Socioeconomic Class in Physical Activity Wearables Research and Design

2.1 Time, Labour, and Culture As Modes of Contention

We will frame class, and the accompanying discussion, in two ways. First, we will examine socioeconomic class as it relates to the actual state of one’s earnings and associated labor. We base this on a categorization that places income, educational attainment, and occupation on a scale  [5, 60]. Therefore, where socioeconomic status, ‘SES’, is notated, we refer to this materiality of socioeconomic class. We frame the discussion in this way in order to dispel societal limitations or lack thereof, for people by SES and what this means for their potential capacity to engage in common constructions of wearables research studies. In this case, SES is used to describe trends in access, sorted by the range of literal capital that an individual holds as both an independent person and as part of a group of people in similar conditions.

Class is also represented as a multi-layered cultural and material concept which encompasses the way people maneuver through “society”, and the way that “society”, interacts with them, as both an extension of their ownership of capital, and the subsequent positionalities which are created as a result of these temporalities. We broadly ruminate on the way “cultures” are understood and how they are directly associated within the broader social status for which they are either enticed or shunned (or something in between), and how these same understandings of a specific “culture” can be translated to other similar contexts  [13], p18. Therefore, we view class positionality as one significant factor for how people come to participate and understand culture, inspired by frameworks in  [13, 88, 105].

We employ these social class distinctions because they are far-spreading, and even exist within circumstances which on the surface may seem universal. By example, something as unassuming as the music genre one primarily listens to or is most familiar with, is directly tied to the social and economic background of the listener [125]. The distinction between SES, and class as a culture, serves to offer a more complex conversation about the multiplicities of class and how it impacts the way we interact with research and physical activity.

2.2 Why is Class Important?

Socioeconomic class is one identity marker which we can use to understand more vividly how one experiences the world at all phases, including psychologically and socially  [78]. Therefore, although there are a few competing interests which help to contextualize why class is such a crucial metric for evaluation, we choose to focus on two which are especially complex, the first is culture.

Culturally, there are unique beliefs and practices that are generally imbued by social class. These core beliefs can impact how persons from particular socioeconomic class backgrounds navigate their identities, and engage with the world and systems which are not indicative of their class. For example, those from the middle class are said to champion active independence, personal delegation, and adopt a self-starter view on life  [29, 108, 114]. In these cases, the self is the pillar. On the other hand, what is central to working class survival is community building and interdependence  [114], which can make it harder for working class persons to assimilate into conditions which promote middle to upper class behaviors, such as the corporate workplace  [114]. As personal informatics is largely founded on observations from users with access to discretionary income, time and higher education [19], the cultural foundation of device and research design is largely indicative of high-SES perspectives.

The second is time. Time is another analysis element for understanding the relationship between societal dimensions such as health outcomes, as socioeconomic class has correlations to how time is boxed and valued  [106]. One’s access to “discretionary time”, periods of time where one is free to determine how to use it, largely becomes unstable the lower the income [46]. Lower income workers are more likely to face difficult labor conditions like irregular work schedules or being beholden to practices such as “just in time scheduling”  [50]. Just in time scheduling is a practice of providing shift hours on short notice, often fluctuating by week. This poses difficulties for scheduling important events, child care, medical visits, etc. and places persons in a state of constant time instability  [50]. Such class conditions are transmutable and are even considerable factors for whether one engages in regular exercise, with those who are time poor less likely to participate in physical activity  [110] or self report health status’ [106]. These realities can also influence research participation, which we will further explore within the unique context of wearables intervention studies.

