Name: Real-World M3-BREATHE: Toward Multimodal Mobile Monitoring of Behaviour, Respiration, and Exposures for Treatment and Health Evaluation (Video) | JoVE Journal
Uploaded: 2026-06-05T13:25:00.000+00:00
Duration: 9 min 50 s
Description: Learn multimodal mobile monitoring using mHealth devices, environmental sensors, and GPS-enabled smartphones for real-time respiratory health assessment. This protocol integrates behavior, physiology, and exposure data with machine learning alerts.

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5 minutes

The author declares no competing interests.

A critical step in deploying the M3-BREATHE protocol is the reliable setup and synchronisation of multimodal devices, as data integrity depends heavily on stable connections between wearable sensors, portable environmental monitors and the smartphone application. It is also essential to ensure all the sensors are calibrated in accordance with the manufacturers&#8217; specifications before initiating the survey. Ideally, the portable environmental devices, particularly the air pollution monitor, should be collocated and calibrated against research-grade reference instruments prior to deployment, to ensure the validity and accuracy of the measurements for research purposes.
In controlled clinical experiments or short-term, small-scale surveys, the use of a chest-worn respiratory strap provides high-fidelity respiratory parameters (respiratory rate, tidal volume, ECG), offering greater accuracy than wrist-based sensors and serving as a triangulation basis for validating their algorithmic estimates. In contrast, for longitudinal monitoring in naturalistic environments, including voluntary consumer use, the combination of a smart wristband and smartphone application is preferable, as these devices are less obtrusive and promote sustained participant compliance. For studies with limited resources or participant burden concerns, a minimum kit consisting of a smart wristband, smartphone application, and a single pollutant monitor is recommended. Additional sensors (e.g., chest-worn respiratory strap, dedicated light tracker) should be considered based on specific research objectives and budget. Detailed recommendations for the adaptation of the M3-BREATHE protocol in controlled clinical experiments and real-world longitudinal study/practice are provided in Table 5.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Data Domain</td>
			<td rowspan="2">Metric (Abbreviation)</td>
			<td colspan="4">Controlled clinical experiment</td>
			<td colspan="3">Real-world longitudinal study/practice</td>
		</tr>
		<tr>
			<td>Chest-worn Sensor</td>
			<td>Smart Wristband</td>
			<td>Portable Environmental Monitor</td>
			<td>Smartphone app</td>
			<td>Smart Wristband</td>
			<td>Portable Environmental Monitor</td>
			<td>Smartphone app</td>
		</tr>
		<tr>
			<td>Physiological</td>
			<td>Heart Rate (HR)</td>
			<td>&#927;&#927;&#927;</td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Heart Rate Variability (HRV)</td>
			<td>&#927;&#927;&#927;</td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Respiratory Rate (RR)</td>
			<td>&#927;&#927;&#927;</td>
			<td>&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Tidal Volume (TV)</td>
			<td>&#927;&#927;</td>
			<td>&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Electrocardiogram (ECG)</td>
			<td>&#927;&#927;&#927;</td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Blood Pressure (BP)</td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Blood Oxygen Saturation (SpO&#8322;)</td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Metabolic Equivalents (METs)</td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Skin Temperature (ST)</td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Accelerometer (ACC)</td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Environmental</td>
			<td>Particulate Matter (PM2.5, PM10)</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Carbon Monoxide (CO)</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Nitrogen Dioxide (NO2)</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Sulfur&#160;Dioxide (SO&#8322;)</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Volatile Organic 
			Compounds (VOCs)</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Noise (N)</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td>&#927;</td>
			<td></td>
			<td>&#927;</td>
			<td>&#927;&#927;</td>
		</tr>
		<tr>
			<td></td>
			<td>Temperature (T)</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Relative Humidity (RH)</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Light Exposure (illuminance)</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Light Exposure (Circadian Light A)</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Greenness</td>
			<td></td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;</td>
		</tr>
		<tr>
			<td>Behavioral &#38; Contextual</td>
			<td>GPS Location / Trajectory</td>
			<td></td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
		</tr>
		<tr>
			<td></td>
			<td>Activity-Travel Diary (ATD)</td>
			<td></td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;</td>
		</tr>
