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A2010-578 | Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

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A2010-578 - Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma - braindump

Vendor IBM
Exam Number A2010-578
Exam Name Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Questions 120 Q & A
Recent Update December 11, 2018
Free PDF Download A2010-578 Brain Dump
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A2010-578 exam Dumps Source : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

Test Code : A2010-578
Test Name : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Vendor Name : IBM
Q&A : 120 Real Questions

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IBM IBM Assess: Fundamentals of

How safe Is IBM's Dividend? | killexams.com Real Questions and Pass4sure dumps

No influence discovered, try new key phrase!Dividend safeguard Relative to Its present Debt Load The remaining angle that we are going to use to determine IBM's current ... is currently lined by way of its fundamentals. IBM's dividend looks protected for the ...

IBM Shares Drop 22% This 12 months as Hope of Turnaround Dims | killexams.com Real Questions and Pass4sure dumps

Shares of foreign company Machines Corp. (NYSE: IBM) are down 22% this year as hopes of a turnaround promised by means of CEO Ginni Rometty dissolve. She has been at the task given that 2012 and has taken IBM through a couple of reinventions.

Rometty recently bought pink Hat, a big issuer of open source application essentially for companies. IBM paid $34 billion, which is an strangely high diverse of each profits and income. The deal should bolster IBM’s cloud-related organizations Rometty argues, however buyers appear to consider IBM made that case poorly.

Most weeks, IBM continues to punch out a few press releases, which is extraordinary for publicly held companies. among the most fresh:

Ilusión, Fiorentina & David’s Bridal open the door for digital transformation in vogue with IBM

Most have little to claim concerning the economic consequences of IBM’s plans. Many must do with IBM’s cloud initiatives, a neighborhood through which the enterprise needs to garner market share from leaders Amazon.com and Microsoft. Most research suggests that IBM’s share of the market is a small fraction of its fundamental competitors. IBM has now not made a believable case this may change, most likely as a result of there is none.

The largest knock against IBM is that it has not grown in years, in line with earnings, while its fundamental competition has grown at double digits quarter after quarter. The market remains stinging from another drop in IBM’s profits remaining quarter, down 2% to $18.8 billion. IBM said its cloud features brought in $19 million over the 365 days that led to the fresh quarter. it is challenging to tease that number out from what IBM calls “strategic imperatives” so a assessment to consequences from different agencies is challenging to make.

What isn't complicated to determine is that salary from market leader Amazon net services hit $6.7 billion closing quarter, up 48% from the same period the yr before. Its operating profits margin turned into an spectacular 31%.

IBM’s items and capabilities haven't been knit together in a way so that Wall road can believe the enterprise has a coherent method beyond grabbing at opportunities. One does not need to examine simply the year-to-date stock expense for proof. As an aside, IBM’s shares are down 32% over five years, whereas the Nasdaq is better via seventy four%.

i am drawn to the publication Get Newsletterterms and stipulations  

After a Disastrous Run, IBM inventory is too low priced to ignore | killexams.com Real Questions and Pass4sure dumps

It’s in fact been an ugly run for IBM (NYSE:IBM). overseas business Machines inventory has been hammered considering that early October, falling 25% at one factor. IBM inventory touched a nine-12 months low at one factor earlier than a modest rebound.

Admittedly, there are some explanations for the pullback. in spite of the fact that IBM inventory looked tremendously low-cost earlier than the declines, Q3 revenue disappointed, with income increase again turning poor after three quarters of raises.

on the conclusion of October, IBM agreed to acquire purple Hat (NYSE:RHT) for $34 billion in cash, an acquisition the market looks to dislike.

average weak spot in tech stocks doubtless added to the power. Mature, low-growth tech performs like Cisco techniques (NASDAQ:CSCO) and Oracle enterprise (NYSE:ORCL) have pulled returned as well. Neither stock, of direction, has considered declines like that of international business Machines stock.

IBM’s performance admittedly has been disappointing. I argued as these days as August that IBM gave the look of a purchase, writing that “I’d be bowled over” to look IBM trade decrease than $one hundred twenty five, at which element it could present a 5%+ dividend yield.

IBM is beneath $125, and that i am slightly bowled over. and i feel the sell-off in IBM stock has gone too far.

Is IBM a price trap, or a value Play?

fundamentally, IBM is inexpensive. It trades at lower than 9x consensus 2019 EPS estimates. Free cash stream advice for this 12 months suggests a similar diverse according to free money flow.

With IBM administration guiding for red Hat to be accretive to the ~$12 billion FCF figure, that assorted should fall even extra next yr. And it leaves ample room for IBM to pay out its present ~$6 billion in dividends.

So the simple argument right here seems reasonably easy to make. pink Hat itself adds roughly two points of salary boom a yr, helping to stabilize the business going ahead. IBM inventory is priced for a decline when it comes to each profits and free money move.

The dividend may still be fairly protected; there doesn’t seem to be room for a circumstance like that of standard electric powered (NYSE:GE) the place onerous debt leads to a dividend reduce. Even with the crimson Hat deal, IBM’s debt (and pension) load is still manageable.

but that fundamental case itself highlights the knowledge risk right here. The market in customary, and this market exceptionally, isn’t leaving pleasant organizations sitting around with a 9x P/E and a 5%+ dividend yield, even after some recent weakness. IBM stock didn’t hit a nine-yr low because the market wasn’t paying attention. The market turned into.

The risks to IBM inventory

the fundamentals here imply that buyers are pricing overseas company Machines as if it had been a declining company. looking backward, it is. revenue fell year-over-year for 23 consecutive quarters earlier than closing year’s this fall. operating margins have compressed over that duration.

And so the easiest bear case for IBM in the mean time is based on a single question: even with purple Hat, what’s distinct? The argument for buying IBM going again to 2012 has been, essentially, that the inventory is just too low-cost if it may stabilize revenue and margins. That bull case has been suitable, but IBM hasn’t been able to obtain that stabilization.

Even the good information of the closing few quarters doesn’t look necessarily that decent. IBM’s centered areas of growth (which it refers to as “strategic imperatives”), like cloud and AI, have extended salary 13% over the last yr. those categories force roughly half of earnings, which is respectable information.

The bad information is that IBM on the complete has grown income a bit over 2%. That in turn suggests the relaxation of IBM is seeing income fall anything like eight%. And the modest margin drive on the enterprise suggests that IBM is relocating from stronger revenue to weaker sales. It’s trying to trap up in cloud  while seeing its mainframe business, for example, wither away.

And on that front, Q3 in fact was disappointing. Cognitive options (which homes the established Watson) earnings declined in steady currency for the 2nd straight quarter. programs growth of 1% was a fantastic deceleration. The Q3 document harm the case right here. And with tougher comparisons on the style for the next three quarters, buyers doubtless can’t are expecting too a great deal within the manner of fireworks any time soon.

nonetheless intriguing

So IBM bulls ought to have their eyes open to the capabilities draw back. That said, $121 does appear too low-priced for IBM stock. The purple Hat deal could had been too pricy, however I consider Luke Lango, who made a forceful case for the strategic price of the acquisition. And with IBM having misplaced about $30 billion in market cap for the reason that early October, IBM inventory has more than priced within the expense tag.

The dividend appears secure in the mid-term. The balance sheet is safe. And eight-9x profits and free cash movement gives the company loads of flexibility to either pay off the crimson Hat-connected debt or ramp up shareholder returns.

extra vital, these multiples do exchange the bull case here somewhat. The argument for the last few years became that if IBM stabilized, the inventory would go up. At these levels, if IBM stabilizes, the stock can soar.

whatever like 13x $13 in 2019 EPS receives the stock to ~$170 – about 40% upside even before the dividend. The market now's pricing within the fresh fashion which makes some experience. however that additionally ability investors aren’t accounting for what occurs if the purple Hat deal become a very good one and IBM’s turnaround finally takes hold.