2.3 Class and HCI

The HCI research community is seeing an increasingly non-white, working class HCI audience  [35, 116, 137]. As a result, some research has emphasized a need to consider identity in the process, such as race, education, and gender in technology design [103]. In the greater HCI research community, there have been a few efforts to contend with socioeconomic class. Although this is not an exhaustive list, class has served as an analysis tool in evaluating family technology use and monitoring differences by SES and ethnicity  [40], how unhoused teens engage with information technology  [136], hiring  [20, 21], and labor [76] by socioeconomic class. In many instances, socioeconomic class is used as an inter-sectional boundary for a larger formula which usually includes race and gender. For example, in reviewing the impact of mHealth technologies on vulnerable populations in the U.S., Stowell et al. consider vulnerable groups to be of low-SES, racial/ethnic minorities, and/or individuals with disabilities [116].

In other cases, the conversation on class and research studies transgresses tangentially, particularly through considering participant compensation. In these instances, researchers from other disciplines have conducted comprehensive literature reviews on common compensation reporting metrics, evaluated the ethical implications of these patterns, and express a desire for the development of purported best methods  [68, 71, 90]. Therefore, the conversation largely is centered on establishing transparency on compensation practices and the line between coercion and incentive. Mainly class is circumvented and the emphasis is more so on replicability of research projects and the ethical implications of non-reporting for the larger research space, such as in this systematic review of how participant compensation is reported (in HCI) by Pater et al.  [92]. What is missing notably, is a nuanced exchange about how current class standing and class cultures affect someones agency to participate in a study dependent on the compensation structures that are in place.

Within the personal informatics discipline, there has been entanglement with socioeconomic class, but these studies tend to be the minority, with many saying as much. For example, of 83 mHealth interventions targeted towards vulnerable populations, like low-SES individuals, Stowell et al. identify just two which leverage wearable fitness trackers [116]. Similarly, Huh et al. identified just 13 papers which identify barriers and facilitators to adoption of consumer health informatics technology by underserved populations, of which most examined patient-facing portals or educational tools, rather than wearables [58].

There have been studies which more directly provoke our target populations and their perspectives on general wearable use. For example, Holko et al. [57] conducted a survey study of patients of federally qualified health centers around the U.S., where they learn that most participants positively answered the question, “whether participants would like a fitness tracker”. Some of those who did not reasoned that it was because they did not find trackers to be helpful, among other reasons like not being able to commit to daily use, a lack of knowledge, and general disinterest.

There are also a few intervention studies which lie at the intersection of wearables and class. Take for example, this 2019 study by Saksono et al., where the objective was to understand how families can reflect on past fitness tracker activity data to support future physical activity. What makes this paper particularly unique, is both that the families studied were from low-SES backgrounds (alongside being racial minorities), and had family histories of obesity  [100]. Another paper, also by Saksono et al., describes a two month wearable deployment study conducted with families in “low-SES neighborhoods” from a North-Eastern city in the U.S.  [99]. The primary data analysis focuses on the social, emotional, and environmental relationships between PA and wearables use choices. These two studies offer a glimpse into tracking as a community effort.

Similarly, Cruz et al.  [26] describe an interview study conducted with economically disadvantaged, minoritized participants, living in “high crime” areas. The purpose of the study was to probe their perceptions of wearables device use, and notably they found that participants emphasized neighborhood walkability and vulnerability to crime as contention points for investigation, similar to meditations in the 2018 and 2019 papers by Saksono et al. [26, 99, 100].

While these lines of research have helped introduce class and race perspectives to personal informatics and HCI more broadly, there is space for an even more expansive view on class, which discusses other more granular influences on device use and study participation. We add to this literature and this conversation by discussing some of the reasons for how the way we design our studies contributes to this critical lack. We surface a more nuanced perspective which regards class as more than a fiscal and access measurement, but also as a phenomenon which has temporal and cultural dimensions. This is a necessary junction to address in order to provoke a critical discussion about what this may mean for the field, the data that we collect, and the knowledge we produce.

