Background: Home blood pressure (BP) monitoring is recommended for people with hypertension; however, meta-analyses have demonstrated that BP improvements are related to additional coaching support in combination with self-monitoring, with little or no effect of self-monitoring alone. High-contact coaching requires substantial resources and may be difficult to deliver via human coaching models.
Objective: This observational study assessed changes in BP and body weight following participation in a fully digital program called Lark Hypertension Care with coaching powered by artificial intelligence (AI).
Methods: Participants (N=864) had a baseline systolic BP (SBP) ≥120 mm Hg, provided their baseline body weight, and had reached at least their third month in the program. The primary outcome was the change in SBP at 3 and 6 months, with secondary outcomes of change in body weight and associations of changes in SBP and body weight with participant demographics, characteristics, and program engagement.
Results: By month 3, there was a significant drop of –5.4 mm Hg (95% CI –6.5 to –4.3; P<.001) in mean SBP from baseline. BP did not change significantly (ie, the SBP drop maintained) from 3 to 6 months for participants who provided readings at both time points (P=.49). Half of the participants achieved a clinically meaningful drop of ≥5 mm Hg by month 3 (178/349, 51.0%) and month 6 (98/199, 49.2%). The magnitude of the drop depended on starting SBP. Participants classified as hypertension stage 2 had the largest mean drop in SBP of –12.4 mm Hg (SE 1.2 mm Hg) by month 3 and –13.0 mm Hg (SE 1.6 mm Hg) by month 6; participants classified as hypertension stage 1 lowered by –5.2 mm Hg (SE 0.8) mm Hg by month 3 and –7.3 mm Hg (SE 1.3 mm Hg) by month 6; participants classified as elevated lowered by –1.1 mm Hg (SE 0.7 mm Hg) by month 3 but did not drop by month 6. Starting SBP (β=.11; P<.001), percent weight change (β=–.36; P=.02), and initial BMI (β=–.56; P<.001) were significantly associated with the likelihood of lowering SBP ≥5 mm Hg by month 3. Percent weight change acted as a mediator of the relationship between program engagement and drop in SBP. The bootstrapped unstandardized indirect effect was –0.0024 (95% CI –0.0052 to 0; P=.002).
Conclusions: A hypertension care program with coaching powered by AI was associated with a clinically meaningful reduction in SBP following 3 and 6 months of program participation. Percent weight change was significantly associated with the likelihood of achieving a ≥5 mm Hg drop in SBP. An AI-powered solution may offer a scalable approach to helping individuals with hypertension achieve clinically meaningful reductions in their BP and associated risk of cardiovascular disease and other serious adverse outcomes via healthy lifestyle changes such as weight loss.
The American Heart Association (AHA) defines high blood pressure (BP), called hypertension, as systolic BP (SBP) ≥130 mm Hg or diastolic BP (DBP) ≥80 mm Hg that remains elevated over time . Nearly half of US adults have hypertension or are taking medication for hypertension, and only 1 in 4 have their BP under control [ ]. Effective strategies to improve self-management of BP are critical since hypertension is a leading modifiable risk factor for cardiovascular disease [ ], ranks as the leading cause of mortality in the United States [ ], and is associated with a higher risk for other serious and costly conditions such as stroke, kidney disease, dementia, and eye damage [ ]. In a large meta-analysis, lowering SBP by at least 5 mm Hg reduced the risk of major cardiovascular events by 10% even at normal (<120 mm Hg) and high-normal (120-129 mm Hg) values of SBP [ ].
The AHA recommends home monitoring of BP as a part of self-management for all people with hypertension because it provides a better estimate of BP under “normal” conditions and may help improve BP control . However, meta-analyses have provided strong evidence that BP improvements are related to cointerventions involving individually tailored coaching support in combination with self-monitoring, with little or no effect of self-monitoring alone [ , ]. Coaching can provide personalized support for increasing health-promoting lifestyle behaviors that are known to reduce BP, such as reaching and maintaining a healthy weight, eating a healthy diet, limiting alcohol consumption, avoiding smoking, adhering to prescribed medications, and being physically active [ ]. Compared to a healthy weight, prior research has attributed an estimated 32% excess risk of hypertension to being overweight and 47% to being obese [ ]. There is a well-characterized linear relationship between SBP and BMI, with a higher prevalence of hypertension in individuals in higher BMI classes [ ]. Weight loss is a particularly important focus for individuals with hypertension who are overweight or obese because it is associated with improvements in BP control [ , ].
