
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

70461
10.1038/s41598-024-70461-z
Article
Sleeping behaviors are determined by lockdown and not work-from-home arrangements
Raman Gururaghav 12
Peng Jimmy Chih-Hsien jpeng@nus.edu.sg

1
1 https://ror.org/01tgyzw49 grid.4280.e 0000 0001 2180 6431 Department of Electrical and Computer Engineering, National University of Singapore, 2 Engineering Drive 3, Singapore, 117581 Singapore
2 grid.514054.1 0000 0004 9450 5164 Singapore-ETH Centre, Future Resilient Systems, CREATE campus, 1 CREATE Way, #06-01 CREATE Tower, Singapore, 138602 Singapore
3 9 2024
3 9 2024
2024
14 204654 3 2024
16 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Lockdowns and work-from-home arrangements became abrupt realities for people at an unprecedented scale during the COVID-19 pandemic. Here, considering the case of Singapore, we study how peoples’ sleep behaviors—which are closely linked to their mental health—varied as a result. However, different from most studies, this paper uses household electricity consumption data to estimate the sleeping behaviors of nearly 10,000 households in the city-state. With this, we study how the residents’ daily sleep durations changed dynamically during the lockdown and afterwards when restrictions were progressively eased, and show their strong connection to major changes in the public health policy and current events during this period. Our results add to the evidence for the stress endured by the populace during the lockdown; we find that sleep durations for all demographics, while higher than before the lockdown, became more fluctuating across days. A major, and surprising, finding is that it was the lockdown that determined the residents’ sleeping duration, rather than simply working-from-home arrangements. That is, the sleeping durations largely reverted back to their pre-pandemic levels when the lockdown was lifted—with small variations based on demographic factors—although a vast majority of people continued to work from home. This highlights the resilience of the daily routines of the Singapore populace. While providing insights into how a pandemic influences the dynamics of urban sleep patterns, our finding also has broader implications regarding the efficiency of the workforce, suggesting that concerns about asynchronous work routines and productivity may be overblown.

Subject terms

Energy and society
Energy grids and networks
http://dx.doi.org/10.13039/501100001381 National Research Foundation Singapore issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Significant disruptions in the everyday routine can affect the well-being of an individual. The coronavirus disease 2019 (COVID-19) pandemic resulted in unprecedented and drastic changes in the daily habits of people around the world, as social distancing rules were implemented by cities and work-from-home arrangements became the norm. These disruptions, along with concerns about the public health situation and the associated economic hardships, have had an adverse impact on the mental health of the society, with the latter becoming a focus of much public attention1–3 and research4–9. As such, important clues about a population’s mental health status may be found from studying peoples’ nocturnal sleep behaviors, given the strong association between the mental health and sleep10–17.

Other studies have focused on this association, both in a general sense10 and specifically in the context of the COVID-19 pandemic12–16. Potential influencing mechanisms that have been considered include changes to working from home as opposed to commuting to workplaces15, physical confinement13,14,16, work and school schedules15, anxiety about health of family and friends12, caregiving15, etc. Nevertheless, observing sleep patterns is not straightforward, with a vast majority of such studies relying on self-reporting through surveys (e.g., see12–15). Yet, these methods do not obtain a dynamic picture of the mental health status, i.e., day-to-day data at high resolution is not available as surveys are only performed at longer intervals, typically a few weeks or months apart (with very few exceptions such as in13). Furthermore, these are usually retrospective in nature and their accuracy is affected by the recall of the people surveyed, among other factors8. Alternatives such as digital sleep-tracking devices could be employed, e.g., polysomnography18, actigraphy19, and smart wearables20. While these can accurately capture the sleep patterns of (a set of) individuals, the scale of adoption required to obtain representative inferences may prove difficult. Moreover, privacy risks would need to be carefully mitigated, e.g., see21.

For the first time, this study aims to demonstrate that household electricity consumption data can also be used to deduce residents’ nocturnal sleep patterns by analyzing the peaks and valleys in the data across each day. This follows our previous study22 which showed how these data can be used to study populations’ behaviors during unprecedented times such as a pandemic, and evaluate the efficacy of public health policy. The metered electricity consumption patterns (1) are a real-time result of the actions of individual households, and thereby can capture the dynamic change in daily activities of residents; (2) are measured as opposed to self-reported and therefore avoid reporting errors; (3) are available for all demographics in the society as all households’ electricity consumptions are recorded; and (4) can be anonymized by the electric utility to avoid privacy concerns.

Considering the city state of Singapore, we use electricity consumption and demographic data to assess how residents’ sleep behaviors changed during the COVID-19 pandemic in 2020. Thereby, given the strong association between the mental health and sleep patterns of people, we use the latter as an indicator of mental health. The specific research questions that we answer in this article are as follows: Demonstrate that household electricity consumption data can also be used to deduce residents’ sleep patterns by analyzing the peaks and valleys in the data across each day.

