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. Author manuscript; available in PMC: 2026 Jan 30.
Published in final edited form as: Hypertension. 2026 Jan 22;83(3):e25783. doi: 10.1161/HYPERTENSIONAHA.125.25783

ASSOCIATION OF CLINIC BLOOD PRESSURE AND OUT-OF-CLINIC BLOOD PRESSURE DIFFERENCE WITH FALLS AMONG OLDER ADULTS WITH HYPERTENSION

Daichi Shimbo 1, C Barrett Bowling 2,3, Kimberly Cannavale 4, Chloe Fang 1, Teresa N Harrison 4, Lei Qian 4, Paul Muntner 5,6, Joseph E Schwartz 7, John J Sim 4,8, Rong Wei 4, Kristi Reynolds 4,9
PMCID: PMC12854529  NIHMSID: NIHMS2135814  PMID: 41568454

Abstract

Background:

A barrier to intensification of antihypertensive medication among older adults with hypertension is the perceived risk of falls. Blood pressure (BP) measured in the clinic setting is primarily used to decide whether antihypertensive medication should be intensified. Scarce data exist on whether a lower out-of-clinic BP relative to in-clinic BP is associated with an increased risk of falls among older adults with hypertension.

Methods:

The sample included 630 participants, enrolled from May 2019 through November 2022 from Kaiser Permanente Southern California (KPSC), who were ≥65 years of age, had hypertension, were taking antihypertensive medication, and had not experienced a serious fall injury since their last clinic visit. The primary exposure was quartiles (Q) of the difference between clinic systolic BP (SBP) from the electronic health record and awake SBP on ambulatory BP monitoring (ABPM) (cSBP minus aSBP). The primary outcome was time to the first fall, determined using monthly falls calendars over 12 months of follow-up.

Results:

The mean age (SD) was 74.6 (6.2) years with 56.5% female. During follow-up, 240 (38.1%) of the 630 participants fell. After adjusting for demographics, clinical characteristics, and geriatric measures, participants in Q4 (7.2 to 47.7 mm Hg) versus Q1-Q3 (−56.7 to <7.2 mm Hg) of cSBP minus aSBP did not have an increased fall risk: adjusted hazard ratio (HR) 0.79 (95% CI: 0.57–1.09).

Conclusions:

There was no evidence of an association of a lower awake SBP on ABPM relative to clinic SBP with an increased risk of falls.

Keywords: clinic blood pressure, ambulatory blood pressure monitoring, falls, older adults

Graphical Abstract

graphic file with name nihms-2135814-f0001.jpg

Nearly two-thirds of adults aged 65 years or older in the United States (U.S.) have hypertension, a major risk factor for cardiovascular disease (CVD).1 Although antihypertensive medication lowers CVD risk for older adults with hypertension,2 many older adults with hypertension do not achieve guideline-recommended blood pressure (BP) goals.3 The percentage of US adults with hypertension who were 65 to 74 years old and 75 years and older with controlled BP fell from 59% and 50%, respectively, in 2009–2010, to 54% and 37%, respectively, in 2017–2020.4

A barrier to antihypertensive medication intensification among older adults with hypertension is the perceived risk of falls.5 Falls are a leading cause of injury-related hospitalizations and preventable deaths among older adults.6, 7 However, results from prior studies have not been consistent with respect to whether lower clinic BP is associated with an increased risk for falling.8–10 Further, clinic BP may not accurately reflect BP levels that an individual experiences outside the clinic.11, 12 As BP measured outside the clinic setting is often lower than in-clinic BP, particularly among older adults taking antihypertensive medication,13, 14 measures of out-of-clinic BP could provide additional information for evaluating an older person’s risk for falling. While ambulatory BP monitoring (ABPM) quantifies out-of-clinic BP over a 24-hour period, and is commonly used in studies of CVD risk,12, 15 it is not known if ABPM in conjunction with clinic BP measurement is useful for predicting the risk of falls among older adults with treated hypertension.16

If a lower BP on ABPM relative to clinic BP is associated with an increased risk of falls, ABPM performed in conjunction with office BP would be advantageous for assessing fall risk and helping guide decisions about intensifying antihypertensive medication for older adults. The goal of the AMBROSIA (AMBulatoRy blOod preSsure In older Adults) study was to determine whether BP on ABPM along with clinic BP measurement is useful for identifying older adults with treated hypertension who are at increased risk for falls.

Methods

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Sample population

The design of the AMBROSIA study was previously described.17 The AMBROSIA study enrolled 670 participants from May 2019 through November 2022. AMBROSIA was conducted at Kaiser Permanente Southern California (KPSC), an integrated health care delivery system that provides comprehensive services to over 4.8 million residents of Southern California. KPSC members are diverse and highly representative of the Southern California population.18, 19 Participants were identified from a recruitment base of >123,000 KPSC members aged 65 years and older who had hypertension, were taking antihypertensive medication, and resided in the greater Los Angeles area. Inclusion and exclusion criteria are provided in Table S1 in the Supplement. Potentially eligible participants were identified using the KPSC electronic health record (EHR) and contacted by mail to introduce the study: 1,068 people were prescreened by telephone to verify eligibility and 948 people were scheduled for an in-person visit (Visit 1) at a KPSC research clinic (Figure 1). Of these 948 potential participants, 705 provided informed consent, and 670 were eligible at Visit 1 and participated in the study. Among these 670 participants, demographics, clinical characteristics, and geriatric measures (i.e. frailty, cognition, mobility, functional impairment, and physical performance) were assessed (Table S2). Frailty was defined by the presence of ≥3 of 5 characteristics: shrinking (unintentional weight loss ≥10 lbs in the prior year), grip strength in the lowest 20% according to age and body mass index, exhaustion (self-reported exhaustion), slow gait speed (lowest 20% based on time to walk 15 feet, according to gender and height), and physical activity (for men: kcals/week <383, for women: kcals/week <270 based). Pre-frailty and robust was defined as having the presence of 1 or 2, or no characteristics, respectively. Research grade clinic BP measurement and 24-hour ABPM were performed at Visit 1, and a subsequent in-person visit was conducted the next day (Visit 2). Of the 670 participants, 656 completed ABPM. Participants were followed for 12 months after Visit 2 to ascertain self-reported falls. The analytical sample was 630, after excluding 24 participants who did not return any fall calendars and 2 participants who did not have a sufficient number of systolic BP (SBP) and diastolic BP (DBP) readings on ABPM as defined below. The study was approved by the KPSC and Columbia University Irving Medical Center (CUIMC) institutional review boards.

Figure 1. Assembly of analytical sample.

Figure 1.