The US Preventive Task Force recommends moderate-to-high-intensity behavioral coaching for adults with cardiovascular risk factors including high BP and being overweight or obese . However, behavioral coaching is most effective when it includes personalized content and feedback as well as frequent and timely interactions [ , ]. This type of coaching is highly time and resource intensive and may be difficult to achieve via human coaching models. Fully digital programs powered by artificial intelligence (AI) represent one solution for hypertension care that combines self-monitoring with highly personalized, automated coaching. An AI-powered coaching platform enables the delivery of continuous, synchronous coaching and feedback and offers a scalable, high-touch, and long-term solution to help people make lifestyle changes and sustain healthy behaviors. However, there is little evidence of the effectiveness of AI-powered solutions for facilitating reductions in BP and body weight for individuals with hypertension.
This observational study assessed changes in BP and body weight following participation in a fully digital hypertension care program powered by AI. This program used self-monitoring of BP coupled with conversational AI delivered on a participant’s smartphone to coach participants in lowering their BP, losing weight, and making other healthy lifestyle changes. The primary study objective was to evaluate the change in BP over time (baseline, 3 months, and 6 months), with secondary objectives of assessing changes in weight and associations of BP and weight change with AI-powered coaching and activities. The primary hypothesis was that participants with elevated or greater SBP (ie, ≥120 mm Hg) at baseline would lower their SBP on average by at least 5 mm Hg, which is commonly considered a clinically meaningful threshold for reduced cardiovascular disease risk [, ]. We expected a greater reduction for participants with a higher starting SBP. The secondary hypothesis was that a greater reduction in BP and weight loss would be associated with greater participation in AI coaching and activities within the mobile app.
Participants and Recruitment
The study received exemption status from Advarra Institutional Review Board (protocol #Pro00047181) for retrospective analysis of previously collected and deidentified data.
Description of the Hypertension Care Program
The hypertension care program consisted of educational lessons and fully automated, personalized coaching on healthy lifestyle behaviors powered by conversational AI. Conversational AI technologies facilitate humanlike interactions between a robot (computer) and a human via a text-based interface. See[ , , - ] for a detailed description. After enrollment, participants completed a brief orientation on how to obtain accurate BP measurements, set medication reminders, and select an optional weight loss goal. Participants then progressed through weekly educational lessons spread over 26 weeks.
Participants in the program could opt to receive connected devices (digital BP cuff or weight scale) to measure BP and weight and could enter BP data in a variety of ways in the app, including wirelessly or manually. If using a connected BP monitor, participants could take a measurement through a guided coaching exchange and sync the measurement immediately. Those who already had a home BP cuff could use their existing device and manually enter BP readings in the app. Regardless of the measurement method, participants received detailed instructions on taking at-home BP measurements, as outlined by the AHA . The program has built-in safety mechanisms: in the case of extremely high readings (SBP >180 mm Hg or DBP >110 mm Hg) or low readings (<90 mm Hg or <60 mm Hg) and symptoms like dizziness, the AI coach prompts participants to seek assistance or call their medical provider and assists them in taking these actions.
Primary Outcome of Change in SBP
The primary outcome was the change in SBP from the start of the program to 3 and 6 months, respectively. Starting BP was a participant’s average measurement within the first week of the program, and 3- and 6-month BPs were a participant’s average measurement during the third and sixth month in the program, respectively. A few participants had readings that occurred soon after the sixth month, and we included these data points as well to maximize the sample size available for analysis. This was necessary for a real-world study where participants were not aware that they were supposed to provide readings at a particular time point. We considered a clinically meaningful improvement to be a drop of ≥5 mm Hg at any time point. We also conducted subgroup analyses on participants classified as having elevated SBP at baseline (SBP 120-129 mm Hg), stage 1 hypertension (SBP 130-139 mm Hg), and stage 2 hypertension (SBP ≥140 mm Hg). We assessed corresponding changes in DBP based on starting SBP classification.
