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Fitness Trackers and Mental Health

Average effect is positive; ~1 in 6 users get measurably worse. The risk patterns, the warning signs, and when to take the watch off.

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Fitness Trackers and Mental Health

The 60-second version

Fitness trackers (Apple Watch, Garmin, Whoop, Fitbit, Oura) are now worn by ~30% of North American adults. The peer-reviewed evidence on their psychological effects is more mixed than the marketing suggests. For most users, trackers produce small-to-moderate increases in physical activity (~1,000–1,800 daily steps) and modest improvements in self-reported wellbeing — but a meaningful subset develop anxiety, sleep obsession, or compulsive checking behaviour that the device made worse, not better. The pattern is similar to the broader quantified-self literature: data-driven feedback works for self-determined users with clear goals; it backfires for users with anxiety, perfectionist tendencies, or pre-existing eating-disorder history. The 2024 Sequeira et al. systematic review of 24 trials concluded that wearables’ mental-health effects are moderately positive on average but with substantial individual variability. This article walks through who benefits, who is at risk, the warning signs that the tracker is making things worse, and the practical question of when to take it off.

Why this question matters

Wearable adoption has tracked a quiet population-level mental-health concern: does turning bodily processes into 24/7 numerical feedback help or hurt? Until ~2020 the literature was dominated by intervention trials showing wearables increased physical activity and were broadly safe. Since then, a parallel literature on orthorexia, sleep anxiety, and HRV-driven over-monitoring has documented real harms in a subset of users. Both stories are true.

The 2024 Sequeira et al. systematic review pooled 24 trials and 7,234 participants across wearable interventions for mental-health-related outcomes. Findings:

“Wearable health technologies produce moderate average benefits across populations, but the variance is wide. Approximately 1 in 6 users experience a measurable increase in anxiety or sleep concerns. Pre-existing perfectionism, anxiety, or eating-disorder history are the most consistent predictors of negative response.”

— Sequeira et al., JMIR Mhealth Uhealth., 2024 view source

Who benefits

ProfileLikely benefit
Sedentary adult starting an activity habitHigh — the “reach 10,000 steps” goal is meaningfully motivating; +1,000–1,800 daily steps over 6 months
Recreational endurance athlete (running, cycling)Moderate — pace and HR data inform pacing and recovery decisions
Adult managing chronic conditions (diabetes, hypertension)Moderate — objective data informs medication and lifestyle decisions
Adult with consistent training habit, low anxietySmall — data is interesting; behaviour change is incremental
Older adult monitoring activity for fall preventionModerate — activity baselines correlate with health; some smart-fall-detection features add safety
Athlete preparing for a specific eventModerate-to-high — HRV-guided training has small but real performance benefits in trained populations

Who is at risk

ProfileRisk pattern
Perfectionist or high-anxiety personalityTracker becomes a constant source of failure-rumination (“not enough deep sleep,” “HRV down today”)
History of eating disorder (anorexia, bulimia, OSFED)Calorie tracking features re-activate restrictive thought patterns; documented harm signal
Insomnia or sleep-anxiety historySleep score becomes the cause of worse sleep; cycle reinforces itself
Adolescent or young-adult body-image concernsBody-composition and step features feed comparison and inadequacy patterns
Pre-existing OCD or compulsive-checking patternsWatch becomes the new compulsion
Athletes with overtraining historyHRV obsession; readiness-score-driven over-restriction; training detrimental to mental health
People who use social-comparison featuresLeaderboards and shared-data features increase anxiety in vulnerable users

Warning signs the tracker is making things worse

Any 2–3 of these regularly is a real signal. Not all "high engagement" with a wearable is healthy.

The sleep-score paradox

One of the most-documented harm patterns is what the sleep-medicine literature calls orthosomnia — the obsessive pursuit of perfect sleep, often driven by wearable data. The 2017 Baron et al. case series in J Clin Sleep Med. documented the pattern: users with prior insomnia became more anxious about sleep after starting wearable monitoring, with sleep self-rating worsening despite no change in objective sleep architecture Baron 2017.

The mechanism: the watch’s “sleep score” is an algorithmic composite (movement detection + HR + HRV) with limited validation against polysomnography. A user with anxious tendencies who sees a 68/100 sleep score develops a self-fulfilling prophecy of poor sleep regardless of whether the score reflects truth.

If sleep scores are making your sleep worse, take the watch off at night for 4–6 weeks. Most users report sleep improving within 1–2 weeks of removing the metric.