3.1 Process for Engaging Examples

Regardless, although our goal was not to systematically analyze the literature, we did have a procedure for collecting and documenting examples to base our engagement. The basis of this procedure is in alignment with Rapid Reviews, which aim to be structured and rigorous but are not exhaustive of all relevant literature [52]. We began with an initial search of the ACM digital library for the keywords “personal informatics”, and “physical activity” for full papers published between January 2015 and December 31, 2022. The purpose of this initial search was to develop our inclusion and exclusion criteria, and to finalize a consistent rubric of data points for which we would base our analysis.

4.1 Time

In this section, we will discuss time as a dimension of concern, which will be the basis of our later discussion. The following variables are areas of concern both independently and in relation to each other. This means that although the variables have their own impacts on study participation, they coexist functionally within the larger structure of the intervention study. We evaluate four distinct areas in which time, as it exists as an entity of SES, can impact participation in wearables intervention studies:

4.2 Activity Type

The activity type dimension surrounds the type of activities that are centered in intervention studies and wearables device tracking generally. Within this section, we will investigate the current trends in wearables study activity types mainly:

4.3 Positive Examples from the Literature

Amidst the critique, we did find studies where researchers centered class perspectives as part of recruitment, analysis, or framing of participant experiences. These examples are undoubtedly not the only papers which center class perspectives, but are rather exemplars we believe are worth holding up as ways that researchers can contend with class as they design studies and report on findings.

As mentioned earlier, Saksono et al.  [99, 100] do much work in terms of low-SES demographic inclusivity and perspective centering. Noteworthy examples from these works include describing class-specific challenges associated with use of wearables, such as neighborhood walkability. These studies were also at the high end of financial compensation for study participation, which helps offset the time expectations of the studies. An interview study by Niess et al.  [85], examined differences (and similarities) in perspective between “Western, Educated, Industrialised, Rich, Democratic (WEIRD)” fitness tracker users from America and Europe, and those of Arab Egyptian ethnicity and nationality. Their decision to contrast Egyptian participants was in direct response to the distinct lack of non-western voices in the common lexicon of fitness tracker related research. They found notable differences in perspectives between the two groups regarding varying topics related to fitness tracker use, notably about the prioritization of specific metrics. Studies like these which make demographic a central part of analysis, make it much easier to trace the subjectivity of user experience in data findings because we can contextualize interactions within the lines of our participants independent experiences, and those of larger socio-cultural significance.

In another case, frequent support and communication with participants was a central part of a deployment study by Abrantes et al. [1] with women both enrolled in an alcohol and drug partial program and also managing depression. Aligning with recommendations around how to engage with running studies with marginalized populations [35], researcher support helped maintain and build rapport with the women. Researchers conducted phone sessions, held a pre-study informational coaching session on resources, device use, and best practices for incorporating study requirements into their daily lives. There were also 30 minute, bi-weekly, in-study phone calls to discuss participants PA and device use and other counseling topics, and to generally provide assistance as needed. This form of focused study design is more in line with an SES-conscious practice, designed to help overcome barriers around long term retention and low technology literacy. However, there exists a tension in that the frequency of these check-ins may introduce further time requirements related to participation, which may not align with other needs of low-SES individuals. As it is unclear the SES of these participants, we cannot further extrapolate these tensions.

Although few and far between, some researchers reported participant yearly salary, like in this workplace study by Wentz et al.  [131], and Saksono et al. [100] where they reported class based demographic inclusion criteria (less than $21,979 annually). It is more often that studies described participant occupations, by listing or summarizing them (such as in a table [18]) or explicitly stating that participants engaged in sedentary work practices [47]. while studies predominantly understood the needs of sedentary workers who work 9-5 jobs, reporting this context is helpful for surfacing the availability of PA as a leisure activity and participant access to discretionary time. Additionally when salary or income is provided, there is useful context which is produced. For example, high salary could suggest the opportunity to purchase the equipment needed for certain types of physical activities, and suggests that high study compensation may not be needed for sustained engagement.