Secondary Outcomes of Change in Body Weight and Associations With Program Engagement
The secondary outcome was the percent weight change at month 3 and month 6, respectively. We calculated the percent weight change at 3 months as follows: (first weight – 3-month nadir weight)/first weight. We calculated the percent weight change at 6 months as follows: (first weight – 6-month nadir weight)/first weight. We removed any abnormal weigh-ins indicating a weight loss or gain rate of >7 lbs/week unless confirmed by the user to be a correct measurement. We assessed associations between the change in SBP and body weight at month 3 with participant demographics, characteristics, and engagement metrics using 2 separate regression models. For the regression with change in SBP as the dependent variable, independent variables included participant demographics (age, sex), characteristics (initial BMI, starting SBP, percent weight change), and program engagement metrics (number of sessions with the AI coach, number of BP measurements). For the regression with percent weight change as the dependent variable, independent variables included participant demographics (age, sex), characteristics (initial BMI, starting SBP), and program engagement metrics (number of sessions with the AI coach, number of weight measurements). We examined these associations at month 3 instead of month 6 due to the larger sample size available at month 3 (for statistical power) and since most of the change in BP occurred by month 3 and was maintained through month 6 for participants who provided both measurements.
We conducted all statistical analyses in RStudio 4.0.5. We compared participant demographic and characteristic data between subgroups categorized by baseline SBP classification. We used paired t tests to evaluate the change in BP and body weight between each pair of time points (baseline, 3 months, 6 months) to maximize the sample size available for each comparison (more participants had made it to 3 months in the program [N=864] compared to 6 months [n=717]). We conducted two separate regression analyses: (1) a multiple logistic regression to assess the effects of participant demographics, characteristics, and program engagement on participants’ likelihood of achieving a clinically meaningful drop in SBP of ≥5 mm Hg by month 3 in the program; and (2) a linear regression to assess the effects of participant demographics, characteristics, and program engagement on percent weight change by month 3 in the program. We conducted these analyses separately to consider each outcome independently and because not all participants had both BP and weight data available at 3 months. Engagement variables in both regressions had no issues of multicollinearity; all variance inflation factors in both models were <2. The results of the regression analyses suggested that percent weight change was a mediator of the relationship between program engagement (number of sessions with the AI coach) and SBP drop, so we conducted an exploratory full mediation analysis  to confirm this observation. The a priori α was ≤.05 for all statistical tests.
Participant Demographics and Characteristics
There were significant differences across participants based on the SBP category for initial BMI (). Initial BMI increased, on average, by 1 unit for each increase in the SBP classification category.
|BPa category||F test/ chi-square (df)b||P value|
|All, mean (SE)c||Elevated, mean (SE)c||Stage 1 hypertension, mean (SE)c||Stage 2 hypertension, mean (SE)c|
|Age (years)||51.5 (0.34)||52.3 (0.54)||51.2 (0.55)||50.7 (0.70)||1.9 (2,847)||.14|
|Initial BMI (kg/m2)||34.0 (0.25)||33.2 (0.37)||34.2 (0.39)||35.2 (0.56)||5.4 (2,861)||.005|
|No. of BP readings included in baseline BP||3.6 (0.11)||3.5 (0.16)||3.9 (0.21)||3.3 (0.19)||2.6 (2,861)||.07|
|Baseline systolic BP (mm Hg)||134.5 (0.38)||125.0 (0.15)||134.3 (0.17)||149.5 (0.60)||1506.0 (2,861)||<.001|
|Baseline diastolic BP (mm Hg)||85.3 (0.30)||81.4 (0.38)||85.4 (0.43)||91.4 (0.63)||110.5 (2,861)||<.001|
|Female sexc||500/861 (58.1)||216/351 (61.5)||162/286 (56.6)||122/224 (54.5)||3.2 (2)||.21|
|White racec||370/499 (74.1)||157/207 (75.8)||128/168 (76.2)||85/124 (68.5)||2.7 (2)||.26|
|Taking BP medsc||530/578 (91.7)||228/245 (93.1)||156/177 (88.1)||146/156 (93.6)||4.3 (2)||.12|
aBP: blood pressure.
bChi-square is applicable only to female sex, White race, and taking BP meds. For the other demographics, F test is applicable.
cMean (SE) here is not applicable to categories of female sex, White race, and taking BP meds; for these categories, data are expressed as n/N (%).
Changes in BP
Participants provided a mean of 3.6 (SE 0.1) BP readings for the calculation of average starting BP, a mean of 15.0 (SE 1.2) readings for the calculation of average BP at month 3, and a mean of 17.0 (SE 1.6) BP readings for the calculation of average BP at month 6.
There was a significant overall drop of –5.4 mm Hg in mean SBP following 3 months (t=9.5348; P<.001; 95% CI –6.5 to –4.3), and no change in SBP from 3 to 6 months for those who provided readings at both time points (t=0.7139; P=.49;). Participants with a starting SBP classified as hypertension stage 2 had the greatest change in SBP at both time points, with a drop of –12.4 mm Hg (SE 1.2 mm Hg) by month 3 and a drop of –13.0 mm Hg (SE 1.6 mm Hg) by month 6.
