Pairwise meta-analyses are methodologically constrained to solely evaluating two RTxs. 28 A number of RTxs are conceivable, and a number of pairwise meta-analyses are unlikely to yield congruent insights. Community meta-analysis (NMA) expands on pairwise meta-analysis by allowing the simultaneous comparability of a number of remedies. 29 NMA leverages direct and oblique proof to provide enhanced impact estimates between all remedies, even when some comparisons have by no means been examined in randomised trials. 30 Moreover, NMA permits the rank-ordering of all included remedies and the incorporation of knowledge from multi-arm trials. 28 Inside train science, NMA has been used to match several types of train 31–34 ; inside RT, NMA has solely been used to match totally different load doses. 35 Importantly, NMA can examine a number of multivariate RTxs.
Guideline builders depend on systematic opinions and meta-analyses for figuring out suggestions, as these research designs are, generally, essentially the most strong types of proof. 14 Certainly, numerous meta-analyses have supplied seminal proof on the univariate influence of load, 15–18 units 19–22 or frequency 23–27 to enhance muscle energy, mass and bodily perform. Nevertheless, these univariate analyses restrict RT guideline growth as a result of particular person RT variables are neither mutually unique nor prescribed independently; fairly, a number of variables are collectively inherent to any RTx. Comparisons between multivariate RT prescriptions are wanted to advance optimum RTx tips.
Skeletal muscle is important for quite a few useful and metabolic processes important to good well being. Resistance coaching (RT), muscle contraction towards exterior weight, potently will increase muscle energy and mass (hypertrophy), improves bodily efficiency, offers a myriad of metabolic-health advantages and combats persistent illness threat. 1–4 Though endogenous organic and physiological elements are pertinent to maximising RT-induced skeletal muscle diversifications, 5 6 RT programming variables can have an effect on RT diversifications. 7–13 Subsequently, a RT prescription (RTx) needs to be decided appropriately. Every RTx is comprised of a definite mixture of RT variables, and the most-studied RTx variables embrace the load lifted per repetition, units per train (usually involving a single RT manoeuvre or muscle group) and weekly frequency (the variety of RT classes accomplished per week).
Our writer group contains numerous disciplines, profession levels and genders. Information assortment, evaluation and reporting strategies weren’t altered primarily based on regional, academic or socioeconomic variations of the neighborhood wherein the included research had been carried out. The one persistently reported fairness, range and inclusion-relevant variable on which we’ve got analysed the info is organic intercourse.
Sensitivity analyses had been carried out to discover the influence of outliers, influential circumstances and sources of community inconsistency on mannequin match, relative results and therapy rankings. The primary sensitivity evaluation excluded research recognized throughout pairwise meta-analyses and node-splitting, and the second sensitivity evaluation excluded node(s) comprised of just one research. Community meta-regression (NMR), assuming impartial therapy interactions, 56 was carried out to find out if further elements improved mannequin match and altered therapy results. NMR covariates included age, coaching standing, the proportion of females, length, volitional fatigue, relative weekly quantity load, end result measurement software, end result measurement area and publication 12 months. Lacking information on covariates had been managed by multivariate imputation by chained equations (n imputations=20). 57 NMR is detailed in on-line supplemental appendix 12 .
