# A sensor-based study on the environmental determinants of sleep in older adults

Andrea Montanari, Giovanna Fancello, Cédric Sueur, Yan Kestens, Frank J van Lenthe, Basile Chaix

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## To cite this version:

Andrea Montanari, Giovanna Fancello, Cédric Sueur, Yan Kestens, Frank J van Lenthe, et al. A sensor-based study on the environmental determinants of sleep in older adults. Environmental Research, 2025, 274, pp.120874. 10.1016/j.envres.2025.120874. hal-05017197

HAL Id: hal-05017197

https://hal.sorbonne-universite.fr/hal-05017197v1

Submitted on 3 Apr 2025

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Distributed under a Creative Commons Attribution 4.0 International License
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A sensor-based study on the environmental determinants of sleep in older adults

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andrea.montanari@iplesp.upmc.fr

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**Introduction:**

The residential environment is hypothesized to influence sleep quality within urban settings. Factors associated with the residential environment include air and noise pollution, area socioeconomic status, green and blue spaces, and other neighborhood features. This study seeks to quantify the association of selected environmental factors with sleep quality in the daily lives of 211 older adults residing in the Paris metropolitan area with sensor-based methods.

**Methods:**

Participants' sleep and physical activity were monitored over a 7-day period using 2 accelerometers. Ecological momentary assessment (EMA) surveys were administered 4 times a day to assess depressive and anxiety symptoms. Environmental factors surrounding participants' residential addresses, including noise and air pollution, walkability, green and blue space availability, median income, and population density, were computed using geoprocessing methods. Hierarchical mixed models with a random intercept at the individual level were fitted to estimate the adjusted association between residential environmental factors and sleep outcomes [total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO)]. Potential effect modification of or mediation by physical activity and depression and anxiety levels were explored in the analyses.

**Results:**

We observed an effect size of 1.4 more minutes of sleep for each increase of one thousand euro in neighborhood median income (Confidence Intervals: 0.35, 2.45). The average adjusted difference in total sleep time between the 10th and 90th percentiles of neighborhood median income was 23.6 minutes of sleep. Other environmental factors and depression and anxiety levels did not exhibit correlations with sleep outcomes.

**Conclusions:**

The results reveal a positive association between median income at the residential level and TST, while no associations were identified for SE and WASO. In conclusion, these findings underscore the impact of neighborhood socioeconomic status on total sleep time within the context of urban living, highlighting the need for further research.

**Keywords:** Environmental Exposures, Sleep, Mental Health, Physical activity, Wearable Sensors
## Highlights

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* Dual Actigraph monitoring and repeated Ecological Momentary Assessments were adopted to assess sleep, physical activity and mental health in older residents of the Île-de-France region.

* Geoprocessing methods were employed to perform a comprehensive assessment of various environmental factors around participants' residences.

* The study finds a positive association between neighborhood median income and total sleep time, highlighting the significant impact of neighborhood socioeconomic conditions on sleep quality among older adults in metropolitan areas.
# 1 Introduction

## 1.1 The impact of sleep on health

Sleep is a modifiable health behavior with both acute and cumulative long-term effects on physical and mental well-being, as evidenced by extensive research (Czeisler, 2015). Insufficient sleep duration and quality is associated with higher mortality risk and various health comorbidities (Gallicchio & Kalesan, 2009; Grandner et al., 2021) including elevated body mass index (BMI), type-2 diabetes, and several cardiovascular disorders, primarily due to a disrupted circadian rhythm, neuroendocrine control and glucose regulation (Wu et al., 2019). At the neurocognitive level, insufficient sleep is linked to reduced executive functions, impaired attention and memory processes as a consequence of diminished neuroplasticity during sleep (Lim & Dinges, 2010). In addition, observational studies report strong correlation between inadequate sleep duration and common psychiatric disorders, such as depression and anxiety, suggesting mutual interactions between sleep and mental health (Baglioni et al., 2016; Benca et al., 1992).

Insufficient sleep is highly prevalent across all age groups, constituting an overlooked and underreported public health crisis, and creating substantial economic burdens (Chattu et al., 2019). Sleep is especially critical for older persons, as age-related changes profoundly disrupt their sleep-wake cycle. In the USA alone, one in two older adults present at least one sleep problem and report difficulties in initiating or maintaining sleep (Crowley, 2011; Foley et al., 1995). Meta-analytic findings based on objective measures such as actigraphy and polysomnography report that older adults spend more time in bed trying to sleep and experience more fragmented sleep (Wake after sleep onset (WASO)) and a lower total sleep time (TST) as they age. More specifically the proportion of time spent in lighter sleep stages (N1 and N2) tends to rise with age, while the percentages of REM (Rapid eye movement) and SWS (Slow-wave sleep) decrease linearly as individuals grow older (Mander et al., 2017; Ohayon et al., 2004) resulting in less restorative sleep.

## 1.2 The environmental determinants of sleep

Regarding the physical aspects of the urban environment, factors like nocturnal light, air and noise pollution, a low walkability and the scarcity of green and blue areas at the neighborhood level, have been linked to worse sleep outcomes (Astell-Burt et al., 2013; Basner et al., 2011; Johnson, Billings, et al., 2018; Patel, 2019; Sygna et al., 2014). As for the social environment, other neighborhood factors such as population density, neighborhood disinvestment, or area-level low socioeconomic status, have also been identified as risk factors for poor sleep health (Johnson, Billings, et al., 2018; Johnson, Hirsch, et al., 2018). Furthermore, the subjective perception of individuals of their neighborhood social cohesion and sense of safety has been highlighted by several empirical studies to be a determinant for sleep health (Hale et al., 2013; Hill et al., 2016).

The socioecological model of sleep health emphasizes the interconnectedness between individual-level and supra-individual determinants, encompassing familial, social, and built environments (Grandner, 2019b). Many of these environmental factors are thought to play a role in inducing both physical and psychological stress, as evidenced by biomarkers of stress and allostatic load (Chen et al., 2014). This stress is then believed to directly impact sleep health, suggesting a potential causal pathway between environmental influences and sleep outcomes (Chen et al., 2014). Moreover, the unequal allocation of adverse environmental factors across urban areas could disproportionately affect individuals with lower socioeconomic status. It is thus believed that inequalities in mental health and sleep outcomes across geographical areas tend to mirror the socioeconomic variations within urban settings perpetuating and strengthening health inequalities (Billings et al., 2020; Hunter & Hayden, 2018; Jackson, 2017).

## 1.3 Gaps in the literature and aim of the study

According to recent reviews, the literature on neighborhood and sleep health reveals several notable gaps across various dimensions (Johnson, Billings, et al., 2018; Kim et al., 2022). In terms of study characteristics, while there has been a growing number of empirical studies since the mid-2000s, the majority employ cross-sectional designs instead of longitudinal frameworks, limiting our
understanding of temporal relationships and causal pathways. Regarding neighborhood definitions and measurements, most studies rely on participants' perceptions of their neighborhoods, which are lacking standardized definitions and rely on subjectivity, or on local administrative boundaries which vary in size and are vulnerable to spatial misclassification (Chaix, 2018). This heterogeneity in neighborhood definition makes it challenging to compare findings across studies and hampers the generalizability of results. In addition, most studies typically focus on a single aspect of the residential environment, such as social factors (e.g., social capital, neighborhood socioeconomic status), environmental stressors (e.g., noise and air pollution), or the built environment (e.g., neighborhood walkability), whereas it is recommended to adopt a multi-exposure approach to encompass all aspects simultaneously. Additionally, the measurement of sleep outcomes varies widely, with a predominant focus on self-reported measures rather than objective assessments, such as actigraphy-based sleep metric (TST, SE, WASO). While the formers are susceptible to recall biases, the latter sleep measures are validated against polysomnography (Zinkhan et al., 2014a), the gold standard for sleep assessment, thus providing superior measurements. Analytic approaches also exhibit substantial heterogeneity with inconsistencies in the adjustment for confounders. Moreover, very few studies explore the mediating roles of psychological (e.g. mental health variables) and behavioral factors (e.g. physical activity), which might play a role in the relationship between residential environments and sleep health.

