The associations between thermal variety and health: Implications for space heating energy use.

Fossil fuels dominate domestic heating in temperate climates. In the EU, domestic space heating accounts for around 20% of final energy demand. Reducing domestic demand temperatures would reduce energy demand. However, cold exposure has been shown to be associated with adverse health conditions. Using an observational dataset of 77,762 UK Biobank participants, we examine the standard deviation of experienced temperature (named here thermal variety) measured by a wrist worn activity and temperature monitor. After controlling for covariates such as age, activity level and obesity, we show that thermal variety is 0.15°C 95% CI [0.07-0.23] higher for participants whose health satisfaction was 'extremely happy' compared to 'extremely unhappy'. Higher thermal variety is also associated with a lower risk of having morbidities related to excess winter deaths. We argue that significant CO2 savings would be made by increasing thermal variety and reducing domestic demand temperatures in the healthiest homes. However, great care is needed to avoid secondary health impacts due to mould and damp. Vulnerable households should receive increased attention.

Introduction

In the temperate climates of the Northern Hemisphere, domestic energy demand is dominated by space heating. In the USA there are four times as many heating degree days as cooling degree days [1]. In the EU, domestic space heating accounts for 78% of domestic energy use, at least 60% of which comes directly from fossil fuel sources [2]. A sensitivity analysis of the Cambridge Housing Model for the UK government [3] estimated that a 1°C drop in demand temperature decreased CO2 emissions by 13%, making heating demand temperature one of the behavioural factors with the highest potential impact on emissions. Therefore, given the necessity of clear action on carbon emissions, reduction in domestic heating demand is vital.

At the same time, there is broad epidemiological consensus that observed seasonal variations in mortality in temperate countries is attributable to cold external temperatures and cold exposure. A recent meta-review of existing systematic reviews concluded that cold exposure and cold spells increase the risk of cardiovascular and respiratory illness and mortality [4]. Low temperatures in particular are known to exacerbate respiratory health conditions such as chronic obstructive pulmonary disease [5]. Cold exposure is known to increase blood pressure [6,7]. A review by Jevons et al. found sufficient evidence to recommend a threshold ambient temperature for health in the UK [8]. They advocated for a population wide threshold of 18°C to minimise potential harm to both vulnerable and healthy portions of the population. However, under conditions of milder exposure for healthy individuals, the relationship between cold and morbidity is complex. Recent work has found evidence that mild cold exposure may moderately improve metabolic health [9]. In general, humans exhibit a wide range of adaptive physiological responses to cold [10] and emerging evidence suggests that cold adaptation could lead to a decreased risk of cardiovascular disease [11].

From a thermal comfort perspective, there is increased interest in indoor environments which do not provide static, isothermal conditions on a room/dwelling basis [12]. Presently, UK domestic temperatures tend to be controlled by gas central heating systems operated by a single thermostat [13]; UK offices are typically regulated by centrally controlled air supply systems (HVAC), which lack opportunities for personal control [14]. The deployment of personal comfort systems allows the user to tune their local environment to their personal preferences, against a background heating or cooling load which would provide minimal comfort by itself. This may take the form of heated seating, or air-flow control systems, which are provided at the user’s workspace. Such systems have the potential to reduce heating demand considerably [15, 14]. Heterogeneous indoor microclimates provided by such systems could be deliberately constructed to introduce thermal asymmetries and local air movement [16]. As a result, the range of temperatures experienced throughout the day would increase, as well as offering opportunities for personal control [17].

This paper reports the findings of a novel research project which aimed to characterise the immediate thermal environment of a study participant using a wrist worn monitor. No program of study prior to this has sought to understand the variations of experienced temperature at the population level, although earlier findings from this study were reported previously [18].

In this study, we used data collected as part of the UK Biobank–a large on-going longitudinal health study of older UK adults [19]. Participants wore an Axivity AX3 wristband for a single week between June 2013 and December 2015, which recorded the experienced temperature and activity levels of the participant. Earlier work showed the experienced temperature to be a mix of ambient temperature and heat from the wrist [18]. The study design is cross-sectional in nature. Two models were constructed to understand how the standard deviation of experienced temperature, the thermal variety, is related to health outcomes. The experienced temperature was down-sampled to a 1-minute interval. Model 1 used self-reported health satisfaction as its primary independent variable of interest, while model 2 used diagnosed health conditions. The overall aim of this study was to contribute empirical evidence to our understanding of the relationship between health and the temperatures that people experience in daily life.

