Title: Hot-water bathing before bedtime and shorter sleep onset latency are accompanied by a higher distal-proximal skin temperature gradient in older adults
Abstract: Free AccessScientific InvestigationsHot-water bathing before bedtime and shorter sleep onset latency are accompanied by a higher distal-proximal skin temperature gradient in older adults Yoshiaki Tai, MD, PhD, Kenji Obayashi, MD, PhD, Yuki Yamagami, RN, PHN, PhD, Kiyomi Yoshimoto, MD, PhD, Norio Kurumatani, MD, PhD, Kenji Nishio, MD, PhD, Keigo Saeki, MD, PhD Yoshiaki Tai, MD, PhD Address correspondence to: Yoshiaki Tai, MD, Department of Epidemiology, Nara Medical University School of Medicine, 840 Shijocho, Kashiharashi, Nara, 634-8521, Japan; Email: E-mail Address: [email protected] Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan Department of General Medicine, Nara Medical University School of Medicine, Nara, Japan , Kenji Obayashi, MD, PhD Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan , Yuki Yamagami, RN, PHN, PhD Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan , Kiyomi Yoshimoto, MD, PhD Department of General Medicine, Nara Medical University School of Medicine, Nara, Japan , Norio Kurumatani, MD, PhD Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan , Kenji Nishio, MD, PhD Department of General Medicine, Nara Medical University School of Medicine, Nara, Japan , Keigo Saeki, MD, PhD Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan Published Online:June 1, 2021https://doi.org/10.5664/jcsm.9180SectionsAbstractPDFSupplemental Material ShareShare onFacebookTwitterLinkedInRedditEmail ToolsAdd to favoritesDownload CitationsTrack Citations AboutABSTRACTStudy Objectives:Passive body heating in controlled settings could shorten sleep onset latency (SOL). The hypothesized mechanism is vasodilation-induced heat loss before bedtime. However, this evidence is based on small sample–sized studies in specific populations. Thus, we analyzed the association of hot-water bathing and its before-bedtime timing with SOL and heat loss in a large study population of older adults.Methods:We conducted a longitudinal analysis using repeated measurements of hot-water bathing and sleep among 1,094 older adults (mean age, 72.0 years). SOL was recorded using actigraphy and self-reported sleep estimates and was categorized into conditions (intervals of 1–60, 61–120, 121–180, and > 181 minutes between hot bath and bedtime) and compared with the control condition of no bathing. The heat-loss indicator, distal-proximal skin temperature gradient, was examined in the same categorization.Results:Mixed-effects linear regression models suggested that the bathing conditions of 61–120 minutes and 121–180 minutes showed significantly shorter log-transformed actigraphic SOL by 0.23 log-minutes (95% confidence interval (CI), 0.03–0.42) and 0.32 log-minutes (95% CI, 0.09–0.56), shorter self-reported SOL by 0.16 log-minutes (95% CI, 0.02–0.30) and 0.18 log-minutes (95% CI, 0.01–0.35), and higher distal-proximal skin temperature gradient for 30 minutes before bedtime by 0.49°C (95% CI, 0.22–0.75) and 0.51°C (95% CI, 0.20–0.83), respectively, independent of potential confounders.Conclusions:Hot-water bathing before bedtime is significantly associated with shorter SOL and higher distal-proximal skin temperature gradient among the large-scale older population. This finding could enhance the generalizability of hot-water bathing habits for ameliorating sleep initiation difficulty.Citation:Tai Y, Obayashi K, Yamagami Y, et al. Hot-water bathing before bedtime and shorter sleep onset latency are accompanied by a higher distal-proximal skin temperature gradient in older adults. J Clin Sleep Med. 2021;17(6):1257–1266.BRIEF SUMMARYCurrent Knowledge/Study Rationale: Previous laboratory studies have shown that passive body heating could accelerate sleep initiation. However, it is uncertain whether hot bathing in home settings is associated with an acceleration of sleep initiation and whether a large-scale study would demonstrate the same trend.Study Impact: We showed that hot-water bathing was associated with acceleration of sleep initiation, particularly when bathing was scheduled 1–3 hours before bedtime, independent of age, sex, body mass index, income, sleep