Objective sleep assessment in >80,000 UK mid-life adults: Associations with sociodemographic characteristics, physical activity and caffeine.

STUDY
OBJECTIVES: Normal timing and duration of sleep is vital for all physical and mental health. However, many sleep-related studies depend on self-reported sleep measurements, which have limitations. This study aims to investigate the association of physical activity and sociodemographic characteristics including age, gender, coffee intake and social status with objective sleep measurements.
METHODS: A cross-sectional analysis was carried out on 82995 participants within the UK Biobank cohort. Sociodemographic and lifestyle information were collected through touch-screen questionnaires in 2007-2010. Sleep and physical activity parameters were later measured objectively using wrist-worn accelerometers in 2013-2015 (participants were aged 43-79 years and wore watches for 7 days). Participants were divided into 5 groups based on their objective sleep duration per night (<5 hours, 5-6 hours, 6-7 hours, 7-8 hours and >8 hours). Binary logistic models were adjusted for age, gender and Townsend Deprivation Index.
RESULTS: Participants who slept 6-7 hours/night were the most frequent (33.5%). Females had longer objective sleep duration than males. Short objective sleep duration (<6 hours) correlated with older age, social deprivation and high coffee intake. Finally, those who slept 6-7 hours/night were most physically active.
CONCLUSIONS: Objectively determined short sleep duration was associated with male gender, older age, low social status and high coffee intake. An inverse 'U-shaped' relationship between sleep duration and physical activity was also established. Optimal sleep duration for health in those over 60 may therefore be shorter than younger groups.

Introduction

Sleep is vital for the normal regulation of mood, cognition and metabolism[1]. Sleep disorders are common amongst the ageing population and sleep disturbance from any cause is increasingly recognised as a biomarker for unhealthy ageing[2]. Ideal duration of sleep remains debated and undoubtedly total sleep time changes with age, but “8 hours/night” has become defined as a somewhat idealised norm. Large cohort studies assessing self-reported sleep duration showed that 7–8 hours consistently emerged as the ideal duration for good health in 18–65 year olds[3].

Sleep architecture changes with age. During ageing, there is a degeneration of the circadian pacemaker, a progressive decline in melatonin output and decrease in rhythm amplitude which contributes to increasing sleep fragmentation and waking up earlier in the morning[4,5]. Additionally, >50% of those older than 65 years of age have chronic sleep complaints including difficulties in initiating and maintaining sleep. Sleep disturbance is associated with worse physical and mental health, cognitive impairment and falls but correlation remains debated[4]. Sleep is also influenced by gender and comorbid illnesses[6]. Females are more likely to self-report a longer sleep duration than males[7] and males tend to experience lighter sleep than females[8]. However, females self-report more sleep complaints and insomnia[9].

Chronic partial sleep deprivation has long term impact on health and longevity, including higher risks of hypertension, diabetes, obesity and depression[10]. Sleep deprivation has been proposed as a novel risk factor for dementia[11] and also insulin resistance and type 2 diabetes[12] and it is therefore important to know what the ideal sleep duration is for healthy ageing. In sleep-deprived individuals <65 years old, sleep extension by 1 hour/night for 6 weeks had significant beneficial effects on their insulin sensitivity[13]. Sleep deprivation also has impact on mental health. Those who self-reported 7–8 hours of sleep per night have higher optimism and self-esteem than those who slept <6 hours/night and >9 hours/night[14].

Caffeine is a widely consumed stimulant to counter the effect of fatigue as it can improve alertness, but it has adverse effects on the quantity and quality of sleep[15]. Caffeine intake prior to bedtime leads to reduced sleep efficiency, increased sleep onset latency, shortened second stage of sleep and reduced sleep duration in healthy adults with habitual high caffeine intake[16,17].

Sleep can be assessed subjectively by self-report such as sleep diaries or objectively using wrist-worn accelerometers and laboratory-based polysomnography[1]. There is often a mismatch between subjective and objective assessment of sleep. This is partly due to sleep state misperception which is especially common amongst people with sleep disorders[18].

Prior studies of the large UK biobank cohort have suggested that self-reported short sleep duration is a risk factor for poor cardiometabolic health[19]. Both short and long sleep durations were also found to be independently correlated with cognitive impairment[20]. The Newcastle 85+ study also found that objectively measured, disturbed sleep-wake cycles in the oldest old population was associated with multimorbidity, worse cognitive function and reduced survival[21]. However, the prior Biobank studies have been based on self-reported sleep assessments and may have limitations. Therefore, we wished to understand the association between objective sleep assessments and age, gender, habitual caffeine intake, physical activity and social deprivation in the Biobank population cohort.