Methods

With the exception of external temperature, all variables used in this study were collected as part of the UK Biobank. All data were fully anonymised by UK Biobank prior to being shared with the study authors. The thermal variety and activity level variables were derived from the Axivity AX3 wristband measurements using the cluster computing environment of University College London (UCL). The computational script was a modified version of one produced by Doherty et al. for their work on the AX3 monitor [20]. A calibration error by the Axivity manufacturers was discovered and corrected by the author. 103,707 files were processed, comprising 27TB of data in total. This processing produced a down-sampled timeseries at a 1-minute period. The temperature data were recorded at a period of between 1.1–1.3 seconds. The accuracy of the AX3 temperature sensor is ±1°C under standard operating conditions and the resolution is 0.3°C [21]. The response time is on the order of industry standard temperature monitors (i.e. Onset's HOBO U12 Data Logger).

While the data were being processed, 4 participants withdrew from the study. UK Biobank provided the anonymised identification codes of these participants and their data were deleted. A minimum wear-time criterion of 90% was imposed (9072 minutes out of a total possible 10800 for the study week). Participants who conducted nightshift work were excluded, as were those who were diagnosed with dementia or Alzheimer’s disease, as these may be associated with circadian disruption [22]. Participant’s whose average activity was greater than 0.1g were also excluded, as were timeseries which exhibited clear sensor malfunction or substantial missing data. From these processed timeseries, thermal variety was calculated as the standard deviation of the temperature measured by the device during the week of wear.

UK Biobank variables

The variables selected for inclusion were based on a pre-analysis plan for a previous portion of the present study [23]. The variables of activity level and thermal variety were both recorded using the AX3 device between June 2013 and December 2015. All other variables were collected at the time of initial assessment, between 2006 and 2010, and subsequent follow-up visits [19] with the exception of external temperature (see below). The variables of age, household size, body mass index and activity level were binned into appropriate categories to aid interpretability.

The variable CEWD (whether or not a participant had a condition associated with excess winter deaths) used in Model 2, was constructed from the UK Biobank data on diagnosed disease. CEWD was given the value 1 if participants had been diagnosed with either a respiratory disease (defined under the 10th iteration of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) as codes J00 to J99) or a circulatory disease (ICD-10 codes I00 to I99). Alzheimer’s disease and dementia (ICD-10 codes F01 –F03) are also related to excess winter deaths [24] but they were excluded due to the potential for circadian disruption as described above—only six participants in the study had such diseases.

External temperature

For all participants the average local external temperature for the week in which the AX3 device was worn was calculated. The rounded (1 km) home location of each participant was matched to the corresponding grid square of NASA’s MEERA-2 surface temperature dataset [25, 26]. The grid resolution was 0.625°×0.5° (approximately 70×35 km).

Model 1 (N = 37,730)

Model 1 was a linear model using thermal variety as the dependent variable, with the following independent variables: external temperature, age, health satisfaction, financial situation satisfaction, heating type, sex, ethnic background, household income, tenure type, accommodation type, household size, employment status, gas or solid-fuel cooking/heating, body mass index and activity level. The inclusion of self-reported health satisfaction was designed to capture the subjective sense of well-being and its association with thermal variety.

Model 2 (N = 77,762)

Model 2 was a binomial linear model using a log link function between the dependent variable CEWD and the following independent variables: age, sex, ethnic background, household income, tenure type, accommodation type, household size, employment status, gas or solid-fuel cooking/heating, body mass index, activity level and thermal variety. Model 2 did not include health satisfaction, financial situation satisfaction or heating type as these variables were only available for 37,770 participants and the model did not converge with this lower number of participants. The use of CEWD as the independent variable in model 2 was designed to triangulate any findings of model 1 in relation to health satisfaction.

Results

In model 1, thermal variety was the main outcome variable in a multiple linear regression model against various demographic and health factors. The average external temperature for the week in which the thermal variety was recorded was also included, the relationship between them is shown in Fig 1, which shows that thermal variety is greater at the coldest times of the year.

Fig 1

The relationship between mean external temperature and thermal variety.

The relationship is approximately linear and shows higher thermal variety during the coldest periods of the year. Data were not sampled across a uniform distribution of external temperatures. The least square regression line is shown in red (β = -0.05, p<2x10-16). Since 77,762 participants are plotted, the data is represented as a density cloud.

