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Mahipal Jadeja
Digital Marketing Manager| #tech enthusiast | Blogger

Thriving mobile app technology has made our daily activities easier. Hundreds of thousands of mobile apps are available in the app store in the ‘health and fitness’ category to make our life more comfortable and healthier. This surge in the number of fitness apps is largely attributed to the fitness boom that occurred in the year 2014 with the advent of wearables. However, the trend quickly shifted in favor of the mobile apps because they can effectively control the wearables as well.

Today, as the number of smartphone users increases by leaps and bound, the popularity of customized fitness apps is increasing continually. Statista has predicted that globally, the number of fitness app users will surpass the figure of 353 million by 2022. Another report has estimated that the mHealth apps revenue will expand to $31 billion by 2020. Taking cognizance of this scenario, both Google and Apple have come up with Google Fit and Health Kit respectively to facilitate mobile app development for the Health and Fitness domain.

Types of Health and Fitness Apps

It is indeed quite a task for the fitness companies to stand ahead of the curve with a tailored mobile app. It is necessary to keep the health and fitness trends in mind along with the current direction of the niche market and the user behavior while developing a health and fitness app. \

These days, four types of health and fitness apps are in vogue to improve the user’s health and lifestyle:

A high-end health and fitness app integrates the features of two or more categories to give end-to-end fitness solutions. However, new players should stick to any one category for ensuring better penetration in the fitness app domain. Also, feature integration is proportional to the development duration and the cost of an app, and therefore, it is advisable to start with minimum necessary features.

Most Sought After Features of Health and Fitness Apps

Here we describe a few noteworthy features to be included in the tailored health and fitness mobile app to attract and retain more people:

Users hate a lengthy and complex registration process. You need to enable the first-time users to create their account with social media accounts. Or, you can make the entire process short and simple by asking for name, email id, and mobile number. The app will send the verification code (OTP) to the given mobile number, and the user gets registered.

The second most important feature is a user profile. Here, the users enter their personal data like age, weight, etc. You can make the profile stronger and inclusive to give your users all types of personal information at a stretch. Also, in future, if you want to add functionalities related to healthcare in your fitness app, the detailed user profile can help you a lot.

From signing up to sharing fitness-related activities, the social media integration can serve many objectives for your app users. You can also get a social media advantage as the users spread a good word on their social accounts for your app. The social media integration can also boost the online search for your application.

2. Wearable Device Connection

As the wearable devices have started gaining ground swiftly, it is beneficial for you to connect the app with the wearable devices. It enables the users to monitor or track their fitness activities on the wearables with ease. In a way, the wearable device connectivity also makes your app more user-friendly.

3. Record Tracking

Be it a workout plan or diet plan, your app needs to keep the record of the user’s activity and fitness schedule. From individual activities like cycling, walking, etc. to calories burned and weight reduction- the fitness app should track everything and show the real-time situation to the users. You can also add a functionality of planning a meal.

4. Geo location Facility

This is one of the most important features for a health and fitness application. The geolocation feature enables your app to monitor direction and routes when the users go for walking, cycling, or jogging. Also, it enables the users to know their exact location.

5. Push Notification

Well, push notifications can motivate the app users to achieve their goals. You can encourage the users for regular exercises with inspirational quotes through notifications. Also, the users can set up reminders for the workout, and push notification can effectively remind them on time.

6. Video Tutorials

Many people like workouts and exercises, but they have no idea where to start from. Live video tutorials can help them to know the ins and outs of workout. Video tutorials also show the right way to do a particular exercise. It is easy to give necessary tips regarding workout and their impact on a particular part of the body with video tutorials.

7. In-app Purchase

You can offer the option of purchasing fitness equipment and health drinks through in-app purchase features. It makes your fitness app complete and the users do not need to visit some other app or website for buying the equipment or a complementary health drink. This feature can save the user’s time and offers you an opportunity to earn some money.

8. Safe and Simple Payment Options

You can offer premium features or an ad-free version of your app with a small fee. Also, the in-app purchase feature can also need payment gateway integration. You need to make sure that the payment options are safe and convenient for the users to get the most from app monetization methods.

