Orthometria: Rethinking Metric Fixation for Responsible Digital Health in Elite Sport

A New Name for When Health Data Starts Running the Athlete’s Life

Every morning, millions of people may soon wake up to a verdict from a wrist, finger, mattress or smartphone: a sleep score, a recovery percentage, a readiness color or a heart-rate-variability trend. For elite athletes, these numbers can influence training plans, rest days and perceptions of whether the body is prepared to perform. But a new perspective published in Sports Medicine – Open argues that the same technology intended to improve health and performance can sometimes become psychologically counterproductive. The authors propose the term “orthometria”—from the Greek-derived roots ortho, meaning correct, and metria, meaning measurement—to describe a fixation on digital health metrics that creates anxiety or harms well-being or performance. The concept is not presented as a medical diagnosis, and the researchers stress that its prevalence and consequences remain unknown. Instead, it is a proposed behavioral descriptor for a rapidly expanding problem: when measuring the body begins to matter more than experiencing it.

The timing is significant. The article estimates that more than 740 million smartwatch users could have access to daily health tracking by 2029, without counting smart rings, wrist bands, portable electroencephalography devices or sensor-equipped mattresses. Once confined largely to hospitals, sleep laboratories and exercise physiology facilities, physiological monitoring has become part of everyday life and the wider “quantified-self” movement. Consumer devices can estimate sleep duration, sleep stages, heart rate, heart-rate variability and activity patterns, allowing users to observe biological signals continuously. In high-performance sport, wearables can collect information during travel, competition and recovery, including environments where conventional laboratory equipment would be impractical. Professional cyclists, for example, have been monitored during major stage races to examine sleep and autonomic activity. Yet the expanding availability of data has outpaced sport-specific guidance on how athletes should interpret it, who should control it and when it should influence decisions.

The technology itself is scientifically ingenious but not equivalent to a clinical instrument. Many wrist-worn devices use photoplethysmography, or PPG, a technique in which green or infrared light-emitting diodes illuminate the skin while photodiodes detect changes in reflected light caused by blood-volume fluctuations. From those optical signals, algorithms estimate pulse rate and, under suitable conditions, beat-to-beat variation. Motion, sweat, loose contact and device placement can introduce artifacts, while sleep stages and recovery scores are often inferred through proprietary models combining several indirect measurements. Some commercial devices have been compared with reference methods such as polysomnography and have shown useful performance for particular metrics. That does not mean every output is equally accurate, or that a device’s interpretation should be treated as a direct reading of physiology. Accuracy can also vary across users and exercise intensities. Evidence concerning skin pigmentation and PPG remains mixed, but studies cited by the authors indicate that heart-rate error may increase during intense exercise for people with darker skin tones and that missing or lower-quality data may be disproportionately common.

The greatest interpretive leap occurs when raw or relatively transparent measurements are converted into composite scores. A platform might combine sleep duration, resting heart rate, heart-rate variability, recent activity and other inputs into a single “readiness,” “recovery” or “training status” number displayed on a scale from zero to 100 or as a traffic-light warning. Such scores are attractive because they turn complicated time series into an apparently simple instruction. But their proprietary inputs and algorithms can make them difficult to validate independently. Two devices may process similar signals differently and produce different conclusions about the same night of sleep. A low score may reflect a real change, ordinary biological variation, sensor noise or an algorithm’s assumptions. The authors therefore emphasize that digital health metrics are most useful as longitudinal signals rather than daily judgments. Average sleep duration may be estimated from roughly three to seven nights, they note, while understanding night-to-night variability may require six to ten weeks of continuous monitoring.

That distinction is especially important for athletes, who often work in environments where perfectionism is rewarded. The perspective cites evidence that as many as 39 percent of some athlete groups display unproductive perfectionism, a pattern that can turn a useful training aid into a source of pressure. A competitor who feels rested but sees a poor recovery score may worry unnecessarily, reduce training or begin repeatedly checking the device. Another athlete may feel exhausted despite an apparently excellent sleep score and conclude that their own perceptions are wrong. The proposed construct differs from “orthosomnia,” a term used for excessive pursuit of ideal sleep data, because orthometria encompasses any physiological metric gathered by consumer digital health technology. It also differs from the broader quantified-self movement: tracking is not inherently harmful, and orthometria refers specifically to situations in which engagement with the data produces a negative outcome. The authors are careful not to claim that this behavioral pattern has yet been established through prevalence studies or validated psychological measurement.

To reduce the risk, the researchers outline a five-stage implementation framework based on David Kolb’s experiential learning theory, which treats learning as an iterative cycle of experience, reflection and application. The process begins with preparatory education before data collection starts. Athletes should be told which metrics will be recorded, how often collection will occur, who will have access to the information, who owns it and how it may affect training decisions. Education should address not only what a metric represents but also how often it should be viewed and how much weight it deserves. Athletes and support staff should be prepared for disagreements between subjective experience and device outputs, because such discrepancies are normal when indirect estimates are compared with the complex combination of physical and psychological information represented by a person’s own perception. Staff should also understand the warning signs of counterproductive preoccupation and know when referral is appropriate.

The second stage involves a supervised period of direct experience with the device. Athletes begin collecting and viewing their own information, ideally for at least one to two weeks, but are encouraged to observe rather than immediately act on early readings. This delay allows an individual baseline to develop. In the third stage, an expert such as a sport scientist, physiologist, psychologist or medical professional helps the athlete examine trends across weeks rather than reacting to isolated daily values. The fourth stage shifts the emphasis toward athlete-led interpretation. In one-on-one sessions, the expert facilitates reflection, helping the athlete integrate digital signals with perceived fatigue, mood, training demands, travel and performance context. The framework even proposes periodic metric-minimized training blocks, during which athletes make decisions using subjective experience and coach feedback rather than live scores, then review the data afterward to calibrate their perceptions. Finally, the fifth stage moves toward independent use: athletes retain agency over whether and how they review their information, while coaches and specialists use agreed-upon data alongside the athlete’s observations to inform training and recovery.

The framework also identifies a social risk that could be more powerful than personal perfectionism. If athletes believe wearable data influences selection, playing time, contracts, training opportunities or staff judgments, monitoring can begin to feel like surveillance. Anxiety may then arise not because an athlete is fascinated by a score, but because a low number appears to threaten their career. For that reason, responsible implementation requires explicit rules governing data ownership, privacy, access, downstream sharing and use in performance evaluations. The authors say the approach should apply to organization-issued devices and personally owned wearables alike, provided that multidisciplinary experts are available to interpret the information. It should also begin only after an organization has selected metrics and devices with evidence of adequate validity and relevance. Without that groundwork, a carefully staged education program could lend undeserved authority to unreliable measurements.

The researchers present orthometria as a starting point rather than a final diagnosis. They call for interviews and other qualitative research with athletes to determine which metrics most strongly influence behavior, what fixation looks like in practice and which organizational conditions increase risk. They also propose developing and validating an orthometria-specific questionnaire, similar in spirit to existing measures of anxiety and preoccupation about sleep. Intervention studies could test whether education, reduced device exposure and feedback based on longer-term trends actually protect well-being without sacrificing useful information. The broader message is not that athletes should abandon wearable technology. These devices can provide valuable physiological signals beyond the laboratory, and their data may complement expert observation and athletes’ own experiences. But a number is still an estimate, a score is still an interpretation and neither should become the sole authority over a living body. As personal health technology spreads, learning when not to measure may become as important as learning how measurement works.