For model 1, the full results are given in Tables 1 and 2. The clearest statistically significant results from model 1 show that thermal variety decreases with increasing age, increasing unhappiness with health satisfaction and increasing body mass index. Thermal variety increases with activity levels (S1 Fig). This is unlikely to be accounted for by physiological changes of wrist temperature alone. Studies of wrist temperature variation find the amplitude of variation to be around 1.0–1.5°C [27, 28], which would equate to at most a standard deviation of around 0.4°C. Those living in accommodation they own outright have higher thermal variety than those living in homes rented from the local authority. There were no significant differences as a function of household income.

Table 1

Multiple linear regression of thermal variety with demographic, building and health factors.

N = 77,762. R2 = 0.24. Significance levels: * p<0.01, ** p<0.001, *** p<1x10-9.

Predictor (relative subcategory, N)	Sub-category (N)	tsd°C
Intercept	-	3.46 [3.43–3.48] ***
External temperature°C	-	-0.05 [-0.06 –-0.05] ***
Age (40–49, 6075)	50–59 (21320)	-0.06 [-0.08 –-0.04] ***
	60–69 (35407)	-0.10 [-0.12 –-0.08] ***
	70–79 (14960)	-0.16 [-0.18 –-0.14] ***
Sex (Female, 43770)	Male (33992)	-0.05 [-0.06 –-0.04] ***
Ethnic background (White, 75365)	Mixed (398)	0.07 [0.01–0.13]
	Asian (654)	-0.01 [-0.05–0.04]
	Black (582)	0.09 [0.04–0.14] **
	Chinese (157)	0.11 [0.02–0.21]
	Other ethnic group (395)	0.03 [-0.03–0.09]
	Do not know (20)	0.08 [-0.18–0.35]
	Prefer not to answer (191)	-0.05 [-0.13–0.04]
Household Income (Less than £18,000, 10592)	£18,000 to £30,999, (17779)	-0.02 [-0.04 –-0.01] *
	£31,000 to £51,999 (20016)	-0.01 [-0.03–0.00]
	£52,000 to £100,000 (17021)	-0.01 [-0.03–0.01]
	Greater than £100,000 (4850)	-0.02 [-0.04–0.01]
	Prefer not to answer (5475)	-0.01 [-0.03–0.01]
	Do not know (2029)	-0.07 [-0.10 –-0.04] **
Accommodation type (House/bungalow, 71554)	Flat (6058)	-0.07 [-0.09 –-0.05] ***
	Temporary (54)	0.02 [-0.14–0.18]
	None of above (83)	-0.05 [-0.18–0.08]
	Prefer not to answer (13)	-0.17 [-0.51–0.17]
Tenure type (Own outright, 44537)	Mortgage (28498)	-0.05 [-0.07 –-0.04] ***
	Rent Local Authority (2096)	-0.16 [-0.18 –-0.13] ***
	Rent private (1497)	-0.04 [-0.07 –-0.01]
	Shared (174)	-0.07 [-0.16–0.02]
	Rent free (469)	-0.09 [-0.15 –-0.04]
	None of above (276)	-0.07 [-0.14–0.00]
	Prefer not to answer (215)	-0.01 [-0.09–0.08]
Household size (single occupant, 12854)	Two (37905)	-0.04 [-0.05 –-0.02] **
	Three (12141)	-0.05 [-0.06 –-0.03] ***
	Four or more (14862)	-0.03 [-0.05 –-0.01] ***
Employment status (In paid employment or self-employed, 39797)	Retired (27472)	0.03 [-0.03–0.09]
	Looking after home/family (3235)	0.03 [-0.09–0.15]
	Unable to work, sickness/disability (1411)	0.01 [0.00–0.02]
	Unemployed (901)	0.02 [-0.00–0.04]
	Doing unpaid or voluntary work (3759)	-0.10 [-0.13 –-0.06] **
	Full/ part-time student (738)	-0.02 [-0.06–0.02]
	None of the above (350)	0.04 [0.01–0.06] **
	Prefer not to answer (99)	0.04 [-0.00–0.08]
Fuel type (Gas hob or gas cooker, 28957)	Gas fire (6379)	0.01 [-0.00–0.03]
	Open solid fuel fire (2335)	0.12 [0.09–0.14] ***
	Gas hob & Gas fire (20188)	0.01 [-0.00–0.02]
	Gas hob & Open solid fuel fire (4481)	0.09 [0.07–0.11] ***
	Gas fire & Open solid fuel fire (195)	0.21 [0.12–0.29] **
	Gas hob & Gas fire & Open fire (956)	0.08 [0.04–0.12] **
	None of the above (14221)	-0.01 [-0.02–0.00]
	Prefer not to answer (37)	-0.21 [-0.41 –-0.01]
	Do not know (13)	-0.18 [-0.50–0.15]
Body Mass Index (Normal, 30562)	Underweight (477)	0.11 [0.06–0.17] **
	Overweight (45722)	-0.18 [-0.19 –-0.18] ***
	Obese (1001)	-0.37 [-0.41 –-0.34] ***
Activity level recorded (1st quintile, 15463)	2nd quintile (15567)	0.14 [0.13–0.16] ***
	3rd quintile (15567)	0.24 [0.22–0.25] ***
	4th quintile (15578)	0.33 [0.31–0.34] ***
	5th quintile (15587)	0.50 [0.49–0.51] ***