Methods

Population and study design

A cross-sectional analysis was conducted on baseline data and objectively assessed accelerometry data from the UK Biobank. The UK Biobank recruited approximately 500,000 participants aged 40–69 within the general population of the UK. Full-scale recruitment took place between 2007 and 2010[22]. Participants were invited to a baseline assessment visit where physical measurements and biological samples were collected. Sociodemographic, occupation, health-related and lifestyle information were collected through the use of touch-screen questionnaires which contains approximately 314 questions. To measure self-reported sleep duration, participants were asked “About how many hours sleep do you get in every 24 hours? (Please include naps)”. Work patterns including shift work and night shift was asked about but sleep diary data was not collected[22].

After appropriate written consent, 103,712 participants from the UK Biobank study were later invited to wear wrist acceleration sensors (Axivity AX3 triaxial accelerometer) on their dominant wrist continuously for 7 consecutive days. Recruitment occurred between 2013 and 2015 (participants aged between 43 and 79). This allowed objective measurements of their physical activity and sleep-wake patterns[22,23].

Sleep categories

Accelerometry data obtained from the UK Biobank were processed using R Package GGIR version 1.7–1. This has been validated and published as open source[24]. The generic algorithm has been assessed in a large UK study[25]. The accuracy of the algorithm at detecting sleep period time window has also been tested[26]. Only participants with at least 5 days of complete accelerometry data were included in the current study. Both sleep duration and sleep efficiency were examined. Participants were categorised into 5 groups based on their sleep durations using previously published self-report thresholds[27]. (1) Subjects who slept <5 hours/night. (2) Subjects who slept 5–6 hours/night. (3) Subjects who slept 6–7 hours/night. (4) Subjects who slept 7–8 hours/night. (5) Subjects who slept >8 hours/night. Group 1 and 2 are used to investigate the impact of extremely short objective sleep durations.

Baseline measurements

Sociodemographic characteristics (including age, gender and deprivation scores) and dietary information such as tea and coffee intakes were collected from the touch-screen questionnaires at recruitment.

The age of each participant at accelerometry data collection was calculated. Participants were categorised into 4 age groups (43–49 years, 50–59 years, 60–69 years and 70–79 years). Gender was recorded at recruitment. Townsend deprivation index was calculated immediately prior to participant joining the UK Biobank based on the national census data. Participants were each assigned a score depending on the location of their post codes at recruitment[28]. This index takes account of home ownership, car ownership and employment status. Townsend deprivation index was divided into quintiles (0 represents the least deprived individuals and 4 represents the most deprived individuals).

At the baseline assessment visit, participants were also asked about how many cups of tea and coffee (include decaffeinated coffee) they drink per day over the last year. If participants answered >20 for tea or >10 for coffee, they were asked to confirm their answers. If they are unsure, participants could provide an estimate or select ‘Do not know’. For those participants who indicated they drink <1 or ≥1 cup coffee per day, they were then asked ‘What type of coffee do you usually drink?”[28].

Physical activity measurements

Acceleration levels (measured in milli-g (mg)) of each participants were extracted from the accelerometry data which is a measurement of physical activity level[23]. Acceleration measurements were separated into each wear day allowing comparison between week days and weekend sleep and activity pattern. This method of assessing physical activity has previously been validated and published in detail using the biobank cohort which has also compared to other accelerometry devices[23].

Statistical analysis

Both Biobank baseline data and accelerometry data were analysed using IBM SPSS Statistics version 24 (Armonk, New York, USA). Individuals with missing data on accelerometry measurements were excluded. Chi-square test was used to investigate the association between sleep groups and categorical variables. Once a significant difference is detected between any sleep groups, z-test was used to compare column proportions of each variables and p-values were adjusted using the Bonferroni method. If columns in the same row have been assigned the same letter, then their column proportions do not differ from each other significantly. Monte carlo method with 99% confidence level was used to estimate the exact significant level. Kruskal-Wallis H test was used to investigate the association between sleep groups and sleep efficiency with continuous variables.