How did the sleep behaviors of the Singapore populace change during COVID-19, specifically, before, during, and after the lockdown in 2020?

Did demographic factors play a role in this?

Doing so, we observe direct links between the dynamic sleep behaviors and current events or changes in the public health policy. Furthermore, tracking how the daily sleep durations of the households changed during the lockdown, we surprisingly find that sleep patterns return to pre-COVID levels once the lockdown ended, despite most workplaces imposing work-from-home arrangements for their employees. Our results imply that it is the lockdown that determines sleep durations and not simply whether or not people work from home, and that the time saved in commuting while working from home does not go into increasing the duration of sleep.

Results

In this study, we assess the sleep behaviors of 9967 households in Singapore for the period 1 November 2019–30 November 2020 during the COVID-19 pandemic using anonymized electricity meter data obtained from the Energy Market Authority23. A household’s electricity consumption patterns are directly linked to activities performed by people who reside there. For instance, it is intuitive that when the electricity consumption increases, we can infer that residents are awake—though we remain unaware what specific activities trigger the increase. This is all the more reasonable when automated home appliances are not very prevalent, which is the case for Singapore24.

Continuing this line of reasoning, when the electricity consumption begins to reduce in the evening, this is indicative of residents retiring to sleep for the day. This way, by determining the troughs and peaks in the electricity consumption patterns, we can approximately determine peoples’ sleep patterns. We validated this technique by comparing the estimated sleep duration values per night against reported numbers from surveys in Singapore25, finding a difference of only 0.1–0.5 h on average; see “Methods” for more details.Fig. 1 Sleep duration of Singapore residents during the COVID-19 pandemic. The blue plot presents the 7-day moving average of the daily sleep duration of 9967 households in Singapore estimated from their aggregate electricity consumption data. The time period under consideration is from 1 November 2019 until 30 November 2020. Also shown (in red) is the average daily temperature for the same period. Notable changes in the national COVID-19 situation are highlighted. [CB, circuit breaker; WFH, work-from-home].

Singapore households’ sleep behaviors during the pandemic

Our results are summarized in Fig. 1, which presents a 7-day moving average of how long residents slept each day during the period under study. (The unfiltered version is presented in Supplementary Note 1). In particular, we analyzed how the sleep duration changed with the progression of the COVID-19 pandemic in Singapore26, beginning when the populace was advised by public health authorities to stay at home (on 31 March 2020), followed by the subsequent lockdown termed as the Circuit Breaker (CB) (from 7 April–1 June 2020), and the gradual reopening (from 2 June 2020 onwards). Notably, work-from-home was mandated for all workers (except those in essential sectors such as healthcare and global supply chains) when the CB began on 7 April 2020 and continued to remain as the default arrangement for employees in most non-essential sectors until after November 202027. For a more detailed timeline, see Supplementary Note 2. Fig. 1 shows that people slept longer during the lockdown, a finding that has been shown to be true in other countries as well (e.g., see12). In more detail, while the average sleep duration before the CB was 6.54 h, we observe an increase of 29.8% to 8.49 h during the CB. This increase happened immediately, starting from 31 March 2020, when people were encouraged to stay at home as much as possible.

These changes need to be put in context by considering another potential influence, the weather, which normally affects the household electricity consumption, particularly in Singapore26,28. Analyzing the electricity data and daily average temperatures during this time period, we find that the temperature has a strong positive correlation (Pearson’s r=0.749, p=1.548e-72) with the net daily energy consumption values; see Supplementary Note 3. Another way that weather could affect the consumption patterns—and hence our estimation of sleep behaviors—is that the evening consumption could peak earlier when residents turn on air-conditioning earlier in the evening as the weather becomes warmer. Yet, while the weather very likely did affect the electricity consumption on a day-to-day basis, we nevertheless find little evidence of it being the cause of the sustained increase in the sleep duration observed in Fig. 1, with Granger causality tests29 establishing that the average temperature Granger-causes neither the evening peak timing (p=0.3814), nor the estimated sleep duration (p=0.9798). As such, the correlation between the average temperature and the estimated sleep duration is weak (Pearson’s r=0.401, p=1.045e-16).

With the weather eliminated as a likely influence, we now comment on the observed changes in the sleep behaviors in Fig. 1. Previous studies30 have shown that sleep patterns typically experience only gradual changes, and that discontinuities within such a relatively short time period are unusual. This means that any changes we observe in Fig. 1 are very likely to be due to the changes in the residents’ behavior caused by the pandemic and the lockdown.