As of this writing, Vince Martin has no positions in any securities mentioned.




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A2010-578 exam Dumps Source : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

Test Code : A2010-578
Test Name : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Vendor Name : IBM
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Resilience and efficiency in transportation networks | killexams.com real questions and Pass4sure dumps

Abstract

Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions.

INTRODUCTION

Existing roadway design standards emphasize the efficient movement of vehicles through a transportation network (1–4). Efficiency in this context may include identification of the shortest or fastest route (1, 5–7), or the route that minimizes congestion (8). It is the primary criterion on which road networks are modeled and design alternatives are considered (6, 7, 9, 10). The Texas A&M Transportation Institute defines and reports traffic delay in urban areas as the annual delay per auto commuter (11). Other studies define efficiency as delay for the individual driver in terms of time spent moving or stopped (7), or mean travel time between all origin-destination pairs in the network (9). However, as the experience of any motorist in large American cities can attest, conditions beyond the scope of the roadway design, including congestion, accidents, bad weather, construction, and special events (for example, a marathon race), can cause costly delays and frustrating inefficiencies that result in fuel waste, infrastructure deterioration, and increased pollution (12, 13). Evaluating road networks based only on efficiency under normal operating conditions results in little to no information about how the system performs under suboptimal or disrupted conditions.

Infrastructure systems that exhibit adaptive response to stress are typically characterized as resilient (14–21). Given the essential role of transportation in emergency response, provision of essential services, and economic well-being, the resilience of roadway networks has received increasing policy attention. Nonetheless, scholars have yet to converge on a shared understanding of resilience suitable to guide design, operation, and reconstruction of roadway networks. Although resilience in infrastructure systems is characterized as a multidimensional concept (22, 23), in many engineering and civil infrastructure implementations, resilience is defined as the ability of a system to prepare for, absorb, recover from, and adapt to disturbances (16). Specific to transportation, resilience has been defined as “the ability of the system to maintain its demonstrated level of service or to restore itself to that level of service in a specified timeframe” (24). Others describe transportation resilience as simply the ability of a system to minimize operational loss (25) or use the term synonymously with robustness, redundancy, reliability, or vulnerability (26–28).

Current efforts in transportation resilience research have focused on framework development and quantification methods. These efforts include the specification of resilience indicators, such as total traffic delay (24), economic loss (29), post-disaster maximum flow (30), and autonomous system components (31). Practical concerns with this type of resilience evaluation are that it relies on uncertain performance data and often omits indicators that are unquantifiable (19). Other resilience approaches apply traffic network modeling to identify locations for critical buildings (for example, hospitals and fire stations) (32), minimize trip distance for individual passengers (33), and minimize travel time across the system (12). One drawback of existing network resilience methods is that they are data-intensive, often requiring limited information about resources for unusual road system repair (26, 28) or network behavior following a disruptive event (34). Moreover, existing resilience quantification approaches lack calibration and testing across a range of transportation systems. Because many disruptive events, and their associated consequences, are difficult to predict, resilient road systems must be characterized and evaluated by the capacity to adapt to a variety of different stress scenarios. Partly because of these obstacles, joint consideration of efficiency and resilience has yet to be implemented for transportation networks.

Here, we study the interconnections between resilience and efficiency (20) among road transportation networks in 40 major U.S. cities. We develop an urban roadway efficiency model, calibrate it on the basis of the observed data (11) of annual delay per peak-period auto commuter, and apply the model to calculate efficiency in 40 cities. Then, we model traffic response to random roadway disruptions and recalculate expected delays to determine the sensitivity of each city to loss of roadway linkages. The results may reveal important considerations for assessing proposals for improvement of roadway infrastructure that maintain efficiency under stress conditions.

METHODS

The Methods section appears here to help clarify the subsequent sections. To develop the urban roadway efficiency model, we defined the urban area boundaries, constructed the road networks, and evaluated the population density within cities using the Census Bureau data sets (35, 36) and OpenStreetMap (OSM) data sets (37). We relied on these data to assess commuter patterns, which we used to measure efficiency and resilience of road networks.

Alternative approaches to transportation have been offered and include those based on percolation theory and cascading failures (38–40), human mobility pattern studies (41–43), queueing (44, 45), and the use of historical data to predict traffic. We review these approaches in the Supplementary Materials and note that the main benefit of our model is that it relies solely on readily available public data, rather than on particular data sets that may or may not be practical to obtain for any particular region. The model’s algorithmic simplicity allows us to consider spatial topologies of cities in high resolution including tens of thousands of nodes and links. We did not create a more accurate transportation model than the existing ones, but we were able to obtain measurable characteristics of transportation systems (average delays) using our model.

Geospatial boundaries and population density

To define geospatial boundaries for the transportation infrastructure networks, we used the U.S. Census Bureau geospatial data set (35) for urban areas—densely developed residential, commercial, and other nonresidential areas (46). We approximated the exact urban area polygon with a simplified manually drawn one (Fig. 1A) and included all roadways within 40 km (25 miles) of it in the network. For each of the links, we calculated its length on the basis of the polyline defining the link and assigned a number of lanes m and the FFSs (see the Supplementary Materials).

Fig. 1 Definition of urban areas and assignment of nodes’ population.

(A) Boston, MA-NH-RI urban area as defined by the U.S. Census Bureau shapefiles (gray background). To simplify the model and the algorithms calculating the distance from network nodes to the city boundary, we approximate each of the urban areas shapefiles with a coarse manually drawn polygon (pink outline). (B) Assignment of the number of people departing from each of the network nodes. Population distribution (color polygons; red corresponds to higher population density), Voronoi polygons (black outline), and network nodes (dots) in Downtown Boston.

We next estimated population in vicinity of each intersection i using the Census Tract data (36). To this end, we split the map into Voronoi cells centered at intersections and then evaluated the population of each cell Ni asEmbedded Image

Embedded Image

(1)

Above, Nt is the population of Census Tract t, and Pi and Pt are the polygons of the cell and the tract, respectively (Fig. 1B and table S2).

Transportation model

We built on the gravity model to generate commuting patterns. The gravity model (47) is a classical model for trip distribution assignment and is extensively adopted in most metropolitan planning and statewide travel demand models in the United States (48–51). Other trip distribution models include, for example, destination choice models (52, 53). However, these models are not as widely used in large scale, because the detailed data required by these models are frequently unavailable (48).

We assumed that (i) the flow of commuters from origin region o to destination region d is proportional to the population at the destination Nd and that (ii) the flow of commuters depends on the distance xod between the origin and destination and is given by a distance factor, P(xod). Using these assumptions, we assessed the fraction of individuals commuting from region o to destination region d, fod, asEmbedded Image

Embedded Image

(2)

Then, the commuter flow from origin region o to destination region d isEmbedded Image

Embedded Image

(3)

Although individual driving habits may vary (54), we assumed that all drivers tended to optimize their commute paths such that their travel time was minimized. This assumption allowed us to calculate commute paths for every origin-destination pair using inferred FFSs. To calculate commuter flows between all pairs of intersections, we estimated distances xod as the distance of the shortest time path from o to d. Furthermore, in place of the distance factor P(xod), we used the distribution of trip lengths from the U.S. Federal Highway Administration National Household Travel Survey (55, 56), which we approximated with the exponential function (Fig. 2A and table S3).

Fig. 2 Model details.

(A) Distance factor P(xod) (Eq. 2) of trips given the distance between nodes (solid line) and the statistical data (bars). (B) Dependency of speed on density for V = 100 km/hour.

Next, we defined the commuter load on each road segment asEmbedded Image

Embedded Image

(4)where θod(ij) is a binary variable equal to 0 when the link ij is not on the shortest path connecting nodes o and d, and 1 otherwise. Note that in Eq. 4, we only considered origins that were not farther than 30 km from the urban area boundary polygon. The nodes farther than 30 km from the boundary were only used as destinations to evaluate the fraction of commuters not going toward the urban area (Eq. 2).