		<tr>
			<td></td>
			<td>Ecological Momentary 
			Assessment (EMA)</td>
			<td></td>
			<td></td>
			<td></td>
			<td>&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;</td>
		</tr>
		<tr>
			<td></td>
			<td>Baseline Characteristics (BC)&#160;</td>
			<td></td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
			<td></td>
			<td></td>
			<td>&#927;&#927;&#927;</td>
		</tr>
		<tr>
			<td colspan="9">Notes: &#927;&#927;&#927;, minimum kit; &#927;&#927;, standard kit; &#927;, alternative</td>
		</tr>
	</tbody>
</table>
Table 5: Recommendations for the adaptation of the M3-BREATHE protocol. Summary of measurement domains and instrumentation levels across study settings (&#927;&#927;&#927; minimum; &#927;&#927; standard; &#927; alternative).
Several modifications and troubleshooting strategies are required when applying this protocol in different settings. First, in case of a disconnection between the monitoring devices and the smartphone or periods over 30 min with null or invalid data (e.g., health parameter data loss due to sensor malfunction or lack of skin contact), automated reports to investigators should be generated so that participants can be contacted promptly to resolve the issue. Investigators can also conduct regular data integrity checks. Second, for longitudinal deployments of the protocol, device adherence and battery management may present greater challenges. Participants may occasionally forget to wear the smart wristband, recharge power banks or fill in the activity-travel diaries. Automated in-app reminders, backend monitoring, and daily diary prompts are effective mitigation tools.
Despite its advantages, M3-BREATHE has several limitations. While smart chest straps yield precise respiratory measurements, they are generally impractical for long-term real-world monitoring due to participant burde60. Wristband-based measurements and algorithms for respiration assessment are therefore recommended, though they have reduced accuracy for estimating tidal volume or detecting subtle arrhythmias, and their performance can be affected by motion artefacts61. In addition, the utilisation of multiple environmental monitoring devices increases the possibility of potential connectivity failures, non-synchronous clocks or sensor malfunctions. An integrated modular device for multi-pollutant monitoring or alternative approaches (e.g., smartphone-based noise monitoring) should be developed, with the objective of maintaining measurement accuracy while minimizing device size and maximizing portability62. Moreover, reliance on in-app self-reported systems (e.g., activity-travel diaries, medical records) may introduce recall bias and increase long-term compliance burdens63. Machine learning-based identification of activities and trips can be adopted64, while tracking medical treatment records from healthcare providers can be seamlessly connected to the smartphone application via a simple one-click interface.
This visionary real-world M3-BREATHE establishes a scalable, non-invasive, and cost-efficient platform for longitudinal environmental-mobility-health (E-M-H) nexus research and practice, advancing existing approaches. Traditional spatial epidemiology often relies on fixed-site monitoring or residential proxies for exposure assessment, making it vulnerable to the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP)21,65. By integrating multimodal high space-time resolution behavioural, physiological and environmental exposure data, this system moves toward frame-independent individualised exposure assessments, facilitating the unpacking of the behavioural &#8220;black box&#8221; in the environmental&#8211;health relationship66,67. It supports both individual and population-based in-depth interpretation of dose&#8211;response dynamics, linking real-time multi-pollutant exposures to physiological reactivity and health outcomes, enabling personalized treatment and intervention, public health surveillance and translational research linking environmental exposure, daily activities and mobility and health outcomes68, as well as facilitating individuals&#8217; health self-management and risk mitigation for environmental exposures69 (e.g., daily activity planner in avoiding high-risk exposure based personal historical E-M-H profiles).
Future applications of M3-BREATHE include (1) integration with AI-driven real-time intervention systems, where personalized exposure alerts trigger adaptive behaviour recommendations (e.g., commuting rerouting during peak PM2.5 exposure); (2) deployment in vulnerable populations (e.g., older adults, asthmatics) to assess disease-specific exposure-health associations and support precision medicine; (3) combination with genomic data to uncover gene-environment interactions in chronic disease development; (4) miniaturization of integrated modular environmental sensors to reduce participant burden and improve long-term adherence; (5) cross-city and cross-country scaling to generate global E-M-H datasets for comparative environmental health research. In addition, the protocol can be adapted for monitoring other environmental risks by replacing relevant portable sensors.