Impact of lockdown vs. working from home on sleep behaviors

Next, we study whether these new sleep behaviors persisted when the lockdown ended. Fig. 1 shows that immediately after the CB ended on 1 June 2020, the average sleep duration fell, resulting in an average post-CB reduction of 20.49%. This is quite surprising given the fact that most workplaces did not open up immediately (see26,31,32 and Supplementary Note 2). Employers were required to implement work-from-home arrangements for most employees, and only those whose duties could not be performed remotely, e.g., restaurant staff, factory workers, etc., could return to work. Nevertheless, we notice that households on average came back to within 3.5% of their pre-pandemic sleep duration levels. This suggests that peoples’ sleep behaviors were based on whether they were locked down at home, and not on whether or not they had to commute to their workplaces. We note here that while trends in the mean were highlighted in this article, similar trends are also observed for both the minimum and maximum sleep duration values assessed weekly; see Supplementary Note 1.Fig. 2 How demographic factors influenced sleeping behaviors during the pandemic. (a) Results of the classification of the 9967 households into 6 different dwelling-types; and (b) the 7-day moving average of the daily sleep duration of the households, aggregated by dwelling-type.

Fig. 3 Variability in the sleep routines of households. (a) Distribution of the sleep duration for residents in each dwelling-type before, during, and after the CB. The central mark, bottom, and top edges of each box plot represent the median, 25th, and 75th percentiles, respectively. Outliers are marked as individual circles. (b) Mean sleep duration for households by dwelling-type across days in each time period: before, during, and after CB. (c) Same as (b) but presents the standard deviation across days.

Impact of demographic factors

Until now, we have used the electricity consumption patterns of the 9,967 households, as an aggregate, in order to study the average sleep behaviors of Singapore residents. We now wish to assess whether socio-economic factors played any role in determining the changes in the sleep behaviors brought about by the pandemic and the CB. This is driven by previous studies (e.g., see33–35) that have shown that the ability to work-from-home and reduce mobility during a lockdown is strongly dependent on peoples’ socio-economic status—this could, in turn, have had an effect on their sleep routines as well.

To this end, we classify the households into six different dwelling-types (see “Methods”): 1-room/2-room HDB, 3-room HDB, 4-room HDB, 5-room/executive HDB, private apartment/condominium, and landed property. Note that HDB here refers to public-housing apartments leased out by the Housing and Development Board, Singapore. These dwelling-types present significant variations in terms of family composition, income levels, and number of occupants per residence22. In more detail, households in landed properties have the highest incomes and are likely to be families with a larger number of occupants when compared to those living in 1-room HDBs. Figure 2a presents the results of the classification. We then aggregate the electricity consumption of households belonging to each dwelling-type, and extract their daily sleep durations, see Fig. 2b. The distributions as well as the mean and standard deviation (std) values are plotted in Fig. 3, which also differentiates between different time periods: before, during, and after the lockdown. Furthermore, Table 1 quantifies the relative change in the mean sleep duration during and after the CB with respect to the pre-CB values for each dwelling-type.Table 1 Relative change, by dwelling-type, in average sleep duration when compared to the pre-CB values.

Dwelling-type	Relative increase during CB (%)	Relative increase after CB (%)	
1/2-room HDB	24.08	8.29	
3-room HDB	13.64	4.50	
4-room HDB	14.36	5.38	
5-room/executive HDB	20.36	- 0.18	
Private apartment and condominium	32.31	1.97	
Landed property	21.10	5.17	

The following observations can be gleaned from these results. First, referring to Figs. 2 and 3a, residents’ daily sleep duration for all dwelling-types increased during the CB when compared to the pre-CB period, and reduced once the CB ended. Overall, residents slept longer in most cases post-CB than pre-CB (except for 5-room/executive HDBs, where the mean sleep durations are nearly the same). Notably, the largest variation is observed for households living in private apartments/condominiums, which also exhibit the longest sleep duration during the CB.

Second, we observe that before the pandemic, residents in smaller apartments slept for smaller durations on an average when compared to those living in larger apartments (see Fig. 3b). Interestingly, this trend is maintained valid both during, and after, the CB.

Third, assessing the variability in the mean sleep duration across different dwelling-types and within each time period, we find that the variability increased during the lockdown and reduced afterwards (std before, during, and after CB: 0.362 h, 0.675 h, and 0.345 h). That is, the various demographics exhibited more diverse sleep behaviors during the lockdown.

Fourth, comparing the variability across time within each dwelling-type, we observe from Fig. 3b that larger and more affluent households had a higher variance in the mean sleep duration before the pandemic, but this reversed during and after the CB. This trend was the opposite for smaller households whose variance in sleep routines across days increased during and after the CB. This could perhaps be explained in two ways: (1) residents in smaller households, with their smaller incomes, may have a smaller flexibility in deciding to work from home as opposed to commuting to their workplaces. Therefore, some residents may have been forced to commute to work during and after the CB, thereby increasing the variance. In contrast, residents in more affluent households are probably employed in higher-paying jobs that offer the flexibility to work from home. This means that most residents would have been able to work from home during and after the CB, resulting in the lower variance observed in Fig. 3b. (2) Simultaneously or alternatively, given the fact that smaller households are also likely to have fewer residents, the electricity consumption patterns are likely to change even if one resident has to commute to work. This could have resulted in the higher variance across days during and after the CB.