Approximately half of the overall sample achieved a clinically meaningful SBP drop of ≥5 mm Hg by month 3 (178/349, 51%) and by month 6 (98/199, 49.2%). The drop in SBP resulted in 47.6% (166/349) of participants lowering their SBP by at least 1 classification category (eg, hypertension stage 2 to hypertension stage 1; hypertension stage 1 to elevated) by month 3.
|Change in BPa at 3 months, Δmean (95% CI)b||t test (df)||P value||With ≥5 mm Hg SBPc drop at month 3, n/N (%)||Change in SBP at 6+ months, Δmean (95% CI)b||t test (df)||P value||With ≥5 mm Hg SBP drop at month 6, n/N (%)|
|SBPb (mm Hg)|
|Overall||–5.4 (–6.5 to –4.3)||9.5 (348)||<.001||178/349 (51.0)||–5.3 (–6.9 to –3.6)||6.3 (198)||<.001||98/199 (49.2)|
|120-129||–1.1 (–2.5 to 0.4)||1.5 (148)||.14||51/149 (34.2)||0.6 (–1.7 to 2.8)||–0.5 (85)||.62||25/86 (29.1)|
|130-139||–5.2 (–6.8 to –3.7)||6.6 (106)||<.001||53/107 (49.5)||–7.3 (–9.8 to –4.8)||5.8 (64)||<.001||36/65 (55.4)|
|≥140||–12.4 (–14.9 to –10.0)||10.2 (92)||<.001||74/93 (79.6)||–13.0 (–16.2 to –9.8)||8.2 (47)||<.001||37/48 (77.1)|
|DBPd (mm Hg)e|
|Overall||–1.3 (–2.1 to –0.5)||3.1 (348)||.002||N/Ae||–1.2 (–2.3 to –0.2)||2.3 (198)||.02||N/Af|
|Elevated||0.6 (–0.6 to 1.7)||–1.0 (148)||.34||N/A||1.0 (–0.6 to 2.5)||–1.2 (85)||.22||N/A|
|Stage 1||–0.9 (–2.4 to 0.5)||1.3 (106)||.21||N/A||–2.4 (–4.2 to –0.7)||2.8 (64)||.007||N/A|
|Stage 2||–4.6 (–6.2 to –3.0)||5.8 (92)||<.001||N/A||–3.5 (–5.7 to –1.3)||3.2 (47)||.002||N/A|
aBP: blood pressure.
bNegative Δ values indicate a drop in BP and positive values an increase.
cSBP: systolic blood pressure.
dDBP: diastolic blood pressure.
eDBP categories based on initial SBP classification: elevated, hypertension stage 1, or hypertension stage 2.
fN/A: not applicable.
Associations With BP Drop and Weight Change
Results of the multiple logistic regression for BP revealed associations of participant demographics, characteristics, and program engagement metrics with the likelihood of achieving a clinically meaningful drop of ≥5 mm Hg in SBP by month 3. The overall regression was statistically significant (log-likelihood –152.3; McFadden's pseudo R2=0.18; P<.001. Starting SBP, initial BMI, and percent weight change at month 3 were significantly associated with the likelihood of achieving a drop of ≥5 mm Hg in SBP ().
Of the participants who provided weigh-ins in the third month, 90.1% (374/415) remained weight stable or lost weight over the first 3 months of the program ().
Results of the multiple linear regression for weight change revealed associations of participant demographics, characteristics, and program engagement metrics with the magnitude of percent weight change by month 3. The overall regression was statistically significant (F7,397=5.97; R2=0.10 P<.001). Initial BMI, the number of sessions with the AI coach, and the number of weigh-ins recorded in the first 3 months were significantly associated with percent weight change ().
The 2 regression models together demonstrated that percent weight change at month 3 was significantly associated with the likelihood of achieving a ≥5 mm Hg drop in SBP, and program engagement variables were significantly associated with the magnitude of percent weight change but not SBP drop. Thus, it was important to consider whether percent weight change acted as a statistical mediator between program engagement and SBP drop. The results of the mediation analysis indeed demonstrated that the effect of program engagement on the drop in SBP was fully mediated by percent weight change at month 3.