You can also consider offering unique features like loyalty program, reward for workouts, and the like to attract more people toward your app. Along with these features, simple yet attractive UI and a pleasant user experience are also necessary to ensure your app’s success in the app stores.

Additional features of the fitness mobile app are a direct chat with the gym instructors and built-in soothing music during the workouts. Such features can make your app more acceptable to a huge audience.

Here it is worth to mention that you should prefer a reputed and reliable mobile app development company to the freelancers to get a high-quality fitness app. After discussing the features, let us move to the cost estimation for your health and fitness app.

Cost of Health and Fitness Apps

Costing of a fitness application largely depends on two factors: feature integration and development duration. Though both these factors are interrelated, they need to be considered as major factors. Another factor is an hourly rate. Let us go through these factors one after another:

2. Development Duration- The duration of app development largely depends on the number of features you want to include in the app. Also, the development platform is also important in determining the cost. For example, Android app development requires more extensive testing because of a plethora of devices with different screen sizes.

3. Hourly Rate- There is a great variation in the hourly rate across the world. On one hand, American and Canadian app developers charge above $160 an hour, and on the other hand, Indian app developers take just above $25 an hour. It is better to hire mobile app developers from a reliable fitness application development company to meet the requirements in a cost-effective way.

4. Approximately, the fitness app development process takes over 1000 hours depending on the feature integration and other development procedures. For example, the documentation takes up to 40 hours, whereas the designing phase can last up to 70 hours, etc.

Summing up all the development aspects, the cost of fitness app remains in a range of $25000 to $50000 . You need to consult the mobile application development company to get the exact estimate depending on your project’s requirements.

Also, it is advisable to keep some amount aside for support and maintenance cost to keep your app up-to-date.

Conclusion

The health and fitness domain thrives as people tend to focus on remaining fit and active. A feature-rich fitness app can leverage you the benefits from this prevalent trend. All you need to contact the reputedapp development company and give them your app idea.

We offer enterprise-grade mobile app development services . Just connect with us to get 360-degree fitness app solutions.

All You Need to Know about Fitness App Development
Figure2

Event Rate for the Combined Outcome of Cardiovascular Hospitalization or Death by Quartile of UACR

Q= quartile; UACR= urinary albumin-to-creatinine ratio.

Figure3

Kaplan-Meier Survival Curve for the CompositeOutcome of CV Hospitalization or Death, Stratified by Median UACR

CV= cardiovascular; UACR= urinary albumin-to-creatinine ratio.

View this table:
Table4

Association of UACR With the Composite Outcome ofCardiovascular Hospitalization or Death: Unadjusted and Multivariable-Adjusted Cox Proportional Hazards Analyses

In a well-characterized HFpEF cohort, we evaluated the relationship between albuminuria, as measured by UACR, and clinical characteristics, echocardiographic parameters, and outcomes. Higher UACR was associated with higher rates of DM and CKD, and UACR was also associated with higher creatinine, higher blood urea nitrogen, and lower hemoglobin. We also found that higher levels of UACR were associated with markers of biventricular dysfunction and remodeling, even after adjustment for potential confounders, including DM and CKD. These cardiac markers included PRSW, log BNP, RV wall thickness, and RVFAC. Furthermore, UACR was associated with the composite outcome (cardiovascular hospitalization, HF hospitalization, or death) even after adjustment for age, sex, DM, CKD, CAD, and multiple markers of HF severity (except for BNP). Our study increases understanding of the echocardiographic correlates of albuminuria in HFpEF, particularly parameters of RV remodeling and dysfunction.