Binary logistic regression was used to investigate the odds of being male, aged over 70 years, live in the most deprived area and reporting high coffee intake across the 5 sleep groups. Adjusted odds ratios (OR), with 95% CIs were reported. Logistic regression models were adjusted for: age (reference = ‘43–49’); gender (reference = ‘Female’) and Townsend deprivation index (reference = ‘Least deprived). Significance for all statistical tests was set at p<0.05)

Results

Sociodemographic characteristics

Of the total UK Biobank participants, after excluding those with missing data, there were 82,995 participants in total. 14,333 (17.3%) slept <5 hours/night, 21,559 (26.0%) slept 5–6 hours/night, 27,783 (33.5%) slept 6–7 hours/night, 15,503 (18.7%) slept 7–8 hours/night and 3,817 (4.6%) slept >8 hours/night (Fig 1). There was no statistically significant difference in sleep duration between weekdays and weekends across the entire group or within those under and over the age of 65.

Fig 1

A: Percentage of total participants in each sleep group (n = 82995). B: Percentage of males in each sleep group (n = 36293). C: Percentage of females in each sleep group (n = 46702). Those who sleep 6–7 hours/night are more prevalent and females have longer objective sleep duration than males.

The percentage of males that slept <5 hours/night was significantly higher, while more females slept >7 hours/night (Fig 1). The greatest proportion of participants were within the ‘60–69 years’ age group. According to the Townsend deprivation index, socioeconomic status increased across the sleep groups up to the second quintile then it decreased across the groups. Overall, no significant differences in terms of gender, age and Townsend deprivation index were detected between ‘7–8 hours’ and ‘>8 hours’ groups (Table 1).

Table 1

Sociodemographic and dietary characteristics of the 5 sleep groups (n = 82995).

	Percentage within each sleep group
	<5 hours (n = 14333)	5–6 hours (n = 21559)	6–7 hours (n = 27783)	7–8 hours (n = 15503)	>8 hours (n = 3817)
Gender
Male (%)	54.9 a	47.3 b	40.5 c	36.3 d	35.4 d
Female (%)	45.1 a	52.7 b	59.5 c	63.7 d	64.6 d
Age groups (years)
43–49 (%)	6.0 a	7.7 b	7.5 b	7.1 b	6.2 a,b
50–59 (%)	27.0 a	30.0 b	28.7 b,c	25.9 a	26.0 a,c
60–69 (%)	42.8 a,b	42.2 b	44.2 a,c	45.9 c	46.8 c
70–79 (%)	24.2 a	20.1 b,c	19.6 c	21.1 b	21.0 b,c
Townsend deprivation index quintile
0 (Least deprived) (%)	20.5 a	22.0 b	23.9 c	24.3 c	24.4 c
1 (%)	20.2 a	20.9 a	22.0 b	22.6 b	23.0 b
2 (%)	19.9 a	20.6 a,b	20.7 a,b	21.5 b	19.9 a,b
3 (%)	20.6 a	20.3 a	19.1 b	18.7 b	18.8 a,b
4 (Most deprived) (%)	18.7 a	16.2 b	14.2 c	13.0 d	13.9 c, d
Average coffee intake per day
0/<1 cup (%)	28.3 a	26.8 b	27.3 a,b	28.1 a	28.8 a,b
1–3 cups (%)	50.3 a	53.6 b	54.1 b	54.7 b	53.6 b
>4 cup (%)	21.3 a	19.6 b	18.6 b,c	17.2 d	17.5 c,d
Coffee type
Decaffeinated coffee (any type) (%)	17.6 a	18.1 a,b	19.1 b	20.5 c	21.6 c
Instant coffee (%)	52.8 a	51.5 a,b	50.6 b,c	49.7 c	51.9 a,b,c
Ground coffee (include espresso, filtered etc) (%)	27.7 a	28.8 a	28.9 a	28.3 a	24.6 b
Other types of coffee (%)	1.6 a	1.3 a,b	1.2 b	1.3 a,b	1.6 a,b
Prefer not to answer (%)	0.1 a	0.0 a	0.1 a	0.1 a	0.1 a

Column proportions test was carried out and column proportions (for each row) are compared using a z-test. Each letter denotes a subset of sleep group categories whose column proportion do not differ significantly from each other at 0.05 level. Short sleep duration is significantly associated with male gender, older age, social deprivation and high coffee intake.

Those with the shortest sleep duration (<5 hours/night) were 115% (OR (95% CI) 2.15 (2.06 to 2.26)), 15% (OR (95% CI) 1.15 (1.08 to 1.21)) and 221% (OR (95% CI) 3.21 (2.24 to 4.61)) more likely to be male, aged over 70 years, and live in the most deprived area, respectively, compared with the ‘7–8 hours’ sleep group (Table 2).