Finally, we observe a dip in the sleep durations for all dwelling-types in the middle of the CB, see Fig. 2(b). This dip begins between 21 Apr–6 May 2020 for the different demographics, and the sleep durations begin to restore between 30 Apr and 12 May 2020. Though the weather could be the potential explanation—see the dip in the temperature around the same time period in Fig. 1—this is likely not the case due to the following reasoning. We find that in general, the weather has an immediate, as opposed to a delayed, effect on the electricity consumption as evidenced by the peak cross-correlation between the daily average temperature and energy consumed occurring at zero; see Supplementary Note 3. As such, it is unlikely that the weather, which is common for all dwelling-types, causes an effect that begins and ends at different time instances for the various dwelling-types, which is what happens in our case.

We then explore alternate explanations for this dip, and find that the only other plausible one is a major change in the Singapore COVID-19 situation that happened on 21 Apr 2020, which was a televised speech by the Prime Minister announcing further tightening of restrictions and a one-month extension of the CB until the end of May 202036. While this change in the public health policy did not directly affect the routines of the vast majority of the populace that was already working from home—and this proportion only increased after this change27—we believe that it could have impacted the mental health of the residents and therefore their sleep behaviors. This is supported by the fact that the dips in the sleep duration were the result of residents waking up earlier in the day (see Supplementary Note 1); a number of previous studies (e.g., see37,38) provide evidence that increased stress and depression could result in early morning awakenings. As such, given that no other weather-related changes happened in the Singapore context or in the global COVID-19 situation at this time, we attribute the changes observed in the sleep behaviors to mental stresses brought about by a worsening public health emergency.

Discussion

We now present a few noteworthy remarks regarding the scope and implications of our study.

Broadly, this article presented a novel and non-intrusive way of studying a city’s sleep behaviors—using household electricity consumption patterns—complementing policymakers’ ability to glean insights into peoples’ mental health. While it already has clear advantages in terms of its scalability and simplicity over the conventional methods which are primarily survey-based, our analysis could be made richer by integrating the electricity data with demographic information, which while not available to us due to privacy concerns, is nevertheless accessible to public health authorities through the power utility. An extension could also be to employ our sleep-pattern-extraction methodology to individual households rather than by aggregating households according to dwelling-types as we have in this study. While this approach promises more insights into intra-dwelling-type statistics of sleep behaviors, the challenge here is the high level of randomness in the appliance usage patterns of an individual household. This randomness makes it harder to determine information—such as the time of the first local minimum in the electricity consumption data—that is required for assessing the sleep duration of the residents (see “Methods”). However, one important caveat in our approach is that while the electricity consumption patterns can reveal when residents retire for the evening, this may be due to other nocturnal activities as well and not exactly when they actually sleep. At the same time, it is arguably impossible, other than by using invasive electronic monitoring, to capture peoples’ exact sleep timings. Even in the latter case, scalability of monitoring and peoples’ willingness to share data may be insurmountable concerns. Finally, though this article has dealt with a pandemic context, the approach is not limited to it, particularly in an era where mental well-being in cities has risen to the forefront of public discourse.

Our results have shown for the first time that Singapore residents reverted to their pre-pandemic sleep routines to a large extent immediately after the lockdown ended, even though they continued to work from home. This implies that the daily routines of Singapore households are quite resilient, though we observe some variations depending on the dwelling-type. Moreover, although our study claims no insights into what specific activities residents performed when they were not in bed, the above finding may also be relevant to the discussion of whether or not employees can remain productive and on synchronous work schedules while working from home (e.g., see39–41), at least in the Singapore context. As our results show, peoples’ sleep routines more or less returned to their pre-pandemic patterns once the lockdown ended (see Table 1. Furthermore, our findings also support studies and surveys (e.g., see42) that have shown that the productivity of people working from home remains the same or, at least, does not decrease: we find that the time saved by avoiding commuting to workplaces does not go towards increasing the sleep duration, with the latter being much less than that during the lockdown period. As such, the sleep durations while working from home after the lockdown remains below optimal levels12 as defined by medical professionals.

Methods

Data collection

Household electricity consumption data for 9967 households in Singapore were obtained from the SP Group43 with the consent of the Energy Market Authority (EMA), Ministry of Trade and Industry, Singapore23. This dataset covers the time period from 1 Nov 2019 until 30 Nov 2020, and consists of half-hourly kilowatt-hour values (i.e., 48 data points per day) for each household. While there were data pertaining to more households included in the dataset, only the 9967 households that were considered in this study had no missing entries (except for a very small subset that had zero values for an 8 h period for one day; this does not impact the trends or conclusions presented in the study and was ignored, see Supplementary Note 4 for details).

