How do we square real-time health data with our own bodily experiences? Claire Zhu considers the relationship- or disjuncture- between wearable health devices and somatic embodiment.
From the portable Holter monitor to the first electrocardiograms, the desire to externally quantify the invisible interiority of human physiology has persisted throughout history. Yet, driven by proprietary machine-learning models, modern devices like Fitbits and Apple Watches synthesize disparate metrics, from heart rate variability to sleep quality, into real-time scores on hyper-accessible screen interfaces or synchronized smartphone apps. Whether strapped to our wrists or slipped onto our fingers, this novel ‘wearability’ encloses the user in a continuous bi-directional feedback loop. Viewed through the lens of embodiment theory- the understanding that we make meaning of ourselves and the world directly through the sensory reality of our bodies (Csordas, T.J 2002) – such devices naturally beg the question: How do we weigh our lived somatic experiences against algorithmic certainty? Conducting a recent feasibility study on wearable tracking for individuals with brain injury prompted me to extend this inquiry further: has the rapid implementation of digital health outpaced the ethical considerations for disabled and diverse physiologies?
In studies examining patient populations, particularly those struggling with post-discharge physical activity, commercial wearables offer therapeutic promise as tools for monitoring, screening, and detecting various somatic and clinical progress (Canali et al. 2022). By externalising physiological trends, these tools help users map measurable proxies of their actions, fostering self-regulation that hopes to improve adherence to disease regimens (Sullivan and Lachman 2017). Because they are marketed as “health and wellness” tools rather than as medical devices, tech companies can bypass strict Food and Drug Administration (FDA) regulations in the United States (U.S. Food and Drug Administration 2026). Despite this lack of formal clinical oversight, clinicians increasingly view the integration of these digital biomarkers as a milestone for preventive medicine, and their usage has proliferated across post-injury rehabilitation activity monitoring studies (Coravos, Khozin, and Mandl 2019).
Nevertheless, as Kristen Ostherr argues, most research thus far has focused on privileged social groups in the Global North who already conform to the “responsibilized, self-managing, and entrepreneurial citizen” (Ostherr 2022). A critical gap in literature remains in evaluating how medically vulnerable populations engage with, resist, or reinvent self-tracking.
In the aftermath of a traumatic brain injury (TBI), individuals encounter cognitive challenges such as headaches or memory loss that lack overt physical manifestations, rendering it an “invisible condition.” Consequently, the phenomenological labor of rehabilitation is fundamentally an act of re-embodiment: a conscious process of forging new ways of navigating and making meaning of an altered body and world (Timothy, Graham, and Levack 2016). Survivors consistently describe a loss of connection to their prior self, feeling “reborn” into an unfamiliar life, becoming a “different person,” or mourning a pre-injured self and describe a body that acts on its own impulses or becomes ‘unfamiliar, a frightening hindrance, and something they could never trust’ (Villa, Causer, and Riley 2021; Timothy, Graham, and Levack 2016). Recovery is thus a complex journey involving both a retraining of physical and cognitive skills such as motor coordination and executive functioning, and a renegotiation of one’s personal narrative, acceptance of disability, and redefinition of ‘success’.
Introducing wearable devices into a tumultuous renegotiation of the embodied experience raises complications. On the one hand, digital biomarkers can make the fraught uncertainty of the body manifest, providing evidence of fatigue, sensory overload, and restricted stamina. Yet, beyond privacy and security concerns, in privileging quantifiable output over the subjective and expressive, messy nuances of human meaning-making, do we risk compressing the non-linear arc of recovery into a gamified metric of daily optimisation?
The assumption that an algorithm can objectively narrate the state of a human body ignores the reality that biometric databases are built upon normative, able-bodied standards. What a machine designates as “healthy” or “rested” is an algorithmic guess derived from aggregated data that frequently excludes marginalised physiologies (Nafus and Sherman 2014). Approximately 80% of people who have had a stroke experience some form of motor impairment (Gittins et al. 2021). Yet, because they are calibrated based on able-bodied walking patterns, commercially available activity monitors prove highly unreliable for patients who use mobility aids across cardiology, neurology, and geriatric studies (Kooner et al. 2024). This oversight carries real clinical risk: consistent underreporting frustrates individuals who are actually meeting their milestones and can prompt dangerous overexertion as patients strain to satisfy daily thresholds. Manipulated metrics, such as deflated step counts, correlate with decreased self-esteem, higher blood pressure, and worse mental health outcomes (Zahrt et al. 2023). Beyond questions of accuracy, quantified health is bound to specific standards of numbers, profiles, and “healthy” lifestyle patterns. Such bodily regulation sounds insidiously resonant of Foucaultian biopower, promoting self-management rooted in normalized conceptions of the “healthy” citizen. We must critically interrogate how these algorithms leverage the authority of data to perpetuate certain socionormative ideals.