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ABPM = ambulatory blood pressure monitoring

BP = blood pressure

BP measurements

Clinic BP readings, which were obtained as part of outpatient, non-urgent, clinical care within 12 weeks before study enrollment, were extracted from the EHR. At KPSC, clinic BP measurements are performed by medical staff who are certified in BP measurement. Clinic BP was obtained using a validated oscillometric device (Accutorr 7, Mindray DS USA, Inc).20, 21

Research grade clinic BP and ABPM were also conducted as part of the AMBROSIA study (Supplemental Methods). Briefly, research grade clinic BP was measured using a validated Omron Model HEM-907XL device,22, 23 in the seated, supine, and standing positions. For the current analyses, mean research grade clinic BP was defined as the average of the 3 seated BP readings from the non-dominant arm. Orthostatic hypotension was defined as a decline of ≥20 mm Hg in SBP or ≥10 mm Hg in DBP after either 1 or 3 minutes of standing. For 24-hour ABPM, participants were fitted with a validated SpaceLabs 90227 device.24 The 2017 American College of Cardiology (ACC) /American Heart Association (AHA) BP guideline recommends that BP on ABPM be defined using mean awake BP.11 Therefore, the primary BP measure on ABPM was mean awake BP. The analysis was restricted to participants who had ≥14 valid awake SBP and DBP readings,25–27 which has been shown in prior studies to have sufficient accuracy and predictive value for CVD events.28, 29 Participants and their health care providers were masked to research grade clinic BP and ABPM data collected in the study to prevent treatment decisions being altered.

Difference between clinic BP and BP on ABPM

The primary exposure was the difference between clinic SBP from the EHR and awake SBP (cSBP minus aSBP). Clinic SBP from the EHR, rather than research grade SBP, was chosen because clinic SBP is used at KPSC to guide treatment decisions. Additional exposures included: (1) the difference between clinic DBP and awake DBP (cDBP minus aDBP), (2) the difference between research grade clinic SBP and awake SBP (rcSBP minus aSBP), and (3) the difference between research grade clinic DBP and awake DBP (rcDBP minus aDBP).

Outcomes

Data on falls during the 12 months following Visit 2 were collected prospectively using validated monthly falls calendars.30–36 Participants were provided with calendars, prepaid postage envelopes, and instructions for completing and returning a falls calendar at the end of each month. Participants were asked to report “any fall where part of your body hits a lower surface, including falls that occur on stairs” by marking each day of the calendar indicating if a fall had occurred or not. The primary outcome was the time to first fall. The Supplemental Methods provides additional details on the ascertainment of falls.

Statistical analyses

To test our hypothesis that a lower BP on ABPM relative to clinic BP would be associated with an increased risk of falls, the prespecified primary analysis, was conducted by comparing quartile (Q) 4 versus Q1-Q3 grouped together (referent) of cSBP minus aSBP. Participant characteristics were calculated for the overall analytical sample and compared between Q1-Q3 and Q4 of cSBP minus aSBP, using the Wilcoxon Rank-Sum test or Fisher Exact test. Incidence rates were calculated, and Cox proportional hazards models predicting time to first fall were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) comparing Q4 versus Q1-Q3 of cSBP minus aSBP. Participants with less than 12 months of follow-up were censored as of their last returned falls calendar. HRs and 95% CIs were estimated in an unadjusted model, after adjustment for age, sex, race, and ethnicity (model 1), and after additional adjustment for body mass index, diabetes, alcohol use, number of prescribed antihypertensive medication classes, number of other prescribed chronic medication classes, chronic kidney disease (eGFR <60 ml/min per 1.73 m2), physical performance score, prior history of falls, and orthostatic hypotension (model 2). Assuming a 40% fall rate in the referent group (Q1-Q3 of cSBP minus aSBP), using an α=0.05, a sample size of 630 provided 80% power to detect a minimum HR of 1.43.

In secondary analyses, the above analyses were repeated comparing cSBP minus aSBP ≥10 mm Hg versus <10 mm Hg (referent). Cox proportional hazards models with spline terms were also conducted examining the association of cSBP minus aSBP as a continuous variable with time to first fall in a fully adjusted model (model 2). The analyses were repeated for cDBP minus aDBP, comparing those in Q4 versus Q1-Q3 (referent) of cDBP minus aDBP, and separately ≥5 mm Hg versus <5 mm Hg (referent). Cox proportional hazards models with spline terms were conducted examining the association of cDBP minus aDBP as a continuous variable with time to first fall.

cSBP, aSBP, cDBP, and aDBP.

In additional secondary analyses, instead of analyzing the differences between clinic and awake SBP and DBP, we analyzed the association of each BP measure (cSBP, aSBP, cDBP, and aDBP) with falls. Cox proportional hazards models were used to estimate HRs and 95% CIs of time to first fall comparing participants in Q1 versus Q2-Q4 of cSBP, and separately, participants in Q1 versus Q2-Q4 of aSBP in the unadjusted model, and models 1 and 2. Q2-Q4 was used in the referent for these analyses as we hypothesized that participants with lower BP levels would have a higher risk for falls. An additional model (Model 3) included aSBP when comparing Q1 to Q2-Q4 of cSBP, and cSBP when comparing Q1 to Q2-Q4 of aSBP. The analyses were repeated for Q1 versus Q2-Q4 of cDBP and aDBP.

Sensitivity analyses.

Poisson regression with a robust error variance was used to calculate the risk ratio and 95% CI of the associations of cSBP minus aSBP, and cDBP minus aDBP with the number of falls, accounting for length of follow-up. Cox proportional hazards models were used to estimate HRs and 95% CIs of time to first fall comparing the following: (1) Q4 versus Q1-Q3 of rcSBP minus aSBP, (2) rcSBP minus aSBP ≥10 mm Hg versus <10 mm Hg, (3) Q4 versus Q1-Q3 of rcDBP minus aDBP, and (4) rcDBP minus aDBP ≥5 mm Hg versus <5 mm Hg.

Statistical analyses were conducted with SAS v. 9.4 (SAS Institute Inc., Cary, NC, United States) and R software version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Associations of the difference between clinic SBP and awake SBP with the risk of a first fall

Among the 630 participants in the analysis sample, the median (25th %-ile, 75th %-ile) number of awake readings on ABPM was 44 (39, 47). Figure 2 portrays the distribution of cSBP minus aSBP.

Figure 2. Distribution of cSBP minus aSBP.

Figure 2.

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cSBP minus aSBP is defined as the difference between clinic systolic blood pressure from the electronic health record and awake systolic blood pressure.

Q4 versus Q1-Q3 of cSBP minus aSBP.

Mean (SD) age was 74.6 (6.2) years, 56.5% were female, 44.3% were non-Hispanic White, 13.3% were Non-Hispanic Asian or Pacific Islander, 22.4% were Non-Hispanic Black, 18.1% were Hispanic, and 1.9% were Other or unknown race and ethnicity (Table 1). Compared to the 473 participants in Q1-Q3 (−56.7 to <7.2 mm Hg) of cSBP minus aSBP, the 157 participants in Q4 (7.2 to 47.7 mm Hg) had a higher proportion of non-Hispanic Blacks and Hispanics, a lower life-space mobility score, higher clinic SBP and DBP, lower research grade clinic SBP, lower awake SBP and DBP, and lower 24-hour SBP and DBP on ABPM.

