Geographic environments, daily activities and stress in Luxembourg (the FragMent study): a protocol combining map-based questionnaires, geographically explicit ecological momentary assessment and vocal biomarkers of stress.
MENTAL HEALTH; Stress, Physiological; Stress, Psychological; Wearable Devices; Biomarkers; Humans; Luxembourg/epidemiology; Adult; Middle Aged; Surveys and Questionnaires; Male; Young Adult; Female; Biomarkers/analysis; Adolescent; Aged; Stress, Psychological/epidemiology; Ecological Momentary Assessment; Activities of Daily Living; Luxembourg; Medicine (all)
Abstract :
[en] [en] INTRODUCTION: Stress is nearly ubiquitous in everyday life; however, it imposes a tremendous burden worldwide by acting as a risk factor for most physical and mental diseases. The effects of geographic environments on stress are supported by multiple theories acknowledging that natural environments act as a stress buffer and provide deeper and quicker restorative effects than most urban settings. However, little is known about how the temporalities of exposure to complex urban environments (duration, frequency and sequences of exposures) experienced in various locations - as shaped by people's daily activities - affect daily and chronic stress levels. The potential modifying effect of activity patterns (ie, time, place, activity type and social company) on the environment-stress relationship also remains poorly understood. Moreover, most observational studies relied quasi-exclusively on self-reported stress measurements, which may not accurately reflect the individual physiological embodiment of stress. The FragMent study aims to assess the extent to which the spatial and temporal characteristics of exposures to environments in daily life, along with individuals' activity patterns, influence physiological and psychological stress.
METHODS AND ANALYSIS: A sample of 2000 adults aged 18-65 and residing in the country of Luxembourg completed a traditional and a map-based questionnaire to collect data on their perceived built, natural and social environments, regular mobility, activity patterns and chronic stress at baseline. A subsample of 200 participants engaged in a 15-day geographically explicit ecological momentary assessment (GEMA) survey, combining a smartphone-enabled global positioning system (GPS) tracking and the repeated daily assessment of the participants' momentary stress, activities and environmental perceptions. Participants further complete multiple daily vocal tasks to collect data on vocal biomarkers of stress. Analytical methods will include machine learning models for stress prediction from vocal features, the use of geographic information systems (GIS) to quantify dynamic environmental exposures in space and time, and statistical models to disentangle the environment-stress relationships.
ETHICS AND DISSEMINATION: Ethical approval (LISER REC/2021/024.FRAGMENT/4-5-9-10) was granted by the Research Ethics Committee of the Luxembourg Institute of Socio-Economic Research (LISER), Luxembourg. Results will be disseminated via conferences, peer-review journal papers and comic strips. All project outcomes will be made available at https://www.fragmentproject.eu/.
Disciplines :
Public health, health care sciences & services
Author, co-author :
PERCHOUX, Camille ; University of Luxembourg ; LISER, Esch-sur-Alzette, Luxembourg District, Luxembourg
TOPALIAN, Noémie ; University of Luxembourg ; LISER, Esch-sur-Alzette, Luxembourg District, Luxembourg noemie.topalian@liser.lu ; Luxembourg Institute of Health, Strassen, Luxembourg
Geographic environments, daily activities and stress in Luxembourg (the FragMent study): a protocol combining map-based questionnaires, geographically explicit ecological momentary assessment and vocal biomarkers of stress.
This project has received funding from the European Research Council (ERC), under the Horizon Europe research program (Grant Agreement No. 101040492; Project acronym: FragMent). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the European Research Council can be held responsible for them.This project has received funding from the European Research Council (ERC), under the Horizon Europe research program (Grant Agreement No. 101040492; Project acronym: FragMent). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the European Research Council can be held responsible for them.
World Health Organization. Stress. 2023. Available: https://www. who.int/news-room/questions-and-answers/item/stress [Accessed Apr 2025].
McCarty R. The fight-or-flight response: a cornerstone of stress research. In: In Stress: Concepts, cognition, emotion, and behavior. Elsevier, 2016: 33-7.
Turner AI, Smyth N, Hall SJ, et al. Psychological stress reactivity and future health and disease outcomes: A systematic review of prospective evidence. Psychoneuroendocrinology 2020; 114:S0306-4530( 20)30018-4.
Cohen S, Janicki-Deverts D, Miller GE. Psychological Stress and Disease. JAMA 2007; 298:1685.
Smith MD, Wesselbaum D. Global evidence on the prevalence of and risk factors associated with stress. J Affect Disord 2025; 374:179-83.
