Previous studies examined occupants’ comfort and satisfaction to determine comfortable and acceptable levels of different IEQ factors. A detailed questionnaire was often used at a single or few points in time to elicit responses evaluating satisfaction with various environmental factors (Choi et al., 2012a; Heinzerling et al., 2013; Elzeyadi et al., 2017; Park et al., 2019). While many IEQ factors can affect occupant’s comfort, a recent analysis of a large database revealed that satisfaction levels were lowest for sound privacy, noise level, and temperature (Graham et al., 2021). While detailed questionnaires can be helpful for general evaluations and for identifying sources of discomfort, they are unlikely to capture temporal variations in comfort and satisfaction as a result of changing IEQ.

To track such variations, EMAs on smartphones or wearables can be used (Wei et al., 2014; Konis and Annavaram, 2017; de Vries et al., 2021; Peeters et al., 2021). μEMAs can provide higher granularity, response rate, and are perceived to be less distracting compared to online questionnaires or EMAs administered on smart-phones (Intille et al., 2016; de Vries et al., 2021). Jayathissa et al. (2020b) demonstrated potential for using μEMAs on a Fitbit smartwatch to record thermal, lighting, and acoustical preferences (5–15 prompts per day). These preferences can be used to identify occupant and space profiles and make comfort predictions that could potentially be used to control building systems.

Sick building syndrome (SBS) symptoms describe building-related symptoms experienced by building occupants that are relieved or go away after leaving the building. These symptoms can be influenced by environmental and personal factors and can be classified as (1) upper respiratory and mucosal symptoms such as dry or itchy eyes; (2) lower respiratory irritation like cough; (3) neurophysiological symptoms such as headache and mental fatigue; and (4) skin irritation symptoms like itching or reddening (Apte et al., 2000; Bluyssen et al., 2016; Sakellaris et al., 2020). Several studies associated air quality factors such as CO₂, NO₂, nicotine, particulate matter (PM), volatile organic compounds (VOCs), and microbial contamination with SBS symptoms (Erdmann et al., 2002; Menzies et al., 2003; Mitchell et al., 2007; Colton et al., 2014; Azuma et al., 2018). Additionally, air temperature and relative humidity were also linked to SBS symptoms (Arundel et al., 1986; Fang et al., 2004; Seppänen and Fisk, 2006; Wolkoff, 2017). To the authors’ knowledge, previous studies have not used μEMAs to check SBS symptoms. This is expected because participants typically report the frequency of experiencing symptoms over the past month using detailed questionnaires (Apte et al., 2000).

In addition to ratings of comfort and satisfaction, variations in sleepiness can be captured using EMAs. Generally, lighting has been shown to decrease subjective sleepiness, but this response may vary by time of the day (Vetter et al., 2021). One of the studies that had a different conclusion was a 3-weeks study where higher illuminance at the eye led to negative effects on sleepiness in the spring and no effects in the winter (Peeters et al., 2021). This study evaluated sleepiness using the Karolinska Sleepiness Scale (Åkerstedt and Gillberg, 1990) that was completed eight times a day using EMAs on mobile phones. This highlights the importance of capturing temporal and seasonal variations in sleepiness. In addition to lighting, high noise levels can affect self-reported tiredness (self-reported number of yawns in the last 10 min) and motivation, compared to low noise (Jahncke et al., 2011).

Previous field studies utilized wearables that track activity (actigraphs) to track sleep in relation to variations in light intensity and spectral power distribution (de la Iglesia et al., 2015; Wams et al., 2017; Cain et al., 2020; Peeters et al., 2020). For example, a study by Boubekri et al. (2020) used a wrist-worn device to track sleep duration over a week and found a significant increase of 37 min in sleep duration associated with working in an office that had optimized daylight and views, compared to roller shades. Sleep quality parameters such as duration of deep sleep, number of awakenings, sleep efficiency, and sleep onset latency—all assessed using actigraphy—can be negatively affected by other IEQ factors such as CO₂ levels (Akimoto et al., 2021) and air temperature (Pan et al., 2012; Caddick et al., 2018).

The stress response can be elicited by several IEQ factors. Thayer et al. (2010) examined two aspects of physiological stress, circadian variations in heart rate variability and morning rise in cortisol, for a group of 60 participants working in a traditional or a modern office building. They found that physical features of the work environment such as lighting, views, acoustics, and air quality may affect both physiological stress indicators. In another study, Razjouyan et al. (2020) used a chest-worn sensor to examine the effects of relative humidity in offices on the stress response. They found significant effects of relative humidity on heart rate variability indices while occupants were in the office. Participants that spent the majority of their work time with relative humidity between 30 and 60% experienced 25% less stress, compared to those that spent the majority of their time in relative humidity levels under 30%. The chest-worn sensor also estimated sleep quality, with the authors finding a significant indirect effect of relative humidity on sleep quality, mediated by the stress response.