Table 1.

Baseline characteristics overall, and stratified by Q4 versus Q1-Q3 of cSBP minus aSBP

Total
(N=630)		 Q1-Q3 of cSBP minus aSBP
(−56.7 to <7.2 mm Hg)
(N=473)		 Q4 of cSBP minus aSBP
(7.2 to 47.7 mm Hg)
(N=157)		 P-value
Mean (SD) age at visit 1, years 74.6 (6.2) 74.5 (6.2) 74.6 (6.3) 0.9712
Age categories at visit 1 0.8631
 65–70 years 182 (28.9%) 137 (29.0%) 45 (28.7%)
 71–75 years 195 (31.0%) 142 (30.0%) 53 (33.8%)
 76–80 years 149 (23.7%) 116 (24.5%) 33 (21.0%)
 81–85 years 67 (10.6%) 51 (10.8%) 16 (10.2%)
 86+ years 37 (5.9%) 27 (5.7%) 10 (6.4%)
Female sex, n (%) 356 (56.5%) 259 (54.8%) 97 (61.8%) 0.1371
Race and ethnicity, n (%) 0.0541
 Non-Hispanic White 279 (44.3%) 222 (46.9%) 57 (36.3%)
 Non-Hispanic Asian or Pacific Islander 84 (13.3%) 66 (14.0%) 18 (11.5%)
 Non-Hispanic Black 141 (22.4%) 100 (21.1%) 41 (26.1%)
 Hispanic 114 (18.1%) 76 (16.1%) 38 (24.2%)
 Other or unknown 12 (1.9%) 9 (1.9%) 3 (1.9%)
Body mass index, n (%) 0.1631
 Underweight or normal Weight: <25 kg/m2 155 (24.6%) 121 (25.6%) 34 (21.7%)
 Overweight: 25–29.9 kg/m2 247 (39.2%) 190 (40.2%) 57 (36.3%)
 Obese: 30–34.9 kg/m2 149 (23.7%) 100 (21.1%) 49 (31.2%)
 Severely obese: 35–39.9 kg/m2 58 (9.2%) 46 (9.7%) 12 (7.6%)
 Morbidly obese: ≥40 kg/m2 21 (3.3%) 16 (3.4%) 5 (3.2%)
Active smoking, n (%) 11 (1.8%) 10 (2.1%) 1 (0.6%) 0.3081
Alcohol use, n (%) 391 (62.4%) 299 (63.5%) 92 (59.0%) 0.3411
Diabetes, n (%) 189 (30.0%) 146 (30.9%) 43 (27.4%) 0.4241
History of coronary heart disease, n (%) 107 (17.0%) 75 (15.9%) 32 (20.4%) 0.2201
History of heart failure, n (%) 25 (4.0%) 20 (4.2%) 5 (3.2%) 0.6451
Chronic kidney disease, n (%) 144 (23.1%) 103 (22.0%) 41 (26.5%) 0.2721
History of neuropathy, n (%) 69 (11.0%) 54 (11.4%) 15 (9.6%) 0.5591
History of depression, n (%) 115 (18.3%) 89 (18.8%) 26 (16.6%) 0.5541
History of Parkinson’s disease, n (%) 6 (1.0%) 4 (0.8%) 2 (1.3%) 0.6421
History of arthritis, n (%) 289 (45.9%) 221 (46.7%) 68 (43.3%) 0.4621
Charlson comorbidity index score 0.1231
 0 105 (16.7%) 69 (14.6%) 36 (22.9%)
 1 141 (22.4%) 110 (23.3%) 31 (19.7%)
 2 122 (19.4%) 93 (19.7%) 29 (18.5%)
 3+ 262 (41.6%) 201 (42.5%) 61 (38.9%)
Number of antihypertensive medication classes 0.6371
 0 6 (1.0%)* 4 (0.8%) 2 (1.3%)
 1 221 (35.1%) 169 (35.7%) 52 (33.1%)
 2 5 (40.5%) 194 (41.0%) 61 (38.9%)
 3+ 148 (23.5%) 106 (22.4%) 42 (26.8%)
Antihypertensive medication class 0.8641
 Angiotensin-converting enzyme (ACE) inhibitors only 50 (8.0%) 39 (8.3%) 11 (7.1%)
 Angiotensin II receptor blocker (ARB) only 49 (7.9%) 34 (7.2%) 15 (9.7%)
 Beta blocker only 35 (5.6%) 27 (5.8%) 8 (5.2%)
 Calcium channel blocker only 50 (8.0%) 41 (8.7%) 9 (5.8%)
 Diuretic only 25 (4.0%) 19 (4.1%) 6 (3.9%)
 Other only 12 (1.9%) 9 (1.9%) 3 (1.9%)
 Combination therapy 403 (64.6%) 300 (64.0%) 103 (66.5%)
 ACEI + Other(s) 172 (42.7%) 130 (43.3%) 42 (40.8%)
 BB + Other(s) 182 (45.2%) 136 (45.3%) 46 (44.7%)
 CCB + Other(s) 172 (42.7%) 127 (42.3%) 45 (43.7%)
 ARB + Other(s) 148 (36.7%) 108 (36.0%) 40 (38.8%)
 Diuretic + Other(s) 190 (47.1%) 137 (45.7%) 53 (51.5%)
Number of other medication classes 0.4021
 0–1 221 (35.1%) 159 (33.6%) 62 (39.5%)
 2–3 266 (42.2%) 203 (42.9%) 63 (40.1%)
 4+ 143 (22.7%) 111 (23.5%) 32 (20.4%)
Use of assistive devices, n (%) 108 (17.3%) 78 (16.5%) 30 (19.5%) 0.5431
Difficulty with >1 ADL or IADL, n (%) 76 (12.1%) 59 (12.5%) 17 (10.8%) 0.6721
MoCA Score, n (%) 0.1811
 <26 446 (71.8%) 328 (70.4%) 118 (76.1%)
 26+ 175 (28.2%) 138 (29.6%) 37 (23.9%)
Mean (SD) life-space mobility score 82.3 (22.9) 83.9 (22.8) 77.5 (22.6) 0.0042
Mean (SD) total short physical performance battery assessment score 9.5 (2.0) 9.6 (1.9) 9.4 (2.2) 0.4402
Frailty, n (%) 0.5161
 Robust: frailty score 287 (45.6%) 219 (46.3%) 68 (43.3%)
 Pre-frailty: frailty score 1 or 2 316 (50.2%) 236 (49.9%) 80 (51.0%)
 Frail: frailty score >3 27 (4.3%) 18 (3.8%) 9 (5.7%)
Self-reported history of fall, n (%) 247 (39.8%) 188 (40.4%) 59 (37.8%) 0.5721
Mean (SD) systolic BP, mm Hg
 Clinic systolic BP 132.8 (12.5) 130.0 (11.5) 141.3 (11.7) <0.00012
 Research grade clinic systolic BP 134.6 (15.8) 135.8 (16.1) 131.2 (14.7) 0.0032
 Awake systolic BP on ABPM 134.5 (13.7) 137.9 (12.9) 124.2 (10.6) <0.00012
 24-hour systolic BP on ABPM 130.1 (13.1) 132.8 (12.7) 121.6 (10.5) <0.00012
Mean (SD) diastolic BP, mm Hg
 Clinic diastolic BP 70.1 (9.6) 69.3 (9.0) 72.4 (11.0) 0.0042
 Research grade clinic diastolic BP 69.1 (10.3) 69.4 (10.2) 68.3 (10.5) 0.2032
 Awake diastolic BP on ABPM 73.5 (8.5) 74.8 (8.3) 69.3 (7.7) <0.00012
 24-hour diastolic BP on ABPM 70.6 (7.8) 71.7 (7.7) 67.2 (7.1) <0.00012
Orthostatic hypotension, n (%) 106 (16.9%) 79 (16.8%) 27 (17.2%) 0.9031
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*