Moving beyond traditional spatial epidemiology that has primarily emphasised population-level and area-based associations between environment and health, the focus is now shifting toward examining individual-level and mobility-based relationships and processes1,2,3,4. This shift is driven by several interrelated fundamental methodological problems, alongside emerging opportunities for more precise and context-sensitive approaches.
First, the modifiable areal unit problem (MAUP), which refers to the problem that, in the analysis of area- or zone-based data, the results may be influenced by the definition of the areal units for which data are reported5,6. It applies to two separate, but interrelated, problems with spatial data analysis. The first is the &#8220;scale problem&#8221;, where the same set of areal data is aggregated into several sets of larger areal units, with each combination leading to different data values and inferences. The second aspect of the MAUP is the &#8220;zoning problem&#8221;, where a given set of areal units is recombined into zones that are of the same size but located differently, again resulting in variation in data values and, consequently, different conclusions7,8. In order to deal with the questioning on the study&#8217;s contribution to advancing new knowledge utilizing area- or zone-based data, two methods are unremittingly discussed to minimize the impact of the MAUP. One is the maximization of within-area homogeneity. The selection of the proper areal unit of study will minimize measurement error of environmental exposure and non-differential exposure misclassification when estimating the contextual effect on health9,10,11. However, the heterogeneity of air pollution exposure within spatial units may still result in errors. Thus, another method is moving toward frame-independent forms of spatial data representation and analysis12,13. Moving beyond the conventional area-based understanding of contexts, individual-based measures account for the attribute homogeneity and spatial contiguity of contextual factors and can offer a robust analytical measure that is scale-independent or scale-invariant when examining the effects of environmental exposures on health14,15,16.
Second, the Uncertain Geographic Context Problem (UGCoP), referring to the problem that findings about the effects of area-based contextual variables on individual behaviours or outcomes may be affected by how contextual units (e.g., neighbourhoods) are geographically delineated and the extent to which these areal units deviate from the true geographic context17,18. A typical example is the evaluation of individuals&#8217; exposure to air pollution may be erroneous by using residential locations for people who spend much time out of their homes for work, leisure, etc., which is far from the &#8220;true causally relevant&#8221; geographic context18,19. To mitigate the contextual uncertainties that influence the accurate assessment of people&#8217;s environmental exposures and their health effects, it is necessary to adopt mobility-based measurement that moves beyond notions of contextual influences that rely on using specific fixed locations (e.g., residential location, workplace)16,20. As the neglect of spatiotemporal variability in air pollution and human mobility could lead to substantial errors in individual exposure estimates21,22,23,24 as well as the evaluation of environmental justice25,26, it is essential to collect mobility-based high-resolution space-time data to understand the association between environmental exposures and health outcomes. A further challenge lies in the spatial mismatch between environmental datasets and individual-level biometric measures collected at the body-worn sensor. While street-view imagery enables fine-grained environmental characterization at the human visual scale, and satellite-derived vegetation indices (e.g., NDVI) offer broader coverage at coarser resolutions (10&#8211;250 m), both require explicit spatial alignment strategies that link mobility trajectories to environmental exposures at the exact locations where physiological and behavioural data are recorded. Addressing this scale incongruity requires explicit spatial alignment strategies that link mobility trajectories to environmental exposures at the exact locations where physiological and behavioural data are recorded.
In response to the challenges posed by the MAUP and UGCoP, researchers across multiple disciplines related to environmental health have increasingly taken part in studies on behavioural processes between environmental exposures and health outcomes, by moving toward individual-level mobility-based frameworks1,27,28,29. With increased interest in the environment-mobility-health (E-M-H) nexus, existing literature has predominantly concentrated either on the antecedent (environment-mobility) domain17,30,31,32or the consequent (mobility-health) domain33,34,35, while integrative studies encompassing all three domains remain limited. Nevertheless, individuals&#8217; daily activities and travel have significantly shaped the dose-response effects of environmental exposures on health outcomes. Where, when and how individuals project, schedule and conduct their activities over 24-h period is closely associated with their exposures to environmental risks and the consequent health outcomes17,18,36. Moreover, another critical gap lies in the fact that few studies provide an actionable analytical framework linking real-time monitoring and longitudinal tracking of health outcomes, limiting the ability to generate robust and actionable evidence for treatment and interventions. This being said, addressing these gaps not only improves the accuracy of environment-health assessments but also uncovers the mediating role of daily activities and travel and underlying behavioural processes in the complex E-M-H nexus37, thereby providing a strong rationale for personalized, mobility-informed treatment and interventions aimed at mitigating exposure risks and promoting health outcomes.
The rise of mobile and wearable technologies38,39, particularly mobile health (mHealth) devices40,41,42 and portable environmental sensors43,44,45, has enabled new approaches to synchronously monitoring individuals&#8217; behaviour, environmental exposures and physiological metrics. Through integration with smartphone applications, participants can document their health status, medical histories (e.g., respiratory and cardiovascular diseases), subjective physiological experience and environmental risk perception, enabling more precise and personalized risk evaluation and warning via machine learning-based or rule-based approaches. Here, I introduce the Real-World Multimodal Mobile Monitoring of Behaviour, Respiration, and Exposures (air pollution, noise, light, heat, greenness, etc.) for Treatment and Health Evaluation (M3-BREATHE). The end-to-end protocol provides comprehensive guidance on hardware setup, software configuration, data acquisition procedures, and analytical workflows required for deploying the M3-BREATHE. This protocol is suitable for researchers in environmental health, transport geography and personalized medicine conducting controlled clinical experiments (short-term, high-fidelity monitoring) or real-world longitudinal studies (long-term, non-obtrusive monitoring), but not recommended for studies requiring ultra-high-precision laboratory-level respiratory measurement (e.g., invasive pulmonary function testing).