Ulrich RS. Natural versus Urban Scenes: Some Psychophysiological Effects. Environment and Behavior, 1981.
Ohly H, White MP, Wheeler BW, et al. Attention Restoration Theory: A systematic review of the attention restoration potential of exposure to natural environments. Journal of Toxicology and Environmental Health, Part B 2016; 19:305-43.
Twohig-Bennett C, Jones A. The health benefits of the great outdoors: A systematic review and meta-analysis of greenspace exposure and health outcomes. Environ Res 2018; 166:628-37.
Shuda Q, Bougoulias ME, Kass R. Effect of nature exposure on perceived and physiologic stress: A systematic review. Complement Ther Med 2020; 53:102514.
Kondo MC, Jacoby SF, South EC. Does spending time outdoors reduce stress?. A review of real-time stress response to outdoor environments. Health Place 2018; 51:136-50.
Healey JA, Picard RW. Detecting Stress During Real-World Driving Tasks Using Physiological Sensors. IEEE Trans Intell Transport Syst 2005; 6:156-66.
Yang TC, Matthews SA. The role of social and built environments in predicting self-rated stress: A multilevel analysis in Philadelphia. Health Place 2010; 16:803-10.
Tao Y, Chai Y, Kou L, et al. Understanding noise exposure, noise annoyance, and psychological stress: Incorporating individual mobility and the temporality of the exposure-effect relationship. Appl Geogr 2020; 125:102283.
Kou L, Tao Y, Kwan M-P, et al. Understanding the relationships among individual-based momentary measured noise, perceived noise, and psychological stress: A geographic ecological momentary assessment (GEMA) approach. Health Place 2020; 64:102285.
Wang L, Kwan M-P, Zhou S, et al. Assessing the affective quality of soundscape for individuals: Using third-party assessment combined with an artificial intelligence (TPA-AI) model. Science of The Total Environment 2024; 953:176083.
Tao Y, Kou L, Chai Y, et al. Associations of co-exposures to air pollution and noise with psychological stress in space and time: A case study in Beijing, China. Environ Res 2021; 196:110399.
Tao Y, Chai Y, Zhang X, et al. Mobility-based environmental justice: Understanding housing disparity in real-time exposure to air pollution and momentary psychological stress in Beijing, China. Soc Sci Med 2021; 287:114372.
Plascak JJ, Llanos AAM, Qin B, et al. Visual cues of the built environment and perceived stress among a cohort of black breast cancer survivors. Health Place 2021; 67.
Panlilio LV, Preston KL, Bertz JW, et al. Objective Neighborhood-Level Disorder Versus Subjective Safety as Predictors of HIV Transmission Risk and Momentary Well-Being. AIDS Behav 2024; 28:3326-37.
Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health 2001; 55:111-22.
Steptoe A, Feldman PJ. Neighborhood Problems as Sources of Chronic Stress: Development of a Measure of Neighborhood Problems, and Associations with Socioeconomic Status and Health. Annals of Behavioral Medicine, 2001.
Barrington WE, Stafford M, Hamer M, et al. Neighborhood socioeconomic deprivation, perceived neighborhood factors, and cortisol responses to induced stress among healthy adults. Health Place 2014; 27:120-6.
Algren MH, Ekholm O, Nielsen L, et al. Associations between Perceived Stress, Socioeconomic Status, and Health-Risk Behaviour in Deprived Neighbourhoods in Denmark: A Cross-Sectional Study. BMC Public Health, 2018.
Elliott M. The Stress Process in Neighborhood Context. Health and Place, 2000.
Bak CK, Tanggaard Andersen P, Bacher I, et al. The association between socio-demographic characteristics and perceived stress among residents in a deprived neighbourhood in Denmark. Eur J Public Health 2012; 22:787-92.
Henderson H, Child S, Moore S, et al. The Influence of Neighborhood Aesthetics, Safety, and Social Cohesion on Perceived Stress in Disadvantaged Communities. Am J Community Psychol 2016; 58:80-8.
Scott SB, Munoz E, Mogle JA, et al. Perceived neighborhood characteristics predict severity and emotional response to daily stressors. Soc Sci Med 2018; 200:262-70.
Mennis J, Mason M, Ambrus A. Urban Greenspace is Associated with Reduced Psychological Stress among Adolescents: A Geographic Ecological Momentary Assessment (GEMA) Analysis of Activity Space. Landsc Urban Plan 2018; 174:1-9.
Chaix B, Kestens Y, Perchoux C, et al. An interactive mapping tool to assess individual mobility patterns in neighborhood studies. Am J Prev Med 2012; 43:440-50.