In light of the critical aspects mentioned above, the current paper follows the recommendations of previous literature reviews (Johnson, Billings, et al., 2018; Kim et al., 2022). Firstly, we used geoprocessed measures to ensure an objective assessment of the environmental factors within the residential exposure. Secondly, to overcome limitations associated with measurement bias, we adopt complementary measurement approaches integrating subjective mental health measures from ecological momentary assessment (EMA) surveys (which offer ecological in-situ insights), and sensor-based behavioral and health-related data. Lastly, our paper embraces a repeated-measure framework incorporating seven days of continuous accelerometer-based sleep measures, to capture the dynamic nature of the relationship between the residential environment and sleep health and understand temporal sequences and bidirectional influences.

The present study combines mobile sensing methods (Birenboim et al., 2021; Chaix, 2018) to assess daily changes in health outcomes in the urban environment, with the main goal of assessing the impact of the residential environment on the sleep quality of a sample of older adults in the metropolitan area of Paris over a 7-day monitoring period. The primary objective is to analyze the influence of the residential environment on selected sleep outcomes through a multi-exposure approach. This involves deconstructing the environment into relevant dimensions such as the presence of stressors (e.g., air and noise pollution), physical features (e.g., green and blue spaces), and social aspects (e.g., population density and median income). The secondary objective involves investigating the role of daily levels of physical activity and mental health, assessed with actigraphy monitoring and frequent EMA, in the relationship between the residential environment and sleep metrics the following night, as potential modifiers or mediators. Through this holistic approach, we aim to provide useful insight for future urban planning initiatives and targeted public health interventions

## 2 Methods

### 2.1 Data collection

#### 2.1.1 Study participants, design and setting

The present study aggregated the sub-samples of the HANC (Healthy Aging and Networks in Cities) and MINDMAP sub-studies from the second wave of the RECORD Cohort, into a sample of 211 adults aged 60 years or older (Chaix et al., 2011; Fernandes et al., 2021). The two sub-studies were operated at the same time by the same data collection team on the same study territory. The result is 7-day repeated measures on older adult residents of the Paris metropolitan area. The data
collection took place in the Paris and Île-de-France region (France) from July 2019 to July 2021. The data collection was approved from the French Data Protection Authority (DR-2017-060).

## 2.1.2 Overview of the mobile-sensing protocol

The study data were collected through different channels: computer-based surveys on sociodemographic data, paper-based diary on daily mobility and sleep timing, mobile sensing (wearable sensors) (Birenboim et al., 2021; Chaix, 2018), and Ecological Momentary Assessment (EMA) (Kirchner & Shiffman, 2016)

The computer-based surveys were conducted before the 7-day repeated measure period. The questions assessed included information on health, mobility, health related behaviors, neighborhood perception, and socioeconomic position. Participants wore a set of sensors and replied to smartphone surveys for 7 days during their everyday life activity. The mobile sensing protocol implied: 1) wearing an accelerometer (tri-axial, wGT3X-BT, Actigraph, Pensacola, FL) on the waist on the right side from morning till sleep time to assess physical activity during the day; 2) wearing a second accelerometer (wGT3X+) on the non-dominant arm wrist during sleep time to monitor sleep metrics at night; 3) responding to EMA surveys on a smartphone provided for the study with an application for EMA surveys (Eco emo tracker, INSERM, Paris, FR).

<caption>**Table 1**</caption>

Mobile Sensing protocol

<table><thead><tr><td>Sensor</td><td>Measure</td><td>Frequency</td><td>Construct</td></tr></thead><tbody><tr><td>Tri-axial accelerometer (wGT3X-BT, ActiGraph)</td><td>Sedentary Time (Min, %)<br/>Light Physical Activity (Min, %)<br/>Moderate to Vigorous Physical Activity (Min, %)<br/>Gross acceleration (G)<br/>Metabolic equivalent rate (MET)<br/>Steps (n)</td><td>Five-second epochs, worn on the right hip for 7 days</td><td>Physical activity</td></tr><tr><td>Tri-axial accelerometer (wGT3X+, ActiGraph)</td><td>TST: total sleep time (min)<br/>WASO: wake after sleep onset (min)<br/>SE: sleep efficiency (%)</td><td>Five-second epochs, worn on the wrist on the non-dominant arm for 7 nights</td><td>Sleep</td></tr><tr><td>Smartphone surveys (EMA, Eco emo tracker)</td><td>Depressive symptomatology (CES-D scale) Anxiety levels (State Trait Anxiety Inventory, Y-A form)</td><td>4 EMA per day for 7 days</td><td>Mental health</td></tr></tbody></table>

## 2.2 Sensor and smartphone-based strategy – variables and measurements

### 2.2.1 Sleep outcomes
Participants wore the Actigraph GT3X+ on the wrist for sleep assessment. This validated and widely-used tool estimates key sleep metrics: Total Sleep Time (TST), Wake After Sleep Onset (WASO), and Sleep Efficiency (SE). TST reflects the total duration of sleep, WASO measures time spent awake after initially falling asleep due to interruptions and awakenings, and SE indicates the percentage of time spent asleep relative to the total time spent sleeping or trying to sleep (Zinkhan et al., 2014b).

The GT3X+ data were analyzed with the ActiLife 6 software (Version 6.13.4, ActiGraph). Sleep metrics were scored with the Cole-Kripke algorithm which is a validated algorithm for sleep detection in adult populations (Cole et al., 1992). The Choi algorithm (Choi et al., 2012) was used to distinguish wear time from non-wear time. Participants with less than 4 full nights of data were discarded from the study (see appendix I for more details).

## 2.2.2 Physical Activity

We assessed participants' physical activity and sedentary behavior with the hip-worn wGT3X-BT data which we processed entirely with the Actilife software version 6.13.4. For each participant, we calculated the number of daily steps taken, energy expenditure in kcal determined using the Freedson VM3 Combination method (Sasaki et al., 2011), and metabolic equivalents of task (METs), which were calculated using the refined Crouter equation (Crouter et al., 2010). Additionally, we assessed physical activity and sedentary behavior using specific cut points in accordance with the guidelines for processing accelerometry data for the assessment of physical activity in older adults (Migueles et al., 2017) (see appendix I for more details).

## 2.2.3 Ecological Momentary Assessment (EMA)

Each day, the EMA smartphone surveys were delivered to smartphones provided to participants for the study 4 times at random time during 4 predefined temporal windows (9:00-12:00, 12:00-14:00, 14:00-16:00, 16:00-18:00). The surveys assessed momentary symptoms of depression and anxiety (Fernandes et al., 2021) via the validated short form of the Center for Epidemiologic Studies Depression Scale (CES-D 20) and the State-Trait Anxiety Inventory (STAI) Y-A form, of 8 and 6 items respectively (Radloff, 1977; Spielberger, 2010). The EMA questions were adjusted to the new temporal scale as a way to assess the momentary symptomatology rather than the past 30 days as in the original format (Karim et al., 2015; Tluczek et al., 2009). For both surveys participants could answer on a scale ranging from 1 to 4 (e.g. from 'no, not at all' to 'yes, absolutely). Each CES and STAI item was delivered to participants in varying order once per day during one of the 4 assessment windows. See Table 1 for a summary of our mobile sensing protocol.