medication use, physical activity, bedtime, and indoor temperature. Our findings could expand the generalizability of passive body heating among older adults as a habit to ameliorate sleep initiation difficulty, which is a risk factor for cardiovascular disease, depression, dementia, and all-cause mortality.INTRODUCTIONWith age, sleep initiation and maintenance are impaired, and altered sleep physiology may underlie these disturbances in older adults. An epidemiologic study estimated that over half of adults ≥ 65 years had insomnia symptoms and 10%–25% complained about difficulty in initiating sleep.1 Difficulty initiating sleep has been associated with an increased risk of a vast range of conditions, including diabetes,2 metabolic syndrome,3 cardiovascular disease,4–6 depression,7 dementia,8 and all-cause mortality.9 A nonpharmacological intervention for sleep problems in older adults with no or limited adverse effects is preferred to pharmacological interventions with known adverse effects, such as excessive sedation, cognitive impairment,10 falls,11 and impaired performance of daily activities.12Thermoregulatory processes are associated with sleep initiation.13 Skin temperature increases prior to the onset of sleep. The degree of vasodilation in the distal skin regions, which increases heat loss at extremities, is associated with reduced sleep onset latency (SOL).14 The distal-proximal skin temperature gradient (DPG) is an indirect measure of heat loss in the distal skin region. Selective vasodilation of distal skin regions manipulated by bright light, a meal, or melatonin can shorten SOL,15 and the reverse is also true; patients with vasospastic syndrome have prolonged SOL.16 Additionally, passive body heating (PBH) could shorten SOL by elevating core body temperature (CBT) and triggering subsequent heat loss.17 Further, skin temperature manipulation, even within the normal range of nocturnal fluctuation, is reported to modulate SOL. It is hypothesized that elevated skin temperature without CBT changes may shorten SOL by providing a signal to thermosensitive neurons that play a key role in sleep-awake regulation.18,19A recent meta-analysis (n = 294), including young adults, suggested that water-based PBH was associated with shortening of SOL by a standardized mean difference (Cohen’s d) of 1.01 compared with baseline measurements when scheduled 1–2 hours before bedtime and conducted for at least 10 minutes.20 Japanese individuals follow a custom of soaking themselves in hot water baths in the evenings. Japanese-style bathing at home is also expected to improve difficulty in initiating sleep. However, there is still uncertainty regarding the ecological validity of the effect of water-based PBH on SOL. To our knowledge, previous studies were limited to relatively small sample sizes, specific populations, and controlled laboratory settings, relative to fixed timing, temperature, and duration of PBH recordings. Although 2 randomized controlled trials investigated the effect of PBH on SOL among patients with traumatic brain injury (n = 23) and older men (n = 46), both trials examined only the impact of foot baths.21,22 Small sample size could lead to residual confounding and inaccurate estimation even in a randomized controlled study due to higher measurement variability and bias introduction in the data. Besides, the previous report suggested that Japanese-style bathing before bedtime in real life could occur under varying behaviors with respect to timing and environmental conditions.23We sought to evaluate the association between hot-water bathing, at varying time intervals preceding bedtime, and SOL among the older general population. We conducted a longitudinal analysis using repeated measurements of hot-water bathing (varying time intervals) before bedtime and SOL among 1,094 home-dwelling older adults. In addition, to identify the optimal bathing conditions for shortening SOL, we evaluated different combinations of bath duration and time elapsed between bathing and going to bed. In a subgroup of participants (n = 569), DPGs were compared according to bathing conditions to investigate whether hot-water bathing before bedtime was associated with heat loss in distal skin regions at the time of