Because most commuters travel during peak periods, commuter loads Lij can be regarded as traffic-based centrality measures estimating the number of individuals using corresponding road segments. Then, the cumulative time lost by all commuters isEmbedded Image

Embedded Image

(5)where Vij and vij are, respectively, the FFS and the actual traffic speed along the ij road segment, lij is its length, l0 is the length correction due to traffic signals, and β is the proportionality coefficient same for all urban areas. The summation in Eq. 5 includes only links, whose origins and destinations are within the boundary polygon. A similar equation was obtained for the moving delay in the study of Jiang and Adeli (45), where the authors looked at the delay induced from road repairs.

The actual traffic speed vij depends on many factors including the speed limit, the number of drivers on the road, and road conditions. Although there exist a number of approaches to estimate actual traffic speed (57, 58), we chose to use the Daganzo model (59) to derive the traffic speed, as shown in the Supplementary MaterialsEmbedded Image

Embedded Image

(6)where vmin is the minimum speed in the traffic, vveh is the correction for the finite size of the car, and α is the proportionality coefficient (Fig. 2B).

Efficiency and resilience metrics

We measured efficiency as the average annual delay per peak-period auto commuter. In practice, lower delay means higher efficiency. There are multiple ways to map from delays to efficiency, such as taking the inverse values of delays, taking negative values of delays, etc. To avoid ambiguity and facilitate the interpretation of results, we used the delays themselves to quantify the transportation efficiency of urban areas.

We operationalized resilience through the change in traffic delays relative to stress, which is modeled as loss or impairment of roadway linkages. Looking at resilience from the network science perspective, we focused on topological features of cities, rather than on recovery resources available. Sterbenz et al. (60) evaluated a network’s resilience as a range of operational conditions for which it stays in the acceptable service region and highlighted that remediation mechanisms drive the operational state toward improvement. We are studying how availability of alternate routes helps remediate the consequences of the initial disruption to the network. In the traffic context, the immediate impact of a given physical disruption (and the time for it to unfold) in terms of closing lanes or reducing speed limits on affected roads will not vary much from network to network, although the number and type of these disruptions will. Likewise, the speed of restoring full functionality (through action in the physical domain) is not so much dependent on the road network as it is on the nature of the disruption (snow versus earthquake versus flood) and the resources that the city allocates to such repair. The level of functionality that these repairs achieve ought to be the full predisruption functionality, that is, eventually all roads can be fully cleared or restored. However, the immediate loss of function for a given traffic flow can very quickly be partially recovered after a disruption by action in the information domain, namely, rerouting of traffic. From the new steady state at that level of functionality, full functionality is gradually restored. Thus, our model proxies for resilience and is calibrated against the data that proxy for efficiency. At the same time, we note that to fully capture resilience characteristics of a transportation system, it is required to analyze recovery resources available and the effectiveness of coordination between the relevant authorities. Lower additional delay corresponds to higher resilience, but using the same reasoning that we had for efficiency, we quantified resilience through additional delays.

RESULTS Efficiency

Together, our traffic model has three parameters (proportionality coefficient α, minimum speed vmin, and finite vehicle size correction vveh) and is summarized in Eqs. 5 and 6. Given parameter values of the model, one can estimate the total delay incurred by all commuters in any given suburban area or, equivalently, the average delay per commuter. We take vveh = 9 km/hour and vmin = 5 km/hour and calibrate the model to determine the value of α to match the real data on the annual average delay per peak-period auto commuter provided by the Urban Mobility Scorecard (11).

We divide the 40 urban areas into two equally sized groups for model calibration and validation, respectively. We have found that for the 20 urban areas used for calibration, the R-squared coefficient took values in the range (−0.01 to 0.83) (Fig. 3 and Supplementary Materials). This allows us to set model parameters α and β (see Methods) as follows: α = 4.30 × 104 hour−1 and β = 10.59. These values correspond to the Pearson coefficient of 0.91 (P = 2.17 × 10−8).

Fig. 3 Modeled and observed delays in 40 urban areas.

Pearson correlation coefficients and P values between observed and modeled delays are (0.91, 2.17 × 10−8) for the 20 cities used to calibrate the model and (0.63, 3.00 × 10−3) for the 20 cities used to validate the model. Observed delays were taken from the Texas A&M Transportation Institute Urban Mobility Scorecard (11).

To validate the model, we estimate travel delays in 20 different urban areas. As seen from Fig. 3, the estimated travel delays are significantly correlated (R = 0.63, P = 3.00 × 10−3) with actual delay times (11), validating the transportation model. Figure 4 is a Google Maps representation of real and modeled results for Los Angeles and San Francisco. Road conditions under real, average traffic patterns at 8 a.m. provided by Google Maps are in Fig. 4 (A and D). Modeled conditions are given for comparison in Fig. 4 (B and E). Finally, Fig. 4 (C and F) shows the new, modeled traffic patterns that result from redistribution of travel in response to a disruption of 5% of the links.

Fig. 4 Traffic distributions.

Typical congestion at 8 a.m. for Los Angeles (top) and San Francisco (bottom) as given by Google Maps (A and D), modeled with no disruptions (B and E), and modeled with a 5% link disruption (C and F). Notably, in Los Angeles, the disruption results in traffic redistribution to smaller roads, whereas in San Francisco, it results in increased congestion along the major highways.

Resilience

Our approach to model stress is inspired by percolation theory. For every independent simulation of stress, we select a finite fraction of affected road segments r at random, with the probability of failure proportional to segment length. We collect statistics for 20 realizations of the percolation. On failed segments, free-flow speeds (FFSs) are reduced to 1 km/hour (representing near-total loss), and loads L and traffic delays are then recalculated using the updated FFSs. Low-stress scenarios (r < 0.1) might be caused by accidents or construction. Larger disruptions might occur during power failures that disrupt traffic signals or severe flooding that makes many roadways nearly impassable. Finally, widespread stress might be caused by snow, ice, or dust storms that affect nearly the entire roadway system. Figure 5 displays the analysis of delay times in six representative urban areas for the full spectrum of adverse event severities, r ⋲ [0; 1]. In addition, fig. S5 shows the results for all urban areas. Some routes within a single urban area experience longer delays than others. The inset of Fig. 5 shows the delay distribution for both Los Angeles, which is narrowly clustered, and Boston, where greater variability between roadways is evident. Traffic delay times grow rapidly as r increases and reach saturation (all routes moving at 1 km/hour) as r approaches 1. We determine the most resilient urban transportation network to be Salt Lake City, UT, whereas the least resilient among the 40 metropolitans is shown to be Washington, DC.

Fig. 5 Dependency of the additional delay on the severity of the links disruption for six representative urban areas.

Error bars show mean values ± SD. The inset shows distribution densities for two selected urban areas for 1000 realizations of 5% disruption. Note that San Francisco’s unique topology makes it susceptible to failures of a small number of discrete roadways, and this produces an anomalous impact at 5 to 15% disruption.

Figure 6 shows both the efficiency (in blue) and resilience response (additional delays due to 5% link disruption, in orange) for the 40 urban areas modeled. Some cities with high efficiency under normal operating conditions (that is, low delays) nevertheless exhibit low resilience (that is, a sharp increase in traffic delays) under stress. Virginia Beach, VA; Providence, RI; and Jacksonville, FL all fall into this category of urban areas in which traffic operates well under ordinary circumstances but rapidly become snarled under mild stress. On the other hand, Los Angeles is notorious for traffic delays under all conditions—yet minor stress levels result in little degradation of efficiency. By contrast, normal traffic delays in San Francisco are comparable to Los Angeles, but mild stress in San Francisco results in large increases in additional delays. These examples indicate that resilience (that is, additional delay response to stress) is independent of normal operating efficiency.