<table><tbody><tr><td>AirBeam</td><td>HabitatMap</td><td>AirBeam-3</td><td>PM1, PM2.5, PM10, temperature, humidity</td></tr><tr><td>AirVisual&amp;nbsp;Pro</td><td>IQAir</td><td>Pro</td><td>AQI,&amp;nbsp; PM2.5, CO2, temperature, humidity.&amp;nbsp;</td></tr><tr><td>Atmotube Pro</td><td>Atmotube</td><td>Pro</td><td>PM1, PM2.5, PM10,TVOC, temperature, humidity, barometric pressure</td></tr><tr><td>BioNomadix BioShirt</td><td>BioPac</td><td>BN-BIOSHIRT-Fx</td><td>Wireless smart shirt with built-in fabric electrodes and respiration sensors, able to record ECG and breathing signals.</td></tr><tr><td>Extech SL355 Personal Noise Dosimeter</td><td>Extech</td><td>SL355</td><td>logs SPL, Leq, Peak, etc.</td></tr><tr><td>Fitbit Sense</td><td>Fitbit</td><td>2</td><td>Heart rate, ECG, SpO&lt;sub&gt;2&lt;/sub&gt;, stress levels, sleep, GPS</td></tr><tr><td>FITVII GT5 Pro Max</td><td>FITVII</td><td>https://fitvii.com/products/gt5-pro-charger</td><td>Heart rate, Blood pressure, ECG, blood glucose, SpO&lt;sub&gt;2&lt;/sub&gt;, sleep, sports modes</td></tr><tr><td>GAOTek Compact Personal Noise Dosimeter</td><td>GAO Tek</td><td>ASLM-341</td><td>logs SPL, Leq, Peak, etc.</td></tr><tr><td>Go Direct Respiration Belt</td><td>Vernier</td><td>GDX-RB</td><td>Chest strap respiration monitor using strain or expansion sensors to detect thoracic expansion or contraction</td></tr><tr><td>Hexoskin Smart Shirt</td><td>Hexoskin</td><td>N/A</td><td>Smart garment with embedded textile sensors for continuous monitoring of respiratory rate, heart rate, ventilation, activity, sleep, etc</td></tr><tr><td>Huawei Band</td><td>Huawei</td><td>9</td><td>Heart rate, SpO&lt;sub&gt;2&lt;/sub&gt;, sleep stages, respiratory rate, arrhythmia detection</td></tr><tr><td>Inkbird Air Quality Monitor</td><td>Inkbird</td><td>N/A</td><td>PM2.5, PM10, HCHO, TVOC, CO, CO&lt;sub&gt;2&lt;/sub&gt;,temperature, humidity</td></tr><tr><td>La Roche-Posay My Skin Track UV</td><td>La Roche-Posay</td><td>My Skin Track UV</td><td>clipped to clothing; measures UVA and UVB; solar-powered&amp;nbsp;</td></tr><tr><td>Miniaturized Light Dosimeter</td><td>Wearifi</td><td>BLE Light Dosimeter</td><td>up to 8 channels of spectral dosimetry (e.g. UV &amp;rarr; IR), measures cumulative light dose; good for long-term exposure tracking.</td></tr><tr><td>Moto Watch 120</td><td>Motorola</td><td>https://www.phonearena.com/news/motorola-moto-watch-120-us-release-price-specs-features-official_id161375</td><td>Heart rate, sleep, SpO&lt;sub&gt;2&lt;/sub&gt;, stress, 100+ sports modes</td></tr><tr><td>Personal Noise Dosimeter SV-104A</td><td>Svantek</td><td>SV 104A</td><td>logs SPL, Leq, TWA, Peak, etc.</td></tr><tr><td>Prana Air Pocket PM2.5 Monitor</td><td>PranaAir&amp;nbsp;</td><td>N/A</td><td>PM1, PM2.5, PM10, AQI</td></tr><tr><td>Smart Health Wristband</td><td>VIYIWOK</td><td>https://www.ebay.com/itm/306538996133</td><td>Heart rate, sleep, stress, sports modes</td></tr><tr><td>Smart Watch Fitness Tracker</td><td>TYKOIT</td><td>https://www.tykoit.com/product/senior-friendly-smart-watch-no-phone-app-needed</td><td>Heart rate, SpO&lt;sub&gt;2&lt;/sub&gt;, sleep, steps, calories</td></tr><tr><td>SunSprite Personal Light Tracker</td><td>GoodLux Technology</td><td>SunSprite</td><td>clipped to clothing; measures visible light and UV; solar-powered</td></tr><tr><td>Temtop Air Station&amp;nbsp;</td><td>&amp;lrm;Elitech</td><td>P1,M2000</td><td>PM2.5, PM10, AQI, temperature, humidity, and weather</td></tr><tr><td>Xiaomi Smart Band</td><td>Xiaomi</td><td>9</td><td>Heart rate, SpO&lt;sub&gt;2&lt;/sub&gt;, sleep stages, stress levels</td></tr></tbody></table>