sleep initiation.METHODSParticipants and study protocolThis longitudinal analysis of repeated measurements of hot-water bathing and subsequent sleep was performed on baseline data from a community-based cohort study: the Housing Environments and Health Investigation among Japanese Older People in Nara, Kansai Region (HEIJO-KYO) study.24 We recruited a total of 1,127 volunteers aged at least 60 years between September 2010 and April 2014. Of these, we completed measurements during the colder months (October to April) in 1,122 participants. We excluded 10 participants who only showered, 3 participants who did not complete their bathing diaries, and 15 participants without data on actigraphic SOL. Consequently, our study included 1,094 participants in the analysis (Table 1). All participants provided written informed consent. The study protocol was reported in a previous study and approved by the Nara Medical University ethics committee (No. 301).24,25 In summary, we visited each participant’s house and acquired demographic and medical information using a standardized questionnaire. Subsequently, we placed temperature loggers in multiple places in the home and attached an accelerometer on each participant’s wrist. We then measured the indoor temperature and physical activity for a consecutive 48 hours (from noon on the first day to noon on the third day). We tracked sleep and bathing behaviors by instructing participants to keep a standardized diary during the same period.Table 1 Basic and clinical characteristics of participants (n = 1,094).Days of Taking a Hot Bath in 2-Day SurveyP TrendNeither (n = 76)Either Day (n = 255)Both Days (n = 763)Demographic parameters Age, mean (SD), y76.2 (7.5)74.1 (6.4)70.8 (7.0)< .001 Sex, male37 (48.7%)115 (45.1%)360 (47.2%).883 Body mass index, mean (SD), kg/m222.2 (3.2)23.0 (3.0)23.2 (3.1).022 Current smoker7 (9.2%)14 (5.5%)35 (4.6%).110 Alcohol consumption (≥ 30 g/d)7 (9.2%)34 (13.3%)112 (14.7%).204 Household income (≥ 4 million JPY/y)13 (18.6%)81 (34.2%)702 (47.9%)< .001 Past education (≥ 13 y)15 (19.7%)65 (25.5%)211 (26.6%).138 History of night-shift work7 (10.3%)14 (6.1%)52 (7.3%).746Clinical parameters Antihypertensive medication use30 (39.5%)125 (49.0%)333 (43.6%).731 eGFR, mean (SD), mL/min/1.73 m273.5 (18.3)71.1 (15.5)72.5 (14.8).351 Sleep medication use9 (11.8%)30 (11.8%)76 (10.0%).398 Antidepressant use1 (1.3%)6 (2.4%)8 (1.0%).309 Daytime physical activity,* mean, count264.6 (106.5)277.6 (101.7)308.0 (101.4)< .001 Bedtime,* mean (SD), clock time21:52 (1:15)22:20 (1:14)22:36 (1:07)< .001 Rising time,* mean (SD), clock time6:49 (1:08)6:53 (0:58)6:46 (0:53).193 Self-reported SOL,* median (IQR), min26.5 (13.1–61.9)30.0 (15.5–71.0)28.0 (15.0–52.3).044 Mean (SD), log-min3.38 (0.93)3.47 (0.93)3.28 (0.88).016 Actigraphic SOL,* median (IQR), min22.5 (9.6–55.9)20.5 (10.0–40.5)17.5 (9.0–33.3).006 Mean (SD), log-min2.83 (1.25)2.82 (1.07)2.69 (0.94).118P trend was calculated using linear regression, logistic regression, and Jonckheere-Terpstra test. *Means of days 1 and 2. eGFR = estimated glomerular filtration rate, IQR = interquartile range, JPY = Japanese yen, SD = standard deviation, SOL = sleep onset latency.Self-reported and actigraphic SOLSelf-reported SOL was determined according to the diary log of bedtime and clock time for falling asleep for 2 nights. We measured sleep onset by actigraph based on bedtime recordings in the diary and physical activity. Physical activity was measured at 1-minute intervals using the actigraph (Actiwatch 2; Respironics Inc., Murrysville, PA) worn on the nondominant wrist for a consecutive 48 hours. Data were analyzed using Actiware (version 5.5; Respironics Inc.). Sleep onset was defined as the first minute followed by a 10-minute immobility period that contained no epoch with any motion. Actigraphic SOL was the time from bedtime to the actigraphic sleep onset.Bathing behavior and environmentBehavioral information about bathing was determined using the diary for bathing start time, bathing end time, time of soaking in the bathtub (bathing duration), bedtime, and the time interval between bathing end and bedtime (time before bedtime). Water temperature in the bathtub was measured using the Thermochron iButton DS1992L (Maxim