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:
- Average effect on subjective wellbeing: small-to-moderate positive (effect size ~0.3).
- Anxiety and depression scores: small reduction in average; increase in 12–18% of users.
- Sleep self-report: improved on average; worsened in users with pre-existing insomnia anxiety who began obsessing over “deep sleep” metrics.
- Eating-disorder screening scores: meaningfully increased in subjects with prior body-image concerns when calorie-tracking features were enabled Sequeira 2020.
“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
| Profile | Likely benefit |
|---|---|
| Sedentary adult starting an activity habit | High — 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 anxiety | Small — data is interesting; behaviour change is incremental |
| Older adult monitoring activity for fall prevention | Moderate — activity baselines correlate with health; some smart-fall-detection features add safety |
| Athlete preparing for a specific event | Moderate-to-high — HRV-guided training has small but real performance benefits in trained populations |
Who is at risk
| Profile | Risk pattern |
|---|---|
| Perfectionist or high-anxiety personality | Tracker 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 history | Sleep score becomes the cause of worse sleep; cycle reinforces itself |
| Adolescent or young-adult body-image concerns | Body-composition and step features feed comparison and inadequacy patterns |
| Pre-existing OCD or compulsive-checking patterns | Watch becomes the new compulsion |
| Athletes with overtraining history | HRV obsession; readiness-score-driven over-restriction; training detrimental to mental health |
| People who use social-comparison features | Leaderboards and shared-data features increase anxiety in vulnerable users |
Warning signs the tracker is making things worse
- Checking the watch repeatedly throughout the day for HR, steps, or readiness score.
- Feeling anxious when the watch shows a “low” metric (sleep score, HRV, recovery).
- Adjusting eating, sleep, or training based primarily on watch data rather than how you feel.
- Sleep getting worse as you focus on it more.
- Feeling guilty about not closing rings or hitting goals on rest days.
- Avoiding social events (long dinners, late nights) because they will affect tomorrow’s metrics.
- Wearing the device during recovery from illness and finding the data demotivating.
- Calorie counting becoming compulsive; weighing food becoming distressing.
- Significant other or family notes you talk about your watch data more than feels normal.
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
| Situation | Recommendation |
|---|---|
| You check it more than 5 times/hour | Take it off for a week; reassess |
| It’s ruining your sleep | Remove at night minimum; consider all-day removal |
| You’re skipping sessions because of recovery score | Disable readiness-score notifications |
| Calorie tracking is becoming compulsive | Disable calorie features or remove watch entirely |
| You’re anxious before checking it | Take it off for 30 days; see if quality of life improves |
| You’re comparing your data to friends/strangers obsessively | Disable social and leaderboard features |
| It interferes with eating decisions | Discuss with a clinician familiar with eating disorders |
| You’re recovering from illness/injury | Take 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
- Check the watch 1–3 times per day max, at fixed times: morning summary, post-workout review, end-of-day total. Not throughout the day.
- Don’t open the app multiple times daily; data review can be weekly or monthly for trend.
- Disable notifications for closed-rings, sleep scores, and readiness messages. The notification habit is the addiction.
- Take the watch off at night if sleep tracking causes anxiety.
- Take it off entirely on rest days, vacations, or illness recovery; you don’t need the data and the demotivation is real.
- Don’t use calorie tracking if you have eating-disorder history; talk to your doctor.
- Don’t use social-comparison features; leaderboards make most users feel worse.
- Train on RPE (rate of perceived exertion) sometimes, especially during base-building blocks; rebuild the body-listening skill the watch atrophies.
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:
- Adults rebuilding a movement habit after a sedentary period or illness: the gentle nudge plus visual feedback of progress is genuinely motivating without being compulsive.
- Cardiac rehab and post-event recovery: HR data helps users stay within prescribed ranges and gives clinicians objective data.
- Older adults: activity baselines correlate with quality-of-life and mortality outcomes; fall detection adds real safety value.
- People with chronic conditions who benefit from objective monitoring (atrial fibrillation, post-surgical recovery).
- Endurance athletes with structured training plans who use the watch primarily for pace and HR-zone targets.
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
- Average effect of fitness trackers on mental health is small-to-moderate positive, but with wide individual variability.
- Approximately 1 in 6 users develops measurable increased anxiety, sleep obsession, or compulsive-checking behaviour.
- Risk factors: perfectionism, anxiety, eating-disorder history, sleep-anxiety, OCD, body-image concerns, adolescence.
- Sleep score > sleep itself is a real harm pattern (orthosomnia); take the watch off at night if it’s making sleep worse.
- HRV/readiness-score-driven training can backfire for recreational athletes; train on RPE sometimes.
- Healthy use: check 1–3 times/day, disable notifications, no social comparison features, off on rest days and illness.
- Take it off if it’s making you anxious, ruining sleep, driving compulsive checking, or causing eating-decision distress.
- Don’t gift trackers to adolescents with any body-image vulnerability.
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 →


