Cashback on medical insurance for those who exercise regularly
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Bounty for the best solution
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- Motivate people to exercise
- Governments spend a lot on medicare, especially on lifestyle-related diseases like diabetes and obesity. Most countries (governments) spend on healthcare, probably not on all citizens but on a certain class. Government spending on healthcare increased by 73% in 2021 in India due to the pandemic. Even in the pre-pandemic era, in EU countries, on average, healthcare expenditure was almost 10% of their GDPs. Several studies suggest that regular exercise reduces healthcare expenditure significantly. As an example, people with cardiovascular diseases reduced their medical expenditure by $2500, on average, in a year if they exercised consistently. Even individuals without heart disease saved around $500 a year in healthcare costs. This suggests that governments could benefit from the "Cashback system". There are other intangible benefits like reduced off days by employees, improving work efficacy.
- People pay their medical insurance regularly (as paid previously).
- They wear a fitness tracker that cannot be cheated easily.
- They submit proof of regular physical activity (either quarterly, half-yearly, or yearly)
- They also submit proof that their physical activity has a positive effect on the body (maybe a fitness test , blood report, anthropometric measurements)
- They get a refund based on a quantifiable parameter of physical activity. The refund could be in the form of reduced future premiums, discounts on regular health check-ups, more discounts on healthcare costs, etc.
- This idea relies on finding a way to build a fitness tracker that cannot be cheated.
- We need a quantifiable parameter that correlates with all the parameters of fitness (cardio, muscle strength, agility, etc.) There are a few studies addressing this problem. As an example, health-related physical fitness (HRPF) is a parameter that can be calculated based on a battery of physical tests and appropriately indicates a person's fitness level on a scale of 1 to 100. HRPF has also been shown to differentiate people with comorbidities from healthy ones, validating its use.
- Fitness trackers usually track cardio. We need trackers for resistance training and other aspects of fitness.
- How do we incorporate sports into this?
- The same amount of physical activity can affect individuals differently. What do we measure then - the efforts (time, amount, intensity of activity) or the effect (weight loss, muscle gain, other blood parameters)?
[1]https://economictimes.indiatimes.com/news/india/economic-survey-govt-spending-on-public-healthcare-rose-73-last-year/articleshow/89257199.cms
[2]https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Healthcare_expenditure_statistics#:~:text=Expenditure%20related%20to%20health%20system,Slovakia%20to%204.7%20%25%20in%20Italy.
[3]https://www.excellenceinfitness.com/blog/how-exercise-can-help-you-save-on-healthcare-costs
[4]Patil P, Lalwani P, Vidwans H, Kulkarni S, Bais D, Diwekar-Joshi M, Rasal M, Bhasme N, Naik M, Batwal S, Watve M. A multidimensional functional fitness score has a stronger association with type 2 diabetes than obesity parameters in cross sectional data. PLoS One. 2021 Feb 5;16(2):e0245093. doi: 10.1371/journal.pone.0245093. PMID: 33544739; PMCID: PMC7864668.
Creative contributions
Other ways to prove your physical activity
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Why not measure the final effect on one's body?
- devices and logs are sometimes non-trustworthy (easy to cheat)
- cameras seem good, but can be cheated (lighter weights than what they seem, for example)
- videos require a lot of storage (not scalable)
- different effects of the same physical activity on people's bodies (somebody walks to work and somebody runs a lot and they have the same progress)
- sick people not being able to get a cashback easy as others
- at the beginning, people who apply for the cashback health ensurance insurance program would be asked to complete comprehensive physical examination
- it would require blood and urine check, lung capacity, fat, muscle mass, bone mass, and other things as the standard physical examination
- it would also include tests that define your physical capabilities such as strength, stamina, power, etc. (examples here and here); the tests are standardized and widely used
- parameters such as age, body type, heightetc. would also be taken into account
- after one year, a person would be asked to repeat the same tests (on the same devices, using same methods)
- the results would be then compared to the previous ones, the general change coefficient would be calculated and a personal cashback would be given
- no fitness data would be required since it doesn't necessarily proves you made progress
- standardized tests could be immediately used to prove one's performance
- mentioned tests are harder to cheat since they would be monitored by licensed facilities
- the key thing would be to create an algorithm that takes in the account the most important parameters such as age, body type, etc. and calculates the progress you made
- the best approach would be to perform pilot tests like mentioned above over few years period and gather statistics that would be the base for the algorithm
- people's health history should also be somehow taken into account, but I am still not sure if it would play an important role in progression coefficient calculation or not
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Counting all beneficial physical activity instead of only intentional exercise
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proved quality of life
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[1]Patil P, Lalwani P, Vidwans H, Kulkarni S, Bais D, Diwekar-Joshi M, Rasal M, Bhasme N, Naik M, Batwal S, Watve M. A multidimensional functional fitness score has a stronger association with type 2 diabetes than obesity parameters in cross sectional data. PLoS One. 2021 Feb 5;16(2):e0245093. doi: 10.1371/journal.pone.0245093. PMID: 33544739; PMCID: PMC7864668.
Please leave the feedback on this idea
[1]Patil P, Lalwani P, Vidwans H, Kulkarni S, Bais D, Diwekar-Joshi M, Rasal M, Bhasme N, Naik M, Batwal S, Watve M. A multidimensional functional fitness score has a stronger association with type 2 diabetes than obesity parameters in cross sectional data. PLoS One. 2021 Feb 5;16(2):e0245093. doi: 10.1371/journal.pone.0245093. PMID: 33544739; PMCID: PMC7864668. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245093
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