Integrated, Dallas, TX), which had a measurement accuracy, range, and resolution of ± 0.5°C, −10 to +65°C, and 0.0624°C, respectively, at 1-minute intervals for 48 hours in the same position without changing the measurement conditions. Temperatures of the bathroom, dressing room, bedroom, and living room were measured 60 cm above the floor at 10-minute intervals for 48 hours. We calculated the mean water temperature in the bathtub (water temperature) and the mean temperature of the bathroom and dressing room in the time between bathing start and bathing end. Evening indoor temperature was defined as the mean temperature measured for 2 hours before bedtime at the participants’ home. Whether the participants were in their living room or bedroom was determined according to the diaries. Outdoor temperatures at 10-minute intervals were provided from the local meteorological office in Nara prefecture (latitude of 34 degrees north).To compare bathing conditions with no bath control, we classified bathing duration and time before bedtime according to thresholds as previously described.20 A previously reported meta-analysis of experimental studies showed that before bedtime, PBH for ≥ 10 minutes significantly shortened SOL when participants scheduled a bath 1–2 hours before bedtime. Based on this information, all bathing sessions were divided into bathing duration < 10 minutes and bathing duration ≥ 10 minutes. Furthermore, data of bathing duration ≥ 10 minutes were classified into intervals of 1–60 minutes, 61–120 minutes, 121–180 minutes, and ≥ 181 minutes between the end of bathing and bedtime.Skin temperatureWe measured skin temperature of 569 participants enrolled in this study on or after October 24, 2012. Proximal (abdomen) and distal (mean of wrist and ankle) skin temperatures were measured at 1-minute intervals using the same device for a consecutive 48 hours. We attached the device to the right upper quadrant of the abdominal skin with Tegaderm transparent dressing (3M, St. Paul, MN). The iButtons were attached to a silicone band with adjustable diameter that we then applied to the flexor surface of the dominant wrist (the opposite side of the actigraph) and to the lateral flat surface of the ankle. We instructed participants not to detach the bands for a consecutive 48 hours. We did not cover the silicone band or Tegaderm transparent dressing. Proximal skin temperature measurement of < 25°C and distal skin temperature measurement of < 20°C were excluded from the analysis as measurement error. DPG was calculated as distal skin temperature minus proximal skin temperature, and it was an indirect measure of blood flow as well as heat loss in the distal skin region.14,26 We calculated mean DPG for 30 minutes after participants went to bed as an average of a series of subsequent readings to detect heat loss shortly after getting in bed and mean DPG for 30 minutes before the bathing start time, in the same way, to detect heat loss before PBH. For the days without bathing, mean DPG corresponding to 30 minutes before bathing was calculated as mean DPG for 30 minutes before the median bathing start time of “bathing days.”Other measurementsSmoking and drinking habits, household income, educational qualifications, work-shift history, medication use, and medical history were recorded during an interview to enable adjustment for confounding factors in analysis of associations. The estimated glomerular filtration rates (eGFR) were calculated using the formula that follows, which was published in the Japanese Society of Nephrology– Evidence-based Practice Guideline for the Treatment of CKD:27 eGFR (mL/min per 1.73 m2) = 194 × [serum creatinine (mg/dL)]−1.094 × [age (years)]−0.287 × [sex]. For females and males, the values were 0.739 and 1.000, respectively. Self-reported sleep quality over a 1-month time interval was assessed using the Pittsburgh Sleep Quality Index, which measures 7 areas: self-reported sleep quality, latency, duration, efficiency, and disturbances, along with daytime dysfunction and use of sleep medication. Each component was scored on a scale of 0–3. The global score was calculated by a total of the 7-component score.28 A global score of ≥ 6 was used for detecting sleep disturbance.Statistical analysesNormally