Golledge RD, Stimson RJ. Spatial Behavior: A Geographic Perspective. New York: The Guilford press, 1997.
Perchoux C, Chaix B, Cummins S, et al. Conceptualization and measurement of environmental exposure in epidemiology: accounting for activity space related to daily mobility. Health Place 2013; 21:86-93.
Perchoux C, Kestens Y, Brondeel R, et al. Accounting for the daily locations visited in the study of the built environment correlates of recreational walking (the RECORD Cohort Study). Prev Med 2015; 81:142-9.
Perchoux C, Brondeel R, Wasfi R, et al. Walking, trip purpose, and exposure to multiple environments: A case study of older adults in Luxembourg. J Transp Health 2019; 13:170-84.
Fernandes A, Van Lenthe FJ, Vallée J, et al. Linking physical and social environments with mental health in old age: a multisensor approach for continuous real-life ecological and emotional assessment. J Epidemiol Community Health 2021; 75:477-83.
Kim E-K, Conrow L, Röcke C, et al. Advances and challenges in sensor-based research in mobility, health, and place. Health Place 2023; 79.
Kwan MP. The Neighborhood Effect Averaging Problem (NEAP): An Elusive Confounder of the Neighborhood Effect. Int J Environ Res Public Health 2018; 15:1841.
Chaix B, Méline J, Duncan S, et al. GPS tracking in neighborhood and health studies: a step forward for environmental exposure assessment, a step backward for causal inference?. Health Place 2013; 21:46-51.
Bild E, Coler M, Pfeffer K, et al. Considering Sound in Planning and Designing Public Spaces: A Review of Theory and Applications and a Proposed Framework for Integrating Research and Practice. J Plan Lit 2016; 31.
Su L, Zhou S, Kwan M-P, et al. The impact of immediate urban environments on people's momentary happiness. Urban Stud 2022; 59:140-60.
Hunter MCR, Gillespie BW, Chen SYP. Urban Nature Experiences Reduce Stress in the Context of Daily Life Based on Salivary Biomarkers. Front Psychol 2019; 10:722.
Lan Y, Helbich M. Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories. Int J Health Geogr 2023; 22:27.
Zhang L, Zhou S, Kwan MP. The temporality of geographic contexts: Individual environmental exposure has time-related effects on mood. Health Place 2023; 79.
Jankowska MM, Schipperijn J, Kerr J. A framework for using GPS data in physical activity and sedentary behavior studies. Exerc Sport Sci Rev 2015; 43:48-56.
Folkman S, Lazarus R. Stress, Appraisal, and Coping. New York: Springer Publishing Company, 1984.
Reece R, Bornioli A, Bray I, et al. Exposure to Green and Historic Urban Environments and Mental Well-Being: Results from EEG and Psychometric Outcome Measures. Int J Environ Res Public Health 2022; 19:13052.
Baumann O, Brooks-Cederqvist B. Multimodal assessment of effects of urban environments on psychological wellbeing. Heliyon 2023; 9:e16433.
Beil K, Hanes D. The influence of urban natural and built environments on physiological and psychological measures of stress-a pilot study. Int J Environ Res Public Health 2013; 10:1250-67.
Kestens Y, Chaix B, Gerber P, et al. Understanding the role of contrasting urban contexts in healthy aging: an international cohort study using wearable sensor devices (the CURHA study protocol). BMC Geriatr 2016; 16:96.
Kestens Y, Thierry B, Shareck M, et al. Integrating activity spaces in health research: Comparing the VERITAS activity space questionnaire with 7-day GPS tracking and prompted recall. Spat Spatiotemporal Epidemiol 2018; 25:1-9.
Giddens CL, Barron KW, Byrd-Craven J, et al. Vocal indices of stress: a review. J Voice 2013; 27:390.
Fagherazzi G, Fischer A, Ismael M, et al. Voice For Health: The Use Of Vocal Biomarkers From Research To Clinical Practice. Digital Biomarkers, 2021.
Pisanski K, Kobylarek A, Jakubowska L, et al. Multimodal stress detection: Testing for covariation in vocal, hormonal and physiological responses to Trier Social Stress Test. Horm Behav 2018; 106:52-61.
Holmqvist-Jämsén S, Johansson A, Santtila P, et al. Investigating the Role of Salivary Cortisol on Vocal Symptoms. J Speech Lang Hear Res 2017; 60:2781-91.
Kappen M, van der Donckt J, Vanhollebeke G, et al. Acoustic speech features in social comparison: how stress impacts the way you sound. Sci Rep 2022; 12:22022.