NMA built-in all direct proof, with one community constructed for every end result. NMA fashions had been fitted inside a Bayesian framework utilizing Markov chain Monte Carlo strategies. 47 4 chains had been run with non-informative priors. There have been 50 000 iterations per chain; the primary 20 000 had been discarded as burn-in iterations. Values had been collected with a thinning interval of 10. Convergence was evaluated by visible inspection of hint plots 48 and the potential scale discount issue. Each fixed-effects and random-effects fashions had been match, and the extra parsimonious mannequin was used for evaluation. 49 Mannequin match was assessed with the deviance info criterion (DIC) and posterior imply residual deviance. 49 50 Heterogeneity was assessed by analyzing the between-study SD (τ) and 95% credible intervals (95% CrI). International inconsistency was assessed by evaluating mannequin match, DIC and variance parameters between the NMA mannequin and an unrelated imply results (UME) mannequin. 51 Native inconsistency was assessed with the node-splitting technique, 52 and inconsistency was thought-about to be detected when the Bayesian p worth<0.05. Forest plots and league tables had been generated to show relative results. Floor below the cumulative rating curve values wereused to rank-order every situation from top-to-bottom; moreover, the chance of every situation rating within the prime three was calculated as a share of the world below the curve. NMA outcomes had been offered as posterior SMD and 95% CrI, interpreted as a spread wherein a parameter lies with a 95% chance. 53 Standardised imply variations (SMD), adjusted for small-sample measurement bias, 42 had been calculated because the abstract statistic as a result of every end result was measured with numerous instruments. 37 The course of impact was standardised to analyse mobility, gait pace and steadiness to make sure consistency of fascinating outcomes. 43 When a number of research in contrast two situations, random-effects pairwise meta-analyses had been carried out to determine comparison-level heterogeneity, publication bias, outliers and influential circumstances. 40 44 To account for within-trial correlations in multi-arm trials (≥3 situations), the SE within the base/reference arm was calculated because the sq. root of the covariance between calculated results, 45 assuming a correlation of 0.5 between impact sizes. 46 Reviewers independently evaluated the within-study threat of bias utilizing the Cochrane Danger of Bias V.2.0. software. 41 Signalling questions and standards had been adopted to tell the danger of bias value determinations for the intention-to-treat impact. Articles had been assessed in duplicate on the energy and hypertrophy end result stage for bias: (1) arising from the randomisation course of, (2) resulting from deviations from meant interventions, (3) resulting from lacking end result information, (4) within the measurement of the end result and (5) within the number of reported end result. Each area was decided to be of excessive, reasonable (some considerations) or low threat of bias, and research had been subsequently given an general classification of excessive, reasonable or low threat of bias. Any disagreement was resolved by consensus (BSC and JCM). Imply change from baseline and SD change (SD change ) from baseline had been the outcomes of curiosity and extracted when reported. When unreported, SD was calculated with SEs, CIs, p values or t-statistics, 37 and SD change was imputed from pre-SD and post-SD values with a correlation coefficient of 0.5. 35 RT masses reported as repetition most (RM) had been transformed to a share of one-repetition most (%1RM) with the equation: %1RM=100−(RM(2.5)). 38 The best-ranked measurement was extracted, per predetermined hierarchy ( on-line supplemental appendix 5 ), when a number of measurements had been reported for a single end result (eg, MRI and ultrasonography for muscle measurement). The longest interval that every one situations had been unchanged from baseline was analysed when the end result(s) of curiosity had been measured at a number of time factors. 37 Cohorts randomised individually however reported collectively (eg, younger and previous 39 ) had been analysed independently. Inside-group outcomes reported by participant intercourse had been grouped by situation. 37 40 Information from included research had been extracted independently by pairs of reviewers, with any discrepancies resolved by consensus with a 3rd reviewer (BSC or JCM). Extracted information included research and participant traits, RTx particulars and measurements of muscle energy and/or measurement ( on-line supplemental appendix 4 ). Measures of mobility, steadiness and/or gait pace had been extracted when the imply participant age was ≥55 years previous. Authors of research with lacking information had been contacted twice with a request for the lacking information. The systematic assessment software program Covidence (Veritas Well being Innovation, Melbourne, Australia. Obtainable at www.covidence.org ) was used for document screening and information