The gamified design of these devices could further jeopardize a user’s relationship with their body. A user may wake up feeling rejuvenated, only to be told by their device that their “recovery score” is suboptimal. Through interfaces that leverage variable rewards, streaks, and nudges, wearables can promote hyperfixation and an unhealthy obsession with optimizing health outputs. Features such as Apple’s “Activity Rings” or Fitbit’s leaderboards, for instance, facilitate a “social-mediaficiation” of the body. Even when trackers calibrate against an individual’s own baseline, the mandate for constant optimization risks framing the user as a closed system to be improved rather than a fluid process of perpetual becoming. That the wearable assumes the body should—or can—perform at a consistent level risks flattening the volatile, essential labor of rehabilitation into a binary of “success” or “failure.” The psychological toll of this dependency is well-documented. In a study with over 200 women, some participants reported feeling ‘naked’ without their Fitbits, and that activities felt “wasted” when completed without the device (Duus and Cooray 2015). Excessive trust in sensors can also trigger unnecessary anxiety (Predel and Steger 2021); in studies with individuals living with depression, Parkinson’s Disease, or multiple sclerosis, inappropriately designed visualizations have been described as actively demoralising (Polhemus et al. 2022).
The promise of wearable sensors to catalyze wellness is undeniable, yet their unchecked integration into clinical practice risks a tyranny of the score. Existing feasibility studies prioritize adherence and compliance but largely ignore the longitudinal phenomenological impact of these devices on a patient’s evolving relationship with their body. As Pols (2012) argues, controlled study protocols often ignore the creative, agential ways patients challenge or resist prescriptive technologies. Future inquiry thus must shift from quantitative analysis and compliance to qualitative investigations of how self-tracking mediates epistemic self-confidence and somatic agency, particularly for populations navigating the non-linear, unpredictable recovery.
Manufacturers must prioritise technical sensitivity and specificity, both for clinical accuracy and to minimise anxiety from false-positive alerts. Devices should incorporate visualizations sensitive to symptom volatility, helping patients recognize stabilization and stagnation as natural parts of recovery. Clinicians, meanwhile, must ensure these technologies remain complementary instruments, not substitutive authorities, alongside expanded digital literacy of device readings.
Yet, even with these safeguards, such reliance on technologised knowledge moves us further from the invaluable wisdom of our bodily experience—the reflective awareness of pain, effort, and rhythm that constitutes the human experience. Indeed, techniques in post-stroke management such as Body Awareness Therapy have demonstrated that when rehabilitation prioritises the experience of movement rather than its result, patients regain agency, confront their limits, and develop an integrated understanding of their own physiology (Cardile et al. 2025). True control perhaps is not found in the device alone, but in the entanglement of the tool with the patient’s own lived expertise. It is the synthesis of medical knowledge, reflective life-journaling, and sensory awareness that empowers the patient to move from being a passive recipient of data to an active agent of their own recovery (Ayobi et al. 2017). As artificial intelligence becomes increasingly embedded in wearable sensors, we must critically evaluate the cost of outsourcing our subjective somatic interpretations to relentless algorithmic systems that continuously monitor, process, and judge our bodies.
About the author:
Claire Zhu is an undergraduate at Columbia University, double-majoring in Neuroscience and Behavior and Medical Humanities, and is passionate about the intersection of the two fields. She serves as the President of Columbia Synapse and is a dedicated advocate for TBI awareness.
References
Ayobi, Amid, Paul Marshall, Anna L. Cox, and Yunan Chen. 2017. “Quantifying the Body and Caring for the Mind: Self-Tracking in Multiple Sclerosis.” In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 6889–6901. New York: Association for Computing Machinery. https://doi.org/10.1145/3025453.3025869.