Kappen M, Vanhollebeke G, Van Der Donckt J, et al. Acoustic and prosodic speech features reflect physiological stress but not isolated negative affect: a multi-paradigm study on psychosocial stressors. Sci Rep 2024; 14:5515.
Kumar A, Shaun MA, Chaurasia BK. Identification of psychological stress from speech signal using deep learning algorithm. e-Prime-Advances in Electrical Engineering. Electronics and Energy 2024; 9:100707.
Adams P, Rabbi M, Rahman T, et al. Towards personal stress informatics: comparing minimally invasive techniques for measuring daily stress in the wild. 8th International Conference on Pervasive Computing Technologies for Healthcare; Oldenburg, Germany, 2014 10.4108/icst.pervasivehealth.2014.254959 Available: http://eudl.eu/proceedings/PervasiveHealth/2014
Langer M, König CJ, Siegel R, et al. Vocal-Stress Diary: A Longitudinal Investigation of the Association of Everyday Work Stressors and Human Voice Features. Psychol Sci 2022; 33:1027-39.
Whitaker C, Stevelink S, Fear N. The Use of Facebook in Recruiting Participants for Health Research Purposes: A Systematic Review. J Med Internet Res 2017; 19:e290.
Naud A, Sueur C, Chaix B, et al. Combining social network and activity space data for health research: tools and methods. Health Place 2020; 66:102454.
Röcke C, Luo M, Bereuter P, et al. Charting everyday activities in later life: Study protocol of the mobility, activity, and social interactions study (MOASIS). Front Psychol 2022; 13:1011177.
de Vries LP, Baselmans BML, Bartels M. Smartphone-Based Ecological Momentary Assessment of Well-Being: A Systematic Review and Recommendations for Future Studies. J Happiness Stud 2021; 22:2361-408.
Zhang X, Zhou S, Kwan M-P, et al. Geographic Ecological Momentary Assessment (GEMA) of environmental noise annoyance: the influence of activity context and the daily acoustic environment. Int J Health Geogr 2020; 19:50.
Kingsbury C, Buzzi M, Chaix B, et al. STROBE-GEMA: a STROBE extension for reporting of geographically explicit ecological momentary assessment studies. Arch Public Health 2024; 82:84.
Snyder E, Cai B, DeMuro C, et al. A New Single-Item Sleep Quality Scale: Results of Psychometric Evaluation in Patients With Chronic Primary Insomnia and Depression. J Clin Sleep Med 2018; 14:1849-57.
Jahnel T, Ferguson SG, Shiffman S, et al. Daily stress as link between disadvantage and smoking: an ecological momentary assessment study. BMC Public Health 2019; 19:1284.
Almeida DM, Wethington E, Kessler RC. The daily inventory of stressful events: an interview-based approach for measuring daily stressors. Assessment 2002; 9:41-55.
Baird A, Triantafyllopoulos A, Zänkert S, et al. An Evaluation of Speech-Based Recognition of Emotional and Physiological Markers of Stress. Front Comput Sci 2021; 3.
Kappen M, Hoorelbeke K, Madhu N, et al. Speech as an indicator for psychosocial stress: A network analytic approach. Behav Res Methods 2022; 54:910-21.
Rothkrantz LJM, Wiggers P, Wees J-W, et al. Voice stress analysis. Text, Speech and Dialogue: 7th International Conference, TSD 2004, Brno, Czech Republic, September 8-11, 2004; 2004:449-56.
Tao F, Daudet L, Poellabauer C, et al. A portable automatic pa-ta-ka syllable detection system to derive biomarkers for neurological disorders. Interspeech 2016; 2016 10.21437/Interspeech.2016-789 Available: https://www.isca-archive.org/interspeech_2016/
Rubaiat R, Templeton JM, Schneider SL, et al. Exploring Speech Biosignatures for Traumatic Brain Injury and Neurodegeneration: Pilot Machine Learning Study. JMIR Neurotech 2025; 4:e64624.
Ayadi H, Elbéji A, Despotovic V, et al. Digital Vocal Biomarker of Smoking Status Using Ecological Audio Recordings: Results from the Colive Voice Study. Digit Biomark 2024; 8:159-70.
Elbéji A, Pizzimenti M, Aguayo G, et al. A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. PLOS Digit Health 2024; 3:e0000679.
Namkung J, Kim SM, Cho WI, et al. Novel Deep Learning-Based Vocal Biomarkers for Stress Detection in Koreans. Psychiatry Investig 2024; 21:1228-37.