Asillustrates, the regression coefficient between percent weight change at month 3 and the drop in SBP was significant, even though the regression coefficient between the number of sessions with the AI coach and the drop in SBP was not. Although the total effect was therefore not significant, this is not considered a requirement for statistical mediation [ ]. We tested the significance of the unstandardized indirect effect of the number of sessions with the AI coach on the change in SBP that occurred via the mediator percent weight change at month 3 using 1000 bootstrapped samples. The bootstrapped unstandardized indirect or average causal mediation effect was –0.0024 (95% CI –0.0052 to 0; P=.002).
|Variable||Standardized coefficient (β)||SE||Z value||P value|
|Initial BMI (kg/m2)||–.56||0.15||–3.57||<.001|
|Percent weight change by 3 monthsb||–.36||0.15||2.43||.02|
|No. of sessions with AId coach in first 3 months||–.04||0.16||–0.27||.79|
|No. of BP measurements recorded in first 3 months||.05||0.16||0.28||.78|
aIncluded participants had to have both blood pressure and weight data available at 3 months.
bA negative sign for weight change indicates greater weight loss.
cSBP: systolic blood pressure.
dAI: artificial intelligence.
|Variable||Standardized coefficient (β)||SE||t value||P value|
|Initial BMI (kg/m2)||–.48||0.16||3.02||.003|
|No. of sessions with AIc coach in first 3 months||–.62||0.16||3.81||<.001|
|No. of weight measurements recorded in first 3 months||–.44||0.16||2.78||.006|
aLarger sample size due to removed requirement for blood pressure data at 3 months.
bSBP: systolic blood pressure.
cAI: artificial intelligence.
The primary aim of this study was to assess changes in SBP following participation in an AI-powered hypertension care program. We further assessed changes in weight and associations of BP and weight change with participant characteristics and program engagement. In support of the primary hypothesis, participation in the AI-powered hypertension care program was associated with clinically meaningful reductions in SBP over 3 to 6 months, with larger drops observed in the subgroups of participants classified as stage 1 or 2 hypertension at baseline. More than half of all participants achieved a clinically meaningful drop in SBP of ≥5 mm Hg. The percentage of participants that achieved a clinically meaningful reduction was higher for those classified as hypertensive at baseline, with 79.6% (74/93) of participants with a starting SBP ≥140 mm Hg achieving a drop of ≥5 mm Hg by month 3 in the program. Percent weight change at month 3 was significantly associated with program engagement and the likelihood of achieving a clinically meaningful drop in SBP, and percent weight change mediated the relationship between program engagement and change in SBP.
The overall average reduction in SBP of –5.4 mm Hg in this study corresponded to roughly half of the participants lowering their initial SBP classification by at least 1 classification category by month 3. Key aspects of the program included reminders to monitor BP, medication adherence support, personalized in-the-moment feedback about progress, hypertension-specific nutrition coaching, stress-reduction coaching, and educational material about hypertension. Prior research has shown that this type of personalized and multifaceted intervention is critical for success in digital hypertension care programs . Compared to individuals with normotensive BP, individuals with treated but uncontrolled hypertension are at a higher risk of cardiovascular, cerebrovascular, and all-cause mortality [ ]. Lowering BP is of well-established importance for those with hypertension stage 2; however, lowering BP for individuals with elevated BP or hypertension stage 1 is also clinically meaningful because absolute reductions in the risk of stroke, major cardiovascular events, and cardiovascular and all-cause mortalities have been shown to be progressively lower with a lower attained value of SBP [ ].
The observed change in systolic BP in this study is comparable to the drops reported in published human coach–led behavioral lifestyle interventions. In the meta-analysis by Tucker et al , only 1 of 5 interventions that investigated self-monitoring alone showed a significant drop in SBP, with a pooled average of −1.0 mm Hg (95% CI −3.3 to 1.2). In contrast, self-monitoring with intensive coaching support via counseling or telecounseling showed statistically significant drops in SBP compared to a control group, with a pooled average reduction of –6.1 mm Hg (95% CI −9.0 to −3.2). The present study provides new evidence that members of a fully digital program powered by AI coaching experienced improvements in their BP while enrolled in the program.