Six participants reported at their baseline visit that they were not taking their antihypertensive medication although they had a current antihypertensive medication prescription.

1

Fisher Exact p-value;

2

Wilcoxon rank sum p-value

cSBP minus aSBP is defined as the difference between clinic systolic blood pressure from the electronic health record and awake systolic blood pressure

Chronic kidney disease is defined as eGFR <60 ml/min per 1.73 m2

ABPM=ambulatory blood pressure monitoring

ADL=Activities of daily living

BP=blood pressure

IADL=Instrumental activities of daily living

MoCA= Montreal Cognitive Assessment

Q=quartile

During 12 months of follow-up, 240 (38.1%) of the 630 participants fell with 190 (40.2%) falls in the 473 participants in Q1-Q3 of cSBP minus aSBP, and 50 (31.8%) in the 157 participants in Q4 (Table 2). In the primary analysis, compared to the participants in Q1-Q3 of cSBP minus aSBP, participants in Q4 did not have an increased fall risk in an unadjusted model (HR 0.77 [95% CI: 0.56–1.04]), model 1 (HR 0.78 [95% CI: 0.57–1.07]), or model 2 (HR 0.79 [95% CI: 0.57–1.09]).

Table 2.

Association of Q4 versus Q1-Q3 of cSBP minus aSBP with falls

Q1 - Q3 of cSBP minus aSBP
(−56.7 to <7.2 mm Hg)		 Q4 of cSBP minus aSBP
(7.2 to 47.7 mmHg)		 Hazard Ratio (95% CI)
N Number of cases Number of person years Incidence per 100 person-years
(95% CI)		 N Number of cases Number of person years Incidence per 100 person-years
(95% CI)		 Unadjusted Model 1 Model 2
473 190 335.02 56.71
(49.20–65.38)		 157 50 116.34 42.98
(32.57–56.70)		 0.76
(0.56–1.04)		 0.78
(0.57–1.07)		 0.79
(0.57–1.09)
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cSBP minus aSBP is defined as the difference between clinic systolic blood pressure from the electronic health record and awake systolic blood pressure

Model 1 adjusts for age, sex, race, and ethnicity as covariates

Model 2 adjusts for covariates in Model 1 plus body mass index, diabetes, alcohol use, number of antihypertensive medication classes, number of other chronic medication classes, chronic kidney disease (eGFR <60 ml/min per 1.73 m2), short physical performance battery score, history of falls, and orthostatic hypotension.

CI=confidence interval

Q=quartile

cSBP minus aSBP ≥10 mm Hg versus <10 mm Hg.

Table S3 provides baseline characteristics stratified by cSBP minus aSBP ≥10 mm Hg versus <10 mm Hg. Compared to the participants with cSBP minus aSBP <10 mm Hg, participants with ≥10 mm Hg did not have an increase in the risk for a first fall in an unadjusted model (HR 0.75 [95% CI: 0.53–1.05]), model 1 (HR 0.74 [95% CI: 0.52–1.04]), or model 2 (HR 0.75 [95% CI: 0.52–1.07]) (Table S4).

cSBP minus aSBP as continuous measure.

Spline analysis confirmed that there was no association of cSBP minus aSBP with the risk of a first fall (Figure 3).

Figure 3. Spline analysis of the association of cSBP minus aSBP with falls.

Figure 3.

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cSBP minus aSBP is defined as the difference between clinic systolic blood pressure from the electronic health record and awake systolic blood pressure.

Associations of the difference between clinic DBP and awake DBP with the risk of a first fall

Figure S1 shows the distribution of cDBP minus aDBP.

Q4 versus Q1-Q3 of cDBP minus aDBP.

Table S5 provides baseline characteristics stratified by Q4 (2.2 to 26.9 mm Hg) versus Q1-Q3 (−38.5 to <2.2 mm Hg) of cDBP minus aDBP. Participants in Q4 of cDBP minus aDBP did not have an increased risk for a first fall in an unadjusted model or in adjusted models (Table S6).

cDBP minus aDBP ≥5 mm Hg versus <5 mm Hg.

Table S7 shows baseline characteristics stratified by cDBP minus aDBP ≥5 mm Hg versus <5 mm Hg. Compared to the participants with cDBP minus aDBP <5 mm Hg, participants with ≥5 mm Hg did not have an increase in the risk for a first fall (Table S8).

cDBP minus aDBP as continuous measure.

There was no association of cDBP minus aDBP with the risk of a first fall (Figure S2).

Associations of cSBP, aSBP, cDBP and aDBP with the risk of a first fall

Compared to the participants in Q2-Q4 (125.0 to 188.0 mm Hg) of cSBP, participants in Q1 (90.0 to <125.0 mm Hg) did not have an increase in the risk for a first fall in an unadjusted model (HR 1.23 [95% CI: 0.92–1.63]), model 1 (HR 1.24 [95% CI: 0.93–1.66]), or model 2 (HR 1.25 [95% CI: 0.93–1.69]) (Table S9). The results were similar after adjusting for aSBP in model 3 (HR 1.28 [95% CI: 0.94–1.74]). Compared to the participants in Q2-Q4 (124.7 to 183.3 mm Hg) of aSBP, participants in Q1 (95.3 to <124.7 mm Hg) did not have an increase in the risk for a first fall in an unadjusted model (HR 0.88 [95% CI: 0.65–1.19]), model 1 (HR 0.91 [95% CI: 0.67–1.23]), or model 2 (HR 0.96 [95% CI: 0.70–1.31]). The results were similar after adjusting for cSBP in model 3 (HR 0.93 [95% CI: 0.68–1.27]).