To classify the households into different dwelling-types, we used the average monthly energy consumption data reported by the EMA44. Comparing the monthly average kWh consumption of a given household in our dataset with the reported averages for the different dwelling-types in the same month, we assigned the household to the dwelling-type with the closest match. We performed this classification thrice for each household while considering the data pertaining to the months Nov 2019, Dec 2019, and Jan 2020 (before the pandemic began in Singapore), and assigned the household to the dwelling-type that resulted in at least two classifications. If all three classifications resulted in different outcomes, the final dwelling-type was assigned based on the classification performed using the Nov 2019 data.

The weather data, specifically, the average temperature values in Singapore for the same time period, were obtained from Meteoblue45.Fig. 4 Estimating sleep behaviors from household electricity consumption patterns.

Estimating sleep behaviors

We estimate residents’ sleep behaviors based on their electricity consumption patterns, see Fig. 4. In particular, the time when residents awake in the morning is determined by the first local minimum occurring between 12:30 AM and 12:00 PM in the electricity consumption curve. As for when they go to bed, this is estimated as the time when the electricity consumption peaks in the evening, after 7:00PM. The results presented in Fig. 1 utilize the aggregate electricity consumption curve, which is obtained as the sum of the individual consumption curves of the 9967 households. As for the results presented in Fig. 2b, we aggregate the consumption of all households belonging to each dwelling-type, and use these to estimate the sleep duration values for the different dwelling-types.

We validate our method of estimating sleep behaviors using ref.46, which reports the results from a sleep survey of 1000 participants in Singapore before (2020) and during the COVID-19 pandemic (2021). Compared to the reported numbers before the pandemic (i.e., before the CB began) which show an average of 7 h (6.7 h for weekdays only and 7.5 h for weekends only) per night, our method shows good agreement with an estimated 6.5 h (6.3 h for weekdays only and 7.1 h for weekends only). As for the time period after the pandemic begins (i.e., after the CB ends), the reported average sleep duration is 6.8 h (6.6 h for weekdays only and 7.3 h for weekends only) per night whereas our method estimates 6.7 h (6.6 h for weekdays only and 7.1 h for weekends only). This comparison shows that our method of using electricity consumption provides reasonable estimates of residents’ sleep durations. Note that there is an apparent discrepancy here: while our method shows a slight increase in the average sleep duration from 6.5 to 6.7 h, the data from ref.46 shows a decrease from 7.0 to 6.8 h. This is explained by the fact that our data corresponds to the period immediately after the CB, which is from June 2 to November 30, 2020; this time period starts before the survey period in ref.46. As we see from Fig. 1, the sleep duration from our model gradually reduces after the CB ended. Therefore, if we average the results of our model for a later time period, we would indeed obtain a lower number for the sleep duration. For example, only averaging the sleep duration values for the last week of November 2020 results in 6.3 h, as opposed to 6.7 h when considering the entire period of June 2–November 30, 2020. Therefore, only considering the last week of November 2020 as the ‘after CB’ period, we find a small reduction in the average sleep duration when compared to the pre-CB period, from 6.5 to 6.3 h. This is the same finding as that of Ref.46.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-70461-z.

Acknowledgements

The authors thank the Energy Market Authority Singapore and SP Group for providing the electricity consumption data. The research was conducted at the Future Resilient Systems at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and the National Research Foundation Singapore. This research is supported by the National Research Foundation Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.

Author contributions

G.R. and J.C.-H.P conceived the study. G.R. performed the analyses and generated the figures. G.R. and J.C.-H.P. wrote the paper.

Data availability

The data that support the findings of this study are available from the Energy Market Authority, Ministry of Trade and Industry Singapore but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author (J.C.-H. Peng) upon reasonable request and with permission of the Energy Market Authority.