distributed continuous variables were expressed as mean and standard deviation; nonnormally distributed variables were reported as median (interquartile range, [IQR]). Mean values were compared utilizing unpaired t tests and 1-way analysis of variance. Median values were analyzed using the Mann-Whitney U test, Kruskal-Wallis test, and Steel-Dwass test. Trends in acquired data were evaluated using linear regression, logistic regression, and the Jonckheere-Terpstra trend test. To evaluate the interdependency of bathing conditions, SOL, and DPG within participants, we calculated the intraclass correlation coefficient using a 1-way random model.Median of SOL for each of the 5 categories of bathing conditions described previously was compared to median without hot-water bathing using all data irrespective of the first or second night. Sensitivity analyses of SOL with varying bathing duration were performed, and data of bathing duration ≥ 1, 5, and 15 minutes were also sorted into intervals of 1–60 minutes, 61–120 minutes, 121–180 minutes, and ≥ 181 minutes between the end of bathing and bedtime.We evaluated the association of each bathing condition with log-transformed SOL using a linear mixed-effect regression model consisting of participant-level variables (age, sex, body mass index [BMI], smoking and drinking status, household income, educational qualifications, work-shift history, use of sleep medication, use of antidepressants, use of antihypertensive medication, and eGFR) and measurement of day-level variables (bathing conditions, bedtime, evening indoor temperature, and daytime physical activity). We also evaluated the association of each bathing condition with DPG and the association between DPG and log-transformed SOL using a linear mixed-effect regression model adjusted for age and sex. We considered the measurements of bathing conditions, DPG, and SOL to be nested within each participant in a linear mixed-effect regression. The bathing conditions were expressed as 5 dummy variables according to the 5 previously described categories. Differences in SOLs between the control and each bathing condition were derived from regression coefficients of the dummy variables using a linear mixed-effect regression, representing the following equation with a subscript for measurement-days (i) and participants (j). Measurement of day-level: log-transformed SOLij = β0j + β1 × dummy1 + β2 × dummy2 + β3 × dummy3 + β4 × dummy4 + β5 × dummy5 + β6 × bedtime + β7 × evening indoor temperature + β8 × daytime physical activity + εij. Participant-level; β0j = γ00 + γ01 × age + γ02 × sex + γ03 × BMI + γ04 × smoking status + γ05 × drinking status + γ06 × income + γ07 × educational qualifications + γ09 × work-shift history + γ010 × sleep medication use + γ011 × antidepressants use + γ012 × antihypertensive use + γ013 × eGFR + u0j, where β0j is the means of intercept for the jth participant; γ00 is the participant-level intercept; εij and u0j represent measurement of day-level and participant-level random effects, respectively; and dummy1–5 represent dummy variables of the 5 bathing conditions. Regression coefficients were estimated by maximum likelihood.The Steel-Dwass test was performed using EZR 1.36 for Windows (Jichi Medical University, Shimotsuke, Japan).29 The other analyses were performed using SPSS 26.0 for Windows (IBM SPSS Inc., Chicago, IL). All test distributions were 2-sided, and a P value < .05 was considered statistically significant.RESULTSAmong the 1,094 participants, 1,045 completed the consecutive 48-hour session, 29 completed only the first 24-hour session, and 20 completed only the last 24-hour session. We analyzed a total of 2,139 nights (1,781 nights bathing and 358 nights not bathing). The mean age of all 1,094 participants was 72.0 years (standard deviation, 7.1 years), and 512 participants (46.7%) were male. The median age was 72 years (IQR, 66–77). The mean bedtime was 22:30 (standard deviation, 1:11). Median actigraphic and self-reported SOL was 18.5 minutes (IQR, 9.5 –35.5) and 29.0 minutes (IQR, 15.0–55.0), respectively. Sleep disturbance with a Pittsburgh Sleep Quality Index score ≥ 6 was found in 383 participants (35.0%). The number of participants who took medication for insomnia, depression, hypertension, and