Table 2

OR (95% CI) of being male, aged over 70 years, live in the most deprived area and reporting high coffee intake across sleep groups.

	Male	Aged >70 years	Social deprivation	>4 cups of coffee/day
7–8 hours	1.00	1.00	1.00	1.00
<5 hours	2.15 (2.06 to 2.26)	1.15 (1.08 to 1.21)	3.21 (2.24 to 4.61)	1.26 (1.17 to 1.36)
5–6 hours	1.62 (1.55 to 1.68)	0.93 (0.88 to 0.98)	1.58 (1.09 to 2.30)	1.13 (1.06 to 1.21)
6–7 hours	1.21 (1.17 to 1.26)	0.90 (0.86 to 0.95)	1.29 (0.89 to 1.87)	1.07 (1.00 to 1.14)
>8 hours	0.97 (0.90 to 1.04)	1.03 (0.95 to 1.12)	1.55 (0.86 to 2.81)	0.98 (0.87 to 1.10)

Statistical models were adjusted for age, gender and Townsend Deprivation Index.

Sleep efficiency was significantly higher in females, those aged 60–69 years old and participants with higher social status (p<0.001) (Table 3).

Table 3

Association between sociodemographic characteristics, coffee intake, social deprivation and sleep efficiency.

	Sleep efficiency (mean + standard deviation)	P-value
Gender		<0.001
Male	0.811 ± 0.117
Female	0.829 ± 0.110
Age (years)		<0.001
43–49	0.821 ± 0.109
50–59	0.820 ± 0.113
60–69	0.823 ± 0.113
70–79	0.820 ± 0.118
Townsend deprivation index quintile		<0.001
0 (Least deprived)	0.824 ± 0.114
1	0.824 ± 0.113
2	0.822 ± 0.114
3	0.820 ± 0.112
4 (Most deprived)	0.816 ± 0.116
Average coffee intake per day		<0.001
0/<1 cup	0.821 ± 0.113
1–3 cups	0.823 ± 0.115
>4 cups	0.818 ± 0.117
Coffee type		<0.001
Decaffeinated coffee (any type)	0.825 ± 0.115
Instant coffee	0.821 ± 0.115
Ground coffee (include espresso, filtered etc)	0.821 ± 0.117
Other types of coffee	0.816 ± 0.114

Higher sleep efficiency correlated with lower coffee intake and decaffeinated coffee type. The differences between coffee intake and between different coffee types were all found to be statistically significant (p<0.05).

Habitual coffee intake

The percentage of participants with prior high coffee intake (>4 cups/day) was significantly higher in those who slept <5 hours/night (Table 1). Those with the shortest sleep duration (<5 hours/night) were 26% (OR (95% CI) 1.26 (1.17 to 1.36)) more likely to be have high coffee intake (>4 cups/day) compared with the ‘7–8 hours’ sleep group (Table 2). When investigating the relationship between coffee type and sleep duration; the percentage of participants who drink decaffeinated coffee is significantly higher in those who sleep >7 hours/night (Table 1). The type of caffeinated coffee (instant versus espresso) did not affect sleep duration. In addition, males drank significantly more coffee than females (Table 4).

Table 4

Association between gender and average coffee intake per day.

	Percentage within gender
	Female	Male
Average coffee intake per day
0/<1 cup (%)	29.7 a	24.7 b
1–3 cups (%)	53.4 a	53.4 a
>4 cup (%)	16.8 a	21.8 b
Do not know (%)	0.1 a	0.1 a
Prefer not to answer (%)	0.0 a	0.0 a

Column proportions test was carried out and column proportions (for each row) are compared using a z-test. Each letter denotes a subset of sleep group categories whose column proportion do not differ significantly from each other at 0.05 level. Males were found to consume significantly more cups of coffee per day compared to females.

Sleep efficiency was negatively correlated with coffee intake (p = 0.001 and p<0.001, respectively). Not only did the amount of coffee drank affect the sleep efficiency, but those who drank decaffeinated coffee had significantly higher sleep efficiency (p = 0.023) (Table 3).

Association between acceleration level and sleep duration

Physical activity levels were monitored continuously throughout the day. This allowed the determination of the most active and least active 5 hours of each day using the detected acceleration level measured in milli-g (Table 5). An inverse ‘U-shaped’ association between activity level and sleep duration was observed. Average acceleration level during the most active 5 hours of the day (M5) was found to be the highest in those who slept 6–7 hours and the lowest in participants who slept >8 hours/night (Fig 2A). On the other hand, the average acceleration level during the least active 5 hours of the day (L5) decreased across the sleep groups. It was the highest in those who slept <5 hours/night, while the difference between the other 4 sleep groups was relatively small. As a biomarker for healthy ageing, the difference between the most and least active 5 hours of the same day (ΔM5L5) was calculated from the M5 and L5 readings[21] and it was the highest in ‘6–7 hours’ sleep group (Fig 2B). It was significantly lower in those who slept <5 hours/night and >8 hours/night (p<0.001).