extraction. MEDLINE, Embase, Emcare, SPORTDiscus, CINAHL and Internet of Science had been systematically searched till 7 February 2022. A number of specialists developed the search technique, which included topic headings and key phrases particular to the analysis query and every database. No language nor research design limits had been used within the search technique. The entire search technique is supplied in on-line supplemental appendix 2 . Related systematic opinions ( on-line supplemental appendix 3 ) had been manually chosen, and the references had been scrutinised for eligibility. Arms of included research had been categorized as 1 of 12 RTxs or non-exercise management (CTRL). Every RTx was categorized primarily based on the load, set and frequency prescription ( field 1 ). RTxs had been denoted with a three-character acronym—XY#—the place X is load (H, ≥80% one-repetition most (1RM); L, <80% 1RM); Y is units (M, multiset; S, single-set); and # is the weekly frequency (3, ≥3 days/week; 2, 2 days/week; 1, 1 day/week), respectively. For instance, HM2 denotes higher-load, multiset, twice-weekly RT inside this framework. CTRL was comprised of topics who acquired no intervention. The eligibility standards are detailed in desk 1 . Solely trials that included wholesome adults ≥18 years previous, had been randomised, in contrast not less than 2 of 13 distinctive situations ( field 1 ), and measured muscle energy, measurement and/or bodily perform had been included. Bodily perform was subdivided into three domains: mobility, the power to bodily transfer; steadiness, the power to take care of a physique place throughout a activity; and gait pace, the time taken to locomote over a given distance. Trials that included athletes, individuals with comorbidities or army individuals; spanned <6 weeks; concerned unsupervised RT (eg, home-based train); had been reported in a non-English language; or had been non-randomised had been excluded. The hypertrophy community included 115 research (n=3240) and 9 situations (HS2 and HM1 excluded). The relative impact for every RTx versus CTRL was roughly unchanged, with the biggest relative impact from HM2 (0.59 (0.39 to 0.78)) and the smallest from HS3 (0.30 (−0.05 to 0.66)). Between prescriptions, there was a 95% chance that LM2 was superior to LS3. HM2 (82.8%) and LM2 (80.4%) remained more than likely to rank within the prime three for muscle hypertrophy. Sensitivity evaluation outcomes are displayed within the on-line supplemental appendix 11 . For each the energy and hypertrophy NMAs, the second sensitivity evaluation (mentioned herein) most improved mannequin match. The energy community included 155 research (n=4397) and 11 situations (LS1 and HS1 excluded). The relative results for all RTx versus CTRL had been tempered, such that posterior SMDs ranged from 0.77 to 1.49, with the biggest relative impact from HM2 (1.49 (1.29 to 1.70)) and smallest from LS3 (0.77 (0.56 to 0.98)). The 95% CrI for every RTx versus CTRL excluded zero. There was a 95% chance that HM2 yields bigger relative results than LS2, LS3, LM1, LM2, LM3 and HS3; that HM3 was superior to LS2, LS3, LM1, LM2 and LM3; and that LM2 was superior to LS3. HM2 (99.9%) and HM3 (95.7%) remained more than likely to rank within the prime three for muscle energy. Threshold evaluation outcomes for energy and hypertrophy are proven in on-line supplemental appendix 10 . HM3 was the top-ranked situation for energy; nonetheless, 65 comparisons indicated some sensitivity to the extent of uncertainty and potential biases within the proof. The revised top-ranked energy situation was HM2 in 92% (60/65) or HM1 in 8% (5/65) of comparisons. HM2 was the top-ranked situation for hypertrophy, and this discovering was strong. Two comparisons indicated some sensitivity to the extent of uncertainty and potential biases within the proof, and HM1 was the revised top-ranked situation in each circumstances. Mannequin match outputs and node-splitting plots are reported within the on-line supplemental appendix 9 . Within the energy community, the UME mannequin (DIC=402.3) was not meaningfully totally different than the random-effects NMA mannequin (DIC=400.8). Node-splitting was carried out on 29 comparisons; the one vital distinction was LM1 versus HM1 (p<0.01). Within the hypertrophy community, the UME mannequin (DIC=143.1) was meaningfully totally different than the random-effects NMA mannequin (DIC=137.8). Node-splitting was carried out on 22 comparisons; the one vital distinction was LS2 versus CTRL (p<0.01). Chance for every situation rating within the prime three only for energy (A) and hypertrophy (B). Scores nearer to 100% point out a higher probability of being ranked within the prime three. Resistance coaching prescriptions are denoted with a three-character acronym—XY#—the place X is load (H, ≥80% 1-repetition most (1RM); L, <80% 1 RM); Y is units (M, multiset; S, single-set); and # is the weekly frequency (3, ≥3 days/week; 2, 2 days/week; 1, 1 day/week), respectively. For instance, ‘HM2’ denotes higher-load, multiset, twice-weekly coaching. CTRL, non-exercising management group. Determine 4 shows the chance that every situation would rank within the prime three