diabetes was 115 (10.5%), 15 (1.4%), 488 (44.6%), and 102 (9.3%), respectively.For our 2-day measurements, among the 1,094 participants, 763 took a hot bath on both days of measurement, 255 took a hot bath on day 1 or 2, and 76 did not take a hot bath on either day. Bathing for both days during the 2-day survey was significantly associated with increased BMI, household income, daytime physical activity, bedtime, decreased age, actigraphic, and self-reported SOL. A prolonged time interval between bathing and bedtime was significantly associated with earlier bathing, higher water temperature, and higher dressing room temperature (Table 2). Day-to-day correlation of log-transformed time before bedtime, bathing duration, water temperature, log-transformed self-reported/actigraphic SOL, and mean DPG for 30 minutes before going to bed and for 30 minutes after going to bed was 0.70 (95% confidence interval [CI], 0.66–0.73), 0.76 (95% CI, 0.73–0.79), 0.69 (95% CI, 0.64–0.72), 0.55 (95% CI, 0.50–0.59)/0.26 (95% CI, 0.21–0.32), 0.53 (95% CI, 0.47–0.58), and 0.52 (95% CI, 0.45–0.59), respectively.Table 2 Bathing and sleep parameters according to time before bedtime.Time Before Bedtime (min)P Trend1–6061–120121–180≥ 181Number of nights803467261250Time Bathing start, mean21:38 (1:17)20:39 (1:13)19:35 (1:15)18:00 (1:17)< .001 Bathing end, mean22:05 (1:17)21:06 (1:13)20:02 (1:15)18:28 (1:15)< .001 Bedtime, mean22:34 (1:15)22:35 (1:11)22:29 (1:12)22:41 (1:02).594 Rising time, mean6:49 (0:58)6:40 (0:56)6:48 (0:59)6:48 (0:53).654 Bathing duration, median, min11 (9–16)13 (11–16)11 (9–16)13 (9–16).838 Time before bedtime, median, min29 (15–41)90 (73–103)146 (135–161)240 (205–284)< .001Temperature, mean, °C Water temperature*40.7 (1.3)40.8 (1.3)40.8 (1.4)40.9 (1.4).036 Bathroom*16.2 (4.4)16.5 (4.3)16.5 (4.5)16.5 (4.3).252 Dressing room*13.3 (4.1)13.5 (4.2)13.4 (3.9)13.9 (4.0).045 Indoor†16.8 (3.9)17.0 (3.8)16.7 (4.0)16.7 (3.9).071 Outdoor†6.5 (4.9)6.6 (4.9)6.3 (4.9)6.1 (5.0).261Data are expressed as means with standard deviation and medians with interquartile range. Unpaired t test and Mann-Whitney U test were used to compare means and medians. P trend was calculated using linear regression and Jonckheere-Terpstra test. *Mean temperature during bathing. †Mean temperature for 2 hours before bedtime. Bathing duration = time of soaking in the bathtub, Time before bedtime = time interval between bathing end and bedtime, Water temperature = temperature of hot water in the bathtub.Self-reported SOL was significantly shorter in hot bathing 61–120 minutes before bedtime (median: 25 minutes, IQR: 10–45, P = .005) and hot bathing 121–180 minutes before bedtime (median: 24 minutes, IQR: 14–40, P = .021) compared with not bathing (median: 30 minutes, IQR: 15–60) (Table 3). Hot bathing 61–120 minutes, 121–180 minutes, and ≥ 181 minutes before bedtime showed a significantly shorter log-transformed self-reported SOL by 0.18 (95% CI, 0.06–0.31), 0.18 (95% CI, 0.03–0.33), and 0.17 (95% CI, 0.02–0.33) log-minutes, respectively, compared with not bathing (Table 3). The difference remained significant in hot bathing 61–120 minutes and 121–180 minutes before bedtime when adjusted for participant-level variables (age, sex, BMI, smoking and drinking habits, household income, educational qualifications, work-shift history, use of sleep medication, use of antidepressants, use of antihypertensive medication, and eGFR). Hot bathing for 61–120 minutes and 121–180 minutes before bedtime showed an adjusted difference of −0.19 log-minutes (95% CI, −0.33 to −0.06, P = .006) and an adjusted difference of −0.21 log-minutes (95% CI, −0.32 to −0.04, P = .014), respectively. The same was true after adjustment for participant-level variables and measurement of day-level variables (bedtime, daytime physical activity, and evening indoor temperature) (adjusted model, Table 4). These differences derived from the estimated coefficients were back-transformed from 0.16 and 0.18 log-minutes (in adjusted model, Table 4) to a 14.8% and 16.5% reduction of self-reported SOL from the control, using the following calculation: e−0.16 = 0.852 (85.2%) and e−0.18 = 0.835 (83.5%), where e is Euler’s number.Table 3 Self-reported and actigraphic sleep onset latency