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March 20, 2026 · 5 min read

Gym Workout Mode: Tracking Resistance Training, Spinning and Elliptical

Gym Workout Modes: Tracking Resistance Training and Indoor Cycling

Gym-based workouts — resistance training and indoor cycling (spinning) — are among the most commonly logged workout types in phone health apps, and they each have distinct tracking requirements that differ meaningfully from outdoor endurance activities. Resistance training is characterised by short, intense efforts (each set 20–90 seconds) separated by rest periods, producing intermittent cardiovascular demand rather than sustained aerobic load. Indoor cycling produces sustained elevated heart rate that closely resembles outdoor cycling but without GPS distance data. Understanding how your smart ring and phone health app capture and interpret each workout type allows you to extract maximum value from every session logged — accurate calorie estimates, meaningful zone data, and progressive performance tracking over weeks and months.

Resistance Training: What Gets Tracked and Why It Matters

When you log a resistance training session with the correct workout mode, the phone app and ring track heart rate continuously throughout the session — capturing the cardiovascular demand of the session in full rather than just peak effort. This reveals several important patterns. First, cardiovascular response: during heavy compound lifts (squats, deadlifts, bench press), heart rate typically spikes to 80–90% of maximum during each set, then recovers to 50–65% during rest intervals — this intermittent pattern accumulates meaningful cardiovascular load over a 45–60 minute session. Second, session-to-session progression: comparing average HR, peak HR, and session duration across equivalent sessions over weeks shows whether you are adapting to the training load (lower HR at the same weight indicates improved cardiovascular fitness relative to the effort). Third, calorie estimation: resistance training calorie estimates require heart rate data — using accelerometer data alone in gym training significantly underestimates the metabolic cost, particularly for upper body exercises and static holds.

Key Insight: Heart rate monitors significantly improve calorie accuracy during resistance training — studies show heart rate-based estimates are 20–30% more accurate than accelerometer-only estimates for strength training. This matters when resistance training is part of a weight management strategy.

Indoor Cycling (Spinning): Zone Targets and Performance Metrics

Indoor cycling sessions are well-suited to heart rate zone training because pedal resistance can be adjusted to maintain a specific HR target throughout the session. A typical structured spinning class moves through multiple heart rate zones: warm-up in zone 1–2, climbing intervals in zones 3–4, sprint intervals in zone 5, and recovery periods between efforts. Tracking HR zone distribution across the session via the ring reveals whether you are spending the intended proportion of the session in each zone — important for ensuring the session delivers the planned physiological adaptation (fat oxidation, aerobic capacity, or anaerobic power depending on the zone target). Over multiple spinning sessions logged in the phone app, the trend data reveals whether your average HR at the same perceived effort is declining (indicating improved aerobic fitness) and whether your heart rate recovery between sprint intervals is improving (indicating better cardiovascular adaptation).

Figure 1: Heart rate trace from a 45-minute spinning session — zone 1–2 warm-up (minutes 0–10), zone 3–4 sustained effort (minutes 10–30), zone 5 sprint intervals with zone 2 recoveries (minutes 30–40), zone 1–2 cool-down (minutes 40–45); the phone app calculates total zone distribution and estimated calories burned from this continuous HR data

Selecting the Correct Workout Mode for Accurate Data

The workout mode selected in the phone app before starting a session determines how the device interprets movement and HR data to produce calorie estimates and session summaries. For resistance training, selecting 'strength training' or 'weight training' rather than 'general fitness' or 'other' activates the app's strength-specific metabolic calculations. For indoor cycling, selecting 'indoor cycling' or 'spinning' rather than 'cardio' or 'cycling' ensures the session is not scored against GPS distance expectations and that HR zone targets are applied appropriately. Some phone health platforms allow you to set pre-session heart rate zone targets — for example, 'maintain zone 3–4 for 20 minutes' — and provide real-time feedback during the session. Post-session, the app provides a complete summary including average HR, peak HR, zone distribution, active calories burned, and session duration, which feeds into weekly training load calculations across all workout types.

Building Progressive Fitness Records from Gym Data

The progressive value of logging gym workouts consistently in the phone app builds over months into a personalised fitness record that gym tracking alone cannot provide. By comparing equivalent sessions — the same lifting programme, the same spinning class — across months of logged data, the phone app reveals the physiological signatures of fitness adaptation: declining HR at equivalent loads, improving HR recovery between sets, lower peak HR at the same perceived effort. For resistance training, pairing HR data with session RPE (rate of perceived exertion) notes in the app annotation system reveals the training readiness trend — sessions that feel hard with elevated HR indicate accumulated fatigue and may warrant a deload; sessions that feel easy with normal HR indicate freshness and readiness for progression. This personalised feedback loop, built from simple consistent logging, replaces guesswork with data.

References

  1. Garber CE, et al. 'Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults.' Med Sci Sports Exerc. 2011;43(7):1334–1359. [Link]
  2. Lyden K, et al. 'Wrist accelerometry in assessing physical activity with hip accelerometry as the reference.' Gait Posture. 2011;33(4):618–622. [Link]
  3. Gellish RL, et al. 'Longitudinal modeling of the relationship between age and maximal heart rate.' Med Sci Sports Exerc. 2007;39(5):822–829. [Link]
  4. Halson SL. 'Monitoring training load to understand fatigue in athletes.' Sports Med. 2014;44(Suppl 2):S139–S147. [Link]
  5. Flatt AA, et al. 'Heart rate variability and training load among national collegiate athletic association division 1 college football players throughout spring camp.' J Strength Cond Res. 2017;31(10):2738–2745. [Link]
  6. Borg G. 'Psychophysical bases of perceived exertion.' Med Sci Sports Exerc. 1982;14(5):377–381. [Link]

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