Kim T, Choi J-Y, Ko MJ, et al. Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study. JMIR Med Inform 2025; 13:e57298.
Patel R, Connaghan K. Park Play: a picture description task for assessing childhood motor speech disorders. Int J Speech Lang Pathol 2014; 16:337-43.
Helbich M. Dy n Amic Urban e Nvironmental e Xposures on D Epression and S Uicide (NEEDS) in the Netherlands: A Protocol for a Cross-Sectional Smartphone Tracking Study and a Longitudinal Population Register Study. BMJ Open 2019; 9:e030075.
Jankowska MM, Yang JA, Luo N, et al. Accounting for Space, Time, and Behavior Using GPS Derived Dynamic Measures of Environmental Exposure. Health and Place, 2021.
Despotovic V, Elbéji A, Fünfgeld K, et al. Digital voice-based biomarker for monitoring respiratory quality of life: findings from the colive voice study. Biomed Signal Process Control 2024; 96:106555.
Boone-Heinonen J, Gordon-Larsen P, Guilkey DK, et al. Environment and Physical Activity Dynamics: The Role of Residential Self-selection. Psychol Sport Exerc 2011; 12:54-60.
Klein S, Brondeel R, Chaix B, et al. What triggers selective daily mobility among older adults?. A study comparing trip and environmental characteristics between observed path and shortest path. Health Place 2023; 79:102730.
Perchoux C, Chaix B, Brondeel R, et al. Residential buffer, perceived neighborhood, and individual activity space: New refinements in the definition of exposure areas - The RECORD Cohort Study. Health Place 2016; 40:116-22.
Green P, MacLeod CJ. SIMR: an R package for power analysis of generalized linear mixed models by simulation. Methods Ecol Evol 2016; 7:493-8.
Yu C, Kwan MP, Liu Y. Assessing momentary stress responses to dynamic real-time greenspace exposure: Unveiling algorithmic uncertainty and the temporality of exposure context. Soc Sci Med 2024; 363:117411.
Eisele G, Vachon H, Lafit G, et al. The Effects of Sampling Frequency and Questionnaire Length on Perceived Burden, Compliance, and Careless Responding in Experience Sampling Data in a Student Population. Assessment 2022; 29:136-51.
Zenk SN, Matthews SA, Kraft AN, et al. How many days of global positioning system (GPS) monitoring do you need to measure activity space environments in health research?. Health Place 2018; 51:52-60.
Teresi JA, Yu X, Stewart AL, et al. Guidelines for Designing and Evaluating Feasibility Pilot Studies. Med Care 2022; 60:95-103.
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983; 24:24:385-96:.
Gilbert S, Kevin Kelloway E. Using single items to measure job stressors. Int J Workplace Health Manag 2014; 7:186-99.
Castetbon K, Méjean C, Deschamps V, et al. Dietary behaviour and nutritional status in underprivileged people using food aid (ABENA study, 2004-2005). J Hum Nutr Diet 2011; 24:560-71.
Topp CW, Østergaard SD, Søndergaard S, et al. The WHO-5 Well-Being Index: a systematic review of the literature. Psychother Psychosom 2015; 84:167-76.
The World Health Organization quality of life assessment (WHOQOL): Position paper from the World Health Organization. Soc Sci Med 1995; 41:1403-9.
Wang C, Colley RC, Roberts KC, et al. Sleep behaviours among Canadian adults: Findings from the 2020 Canadian Community Health Survey healthy living rapid response module. Health Rep 2022; 33:3-14.
Hagströmer M, Oja P, Sjöström M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr 2006; 9:755-62.
Rammstedt B, John OP. Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. J Res Pers 2007; 41:203-12.
Chaix B, Kestens Y, Bean K, et al. Cohort profile: residential and non-residential environments, individual activity spaces and cardiovascular risk factors and diseases-the RECORD Cohort Study. Int J Epidemiol 2012; 41:1283-92.
de Vries S, van Dillen SME, Groenewegen PP, et al. Streetscape greenery and health: stress, social cohesion and physical activity as mediators. Soc Sci Med 2013; 94:26-33.
Kestens Y, Winters M, Fuller D, et al. INTERACT: A comprehensive approach to assess urban form interventions through natural experiments. BMC Public Health 2019; 19:51.
Roe J, McCay L. Restorative Cities. 2021.
Gray J, Kim J, Ciesla JR, et al. Rasch Analysis of the Lubben Social Network Scale-6 (LSNS-6). J Appl Gerontol 2016; 35:508-28.