Table 2

Additional variables for the regression given in Table 1.

These variables were only available for N = 37,730 participants. R2 = 0.24. Significance levels: * p<0.01, ** p<0.001, *** p<1x10-9.

Predictor (relative subcategory, N)	Sub-category (N)	tsd
Health satisfaction (Extremely happy, 2230)	Very happy (13771)	-0.04 [-0.07 –-0.01] *
	Moderately happy (17767)	-0.10 [-0.12 –-0.07] ***
	Moderately unhappy (2955)	-0.15 [-0.19 –-0.12] ***
	Very unhappy (661)	-0.16 [-0.21 –-0.11] **
	Extremely unhappy (249)	-0.15 [-0.23 –-0.07] **
	Prefer not to answer (10)	0.05 [-0.32–0.42]
	Do not know (87)	-0.04 [-0.17–0.08]
Financial situation satisfaction (Extremely happy, 3808)	Very happy (14498)	0.01 [-0.01–0.04]
	Moderately happy (15732)	0.01 [-0.01–0.03]
	Moderately unhappy (2473)	-0.02 [-0.05–0.02]
	Very unhappy (737)	-0.07 [-0.12 –-0.02] *
	Extremely unhappy (369)	-0.06 [-0.13–0.00]
	Prefer not to answer (57)	-0.09 [-0.25–0.07]
	Do not know (56)	-0.12 [-0.28–0.03]
Heating Type (Gas central heating, 34999)	Electric storage heaters (798)	-0.01 [-0.05–0.03]
	Oil (kerosene) central heating (979)	0.09 [0.05–0.13] **
	Portable gas or paraffin heaters (10)	0.17 [-0.20–0.54]
	Solid fuel central heating (128)	0.09 [-0.01–0.20]
	Open fire without central heating (109)	-0.02 [-0.14–0.09]
	Three heating types (5)	-0.17 [-0.69–0.35]
	None of the above (676)	-0.01 [-0.05–0.04]
	Prefer not to answer (15)	-0.19 [-0.53–0.16]
	Do not know (11)	-0.17 [-0.52–0.19]

Model 2 was constructed to understand how conditions associated with excess winter deaths (CEWD) are related to the thermal variety of the participant. CEWD was constructed as a binary variable and denoted whether a participant had been diagnosed with cardiovascular or respiratory conditions. A binomial regression model of the relationship between CEWD, thermal variety and potentially confounding demographic factors was produced. These findings are given in Table 3.

Table 3

Risk ratio of CEWD as a function of both tsd and other demographic, health and building factors.

N = 77,762 Significance levels: * p<0.01, ** p<0.001, *** p<1x10-9.

