Why Runners Should Track the Gym as Carefully as the Miles

Most runners can say how far they ran last week, what pace they held, and how their heart rate behaved. A watch records every split. The strength sessions that support those miles are often reduced to “trained legs”, scattered across a notes app, or left in memory. That creates a blind spot: runners can see the endurance work clearly but struggle to judge whether the gym work is progressing, consistent, and still matching the demands of the running programme.

BeanFit is an independent iPhone app developed by Anthony Grisoni to close this gap. Its uncommon combination is the way set-level strength data is tracked alongside cardio, heart-rate, bodyweight and muscle-load analytics, making all your training work visible to review. Completing a session is not the same as understanding how it was completed.

The missing half of the training log

A useful strength record needs more than an exercise name and a session total. A back squat completed for three sets of five at 130 kg can mean something very different when the sets feel like RPE 6.5, 7, and 7.5 than when they feel like 8.5, 9 and 9.5. The tonnage is identical, but the training exposure is not. Set-level load, repetitions, RPE, and notes preserve the context that helps explain whether a runner is adapting, accumulating fatigue, or simply repeating the same work without progress.

This is especially valuable for mixed training. Your periodisation might dictate running volume rising while gym volume needs to fall. A given strength exercise may stay at the same load while perceived effort drops. A note may explain why a session looked flat despite the numbers appearing normal. Recording these details turns the gym from a separate obligation into information that can shape the next decision.

Why strength training matters for short- and long-distance runners

For middle- and long-distance runners, the strongest evidence centres on running economy: the oxygen cost of maintaining a given pace. Recent systematic reviews and meta-analyses indicate that high-load resistance training, plyometric training, or a planned combination can improve running economy, particularly when added to normal endurance training rather than replacing it.^1-3

The benefits are not confined to one physiological variable. Reviews of distance-running studies have reported improvements in time-trial performance, maximal sprint speed and other performance indicators after appropriately programmed strength work. These qualities matter at both ends of the running spectrum: shorter events demand high rates of force and power, while longer events still depend on economical force production, finishing speed and the ability to maintain running mechanics as fatigue accumulates.^2,4

Strength and endurance training can also coexist without automatically cancelling each other out. An updated meta-analysis found that concurrent aerobic and strength training overall remains compatible for maximal strength and muscle development. However, explosive-strength adaptations may be more sensitive to how sessions are scheduled. In practice, programming, recovery and exercise selection still matter.⁵

Current physical activity guidance recommends both aerobic and muscle-strengthening activity, and recent meta-analyses associate muscle-strengthening activity with lower all-cause and cardiovascular mortality. The greatest health value appears to come from combining aerobic and resistance activity rather than relying on either as sufficient on its own.^6-8

Runners who maintain strength, muscle function and training consistency may find it easier to keep participating, adapt their programme as life changes, and continue enjoying the sport for longer.

How BeanFit works

TL;DR: BeanFit logs every working set and stores it alongside cardio, heart rate, body weight, and longer-term analytics in one iPhone app.

• Resistance sessions can be logged with weight, repetitions, RPE, rest time and notes.
• Progression information appears during a live session as it is being recorded.
• Estimated one-repetition maximums, personal records and exercise history can be reviewed over time.
• Cardio sessions can track duration, distance, pace, average heart rate, maximum heart rate and personal notes.
• The dashboard and analytics views bring together resistance, cardio, heart rate, body weight, and muscle load across one week, one month, three months, six months, one year, or all time.
• Previous periods can be revisited to compare training blocks rather than relying on the most recent week.
• The weekly muscle-load map translates logged resistance work into front-and-back anatomical diagrams across more than 27 muscle groups. This can quickly visualise which muscles have received repeated work and what may have been neglected.
• BeanFit also includes 39 built-in templates, more than 200 resistance and cardio exercises, custom exercises, shareable templates and backup export and restore.