Lastly, there were a few studies which contained unique research metrics that differed from numerical data, such as this study by Miragall et al.  [81] where “enjoyment” of PA participation was a secondary analysis. Among early commercial wearables, the now-defunct Nike+ Fuel Band used a unique measure of “Fuel” to abstractly refer to exercise as measured across different kinds of activities, rather than translating sensor data to “Steps” [133]. While perhaps less human-interpretable, this measure had the advantage of being scalable to different kinds of activities.

5.1 Implications of Time and Participation on Lower Socio-Economic Persons

A study’s length does not necessarily hold the brunt of the burden for the current socioeconomic landscape of HCI studies. Longitudinal studies are not necessarily more burdensome than others simply by their nature of being longer in length. It is the content of the study which determines the relative burden of participation. These participation time burdens cannot only affect inclusivity but also retention. By and large a consistent narrative surrounding low study participation of persons from economically disadvantaged or ethnic minority groups are about difficulties in recruitment and mistrust of researchers by community members [101]. Although it is true that persons who are of a lower SES are often missing from the health research populations as a result of siloed recruitment strategies, it is also important to note that with successful inclusively design, attrition rates can be improved such as in  [118]. However, not much of the HCI intervention research domain focuses on the retention of said populations, or even the development of research studies which account for complex life circumstances.

The latter, retention, is a crucial piece of the puzzle in regards to longitudinal studies. In general, researchers have had trouble with attrition for community members who are low-SES due to instability in life conditions. This could be due to something as innocuous on the surface as a change of contact information, systemic such as a lack of agency to free time, or ultimately personal, like the prioritization of normal day to day responsibilities  [12, 118]. In clinical trials which are also longitudinal in nature, researchers working with low-SES populations note drop offs by week, with trouble maintaining contact as time goes on  [12, 118]. Although these studies were done in the general medical context, some of the reasons for attrition directly correlate to time, which is a variable wearable intervention studies are founded upon (PA, wear time).

In a multi-month studies where participants are given a wearable device to track daily or weekly data, in addition to other specified study requests, a participant must have access to discretionary time. Our studies are long, require increased effort, and the introduction, or increasing, of physical activity. These sorts of study requirements can prove more significantly impractical for working class persons, as often times their labor is within the margin of the rest or leisure of white collar workers. This impedes upon one’s ability to have true agency on their schedules  [105], p66, and in the context of wearables studies, this could limit their ability to fully engage with recruitment processes or study requirements. In many cases, working class people spend their free time sedentary as due to the physical demands of their occupations  [94], this means that it is not rare for working class persons to feel the “materialization” of labor on their physical bodies  [105], p70. Therefore, if we frame physical activity as a thing which must be done in our free time, we must account for the nuances of leisure time. And it is not that there is an inherent disinterest from members of these communities, as underrepresented populations tend to have the same amount of interest in participation as the common study demographics, and in some instances even more  [56].

What is true, however, is that in the case of low-SES populations whose class positionalities most directly necessitate extensive time to generate money and resources for their immediate survival, the trade-off for participation looms more potently. Study components such as forms of low cash, “experience” based, or other types of compensation (e.g., a gifted wearable), can be deterrents to joining or completing a research study for low-SES communities – assuming that the study recruitment methods even reach them. We suspect that even if participation presents potential greater benefit, these benefits may not be sufficient enough to justify accommodation within the material conditions of one’s day-to-day reality. We hypothesize that if a household does not readily have access to discretionary funding, the advantage for partaking in a months long study cannot solely be for altruism. Low compensation is a trade off people from higher SES backgrounds can choose to make.

5.2 Implications of Activity Type on Wearables Design

Traditionally, wearables leverage a mixture of accelerometers and heart rate sensors for detection  [8, 69, 120] as the advent of wearables device design included a focused effort to introduce pedometer based tracking. Therefore, step count as the central tracking measurement for wearables is connected to the history of pedometers in the early 2000’s conceptualizations of personal informatics. In that era of wearables research, pedometers were the chosen tracking devices due to their perceived usability and purported ease for tracking walking movements. Yet, as early as 2008,  [25] researchers expressed the shortcomings of sensing with pedometers and suggested the need for either improvement of the apparatus or for the development of new devices to create a more comprehensive PA tracking foundation.