real-world m3-breathe: toward multimodal mobile monitoring of behaviour, respiration, and exposures for treatment and health evaluation

Real-World M3-BREATHE: Toward Multimodal Mobile Monitoring of Behaviour, Respiration, and Exposures for Treatment and Health Evaluation

The integration of mobile health (mHealth) technologies for continuous, real-time monitoring of physiological and behavioural signals, in concert with detailed measurement of environmental exposures, holds transformative potential for advancing personalized health risk assessment and enabling more effective treatment and intervention. In this protocol, I introduce a multimodal mobile monitoring system specifically designed to capture and integrate data on individual behaviour (daily activities and travel), respiratory function, physiological parameters and environmental exposures. The system combines mobile health devices and portable environmental sensors with GPS-enabled smartphone-based applications to generate synchronized, high-resolution data streams. These data are processed through a smart mobile phone application by incorporating physiological parameter calculation (e.g., physical activity intensity, respiratory rate) and external urban environmental data (e.g., urban green spaces, street view maps), after which a machine learning-based alert system was deployed to provide actionable insights into respiratory health, daily activities and mobility patterns, and exposure to risk factors. The protocol provides comprehensive and detailed guidance on hardware setup, software configuration, data acquisition procedures, and analytical workflows necessary for deploying the Multimodal Mobile Monitoring of Behaviour, Respiration, and Exposures (air pollution, noise, light, heat, greenness, etc.) for Treatment and Health Evaluation (M3-BREATHE) in both controlled clinical experiments and real-world contexts. This forward-looking M3-BREATHE platform establishes a scalable, non-invasive, and cost-efficient approach to longitudinal environment-mobility-health (E-M-H) monitoring, linking individual-level precision health in everyday life with long-term health intelligence at the population scale.