Fig. 6 Comparison of resilience and efficiency metrics.

Annual impact of 5% disruption (additional delay) has a low correlation with normal annual delay per peak-period auto commuter (delay). Pearson R = 0.49, P = 1.18 × 10−3.

DISCUSSION

The disturbances affecting the road infrastructure are often complex, and their impact on the structure and function of roadway systems may be unknown (28, 31). These disturbances might be natural and irregular, such as distributed road closures caused by an earthquake or homogeneous vehicle slowing down because of a snowstorm. The disturbances might also be anthropogenic and intentional, such as a street fair or marathon race. Whatever the disturbance, the results of this analysis allow several meaningful inferences to be made that may have important implications for highway transportation policy. The first is that resilience and efficiency represent different aspects related to the nature of transportation systems; they are not correlated and should be considered jointly as complementary characteristics of roadway networks.

Second, there are characteristic differences in the resilience of different urban areas, and these differences are persistent at mild, medium, or widespread levels of stress (Fig. 5). Except for San Francisco, CA, which is the most fragile of all cities represented in Fig. 5 at stress levels r < 20% but then surpassed by Boston, MA and Washington, DC, the rank ordering of urban area resilience is insensitive to stress levels. That is, cities that exhibit relatively low resilience under mild stress are the same cities that exhibit low levels of resilience (relative to peers) under widespread roadway impairment. This suggests that the characteristics that impart resilience (such as availability or alternate routes through redundancy of links) are protective against both the intermittent outages caused by occasional car crashes and those caused by snow and ice storms. For cities without resilience, a widespread hazard such as snow may lead to a cascade of conditions (for example, crashes) that rapidly deteriorate into gridlock. This was exactly the case for Washington, DC 20 January 2016 under only 2.5 × 10−2 m or 2.5 cm of snow (61), and for Atlanta, GA 2 years earlier, which experienced 5.1 × 10−2 m or 5.1 cm of snow in the middle of the day that resulted in traffic jams that took days to disentangle (62). Whereas popular explanations of these traffic catastrophes focus on the failure of roadway managers to prepare plows and emergency response equipment, Fig. 5 suggests that cities with similar climates (Memphis, TN and Richmond, VA) are less likely to be affected, regardless of the availability of plow or sand trucks.

The third inference follows from Fig. 6, which suggests that urban areas that make capital investments to reduce traffic delays under normal operating conditions may nevertheless be vulnerable to traffic delays under mild stress conditions. Because these stressors are inevitable, whether from crashes, construction, special events, extreme weather, equipment malfunctions, or even deliberate attack, investment strategies that prioritize reduction of normal operating delays may have the unintended consequence of exacerbating tail risks—that is, the risk of worse catastrophe under unlikely but possible conditions.

Finally, the exceptional position of New York City in Fig. 3 calls attention to the fact that substitutes for roadway transportation are available in many cities and have an important role to play in relieving traffic congestion. According to the Texas A&M Institute (63, 64), public transit reduces delays per peak-period auto commuter in the New York urban area by 63 hours, in Chicago by 23 hours, and by less than 20 hours in other urban areas. Because our model considers only roadway transit, and New York City contains a myriad of nonroad-based options to avoid roadway congestion, it is unlikely that our model can provide informative results for the New York urban area.

Although interest has increased in policies that enhance roadway resilience, few analytic tools are available to guide new investments in achieving resilience goals. It is widely understood that roadway infrastructure is expensive, both in acquiring land for rights-of-way and in construction of improvements, and thus, decisions regarding alignment, crossing, and access made over a period of decades may have long-lasting consequences that are observable in traffic data today. Consequently, urban areas exhibit different unintentional traffic characteristics, including delays under normal and random stress conditions. Investments motivated exclusively by expected efficiencies under normal operating conditions are unreliable safeguards against loss of efficiency under stress conditions. Therefore, new analytic tools are required that allow designers to assess the adaptive capacity of roadway infrastructure and assess the potential of new investments to provide enhanced resilience. The adaptive network-based model described herein is one such approach.

SUPPLEMENTARY MATERIALS

Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/12/e1701079/DC1

Alternative approaches to model transportation

Mapping from OSM Foundation shapefiles to network nodes and links

Population assignment algorithm

Distance factor of the likelihood of travel between nodes

Estimation of the traffic speed from the density of vehicles

Model calibration procedure

Sensitivity of the model to ramp speeds

Additional delay as a function of the severity of link disruption

table S1. Mapping original OSM types to network link types and assignment of the number of lanes.

table S2. The algorithm of the node population assignment.

table S3. Distance factor P(xod) of the likelihood of travel between nodes.

table S4. Model sensitivity to ramp speed coefficient.

fig. S1. Effects of the removal of nodes of degree 2.

fig. S2. Density-flow relationship in the Daganzo traffic model.

fig. S3. Model calibration.

fig. S4. Modeled delays for ramp speed coefficients of 1/3 and 1/2.

fig. S5. Dependency of the additional delay on the severity of the link disruption for all 40 urban areas.

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

REFERENCES AND NOTES
  • K. Beverly, Efficient Use of Highway Capacity (FHWA-HOP-10-023, Texas Transportation Institute, 2010), p. 100.

  • T. Yamashita, K. Izumi, K. Kurumatani, Car navigation with route information sharing for improvement of traffic efficiency, in Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems (IEEE, 2004), pp. 465–470.

  • K. Turnbull, Technical Activities Division, Transportation Research Board, National Academies of Sciences, Engineering, and Medicine, Transportation Resilience: Adaptation to Climate Change (Transportation Research Board, 2016).

  • C. S. Holling, Engineering Resilience versus Ecological Resilience, in Engineering within Ecological Constraints, P. C. Schulze, Ed. (The National Academies Press, 1996), pp. 31–44.

  • S. E. Flynn, S. P. Burke, Critical Transportation Infrastructure and Societal Resilience (Center for National Policy, 2012).

  • T. P. Seager, S. Spierre Clark, D. A. Eisenberg, J. E. Thomas, M. M. Hinrichs, R. Kofron, C. N. Jensen, L. R. McBurnett, M. Snell, D. L. Alderson, Redesigning resilient infrastructure research, in Resilience and Risk, I. Linkov, J. M. Palma-Oliveira, Eds. (Springer, 2017).

  • D. Freckleton, K. Heaslip, W. Louisell, J. Collura, Evaluation of transportation network resiliency with consideration for disaster magnitude, paper presented at the 91st Annual Meeting of the Transportation Research Board, Washington, DC, 2012).

  • S. B. Pant, Transportation network resiliency: A study of self-annealing, thesis, Utah State University (2012).

  • D. King, A. Shalaby, Performance metrics and analysis of transit network resilience in Toronto, paper presented at the 95th Annual Meeting of the Transportation Research Board, Washington, DC, 10 to 14 January 2016.

  • D. Li, Resilience of spatial networks, in Complex Systems and Networks, J. Lü, X. Yu, G. Chen, W. Yu, Eds. (Springer Berlin Heidelberg, 2016), pp. 79–106.

  • P. M. Murray-Tuite, A Comparison of Transportation Network Resilience under Simulated System Optimum and User Equilibrium Conditions, in Proceedings of the Winter Simulation Conference WSC 06, 3 to 6 December 2006, pp. 1398–1405.

  • A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, J. Eriksson, VTrack: Accurate, energy-aware road traffic delay estimation using mobile phones, in Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (ACM Press, 2009), p. 85.

  • E. Cho, S. A. Myers, J. Leskovec, Friendship and mobility: User movement in location-based social networks, in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM Press, 2011), p. 1082.

  • D. Gross, J. F. Shortie, J. M. Thompson, C. M. Harris, Fundamentals of Queueing Theory (Wiley Series in Probability and Statistics, Wiley, Hoboken, NJ, ed. 4, 2008).

