Sleep tracking apps alter behavior and self-perception through data

Consumer sleep-tracking technologies are drastically transforming how individuals understand rest, health, and self-care, but a new study suggests these systems may be doing more than just monitoring sleep. Researchers argue that sleep trackers are actively reshaping users into data-driven subjects, exposing a growing tension between human-centered design and the realities of algorithmic life.

Authored by Sanonda Datta Gupta and John S. Seberger, the study titled "Sleep by the numbers: human-centeredness and the immanent posthumanism of users," published in AI & Society, analyzes 120 consumer-facing sleep-tracking studies conducted between 2014 and 2024 to examine how these technologies redefine user identity, behavior, and agency.

The research argues that while such systems are designed under the banner of human-centeredness, they often produce outcomes that shift control toward data, metrics, and algorithmic interpretation.

Sleep tracking transforms rest into measurable performance

Sleep tracking devices and applications have become a central feature of the consumer health technology market, offering users detailed metrics such as sleep duration, sleep stages, movement patterns, and recovery scores. These systems promise greater awareness and control over sleep habits, positioning themselves as tools for self-improvement and well-being.

The study finds that this promise is grounded in a broader trend of quantification, where sleep is reframed from a subjective, lived experience into a set of measurable indicators. Through wearable sensors and mobile applications, biological signals such as heart rate variability, motion, and breathing patterns are translated into numerical scores and visual dashboards.

By presenting sleep as a performance metric, these systems encourage users to evaluate their rest in terms of efficiency and optimization rather than personal experience. Over time, users begin to align their behaviors with the metrics provided, adjusting routines to improve scores rather than focusing on how they feel.

The research highlights that this shift is not merely a matter of interface design but reflects a deeper epistemological change. Knowledge about sleep is increasingly mediated by algorithms, which determine what counts as good or bad sleep. Users are encouraged to trust these outputs, even when they conflict with personal perception.

This dynamic introduces a new form of dependence, where individuals rely on external systems to interpret their own bodily states. Sleep trackers, in this sense, do not just measure sleep; they redefine it.

Human-centered design collides with posthuman user realities

Sleep tracking technologies expose a contradiction within human-centered design. HCI has traditionally emphasized designing systems that prioritize human needs, values, and experiences. However, the operation of sleep trackers suggests a different reality.

The study introduces the concept of immanent posthumanism to describe how users are reconfigured through their interaction with these systems. Rather than being autonomous individuals who use technology as a tool, users become part of a data-driven assemblage where human and machine processes are deeply intertwined.

In this framework, user identity is no longer fixed or purely human-centered. Instead, it is shaped through continuous interaction with data streams, algorithms, and feedback loops. Sleep trackers collect data, process it through proprietary models, and return insights that influence user behavior, creating a cycle in which users adapt to the system as much as the system adapts to them.

This process challenges the assumption that users remain in control. While interfaces present choices and recommendations, the underlying structure of the system guides behavior in subtle but powerful ways. Notifications, goal-setting features, and performance scores nudge users toward specific actions, often without explicit awareness.

The human-centered framing of these technologies can obscure their effects. By emphasizing personalization and user benefit, designers may overlook how systems impose standardized norms of health and performance. These norms are embedded in algorithms that define optimal sleep patterns, often without accounting for individual variability or contextual factors.

Consequently, users may internalize these norms, measuring themselves against algorithmic standards that may not reflect their actual needs or circumstances. This creates a tension between lived experience and data-driven evaluation, where the latter often takes precedence.

Data-driven feedback loops reshape behavior and self-perception

Sleep tracking systems operate through continuous feedback loops that influence both behavior and self-perception. Users receive daily reports, scores, and recommendations, which encourage them to modify routines in pursuit of improved metrics.

These feedback mechanisms can produce positive outcomes, such as increased awareness of sleep habits and motivation to adopt healthier behaviors. However, the research also highlights potential downsides, particularly when users become overly focused on achieving specific scores.

One emerging phenomenon is the prioritization of numerical outcomes over subjective well-being. Users may feel dissatisfied with their sleep if scores are low, even when they feel rested, or conversely feel reassured by high scores despite experiencing fatigue. This disconnect illustrates how algorithmic feedback can override personal judgment.

The study also notes that these systems can create new forms of anxiety. The pressure to optimize sleep, combined with constant monitoring, may lead to stress and overthinking, a condition sometimes referred to as orthosomnia. In such cases, the pursuit of better sleep metrics paradoxically undermines actual sleep quality.

The research suggests that sleep tracking contributes to the normalization of self-surveillance. By encouraging continuous monitoring and evaluation, these technologies embed data-driven practices into everyday life, extending beyond sleep into other domains of health and behavior. This normalization raises important questions about autonomy and control. As users integrate these systems into daily routines, the boundary between voluntary self-improvement and algorithmic influence becomes increasingly blurred.

Rethinking design and governance in data-driven health technologies

The findings call for re-assessing how consumer health technologies are designed and governed. While sleep trackers offer clear benefits, their broader implications require careful consideration.

Among others, one key issue is transparency. The study highlights that many sleep tracking systems rely on proprietary algorithms that are not fully disclosed to users. This lack of transparency makes it difficult for individuals to understand how metrics are generated or to evaluate their accuracy. Improving transparency could help users make more informed decisions about how to interpret and act on the data provided. This includes clearer communication about the limitations of sleep tracking and the variability of individual sleep patterns.

Another important consideration is the role of user agency. Designing systems that support rather than override personal judgment may help mitigate some of the negative effects identified in the study. This could involve providing contextual information, encouraging reflection, and allowing users to customize how data is presented and used.

The study also calls for a broader shift in how human-centered design is understood. Rather than assuming a stable, autonomous user, designers must account for the ways in which technology reshapes identity, behavior, and knowledge. This requires integrating insights from fields such as philosophy, sociology, and critical data studies into HCI practice.

From a policy perspective, the research underscores the importance of regulating consumer health technologies to ensure they align with user well-being. This includes addressing issues such as data privacy, algorithmic accountability, and the potential psychological impact of continuous monitoring.

Sleep tracking as a lens into the future of human–technology interaction

Sleep tracking serves as a microcosm of larger trends in datafication, where everyday activities are increasingly captured, analyzed, and optimized through technology.

The concept of immanent posthumanism introduced in the study suggests that these changes are not external to human experience but are deeply embedded within it. As individuals interact with data-driven systems, they become part of hybrid networks that combine human and machine elements.

This perspective challenges traditional boundaries between user and system, raising questions about what it means to act, decide, and know in an algorithmically mediated world. It also highlights the need for new frameworks to understand and navigate these interactions.