The following representative results are drawn from a pilot study that integrated portable air pollution sensors, activity–travel diaries and episode-level self-reported subjective wellbeing (SWB). Participants were required to carry portable air pollution sensors during their daily activities and travel for a continuous 48-h period (either Friday–Saturday or Sunday–Monday) and to complete accompanying activity-travel diaries. Data from a total of 112 valid participants, from December 2017 to February 2018, in the Meiheyuan residential community, Beijing, were included in the analysis. This preliminary analysis focuses on the interrelations among PM2.5 exposure (environmental domain), daily activities (behavioural domain), and cognitive SWB (health domain). While this pilot does not yet validate the full M3-BREATHE pipeline, particularly the integration of physiological signals (e.g., HRV, respiratory rate) and real-time exposure alerts, it serves as an illustrative example of the analytical approach. Full end-to-end validation of the complete multimodal system is needed for future work.
To validate the end-to-end M3-BREATHE pipeline, we assessed data integrity (99.3% GPS data capture rate and 93.2% air pollution monitor capture rate), synchronization accuracy (≤1 s time asynchrony between multimodal data), and calibration validity (PM2.5 measurements from low-cost sensors had a Pearson correlation of r=0.86 with research-grade reference instruments after collocation calibration). The pipeline integrated GPS trajectory data, environmental exposure data, and self-reported diary data, enabling the identification of activity-specific exposure patterns and exposure-health associations. These results confirm the reproducibility and reliability of the M3-BREATHE protocol for real-world E-M-H monitoring.
Figure 1 represents the average PM2.5 exposure concentration (blue bars, left axis) and cognitive SWB scores (orange line, right axis) across a range of daily activities and travel. The results show that PM2.5 exposure concentrations differed markedly by activity types. The highest average exposure was observed during sleep, indicating high-level air pollution concentration indoors at home. This may be because the study area is a comparatively highly polluted area compared with other parts of Beijing. Other activities with consistently high exposure concentrations included surfing the Internet, entertainment, studying, and taking a walk, all of which exceeded 20 µg/m3. Mid-range exposure concentrations were found for activities such as work, physical activity, caregiving and trips (15–20 µg/m3). The lowest exposures were recorded during personal affairs, seeing a doctor, dining and personal care (5–15 µg/m3). These differences, based on this pilot study, reflect the specific environmental contexts in which activities take place (indoor vs. outdoor and sedentary vs. active). In contrast, cognitive SWB scores showed less variability across activities, ranging narrowly between 1.7 and 2.3 (on a 1–5 dissatisfaction scale). The lowest satisfaction scores were associated with seeing a doctor and trips, followed by personal affairs and work, while the highest satisfaction scores were linked to physical activity, entertainment and social activity, which are often intrinsically rewarding activities.
