Does day-to-day home blood pressure variability change with the seasons?

Levels swing with winter; variability mostly does not

The paper asks whether the same person's day-to-day home blood pressure variability follows a seasonal pattern. Using repeated morning home readings from a Japanese health-insurance database linked to a mobile health application, the authors found that average home systolic pressure peaked in December or January and fell to its lowest in July–August, a winter–summer gap of roughly 3–4 mmHg. That pattern held regardless of antihypertensive treatment, sex, or age category.

Day-to-day variability told a different story. When variability was measured with indices designed to be less dependent on the average pressure level, seasonal differences were statistically significant in several subgroups but small in magnitude. The authors' conclusion is that the apparent winter rise in variability is largely a by-product of the winter rise in blood pressure level, not a separate seasonal phenomenon.

Why the choice of variability index decides the answer

Day-to-day variability is not a single number. The study computed three indices for each person in each calendar month: the standard deviation (SD) of that month's daily morning readings, the coefficient of variation (CV, the SD divided by the mean), and variability independent of the mean (VIM). VIM is built to strip out the dependence of SD on the average level: the authors first modeled how SD scales with mean home pressure across the population, then rescaled each person-month so that the result is comparable across people with different average pressures.

This matters because SD and average real variability (ARV, the mean absolute difference between consecutive readings) both rise when average pressure rises. In this cohort, home systolic pressure correlated positively with SD (r ≥ 0.194) and ARV (r ≥ 0.176), but correlations with VIM (|r| ≤ 0.067) and CV (|r| ≤ 0.035) were weak. So an index that tracks the mean will show a winter increase almost automatically, while an index that does not track the mean can reveal whether variability itself is seasonal.

How the study was built from repeated home readings

The analysis drew on 36,519 people with home blood pressure records in the application and narrowed to 7,908 who met the entry conditions. Participants needed a health checkup within one year of starting home monitoring, morning home readings on at least five days per month in at least three calendar months, and at least one eligible month in both winter (December–February) and summer (June–August). The final sample had a mean age of 55 years and was 13.6% untreated females, 10.2% treated females, 31.1% untreated males, and 45.1% treated males, contributing 219,832 person-month observations.

Monthly trajectories and winter–summer differences were estimated with linear mixed-effects models stratified by antihypertensive treatment and sex, with age category as a fixed effect and adjustment for region, manual versus automatic data entry, body mass index, smoking, alcohol, glucose- and lipid-lowering treatment, proteinuria, cardiovascular and kidney disease history, HbA1c, and LDL cholesterol. To compare indices on a common scale, winter–summer differences were divided by the model-based total SD. One measurement caveat belongs here: the exact conditions of each reading could not be verified, and most records were entered manually rather than transferred from a device.

The standardized gap between level and variability

Expressing everything in the same standardized units makes the comparison direct. Winter–summer differences in average home systolic pressure ranged from 0.28 to 0.41 standardized units across strata. Differences in the SD of home systolic pressure were smaller, at 0.07 to 0.18. Differences in VIM were smaller still, at 0.09 or below, even where they reached statistical significance.

The same ordering appeared for the other indices. ARV, which correlates with pressure level, showed standardized winter–summer differences of 0.07 to 0.19 for systolic pressure, while CV, which barely correlates with level, ranged from −0.01 to 0.10. The authors note that the size of each index's seasonal difference tracked how strongly that index correlated with average home pressure. A sensitivity analysis using inverse probability weighting gave a similar picture: 0.24 to 0.42 for average systolic pressure and −0.03 to 0.10 for systolic VIM.

Where statistical significance and practical size diverge

Significance was not evenly distributed. A winter increase in VIM reached significance in untreated females aged 65 and older, in treated females aged 55–64, and across all male subgroups. The authors flag the male pattern as apparent but offer no confirmed explanation, noting only that day-to-day variability has been linked to lifestyle factors such as alcohol consumption and long sleep duration.

The practical point is that significance and magnitude pointed in different directions. Even in the male strata where VIM differences were significant, the standardized gap stayed at or below 0.09, against 0.28–0.41 for pressure level. An exploratory region-stratified analysis found VIM differences small in most regions (−0.03 to 0.09) but larger, at 0.39, in Kyushu and Okinawa, the warmest regions in Japan — a subgroup result the authors present as exploratory rather than established.

Why variability may be the more stable quantity

The authors' explanation is that pressure level responds acutely to cold-induced vasoconstriction and to environmental temperature, with indoor temperature in particular closely tied to home blood pressure in Japan. Day-to-day variability, by contrast, may reflect more durable individual characteristics — age-related vascular change, impaired baroreflex sensitivity, arterial stiffness, and habitual measurement behavior — that seasonal weather does not readily reshape.

That reading is the authors' interpretation, not a directly tested mechanism in this dataset. It is consistent with the observed ordering of results, but the study measured associations across calendar months rather than manipulating temperature or tracking the proposed physiological pathways. The claim that variability behaves as a relatively stable within-individual characteristic should be read as the authors' synthesis of their findings.

What this means for a variability threshold

The authors connect the result to a practical decision. They had previously proposed a cutoff of 8.5% for the CV of home systolic pressure; because CV showed only small seasonal differences here, they suggest that substantial seasonal adjustment of that cutoff may not be necessary. Variability indices derived from home readings, on this reading, can be interpreted without strong seasonal correction, whereas the pressure level itself should still be read with the measurement season in mind.

The boundary is the population studied: adults aged 19–74 enrolled in Japanese employment-based or national health insurance who voluntarily used the application and met the minimum measurement density. Eligible participants were older, more often treated, had higher office and home pressures, and were less likely to enter data manually than those excluded, so the sample is not a random slice of home-monitoring users. The finding that variability is largely season-independent is therefore best treated as a working assumption for app-collected home readings in comparable populations, not a universal rule.

The priority this paper establishes

The paper's most useful contribution is a ranking of what to correct for. Seasonal adjustment matters most for average home blood pressure, where the winter–summer gap reaches 3–4 mmHg and 0.28–0.41 standardized units across every treatment, sex, and age stratum examined. For day-to-day variability measured with mean-independent indices, the seasonal signal is small enough — at or below 0.09 standardized units — that the choice of index, not the season, is the more consequential decision.

That reframes how to read a home blood pressure report. A winter rise in SD or ARV should not be taken at face value as a rise in instability, because those indices move with the level; a mean-independent index such as VIM or CV is the one that answers whether the person's day-to-day swings themselves changed. The paper's own priority is clear: correct the level for season, and be far more cautious about correcting the variability.

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