## 2.3 Geographic Information System data

For each participant's residential address, we conducted an analysis of spatial attributes characterizing the surrounding environment using the ArcGIS software to geo-process a set of parameters in different buffer sizes. We purposely employed two distinct types of buffers, namely aerial and street network buffers, to conduct an assessment of environmental factors. The aerial buffer was designed to capture macro effects at a more global level (e.g. noise and air pollution) that operate independently of the street network. On the other hand, the street network buffer was tailored to evaluate localized accessibility, (e.g. proximity to green and blue areas). The 1 km² buffer from geocoded residence was adopted to encompass a spectrum of local and global neighborhood influences. In contrast, the more focused 25 m buffer from geocoded residence was set to identify more proximate effects on sleep. 1 km and 25 m area buffers were applied to assess environmental pollutants, including noise (day and night) and air pollution (NO₂, PM₁₀, PM2.5). 1 km street network buffers were used for environmental landscape features such as green and blue areas, and walkability (service density). Additionally, sociodemographic characteristics such as population density and the mean income level of the area were analyzed with 1 km² area buffer zones. Finally, we used a 25 m radius buffer to calculate daily minimum temperatures at the residential level as a complementary covariate for the analyses. See table 2 for a summary of our geoprocessing strategy and data source.
248 Table 2

249 Geoprocessing strategy

<table><thead><tr><th>Phenomenon</th><th>Radius</th><th>Buffer</th><th>Rationale</th><th>Source (yearly)</th></tr></thead><tbody><tr><td>Noise (nocturnal)</td><td>25 m</td><td>Area</td><td>Direct impact on sleep</td><td>Bruitparif centre (2017 LDEN - Day-evening-night level - strategic noise pollution map for road, railroad, and airborne traffic at 5-meter resolution, using the NMPB 2008 European method - SETRA, 2009)</td></tr><tr><td>Noise (total)</td><td>1 km</td><td>Area</td><td>Neighborhood levels</td><td></td></tr><tr><td>Air pollution (NO<sub>2</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>)</td><td>25 m</td><td>Area</td><td>Direct impact on sleep</td><td>Airparif (Annual concentrations of nitrogen dioxide (NO<sub>2</sub>), particulate matter (PM10) and PM2.5 over the Île-de-France region (spatial resolution: 12.5 to 50 metres, projection/EPSG: 27572 - Lambert II) for the years 2019, 2020 and 2021 in Shapefile format, i.e. 9 annual maps.)</td></tr><tr><td>Air pollution (NO<sub>2</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>)</td><td>1 km</td><td>Area</td><td>Neighborhood level</td><td></td></tr><tr><td>Median income</td><td>1 km</td><td>Area</td><td>Neighborhood level</td><td>National Institute of Statistics Economic Studies (census data, FiLoSoFi database, Permanent equipment database)</td></tr><tr><td>Population density</td><td>1 km</td><td>Area</td><td>Neighborhood level</td><td></td></tr><tr><td>Walkability (service density)</td><td>1 km</td><td>Street Network</td><td>Accessibility</td><td></td></tr><tr><td>Green & blue</td><td>1 km</td><td>Street Network</td><td>Accessibility</td><td>The Regional Institute of Paris (street and road network, urban tissue data - TUF, land use data - MOS),</td></tr><tr><td>Daily outdoor minimum temperature</td><td>25 m</td><td>Area</td><td>Direct impact on sleep</td><td>Météo France [meteorological analysis system SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie); grid with an approximate spatial resolution of 8 km]</td></tr></tbody></table>

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252 2.4 Sociodemographic covariates

253 The study collected information on participants, including age (categorized as 60-70, 71-80, and >80 years), gender (male, female), marital status (coupled, divorced, widowed, unmarried), education level (higher education, secondary education, lower education), monthly household income per consumption unit (<2000, 2000-4000, >4000), and employment status (employed, retired, other).

257 2.5 Statistical methods
We initially assessed the influence of environmental factors and individual covariates on daily sleep outcomes within the entire sample. This involved consideration of statistical relevance, optimal fit to the data [such evaluation was conducted using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)], and control for autocorrelation in daily sleep data. We implemented hierarchical mixed effect linear models with a random effect at the individual level to accommodate the repeated measurements (for repeated nights as the unit of analysis) obtained for each participant. This modeling approach allowed us to capture between-individual variations in the outcomes and within-individual correlation across multiple nights. The selected sleep outcomes included: TST, SE and WASO. Individual characteristics, such as demographic, socioeconomic and health factors, were included as covariates to account for potential confounding. Additionally, in preliminary and supplementary analyses, we evaluated several temporal variables, including season, day of the week, and daily minimum outdoor temperature, and incorporated only those demonstrating associations with the outcomes into the final models. Physical activity metrics and EMA mental health surveys were controlled for in subsequent models.

To estimate how depression and anxiety levels varied over the week, we employed a Markov chain Monte Carlo approach following the methodology outlined by Chaix, Fancello et al (Chaix et al., 2011; Fancello, Vallée, et al., 2023); this approach provided the day-level time-varying explanatory variables for each individual on the depressive and anxiety symptomatology that were subsequently included as covariables in the model for sleep as the outcome. Finally, to unravel potential reverse causality, we tested the well documented bidirectional relationship between mental health, physical activity, and subsequent sleep patterns (Atoui et al., 2021; Baglioni et al., 2016). See Table 3 for a summary of our analytical framework. All models were estimated using the latest version of RStudio. See the appendix II for more details.

<caption>**Table 3**</caption>

Analytical framework

<table><thead><tr><th>Predictor</th><th>Covariate</th><th>Effect modifier/mediator</th><th>Outcome</th></tr></thead><tbody><tr><td><strong>Residential Environment<sup>1</sup></strong><br>Green area<br>Blue area<br>Walkability<br>(service density)<br>Noise (day/night)<br>Pollution (NO<sub>2</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>)</td><td><strong>Demographics and socioeconomic factors<sup>2</sup></strong><br>Age<br>Gender<br>Marital status<br>Education level<br>Income of the household<br>Employment<br><strong>Health<sup>2</sup></strong><br>Body mass index<br>Chronic pain<br>Health perception<br>Smoker status<br>Alcohol consumption<br>Smoking<br><strong>Temporal variables<sup>3</sup></strong><br>Daily outdoor minimum temperature</td><td><strong>EMA mental health<sup>4</sup></strong><br>CES<br>STAI Y-A<br><strong>Physical Activity<sup>4</sup></strong><br>Sedentary Time (Min, %)<br>Light Physical Activity (Min, %)<br>Moderate to Vigorous Physical Activity (Min, %)<br>Metabolic equivalent rate (MET)<br>Steps (n)</td><td><strong>Sleep<sup>4</sup></strong><br>Total Sleep Time<br>Sleep Efficiency<br>Wake after Sleep onset</td></tr></tbody></table>
<table><tr><td></td><td>Weekend</td><td></td><td></td></tr><tr><td></td><td>Season</td><td></td><td></td></tr></table>

¹Yearly metrics; ²Time-invariant factors; ³Daily, weekly and seasonal variables; ⁴Daily metrics.

## 3 Results

### 3.1 Descriptive statistics

Data were gathered from a cohort of 211 individuals aged 60 years and above, with a majority being male (64%), residing in couples (67%), and retired (85%). The sample exhibited a bias toward elevated educational attainment levels (see Tables 4 and 5 for more descriptive statistics).

<caption>**Table 4**</caption>

Descriptive characteristics of study participants

<table><thead><tr><th rowspan="2">Characteristics</th><th colspan="2">Individuals</th></tr><tr><th>(N = 211)</th><th>%</th></tr></thead><tbody><tr><td><strong>Gender</strong></td><td></td><td></td></tr><tr><td>Female</td><td>75</td><td>(36%)</td></tr><tr><td>Male</td><td>136</td><td>(64%)</td></tr><tr><td><strong>Age (years)</strong></td><td></td><td></td></tr><tr><td>60-70</td><td>105</td><td>(50%)</td></tr><tr><td>71-80</td><td>89</td><td>(42%)</td></tr><tr><td>&gt;80</td><td>17</td><td>(8.1%)</td></tr><tr><td><strong>Marital status</strong></td><td></td><td></td></tr><tr><td>Couple</td><td>141</td><td>(67%)</td></tr><tr><td>Divorced/widowed/unmarried</td><td>70</td><td>(33%)</td></tr><tr><td><strong>Education</strong></td><td></td><td></td></tr><tr><td>Lower education</td><td>65</td><td>(31%)</td></tr><tr><td>Secondary education</td><td>67</td><td>(32%)</td></tr><tr><td>Higher education</td><td>79</td><td>(37%)</td></tr><tr><td><strong>Household income</strong></td><td></td><td></td></tr><tr><td>&lt;2000</td><td>31</td><td>(15%)</td></tr><tr><td>2000-4000</td><td>124</td><td>(59%)</td></tr><tr><td>&gt;4000</td><td>56</td><td>(27%)</td></tr><tr><td><strong>Employment</strong></td><td></td><td></td></tr><tr><td>Employed</td><td>29</td><td>(14%)</td></tr><tr><td>Other/unemployed</td><td>4</td><td>(1.9%)</td></tr><tr><td>Retired</td><td>178</td><td>(84%)</td></tr><tr><td><strong>Body mass index</strong></td><td></td><td></td></tr><tr><td>Normal</td><td>106</td><td>(50%)</td></tr><tr><td>Overweight</td><td>86</td><td>(41%)</td></tr></tbody></table>
<table><tr><td>Obese</td><td>19</td><td>(9.0%)</td></tr></table>

N = Number of participants; % = Percentage of sample.