Predictor (relative subcategory)	Sub-category	Risk ratio (tsd)
tsd	-	0.95 [0.93–0.98] **
Age (40–49)	50–59	1.48 [1.32–1.64] ***
	60–69	2.10 [1.88–2.34] ***
	70–79	2.70 [2.41–3.03] ***
Sex (Female)	Male	1.52 [1.47–1.58] ***
Ethnic background (White)	Mixed	0.99 [0.74–1.32]
	Asian or Asian British	1.16 [0.97–1.38]
	Black or Black British	0.90 [0.71–1.15]
	Chinese	0.86 [0.51–1.44]
	Other ethnic group	1.12 [0.88–1.44]
	Do not know	1.17 [0.41–3.39]
	Prefer not to answer	0.95 [0.68–1.33]
Household income per year (less than £18,000)	£18,000 to £30,999	0.91 [0.86–0.96] **
	£31,000 to £51,999	0.80 [0.75–0.85] ***
	£52,000 to £100,000	0.72 [0.67–0.77] ***
	Greater than £100,000	0.64 [0.57–0.71] ***
	Prefer not to answer	0.84 [0.77–0.91] **
	Do not know	0.92 [0.82–1.03]
Tenure type (Own outright)	None of above	0.91 [0.66–1.26]
	Prefer not to answer	1.08 [0.78–1.49]
	Mortgage	1.07 [1.03–1.12] *
	Rent Local Authority	1.22 [1.10–1.35] **
	Rent private	1.11 [0.98–1.26]
	Shared	1.47 [1.07–2.01]
	Rent free	1.04 [0.83–1.30]
Accommodation type (House or bungalow)	Flat	0.97 [0.91–1.05]
	Temporary	0.81 [0.41–1.62]
	None of above	0.80 [0.45–1.41]
	Prefer not to answer	1.18 [0.40–3.52]
Employment status (In paid/self-employment)	Retired	1.06 [1.01–1.11]
	Looking after home and/or family	0.96 [0.86–1.08]
	Unable to work due to sickness/disability	1.82 [1.66–1.99] ***
	Unemployed	0.85 [0.71–1.01]
	Doing unpaid or voluntary work	1.06 [0.97–1.15]
	Full or part-time student	1.04 [0.84–1.28]
	None of the above	1.10 [0.85–1.41]
	Prefer not to answer	0.76 [0.42–1.36]
Fuel type (Gas hob or gas cooker)	Open solid fuel fire	1.01 [0.91–1.13]
	Gas hob & Gas Fire	1.05 [1.00–1.09]
	Gas hob & solid fuel open fire	0.92 [0.85–1.01]
	Gas fire & solid fuel open fire	1.05 [0.74–1.50]
	Gas hob & Gas fire & solid fuel open fire	0.98 [0.83–1.16]
	None of the above	1.01 [0.96–1.06]
	Prefer not to say	1.64 [0.95–2.85]
	Do not know	1.52 [0.63–3.67]
Body mass index (Normal)	Underweight	1.00 [0.76–1.31]
	Overweight	1.15 [1.11–1.20] ***
	Obese	1.49 [1.32–1.68] ***
Activity level recorded (1st quintile)	2nd quintile	0.84 [0.80–0.89] ***
	3rd quintile	0.81 [0.77–0.86] ***
	4th quintile	0.77 [0.73–0.82] ***
	5th quintile	0.72 [0.68–0.77] ***
Household size (single)	Two	1.13 [1.07–1.19] **
	Three	1.20 [1.12–1.29] **
	Four or more	1.18 [1.09–1.27] **

Model 2 found that the risk of CEWD decrease with increasing thermal variety, activity and income. Risk of CEWD increase as a function of age and body mass index. Being unable to work because of sickness or disability also showed a strong increased risk of CEWD, as might be expected. Renting from the local authority had a strong increased risk of CEWD over owning a home outright; having a mortgage showed moderate increased risk. Unlike model 1 there was a clear effect as a function of income–a higher household income was associated with decreased risk of CEWD across all income brackets. The absence of a significant relationship for household income in model 1 is addressed in the supporting information section.

It is important to stress that the associations highlighted by both models in this study do not necessarily point to causal mechanisms. It is possible that those who have health conditions are less able to access, or seek to avoid, wide thermal ranges.

Discussion

A conceptual representation of the above findings, informed by the literature as a whole, is given in Fig 2. This shows that, for healthy individuals, a wider range of experienced temperature is found. This is evidenced by the findings of both models in this study; individuals with greater health satisfaction have larger thermal variety, and the risk of having a condition associated with excess winter deaths is lower for each degree increase of thermal variety. This is equivalent to a larger range of temperatures which are not harmful for healthy individuals. For individuals who are less healthy, the range of healthy temperatures is narrower. Ultimately this effect likely contributes to the greater mortality levels in winter in temperate climates i.e. those living with respiratory or cardiovascular diseases experience higher risks in extreme temperatures than those without such conditions.

Fig 2

A schematic summary of the results of this study (dotted black lines) and the conceptual structure of the broader literature.