Worked example: a half-marathon runner who lifts

Consider an endurance runner building towards an autumn half-marathon. The objective is to increase running volume, retain sufficient strength work to support performance and robustness, and flag when the two parts of the programme no longer fit together.

Day	Session	Recorded detail
Monday	Easy run + drills	8 km easy in 48 minutes; average HR 146 bpm; maximum HR 161 bpm; plus 6 × 20 s strides and running drills.
Tuesday	Strength	Back squat: 3 × 5 at 130 kg, RPE 8.5 / 9 / 9.5. Romanian deadlift: 3 × 8 at 100 kg, RPE 8 / 8.5 / 9.
Wednesday	Tempo / HM-pace run	2 km warm-up + 3 × 10 min at tempo with 2 min jog; total 12 km in 64 minutes; average HR 164 bpm; maximum HR 178 bpm.
Friday	Strength	Lower-volume top-up session using similar movements at a lower overall dose.
Saturday	Long run	18 km easy-steady in 1 hour 42 minutes; average HR 150 bpm; maximum HR 166 bpm.

In a conventional running app, Monday’s run is recorded in detail while Tuesday’s lifting may be almost invisible. In BeanFit, both sessions sit in the same training history. Four weeks later, the runner can ask more useful questions: Is the same squat load moving at a lower RPE? Has cardio duration fallen while gym volume has risen? Are calves, hamstrings, or upper-body support work repeatedly under-dosed?

Suppose the same 130 kg squat is later completed at RPE 7.5, 8 and 8.5. That is a clearer sign of improved tolerance than total tonnage alone. If easy-run heart rate is also lower at a similar pace, that may be encouraging. However, heart rate should still be interpreted alongside heat, sleep, terrain and fatigue rather than treated as a diagnosis.

The example is illustrative rather than prescriptive. Its value lies in the review process: the athlete can see the support work clearly enough to decide whether to progress, maintain, or reduce it.

Why progression over time matters

Most useful training decisions are not made based on a single workout. They emerge from trends. The same load may feel easier. An extra repetition may be achieved at the same RPE. Cardio frequency may drift down while gym volume climbs. Bodyweight may remain stable while estimated strength rises. None of these signals is decisive alone, but together they illuminate your training picture.

This is where integrated analytics can directly improve decision-making. They allow runners to review support work in the same rhythm as their mileage: week to week, block to block and across an entire season. That makes it easier to distinguish a planned reduction from inconsistency, a genuine progression from a one-off good day, and a balanced programme from one that repeatedly ignores the same tissues or movement patterns.

BeanFit does not predict injury, prescribe rehabilitation or replace coaching. Its role is specific: it preserves your context for you (and your coach) to make a better-informed judgement. Better records do not guarantee better outcomes, but poor records make it much harder to know what should change.

Compatibility, privacy and price

BeanFit is designed for iPhone and requires iOS 17.2 or later. Apple’s listing also permits installation on Apple-silicon Macs running macOS 14.2 or later, although it is not verified for macOS. The app is currently available in English and supports Family Sharing for up to six family members.

It is intentionally self-contained rather than via an integration hub. BeanFit is not positioned as a replacement for a GPS watch, route platform or cloud coaching service. Its job is to let users record resistance and cardio sessions, review the data locally, share template files when they choose, and export or restore backup files.

The privacy model is straightforward. No account is required, no training history is sent to a BeanFit server, the developer collects no data, and there is no cloud sync, advertising or social feed. Data remains on the device unless the user chooses to export or share it.

BeanFit costs £7.99 as a one-off purchase. There is no free tier, subscription, premium plan or upsell. For runners who are already paying for watches, race entries, and other platforms, the value proposition is deliberately simple: buy the training log once, keep the data, and receive future app updates without an additional monthly charge.

Summary

BeanFit will appeal most to runners who have identified that resistance training supports their performance but do not, or have not yet, track it with the same level of detail as their mileage. Its distinctive value is the combination of set-level strength logging, cardio and heart-rate records, muscle-load mapping and long-term analytics in one private iPhone app.