  • M. Sabyasachee, Y. Wang, X. Zhu, R. Moeckel, S. Mahapatra, Comparison between gravity and destination choice models for trip distribution in Maryland, paper presented at the TRB 92nd Annual Meeting of Compendium of Papers, 13 to 17 January 2013.

  • J. de Dios Ortúzar, L. G. Willumsen, Modelling Transport (John Wiley & Sons, ed. 4, 2011).

  • National Research Council (U.S.), Metropolitan Travel Forecasting: Current Practice and Future Direction (Transportation Research Board, 2007).

  • R. Van Haaren, Assessment of Electric Cars’ Range Requirements and Usage Patterns based on Driving Behavior recorded in the National Household Travel Survey of 2009 (Solar Journey, 2012), p. 25.

  • B. D. Greenshields, J. R. Bibbins, W. S. Channing, H. H. Miller, R. W. Crum, A study of traffic capacity, in Proceedings of the 14th Annual Meeting of the Highway Research Board, 6 to 7 December 1934, vol. 1.

  • Acknowledgments: We would like to thank S. Buldyrev (Yeshiva University) and J. Palma-Oliveira (University of Lisbon) for their insightful comments. Funding: This study was supported by the U.S. Army Engineer Research and Development Center and by the Defense Threat Reduction Agency, Basic Research Program (P. Tandy, program manager). A.A.G. was additionally supported by the Virginia Transportation Research Council and Virginia Department of Transportation. T.S. was supported by the NSF under grant no. 1441352. Author contributions: A.A.G., M.K., and I.L. conceived the model and designed the simulations. A.A.G. developed software and performed data retrieval and simulations. A.A.G. and M.K. analyzed results. I.L. provided senior guidance. A.A.G., M.K., J.M.K., T.S., and I.L. wrote the paper and contributed to the interpretation of the results. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. Map data were copyrighted by OSM contributors and are available at www.openstreetmap.org.

    Effects and dose–response relationships of resistance training on physical performance in youth athletes: a systematic review and meta-analysis | killexams.com real questions and Pass4sure dumps

    Introduction

    Resistance training (RT) is a safe and effective way to improve proxies of physical performance in healthy children and adolescents when appropriately prescribed and supervised.1–4 Several meta-analyses have shown that RT has the potential to improve muscle strength and motor skills (eg, jump performance) in children and adolescents.1 ,5–7 However, youth athletes have different training capacities, adherence, physical demands of activities, physical conditions and injury risks compared with their non-athlete peers; so the generalisability of previous research on youth athletes is uncertain.8–10

    To the best of our knowledge, there is only one meta-analysis available that examined the effects of RT on one specific proxy of physical performance (ie, jump performance) and in one age group (ie, youth aged 13–18 years).11 It is reasonable to hypothesise that factors such as age, sex and sport may influence the effects of RT. Therefore, a systematic review with meta-analysis is needed to aggregate findings from the literature in terms of age, sex and sport-specific effects of RT on additional physical performance measures (eg, muscle strength, linear sprint performance, agility, sport-specific performance) in youth athletes.

    There is also little evidence-based information available regarding how to appropriately prescribe exercise to optimise training effects and avoid overprescription or underprescription of RT in youth athletes.12 The available guidelines for RT prescription are primarily based on expert opinion, and usually transfer study findings from the general population (ie, healthy untrained children and adolescents) to youth athletes. This is important because the optimal dose to elicit a desired effect is likely to be different for trained and untrained youth.13

    Therefore, the objectives of this systematic literature review and meta-analysis were (1) to analyse the effectiveness of RT on proxies of physical performance in youth athletes by considering potential moderator variables, including age, sex, sport and the type of RT, and (2) to characterise dose–response relationships of RT parameters (eg, training period, training frequency) by quantitative analyses of intervention studies in youth athletes. We hypothesised that (1) RT would have a positive effect on proxies of physical performance in youth athletes, and (2) the effects would be moderated by age, sex, sport and RT type.

    Methods

    Our meta-analysis was conducted in accordance with the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).14

    Literature search

    We performed a computerised systematic literature search in the databases PubMed and Web of Science.

    The following Boolean search syntax was used: (‘strength training’ OR ‘resistance training’ OR ‘weight training’ OR ‘power training’ OR ‘plyometric training’ OR ‘complex training’ OR ‘weight-bearing exercise’) AND (athlete OR elite OR trained OR sport) AND (children OR adolescent OR youth OR puberty OR kids OR teens OR girls OR boys). The search was limited to: full-text availability, publication dates: 01/01/1975 to 07/31/2015, ages: 6–13; 13–18 years, and languages: English, German. The reference list of each included study and relevant review article1 ,4–6 ,11 ,15–19 was screened for title to identify any additional suitable studies for inclusion in our review.

    Selection criteria

    Based on the defined inclusion and exclusion criteria (table 1), two independent reviewers (ML and OP) screened potentially relevant articles by analysing titles, abstracts and full texts of the respective articles to elucidate their eligibility. In case ML and OP did not reach an agreement concerning inclusion of an article, UG was contacted.

    Table 1

    Selection criteria

    Coding of studies

    Each study was coded for certain variables listed in table 2. Our analyses focused on different outcome categories. If studies reported multiple variables within one of these outcome categories, only one representative outcome variable was included in the analyses. The variable with the highest priority for each outcome is mentioned in table 2.

    If a study solely used other tests, we included those tests in our quantitative analyses that were most similar with regard to the ones described above in terms of their temporal/ spatial structure.

    Further, we coded RT according to the following training parameters: training period, training frequency, and training volume (ie, number of sets per exercise, number of repetitions per set), training intensity, temporal distribution of muscle action modes per repetition, and rest (ie, rest between sets and repetitions). Training parameters were categorised according to common classifications of RT protocols.21 If a study reported exercise progression over the training period, the mean number of sets per exercise, repetitions per sets, rest between sets and training intensity were computed.

    To obtain sufficient statistical power to calculate dose–response relationships, we summarised RT types as conventional RT (ie, machine based, free weights, combined machine based and free weights, functional training) and plyometric training (ie, jumping). As it is not possible to classify complex training as either conventional RT nor plyometric training,22 we excluded these studies23–27 from dose–response analyses. Our dose–response analyses were computed independent of age, sex and sport.

    Assessment of risk of bias

    The Physiotherapy Evidence Database (PEDro) scale was used to quantify the risk of bias in eligible studies and to provide information on the general methodological quality of studies. The PEDro scale rates internal study validity and the presence of statistical replicable information on a scale from 0 (high risk of bias) to 10 (low risk of bias) with ≥6 representing a cut-off score for studies with low risk of bias.28

    Statistical analyses

    To determine the effectiveness of RT on proxies of physical performance and to establish dose–response relationships of RT in youth athletes, we computed between-subject standardised mean differences (SMD=(mean postvalue intervention group−mean postvalue control group)/pooled standard deviation). We adjusted the SMD for the respective sample size by using the term (1−(3/(4N-9))).29 Our meta-analysis on categoric variables was computed using Review Manager V.5.3.4 (Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2008). Included studies were weighted according to the magnitude of the respective SE using a random-effects model.

    At least two RT intervention groups had to be included to calculate weighted mean SMDs, hereafter refered to as SMDwm, for each performance category.30 We used Review Manager for subgroup analyses: computing a weight for each subgroup, aggregating SMDwm values of specific subgroups, comparing subgroup effect sizes with respect to differences in intervention effects across subgroups.31 To improve readability, we reported positive SMDs if superiority of RT compared with active control was found. Heterogeneity was assessed using I² and χ2 statistics.