Healthy individuals have a wider range of temperatures that are not harmful, and typically do not experience them. Individuals with poor health have a narrower range of experienced temperature, and are more likely to experience harmful thermal conditions, especially when living in poor housing which fails to guard against harmful temperature exposure. This harmful exposure is understood to contribute to the observed seasonal variation in mortality.

Differences in experienced temperature, and thermal variety, are associated with a number of demographic and housing factors. In terms of Fig 2, reducing harmful exposure necessitates modifying internal temperatures in underheated dwellings. Thermal variety can also be framed as an issue of flexibility justice [29]. This concept suggests that the ability to be flexible in daily life constitutes a form of capital, which is unevenly distributed in society. In many future energy scenarios flexibility will be increasingly valuable. The figure can therefore be interpreted from a flexibility justice perspective; those in good health can potentially tolerate increased thermal variety, whereas those in poor health might require a narrowing of their thermal variety to avoid harmful exposure. Since only associations are highlighted by the present study, these interpretations should be caveated by noting that causal relationships have not be revealed by this study.

Low thermal variety, especially in winter, may also point to the problem of chronically low experienced temperature [30]. The data as a whole (see S2 Fig) show that lower mean temperatures are weakly associated with higher thermal variety, although this is most likely a seasonal effect. For fuel poor households that struggle to afford warmth due to a combination of low income, poor thermal dwelling performance and high energy costs, low thermal variety may be more likely. It is vital that attention is focused on at-risk populations who lack the means to avoid harmful cold exposure. Secondary health impacts associated with temperature such as mould growth and damp, which are more prevalent in underheated homes, are also a priority.

In the EU, space cooling remains uncommon in homes [1]. Since the vast majority of domestic heating systems there are fuelled by carbon intensive resources, allowing more thermal variation in dwellings occupied by healthy individuals could yield carbon emissions savings. From a policy standpoint, such a position is currently controversial given that government and health body recommendations typically avoid differentiating between thermal environments for healthy and unhealthy individuals. However, when coupled with the emerging evidence from the thermal comfort literature on the comfort potential of indoor environments which avoid thermal monotony, such a proposal has broader appeal. Heating reduction campaigns could be targeted at healthy, well-off and environmentally conscious portions of the population as a means of raising awareness of the climate impacts of CO2 intensive heating. As economies of the Northern Hemisphere undergo the transition away from carbon, providing low-carbon comfort for those able to tolerate wider thermal variety would allow carbon intensive heating to be reserved for those most in need. Practically, this could take the form of health differentiated heating recommendations, moving away from a one size fits all approach, towards policy which focuses on health-related needs as well as also carbon emission reductions targets.

The relationship between mean activity in mg and thermal variety.

, where is the mean recorded activity for the study week.

(TIF)

Click here for additional data file.

The relationship between mean experienced temperature and thermal variety.

, where is the mean experienced temperature.

(TIF)

Click here for additional data file.

12 May 2020

PONE-D-20-02222

The associations between thermal variety and health: implications for space heating energy use

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: Generally, it is scientifically sound and well written. Only minor revision may be required to clarify some ambiguities. You can find my detail comments from the attached 'Reviewer's Comment' word document.

Reviewer #2: The original research article presents a interesting topic pertaining to environmental health. The manuscript is written in a intelligent fashion although the methodology section can be further strengthened by describing the study design. There were some topographical and grammatical errors detected throughout the manuscript. A professional editing is suggested. and A few suggestions are provided of relevant literature which can be incorporated in the introduction and discussion section.

Houghton A, Castillo-Salgado C. Associations between Green Building Design Strategies and Community Health Resilience to Extreme Heat Events: A Systematic Review of the Evidence. International journal of environmental research and public health. 2019 Jan;16(4):663.

Tsoulou I, Andrews CJ, He R, Mainelis G, Senick J. Summertime thermal conditions and senior resident behaviors in public housing: A case study in Elizabeth, NJ, USA. Building and Environment. 2020 Jan 15;168:106411.

Asumadu-Sakyi AB, Barnett AG, Thai PK, Jayaratne ER, Miller W, Thompson MH, Rahman MM, Morawska L. Determination of the association between indoor and outdoor temperature in selected houses and its application: A pilot study. Advances in Building Energy Research. 2019 Apr 23:1-35.

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Reviewer #1: Yes: Serebe Gebrie

Reviewer #2: No

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Submitted filename: Reviewer Comment_SerebeGebrie.docx

Click here for additional data file.