Compared to the participants in Q2-Q4 (63.0 to 98.0 mm Hg) of cDBP, participants in Q1 (45.0 to <63.0 mm Hg) did not have an increase in the risk for a first fall in an unadjusted model (HR 1.09 [95% CI: 0.81–1.46]), model 1 (HR 0.98 [95% CI: 0.73–1.33]), or model 2 (HR 0.96 [95% CI: 0.70–1.31]). The results were similar after adjusting for aSBP in model 3 (HR 1.03 [95% CI: 0.74–1.43]) (Table S10). Compared to the participants in Q2-Q4 (67.2 to 113.7 mm Hg) of aDBP, participants in Q1 (50.7 to <67.2 mm Hg) did not have an increase in the risk for a first fall in an unadjusted model (HR 0.88 [95% CI: 0.65–1.19]) or model 1 (HR 0.81 [95% CI: 0.60–1.10]). In model 2, participants in Q1 versus Q2-Q4 of aDBP had a lower risk of a first fall (HR 0.70 [95% CI: 0.50–0.97]). However, this association was no longer statistically significant after adjustment for cSBP in model 3 (HR 0.72 [95% CI: 0.50–1.02]).

Sensitivity analyses

Associations of the differences between clinic SBP and awake SBP, and clinic DBP and awake DBP with the number of falls.

In a fully adjusted model (Model 2), participants in Q4 versus Q1-Q3 of cSBP minus aSBP and participants in cSBP minus aSBP ≥10 mm Hg versus <10 mm Hg had a decrease in the number of falls (Table S11). There were no differences in the number of falls for Q4 versus Q1-Q3 of cDBP minus aDBP, or cDBP minus aDBP ≥5 mm Hg versus <5 mm Hg.

Associations of differences between research clinic SBP and awake SBP, and research clinic DBP and awake DBP with the risk of a first fall.

Figure S3 shows the distributions of rcSBP minus aSBP and rcDBP minus aDBP. There were no associations of rcSBP minus aSBP (Q4 versus Q1–3; or ≥10 mm Hg versus <10 mm Hg), or rcDBP minus aDBP (Q4 versus Q1–3; or ≥5 mm Hg versus <5 mm Hg) with the risk of a first fall (Table S12).

Discussion

A lower awake SBP on ABPM relative to clinic SBP was not associated with an increased risk of a fall among older adults with hypertension who were taking antihypertensive medication. No association was present when DBP was used instead of SBP, and when research grade clinic SBP and DBP were used instead of clinic SBP and DBP, respectively. These findings indicate that knowledge that out-of-clinic BP is substantially lower than clinic BP may not be helpful for identifying older adults who have an increased risk of falls.

Findings from observational studies have suggested that antihypertensive medication intensification among older adults might precipitate falls.37–40 The possible mechanism explaining the association between antihypertensive medication and falls has been hypothesized to be a reduction in BP, leading to dizziness, balance problems, or syncope.41 However, there are scarce data examining the association of BP level with the risk of falls. In a cross-sectional study by Sim et al., an office SBP <110 mm Hg versus ≥110 mm Hg was associated with a greater odds of a serious fall injury among individuals ≥65 years of age who were taking antihypertensive medication.9 In a prospective cohort study by Bromfield et al., among participants ≥65 years of age who were taking antihypertensive medication, lower office SBP and DBP were not associated with the risk of a serious fall injury.8 In the Systolic Blood Pressure Intervention Trial (SPRINT), the risk of an injurious fall was not increased in the intensive-treatment group versus standard-treatment group among adults with hypertension ≥50 years of age.42 In the current study, lower clinic SBP and DBP, and lower awake SBP and DBP were not associated with an increased risk of a fall.

Falls are conceptualized as a multifactorial geriatric syndrome, characterized by an accumulation of impairments in multiple systems (i.e., having several health conditions that can cause dizziness or affect balance, impairments in vision, low muscle strength).43–45 Related to the accumulation of multiple impairments is the concept of frailty, which is common and associated with an increased risk of a fall among individuals with hypertension. A meta-analysis by Liu et al. indicated that among older adults with hypertension, the pooled prevalence of frailty and prefrailty were 23% (95% CI: 0.09–0.36) and 46% (95% CI: 0.38–0.54), respectively.46 In the study by Bromfield et al., indicators of frailty were associated with the risk of a serious fall injury.8 In a meta-analysis by Hu et al., the pooled HR of an injurious fall associated with frailty was 1.89 (95% CI: 1.56–2.27).47 These findings indicate that frailty may be more important than BP level for predicting an older adult’s risk of falling. The 2024 European Society of Cardiology hypertension guidelines recently endorsed screening for frailty when considering antihypertensive medication treatment and goals among older adults.48 However, limited data from randomized controlled trials suggest that among older adults with hypertension, individuals with frailty may derive the same cardiovascular benefit as those without frailty.49, 50 The extent to which frailty status should be incorporated into fall-risk assessment during treatment intensification is uncertain. Only 4.3% of participants in the AMBROSIA cohort were frail. Therefore, the results may not be generalizable to all older adults with hypertension. Future studies should include a higher proportion of frail, older adults with hypertension, and compare the associations of frailty and awake BP with falls during antihypertensive medication intensification. From a prevention perspective, it may be advantageous to identify older adults with hypertension before the onset of frailty. Studies have demonstrated that comorbid conditions such as impaired glucose metabolism and chronic kidney disease are associated with worse cognition and physical function as well as frailty, suggesting potential directions for prevention.51–53 Further, there is some evidence to suggest that sodium glucose co-transporter 2 (SGLT2) inhibitors may improve cognitive and physical impairment among older adults with hypertension and diabetes.54

There are several strengths of this study. The study is novel as there are scarce data on the associations of clinic BP relative to out-of-office BP with the risk of falls. ABPM, a reference standard for out-of-clinic BP measurement, was used to determine out-of-clinic BP, and ABPM was conducted in a standardized manner with participants and their clinicians being masked to the results of the ABPM. Therefore, the ABPM results did not affect clinical decision making related to BP control. Further, another unique feature of this study was the incorporation of both clinic BP from the EHR and research-grade clinic BP. Clinic BP measurements, which are used for determining whether antihypertensive medication should be intensified, are different than research-grade clinic BP.55, 56 The design of the study was prospective with falls being determined using a validated monthly falls calendar for 12 months. The study also incorporated other state-of-the-art assessments for geriatric fall-related factors including assessments of cognition, functional status, and frailty. The study had a high retention rate with only 24 (3.7%) of the 656 participants that completed ABPM at baseline not returning any falls calendars. The study population was racially and ethnically diverse, and the participants were patients recruited from a real-world clinical environment.