The app does not promise that more data automatically produces better training. It claims that when all your training (strength and endurance work) can be reviewed together, progression becomes clearer to achieve, imbalances become visible, and support work is less likely to disappear into a separate log that nobody revisits. BeanFit: a clearer path to chase your goals.

Quick answers

Is BeanFit only for lifters?
No. It is useful for runners, triathletes, strength-based, hybrid, and mixed-sport athletes who want strength, cardio, and heart rate information in the same training record.

Does BeanFit replace Garmin, Strava or a running watch?
No. BeanFit is a local training log and analytics app rather than a GPS route platform or automatic wearable-sync service. It complements a running watch by preserving the strength and cardio detail that often sits outside the main endurance record. Smart tech integrations are in the design phase.

Does strength training prevent running injuries?
Strength work can improve capacity and performance, but evidence for runner-specific prevention is mixed. Programme design, supervision, adherence, recovery, and the nature of the individual problem are all factors to be considered.

BeanFit is a fitness-tracking tool and does not provide medical advice. Exercise within your own abilities, and consult a healthcare professional if you have an injury, medical condition, or concerns about exercising. Stop immediately and seek appropriate medical advice if you experience pain or other concerning symptoms.

More: BeanFit on the App Store.

Related reading on the5krunner

• RiversideArt: Eighteen Garmin Apps Built Against the Subscription Tide: another indie shop shipping one-time-purchase apps against the subscription direction.
• Vitara: Apple Health Tells You What Happened, Vitara Shows What Changed: another privacy-first iPhone training app with on-device computation and no server.
• Negative Split: Free Local Running Analytics for Strava, No API to Lose: a local-first analytics tool that computes everything in the browser with no login or account.

References

1. Llanos-Lagos C, Ramirez-Campillo R, Moran J, Saez de Villarreal E. Effect of strength training programs in middle- and long-distance runners’ economy at different running speeds: a systematic review with meta-analysis. Sports Med. 2024;54:895-932. doi:10.1007/s40279-023-01978-y.
2. Blagrove RC, Howatson G, Hayes PR. Effects of strength training on the physiological determinants of middle- and long-distance running performance: a systematic review. Sports Med. 2018;48:1117-1149. doi:10.1007/s40279-017-0835-7.
3. Eihara Y, Takao K, Sugiyama T, Maeo S, Terada M, Kanehisa H, et al. Heavy resistance training versus plyometric training for improving running economy and running time-trial performance: a systematic review and meta-analysis. Sports Med Open. 2022;8:138. doi:10.1186/s40798-022-00511-1.
4. Beattie K, Carson BP, Lyons M, Rossiter A, Kenny IC. The effect of strength training on performance indicators in distance runners. J Strength Cond Res. 2017;31(1):9-23. doi:10.1519/JSC.0000000000001464.
5. Schumann M, Feuerbacher JF, Sünkeler M, Freitag N, Rønnestad BR, Doma K, et al. Compatibility of concurrent aerobic and strength training for skeletal muscle size and function: an updated systematic review and meta-analysis. Sports Med. 2022;52(3):601-612. doi:10.1007/s40279-021-01587-7.
6. Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organisation 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451-1462. doi:10.1136/bjsports-2020-102955.
7. Momma H, Kawakami R, Honda T, Sawada SS. Muscle-strengthening activities are associated with lower risk and mortality in major non-communicable diseases: a systematic review and meta-analysis of cohort studies. Br J Sports Med. 2022;56(13):755-763. doi:10.1136/bjsports-2021-105061.
8. Saeidifard F, Medina-Inojosa JR, West CP, et al. The association of resistance training with mortality: a systematic review and meta-analysis. Eur J Prev Cardiol. 2019;26(15):1647-1665. doi:10.1177/2047487319850718.

Author: Anthony Grisoni, developer of BeanFit, edited by the5krunner.

Last Updated on 17 July 2026 by the5krunner