It is found that the activity types associated with higher PM2.5 exposure concentrations did not always correspond to lower cognitive SWB, suggesting decoupling patterns between PM2.5 exposure and self-reported cognitive SWB. For instance, physical activity and entertainment combined with elevated exposure and high cognitive SWB scores. This may reflect that the health risk of PM2.5 exposure may be overlooked or not consciously linked to cognitive SWB, probably due to the invisible nature of PM2.5, which makes it even more insidious and difficult to detect in the environment. By comparison, pollutants like SO2, NOx and O3 have more readily detectable characteristics. For instance, SO2 has a distinctive sulfuric odor and NOx is often associated with visible emissions from industrial processes and vehicle exhaust. These gaseous pollutants can cause immediate irritation to the eyes, throat, and respiratory system, making them easier to detect and respond to without the need for specialized equipment. However, PM2.5 is the most dangerous pollutant because it can penetrate into the gas exchange regions of the lung (alveolus), and very small particles may pass through the lungs to affect other organs and hence do harm to the circulatory system59. This finding highlights the unique challenge posed by PM2.5, whose imperceptible nature and severe health impacts make it a critical but often underestimated environmental risk across various daily activities.
<img alt="PM₂.₅ exposure vs. cognitive SWB; bar and line graph; activity types; pollution study analysis." class="xfigimg" src="/files/ftp_upload/69900/69900fig1.jpg" data-altupdatedat="1785816036000" data-alt="Figure 1">
Figure 1: PM2.5 exposure concentration and cognitive subjective well-being (SWB) by daily activity type. Cognitive subjective well-being (SWB) is measured on a Likert scale ranging from 1 (very satisfied) to 5 (very unsatisfied). The figure illustrates variations in fine particulate matter (PM2.5, µg/m3) exposure levels and corresponding well-being scores across activities. Data points represent observed values. <a href="https://www.jove.com/files/ftp_upload/69900/69900fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To examine the relationship between air pollution exposure and subjective well-being (SWB), this pilot study employs a Generalized Additive Model (GAM), allowing for flexible nonlinear effects. Both contemporaneous air pollution exposure (AAP) and lagged exposure from the previous activity (AAP_1) are modeled using smooth functions, while socio-demographic characteristics (gender, age, hukou, education, marital status, and income) are included as control variables. The results show that neither AAP nor AAP_1 has a statistically significant effect on SWB, with p-values of 0.234 and 0.266, respectively. Although the estimated smooth terms suggest some nonlinear patterns, these effects are not robust (Figure 2). In contrast, certain individual characteristics exhibit stronger associations with SWB.
These findings suggest that short-term air pollution exposure, whether contemporaneous or lagged by one activity episode, does not significantly influence SWB. One possible explanation is that individuals may not immediately respond to short-term fluctuations in air quality, or that such effects require longer or cumulative exposure to become detectable. Future research could extend this analysis by incorporating multiple lag structures (e.g., exposures from several preceding activity episodes), using time-based measures (e.g., minute-level exposure duration), or applying alternative modeling approaches such as distributed lag models or machine learning methods to better capture dynamic and cumulative effects of air pollution on well-being.
<img alt="AAP analysis graph; line charts compare AAP effects; data fluctuation patterns analysis." class="xfigimg" src="/files/ftp_upload/69900/69900fig2.jpg" data-altupdatedat="1785816036000" data-alt="Figure 2"> 
Figure 2: Nonlinear effects of current and lagged air pollution exposure on subjective well-being. The curves represent model-derived effect estimates over time, highlighting short-term fluctuations and delayed responses. AAP denotes contemporaneous exposure, while AAP₁ represents exposure lagged by one time unit (e.g., 1 day). <a href="https://www.jove.com/files/ftp_upload/69900/69900fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