There are also several potential limitations. Injurious falls were not studied as an outcome; the study examined all falls whether injurious or not. Even a non-injurious fall is clinically significant as it is associated with functional decline and nursing home placement.57, 58 All participants were insured members of KPSC, an integrated health care delivery system in Southern California. Therefore, it is unclear whether the results are generalizable to uninsured patients or those from other health care systems in the U.S. The study did not examine the lowest achieved BP level during antihypertension medication treatment, and did not investigate whether the association of cSBP minus aSBP with the risk of falls was modified by the intensification of antihypertensive medications during the one-year follow-up period. The study was a prospective observational study and was not a clinical trial. Further, near falls assessed by self-report or using wearable devices were not determined in this study. Finally, it is possible that the study was underpowered for the primary analysis comparing Q4 with Q1-Q3 of cSBP minus aSBP to detect a HR below 1.43; however, contrary to our expectation, the HR point estimate was not greater than 1.00.

Perspectives

Among older adults with hypertension who are taking antihypertensive medication, there was no evidence of an association of a lower awake BP on ABPM relative to clinic BP with an increased risk of falls. Determining awake BP via ABPM may not be useful for identifying older adults with treated hypertension who are at increased risk for falls. In contrast, when evaluating fall risk, non-BP related factors including frailty may inform hypertension treatment decisions.

Supplementary Material

Supplement

Supplemental Methods

Tables S1–S12

Figures S1–S3

Novelty and Relevance.

What Is New?

  • In this prospective cohort study of 630 older adults with hypertension and who were taking antihypertensive medication, there was no evidence of an association of a larger difference between clinic BP and awake BP on ABPM with an increased risk of falls.

What Is Relevant?

  • Clinic BP is primarily used to decide whether antihypertensive medication should be intensified.

  • This study determined whether lower awake BP on ABPM relative to clinic BP is associated with an increased risk of falls among older adults with treated hypertension.

Clinical/Pathophysiological Implications.

  • Using ABPM to determine awake BP in addition to clinic BP measurement may not be warranted for the purpose of evaluating the risk of falls.

Funding

This study was supported by grant R01 HL136445 from NHLBI.

Nonstandard abbreviations and acronyms

ABPM

ambulatory blood pressure monitoring

ACC

American College of Cardiology

aDBP

awake diastolic blood pressure

AHA

American Heart Association

AMBROSIA

AMBulatoRy blOod preSsure In older Adults

aSBP

awake systolic blood pressure

cDBP

clinic diastolic blood pressure from the electronic health record

cSBP

clinic systolic blood pressure from the electronic health record

CUIMC

Columbia University Irving Medical Center

EHR

electronic health record

KPSC

Kaiser Permanente Southern California

Q

quartile

rcDBP

research grade clinic diastolic blood pressure

rcSBP

research grade clinic systolic blood pressure

SPRINT

Systolic Blood Pressure Intervention Trial

Footnotes

Disclosures

No disclosures were reported.