Table 5

Association between activity level and sleep duration (p<0.001).

	<5 hours	5–6 hours	6–7 hours	7–8 hours	>8 hours
Average acceleration during M5 (mean ± SD) (mg)	55.56 ± 22.76	58.21 ± 20.94	58.39± 20.70	57.39 ± 19.70	54.23 ± 18.98
Average acceleration during L5 (mean ± SD) (mg)	4.58 ± 3.11	3.76 ± 1.86	3.44 ± 1.94	3.27 ± 1.48	3.24 ± 2.11
ΔM5L5 (mean + SD) (mg)	50.98 ± 22.32	54.45 ± 20.87	54.95± 20.59	54.11 ± 19.63	50.99 ± 18.44

Acceleration levels were monitored throughout the day which allowed the determination of average acceleration during the most active 5 hours of the day (M5) and the least active 5 hours of the same day (L5). The difference between these 2 measurements (ΔM5L5). Those who slept 6–7 hours/night were found to be more active compared to other sleep groups.

Fig 2

Distribution of acceleration measured in milli-g (mg) across the 5 sleep groups (p<0.001).

A: average acceleration over the most active 5 hours of the day (M5). B: Difference in acceleration between the most active 5 hours (M5) and least active 5 hours (L5) of the same day (ΔM5L5). Those who sleep 6–7 hours/night had the highest acceleration level.

Discussion

Using the largest accelerometer cohort to date, this study found that short objective sleep duration is associated with male gender, older age, social deprivation and habitual high caffeine intake. An inverse ‘U-shaped’ relationship between objective sleep duration and physical activity level was also identified suggesting that the most physically active slept between 6–7 hours. All prior UK biobank sleep studies have used the self-reported sleep data. An association between worse cardiometabolic health and impaired task performance with sleep duration <7 hours or >9 hours per night was reported[20,29]. Previous large cohort studies also utilised wrist-worn actigraphy to assess sleep objectively[30-34]. There have been smaller accelerometry studies within the Whitehall cohort (n = 3749) showing an association between longer duration and higher intensity of daily moderate-to-vigorous physical activity level and successful ageing[35]. However, no one has to date assessed the objective sleep-wake patterns and physical activity using accelerometry in the Biobank cohort.

We found 43.3% of the UK Biobank cohort had an objective sleep duration of <6 hours/night. The American Academy of Sleep Medicine and Sleep Research Society have recommended >7 hours of sleep per night for 18–60 years old adults for good health and reduced mortality[36]. Only 23.3% of the UK Biobank participants reached this recommended sleep duration, this may mean that in an older population the ideal sleep duration may not be 7–8 hours but instead somewhat shorter when assessed using this accelerometer based objective method rather than self-report. Age adjusted norms remain debated and are key when considering recommendations and advice for an ageing population. In fact, careful assessment of healthy older adults excluding sleep disorders has shown a clear decrease in sleep need with age without effects on daytime alertness[37]. Social jet lag (sleep duration significantly longer at weekends compared to the working week) was also assessed in the UK Biobank cohort as a possible marker of societal sleep restriction and therefore one possible reason for objective short sleep time. Participants’ sleep duration was not significantly longer on Fridays or Saturdays compared to other days of the week. This could be due to many of the participants having reached retirement age and therefore, their sleep duration is not significantly affected by longer working hours during the week. However, it could also suggest that their shorter sleep times were not indicative of restricted or inadequate sleep and supports 6–7 hours being the likely optimum sleep duration for many over the age of 60.