Prior study of albuminuria in HFpEF has been limited. Forty-two percent (n= 967) of the subjects from the CHARM (Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity) Programme had LV EF >40% (6) . The CHARM investigators described a prevalence of 30% and 11%for microalbuminuria and macroalbuminuria, respectively, in the combined HFpEF and HFrEF cohorts and showed that continuous UACR was similarly predictive of outcomes in HFpEF and HFrEF. Work from Miura etal. demonstrated the prognostic importance of positive urine dipstick test for urine albumin (a dichotomous indicator of albuminuria) in HFpEF but did not evaluate albuminuria as a continuous marker (9) . Recently, albuminuria was reported to predict incident HFpEF rather than HFrEF (10) . Like these previous studies, our work confirms the prognostic importance of UACR in HFpEF; however, as shown by our AUC and IDI analyses, the incremental benefit for UACR beyond traditional risk factors is limited. Aside from our results on UACR as a prognostic variable, we also found that the prevalence of microalbuminuria and macroalbuminuria in our cohort was similar to that recorded in CHARM patients with HFpEF, and associations of greater albuminuria with DM and CKD were similarly present in our study. In the Strong Heart Study, UACR was linked to systolic and diastolic dysfunction in type 2 DM (31) ; however, that study did not use tissue Doppler imaging and rather relied on E/A ratio and mitral deceleration time to measure diastolic function.

View this table:
Table 1. Baseline Demographic and Clinical Characteristics of Incident AF

Prevalence of associated comorbidities at the time of AF diagnosis varied over time (Table 1 ). The proportions of AF patients with a previous stroke/TIA and vascular disease remained fairly constant (11–15%), whereas clear increases were observed in the prevalence of hypertension and diabetes mellitus (46–61% and 9–14%, respectively). Ischemic heart disease at the time of AF diagnosis decreased (44–37%). Although we noted a decrease in the number of incident AF patients with a clinical diagnosis of heart failure (17–10%), the adoption of more‐stringent diagnostic criteria during the study period was a confounding factor. Overall, the CHA 2 DS 2 ‐VASc score remained similar across all time periods (mean, 3.2; SD, 1.9). Type of AF was not consistently reported.

Medications at the time of incident AF diagnosis are displayed in Table 1 . Anticoagulation pre‐existing the diagnosis of AF was uncommon (n=2709; 4.7%). There was a steady increase in use of angiotensin‐converting enzyme (ACE) inhibitors and beta‐blockers from 1998 to 2010 and a steep decline in the use of digoxin.

The incidence of AF increased with age from 0.13 per 1000person‐years in those aged <55years to 7.65 per 1000person‐years in those aged ≥85years (Table 2 ). Incident AF was greater in men compared to women (1.33 vs 1.18 per 1000person‐years, respectively).

View this table:
Table 2. Age‐Adjusted Incidence Rates (With 95% Confidence Intervals) of AF in the UK Overall and by Sex, Stratified by Calendar‐Year of Diagnosis

Comparing the 3 time periods, the overall age‐adjusted incidence rate of AF per 1000person‐years was 1.11 in 1998–2001 (95% CI, 1.09–1.13), 1.33 in 2002–2006 (1.31–1.34), and 1.33 in 2007–2010 (1.31–1.35; Table 2 ). Although incidence rates were fairly static for younger patients, they continued to increase in older patients, both in women and men (Figure 1 ).

Figure 1.

Age‐adjusted annual incidence rate of atrial fibrillation per 1000 in the UK (1998–2010) showing (A) increases in incidence over time with increasing age, particularly in older patients, and (B) sex‐stratified incidence of atrial fibrillation with higher rates in men.

Averaged over all time periods, the crude 1‐year mortality rate was 8.8% in women with incident AF (95% CI, 8.4–9.1%) and 10.6% in men (95% CI, 10.2–11.0%). As expected, older patients had substantially higher mortality: 1.0% (18–39years), 2.2% (40–54years), 3.2% (55–64years), 6.0% (65–74years), 10.4% (75–84years), and 23.7% (≥85years) per year.

Two age groups were modeled to assess mortality over time (Figure 2 ). In patients aged 55 to 74 there was a reduction in mortality (IRR per calendar year, 0.97; 95% CI, 0.95–0.99; P <0.001). In contrast, patients aged ≥75years had similar mortality between 1998 and 2010 (IRR, 1.00; 95% CI, 0.99–1.01; P =0.84).

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