TY - JOUR
T1 - Comprehensive Cardiovascular Risk Factor Control With a Mobile Health Cardiovascular Risk Self-Management Program
AU - Paz, Edo
AU - Pargaonkar, Vedant S.
AU - Roach, Brian J.
AU - Meadows, Morgan
AU - Roberts, Jennifer M.
AU - Gazit, Tomer
AU - Zaleski, Amanda L.
AU - Craig, Kelly Jean Thomas
AU - Serra, Steven J.
AU - Dunn, Pat
AU - Michos, Erin D.
N1 - Publisher Copyright:
© 2024 The Authors and Hello Doctor, Ltd.
PY - 2024
Y1 - 2024
N2 - BACKGROUND: Mobile health technology’s impact on cardiovascular risk factor control is not fully understood. This study evalu-ates the association between interaction with a mobile health application and change in cardiovascular risk factors. METHODS AND RESULTS: Participants with hypertension with or without dyslipidemia enrolled in a workplace-deployed mobile health application-based cardiovascular risk self-management program between January 2018 and December 2022. Retrospective evaluation explored the influence of application engagement on change in blood pressure (BP), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and weight. Multiple regression analyses examined the influence of guideline-based, nonpharmacological lifestyle-based digital coaching on outcomes adjusting for confounders. Of 102 475 participants, 49.1% were women. Median age was 53 (interquartile range, 43–61) years, BP was 134 (interquartile range, 124–144)/84 (in-terquartile range, 78–91) mm Hg, TC was 183 (interquartile range, 155–212) mg/dL, LDL-C was 106 (82–131) mg/dL, and body mass index was 30 (26–35) kg/m2. At 2 years, participants with baseline systolic BP ≥140 mm Hg reduced systolic BP by 18.6 (SEM, 0.3) mm Hg. At follow up, participants with baseline TC ≥240 mg/dL reduced TC by 65.7 (SEM, 4.6) mg/dL, participants with baseline LDL-C≥160 mg/dL reduced LDL-C by 66.6 (SEM, 6.2) mg/dL, and participants with baseline body mass index ≥30 kg/m2 lost 12.0 (SEM, 0.3) pounds, or 5.1% of body weight. Interaction with digital coaching was associated with greater reduction in all outcomes. CONCLUSIONS: A mobile health application-based cardiovascular risk self-management program was associated with favorable reductions in BP, TC, LDL-C, and weight, highlighting the potential use of this technology in comprehensive cardiovascular risk factor control.
AB - BACKGROUND: Mobile health technology’s impact on cardiovascular risk factor control is not fully understood. This study evalu-ates the association between interaction with a mobile health application and change in cardiovascular risk factors. METHODS AND RESULTS: Participants with hypertension with or without dyslipidemia enrolled in a workplace-deployed mobile health application-based cardiovascular risk self-management program between January 2018 and December 2022. Retrospective evaluation explored the influence of application engagement on change in blood pressure (BP), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and weight. Multiple regression analyses examined the influence of guideline-based, nonpharmacological lifestyle-based digital coaching on outcomes adjusting for confounders. Of 102 475 participants, 49.1% were women. Median age was 53 (interquartile range, 43–61) years, BP was 134 (interquartile range, 124–144)/84 (in-terquartile range, 78–91) mm Hg, TC was 183 (interquartile range, 155–212) mg/dL, LDL-C was 106 (82–131) mg/dL, and body mass index was 30 (26–35) kg/m2. At 2 years, participants with baseline systolic BP ≥140 mm Hg reduced systolic BP by 18.6 (SEM, 0.3) mm Hg. At follow up, participants with baseline TC ≥240 mg/dL reduced TC by 65.7 (SEM, 4.6) mg/dL, participants with baseline LDL-C≥160 mg/dL reduced LDL-C by 66.6 (SEM, 6.2) mg/dL, and participants with baseline body mass index ≥30 kg/m2 lost 12.0 (SEM, 0.3) pounds, or 5.1% of body weight. Interaction with digital coaching was associated with greater reduction in all outcomes. CONCLUSIONS: A mobile health application-based cardiovascular risk self-management program was associated with favorable reductions in BP, TC, LDL-C, and weight, highlighting the potential use of this technology in comprehensive cardiovascular risk factor control.
KW - artificial intelligence
KW - blood pressure
KW - cardiovascular diseases
KW - cholesterol
KW - heart disease risk factors
KW - mobile health
KW - weight loss
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U2 - 10.1161/JAHA.123.033328
DO - 10.1161/JAHA.123.033328
M3 - Article
C2 - 38757455
AN - SCOPUS:85194014007
SN - 2047-9980
VL - 13
JO - Journal of the American Heart Association
JF - Journal of the American Heart Association
IS - 10
M1 - e033328
ER -