When comparing physical activity levels of our 5 sleep groups; those who slept 6–7 hours/night were most active with an inverse U-shaped curve and decreased activity in very short and long sleepers. Apart from healthy sleeping habits, the beneficial effects of an active lifestyle on health are well known. The ability of objectively measured physical activity in improving sleep has been shown. Those who have met the recommended World Health Organisation physical activity guidelines (>150mins of moderate intensity or >75mins vigorous intensity per week) self-reported shorter sleep latency, less early awakenings and less leg cramps during sleep fewer usage of sleeping pills[38]. Our findings may suggest that physical activity can promote healthy sleep duration, but it may also be that healthy sleeping habits promote a more active lifestyle. Strong evidence suggests inadequate moderate-to-vigorous physical activity is closely associated with increased risks of metabolic diseases and all-cause mortality after controlling for age, gender, race and weight[39]. Physical inactivity also leads to direct and indirect costs which result in considerable financial burdens on the society[40]. Due to the cross-sectional nature of the study, we are unable to prove the direction of relationship between physical activity and sleep.

A significant ‘U-shaped’ association between sleep duration and metabolic diseases has previously been published with short sleep associating with worse cardiometabolic health. However, after adjusting for confounders, this association only remained in short sleepers (<6 hours/night)[3]. In this study, shorter objective sleep duration and lower sleep efficiency were found to correlate with the male gender, social deprivation and high coffee intakes. These are groups previously shown to be at higher risk of poor cardiometabolic health and increased mortality. Our results are also consistent with previous studies showing the correlation of lower socioeconomic status with longer sleep latency and poorer quality of sleep as measured by actigraphy and polysomnography[41,42]. Many factors including poor mental health and physical diseases can affect sleep quality and they are more prevalent in socially deprived individuals. This may well confound any correlations between social deprivations and sleep parameters[41,42]. Within our cohort, a habitual high coffee intake (>4 cups/day) was associated with longer sleep latency, more awakenings during the night and sleep complaints[43]. The difference in sleep efficiency between different types of coffee is relatively small but there was a significant decrease in sleep time in those with habitual high coffee intake and this group were more likely to be male. Our results demonstrated that caffeinated coffee does have an impact on sleep efficiency compared to decaffeinated coffee. Although 3–4 cups of coffee per day was not significantly associated with health risks. The prevalence of coffee drinking and usual intake increases with age[44] and coffee is the main source of dietary caffeine in the western society so may be one factor in short sleep in an older population. Therefore, the relationship between habitual high coffee intake and sleep in ageing populations is worthy of further investigation[45]. However, limitations of the study were that habitual tea/coffee intake data were collected some years prior to accelerometry data collection. This means that any association detected needs to be interpreted with care.

The current study found a discrepancy between self-reported and accelerometry assessments of sleep. The prior self-reported sleep duration of the Biobank cohort showed that 78% of participants had self-reported a sleep duration >7 hours/night, but accelerometry data showed that only 23% of participants slept >7 hours/night. Therefore, participants may over-estimate their sleep duration. Moreover, we found that females self-reported shorter sleep durations but they have significantly longer objective sleep duration and higher sleep efficiency than males. Similar results were also found in the Rotterdam study[46]. Within the prior UK Biobank studies, females were more likely to self-report insomnia symptoms and shorter sleep duration[20]. However, evidence from polysomnographic measures and quantitative electroencephalographic analysis does not support this[47]. Our results suggested that sleep state misperception may be more common amongst females. However, any potential association needs to be interpreted with care as there is on average a 5 year time lag between self-report sleep duration and accelerometry assessment of sleep. The sleep pattern of some of the participants might have changed during this period of time.

The main strength of the current UK Biobank study is the large sample size and extensive information collected. It provided detailed, objective measurements of sleep duration and efficiency, physical activity, sociodemographic and dietary characteristics. The specific accelerometers allowed the possibility of measuring physical activity level and sleep/wake patterns in a single device using open access algorithms. This is more likely to be an accurate measure of sleep duration compared to limited, short questions of self report sleep duration previously collected in this cohort. This is by far the largest UK accelerometry cohort and subsequent long term follow up studies will be able to determine whether an optimum sleep duration and physical activity level predicts healthy ageing. However, we accept the limitations of a cross-sectional study design and the time lag between accelerometry data and other biometric assessments within the Biobank cohort. Many other potential confounders such as physical or psychiatric disease were not assessed due to the time lag between accelerometry data collection and original biometric variables and we accept that future studies would ideally include these variables. Finally, due to the constraints of the Biobank, other sources of caffeine intake such as coke and energy drink consumption were not taken into account.

In conclusion, our study provided further insights into the relationship between objective sleep duration, sociodemographic and physical activity using the largest ever accelerometer cohort. Males, socially deprived individuals and habitual high coffee drinkers were found to have shorter objective sleep duration and lower sleep efficiency. 6–7 hours of sleep per night was associated with the highest physical activity levels. This raises the possibility that objectively assessed 6–7 hours of sleep per night may be optimal for health, at least for those aged over 60 years old. Understanding the association with sleep and health could help to design and optimise interventions to targeted groups and therefore reduce the adverse health impact of poor sleep.

