CortexLit tool · learning attention

Tracking Fit Check

Decide whether to keep, loosen or drop a tracking habit, using the tracker meta-analysis, the pedometer review and the orthosomnia case series.

Runs offline in your browserNo account Nothing is uploaded

← Back to the article · See the evidence

What this is based on

Every output names at least one verified PubMed source.

Brickwood 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.
PMID 30977740 (verification gap: Effects were modest and follow-up was generally short, so it does not show that tracked activity persists.)
Systematic review and meta-analysis of interventions using consumer wearable activity trackers with feedback, compared with interventions that did not use tracker feedback. · weight 1.0 · Tracker interventions increased daily step count (SMD 0.24) and moderate-to-vigorous physical activity (SMD 0.27) compared with interventions that did not use tracker feedback.
Freak-Poli RLA, Cumpston M, Albarqouni L, Clemes SA, Peeters A. Workplace pedometer interventions for increasing physical activity. Cochrane Database Syst Rev. 2020;7(7):CD009209.
PMID 32700325 (verification gap: Very low certainty evidence and heterogeneous outcomes; most studies were at unclear or high risk of bias.)
Cochrane systematic review of 14 studies with 4762 participants, all multi-component workplace health programmes that included a pedometer, with follow-up after the programme ended. · weight 1.0 · Compared with no or minimal programmes, pedometer programmes may have had no effect on physical activity at least a month after the programme ended (6 studies, very low certainty). The review found insufficient evidence that a pedometer programme beats other activity programmes, and some effects seen during programmes were not consistent.
Baron 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.
PMID 27855740 (verification gap: A small case series: it shows the pattern exists, not how common it is.)
Clinical case series and commentary describing patients who sought treatment for self-diagnosed sleep problems based on consumer sleep-tracker data. · weight 0.2 · The authors described patients whose interpretation of sleep-tracker data produced a perfectionistic drive to improve their sleep scores and, in some cases, worse sleep and distrust of clinical testing. They proposed the term orthosomnia and argued clinicians need to help patients weigh tracker data against validated measures.

Limits: This is a decision aid built from pooled averages and one case series. It does not measure your mood, and it deliberately excludes weight and calorie tracking.

Privacy

Inputs stay in this page's memory. There is no telemetry, no analytics call and no network request. The optional share card below is off until you switch it on, is built on your own device, and never includes anything you did not choose to include.

Free vs Plus

Free (this page)

  • Unlimited fit checks
  • Copy or download the outcome
  • Offline, nothing stored

Plus (optional, one-time)

  • A monthly re-check reminder
  • History of your own decisions

Payment link: PLACEHOLDER_STRIPE_PAYMENT_LINK — not live yet. No account, no subscription, no data leaves this page.

Related

Read the full article · Cautions · All CortexLit articles