Other studies have shown that noise levels can affect the stress response. Physiological stress (indicated by skin conductance levels) and respiratory rate increased at higher noise levels, moderated by working experience (Shafiee Motlagh et al., 2018). Furthermore, cognitive stress self-reported using the cognitive stress scale was lower in open offices in a condition with enhanced sound absorption (Seddigh et al., 2015; Colenberg et al., 2020). Other factors such as workstation type and outdoor view type were also found to affect stress response (Kaplan, 1995; Bjørnstad et al., 2015; Lindberg et al., 2018).

Increased CO₂ levels can have negative physiological and cognitive effects (Azuma et al., 2018). Compared to 600 ppm, Satish et al. (2012) showed that the mean score of nine decision-making tasks dropped 12 and 51% under CO₂ levels of 1,000 and 2,500 ppm, respectively. These results are consistent with another study that found VOCs and CO₂ to be independently associated with cognitive scores (Allen et al., 2016). The mechanism by which CO₂ and VOCs affect cognitive performance remains unclear.

Lighting and access to views can affect cognitive performance. A previous study reported 26–62% higher cognitive function scores across the nine domains of the strategic management simulation test in an office with electrochromic glazing compared to roller blinds (Boubekri et al., 2020). These results are consistent with the results of another study that found improvements in working memory and inhibition in setting with electrochromic glazing or mesh shades, compared to blackout shades (Jamrozik et al., 2019). Using electric lighting only, Ru et al. (2019) found significant improvements in reaction speed at1000 lux compared to 100 lux at the eye.

It is important to note that most previous studies assessed cognitive performance using computer-based tasks. Some studies, outside the IEQ research domain, explored administering cognitive tests using wrist-mounted devices, smartphones, and tablets to overcome challenges related to administering cognitive tests in field studies and to allow for more frequent assessments of cognitive performance (Matsangas et al., 2017; Moore et al., 2017; Arsintescu et al., 2019; Koo and Vizer, 2019; Laurent et al., 2021). Currently, the use of wearables for cognitive tests limits the types of cognitive tests that can be administered, but this may change as more cognitive tests are developed for wearables.

Table 1 summarizes the studied relationships between IEQ factors and HPIs. This table does not show all possible relationships and does not suggest that a causation relationship was demonstrated, but it highlights objective HPIs that were collected using wrist or chest-mounted devices, as well as subjective HPIs that were assessed using EMAs from wearables or smartphones. Based on this table and the reviewed studies in Section 2.1.1 through Section 2.1.6, a few key points can be noted:

• Wearables were used in previous IEQ studies to collect several HPIs including sleep quality, heart rate, heart rate variability, and subjective responses.

The spatial and temporal sampling resolution needed for IEQ measurements will vary based on the research questions and objectives. As discussed by Parkinson et al. (2019), spatio-temporal sampling is limited by the logistical challenges of characterizing different IEQ parameters with significant variability in time and space scales. Physical features of the space or activities of the occupants may also limit the sampling resolution of IEQ measurements as it is important to avoid any distraction or hindrance to participants. Previous studies measured IEQ data at different spatial resolutions ranging from localized measurements from wearables to point-in-time measurements using a handheld device or IEQ carts (Heinzerling et al., 2013).

Wearable devices offer the highest spatial resolution, continuously monitoring IEQ conditions experienced by individual participants as they move throughout multiple rooms or buildings (Figueiro et al., 2019; Boubekri et al., 2020; Cain et al., 2020; Peeters et al., 2020). The use of wearable devices to sense IEQ conditions provides advantages over fixed sensing stations in spaces with high spatial and/or temporal variability (Adamsson et al., 2019; Clements et al., 2019). Personal light exposure is one of the most common IEQ parameters collected via wearables, but it is also possible to measure and track temperature, relative humidity, CO₂, and sound level. One study showed that thermal comfort sensors could be placed into a small enclosure and worn on the wrist; those sensors could then control an air conditioning system based on localized comfort estimates (Feldmeier and Paradiso, 2010). Ghahramani et al. (2018) utilized a wearable sensor worn on the chest to measure sound pressure level, CO₂, illuminance, air temperature, relative humidity, and pressure. A large-scale study used sound measurements from a smart watch to examine personal sound exposures (Smith et al., 2020). Salamone et al. (2021) provided a comprehensive review of previous studies that used wearables for IEQ sensing.