26 Sep 2019

PONE-D-19-20962

Objective sleep assessment in >80,000 UK mid-life adults: associations with sociodemographic characteristics, physical activity and caffeine

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Additional Editor Comments:

This study has the strengths including 1) large data set and 2) using objective measure of sleep.

However, this study also has the weaknesses. The most weakness is the statistical analysis.

1. We do not know what exact methods or models were used for this study.

2. z-test is not clear

3. Actually more details of statistical analysis are needed.

For example how the continuous variable and categorical variables were analyzed.

What tests were used, such as t-test or ANOVA or non-parametric t-test and ANOVA were used?

Did the study tested the data distributions?

For categorical variables, what tests were used, Chi-square test or Fisher exact test? Did the study use contrast to compare the different levels of categories?

4. Since in the data sets there have several demographics variable such as age and gender, and clinical variables. A statistical model is more appropriate for the analysis instead of simple tests.

5. How many percentage of missing data? Excluding those missing data from the analysis is a limitation. The appropriate approach should include the missing data and using a appropriate method or model to deal with the missing data.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this study, objective sleep efficiency and duration were investigated by Zhu et al using wrist-worn devices in large cohort of aging 43-79 in UK. It was concluded that shorter sleep duration was associated with male gender, older age, social deprivation and higher caffeine intake. More importantly, the authors found that 6-7 hours sleep/night was associated with the highest physical activity level, and discussed the differences between objective assessment and subjective questionnaire.

Minors:

It is impossible to include participants aged <43 for the current study.

Letters denoting column proportion z-test in note of Table 1 are confusing, better if authors can explain them more.

Line 174-320: duplicate reference

Line 166-174: font and size need to edit

Line 84: “the oldest old”?

Line 56: “difficulties” in?

Line 93: age “40-69”; line 31: aged “43-69”?

Line 166-168: compared to what? Refer to Fig 1 or Table 1?

Line 330: gender p-value: p < 0.001 in table 2.

Reviewer #2: The authors assessed a relationship between sociodemographics and sleep duration determined by objective actigraphy measures in a large population based sample and showed relevant factors to sleep duration. Also the authors the demonstrated inverse U-shaped relationship between sleep duration and physical activity by objective sleep measures.

Introduction and methods section are clear and well written.

Page 9-16, lines 166-320,

Large font texts and references list are incorrectly inserted here. Please check and correct them.

Are there any correlations between sleep time, measured by objective measures, and subjective sleepiness or sleep complaints?

Can actigraphy differentiate sleep and immobile waking state?

Information about smoking and alcohol intake can interfere with sleep and should be presented if possible.

Statistical analysis should be described in more detail, as this study address a large sample.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Guo Luo

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

14 Oct 2019

Objective sleep assessment in >80,000 UK mid-life adults: associations with sociodemographic characteristics, physical activity and caffeine. PONE-D-19-20962

Dear Dr Yinglin Xia,

We would like thank the reviewer for their time and helpful comments. Amendments to the original text can be identified using track.

Please find our responses to the comments as follows.

Editor comments:

1. No statistical model was previously carried out. Additional explanation on the statistical tests used are added in the method section.

2. More explanation on the z-test are added – z-test was used instead of standard t-test to determine whether 2 population mean was different because of the large sample size in the current study.

3. Details of the statistically analysis are now added as requested – Monte carlo method with 99% confidence level was used to estimate the exact significant level. ANOVA was used on continuous variables and Chi-square test was used on categorical variables. Kolmogorov-smirnov test indicates that sleep duration does not follow a normal distribution (D(84411)=0.065, p<0.001).

4. Statistical model results are added as suggested.

5. 11.6% (n=10918) were excluded from the current study including 1.6% (n=1425) due to participant drop-outs over-time. Comparison between the population in the current study and those excluded was undertaken, there are no significant differences in terms of age and gender, and therefore we believe that there is unlikely to be a selection bias.

Reviewer 1 comments:

Letters denoting column proportion z-test in note of Table 1 are confusing, better if authors can explain them more.

Additional explanation is added in the method section as suggested.

Line 174-320: duplicate reference

This might be an error in printing from Microsoft word. When viewing on the computer or print from PDF, this problem does not occur.

Line 166-174: font and size need to edit

This might be an error in printing from Microsoft word. When viewing on the computer or print from PDF, this problem does not occur.