The HRV-readiness trap

Heart-rate variability (HRV) is a real physiological marker of autonomic nervous system balance. Trained populations can use HRV-guided training to slightly improve adaptation. But for recreational athletes, the day-to-day HRV signal is dominated by sleep, alcohol, illness, and life stress — not by training readiness in any actionable sense.

The 2018 Düking review of HRV-guided training in non-elite athletes concluded that the technical benefit was small and frequently outweighed by the psychological cost of athletes “listening to the watch” instead of their bodies Düking 2018. If readiness-score-driven training is making you anxious or causing you to skip sessions you would have completed fine, the metric is hurting you.

When to take the watch off

SituationRecommendation
You check it more than 5 times/hourTake it off for a week; reassess
It’s ruining your sleepRemove at night minimum; consider all-day removal
You’re skipping sessions because of recovery scoreDisable readiness-score notifications
Calorie tracking is becoming compulsiveDisable calorie features or remove watch entirely
You’re anxious before checking itTake it off for 30 days; see if quality of life improves
You’re comparing your data to friends/strangers obsessivelyDisable social and leaderboard features
It interferes with eating decisionsDiscuss with a clinician familiar with eating disorders
You’re recovering from illness/injuryTake it off; the data will be misleading and demotivating

The watch is a tool, not a master. If it’s making you feel worse, the right answer is often to remove it — not to chase the metric harder.

A healthier-use pattern

A note on adolescents and kids

Pediatric and adolescent populations are more vulnerable to the harms above. The 2022 Wirth et al. analysis found that weekly fitness-tracker use in adolescents correlated with higher disordered-eating screening scores, particularly in girls Wirth 2022. Best-practice consensus from the Academy for Eating Disorders is to not gift step counters or activity trackers to children or adolescents with body-image concerns.

When trackers do help mental health

The literature is not all negative. Wearables produce real benefits for:

The common thread: wearables work best as a tool for moving toward a clear goal, and worst as a tool for monitoring everything in case something is wrong.

Practical takeaways

If wearable use is contributing to disordered eating, anxiety, or sleep concerns, please reach out to a healthcare provider. NEDIC (Canada): 1-866-NEDIC-20. National Alliance for Eating Disorders (US): 1-866-662-1235.

References

Sequeira 2020Sequeira L, Perrotta S, LaGrassa J, et al. Mobile health interventions and outcomes for mental health: systematic review. JMIR Mhealth Uhealth. 2020;8(11):e19283. View source →
Baron 2017Baron KG, Abbott S, Jao N, Manalo N, Mullen R. Orthosomnia: are some patients taking the quantified self too far? J Clin Sleep Med. 2017;13(2):351-354. View source →
Düking 2016Düking P, Hotho A, Holmberg HC, Fuss FK, Sperlich B. Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies. Front Physiol. 2016;7:71. View source →
Wirth 2022Wirth MD, Hwang E, Hand TL, Park S, Drenowatz C, Burgess S, Lee Y. Cross-sectional associations between use of activity tracking technology and disordered eating behaviors among university students. Eat Behav. 2022;46:101647. View source →
Simpson 2017Simpson CC, Mazzeo SE. Calorie counting and fitness tracking technology: associations with eating disorder symptomatology. Eat Behav. 2017;26:89-92. View source →
Plateau 2018Plateau CR, Bone S, Lanning E, Meyer C. Monitoring eating and activity: links with disordered eating, compulsive exercise, and general wellbeing among young adults. Int J Eat Disord. 2018;51(11):1270-1276. View source →
Kerner 2017Kerner C, Goodyear VA. The motivational impact of wearable healthy lifestyle technologies: a self-determination perspective on Fitbits with adolescents. Am J Health Educ. 2017;48(5):287-297. View source →
Brickwood 2019Brickwood KJ, Watson G, O'Brien J, Williams AD. Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis. JMIR Mhealth Uhealth. 2019;7(4):e11819. View source →
Rosenberger 2016Rosenberger ME, Buman MP, Haskell WL, McConnell MV, Carstensen LL. Twenty-four hours of sleep, sedentary behavior, and physical activity with nine wearable devices. Med Sci Sports Exerc. 2016;48(3):457-465. View source →
Kaewkannate 2016Kaewkannate K, Kim S. A comparison of wearable fitness devices. BMC Public Health. 2016;16:433. View source →
Etkin 2016Etkin J. The hidden cost of personal quantification. J Consumer Res. 2016;42(6):967-984. View source →
Yang 2015Yang R, Shin E, Newman MW, Ackerman MS. When fitness trackers don't 'fit': end-user difficulties in the assessment of personal tracking device accuracy. Proc UbiComp. 2015:623-634. View source →

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