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Wall Pilates by Fit & Lean

Wall Pilates by Fit & Lean

At team LLC

Health & Fitness免费v8.4.40
App Store
评分

4.6

1,333 条评分

星级

★★★★★

最近更新

2026年4月16日

发布日期

2023年6月2日

更新内容

v8.4.40

We’ve fine-tuned the app to run faster and more reliably, so you can stay focused on your workouts.

应用信息

开发者
At team LLC
分类
Health & Fitness
价格
免费
版本
8.4.40
App ID
6446575424

简介

If you’re looking for a way to diversify your trainings and try something new and trendy, Wall Pilates is a great option.   The wall can add resistance to your regular workout routine, and it can also take your moves up a notch by adding elevation.   Wall Pilates workouts by Fit & Lean aim to: - Target every problem area (glutes, legs, inner and outer thighs, back, waist, hips, and abs) - Increase muscular endurance - Improve posture and balance - Strengthen core and back muscles - Improve flexibility and deepen stretch   The movements are slow and controlled, which makes exercises perfect for those who are new to Pilates or are looking for a low-impact workout. 
Find a wall nearby and give Fit & Lean a try! You’ll certainly feel like you have worked, and you’ll definitely feel how the wall supports some of the movements and adds challenge to others. Ready? Let's get to work! Fit & Lean offers different types of subscriptions so that you can choose the right one for you (3 months access renewal, 1 month access renewal etc.). Subscription automatically renews unless it is canceled at least 24 hours before the end of the current period. You can cancel your subscriptions at any time in your iTunes account settings. Payment will be charged to your iTunes Account at confirmation of purchase. Any unused portion of a free trial period, if offered, will be forfeited when the user purchases a subscription to that publication, where applicable. Privacy Policy: https://at-team.org/fitandhot-privacy/ Terms of Use: https://at-team.org/fitandhot-terms/

下载量预测

专业 · 预览

预估总下载量

121K89K190K
保守估计乐观估计

2K

低 / 月

3K

预估 / 月

5K

高 / 月

基于1,333 条评分
假设评分率1.1%
应用年龄36 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。