    Owing to a low number of studies in each physical performance outcome category that completely reported information on the applied RT parameters, metaregression was precluded.30 According to a scale for determining the magnitude of effect sizes in strength training research for individuals who have been consistently training for 1–5 years,32 we interpreted SMDwm as: trivial (<0.35); small (0.35–0.79); moderate (0.80–1.50); large (≥1.50). The level of significance was set at p<0.05.

    Results Study characteristics

    A total of 576 potentially relevant studies were identified in the electronic database search (figure 1). Finally, 43 studies remained for the quantitative analyses. A total of 1558 youth athletes participated, and of these, 891 received RT in 62 RT intervention groups. The sample size of the RT intervention groups ranged from 5 to 54 participants (table 3).

    Table 3

    Included studies examining the effects of resistance training in youth athletes

    Figure 1

    Flow chart illustrating the different phases of the search and study selection.

    There were 13 studies (21 RT intervention groups) that included children, and 29 studies (36 RT intervention groups) that included adolescents. In terms of biological maturation, only 15 studies reported Tanner stages. Three (5 RT intervention groups) of those studies examined prepubertal and 12 (15 RT intervention groups) postpubertal/pubertal youth athletes. Thirty studies (44 RT intervention groups) included boys only, whereas 4 studies (4 RT intervention groups) included girls only.

    Youth athletes were recruited from team sports (soccer (20 studies; 34 RT intervention groups), basketball (9 studies; 11 RT intervention groups), baseball (3 studies; 5 RT intervention groups), handball (3 studies; 3 RT intervention groups), tennis (2 studies; 3 RT intervention groups), volleyball (1 study; 1 RT intervention group)), and strength-dominated sports (swimming (3 studies; 3 RT intervention groups), track and field (1 study, 1 RT intervention group)). No included study investigated youth athletes recruited from martial arts or technical/acrobatic sports.

    Regarding the type of RT, 4 studies performed RT using machines, 4 studies using free weights, 4 studies using both machines and free weights, 5 studies performed functional RT, 5 studies performed complex training, and 19 studies applied plyometric training. Classification of studies was not always feasible due to missing information or group heterogeneity.

    The RT interventions lasted between 4 and 80 weeks, with training frequencies ranging from 1 to 3 sessions per week, 1–8 sets per exercise, 4–15 repetitions per set, and 20–220 s of rest between sets. Training intensity ranged from 35% to 88% of the 1 repetition maximum (RM). Training parameters (eg, temporal distribution of muscle action modes per repetition, and rest in-between repetitions) which have gained attention in the literature71 were not quantified due to insufficient data.

    A median PEDro score of 4 (95% CI 4 to 5) was detected and only 4 out of 43 studies reached the predetermined cut-off value of ≥6, which can be interpreted as an overall high risk of bias of the included studies (table 3).

    Effectiveness of RT

    Table 4 shows the overall as well as age, sex, sport and training type-specific effects of RT on measures of muscle strength, vertical jump and linear sprint performance, agility and sport-specific performance.

    Table 4

    Overall as well as age, sex, sport and training type-specific effects of resistance training in youth athletes

    There were moderate effects of RT on measures of muscle strength (SMDwm=1.09; I²=81%; χ2=114.24; df=22; p<0.001; figure 2) and vertical jump performance (SMDwm=0.80; I²=67%; χ2=137.47; df=46; p<0.001; figure 3), while there were small effects for linear sprint performance (SMDwm=0.58; I²=41%; χ2=55.74; df=33; p<0.01; figure 4), agility (SMDwm=0.68; I²=50%; χ2=48.19; df=24; p<0.01; figure 5) and sport-specific performance (SMDwm=0.75; I²=62%; χ2=67.81; df=26; p<0.001; figure 6). By considering only the four studies with high quality (ie, low risk of bias), RT had moderate effects on measures of muscle strength (SMD=1.07; 1 study), vertical jump (SMDwm=0.89; 3 studies) and linear sprint performance (SMDwm=1.19; 2 studies); small effects on agility (SMD=0.28; 1 study); and large effects on sport-specific performance (SMDwm=1.73; 2 studies).

    Figure 2

    Effects of resistance training (experimental) versus active control on measures of muscle strength (IV, inverse variance).

    Figure 3

    Effects of resistance training (experimental) versus active control on measures of vertical jump performance (IV, inverse variance).

    Figure 4

    Effects of resistance training (experimental) versus active control on measures of linear sprint performance (IV, inverse variance).

    Figure 5

    Effects of resistance training (experimental) versus active control on agility (IV, inverse variance).

    Figure 6

    Effects of resistance training (experimental) versus active control on proxies of sport-specific performance (IV, inverse variance).

    There was no statistically significant effect of chronological and/or biological age on any proxy of physical performance. However, a tendency (p=0.05) towards larger RT effects were found for proxies of sport-specific performance in adolescents (SMDwm=1.03) compared with children (SMDwm=0.50; table 4). Subgroup analyses indicated that RT produced significantly larger effects (p<0.05) on proxies of sport-specific performance in girls (SMDwm=1.81) compared with boys (SMDwm=0.72; table 4). Given that most included studies (n=38) examined participants competing in team sports, our subgroup analyses regarding the moderator variable ‘sport’ is limited and did not show any significant subgroup differences (table 4). Subgroup analyses demonstrated that different training types of RT produced significantly different gains in muscle strength (p<0.001), agility (p<0.05) and sport-specific performance (p<0.05). Free weight RT showed the largest effects on muscle strength and agility, while for sport-specific performance, complex training produced the largest effects (table 4).

    Dose–response relationships of RT Training period

    There was a significant difference for the effects of conventional RT on measures of muscle strength (p<0.001), vertical jump height (p<0.05) and agility (p<0.001; figure 7). The dose–response curves indicated that long lasting conventional RT (>23 training weeks) resulted in more pronounced improvements in measures of muscle strength (SMDwm=3.40) and agility (SMDwm=1.31), as compared with shorter training periods (<23 weeks). In terms of vertical jump height, a training period of 9–12 weeks appeared to be the most effective (SMDwm=1.20).

    Figure 7

    Dose–response relationships of the parameter ‘training period’ on measures of muscle strength, vertical jump and linear sprint performance, agility, and sport-specific performance. Each filled grey circle illustrates between-subject SMD per single study with active control. Filled black triangles represent weighted mean SMD of all studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised mean difference.

    Training frequency

    There were no significant differences between the observed training frequencies (ie, 1, 2, 3 times per week) for RT as well as plyometric training (figure 8).

    Figure 8

    Dose–response relationships of the parameter ‘training frequency’ on measures of muscle strength, vertical jump and linear sprint performance, agility, and sport-specific performance. Each filled grey circle illustrates between-subject SMD per single study with active control. Filled black triangles represent weighted mean SMD of all studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised mean difference.

    Training intensity

    There was a significant difference with regard to the effects of conventional RT on measures of muscle strength (p<0.01; figure 9). High-intensity conventional RT (ie, 80–89% of 1 RM) resulted in more pronounced improvements in muscle strength (SMDwm=2.52) compared with lower training intensities (ie, 30–39%, 40–49%, 50–59%, 60–69%, 70–79% of the 1 RM).

    Figure 9

    Dose–response relationships of the parameter ‘training intensity’ on measures of muscle strength, vertical jump and linear sprint performance, agility, and sport-specific performance. Each filled grey circle illustrates between-subject SMD per single study with active control. Filled black triangles represent weighted mean SMD of all studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised mean difference; RM, repetition maximum.

    Training volume (number of sets per exercise)

    There was a significant difference with regard to the effects of conventional RT on muscle strength (p<0.01), and a tendency towards significance for measures of vertical jump performance (p=0.06; figure 10). Five sets per exercise resulted in more pronounced improvements in muscle strength (SMDwm=2.76) compared with fewer sets. Three sets per exercise tended to be more effective in improving vertical jump performance (SMDwm=1.19), as compared with four or five sets per exercise.