Code availability

The codes for generating the specific plots are available from the corresponding author upon request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. The New York Times. Declining mental health. https://www.nytimes.com/2022/07/11/briefing/declining-mental-health.html (2022).
2. The Washington Post. Coronavirus is harming the mental health of tens of millions of people in U.S., new poll finds. https://www.washingtonpost.com/health/coronavirus-is-harming-the-mental-health-of-tens-of-millions-of-people-in-us-new-poll-finds/2020/04/02/565e6744-74ee-11ea-85cb-8670579b863d_story.html (2020).
3. The Straits Times. Mental wellness task force offers 3 recommendations to tackle Covid-19’s impact on S’poreans. https://www.straitstimes.com/singapore/health/mental-health-of-sporeans-impacted-by-covid-19-mental-wellness-taskforce-offers (2021).
4. Penninx BWJH Benros ME Klein RS Vinkers CH How COVID-19 shaped mental health: From infection to pandemic effects Nat. Med. 2022 28 10 2027 2037 10.1038/s41591-022-02028-2 36192553
Penninx, B. W. J. H., Benros, M. E., Klein, R. S. & Vinkers, C. H. How COVID-19 shaped mental health: From infection to pandemic effects. Nat. Med. 28(10), 2027–2037 (2022).36192553 10.1038/s41591-022-02028-2
5. Li L Taeihagh A Tan SY A scoping review of the impacts of COVID-19 physical distancing measures on vulnerable population groups Nat. Commun. 2023 14 1 599 10.1038/s41467-023-36267-9 36737447
Li, L., Taeihagh, A. & Tan, S. Y. A scoping review of the impacts of COVID-19 physical distancing measures on vulnerable population groups. Nat. Commun. 14(1), 599 (2023).36737447 10.1038/s41467-023-36267-9
6. Wu JT Leung K Lam TTY Ni MY Wong CKH Peiris JSM Leung GM Nowcasting epidemics of novel pathogens: Lessons from COVID-19 Nat. Med. 2021 27 3 388 395 10.1038/s41591-021-01278-w 33723452
Wu, J. T. et al. Nowcasting epidemics of novel pathogens: Lessons from COVID-19. Nat. Med. 27(3), 388–395 (2021).33723452 10.1038/s41591-021-01278-w
7. Brülhart M Klotzbücher V Lalive R Reich SK Mental health concerns during the COVID-19 pandemic as revealed by helpline calls Nature 2021 600 7887 121 126 10.1038/s41586-021-04099-6 34789873
Brülhart, M., Klotzbücher, V., Lalive, R. & Reich, S. K. Mental health concerns during the COVID-19 pandemic as revealed by helpline calls. Nature 600(7887), 121–126 (2021).34789873 10.1038/s41586-021-04099-6
8. Liu CH Tsai AC Helpline data used to monitor population distress in a pandemic Nature 2021 600 46 47 10.1038/d41586-021-03038-9 34789884
Liu, C. H. & Tsai, A. C. Helpline data used to monitor population distress in a pandemic. Nature 600, 46–47 (2021).34789884 10.1038/d41586-021-03038-9
9. Aebi NJ Can big data be used to monitor the mental health consequences of COVID-19? Int. J. Public Health 2021 2021 29
Aebi, N. J. et al. Can big data be used to monitor the mental health consequences of COVID-19?. Int. J. Public Health 2021, 29 (2021).
10. Lee YY Lau JH Vaingankar JA Sambasivam R Shafie S Chua BY Chow WL Abdin E Subramaniam M Sleep quality of Singapore residents: Findings from the 2016 Singapore mental health study Sleep Med.: X 2022 4 100043 35243325
Lee, Y. Y. et al. Sleep quality of Singapore residents: Findings from the 2016 Singapore mental health study. Sleep Med.: X 4, 100043 (2022).35243325
11. Lin YN Liu ZR Li SQ Li CX Zhang L Li N Sun XW Li HP Zhou JP Li QY Burden of sleep disturbance during COVID-19 pandemic: A systematic review Nat. Sci. Sleep 2021 13 933 10.2147/NSS.S312037 34234598
Lin, Y. N. et al. Burden of sleep disturbance during COVID-19 pandemic: A systematic review. Nat. Sci. Sleep 13, 933 (2021).34234598 10.2147/NSS.S312037
12. Roitblat Y Stay-at-home circumstances do not produce sleep disorders: An international survey during the COVID-19 pandemic J. Psychosom. Res. 2020 139 110282 10.1016/j.jpsychores.2020.110282 33130483
Roitblat, Y. et al. Stay-at-home circumstances do not produce sleep disorders: An international survey during the COVID-19 pandemic. J. Psychosom. Res. 139, 110282 (2020).33130483 10.1016/j.jpsychores.2020.110282
13. Simor P Polner B Báthori N Sifuentes-Ortega R Van Roy A Sáenz AA González AL Benkirane O Nagy T Peigneux P Home confinement during the COVID-19: Day-to-day associations of sleep quality with rumination, psychotic-like experiences, and somatic symptoms Sleep 2021 44 7 zsab029 10.1093/sleep/zsab029 33567067
Simor, P. et al. Home confinement during the COVID-19: Day-to-day associations of sleep quality with rumination, psychotic-like experiences, and somatic symptoms. Sleep 44(7), zsab029 (2021).33567067 10.1093/sleep/zsab029