Table 5

Descriptive characteristics of residential environments

<table><thead><tr><th>Characteristic</th><th>Mean (SD)</th></tr></thead><tbody><tr><td colspan="2"><strong>Environmental pollutants</strong></td></tr><tr><td>NO<sub>2</sub> (µg/m<sup>3</sup>)</td><td>24 (6)</td></tr><tr><td>PM<sub>10</sub> (µg/m<sup>3</sup>)</td><td>18.78 (2.49)</td></tr><tr><td>PM<sub>2.5</sub> (µg/m<sup>3</sup>)</td><td>10.24 (1.71)</td></tr><tr><td>Noise daily (db, Lden)</td><td>54 (4)</td></tr><tr><td>Noise nightly (db, Lden)</td><td>46 (5)</td></tr><tr><td colspan="2"><strong>Physical Environment</strong></td></tr><tr><td>Green space (%)</td><td>15 (11)</td></tr><tr><td>Blue space (%)</td><td>1.66 (3.14)</td></tr><tr><td>Walkability (# of services)</td><td>657 (648)</td></tr><tr><td colspan="2"><strong>Social environment</strong></td></tr><tr><td>Population density (inhabitants/km<sup>2</sup>)</td><td>2,849 (1,748)</td></tr><tr><td>Median income (€)</td><td>31,966 (6,335)</td></tr></tbody></table>

SD = Standard Deviation

294

The sleep outcomes' mean and standard deviation (SD) were examined in relation to various participant characteristics. Gender differences revealed that females tended to have slightly longer TST at 528 minutes (SD = 69) compared to males at 507 minutes (SD = 77). SE and WASO also showed minor differences between genders. The retired group demonstrated a longer TST of 520 minutes (SD = 71) and higher SE of 87% (SD = 7), but also experienced a longer WASO of 63 minutes (SD = 37) compared to working individuals. See Figure 1 to visualise the outcomes' averages by day.

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Figure 1

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Sleep outcomes averages by day of the week

305
Mean TST by Day of the Week with 95% CI

<figure><img src="image_3.png" /></figure>

Mean SE by Day of the Week with 95% CI

<figure><img src="image_4.png" /></figure>

Mean WASO by Day of the Week with 95% CI

<figure><img src="image_5.png" /></figure>

3.2 Main models

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308

309

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We employed hierarchical mixed models with an individual-level random effect to analyse the sleep outcomes of 211 participants across 1215 night of observation (see Table 6 for the final version of our model).

Among the environmental factors, only income of the residential area was associated with TST, demonstrating a positive association. Results showed a +18.84 minutes of sleep for people in the third tertile of neighborhood income, compared to people of the first tertile (CI: 2.58, 35.09), suggesting a possible influence of median income on sleep duration. However, no relationship of neighborhood income was documented with SE and WASO. No other environmental factor was retained. Among the covariates, we observed an association between gender and TST. Specifically, men exhibited a negative association with TST, with men, on average, experiencing 19 minutes less of sleep (CI: -33.11, -4.89) compared to women. However, SE and WASO did not show any associations with gender. Regarding employment, retired individuals, on average, experienced a 21.57 minutes longer sleep time (CI: 0.75, 42.40) compared to employed participants but no association was detected for SE and WASO. Weekends showed a positive association with TST, suggesting that individuals tended to enjoy longer sleep durations during the weekends with an average of 9.87 more minutes per night (CL: 2.99, 16.75) compared to weekdays. However, no association was found for SE and WASO. The analysis also explored the influence of seasons on sleep outcomes. Spring and Summer exhibited a notable negative association with TST, indicating shorter sleep duration during the hottest months of the year. In addition, Summer demonstrated a negative association with SE, and Spring with WASO, implying decreased efficiency during the Summer season and more sleep fragmentation during Spring. The random effect analysis revealed substantial variability at the individual level, with intra-class correlation coefficients (ICCs) ranging from 0.35 to 0.5 indicating a relevant proportion of total variance attributable to individual differences. The final models exhibited good explanatory power, as reflected in the marginal and conditional R-squared values ranging from 0.122 / 0.431 to 0.082 / 0.537. See appendix I for more details on the main models.

Table 6

Associations between the residential environment and selected sleep outcomes (TST, SE, WASO)

<table><thead><tr><th rowspan="2">Predictors</th><th colspan="2">TST</th><th colspan="2">SE</th><th colspan="2">WASO</th></tr><tr><th>Estimates</th><th>CI</th><th>Estimates</th><th>CI</th><th>Estimates</th><th>CI</th></tr></thead><tbody><tr><td>(Intercept)</td><td>467.99</td><td>428.19, 507.79</td><td>79.99</td><td>74.15, 85.84</td><td>62.04</td><td>46.44, 77.65</td></tr><tr><td>Lagged outcome</td><td>0.09</td><td>0.03, 0.14</td><td>0.06</td><td>0.01, 0.12</td><td>0.10</td><td>0.04, 0.16</td></tr><tr><td>Median income [T2]</td><td>5.16</td><td>-11.40, 21.72</td><td>0.44</td><td>-1.34, 2.21</td><td>-1.20</td><td>-10.12, 7.71</td></tr><tr><td>Median income [T3]</td><td>18.84</td><td>2.58, 35.09</td><td>0.18</td><td>-1.56, 1.92</td><td>2.13</td><td>-6.59, 10.86</td></tr><tr><td>Gender [M]</td><td>-19.00</td><td>-33.11, -4.89</td><td>1.26</td><td>-0.25, 2.77</td><td>-7.93</td><td>-15.51, -0.34</td></tr><tr><td>Age category [70-80]</td><td>0.34</td><td>-14.51, 15.19</td><td>-1.25</td><td>-2.85, 0.34</td><td>3.45</td><td>-4.54, 11.44</td></tr><tr><td>Age category [80]</td><td>-4.58</td><td>-29.88, 20.71</td><td>0.58</td><td>-2.14, 3.29</td><td>-3.94</td><td>-17.55, 9.67</td></tr><tr><td>Employment [other/unemployed]</td><td>-3.88</td><td>-54.24, 46.48</td><td>-0.54</td><td>-5.95, 4.88</td><td>-1.30</td><td>-28.42, 25.82</td></tr><tr><td>Employment [retired]</td><td>21.57</td><td>0.75, 42.40</td><td>-0.48</td><td>-2.71, 1.74</td><td>4.70</td><td>-6.48, 15.88</td></tr><tr><td>Household income [2-4K]</td><td>-5.78</td><td>-25.10, 13.54</td><td>2.14</td><td>0.07, 4.21</td><td>-10.91</td><td>-21.31, -0.50</td></tr><tr><td>Household income [>4K]</td><td>-13.68</td><td>-35.50, 8.13</td><td>2.13</td><td>-0.21, 4.47</td><td>-11.37</td><td>-23.11, 0.37</td></tr><tr><td>BMI [obese]</td><td>-20.04</td><td>-43.85, 3.76</td><td>-1.37</td><td>-3.92, 1.18</td><td>3.28</td><td>-9.51, 16.06</td></tr></tbody></table>
<table><tbody><tr><td>BMI [overweight]</td><td>-1.81</td><td>-16.02, 12.40</td><td>-0.90</td><td>-2.43, 0.62</td><td>4.68</td><td>-2.97, 12.33</td></tr><tr><td>Weekend [weekend]</td><td>9.87</td><td>2.99, 16.75</td><td>0.56</td><td>-0.01, 1.14</td><td>-2.05</td><td>-5.32, 1.23</td></tr><tr><td>Season [Spring]</td><td>-18.98</td><td>-37.46, -0.51</td><td>1.74</td><td>-0.18, 3.65</td><td>-10.39</td><td>-20.18, -0.59</td></tr><tr><td>Season [Summer]</td><td>-21.41</td><td>-38.44, -4.38</td><td>-2.54</td><td>-4.35, -0.74</td><td>8.70</td><td>-0.40, 17.80</td></tr><tr><td>Season [Winter]</td><td>14.58</td><td>-1.56, 30.73</td><td>-0.48</td><td>-2.13, 1.16</td><td>6.38</td><td>-2.11, 14.87</td></tr><tr><td colspan="7"><strong>Random Effects</strong></td></tr><tr><td>σ<sup>2</sup></td><td>3055.86</td><td></td><td>21.85</td><td></td><td>699.86</td><td></td></tr><tr><td>τ<sub>00</sub></td><td>1658.27</td><td></td><td>21.42</td><td></td><td>512.34</td><td></td></tr><tr><td>ICC</td><td>0.35</td><td></td><td>0.50</td><td></td><td>0.42</td><td></td></tr><tr><td>Marginal R<sup>2</sup> /<br>Conditional R<sup>2</sup></td><td>0.122 / 0.431</td><td></td><td>0.082 / 0.537</td><td></td><td>0.082 / 0.470</td><td></td></tr></tbody></table>