Canali, S., Schiaffonati, V., & Aliverti, A. (2026). Evidence-based medicine and the promises and limits of digital health and wearable technology. Studies in History and Philosophy of Science, 116, 102097. https://doi.org/10.1016/j.shpsa.2025.102097
Cardile, Davide, Viviana Lo Buono, Francesco Corallo, Angelo Quartarone, and Rocco Salvatore Calabrò. 2025. “A Scoping Review on the Body Awareness Rehabilitation after Stroke: Are We Aware of What We Are Unaware?” Frontiers in Neurology 15: 1497052. https://doi.org/10.3389/fneur.2024.1497052.
Coravos, Andrea, Sean Khozin, and Kenneth D. Mandl. 2019. “Developing and Adopting Safe and Effective Digital Biomarkers to Improve Patient Outcomes.” npj Digital Medicine 2: 14. https://doi.org/10.1038/s41746-019-0090-4.
Csordas, T.J. (2002). Embodiment as a Paradigm for Anthropology. In: Body/Meaning/Healing. Contemporary Anthropology of Religion. Palgrave Macmillan, New York. https://doi.org/10.1007/978-1-137-08286-2_3
Duus, Rikke, and Mona Cooray. 2015. “How We Discovered the Dark Side of Wearable Fitness Trackers.” The Conversation. https://theconversation.com/how-we-discovered-the-dark-side-of-wearable-fitness-trackers-43363.
Gittins, M., D. Lugo-Palacios, A. Vail, et al. 2021. “Stroke Impairment Categories: A New Way to Classify the Effects of Stroke Based on Stroke-Related Impairments.” Clinical Rehabilitation 35 (3): 446–458. https://doi.org/10.1177/0269215520966473.
Kooner, P., S. Baskaran, V. Gibbs, S. Wein, R. Dimentberg, and A. Albers. 2024. “Commercially Available Activity Monitors Such as the Fitbit Charge and Apple Watch Show Poor Validity in Patients with Gait Aids after Total Knee Arthroplasty.” Journal of Orthopaedic Surgery and Research 19 (1): 404. https://doi.org/10.1186/s13018-024-04892-9.
Nafus, Dawn, and Jamie Sherman. 2014. “This One Does Not Go Up to 11: The Quantified Self Movement as an Alternative Big Data Practice.” International Journal of Communication 8: 1784–1794.
Ostherr, Kirsten. 2022. “Artificial Intelligence and Medical Humanities.” Journal of Medical Humanities 43: 211–232. https://doi.org/10.1007/s10912-020-09636-4.
Polhemus, Ashley, Jan Novak, Sinan Majid, et al. 2022. “Data Visualization for Chronic Neurological and Mental Health Condition Self-Management: Systematic Review of User Perspectives.” JMIR Mental Health 9 (4): e25249. https://doi.org/10.2196/25249.
Pols, Jeannette. 2012. Care at a Distance: On the Closeness of Technology. Amsterdam: Amsterdam University Press.
Predel, Carolin, and Florian Steger. 2021. “Ethical Challenges with Smartwatch-Based Screening for Atrial Fibrillation: Putting Users at Risk for Marketing Purposes?” Frontiers in Cardiovascular Medicine 7: 615927. https://doi.org/10.3389/fcvm.2020.615927.
Sullivan, Alexandra N., and Margie E. Lachman. 2017. “Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity.” Frontiers in Public Health 4: 289. https://doi.org/10.3389/fpubh.2016.00289.
Timothy, Emily K., Fiona P. Graham, and William M. M. Levack. 2016. “Transitions in the Embodied Experience after Stroke: Grounded Theory Study.” Physical Therapy 96 (10): 1565–1575. https://doi.org/10.2522/ptj.20150419.
U.S. Food and Drug Administration. 2026. General Wellness: Policy for Low Risk Devices: Guidance for Industry and Food and Drug Administration Staff.
Villa, Darrelle, Hilary Causer, and Gerard A. Riley. 2021. “Experiences That Challenge Self-Identity Following Traumatic Brain Injury: A Meta-Synthesis of Qualitative Research.” Disability and Rehabilitation 43 (23): 3298–3314. https://doi.org/10.1080/09638288.2020.1743773.
Zahrt, Octavia Hedwig, Kristopher Evans, Elizabeth Murnane, et al. 2023. “Effects of Wearable Fitness Trackers and Activity Adequacy Mindsets on Affect, Behavior, and Health: Longitudinal Randomized Controlled Trial.” Journal of Medical Internet Research 25: e40529. https://doi.org/10.2196/40529.