stratified by bathing conditions.ControlBathBathing Duration < 10 minBathing Duration ≥ 10 minTime Before Bedtime (min)1–6061–120121–180≥ 181Self-reported SOL, median (IQR), min30 (15–60)25 (13.5–55)25 (15–55)25 (10–45)24 (14–40)25 (10–54) Number of nights348436587353181180 P value*—.250.205.005.021.094Actigraphic SOL, median (IQR), min18 (7–41)17 (7–37)18 (8–35)13.5 (5–30)12 (4–26)15 (6–35) Number of nights358445602362187185 P value*—.989.999.021.004.557*SOL in the control condition was compared with 5 bathing conditions. P values were calculated using the Steel-Dwass test. Bathing Duration = time of soaking in the bathtub, IQR = interquartile range, SOL = sleep onset latency, Time Before Bedtime = time interval between bathing end and bedtime.Table 4 Linear mixed-effect model analysis of the unadjusted and adjusted association of bathing conditions with log-transformed sleep onset latency.ControlBathBathing Duration < 10 minBathing Duration ≥ 10 minTime Before Bedtime (min)1–6061–120121–180≥ 181Self-reported SOL Number of participants*298296422290161141 Crude difference (95% CI), log-minRef−0.05 (−0.18 to 0.07)−0.11 (−0.22 to 0.01)−0.18 (−0.31 to −0.06)−0.18 (−0.33 to −0.03)−0.17 (−0.33 to 0.02) P value—.396.066.004.020.030 Adjusted difference (95% CI), log-min†Ref0.03 (−0.11 to 0.17)−0.07 (−0.19 to 0.06)−0.16 (−0.30 to −0.02)−0.18 (−0.35 to −0.01)−0.14 (−0.31 to 0.04) P value—.695.310.022.033.123Actigraphic SOL Number of participants*298297422290162141 Crude difference (95% CI), log-minRef0.03 (−0.14 to 0.20)0.06 (−0.10 to 0.22)−0.21 (−0.38 to −0.03)−0.32 (−0.53 to −0.10)−0.12 (−0.34 to 0.09) P value—.739.459.024.0040.262 Adjusted difference (95% CI), log-min†Ref0.11 (−0.08 to 0.30)0.13 (−0.05 to 0.31)−0.23 (−0.42 to −0.03)−0.32 (−0.56 to −0.09)−0.16 (−0.40 to 0.08) P value—0.2730.1550.0240.0070.186Differences in SOLs were derived from regression coefficients on the dummy variables using the mixed-effect linear regression model. *The sum of the number of participants exceeds the total participants (n = 1,094) because a participant who had 1 night in 1 bathing category and the other night in another bathing category was counted twice. †Adjusted for age, sex, body mass index, smoking and drinking status, income, past education, shift work, sleep medication use, antidepressant use, antihypertensive medication use, eGFR, bedtime, physical activity, and indoor temperature. Bathing Duration = time of soaking in the bathtub, CI = confidence interval, eGFR = estimated glomerular filtration rate, Ref = reference, SOL = sleep onset latency, Time Before Bedtime = time interval between bathing end and bedtime.Consistently, actigraphic SOL was significantly shorter in hot bathing 61–120 minutes before bedtime (median: 13.5 minutes, IQR: 5–30, P = .021) and 121–180 minutes before bedtime (median: 12 minutes, IQR: 4–26, P = .004), compared with the control condition (median: 18 minutes, IQR: 7–41, Table 3). Similarly, hot bathing 61–120 minutes and 121–180 minutes before bedtime showed significantly shorter log-transformed actigraphic SOL by 0.21 log-minutes (95% CI, 0.03–0.38) and 0.32 log-minutes (95% CI, 0.10–0.53), respectively, compared to not bathing (Table 4). Multivariable models adjusted for the all participant-level variables revealed significantly shorter actigraphic SOL in hot bathing 61–120 minutes before bedtime by 0.24 log-minutes (95% CI, 0.05–0.43, P = .015) and in hot bathing 121–180 minutes before bedtime by 0.34 log-minutes (95% CI, 0.10–0.57, P = .005), respectively. The difference remained significant in hot bathing 61–120 minutes and 121–180 minutes before bedtime when adjusted for participant-level variables and measurement of day-level variables (adjusted model, Table 4). These differences derived from the estimated coefficients were back-transformed from 0.23 and 0.32 log-minutes (in adjusted model, Table 4) to a 20.6% and 27.4% reduction of actigraphic SOL from the control, using the following calculation: e−0.23 = 0.794 (79.4%) and e−0.32 = 0.726 (72.6%).In the sensitivity analyses of SOL, when hot-water bathing duration under bathing duration condition varied ≥ 1, 5, and 15 minutes, the significant association between SOL and bathing conditions remained largely consistent (Table S1 in t