Participants: 211; observations: 1215 (nights of sleep); Estimates = Effect size; CI (Confidence Interval) = Range of true effect; σ² (Residual Variance) = Unexplained variability; τ₀₀ (Intercept Variance) = Group variability; ICC (Intraclass Correlation Coefficient) = Group effect proportion; Marginal R² = Fixed effects variance; Conditional R² = Total variance (fixed + random).

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340 **3.3 Secondary models**

341 In our extended analysis, we augmented our previous models by incorporating daily anxiety and depressive symptoms estimated using the Markov chain Monte Carlo approach and daily physical activity indicators, calculated for the day just before the night in question. Various physical activity factors, including daily sedentary time, light physical activity, moderate-to-vigorous physical activity and the number of steps, were tested. Among these, only steps exhibited a negative association with TST and was retained in the final models but not with SE or WASO, as detailed in Table 7. More specifically, the third and second tertile groups of daily steps exhibited an average of 32.95 and 28.29 minutes less of TST compared to the most sedentary group (CIs: -55.13, -10.78; -51.19 -5.38) highlighting a possible linear effect. Contrary to our initial expectations, depressive symptoms and anxiety levels demonstrated no discernible associations with our sleep outcomes.

351 Moreover, we delved deeper into the interplay between mental health, physical activity, and sleep by exploring inverse associations. In distinct models, sleep served as a predictor, while depressive symptoms, anxiety, and physical activity the day just after were examined as outcomes. However, no association was detected in any of our models.

355 **Table 7**

356 *Adjusted associations between median income, behavioral and mental health variables and selected sleep outcomes (TST, SE, WASO)*

<table><thead><tr><th rowspan="2">Predictors</th><th colspan="2">TST</th><th colspan="2">SE</th><th colspan="2">WASO</th></tr><tr><th>Estimates</th><th>CI</th><th>Estimates</th><th>CI</th><th>Estimates</th><th>CI</th></tr></thead><tbody><tr><td>(Intercept)</td><td>490.93</td><td>438.58, 543.29</td><td>79.99</td><td>74.15, 85.84</td><td>59.97</td><td>38.34, 81.60</td></tr><tr><td>Lagged outcome</td><td>0.08</td><td>0.01, 0.14</td><td>0.06</td><td>0.01, 0.12</td><td>0.08</td><td>0.02, 0.15</td></tr><tr><td>STAI binary [low]</td><td>10.35</td><td>-4.37, 25.07</td><td>0.44</td><td>-1.34, 2.21</td><td>3.83</td><td>-4.05, 11.72</td></tr><tr><td>CES binary [low]</td><td>2.74</td><td>-9.14, 14.62</td><td>0.18</td><td>-1.56, 1.92</td><td>-0.25</td><td>-6.27, 5.78</td></tr></tbody></table>
<table><tbody><tr><td>Steps [normal]</td><td>-28.29</td><td>-51.19, -5.38</td><td>-1.25</td><td>-2.85, 0.34</td><td>-0.27</td><td>-11.24, 10.70</td></tr><tr><td>Step [active]</td><td>-32.95</td><td>-55.13, -10.78</td><td>0.58</td><td>-2.14, 3.29</td><td>1.43</td><td>-9.21, 12.07</td></tr><tr><td>Median income [T2]</td><td>3.92</td><td>-13.97, 21.82</td><td>1.26</td><td>-0.25, 2.77</td><td>0.51</td><td>-9.24, 10.26</td></tr><tr><td>Median income [T3]</td><td>18.42</td><td>0.80, 36.04</td><td>-0.54</td><td>-5.95, 4.88</td><td>1.09</td><td>-8.48, 10.65</td></tr><tr><td colspan="7"><strong>Random Effects</strong></td></tr><tr><td>σ<sup>2</sup></td><td>2936.87</td><td></td><td>22.10</td><td></td><td></td><td>644.41</td></tr><tr><td>τ<sub>00</sub></td><td>1668.47</td><td></td><td>20.02</td><td></td><td></td><td>554.72</td></tr><tr><td>ICC</td><td>0.36</td><td></td><td>0.48</td><td></td><td></td><td>0.46</td></tr><tr><td>Marginal R<sup>2</sup> /<br>Conditional R<sup>2</sup></td><td>0.140 / 0.451</td><td></td><td>0.109 / 0.532</td><td></td><td></td><td>0.112 / 0.523</td></tr></tbody></table>

Participants: 207; observations: 821; Estimates = Effect size; CI (Confidence Interval) = Range of true effect; σ² (Residual Variance) = Unexplained variability; τ₀₀ (Intercept Variance) = Group variability; ICC (Intraclass Correlation Coefficient) = Group effect proportion; Marginal R² = Fixed effects variance; Conditional R² = Total variance (fixed + random). Model adjusted for gender, age, employment, household income, BMI, week day and seasonality.

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## 4 Discussion

### 4.1 Main Models

In our model specification, we sought to address time autocorrelation in sleep outcomes across nights through various approaches. Initially, we introduced a binary variable (0 or 1) indicating whether TST lagged 1 (i.e., the previous night) was less than 6 hours, aiming to test the hypothesis of a compensation effect where individuals, after experiencing sleep deprivation on one night, would potentially compensate by sleeping more the next day. Surprisingly, we found no association supporting this hypothesis. Subsequently, we included TST lagged 1 as a standard method to control for autocorrelation and a positive association emerged, revealing that higher sleep the night before was linked to increased sleep the subsequent night. This finding raises the possibility of individual idiosyncrasies or habits influencing sleep patterns rather than a reactive compensation behavior. Notably, this interpretation gains support from the elevated Intraclass Correlation Coefficient (ICC) at the individual level within the mixed-effects model, underscoring the influence of individual factors on sleep dynamics. In the Empty Model, 45% of the variance in TST is attributed to individual differences. As the lagged outcome by one night is introduced to account for autocorrelation, the ICC decreases to 0.36, indicating that a substantial portion of individual variability is explained by previous sleep patterns. Further reduction occurs with the inclusion of individual covariates, resulting in an ICC of 0.32. This emphasizes the significant influence of demographic variables and individual habits in shaping sleep outcomes.