Localized IEQ measurements using wearables can be directly related to other occupant-level data such as HPIs but may not be representative of ambient space-level conditions. For studies where participants spend the majority of their time in one location, like offices, a fixed monitoring station at each desk allows IEQ measurements to be captured and related to each participant (MacNaughton et al., 2016; Jamrozik et al., 2018; Clements et al., 2019). For environments where desktop monitoring stations are not feasible, a coarser approach is to use sensor stations on a mobile cart. These stations are often placed in the center of the room, capturing IEQ measurements overtime, or moved between workstations to capture point-in-time measurements of conditions participants may be experiencing (Chiang et al., 2001; Castaldo et al., 2018; Choi and Lee, 2018; Jin et al., 2018).

In addition to measuring IEQ factors and HPIs, previous studies suggested other data types that need to be collected for more holistic investigations (Figure 1). Personal and work-related factors can directly affect or mediate effects of IEQ on occupant well-being (Veitch, 2001; Boyce, 2003; Bluyssen et al., 2011; Bluyssen, 2020). The model proposed by Bluyssen et al. (2011) considered both physical and psychosocial stressors and their effects on well-being. Many personal factors can be considered including age, gender, ethnicity, life events, body mass index, consumption of stimulants, smoking, use of medications (allergy, anxiety, melatonin, sleep), chronotype, sleep disturbances, and responsibilities outside of work. Work-related factors include workload, job type, satisfaction with work, perceived productivity, workstation type, and level of education (Bluyssen et al., 2011). These factors can be used in the inclusion or exclusion process and can aid in data analysis to account for potential confounding effects. For example, Boubekri et al. (2020) collected information on the medical status of potential participants and excluded those that had sleep apnea, chronic depression, and other health issues that may affect sleep.

In studies where the goal is to examine relationships between building system characteristics and occupant well-being, it is important to document general building attributes and system characteristics. Building system characteristics can be documented using existing checklists, such as the modified checklist from the Health Optimisation Protocol for Energy-efficient Buildings (HOPE) study (Cox, 2005; Bluyssen et al., 2016) and the Technical Attributes of Building Systems (TABS) survey (Aziz et al., 2010).

The use of wearable devices in a research study can raise privacy concerns that may affect recruitment and collected responses. Hence, it is important to develop a strategy to address privacy during early stages of research planning. Safavi and Shukur (2014) proposed a conceptual framework that included ten principles for collecting and handling health data from wearables. The principles highlight the importance of transparency in communicating study purpose, the technology used to collect data, data collection procedures, and ownership of collected data.

Paul and Irvine (2014) tested four wearable devices and found that two had policies allowing them to collect information about the user from other sources. Other issues in privacy policies were related to data ownership, right to privacy of data, and whether device manufacturers can use collected data for commercial purposes. In a study that examined privacy concerns for wrist-mounted devices, participants were concerned about the Global Positioning System (GPS) sensor (Motti and Caine, 2015). Additionally, participants were generally concerned about these devices collecting information about them from a social network, the inability to recall and delete collected data, and having organizations or the government access the data without their awareness and consent. Fitbit’s approach of using social networks, such as Facebook, to log in can lead to concerns on data breaches or misuse of user profile data (Orlosky et al., 2019). To protect the subject’s privacy when publishing data, several techniques are often used such as anonymization, deidentification, and pseudonymization.

Data anonymization can be defined as an irreversible removal of the link between a subject and his/her record data (Kushida et al., 2012). This requires an a priori decision by researchers whether there would be any need in the future to link data to subjects, such as if there might be data removal requests when a subject opts out of a study. A less stringent technique is data de-identification, which is the removal or manipulation of direct and indirect identifiers such that reestablishing a link between a subject and his/her data would require a key that can be used to reverse the de-identification process (Garfinkel, 2015). De-identification can be achieved by replacing identifiers with pseudonyms or codes, or pseudonymization (Ren et al., 2021). For example, Kallio et al. (2020) assigned pseudonymization codes to participants that were used to log in and provide ratings of IEQ. Another study generated an ID for each participant using a cryptographic secure hash algorithm (SHA-1) using father and mother initials and months of birth (Bluyssen et al., 2016).

A complementary procedure that can be considered for certain data types from wearables is generalization, which transforms absolute data to ranges or categories. For example, age 30 can be transformed to age 25–35. Another example is location obfuscation, which is to deliberately reduce the precision of the position info. Like a circular area instead of exact geographical coordinates (Liu et al., 2018).