16 Jun 2020

Dear Mason Sarafraz, editors and reviewers,

The authors would like to thank the reviewers and the editor for their positive review and helpful comments, as well as taking the time to carry the review.

Before addressing specific reviewer and editorial comments, it is helpful to clarify the data availability for this study. The data for this study were derived from the UK Biobank, which places restrictions on data sharing, but allows any bonified researcher to apply for access. Therefore, the authors would like to amend the data availability statement to the following:

“Restrictions limit the direct sharing of the data used in this study. However, the data used in this study are available to any bona fide researchers following application to a third party, UK Biobank at www.ukbiobank.ac.uk. All data were fully anonymised by UK Biobank prior to being shared with the study authors”

For precedence regarding the use of UK Biobank data in Plos One studies, please see, for example: Lyall et al 2016. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0154222. The authors hope that this clarification addresses both the data-sharing and ethics queries raised.

The submission has been proofread to ensure no spelling errors remain. All submission figures have been checked and processed using the PACE provided. An additional reference pertaining to personal thermal control in office systems added.

The specific editorial and reviewer comments are addressed below:

Editorial comments:

Comment Response

It would be more appealing if a paragraph is written on the contribution, gap and novelty of the present work in the "introduction" section. An extra framing paragraph has been included (lines 69-73)

Please upload a copy of Supporting Information Figure 4 which you refer to in your text on page 16. Figure 4 was intended to refer to figure S2. The text has been updated accordingly. (line 277)

Reviewer #2:

Comment Response

“The original research article presents a interesting topic pertaining to environmental health. The manuscript is written in a intelligent fashion although the methodology section can be further strengthened by describing the study design. There were some topographical and grammatical errors detected throughout the manuscript. A professional editing is suggested. and A few suggestions are provided of relevant literature which can be incorporated in the introduction and discussion section.” The study design has been further expounded on lines 79-80.

Typographical errors have been corrected.

The authors are grateful for the suggested additional literature. However, the paper already includes significant recent review articles (refs 4-11) related to the topic in question. The literature suggestion pertaining to green building design is too specific for inclusion here. The second and third suggested studies are a case study and a pilot study respectively, and so also lack general relevance required for inclusion in this paper.

Serebe Gebrie comments:

Comment Response

(Page 4, L61-64) – It shows thermal asymmetries and local air movement would offer opportunities for personal control. Would you explain a little about how temperature is controlled or regulated in the UK? what are the major problems with the current system? A description of typical temperature control systems in both domestic and office settings has been added (lines 55-58) with relevant references.

In this study, you have used two models. What is the rationale to use these two models? It seems model 2 is more reliable than model 1. Self-reported health satisfaction could be more subjective and has a higher risk of bias. It would be clear if you could add the reason why you have used these two models. Lines 69-73 have been added, including relevant references, to address this comment.

Lines 143-144 and 152-153 have been added to clarify the choice of models. The use of self-reported health status is essential if subjective assessments of well-being are to be captured. It is not unreliable per se, since it aims to capture subjective well-being.

In this paper, there are some abbreviations and subject-specific words. Eg. ICD, HOBO, UCL’s cluster. I suggest using abbreviations and operational definitions. Otherwise, you may define or write in full text when first use. All subject specific abbreviations have been defined at first use in the text, and/or clarified where required.

(Page 5, L91&92) – “While the data were being processed, 4 participants withdrew from the study, and their data were deleted.”. This sentence gives me a little confusion. Have you used primary data directly from participants? If so, you need to add ‘Ethical consideration’. Per the updated data availability statement above, data were provided anonymised by the UK Biobank. The text has been amended to clarify the deletion process, lines 101-102.

Results: The reader may expect to see why household income was not associated with health risks in model 1, but it did in model 2. What could be the reason for this variation? Please include it in your discussion. A comment in the main text directing the reader to a statement in the supporting information has been added (lines 449-454).

On behalf of the authors, we hope that this resubmission satisfies the requirements of the journal, editors and reviewers.

With best wishes,

Dr Harry Kennard

30 Jun 2020

The associations between thermal variety and health: implications for space heating energy use

PONE-D-20-02222R1

Dear Dr. Kennard,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Dr Mason Sarafraz

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

1 Jul 2020

PONE-D-20-02222R1

The associations between thermal variety and health: implications for space heating energy use

Dear Dr. Kennard:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

Dr. Mason Sarafraz

Academic Editor

PLOS ONE