Participants in this study were obese on average, with the overall initial BMI of 34.0 kg/m2 falling into class I obesity . There was a 1-unit increase in initial BMI for each increasing classification of starting SBP, with participants classified as elevated having an average BMI of 33.2 kg/m2, hypertension stage 1 an average BMI of 34.2 kg/m2, and hypertension stage 2 an average BMI of 35.2 kg/m2 (class II obesity). Given that weight loss is associated with improvements in BP control [ , ], weight loss was a particularly important target for participants in this study. Percent weight change at month 3 was significantly associated with the likelihood of achieving a clinically meaningful drop in SBP of ≥5 mm Hg. Participants with a higher initial BMI were less likely to achieve this clinically meaningful drop. However, in the regression with percent weight change as the dependent variable, participants with a higher initial BMI lost a greater amount of weight. Taken together, it appears that there were some participants with a higher initial BMI that did not achieve a clinically meaningful reduction in BP. However, for the larger number of users analyzed in the weight change regression, having a higher initial BMI was associated with a greater percent weight loss.
There are multiple reasons for the observed relationship between percent weight change and achieving a clinically meaningful drop in SBP in this study. There are well-established physiological benefits of weight loss for hypertension, such as improvement in insulin sensitivity and a decrease in sympathetic nervous system activity and inflammation [, , ]. However, given that percent weight change was significantly associated with program engagement and statistically mediated the relationship between program engagement and SBP drop in this study, it may also be that percent weight change was an indicator of those participants who were more closely adhering to the recommendations of the AI coach and adopting the healthy lifestyle changes and behaviors (eg, diet, exercise) that are also known to lower BP [ , ]. Indeed, prior research has linked weight loss to behaviors such as frequent tracking of exercise and weight [ ].
Study Strengths and Limitations
This was a single-arm study, preventing any determination of cause and effect. However, participants were real-world users of a commercially available digital health program designed for hypertension management; thus, this study provides evidence for the effectiveness of an AI-powered behavioral coaching program for lowering BP in the target population of interest. Participants were not required to provide socioeconomic information (eg, income, education), which limited insight into potential socioeconomic disparities. Although retention was lower than that in clinical efficacy studies, this is expected for digital health , and retention was substantially higher than what is commonly reported for similar programs in the literature [ ]. Prior investigations have demonstrated that self-monitoring along with self-titration of medications in collaboration with a treating physician yields robust drops in BP and good retention [ ]. Engaging physicians in the member experience could be one way to improve member retention. The exploratory mediation analysis had some inherent limitations: without the ability to infer causal relationships or directionality from the results of this study, this was not “true” mediation. Given the timing of the measurements, we cannot state that engagement caused weight loss, which then caused BP reductions. However, the alternative model (engagement → BP reduction → weight loss) was not significant, which supports the directionality of the relationship between engagement, percent weight loss, and BP change proposed in this study.
This study was an important first step in demonstrating changes in BP and body weight that occurred during a fully digital hypertension management program. Although we had information on other important BP management strategies at baseline (eg, medication status), we did not have the ability to track changes made to participants’ care management that might have occurred during the duration of the study. In future investigations, we intend to examine interactions between program participation and additional aspects of care. Evaluating medication adherence is a new feature within the app, and future investigations will examine whether participating in a fully digital hypertension management program results in improved adherence to prescribed medications. Finally, we did not separately consider coaching sessions per topic area (eg, diet) in the regression analyses due to collinearity issues. However, certain types of coaching may be more important than others. We plan to explore the different factors related to AI coaching in future investigations.
Members enrolled in a fully digital hypertension care program with coaching powered by AI who provided BP readings during their third and sixth months of program participation achieved clinically meaningful reductions in SBP. The magnitude of the drop depended on starting SBP, with participants classified as hypertension stage 2 experiencing the greatest drop. Most participants remained weight stable or lost weight by month 3 in the program, and the percent weight change at month 3 was significantly associated with program engagement and the likelihood of achieving a drop of ≥5 mm Hg in SBP. Taken together, these results provide formative evidence that members enrolled in an AI-powered hypertension care program who remain engaged experience clinically meaningful reductions in their systolic BP and body weight.
We thank the team at Lark for their assistance in managing the data collection.
OHB designed the study and edited the manuscript; MR performed data analyses and edited the manuscript; LAG wrote and edited the manuscript; KGL wrote and edited the manuscript; and SAG designed the study, performed data analyses, and wrote and edited the manuscript.
Conflicts of Interest
OHB, MR, LAG, KGL, and SAG are employed by Lark.