References

  • 1.Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS, American Heart Association Council on E, Prevention Statistics C, Stroke Statistics S. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation. 2023;147:e93–e621 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Blood Pressure Lowering Treatment Trialists C. Age-stratified and blood-pressure-stratified effects of blood-pressure-lowering pharmacotherapy for the prevention of cardiovascular disease and death: an individual participant-level data meta-analysis. Lancet. 2021;398:1053–1064 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Hardy ST, Jaeger BC, Foti K, Ghazi L, Wozniak G, Muntner P. Trends in Blood Pressure Control among US Adults With Hypertension, 2013–2014 to 2021–2023. American Journal of Hypertension. 2025;38:120–128 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Muntner P, Miles MA, Jaeger BC, Hannon Iii L, Hardy ST, Ostchega Y, Wozniak G, Schwartz JE. Blood Pressure Control Among US Adults, 2009 to 2012 Through 2017 to 2020. Hypertension. 2022;79:1971–1980 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Sheppard JP, Koshiaris C, Stevens R, Lay-Flurrie S, Banerjee A, Bellows BK, Clegg A, Hobbs FDR, Payne RA, Swain S, Usher-Smith JA, McManus RJ. The association between antihypertensive treatment and serious adverse events by age and frailty: A cohort study. PLoS Medicine. 2023;20:e1004223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Margolis KL, Barzilay JI, Schwartz AV. Risks and benefits of antihypertensive medications in older adults. JAMA Internal Medicine. 2014;174:1873 [Google Scholar]
  • 7.Setters B, Holmes HM. Hypertension in the Older Adult. Prim Care. 2017;44:529–539 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Bromfield SG, Ngameni CA, Colantonio LD, Bowling CB, Shimbo D, Reynolds K, Safford MM, Banach M, Toth PP, Muntner P. Blood Pressure, Antihypertensive Polypharmacy, Frailty, and Risk for Serious Fall Injuries Among Older Treated Adults With Hypertension. Hypertension. 2017;70:259–266 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Sim JJ, Zhou H, Bhandari S, Wei R, Brettler JW, Tran-Nguyen J, Handler J, Shimbo D, Jacobsen SJ, Reynolds K. Low Systolic Blood Pressure From Treatment and Association With Serious Falls/Syncope. Am J Prev Med. 2018;55:488–496 [DOI] [PubMed] [Google Scholar]
  • 10.Wright JT Jr., Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE, Kimmel PL, Johnson KC, Goff DC Jr., Fine LJ, Cutler JA, Cushman WC, Cheung AK, Ambrosius WT. A Randomized Trial of Intensive versus Standard Blood-Pressure Control. New England Journal of Medicine. 2015;373:2103–2116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Whelton PK, Carey RM, Aronow WS, Casey DE Jr., Collins KJ, Himmelfarb CD, DePalma SM, Gidding S, Jamerson KA, Jones DW, MacLaughlin EJ, Muntner P, Ovbiagele B, Smith SC Jr., Spencer CC, Stafford RS, Taler SJ, Thomas RJ, Williams KA Sr., Williamson JD, Wright JT Jr. 2017 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. Hypertension. 2018;71:E13–E115 [DOI] [PubMed] [Google Scholar]
  • 12.Muntner P, Shimbo D, Carey RM, Charleston JB, Gaillard T, Misra S, Myers MG, Ogedegbe G, Schwartz JE, Townsend RR, Urbina EM, Viera AJ, White WB, Wright JT Jr. Measurement of Blood Pressure in Humans: A Scientific Statement From the American Heart Association. Hypertension. 2019;73:e35–e66 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Tanner RM, Shimbo D, Seals SR, Reynolds K, Bowling CB, Ogedegbe G, Muntner P. White-Coat Effect Among Older Adults: Data From the Jackson Heart Study. J Clin Hypertens (Greenwich). 2016;18:139–145 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Ghazi L, Cohen LP, Muntner P, Shimbo D, Drawz PE. Effects of Intensive Versus Standard Office-Based Hypertension Treatment Strategy on White-Coat Effect and Masked Uncontrolled Hypertension: From the SPRINT ABPM Ancillary Study. Hypertension. 2020;76:1090–1096 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Shimbo D, Abdalla M, Falzon L, Townsend RR, Muntner P. Role of Ambulatory and Home Blood Pressure Monitoring in Clinical Practice: A Narrative Review. Ann Intern Med. 2015;163:691–700 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Reynolds K, Bowling CB, Sim JJ, Sridharan L, Harrison TN, Shimbo D. The Utility of Ambulatory Blood Pressure Monitoring for Diagnosing White Coat Hypertension in Older Adults. Current Hypertension Reports. 2015;17:86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Reynolds K, Bowling CB, Cannavale K, Fang C, Harrison TN, Levitan EB, Muntner P, Poudel B, Qian L, Schwartz JE, Sim JJ, Wei R, Shimbo D, Columbia University Medical C, Kaiser Permanente Southern C, Duke U, University of Alabama at B. Cohort profile: AMBulatoRy blOod preSsure in older adults (AMBROSIA) and AMBROSIA-HOME. BMJ Open. 2025;15:e091142 [Google Scholar]
  • 18.Davis AC, Voelkel JL, Remmers CL, Adams JL, McGlynn EA. Comparing Kaiser Permanente Members to the General Population: Implications for Generalizability of Research. Perm J. 2023;27:87–98 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Koebnick C, Langer-Gould AM, Gould MK, Chao CR, Iyer RL, Smith N, Chen W, Jacobsen SJ. Sociodemographic characteristics of members of a large, integrated health care system: comparison with US Census Bureau data. Perm J. 2012;16:37–41 [Google Scholar]
  • 20.Anwar YA, Tendler BE, McCabe EJ, Mansoor GA, White WB. Evaluation of the Datascope Accutorr Plus according to the recommendations of the Association for the Advancement of Medical Instrumentation. Blood Press Monit. 1997;2:105–110 [PubMed] [Google Scholar]
  • 21.Khawaja RA, Qureshi R, Mansure AH, Yahya ME. Validation of Datascope Accutorr Plus using British Hypertension Society (BHS) and Association for the Advancement of Medical Instrumentation (AAMI) protocol guidelines. J Saudi Heart Assoc. 2010;22:1–5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Ostchega Y, Nwankwo T, Sorlie PD, Wolz M, Zipf G. Assessing the validity of the Omron HEM-907XL oscillometric blood pressure measurement device in a National Survey environment. J Clin Hypertens (Greenwich). 2010;12:22–28 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.El Assaad MA, Topouchian JA, Darne BM, Asmar RG. Validation of the Omron HEM-907 device for blood pressure measurement. Blood Press Monit. 2002;7:237–241 [DOI] [PubMed] [Google Scholar]
  • 24.de Greeff A, Shennan AH. Validation of the Spacelabs 90227 OnTrak device according to the European and British Hypertension Societies as well as the American protocols. Blood Press Monit. 2020;25:110–114 [DOI] [PubMed] [Google Scholar]
  • 25.NICE guideline. Hypertension in adults: diagnosis and management. www.nice.org.uk/guidance/ng136. Updated November 2, 2023. Accessed December 4, 2025.
  • 26.Viera AJ, Lin FC, Tuttle LA, Shimbo D, Diaz KM, Olsson E, Stankevitz K, Hinderliter AL. Levels of office blood pressure and their operating characteristics for detecting masked hypertension based on ambulatory blood pressure monitoring. American Journal of Hypertension. 2015;28:42–49 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Viera AJ, Zhu S, Hinderliter AL, Shimbo D, Person SD, Jacobs DR Jr. Diurnal blood pressure pattern and development of prehypertension or hypertension in young adults: the CARDIA study. Journal of the American Society of Hypertension. 2011;5:48–55 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Brito LC, Rice SPM, Bowles NP, Butler MP, McHill AW, Emens JS, Shea SA, Thosar SS. Identifying an acceptable number of ambulatory blood pressure measurements for accuracy of average blood pressure and nocturnal dipping status. Am J Physiol Heart Circ Physiol. 2024;327:H399–H405 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Yang WY, Thijs L, Zhang ZY, Asayama K, Boggia J, Hansen TW, Ohkubo T, Jeppesen J, Stolarz-Skrzypek K, Malyutina S, Casiglia E, Nikitin Y, Li Y, Wang JG, Imai Y, Kawecka-Jaszcz K, O’Brien E, Staessen JA, International D, on Ambulatory blood pressure in relation to Cardiovascular Outcomes I. Evidence-based proposal for the number of ambulatory readings required for assessing blood pressure level in research settings: an analysis of the IDACO database. Blood Press. 2018;27:341–350 [DOI] [PubMed] [Google Scholar]