Neighborhood income was the sole environmental factor associated with TST, indicating a potential link of area socio-economic factors with sleep duration, with no associations found for SE or WASO. In our further investigation of the relationship between neighborhood income and TST, we reestimated the model with neighborhood income as a continuous variable and we observed a noteworthy effect size of 1.4 more minutes of sleep for each increase of one thousand euro in median income (CL: 0.35, 2.45). The quantile analysis further revealed a substantial difference in TST between the 10th and 90th percentiles of neighborhood median income, which show an average difference of income amounting to €16,850. The corresponding adjusted effect on total sleep time indicated that individuals residing in higher-income areas experienced, on average, 23.6 more minutes of sleep compared to their counterparts in lower-income areas, underscoring a potentially significant
impact of neighborhood socioeconomic factors on sleep patterns. While numerous factors contribute to sleep variability, our results highlight the significance of neighborhood income-related disparities in understanding sleep duration variations within the population in concordance with previous finding (Tomfohr-Madsen et al., 2020). Further research is warranted to delve into the underlying mechanisms and potential interventions aimed at addressing these disparities in sleep health.

Median income area might act as a proxy factor covering the synergistic and cumulative effects of different combinations of environmental factors patterned by income levels Specific combinations of factors might have a stronger impact on sleep than the effect of single elements. Our findings align with previous research, indicating that sleep inequalities tend to be geographically distributed in accordance to socioeconomic differences across areas. This pattern may be hypothetically attributed to varying exposure levels to adverse environmental factors concentrated in these specific regions (Grandner et al., 2015, 2021; Morello-Frosch et al., 2011). Future studies might investigate this case by adopting mixture models to quantify the effect of the concerted effects of the environment while accounting for multicollinearities and interactions between each element of the mixture (Bista et al., 2023). It is noteworthy to observe how TST was associated with median area income but not with household income. It suggests that neighborhood-level dynamics, encapsulated by median area income, may play a more prominent role in influencing sleep duration than individual household financial situations alone. This finding diverges from a range of previous studies that identified associations between sleep duration and individual measures of income and socioeconomic status (Etindele Sosso et al., 2021). See figure 2 for a visual representation of median income differences across the region of Ile de France.

<caption>Figure 2</caption>

Map of neighborhood median income in Ile de France

<figure><img src="image_6.png" /></figure>
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The absence of associations in our model between sleep outcomes and other environmental factors such as air and noise pollution, access to green and blue areas, population density, and walkability contrasts with our initial hypotheses and the existing literature (Johnson, Billings, et al., 2018; Johnson, Hirsch, et al., 2018; Kim et al., 2022). Previous studies have linked these urban environmental factors with sleep conditions and mental health. The lack of detected associations in our model may be due to our ability to correctly capture the dimensions of interest with our variables or to the fact that other unexplored variables or complex interactions could be influencing these relationships, emphasizing the need for comprehensive investigations to unravel the complexity of these relationships. (Billings et al., 2020; Hunter & Hayden, 2018; Johnson, Hirsch, et al., 2018).

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We find congruent with our hypothesis the other associations detected by our model between sleep and selected individual covariates. The gender differences in sleep identified by our model showing a longer sleep duration for female participants are well documented in the literature (Meers et al., 2019; Tang et al., 2017). The higher sleep duration of retired participants is congruent with sleep theory where retired individuals benefit of more sleep opportunity (e.g., the overall availability of time), to dedicate to sleep (Grandner, 2019a). The temporal and seasonal effects on sleep detected by our models are also coherent with the literature; the observed higher sleep duration during weekends indicates compensatory behavior to recover lost sleep hours from the preceding week. This phenomenon, known as ‘social jet lag,’ results in the misalignment between sleep time and the individual endogenous circadian clock (Roenneberg et al., 2003, 2012; Valdez et al., 1996). The shorter sleep duration and increased sleep fragmentation during the Spring and Summer seasons are also in line with prior research. These seasonal variations could be attributed to the disruptive effects of heat, as well as adaptation in circadian entrainment (the synchronization of an organism’s internal biological rhythms with external environmental cues, such as the light-dark cycle), due to increased exposure to sunlight, that posticipate sleep timing and decrease length of sleep (Okamoto-Mizuno & Mizuno, 2012; Stothard et al., 2017; Taylor et al., 2008; Zerbini et al., 2021). However, our study contradicts existing literature on age related changes in sleep patterns, as we did not observe any association between age groups and sleep outcomes. This absence of contrast diverges from expectations, as we anticipated a decrease in SE and an increase in WASO with older age due to neurobiological changes normally associated with aging (Mander et al., 2017).

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## 4.2 Secondary models

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We utilized a Markov chain Monte Carlo approach to estimate daily variations in depressive symptoms and anxiety, which we then integrated as predictors into a subsequent mixed-effect linear model with sleep as the outcome alongside physical activity. While the number of daily steps displayed an association with total sleep time, depression and anxiety levels failed to show discernible correlations with sleep outcomes. In a third iteration, we explored the inverse relationship, testing sleep as a predictor and daily mental health levels and steps count as outcomes. However, we detected no significant associations.

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The finding that daily steps taken exhibited a negative association with total sleep time is consistent with previous research. This observation aligns with a meta-analytical study by Atoui et al., which found that lower physical activity during the day is associated with shorter TST the following night (Atoui et al., 2021). As a potentially straightforward explanation, on days where people allocate more time to be awaken (and so less time to sleep), they have more opportunity to accumulate steps. For a more thorough exploration of this relationship, future studies focusing on sleep and physical activity could consider employing an isotemporal substitution model to account for behavioral changes in a limited span of a 24 hours day (Fancello, Can, et al., 2023; Mekary et al., 2009). Another plausible interpretation is that more physically active individuals generally experience better circadian entrainment (Healy et al., 2021; Yamanaka et al., 2006), potentially requiring less sleep due to a more restorative sleep. However, our models did not detect associations between steps groups and sleep outcomes, consistent with previous meta-analyses that reported a lack of consistent associations between physical activity and sleep (Atoui et al., 2021). Measures of sleep stages, such as those

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Regarding our analysis with mental health variables, contrary to our hypotheses, depression and anxiety levels showed no discernible associations with sleep outcomes. This unexpected finding challenges existing assumptions about the direct impact of daily depressive and anxiety level variations on nightly sleep patterns (Baglioni et al., 2016; Freeman et al., 2020). The absence of associations in the inverse models, where sleep served as a predictor, further underscores the complexity of the temporal interplay between mental health and sleep. Recent studies involving depression and anxiety assessment via EMA surveys have detected bidirectional associations with sleep quality, with larger effects for the impact of sleep on next day symptomatology (Cox et al., 2018; Diffrancesco et al., 2021; Triantafillou et al., 2019). However, our study did not detect any associations in either direction, prompting a reconsideration of this presumed connection.

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In our sample, with-individual daily fluctuations in depressive and anxiety levels were minimal (see appendix II for more details). However, it should be kept in mind that the associations between anxiety or depression and sleep were estimated by comparing with each other all the participants with significantly different anxiety or depression levels, not only through a within-individual comparison. One potential explanation for the absence of associations may be that our tools incurred in measurement error and failed to properly capture the personal fluctuations in mental health as well as differences between participants. As another explanation, the relationship may not be linear, with only EMA scores above a certain threshold impacting sleep, and the sample size may have contributed to limited statistical power to detect associations. Finally, it is also possible that mood or mental health the day before was so much associated with sleep duration and quality the following night in this particular population.

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## 4.3 Implications of findings

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The positive adjusted correlation between neighborhood median income and total sleep time highlights the between-neighborhood socioeconomic inequalities in sleep duration. Our findings suggest the need for further investigation into the actual factors contributing to this disparity and emphasize the importance of monitoring sleep patterns in poorer areas. While our results do not provide direct insights for urban planning and health interventions in socially disadvantaged neighborhoods, they underscore the necessity of understanding and addressing the root causes of sleep inequality. They also suggest that the low socioeconomic status disadvantage in sleep duration is attributable to processes related not only to individual factors but also to neighborhood factors. Future research should aim to identify specific environmental and social stressors that affect sleep in low socioeconomic status neighborhoods, which may eventually inform targeted interventions and urban planning considerations.

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## 4.4 Strengths, limitations and future directions

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This study pioneers the exploration of dynamic mutual influences between sleep, mental health, physical activity, and the residential environment, uniquely monitoring participants daily behavior and activity. The primary strength of our approach lies in its multi-exposure and comprehensive monitoring, utilizing sensor-based and EMA methods to assess individual exposures, confounding factors, and health outcomes across 7 days. Participants were tracked with accelerometry both during the day and during the night using two distinct devices to ensure a continuative assessment of health relevant behaviors such as physical activity and sleep.