14. Hisler GC Twenge JM Sleep characteristics of US adults before and during the COVID-19 pandemic Soc. Sci. Med. 2021 276 113849 10.1016/j.socscimed.2021.113849 33773474
Hisler, G. C. & Twenge, J. M. Sleep characteristics of US adults before and during the COVID-19 pandemic. Soc. Sci. Med. 276, 113849 (2021).33773474 10.1016/j.socscimed.2021.113849
15. Gao C Scullin MK Sleep health early in the coronavirus disease 2019 (COVID-19) outbreak in the United States: Integrating longitudinal, cross-sectional, and retrospective recall data Sleep Med. 2020 73 1 10 10.1016/j.sleep.2020.06.032 32745719
Gao, C. & Scullin, M. K. Sleep health early in the coronavirus disease 2019 (COVID-19) outbreak in the United States: Integrating longitudinal, cross-sectional, and retrospective recall data. Sleep Med. 73, 1–10 (2020).32745719 10.1016/j.sleep.2020.06.032
16. Cellini N Conte F De Rosa O Giganti F Malloggi S Reyt M Guillemin C Schmidt C Muto V Ficca G Changes in sleep timing and subjective sleep quality during the COVID-19 lockdown in Italy and Belgium: Age, gender and working status as modulating factors Sleep Med. 2021 77 112 119 10.1016/j.sleep.2020.11.027 33348298
Cellini, N. et al. Changes in sleep timing and subjective sleep quality during the COVID-19 lockdown in Italy and Belgium: Age, gender and working status as modulating factors. Sleep Med. 77, 112–119 (2021).33348298 10.1016/j.sleep.2020.11.027
17. Morin CM Carrier J Bastien C Godbout R Sleep and circadian rhythm in response to the COVID-19 pandemic Can. J. Public Health 2020 111 5 654 657 10.17269/s41997-020-00382-7 32700231
Morin, C. M., Carrier, J., Bastien, C. & Godbout, R. Sleep and circadian rhythm in response to the COVID-19 pandemic. Can. J. Public Health 111(5), 654–657 (2020).32700231 10.17269/s41997-020-00382-7
18. Neikrug IB Characterizing behavioral activity rhythms in older adults using actigraphy Sensors 2020 2020 20
Neikrug, I. B. et al. Characterizing behavioral activity rhythms in older adults using actigraphy. Sensors 2020, 20 (2020).
19. Withers A Maul J Rosenheim E O’Donnell A Wilson A Stick S Comparison of home ambulatory type 2 polysomnography with a portable monitoring device and in-laboratory type 1 polysomnography for the diagnosis of obstructive sleep apnea in children J. Clin. Sleep Med. 2022 18 2 393 402 10.5664/jcsm.9576 34323688
Withers, A. et al. Comparison of home ambulatory type 2 polysomnography with a portable monitoring device and in-laboratory type 1 polysomnography for the diagnosis of obstructive sleep apnea in children. J. Clin. Sleep Med. 18(2), 393–402 (2022).34323688 10.5664/jcsm.9576
20. Gabinet NM Portnov BA Investigating the combined effect of alan and noise on sleep by simultaneous real-time monitoring using low-cost smartphone devices Environ. Res. 2022 214 113941 10.1016/j.envres.2022.113941 35931188
Gabinet, N. M. & Portnov, B. A. Investigating the combined effect of alan and noise on sleep by simultaneous real-time monitoring using low-cost smartphone devices. Environ. Res. 214, 113941 (2022).35931188 10.1016/j.envres.2022.113941
21. Niksirat KS Wearable activity trackers: A survey on utility, privacy, and security ACM Comput. Surv. 2024 56 7
Niksirat, K. S. et al. Wearable activity trackers: A survey on utility, privacy, and security. ACM Comput. Surv. 56, 7 (2024).
22. Raman G Peng JC-H Electricity consumption of Singaporean households reveals proactive community response to COVID-19 progression Proc. Natl. Acad. Sci. 2021 118 34 10.1073/pnas.2026596118
Raman, G. & Peng, J.C.-H. Electricity consumption of Singaporean households reveals proactive community response to COVID-19 progression. Proc. Natl. Acad. Sci. 118, 34 (2021).10.1073/pnas.2026596118
23. Energy Market Authority Singapore. www.ema.gov.sg (2020).
24. Energy Market Authority Singapore. Demand response programme. https://www.ema.gov.sg/Demand_Response_Program.aspx (2023).
25. Channel News Asia. Nearly 6 in 10 Singaporeans aren’t sleeping well because of COVID-19, study confirms. https://cnalifestyle.channelnewsasia.com/wellness/sleep-tips-insomnia-singapore-philips-global-survey-237866 (2021).
26. Ministry of Sustainability and the Environment Singapore. Impact of COVID-19 on electricity demand. https://www.penglaboratory.com/_files/ugd/e69217_5107bbb4990e46a6a67b3f7df8a33974.pdf (2020).
27. Ministry of Trade and Industry Singapore. Oral reply to PQ on essential services and workers. https://www.mti.gov.sg/Newsroom/Parliamentary-Replies/2020/05/Oral-reply-to-PQ-on-essential-services-and-workers (2020).
28. Raman G Kong Y Peng JC-H Ye Z Demand baseline estimation using similarity-based technique for tropical and wet climates IET Gener. Transm. Distrib. 2018 12 13 3296 3304 10.1049/iet-gtd.2017.1933