Line 84: “the oldest old”?

This refers to the 85+ population.

Line 56: “Difficulties” in?

This refers to difficulties in initiating and maintaining sleep. The sentence has been amended to make it clearer.

Line 93: age “40-69”; line 31: aged “43-69”?

There is a time lag between self-reported data and accelerometry data. Participants were aged 40-69 years during in baseline assessment around 2007-2010, but their age are between 43-79 years when accelerometry data were taken around 2013-2015.

Line 166-168: compared to what? Refer to Fig 1 or Table?

This sentence has now been rearranged to improve clarity.

Line 330: gender p-value: p<0.001 in table 2

This typo has now been changed.

Reviewer 2 comments:

Page 9-16, lines 166-320, large font texts and references list are incorrectly inserted here. Please check and correct them.

This might be an error in printing from Microsoft word. When viewing on the computer or print from PDF, this problem does not occur.

Are there any correlations between sleep time, measured by objective measures, and subjective sleepiness or sleep complaints?

A previous study by Chaput JP et al. (2013) found a ‘U-shaped’ association between subjective sleep duration and metabolic measures and the current study found a similar association between that and objective sleep duration.

Can actigraphy differentiate sleep and immobile waking state?

Vincent T. van Hees et al. (2015) found that GGIR over-estimates sleep duration by 31 minutes with 83% accuracy. If arm angle does not change greater than 5 degrees in 5 minutes then it is considered a bout of sleep. Therefore actigraphy remains an acceptable tool to estimate the major sleep period, widely used across a number of different population cohort studies.

Information about smoking and alcohol intake can interfere with sleep and should be presented if possible.

Smoking status and alcohol intake information are available, but due to the ~5 years’ time lag between these data and accelerometry data, smoking and alcohol data is not presented in this paper.

Statistical analysis should be described in more details, as this study address a large sample.

More information on the statistical tests are added in the method section as suggested.

Yours sincerely

Miss Gewei Zhu and Dr Kirstie Anderson

Submitted filename: Editor comments and responses.docx

Click here for additional data file.

29 Oct 2019

PONE-D-19-20962R1

Objective sleep assessment in >80,000 UK mid-life adults: associations with sociodemographic characteristics, physical activity and caffeine

PLOS ONE

Dear Miss Gewei Zhu and Dr Kirstie Anderson,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Since the Kolmogorov-smirnov test indicates that sleep duration does not follow a

normal distribution (D(84411)=0.065, p<0.001), the non-parametric Kruskal-Wallis ANOVA should be used.The study must re-analyze this part.

We would appreciate receiving your revised manuscript by Dec 13 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

yinglin xia, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: N/A

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: The authors have well addressed my concerns.

Page 9-16, lines 166-320, large font texts and references list are incorrectly inserted here. Please check and correct them.

However, this has not been revised. Can the authors check PDF version of the manuscript before submission?

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Keisuke Suzuki

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

19 Nov 2019

Dear Yinglin Xia,

We would like thank the reviewer for their time and helpful comments. Amendments to the original text can be identified using track.

Please find our responses to the comments as follows.

Comment: Since the Kolmogorov-smirnov test indicates that sleep duration does not follow a normal distribution (D(84411)=0.065, p<0.001), the non-parametric Kruskal-Wallis ANOVA should be used. The study must re-analyze this part.

Response: Analysis between continuous variables has been repeated using the Kruskal-Wallis test. Method and results section has been amended. Largely results were unchanged but all amendments are clearly marked.

Reviewer comments to the author

Reviewer 2: Page 9-16, lines 166-320, large font texts and references list are incorrectly inserted here. Please check and correct them.

However, this has not been revised. Can the authors check PDF version of the manuscript before submission?

Response: No error in font size and reference list are detected on either of our computers, therefore we are unsure whether this is due to errors when uploading online.

Yours sincerely

Miss Gewei Zhu and Dr Kirstie Anderson

22 Nov 2019

Objective sleep assessment in >80,000 UK mid-life adults: associations with sociodemographic characteristics, physical activity and caffeine

PONE-D-19-20962R2

Dear Dr. Anderson,

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

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. 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.

With kind regards,

yinglin xia, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

18 Dec 2019

PONE-D-19-20962R2

Objective sleep assessment in >80,000 UK mid-life adults: associations with sociodemographic characteristics, physical activity and caffeine

Dear Dr. Anderson:

I am 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 notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, 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.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. yinglin xia

Academic Editor

PLOS ONE