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Despite its strengths, the protocol has limitations. Among the limits of our study we can mention the short duration of the study. Seven days of monitoring do not allow to detect medium- and long-term health changes and can only reveal very short-term variations in the variables of interest. Furthermore, a minimum of 12 measures is advocated for a more detailed control of time autocorrelation at different scales beyond the inclusion in the models of the outcome at lag 1 (e.g. the precedent night) (Irish et al., 2014). Previous research detected greater fit with longer lag measures for different predictors of sleep. We therefore advocate for a longer data collection period with a longer set of daily measures and follow up assessments.
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In addition, we acknowledge the limitation of using yearly averages in environmental factors to predict daily outcomes, as this approach may fail to capture the short-term variations and dynamics affecting sleep patterns. Specifically, using annual concentrations of pollutants and only seasonal variations as a proxy for temperature might not accurately reflect the daily fluctuations that could impact sleep outcomes. Future research should aim to incorporate more granular, time-varying environmental exposure data.

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Another limitation of the study consists in the lack of objective measures of the indoor sleep environment which could have a direct impact on sleep. Research on sleep patterns in adults frequently overlooks information about household attributes, hindering our capacity to assess the influence of the domestic environment on sleep. The absence of data on household features in these studies restricts our understanding of potential within-neighborhood variations in environmental exposures. Between-household variations in sleep outcomes are extensively reported in the literature, emphasizing the influence of the internal environment on sleep settings through factors such as ambient temperature, air quality, noise, ventilation, and lighting conditions (Chambers et al., 2016; Johnson, Thorpe, et al., 2018). For future studies, we recommend expanding the protocol to incorporate additional measures such as ambient noise, temperature, and humidity, contributing to a more robust and thorough assessment of sleep environments (Chambers et al., 2016; Johnson, Hirsch, et al., 2018; Sandel & Wright, 2006).

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Another limitation of the study is the lack of genetic data which according to meta-analytical evidence might account for one-third of sleep variability (Kocevska et al., 2021; Madrid-Valero et al., 2021). While our paper focuses on the environmental components of sleep variability rather than genetic factors, it is important to acknowledge that genetics could influence individual idiosyncrasies in health-related behaviors, and sensitivity to environmental exposures.

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On the front of mobile sensing, accelerometer-based sleep data is very limited. The agreement with the sleep gold standard polysomnography is higher with TST and limited with the SE and WASO. In addition, actigraphy tends to overestimate TST and underestimate WASO. Consequently, the more disturbed and fragmented the sleep, the less accurate the sleep assessment. This is particularly relevant for populations with higher rates of sleep troubles such as older adults or individuals with mental and sleep disorders (Quante et al., 2018; Zinkhan et al., 2014). TST provides a quantitative measure of the time a participant spends sleeping, including all awakenings during such sleep period. In essence, it captures the behavioral facet of sleep rather than the actual quality of sleep. Consequently, this measure may lack sensitivity in detecting the influence of environmental factors or other behaviors, as they might differentially impact various sleep stages. For example, psychoactive consumption (e.g., caffeine or alcohol) could diminish deep and REM sleep while augmenting light sleep and micro-awakenings, whereas long bouts of moderate to vigorous physical activity might exert an opposing effect (Atoui et al., 2021; Garcia & Salloum, 2015). We therefore recommend upgrading sleep tracking with wearable EEG. Such tools are validated against polysomnography (Arnal et al., 2020) and can accurately differentiate sleep stages, offering insights into the impact of environment, behavior, and mental health on specific phases of sleep and ultimately on the level of restoration that sleep can provide (Montanari et al., 2024).

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## 5 Conclusions

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The findings demonstrate a notable positive correlation between socioeconomic status at the residential level and TST. Conversely, SE and WASO did not display any association. Contrary to our expectations, other environmental factors, physical activity and depression-anxiety levels showed no substantial correlations with sleep outcomes.

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In examining the association between neighborhood income and Total Sleep Time, we identified an adjusted effect size of 1.4 more minutes of sleep for each 1000€ increase in neighborhood median income, and quantile analysis highlighted a substantial difference in TST between the 10th and 90th percentiles of median area income, emphasizing the substantial impact of area socioeconomic factors on sleep patterns. In summary, these results emphasize the stronger

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Authors' contributions

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Contributors

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AM drafted the manuscript and conducted data processing and data analysis under the supervision of

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BC, with support of GF who also contributed to the GIS analyses. BC is the principal investigator of

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the study. All authors revised the manuscript for intellectual content.

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Competing interests

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The authors have no conflict of interest to disclose.

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Funding

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The current publication is financed by the European Research Council (ERC) under the European

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Union's Horizon 2020 Marie Skłodowska-Curie actions (H2020-MSCA-ITN) (grant agreement ID:

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956780). MINDMAP is funded by the European Commission HORIZON 2020 research and

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innovation action 667661. HANC is funded by a grant from the French National Research Agency

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(ANR-15-CE36-0005).

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Data availability statement

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Data is available on request.

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1

A sensor-based study on the environmental determinants of sleep in older adults

2

3

Appendices

4
# Appendix I

## Mobile Sensing Protocol

### Actigraphy - Processing method

#### Sleep

Following the most recent guidelines on the use of accelerometers for sleep assessment (Littner et al., 2003), a trained researcher visually analyzed each sleep graph provided by ActiLife and compared them with participants' sleep logs (derived from self-reported activity diaries), to verify the sleep periods automatically detected by the Cole-Kripke algorithm. Sleep timestamps were then manually inputted into the software in case of artefacts or wide disagreements between the sleep logs and the scored metrics. Visual inspection and use of self-reported logs are considered complementary methods for accurate sleep scoring and artifact rejection (Littner et al., 2003).

#### Physical Activity

For each participant, we aggregated the 5-second epochs of the Actigraph monitors into 1-minute epochs. We adopted a Low Frequency Extension (LFE) filter to correctly assess physical activity in our sample of older adults. This extension is specifically utilized to identify lower amplitude movements, typical in older adults, that would otherwise be overlooked with default settings (Migueles et al., 2017). Following the literature on older adults, we employed the Choi algorithm (Choi et al., 2012) to distinguish wear time from non-wear time. The algorithm classifies non-wear as 90 minutes of 0 count per minute with a 2-minute allowance for activity, along with two 30-minute windows of 0 count per minute before and after the allowance period. This approach has shown superior performance compared to other methods in detecting non-wear time in older adults (Migueles et al., 2017). Participants without a minimum of four days, each with a minimum of ten hours of wear time, were excluded from the analysis. These criteria were established to ensure a robust representation of participants' physical activity patterns while accounting for potential variations in daily routines. To assess physical activity we adhered to the guidelines for older adults (Migueles et al., 2017). To differentiate various levels of physical activity and sedentary behavior, we integrated the cut points proposed by Aguilar-Farias et al. and Santos-Lozano et al. Following Santos-Lozano et al. we calculated the time spent in the different activity levels (by summing the 1-minute epochs) with the 3-axes cut points of 0-2750, 2751-9359, and >9360 to differentiate light, moderate and vigorous physical activity (Santos-Lozano et al., 2013). As for sedentary behavior we summed the 1-minute epochs with the cut points of vector magnitude <1 count/s, <70 counts/15-s, <200 counts/min and for the vertical axis of <1 count/s, <10 counts/15-s, <25 counts/min as suggested by Aguilar-Farías et al (Aguilar-Farías et al., 2014).

# Appendix II

## Statistical Methods

Employing an integrative modeling approach, our study aims to conduct a comprehensive multi-exposure assessment to assess the impact of the environment on sleep, integrating various behavioral and health covariates and confounders. In the initial models, we selected the most relevant individual covariates to predict sleep quality. Subsequently, we systematically added one by one geoprocessed environmental factors to assess their impact on sleep; this phase was essential to decide which factors to include in the final model. Afterward, we added to the models physical activity indicators (including daily steps) and mental health daily state fluctuations assessed by Ecological Momentary Assessment (EMA). Finally, we explored the potential case of reverse causality by testing sleep and other individual covariates as predictors of the EMA scores of mental health and as
predictors of physical activity. This modeling plan aims to investigate the intricate relationships between environmental factors, individual characteristics and behaviors, and sleep outcomes, providing insights on the complex interplay in urban living conditions.