Today, researchers and commercial agents alike continue to hail walking (step counting) for its diversity of engagement cases  [14] and its low barrier to entry  [104]. Within this framework, 10,000 steps is widely advertised as the metric by which a healthy person should strive for daily. This step count goal is said to provide benefits such as improved cardiovascular health, mental health, potential weight loss, and weight management, among other things [124]. However, the legitimacy of the 10,000 step a day metric has also been countered. In some spaces  [84, 96], it has been argued that steps a day as low as 4,400, or a few thousand steps over, provide nearly the same benefits as the 10,000. In time metrics, 30 minutes a day of exercise is considered to be enough for a healthy adult to support much of the same things that 10,000 steps a day is purported to provide  [11]. Yet, what these figures signify are more than suggestions for health improvement, they embody a larger sociopolitical dynamic.

There are three ways we choose to evaluate step count, and subsequently step count goals, as engendering middle and upper class cultures. The first, calls into question step counting as a reflection of health literacy. The second, surrounds centering physical activity as leisure. The third, unpacks how focusing on step count limits device use.

First, daily step count goals are not necessarily common knowledge. In wellness culture and the popular lexicon of fitness tracking, they float around freely as these communities are largely evocative of middle to upper class lifestyles and cultures which are more likely to have a higher health literacy  [115]. However, what appears to be common knowledge, is not so common when investigating health education and awareness. In thinking of personal health and how ones behavior impacts it, although not exhaustive, this literacy is more likely to be lacking in lower SES and minoritized contexts and can affect information recall,  [6], mortality  [117], increased hospital admission  [7], and general health outcomes.

In Wardle [130], a UK study performed on personal perceptions regarding different health related topics such as leisure PA, life expectancy, dietary choices, amongst other things, they found associations between lower socioeconomic status and a reduced awareness for how ones lifestyle choices affects their overall health. They also found a significant portion of working class individuals were often very present in mind and behavior, not often thinking of the future. When contending with devices such as wearables which center behavior change and reflection, one’s capacity to understand the significance of the data and the behavior change that should go alongside it, is crucial. Thus, when a participant is presented with a step count total, or a heart rate number, how do they begin to interpret what this means for their health? Researchers such as Saksono et al. [100] and Lazar et al.  [73], discuss participants’ limited capacity to reflect on the usefulness of the unprocessed data collected which then makes it difficult to decide next steps.

In designing tracking technology which requires that participants interpret data on their own volition, this brings forth disparities in who can optimize device data. And in the case where devices are used in the medical context, as mentioned earlier, there exist correlations between limited health literacy and information recall. Information/Instructional recall is important for sufficiently understanding and following physician suggestion.

Secondly, low-SES groups are less likely to participate in leisure based physical activity  [44, 110], but this does not mean that persons of a lower socioeconomic class are necessarily largely sedentary  [111]. According to Besser & Dannenberg [11], the working class living in urban areas are more likely to walk for transportation more than 30 minutes a day, in addition to being most likely to participate in workplace PA  [27]. The framing of PA outside of these contexts marks a shift from the previous century where PA was largely defined as a work related activity  [113]. We suggest that by framing step count as the activity for which all activities are based, we are creating a limited frame by which a user can develop a broad understanding of their physical activity patterns and habits.

