolic BP decreased by 9.6 mm Hg (95% CI −12.2 to −6.9; P<.001) and 5.7 mm Hg (95% CI −7.6 to −3.9; P<.001), respectively. At 24 weeks, systolic and diastolic BP decreased by 8.1 mm Hg (95% CI −10.1 to −6.1; P<.001) and 5.1 mm Hg (95% CI −6.2 to −3.9; P<.001), respectively. For participants starting with stage-2 hypertension, systolic and diastolic BP decreased by 14.2 mm Hg (95% CI −17.7 to −10.7; P<.001) and 8.1 mm Hg (95% CI −10.4 to −5.7; P<.001), respectively, at 24 weeks. The percentage of participants with controlled BP increased by 17.2% (22/128; P<.001) and 26.5% (27/102; P<.001) from baseline to 12 and 24 weeks, respectively. The percentage of participants with stage-2 hypertension decreased by 25% (32/128; P<.001) and 26.5% (27/102; P<.001) from baseline to 12 and 24 weeks, respectively. The average weekly participant engagement rate was 92% (SD 3.9%), and only 5.9% (6/102) of the participants required manual outreach over 24 weeks. Conclusions The study demonstrates the potential of fully digital, autonomous, and AI-based lifestyle coaching to achieve meaningful BP improvements and high engagement for patients with hypertension while substantially reducing clinician workloads. Trial Registration ClinicalTrials.gov NCT06337734; https://clinicaltrials.gov/study/NCT06337734

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial

Leitner,

Chiang,

Agnihotri

et al. 2024

JMIR Cardio

Self Cite

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“…Adaptive suggestions and real-time monitoring are made possible by the incorporation of machine learning. [11].…”

mentioning

confidence: 99%

A Survey on Transforming Healthcare with IoMT : The Power of Connected Medical Devices

Arul Raj,

Rukmani,

Rukmani

et al. 2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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This thorough analysis examines current developments in medical technology, with an emphasis on the Internet of Medical Things (IoMT) and its uses. It covers IoMT's contribution in creating wearable internet monitors for dispersed monitoring systems, addressing pregnancy-related issues, and is divided into four subject groups. Along with blockchain adaption, it also highlights privacy and dependability in IoMT-based health monitoring systems. The paper also emphasizes the contributions of IoMT to healthcare diagnosis and monitoring, including machine learning and edge-AI-based cardiovascular health monitoring and fall prediction. It also covers data science and healthcare informatics techniques, such as using lightweight cryptography to ensure real-time security in remote patient monitoring and identifying unusual user behavior. IoMT has the potential to transform patient care, diagnostics, and data-driven decision-making across a range of healthcare areas, as the paper highlights overall.

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Leveraging unlabeled data: Fostering self-regulated learning in online education with semi-supervised recommender systems

Zhang,

Hew

2024

Educ Inf Technol

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An mHealth Lifestyle Intervention Service for Improving Blood Pressure using Machine Learning and IoMTs

Cited by 9 publications

References 14 publications

The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial

The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial

A Survey on Transforming Healthcare with IoMT : The Power of Connected Medical Devices

Leveraging unlabeled data: Fostering self-regulated learning in online education with semi-supervised recommender systems

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