## Main models

To select the most relevant variables, we initially tested a range of covariates. In all models, we included participants' age group (60–70, 71–80, >80 years) and gender (male, female). Additionally, we incorporated marital status (in couple, divorced/widowed/unmarried), household income per consumption unit (<2000, 2000–4000, >4000), employment status (employed, retired, other), day of the week (weekend, weekday), season (autumn, winter, spring, summer), and perceived health (good, bad). Following preliminary analyses, we discarded education level (low-medium education, high education, very high education), smoking status (smoker/occasional smoker, non-smoker), monthly consumption of alcohol units (1-100), perceived burden of chronic pain (low, medium, high), and Covid-19 pandemic timing (before, after) to maximize model fit and parsimony (the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used in the selection process). Subsequently we separately introduced in the model each environmental factor and we retained only the predictors that were associated with the outcome. The Variance Inflation Factor (VIF) for each model is extremely low, indicating minimal multi-collinearity among the predictors in all three models (TST, SE, and WASO). The VIF values ranged for the Total Sleep Time (TST) model from 1.01 to 1.38, for the Sleep Efficiency (SE) model from 1.00 to 1.36, and for the Wake After Sleep Onset (WASO) model from 1.00 to 1.36, confirming very low multi-collinearity among the predictors.

In our analysis of sleep metrics, we compared models with and without an individual-level random effect to understand the variance explained by fixed effects alone versus the combined explanatory power of fixed and random factors. For Total Sleep Time (TST), the marginal R² (considering only the fixed effects of the model) was 0.122, and the conditional R² (also considering the random intercept) was 0.431, indicating that the fixed effects alone explained 12.2% of the variance, while the full model with random effects explained 43.1% of the variance. When removing the random effect, the R² decreased from 0.431 to 0.268, indicating a reduction of 38.0%. For Sleep Efficiency (SE), the marginal R² was 0.082, and the conditional R² was 0.537, with the fixed effects explaining 8.2% of the variance and the full model explaining 53.7% of the variance. The R² dropped from 0.537 to 0.349 when the random effect was excluded, a reduction of 35.0%. Similarly, for Wake After Sleep Onset (WASO), the marginal R² was 0.082, and the conditional R² was 0.470, with fixed effects explaining 8.2% of the variance and the full model explaining 47.0% of the variance. The R² fell from 0.470 to 0.312, a reduction of 33.6% upon removing the random effect. These findings highlight the significant role of random effects, as they account for a substantial portion of the variance in sleep metrics, underscoring the complexity of sleep behavior that fixed effects alone cannot capture.

## Secondary models - Markov chain Monte Carlo (MCMC)

In our study, we employed a two-step modeling approach to analyze the relationship between depression, anxiety, and sleep outcomes. Given that depression and anxiety EMA items from the same scales are assessed over a day, we needed to quantify their overall day level for subsequent modeling. For this reason, we opted for a Markov chain Monte Carlo (MCMC) approach to model the daily levels in depressive symptoms and anxiety. This decision was made due to the complex nature of the model specification. Given the relatively large number of parameters, such as the two-level random effects specified in the models for depressive symptoms and anxiety, i.e., at the day level and individual level, and the limited number of observations available, employing conventional regression techniques could lead to issues of convergence and unreliable estimates. Conversely, MCMC methods offer robustness in analyzing complex longitudinal data, allowing us to account for daily dynamic fluctuations in depressive and anxiety symptoms.
In our application, MCMC is used to derive the day-level random effects for depressive symptoms and anxiety, which are afterwards used as explanatory variables into the main model for sleep. Separate models were estimated for depressive symptoms and anxiety as the outcomes (one observation corresponding to the answer to one item) using MCMC models involving two random effects: one at the individual level and another at the day level, aiming to capture the variation in symptoms each day for each participant. Subsequently, these random effects are integrated into mixed-effect linear models as predictors of sleep. The models to generate the depressive symptoms and anxiety random effects were estimated using Markov chain Monte Carlo simulations (converged with 4 sampling chains and 10,000 iterations) with the brms package in the latest version of R (Bürkner, 2017), while the subsequent models for sleep were estimated using a more traditional frequentist approach. It should be kept in mind that the day-level random effect capturing depressive and anxiety symptoms represents a model estimate implying uncertainty rather than a directly observable quantity, and that the corresponding uncertainty is neglected in our approach.

The MCMC-derived random effects for depressive symptoms (CES) and anxiety (STAI) indicate minimal daily fluctuations for anxiety (median = 0.00017, SD = 0.00670, range = -0.02942 to 0.03022), while depressive symptoms exhibit more daily variability (median = -0.07029, SD = 0.21663, range = -0.54683 to 1.44349). The latter finding suggests that the maximum modulation between days with the lowest and highest levels of symptoms amounts to almost 2 points on a scale from 1 to 4. The random effects represent deviations from the overall mean.

We observed low variability in the EMA mental health scores, likely due to the minimal day-to-day fluctuation within participants when responding to the same EMA items consistently throughout the data collection period. This reduced variability may have contributed to the lack of associations with sleep in our subsequent models, even if it should be kept in mind that the association between EMA scores and sleep were estimated not only on the basis of within-individual but also on the basis of between-individual comparison. For more information on the within-participant variability in the EMA mental health scores, see table 1. The first two columns are based on the within-individual standard deviation of the values for each item. The last two columns are based on the within-individual range of score for each item, which is the difference between the highest score and the lowest score given by a unique individual to each item. These data show that the answer to depressive and anxiety symptoms items is not without within-individual variability over one week.

<caption>**Table 1**</caption>

*EMA Score Variability*

<table><thead><tr><th>Questionnaire and Item</th><th>Participants with standard deviation equal to 0 (%)</th><th>Participants with standard deviation inferior to 1 (%)</th><th>Median of the within-participant range of score</th><th>10<sup>th</sup> and 90<sup>th</sup> percentiles of the within-participant range of score</th></tr></thead><tbody><tr><td>CES Item 1</td><td>33.65</td><td>94.31</td><td>1</td><td>0 – 2</td></tr><tr><td>CES Item 2</td><td>58.29</td><td>98.10</td><td>0</td><td>0 – 1</td></tr><tr><td>CES Item 3</td><td>32.06</td><td>97.13</td><td>1</td><td>0 – 2</td></tr><tr><td>CES Item 4</td><td>36.02</td><td>97.63</td><td>1</td><td>0 – 1</td></tr><tr><td>CES Item 5</td><td>40.28</td><td>93.84</td><td>1</td><td>0 – 2</td></tr><tr><td>CES Item 6</td><td>25.24</td><td>94.76</td><td>1</td><td>0 – 2</td></tr><tr><td>CES Item 7</td><td>53.33</td><td>95.71</td><td>0</td><td>0 – 1</td></tr></tbody></table>
<table><tr><td>CES Item 8</td><td>29.86</td><td>94.31</td><td>1</td><td>0-2</td></tr><tr><td>STAI Item 1</td><td>28.44</td><td>95.26</td><td>1</td><td>0-2</td></tr><tr><td>STAI Item 2</td><td>41.71</td><td>94.31</td><td>1</td><td>0-2</td></tr><tr><td>STAI Item 3</td><td>49.05</td><td>93.33</td><td>1</td><td>0-2</td></tr><tr><td>STAI Item 4</td><td>23.70</td><td>95.26</td><td>1</td><td>0-2</td></tr><tr><td>STAI Item 5</td><td>29.38</td><td>95.73</td><td>1</td><td>0-2</td></tr><tr><td>STAI Item 6</td><td>41.90</td><td>93.81</td><td>1</td><td>0-2</td></tr></table>

Items from the short version of the CES-D (Center for Epidemiologic Studies Depression Scale) and the STAI (State-Trait Anxiety Inventory, Y-A form) questionnaires. Items range of score: 1-4

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