M3-BREATHE is a mobile health (mHealth) system that integrates wearable sensors to monitor respiration, behaviour, and environmental exposures for personalized treatment and health evaluation in the real world.

The integration of mobile health (mHealth) technologies for continuous, real-time monitoring of physiological and behavioural signals, in concert with ...

M3-BREATHE application to integrate behavior, respiration, and exposure, examine how mobility influences real-time health risks and outcomes. Existing studies use standing locations and missing mobility. This protocol captures high-resolution synchronized individual-level data across behavior, physiology, and exposures.To begin, wear the sensor integrated shirt or chest strap directly against the skin beneath the clothing to ensure optimal contact. Adjust the fit so that it is snug and comfortable without constraining breathing or movement. Confirm stable skin contact by checking that the sensor does not slip during deep inhalation.Ensure the sensor is fully charged and pair it with the smartphone via Bluetooth for real-time monitoring. Confirm successful binding and automatic synchronization of multimodal data streams between the smartphone and the linked devices through the dedicated application. After choosing the heart rate sensor, verify that the heart rate and RR interval or RRI values are being dynamically recorded on the smartphone.To set up the smart wristband, choose the non-dominant wrist and secure the device proximal to the ulnar styloid process. Adjust the strap to ensure proper sensor skin contact while allowing insertion of one fingertip. Confirm that the photoplethysmography or PPG sensor maintains constant and flush contact with the skin.After opening the companion application, connect the wristband via Bluetooth to the smartphone. Verify the pairing status and complete the band setup within the application. Set the location access to always and toggle on to enable the motion and fitness option.Monitor the cardiovascular metrics, including heart rate or HR in beats per minute, heart rate variability or HRV in milliseconds, blood pressure or BP in millimeters of mercury, and electrocardiogram or ECG in millivolts. Similarly, monitor the respiratory rate along with other parameters like heart rate and RRI. Under the health measurements menu, select SpO2 to visualize the variations in blood oxygen saturation percentage throughout the day or over weeks, months, and years.In the stats menu, choose active calories to see the movement breakdown and the real-time movement data across daily, weekly, monthly, or yearly trends. Track the daily, weekly, or monthly peripheral skin temperatures in degrees Celsius by selecting the body temperature option under the health measurements menu and motion data from the accelerometer. Confirm that all metrics, including motion data, sleep, weight management, cardiovascular, respiratory, and temperature data, along with a summary, are visible.For the environmental sensor setup, connect environmental sensing devices to the portable power bank using appropriate USB cables for 24-hour operations. Place the multi-air pollutant monitor, noise, and temperature sensors in the fishnet side pocket of the participant's backpack. Monitor air pollution parameters like particulate matters.PM 2.5 values in micrograms per cubic meters. Assess the noise pollution by measuring the live volume levels in decibels and temperature from the temperature sensor in degrees Celsius. Next, attach the light exposure tracker to the shoulder strap facing outward to measure the illuminance in luxe and circadian light A or CLA values as zero to one.Instruct the participant to carry the backpack during daily activities for 24 hours and charge the power bank every night. Confirm that all sensors are powered on and transmitting real-time environmental data, including air particulate matter and formaldehyde concentration. Enable Bluetooth and set location access to always in the menu.Configure the application to record GPS location data at one-minute intervals and sync sensor data every five minutes. In the device's interface, select End Exercise to complete data synchronization. In the device's interface, select the activity records to confirm that all daily tracking reminders are set to on.Select heart rate option to verify that continuous heart rate monitoring is enabled. Under emotional wellbeing, confirm that emotion stress records is active. Then enable both notifications and cloud data sync.Under blood oxygen levels, verify that the automatic SpO2 is enabled. Review a short test recording to ensure data completeness and accuracy, checking for correct timestamping and plausible values across all streams. Record the different modes of daily movements and travel episodes under the action center, along with GPS trajectories.Provide instructional materials to the participants, including manuals and videos for device usage. Assess the demographic data parameters like gender, age, weight, height, and marital status of the participants. Instruct the participant to wear physiological sensors and carry environmental devices continuously for 24 hours.Remind the participant to recharge the power bank daily to maintain uninterrupted operation and data. Monitor the data stream through manual checks or via automated backend alerts. Under the calorie records, enter the diet log for different meals, including breakfast, lunch, dinner, or an extra meal.Average PM 2.5 concentrations and subjective wellbeing scores varied significantly across daily activities with exposure differing markedly between indoor versus outdoor and sedentary versus active settings. The highest average exposure exceeding 20 micrograms per cubic meter was observed during sleep, surfing the internet, entertainment, studying, and taking a walk. Mid-range exposure concentrations in the range of 15 to 20 micrograms per cubic meter were found for activities such as work, physical activity, caregiving, and trips.The lowest exposures in the range of 5 to 15 micrograms per cubic meter were recorded during personal affairs, seeing a doctor, dining, and personal care. The cognitive subjective wellbeing scores showed less variability across activities, ranging narrowly between 1.7 and 2.3 on a one to five dissatisfaction scale. The lowest satisfaction scores were associated with seeing a doctor, trips, personal affairs, and work.The highest satisfaction scores were linked to physical activity, entertainment, and social activity. The activity types associated with higher PM 2.5 exposure concentrations did not always correspond to lower cognitive subjective wellbeing, suggesting decoupling patterns between PM 2.5 exposure and self-reported cognitive subjective wellbeing. M3-BREATHE enables real-time measurements of personalized exposure to higher doses of environmental pollutants and the physiological responses during daily activities and travel.The key challenge is reliable device collaboration, synchronization, and sustained participant compliance in the real world long-term monitoring. Future work can integrate AI interventions, genomics, and core city deployments to study course pathways and improve precision around the house.

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Research

JoVE Journal

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Learn multimodal mobile monitoring using mHealth devices, environmental sensors, and GPS-enabled smartphones for real-time respiratory health assessment. This protocol integrates behavior, physiology, and exposure data with machine learning alerts.

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