Description of the Lark Hypertension Care Program.DOCX File , 2245 KB
- Whelton P, Carey R, Aronow W, Casey D, Collins K, Dennison Himmelfarb C, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2018 May 15;71(19):e127-e248 [FREE Full text] [CrossRef] [Medline]
- Facts about hypertension. Centers for Disease Control and Prevention. URL: https://www.cdc.gov/bloodpressure/facts.htm [accessed 2021-09-05]
- Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. The Lancet 2020 Mar;395(10226):795-808. [CrossRef]
- Ahmad FB, Anderson RN. The leading causes of death in the US for 2020. JAMA 2021 May 11;325(18):1829-1830 [FREE Full text] [CrossRef] [Medline]
- High blood pressure. National Heart, Lung, and Blood Institute. URL: https://tinyurl.com/yrs8j4zm [accessed 2021-09-05]
- Blood Pressure Lowering Treatment Trialists' Collaboration. Pharmacological blood pressure lowering for primary and secondary prevention of cardiovascular disease across different levels of blood pressure: an individual participant-level data meta-analysis. Lancet 2021 May 01;397(10285):1625-1636 [FREE Full text] [CrossRef] [Medline]
- Shimbo D, Artinian NT, Basile JN, Krakoff LR, Margolis KL, Rakotz MK, American Heart Associationthe American Medical Association. Self-measured blood pressure monitoring at home: a joint policy statement from the American Heart Association and American Medical Association. Circulation 2020 Jul 28;142(4):e42-e63. [CrossRef] [Medline]
- Tucker KL, Sheppard JP, Stevens R, Bosworth HB, Bove A, Bray EP, et al. Self-monitoring of blood pressure in hypertension: A systematic review and individual patient data meta-analysis. PLoS Med 2017 Sep;14(9):e1002389 [FREE Full text] [CrossRef] [Medline]
- Carey RM, Whelton PK, 2017 ACC/AHA Hypertension Guideline Writing Committee. Prevention, detection, evaluation, and management of high blood pressure in adults: synopsis of the 2017 American College of Cardiology/American Heart Association Hypertension Guideline. Ann Intern Med 2018 Mar 06;168(5):351-358 [FREE Full text] [CrossRef] [Medline]
- Poorolajal J, Hooshmand E, Bahrami M, Ameri P. How much excess weight loss can reduce the risk of hypertension? J Public Health (Oxf) 2017 Sep 01;39(3):e95-e102. [CrossRef] [Medline]
- Landi F, Calvani R, Picca A, Tosato M, Martone AM, Ortolani E, et al. Body mass index is strongly associated with hypertension: results from the longevity check-up 7+ study. Nutrients 2018 Dec 13;10(12):1976-1988 [FREE Full text] [CrossRef] [Medline]
- Hall ME, Cohen JB, Ard JD, Egan BM, Hall JE, Lavie CJ, American Heart Association Council on Hypertension, Thrombosis Vascular Biology, Stroke Council, Council on Lifestyle Cardiometabolic Health. Weight-loss strategies for prevention and treatment of hypertension: a scientific statement from the American Heart Association. Hypertension 2021 Nov;78(5):e38-e50. [CrossRef] [Medline]
- Mertens I, Van Gaal LF. Overweight, obesity, and blood pressure: the effects of modest weight reduction. Obes Res 2000 May;8(3):270-278 [FREE Full text] [CrossRef] [Medline]
- O'Connor EA, Evans CV, Rushkin MC, Redmond N, Lin JS. Behavioral counseling to promote a healthy diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA 2020 Nov 24;324(20):2076-2094. [CrossRef] [Medline]
- Brandt CJ, Søgaard GI, Clemensen J, Søndergaard J, Nielsen JB. Determinants of successful eHealth coaching for consumer lifestyle changes: qualitative interview study among health care professionals. J Med Internet Res 2018 Jul 05;20(7):e237 [FREE Full text] [CrossRef] [Medline]
- Lv N, Azar KM, Rosas LG, Wulfovich S, Xiao L, Ma J. Behavioral lifestyle interventions for moderate and severe obesity: A systematic review. Prev Med 2017 Jul;100:180-193 [FREE Full text] [CrossRef] [Medline]
- Thomopoulos C, Parati G, Zanchetti A. Effects of blood pressure lowering on outcome incidence in hypertension: 7. Effects of more vs. less intensive blood pressure lowering and different achieved blood pressure levels - updated overview and meta-analyses of randomized trials. J Hypertens 2016 Apr;34(4):613-622. [CrossRef] [Medline]
