This study analyzed the association between GPS-based mobility data and trauma patients to validate geography-based mobility analyses as a tool for assessing orthopedic trauma patient loads. Moderate to high correlations between the MIL and the number of outpatients were found. This indicates that GPS-based mobility data has the potential to serve as a prediction tool for the amount of trauma patients. These findings improved our understanding of the relationship between mobility patterns and trauma patients, and in turn, the ability to predict the number of trauma patients in the future. This could lead to improved resource planning, public health policy, and cost-effectiveness, thereby enhancing patient care and reducing the burden on orthopedic trauma departments, especially during challenging times such as the COVID-19 pandemic. Furthermore, it could be stated that hard mobility restrictions led to a decrease in mobility and trauma patient numbers, albeit with incremental duration a decreasing compliance to the measures was observed. Therefore, one can conclude that at least for a certain time period movement restrictions are a suitable method to reduce trauma patient numbers in emergency situations such as pandemics but a loss of effectiveness over time is to be expected.

A moderate to high correlation between both MIL and the cumulative trauma patient numbers in all cities were observed. The correlations of each distinctive city were equivalently strong. This means that the decrease of the population’s movement is associated with a decrease in trauma patients and vice versa. As similar results using both mobility indices (Google and Apple) has been calculated, the results of this study suggest that the high correlation is not bound to a specific tool or provider. Hence, mobility data have the potential to be used for predictions of trauma patient numbers. This would be of advantage in multiple respects. Besides being prepared for future infection waves and pandemics, it could be useful as a prospective basis for estimating how much personnel, materials or beds will be needed in specific time periods. A simple linear regression model showed that when the Google mobility index increases by 0.76 units, it is expected that the outpatient index will increase by 1 unit. The F‑test shows that the regression model is significant (p < 0.01), meaning that the relationship between the two variables is not due to chance. This suggests that the MIL is a good predictor of the average outpatient index, and that changes in the Google mobility index can be used to estimate changes in the number of trauma patients.

There are multiple other investigations suggesting that GPS-based mobility data are applicable to assess a population’s general mobility patterns and their adherence to the imposed restrictions [7]. Moreover, there is evidence that mobility patterns show correlations with disease spread and infection rates [18]. For example, Periyasamy et. al. analyzed the relationship between the doubling time of the infection rate and Google location data in India [19]. They found a strong negative correlation of mobility patterns and the COVID-19 doubling time and created a model to predict the doubling time based on Google mobility data. Lami et. al. analyzed the relationship between growth ratio and mobility patterns and came to the same conclusion [20]. Scott et. al. found a strong correlation between the number of trauma patients and the Google mobility indices [21]. To our knowledge this is the only other study evaluating the correlation between the Google mobility index and the number of trauma patients and differs from our study in several key ways [21]. Our study provides a more comprehensive and focused examination of this relationship in Austria using data of two providers over a longer time period and hence advances the understanding of this topic.

The governmental imposed mobility restrictions achieved their intended impact on the population’s mobility as well as on the trauma patient numbers. This study found the highest decrease during the first lockdown. These accounted for −47% decrease in mobility and −64% in outpatients compared to the same time period in the previous year. During the subsequent lockdowns, reductions were recorded as well, albeit these were less pronounced. This observation is in accordance with other studies that investigated the development and dynamics of orthopedic and trauma epidemiology [22]. An additional factor that could have added up to the strong reduction in patient numbers is the fear of getting infected in the hospital [23]; however, this explanation might mostly apply to ambulant patients with less severe injuries. Our findings let us conclude that movement restrictions are effective tools for curbing trauma patient numbers. Especially the initial hard constraints yielded the greatest effect. Further repetitive restrictions of long durations led to a decrease in adherence, which was reflected in higher GPS-based mobility. Consequently, this led to higher numbers of patients admitted to trauma units. Studies on effectiveness of governmental containment measures after the first wave suggested that the most effective way to stop the virus spread are hard lockdowns with restriction of social gatherings and movement restrictions [24]; however, it should be remarked that these measures also have damaging effects on economics, mental and physical health and security. They only present as an appropriate approach for a limited time period [25]. This is also reflected in our data, as there seems to be a decreasing compliance to the restrictions. Thus, it should be queried if the risk-benefit ratio of lockdowns still yields positive results regarding reduction of accidents in future studies.