    Figure 10

    Dose–response relationships of the parameter ‘sets per exercise’ on measures of muscle strength, vertical jump and linear sprint performance, agility, and sport-specific performance. Each filled grey circle illustrates between-subject SMD per single study with active control. Filled black triangles represent weighted mean SMD of all studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised mean difference.

    For plyometric training, there was a tendency towards larger training-related effects on measures of muscle strength (p=0.09), linear sprint performance (p=0.07), as well as sport-specific performance (p=0.05) depending on the number of sets per exercise. Four sets per exercise revealed the largest effects for measures of muscle strength (SMDwm=0.79) and sport-specific performance (SMDwm=1.84), while three or four sets appear to be most effective for improving linear sprint performance (SMDwm=0.95).

    Training volume (number of repetitions per set)

    There was a significant difference in terms of the effects of conventional RT on measures of muscle strength (p<0.05; figure 11). Six to eight repetitions per set produced the largest effects on muscle strength (SMDwm=2.42). For plyometric training, there was a tendency towards significance for proxies of sport-specific performance (p=0.05). Six to 8 repetitions per set were less effective (SMDwm=0.15), while 3–5 and 9–12 repetitions per set produced similar effects (SMDwm=0.89 and 0.93).

    Figure 11

    Dose–response relationships of the parameter ‘repetitions per set’ on measures of muscle strength, vertical jump and linear sprint performance, agility, and sport-specific performance. Each filled grey circle illustrates between-subject SMD per single study with active control. Filled black triangles represent weighted mean SMD of all studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised mean difference.

    Rest between sets

    There was a significant difference for the effects of conventional RT on measures of muscle strength (p<0.05; figure 12). Three to 4 min of rest between sets resulted in more pronounced improvements in measures of muscle strength (SMDwm=2.09), as compared with shorter durations of rest.

    Figure 12

    Dose–response relationships of the parameter ‘rest between sets’ on measures of muscle strength, vertical jump and linear sprint performance, agility, and sport-specific performance. Each filled grey circle illustrates between-subject SMD per single study with active control. Filled black triangles represent weighted mean SMD of all studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised mean difference.

    Discussion

    This systematic review with meta-analysis examined the general effects as well as the age, sex, sport and training type-specific impact of RT on proxies of physical performance in healthy young athletes. In addition, dose–response relationships of RT parameters were independently computed. The main findings were: (1) RT has moderate effects on muscle strength as well as on vertical jump performance, and small effects on linear sprint, agility and sport-specific performance in young athletes, (2) the effects of RT were moderated by the variables sex and RT type, (3) most effective conventional RT programmes to improve measures of muscle strength in healthy young athletes comprised training periods of more than 23 weeks, 5 sets per exercise, 6–8 repetition per set, a training intensity of 80–89% of the 1 RM, and 3–4 min of rest between sets.

    Effects of RT on physical performance in youth athletes

    In general, RT is an effective way to improve proxies of physical performance in youth athletes, and our findings support recently published literature.4 ,17 ,72 ,73 We found that the main effects of RT on measures of muscle strength and vertical jump performance were moderate in magnitude, with small effects for secondary outcomes, including linear sprint performance, agility and sport-specific performance (eg, throwing velocity). The lower RT effects on secondary outcomes might be explained by the complex nature of these qualities, with various determinants contributing to the performance level. For instance, agility depends on perceptual factors and decision-making as well as on changes in direction of speed, which is again influenced by movement technique, leg muscle quality and straight sprinting speed.74 Thus, muscle strength appears to be only one of several factors contributing to agility.

    We recommend the incorporation of RT as an important part of youth athletes’ regular training routine to enhance muscle strength and jump performance.

    How age, sex, sport and training type moderate RT effects Age-specific effects of RT in youth athletes

    Biological maturity is related to chronological age, and has a major impact on physical performance in youth athletes.75 However, unlike age, growth and maturation are not linear factors.76 ,77 There is often a discrepancy between chronological age and biological maturity among youth athletes.4 ,16 ,78

    We found no significant differences in effect sizes for any proxy of physical performance between prepubertal and postpubertal athletes. Similarly, we did not find significant differences for the effects of RT on any physical performance measure with respect to the moderator variable ‘chronological age’ (table 4). Merely, a tendency (p=0.05) towards higher sport-specific performance gains following RT in adolescents, compared with children, was identified.

    Although a minimum age has been defined at which children are mentally and physically ready to comply with coaching instructions,4 our subgroup analyses regarding biological and chronological age suggest that youth athletes may benefit to the same extent from RT, irrespective of age. However, it is important to note that most studies did not report the biological maturity status of the participants. Therefore, more research is needed to elucidate biological age-specific RT effects on physical performance in youth athletes and to verify our preliminary findings.

    Sex-specific effects of RT in youth athletes

    Previous research on the effects of RT on proxies of physical performance in youth athletes has primarily focused on boys. However, findings from male youth athletes can only partially be transferred to female youth athletes because the physiology of boys and girls (eg, hormonal status during puberty) varies. We found that male and female youth athletes show similar RT-related gains in muscle strength and vertical jump performance, but girls had significantly larger training-induced improvements in sport-specific performance (SMDwm=1.81) compared with boys (SMDwm=0.72). This suggests preliminary evidence that the RT trainability of female adolescent athletes may be at least similar or even higher compared with males. Given that girls’ and boys’ physiology changes differently with age and maturation,76 ,77 sex-specific effects of RT in youth athletes should be investigated with respect to biological maturity. Owing to an insufficient number of studies that examined female youth athletes and reported their biological maturity status, we were not able to include ‘biological maturity’ as a moderator variable in our subgroup analyses. We consider our sex-specific findings preliminary because these are based on five studies only investigating female youth athletes. More research is needed to elucidate sex-specific RT effects on physical performance in youth athletes and to verify our preliminary findings.

    Sport-specific effects of RT in youth athletes

    The effects of RT in elite adult athletes may be specifically moderated by the respective athlete profile of the sport performed.79 ,80 Whether this is also the case in youth athletes remains unresolved. Given that most included studies (n=38) investigated young athletes competing in team sports, our analyses with regard to the moderator variable ‘sport’ was limited and did not reveal any significant differences between sports disciplines (table 4). Therefore, further research has to be conducted to examine if youth athletes respond differently to RT programmes as per the sport practiced.

    Training type-specific effects of RT in youth athletes

    Various types of RT have been reported (eg, machine-based RT, free weight RT and functional RT). Each of these types has specific benefits and limitations.20 ,73 Machine-based RT may represent a safe environment for young athletes when supervision cannot be ensured, whereas supervised RT using free weights allows full range of motion that better mimics sports-specific movements.20 ,73 We found that RT programmes using free weights were most effective to enhance muscular strength and agility. In addition, complex training produced the largest effect sizes if the goal was to improve sport-specific performance. Therefore, the choice of RT types should be variable and based on the exercise goal (eg, enhancing muscle strength or sport-specific performance).

    Dose–response relationships of RT in youth athletes

    Planning and designing RT programmes is a complex process that requires sophisticated manipulation of different training parameters. Owing to a lack of evidence-based information on dose–response relationships following RT in youth athletes, it is quite common for established and effective RT protocols for healthy untrained children and adolescents to be transferred to youth athletes. However, this may hinder to fully recruit the adaptative potential of young athletes because the optimal dose to elicit the desired effect appears to be different in trained compared with untrained youth.13 Owing to the observed limitations regarding female youth athletes and biological maturation status in the present meta-analysis, the dose–response relationships of RT in youth athletes were determined irrespective of sex and maturity.

    In general, the specific configuration of RT parameters determines the underlying training stimulus and thus, the desired physiological adaptations. However, significant effects were predominantly identified for conventional RT parameters for measures of muscle strength. Therefore, it appears that gains in muscular strength may be more sensitive to the applied training parameters of the conventional RT programmes, as compared with the secondary performance outcomes (eg, linear sprint performance, agility, sport-specific performance).