- Monitoring your blood pressure at home. American Heart Association. 2017. URL: https://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings/monitoring-your-blood-pressure-at-home [accessed 2021-11-21]
- Jacob A, Moullec G, Lavoie KL, Laurin C, Cowan T, Tisshaw C, et al. Impact of cognitive-behavioral interventions on weight loss and psychological outcomes: A meta-analysis.. Heal Psychol 2018;37(7):417-432. [CrossRef] [Medline]
- Celano CM, Gianangelo TA, Millstein RA, Chung WJ, Wexler DJ, Park ER, et al. A positive psychology–motivational interviewing intervention for patients with type 2 diabetes: Proof-of-concept trial. Int J Psychiatry Med 2019;54(2):97-114. [CrossRef] [Medline]
- Barnes RD, Barnes V. A systematic review of motivational interviewing for weight loss among adults in primary care. Obesity reviews 2015 Mar 05;16(4):304-318. [CrossRef] [Medline]
- Löwe B, Kroenke K, Gräfe K. Detecting and monitoring depression with a two-item questionnaire (PHQ-2). J Psychosom Res 2005 Feb;58(2):163-171. [CrossRef] [Medline]
- Fairchild AJ, McDaniel HL. Best (but oft-forgotten) practices: mediation analysis. Am J Clin Nutr 2017 Jun;105(6):1259-1271 [FREE Full text] [CrossRef] [Medline]
- Etminani K, Tao Engström A, Göransson C, Sant'Anna A, Nowaczyk S. How behavior change strategies are used to design digital interventions to improve medication adherence and blood pressure among patients with hypertension: systematic review. J Med Internet Res 2020 Apr 09;22(4):e17201 [FREE Full text] [CrossRef] [Medline]
- Zhou D, Xi B, Zhao M, Wang L, Veeranki SP. Uncontrolled hypertension increases risk of all-cause and cardiovascular disease mortality in US adults: the NHANES III Linked Mortality Study. Sci Rep 2018 Jun 20;8(1):9418 [FREE Full text] [CrossRef] [Medline]
- Bundy JD, Li C, Stuchlik P, Bu X, Kelly TN, Mills KT, et al. Systolic blood pressure reduction and risk of cardiovascular disease and mortality: a systematic review and network meta-analysis. JAMA Cardiol 2017 Jul 01;2(7):775-781 [FREE Full text] [CrossRef] [Medline]
- Defining Adult Overweight & Obesity. Centers for Disease Control and Prevention. URL: https://www.cdc.gov/obesity/adult/defining.html [accessed 2021-11-19]
- Aronow WS. Association of obesity with hypertension. Ann Transl Med 2017 Sep;5(17):350 [FREE Full text] [CrossRef] [Medline]
- Ozemek C, Tiwari S, Sabbahi A, Carbone S, Lavie CJ. Impact of therapeutic lifestyle changes in resistant hypertension. Prog Cardiovasc Dis 2020;63(1):4-9 [FREE Full text] [CrossRef] [Medline]
- Pourzanjani A, Quisel T, Foschini L. Adherent use of digital health trackers is associated with weight loss. PLoS One 2016;11(4):e0152504 [FREE Full text] [CrossRef] [Medline]
- Amagai S, Pila S, Kaat AJ, Nowinski CJ, Gershon RC. Challenges in participant engagement and retention using mobile health apps: literature review. J Med Internet Res 2022 Apr 26;24(4):e35120 [FREE Full text] [CrossRef] [Medline]
- Pratap A, Neto EC, Snyder P, Stepnowsky C, Elhadad N, Grant D, et al. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit Med 2020;3:21 [FREE Full text] [CrossRef] [Medline]
- McManus RJ, Mant J, Haque MS, Bray EP, Bryan S, Greenfield SM, et al. Effect of self-monitoring and medication self-titration on systolic blood pressure in hypertensive patients at high risk of cardiovascular disease: the TASMIN-SR randomized clinical trial. JAMA 2014 Aug 27;312(8):799-808. [CrossRef] [Medline]
|AHA: American Heart Association|
|AI: artificial intelligence|
|BP: blood pressure|
|DBP: diastolic blood pressure|
|SBP: systolic blood pressure|
Edited by A Mavragani; submitted 23.03.22; peer-reviewed by K Margolis, PH Lin; comments to author 30.04.22; revised version received 09.09.22; accepted 26.09.22; published 27.10.22Copyright
©OraLee H Branch, Mohit Rikhy, Lisa A Auster-Gussman, Kimberly G Lockwood, Sarah A Graham. Originally published in JMIR Formative Research (https://formative.jmir.org), 27.10.2022.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.