  • 30.Tinetti ME, Baker DI, McAvay G, Claus EB, Garrett P, Gottschalk M, Koch ML, Trainor K, Horwitz RI. A multifactorial intervention to reduce the risk of falling among elderly people living in the community. The New England Journal of Medicine. 1994;331:821–827 [DOI] [PubMed] [Google Scholar]
  • 31.Hannan MT, Gagnon MM, Aneja J, Jones RN, Cupples LA, Lipsitz LA, Samelson EJ, Leveille SG, Kiel DP. Optimizing the tracking of falls in studies of older participants: comparison of quarterly telephone recall with monthly falls calendars in the MOBILIZE Boston Study. Am J Epidemiol. 2010;171:1031–1036 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Ganz DA, Higashi T, Rubenstein LZ. Monitoring falls in cohort studies of community-dwelling older people: effect of the recall interval. Journal of the American Geriatrics Society. 2005;53:2190–2194 [DOI] [PubMed] [Google Scholar]
  • 33.Peel N. Validating recall of falls by older people. Accid Anal Prev. 2000;32:371–372 [DOI] [PubMed] [Google Scholar]
  • 34.Stevens M, Holman CD, Bennett N, de Klerk N. Preventing falls in older people: outcome evaluation of a randomized controlled trial. Journal of the American Geriatrics Society. 2001;49:1448–1455 [DOI] [PubMed] [Google Scholar]
  • 35.Clemson L, Cumming RG, Kendig H, Swann M, Heard R, Taylor K. The effectiveness of a community-based program for reducing the incidence of falls in the elderly: a randomized trial. Journal of the American Geriatrics Society. 2004;52:1487–1494 [DOI] [PubMed] [Google Scholar]
  • 36.Day L, Fildes B, Gordon I, Fitzharris M, Flamer H, Lord S. Randomised factorial trial of falls prevention among older people living in their own homes. BMJ. 2002;325:128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Tinetti ME, Han L, Lee DS, McAvay GJ, Peduzzi P, Gross CP, Zhou B, Lin H. Antihypertensive medications and serious fall injuries in a nationally representative sample of older adults. JAMA Internal Medicine. 2014;174:588–595 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Shimbo D, Barrett Bowling C, Levitan EB, Deng L, Sim JJ, Huang L, Reynolds K, Muntner P. Short-Term Risk of Serious Fall Injuries in Older Adults Initiating and Intensifying Treatment With Antihypertensive Medication. Circ Cardiovasc Qual Outcomes. 2016;9:222–229 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Callisaya ML, Sharman JE, Close J, Lord SR, Srikanth VK. Greater daily defined dose of antihypertensive medication increases the risk of falls in older people--a population-based study. Journal of the American Geriatrics Society. 2014;62:1527–1533 [DOI] [PubMed] [Google Scholar]
  • 40.Kahlaee HR, Latt MD, Schneider CR. Association Between Chronic or Acute Use of Antihypertensive Class of Medications and Falls in Older Adults. A Systematic Review and Meta-Analysis. American Journal of Hypertension. 2018;31:467–479 [DOI] [PubMed] [Google Scholar]
  • 41.Sheppard JP, Stevens S, Stevens R, Martin U, Mant J, Hobbs FDR, McManus RJ. Benefits and Harms of Antihypertensive Treatment in Low-Risk Patients With Mild Hypertension. JAMA Internal Medicine. 2018;178:1626–1634 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Group SR, Wright JT Jr., Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE, Kimmel PL, Johnson KC, Goff DC Jr., Fine LJ, Cutler JA, Cushman WC, Cheung AK, Ambrosius WT. A Randomized Trial of Intensive versus Standard Blood-Pressure Control. The New England Journal of Medicine. 2015;373:2103–2116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Nowak A, Hubbard RE. Falls and frailty: lessons from complex systems. J R Soc Med. 2009;102:98–102 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Tinetti ME, Inouye SK, Gill TM, Doucette JT. Shared risk factors for falls, incontinence, and functional dependence. Unifying the approach to geriatric syndromes. JAMA. 1995;273:1348–1353 [PubMed] [Google Scholar]
  • 45.Inouye SK, Studenski S, Tinetti ME, Kuchel GA. Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept. Journal of the American Geriatrics Society. 2007;55:780–791 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Liu H, Zhou W, Liu Q, Yu J, Wang C. Global Prevalence and Factors Associated with Frailty among Community-Dwelling Older Adults with Hypertension: A Systematic Review and Meta-Analysis. J Nutr Health Aging. 2023;27:1238–1247 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Hu K, Zhou Q, Jiang Y, Shang Z, Mei F, Gao Q, Chen F, Zhao L, Jiang M, Ma B. Association between Frailty and Mortality, Falls, and Hospitalization among Patients with Hypertension: A Systematic Review and Meta-Analysis. Biomed Res Int. 2021;2021:2690296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.McEvoy JW, McCarthy CP, Bruno RM, Brouwers S, Canavan MD, Ceconi C, Christodorescu RM, Daskalopoulou SS, Ferro CJ, Gerdts E, Hanssen H, Harris J, Lauder L, McManus RJ, Molloy GJ, Rahimi K, Regitz-Zagrosek V, Rossi GP, Sandset EC, Scheenaerts B, Staessen JA, Uchmanowicz I, Volterrani M, Touyz RM, Group ESCSD. 2024 ESC Guidelines for the management of elevated blood pressure and hypertension. Eur Heart J. 2024. Oct 7;45(38):3912–4018. doi: 10.1093/eurheartj/ehae178. [DOI] [PubMed] [Google Scholar]
  • 49.Williamson JD, Supiano MA, Applegate WB, Berlowitz DR, Campbell RC, Chertow GM, Fine LJ, Haley WE, Hawfield AT, Ix JH, Kitzman DW, Kostis JB, Krousel-Wood MA, Launer LJ, Oparil S, Rodriguez CJ, Roumie CL, Shorr RI, Sink KM, Wadley VG, Whelton PK, Whittle J, Woolard NF, Wright JT Jr., Pajewski NM, Group SR. Intensive vs Standard Blood Pressure Control and Cardiovascular Disease Outcomes in Adults Aged >/=75 Years: A Randomized Clinical Trial. JAMA. 2016;315:2673–2682 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Zhu J, Yang K, Liu W. Systolic and diastolic blood pressure time in target range and cardiovascular outcomes in patients with hypertension and pre-frailty or frailty status. J Clin Hypertens (Greenwich). 2024;26:514–524 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Santulli G, Visco V, Varzideh F, Guerra G, Kansakar U, Gasperi M, Marro A, Wilson S, Ferrante MNV, Pansini A, Pirone A, Di Lorenzo F, Tartaglia D, Iaccarino G, Macina G, Agyapong ED, Forzano I, Jankauskas SS, Komici K, Ciccarelli M, Mone P. Prediabetes Increases the Risk of Frailty in Prefrail Older Adults With Hypertension: Beneficial Effects of Metformin. Hypertension. 2024;81:1637–1643 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Santulli G, Visco V, Ciccarelli M, Ferrante MNV, De Masi P, Pansini A, Virtuoso N, Pirone A, Guerra G, Verri V, Macina G, Taurino A, Komici K, Mone P. Frail hypertensive older adults with prediabetes and chronic kidney disease: insights on organ damage and cognitive performance - preliminary results from the CARYATID study. Cardiovasc Diabetol. 2024;23:125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Mone P, De Gennaro S, Frullone S, Marro A, Santulli G. Hyperglycemia drives the transition from pre-frailty to frailty: The Monteforte study. Eur J Intern Med. 2023;111:135–137 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Santulli G, Varzideh F, Forzano I, Wilson S, Salemme L, de Donato A, Lombardi A, Rainone A, Nunziata L, Jankauskas SS, Tesorio T, Guerra G, Kansakar U, Mone P. Functional and Clinical Importance of SGLT2-inhibitors in Frailty: From the Kidney to the Heart. Hypertension. 2023;80:1800–1809 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Dodson JA, Shimbo D. A Tale of 2 Blood Pressures. JAMA Internal Medicine. 2020;180:1663–1664 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Drawz PE, Agarwal A, Dwyer JP, Horwitz E, Lash J, Lenoir K, McWilliams A, Oparil S, Rahbari-Oskoui F, Rahman M, Parkulo MA, Pemu P, Raj DS, Rocco M, Soman S, Thomas G, Tuot DS, Whelton PK, Pajewski NM. Concordance Between Blood Pressure in the Systolic Blood Pressure Intervention Trial and in Routine Clinical Practice. JAMA Internal Medicine. 2020;180:1655–1663 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Tinetti ME, Williams CS. Falls, injuries due to falls, and the risk of admission to a nursing home. The New England Journal of Medicine. 1997;337:1279–1284 [DOI] [PubMed] [Google Scholar]
  • 58.Tinetti ME, Williams CS. The effect of falls and fall injuries on functioning in community-dwelling older persons. J Gerontol A Biol Sci Med Sci. 1998;53:M112–119 [DOI] [PubMed] [Google Scholar]

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