Raman, G., Kong, Y., Peng, J.C.-H. & Ye, Z. Demand baseline estimation using similarity-based technique for tropical and wet climates. IET Gener. Transm. Distrib. 12(13), 3296–3304 (2018).10.1049/iet-gtd.2017.1933
29. MathWorks. Block-wise Granger causality and block exogeneity tests. https://www.mathworks.com/help/econ/gctest.html (2023).
30. Sullivan O Gershuny J Sevilla A Foliano F Vega-Rapun M de Grignon JL Harms T Walthéry P Using time-use diaries to track changing behavior across successive stages of COVID-19 social restrictions Proc. Natl. Acad. Sci. 2021 118 35 e2101724118 10.1073/pnas.2101724118 34426496
Sullivan, O. et al. Using time-use diaries to track changing behavior across successive stages of COVID-19 social restrictions. Proc. Natl. Acad. Sci. 118(35), e2101724118 (2021).34426496 10.1073/pnas.2101724118
31. Hirschmann, R. Share of population who avoided going to work during COVID-19 outbreak in Singapore from February 2020 to July 2022. https://www.statista.com/statistics/1110183/singapore-avoiding-going-to-work-during-covid-19-outbreak/ (2023).
32. Government of Singapore. White paper on Singapore’s response to COVID-19: Lessons for the next pandemic. https://www.gov.sg/article/covid-19-white-paper (2023).
33. Weill JA Stigler M Deschenes O Springborn MR Social distancing responses to COVID-19 emergency declarations strongly differentiated by income Proc. Natl. Acad. Sci. 2020 117 33 19658 19660 10.1073/pnas.2009412117 32727905
Weill, J. A., Stigler, M., Deschenes, O. & Springborn, M. R. Social distancing responses to COVID-19 emergency declarations strongly differentiated by income. Proc. Natl. Acad. Sci. 117(33), 19658–19660 (2020).32727905 10.1073/pnas.2009412117
34. Chang S Pierson E Koh PW Gerardin J Redbird B Grusky D Leskovec J Mobility network models of COVID-19 explain inequities and inform reopening Nature 2021 589 7840 82 87 10.1038/s41586-020-2923-3 33171481
Chang, S. et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature 589(7840), 82–87 (2021).33171481 10.1038/s41586-020-2923-3
35. Jay J Bor J Nsoesie EO Lipson SK Jones DK Galea S Raifman J Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States Nat. Hum. Behav. 2020 4 12 1294 1302 10.1038/s41562-020-00998-2 33144713
Jay, J. et al. Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States. Nat. Hum. Behav. 4(12), 1294–1302 (2020).33144713 10.1038/s41562-020-00998-2
36. Government of Singapore. Circuit breaker extension and tighter measures: What you need to know. https://www.gov.sg/article/circuit-breaker-extension-and-tighter-measures-what-you-need-to-know (2020).
37. Socarras LR Potvin J Forest G COVID-19 and sleep patterns in adolescents and young adults Sleep Med. 2021 83 26 33 10.1016/j.sleep.2021.04.010 33990063
Socarras, L. R., Potvin, J. & Forest, G. COVID-19 and sleep patterns in adolescents and young adults. Sleep Med. 83, 26–33 (2021).33990063 10.1016/j.sleep.2021.04.010
38. Pinto J van Zeller M Amorim P Pimentel A Dantas P Eusébio E Neves A Pipa J Clara ES Santiago T Sleep quality in times of Covid-19 pandemic Sleep Med. 2020 74 81 85 10.1016/j.sleep.2020.07.012 32841849
Pinto, J. et al. Sleep quality in times of Covid-19 pandemic. Sleep Med. 74, 81–85 (2020).32841849 10.1016/j.sleep.2020.07.012
39. Gleb, T. Workers are less productive working remotely (at least that’s what their bosses think). https://www.forbes.com/sites/glebtsipursky/2022/11/03/workers-are-less-productive-working-remotely-at-least-thats-what-their-bosses-think/?sh=58d566bc286a (2022).
40. Prithwiraj, C. Our work-from-anywhere future. https://hbr.org/2020/11/our-work-from-anywhere-future (2020).
41. Forbes. The impact of remote work on productivity and creativity. https://www.forbes.com/sites/forbestechcouncil/2022/01/14/the-impact-of-remote-work-on-productivity-and-creativity/?sh=45d1524a3957 (2022).
42. Barrero, J. M., Bloom, N. & Davis, S. J. Why working from home will stick. In Working Paper 28731, National Bureau of Economic Research (2021).
43. SP Group. https://www.spgroup.com.sg/ (2020).
44. Energy Market Authority Singapore. Average monthly household electricity consumption by dwelling type. https://www.ema.gov.sg/statistic.aspx?sta_sid=20140617E32XNb1d0Iqa (2020).
45. Meteoblue. https://www.meteoblue.com/ (2020).
46. Philips. Philips global sleep study finds singaporeans’ sleep woes compounded by pandemic, yet more turning to telehealth for help. https://www.philips.com.sg/a-w/about/news/archive/standard/news/press/2021/20211703-philips-global-sleep-study-finds-singaporeans-sleep-woes-compounded-by-pandemic-yet-more-turning-to-telehealth-for-help.html (2021).