    Training period

    The effects of short-term (<24 weeks) RT peaked almost consistently with training periods of 9–12 weeks for both conventional RT and plyometric training. However, our subgroup analyses indicated significant differences only for conventional RT for measures of muscle strength and vertical jump performance. Nevertheless, with regard to strength gains, long-term (≥24 weeks) conventional RT was more effective in youth athletes (SMDwm=3.40), as compared with short-term conventional RT (SMDwm=0.61–1.24). Thus, it can be postulated that conventional RT programmes should be incorporated on a regular basis in long-term athlete development.66 Given that continuous performance improvements are difficult to achieve particularly over long time periods, properly varying RT programmes may avert training plateaus, maximise performance gains and reduce the likelihood of overtraining.

    Regular basketball practice during a detraining/reduced training period was sufficient to maintain previously achieved muscular power gains due to its predominantly power-type training drills.81 Therefore, it is reasonable to hypothesise that regular training can maintain RT-based gains in muscular strength for several weeks if similar physical demands are addressed during regular training. Coaches may reduce the time spent on RT for several weeks without impairing previously achieved strength gains during competition periods when the training must emphasise motor skills and competition demands.

    Training frequency

    The phase of periodisation, projected exercise loads and the dose of additional physical training (ie, overall amount of physical stress) may influence training frequency.21 In order to avoid overtraining and achieve maximal benefits of RT, it is important to allow the body sufficient time to recover from each RT session. However, if the rest between RT sessions is too long, adaptive processes from previous RT sessions may get lost.

    Most studies performed RT two or three times per week (figure 8), and there was no significant difference between the observed training frequencies. To our knowledge, there is no study available that directly compared the effects of two RT sessions per week as opposed to three sessions for youth athletes. Although a reduced RT frequency of one session per week may be sufficient to maintain muscle strength gains following RT for several weeks,41 ,82 training twice per week might be preferred to achieve further gains in muscle strength in youth athletes.

    Training volume and training intensity

    Both volume and intensity have to be considered when prescribing RT to maximise physiological adaptations and minimise injury risk.4 Different configurations of training volume and intensity result in different forms of physiological stress, which in turn induce different neural and muscular adaptations.71

    Owing to the large methodological variety in dealing with training intensity during plyometric training, we were not able to consistently quantify the dose–response relationship for training intensity with regard to plyometric training.

    Conventional RT programmes using average training intensities of 80–89% of the 1 RM were most beneficial in terms of improving muscle strength in youth athletes. These findings are in accordance with the position stand of the American College of Sports Medicine for strength training in adults.83 The largest effect sizes for muscle strength gains in adults, trained individuals and athletes were achieved at 80–85% of the 1 RM.8 ,12 However, it should be noted that the individual percentage of 1 RM is a stress rather than a strain factor. Several studies have indicated that a given number of repetitions cannot be associated with a specific percentage rate of the 1 RM.78 ,84 Thus, to individualise RT, future studies should focus on finding a valid strain-based method to quantify RT intensity effectively.

    In terms of the number of sets per conventional RT exercise, our data show similar effect size magnitudes when comparing single-set (SMDwm=2.41) versus multiple-set conventional RT programmes (5 sets: SMDwm=2.76). The primary benefit of a single-set conventional RT is time efficiency. Nevertheless, since our results for single-set conventional RT are based on two intervention groups from one study, this finding has to be interpreted with caution. Although there was no study that directly compared the effects of single-set versus multiple-set conventional RT in youth athletes, there is evidence from adult athletes that single-set conventional RT may be appropriate during the initial phase of RT,85 whereas multiple-set conventional RT programmes should be used to promote further gains in muscle strength, especially in athletes.86 Therefore, multiple-set conventional RT may be necessary to elicit sufficient training stimuli during long-term youth athlete development.

    Regarding the applied plyometric training, 3 (for vertical jump) or 4 sets per exercise (for muscle strength, sport-specific performance) as well as 3–5 or 9–12 repetitions per set (for vertical jump, sport-specific performance) might be beneficial for youth athletes’ physical performance. However, the movement quality of plyometric exercises is more important than the total session volume.87 Therefore, we recommend the use of thresholds for performance variables, such as ground contact time or performance indices, to determine individualised training volume.87

    Rest between sets

    The duration of rest between sets and repetitions depends on parameters like training intensity and volume. The rest interval significantly affects the biochemical responses following RT.71 Owing to an insufficient number of studies that reported the duration of rest between repetitions, we focused on dose–response relationships for rest between sets. Long rest periods (ie, 3–4 min of rest between sets) were most effective for improving muscle strength following conventional RT in youth athletes. This is most likely because long rest periods allow athletes to withstand higher volumes and intensities during training.

    Limitations of this meta-analysis

    A major limitation is that we could not provide insights into the interactions between the reported training parameters. Our analyses are based on a variety of studies using different combinations of training parameters magnitudes (eg, training frequency, number of sets, intensity). It remains unclear if performance gains would still be maximal if, according to the present dose–response relationships, the optimum of each parameter was implemented in RT programmes.81 Thus, further research is necessary to find an analytical method to provide insights into the interactions between the investigated training parameters. The modelling of training variables might help to address this limitation. Holding a set of RT variables constant while changing the effects of one specific variable could determine the unique effects of each training variable.

    Further limitations of this systematic review and meta-analysis are the high risk of bias of the included studies (only 4 out of 43 studies reached a PEDro score of ≥6), the considerable heterogeneity between studies (ie, I²=41–81%), and the uneven distribution of SMDs calculated for the respective training parameters. In addition, the scale for determining the magnitude of effect sizes32 is not specific for RT research in children and adolescents. Another limitation is that almost all studies failed to report RT parameters which had got recent research attention (eg, temporal distribution of muscle action modes per repetition).71 Further, studies used traditional stress-based (ie, RM) instead of recent strain-based (eg, OMNI resistance exercise scale of perceived exertion88) methods to quantify RT intensity.89 We were not able to aggregate the effects of moderator variables, such as sex and maturation, for the dose–response relationships due to an insufficient number of studies that specifically addressed these issues.

    Summary

    RT was effective for improving proxies of physical performance in youth athletes. The magnitudes of RT effects were moderate in terms of measures of muscle strength and vertical jump performance, and small with regard to measures of linear sprint, agility and sports-specific performance in youth athletes. Sex and RT type appeared to moderate these effects. However, most studies were at high risk of bias and therefore, the results should be interpreted cautiously.

    A training period of more than 23 weeks, 5 sets per exercise, 6–8 repetitions per set, a training intensity of 80–89% of 1 RM, and 3–4 min rest between sets were most effective for conventional RT programmes to improve muscle strength in youth athletes. However, these evidence-based findings should be adapted individually by considering individual abilities, skills and goals. Specifically, youth coaches should not use high RT intensities before the youth athlete developed technical skills to adequately perform the RT exercises.

    What is already known on this topic?
  • Resistance training is safe for children and adolescents if appropriately prescribed and supervised.

  • Several meta-analyses have already shown that resistance training has the potential to improve muscle strength and motor skills (eg, jump performance) in healthy, untrained children and adolescents.

  • What this study adds
  • This is the first systematic review and meta-analysis to examine age, sex, sport and training type-specific effects of resistance training on physical performance measures in youth athletes.

  • The effect of resistance training was moderated by sex and resistance training type. Girls had greater training-related sport-specific performance gains compared with boys, and resistance training programmes with free weights were most effective for increasing muscle strength.

  • Dose–response relationships for key training parameters indicate that youth coaches should aim for resistance training programmes with fewer repetitions and higher intensities to improve physical performance measures.

  • Acknowledgments

    The authors would like to thank Dr Andrea Horn for her support during the course of the research project.



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