Additionally, we also note that more modern commercial wearable devices often include detection capabilities for other kinds of PA including swimming, biking, rowing, diving, and golf [4, 41]. While research studies on PA wearables tend not to center these, we note that many of these activities have particularly high requirements with regards to equipment (e.g., a bike or elliptical, a rowing machine, golf clubs) and physical space (e.g., to a swimming pool, a golf course) which result in these activities not permeating across class cultures. This is another barrier for low-SES persons as participation in specific sports types changes by socioeconomic class, with high SES persons significantly more likely to play in racquet based and club based sports due to the relative cost participation  [31, 95]. Disparities also exists by youth sport  [59]. The investment of resources that are going into detecting these activity types and designing complete and compelling experiences around them, points to a deepening reinforcement of wearables serving leisure purposes and favoring individual versus community-based activities. While it is hard to explicitly prescribe detection capabilities which would be beneficial to low-SES individuals, some recreational exercise activities with low indices of inequality include strength training [95]. Beyond these, as we discuss later, we see a particular need to consider detection of activities associated with manual labor.

5.3 Broader Implications of Ignoring Class in Wearable Studies

A clear intent of our critical discussion is to argue for researchers to more consciously consider class when designing wearable studies for physical activity promotion. We can speculate on a variety of reasons as to why there may not be a focused effort on people of lower socioeconomic background in studies of wearables. It could be that researchers have tried to reach communities but have not been able to due to various historical reasons such as mistrust of researchers. However, HCI researchers have written about suggested best practices  [35, 126, 127] for navigating the recruitment of these hard-to-reach populations. It could be that because personal informatics is often associated with middle class wellness, there is a belief that economically disadvantaged people do not have interest in using wearables. However, studies have found that these groups are more interested in device use than not  [57]. There are many other concerns or limitations that may prevent researchers from considering class in their studies, some legitimate. Even so, as part of arguing for greater consideration, we further unpack the short- and long-term consequences of ignoring class in wearables studies.

As wearables become more entrenched in the patient process and become even more ubiquitous within the technological ecosystem, there is incredible potential for researchers in the space to create and perpetuate Intervention Generated Inequalities (IGIs) [127]. Veinot et al. discuss that health technology interventions can generate inequality if they are “more accessible to, adopted more frequently by, adhered to more closely by, or more effective in socioeconomically advantaged groups such as those with more resources or education”  [127]. Our critical discussion surfaces that the wearable, when designed with the goal of physical activity promotion, is well on the way to generating inequality. Veinot et al.  [127] label four distinct areas through which inequalities can precipitate: access, adoption, adherence, and effectiveness. Our critical discussion particularly surfaces that low-SES individuals might face barriers to adherence stemming from time-based expectations, and poor effectiveness rooted in limitations surrounding activity type.

What is specifically unique about the potential for wearables to cause IGIs, is that in addition to being the intervention in health promotion, both in our studies and in the wild, the devices themselves are often used to measure the success of other interventions. For example, ownership of a wearable is the first stage of the intervention, the use of that wearable to promote some part of ones health, is the second stage. This means that owning a device is an intervention in and of itself and having access to personal data could provide another level of knowledge that cannot be necessarily obtained without it. In leveraging the use of a wearable as an outcome measure for research, we are deepening our understanding for how to support health promotion – but for people of high-SES background. Therefore, if the device as it stands is not useful or usable by economically disadvantaged groups, they are now missing out on health interventions which could have otherwise been leveraged. Or worse, our research could perpetuate that a novel health intervention is beneficial, while missing out on the context that this benefit only exists for high-SES individuals.

We have arguably reached the first wave of technological maturity of wearables. As subsequent waves of the technology come into center, it will become even harder to play catch-up in designing wearables which fit the needs of lower-class individuals. The possibilities for the next generation of mass scale medical technology could be with tracking devices  [138] but without sincere contemplation now, we will be forced to reform later. If we do not instead attack the problem in its relative technological infancy, there could be yet another generation of people at risk of being further victim to health disparities because of their socioeconomic backgrounds. As devices mature, the way we conduct our studies, analyze our data, and disseminate findings, will continue to place people of low SES at another significant disadvantage to which they already are.