greatest interventions for muscle energy and hypertrophy, such that scores nearer to 100% point out a higher probability of rating within the prime three. HM3 (85.5%), HM2 (83.5%) and HM1 (60.5%) had been more than likely to rank within the prime three for muscle energy. HM2 (86.9%), LM1 (48.7%) and LM2 (48.3%) had been more than likely to rank within the prime three for muscle hypertrophy. CTRL was the one situation with a 0% probability for energy and hypertrophy. Posterior rankings and distribution curves for all situations are reported within the on-line supplemental appendix 8 . The relative results from all 133 community comparisons for muscle energy and hypertrophy are displayed in desk 2 . For comparisons between RTxs (ie, not CTRL), the 95% CrI excluded zero for 13.6% (9/66) and a couple of.2% (1/45) of comparisons within the energy and hypertrophy NMA, respectively. For muscle energy, there was a 95% chance that HM2 yields a bigger relative impact than LS1, LS2, LS3, LM2 and LM3 and that HM3 yields a bigger relative impact than LS2, LS3, LM2 and LM3. There was a 95% chance for muscle hypertrophy that HM2 yields a bigger relative impact than LS3. The relative impact of every RTx in contrast with CTRL on muscle hypertrophy is displayed in determine 3B . The posterior SMD for all RTx ranged from 0.10 to 0.66, with the biggest relative impact from HM2 (0.66 (0.47 to 0.85)). In contrast with CTRL, the relative impact of HS2 (0.10(-0.57 to 0.80)), HS3 (0.34 (−0.02 to 0.71)) and HM1 (0.40 (−0.35 to 1.17)) had been the one comparisons that the 95% CrI crossed zero. The relative impact of every RTx in contrast with CTRL on muscle energy is displayed in determine 3A . The posterior SMD for all prescriptions ranged from 0.75 to 1.60, with the biggest relative impact from HM3 (1.60 (1.38 to 1.82)). In contrast with CTRL, the relative impact of LS1 (0.75 (−0.16 to 1.68)) and HS1 (0.79 (−0.88 to 2.45)) had been the one comparisons that the 95% CrI crossed zero. Inside-study threat of bias was reasonable–excessive for each energy and hypertrophy outcomes. Within the energy community, 22%, 67% and 1% of research had a excessive, reasonable or low threat of bias, respectively. Within the hypertrophy community, 18%, 82% and 0% of research had a excessive, reasonable or low threat of bias, respectively. Research-level threat of bias assessments for each energy and hypertrophy is detailed in on-line supplemental appendix 7 . Community geometry for all accessible research evaluating energy (A) and hypertrophy (B). Every node represents a novel situation, and the dimensions of every node is proportional to the pattern measurement per situation. Every edge represents direct proof, and the width of every edge is proportional to the variety of research evaluating linked nodes. Resistance coaching prescriptions are denoted with a three-character acronym—XY#—the place X is load (H, ≥80% 1-repetition most (1RM); L, <80% 1 RM); Y is sets (M, multiset; S, single-set); and # is the weekly frequency (3, ≥3 days/week; 2, 2 days/week; 1, 1 day/week), respectively. For example, ‘HM2’ denotes higher-load, multiset, twice-weekly training. CTRL, non-exercising control group. Discussion Twelve distinct RT prescriptions and non-exercising control groups were compared using network meta-analysis to determine their effect on gains in muscle strength, hypertrophy and improvements in physical function in healthy adults. Compared with no exercise, most load, sets and frequency combinations increased muscle strength and hypertrophy, indicating that several RTx resulted in beneficial skeletal muscle adaptations. RT with higher loads characterised the top-ranked strength prescriptions, and RT with multiple sets characterised the top-ranked hypertrophy prescriptions. A diverse range of RT prescriptions improved physical function, but evidence scarcity limited insights. Guideline developers and practitioners may consider these results when forming recommendations and prescribing RT for healthy adults. Network meta-analysis has previously been used to compare different types of exercise31–34 and doses of RT load.35 In the NMA by Lopez et al,35 23 (n=582) and 24 (n=604) studies were included in the strength and hypertrophy networks, respectively. The present strength (178 studies, n=5097) and hypertrophy (119 studies, n=3364) networks were much larger, and this is likely attributable to Lopez et al35 excluding studies not including RT to momentary muscular failure and our more comprehensive search strategy (262935 vs 16 880 records identified). This NMA, to our knowledge, represents the largest synthesis of RT data from randomised trials. All loads, sets and frequency combinations increased muscle strength and size compared with CTRL. There was a 95% probability that RT with at least two sets or two sessions per week increased strength (figure 3A), and training with at least two sets and two sessions per week resulted in hypertrophy (figure 3B). Considering only the lower credible interval limit, each RTx induced at least a moderate (SMD>0.47) and small (SMD>0.16) improve in muscle energy and mass, respectively. Such certainty just isn’t attainable for all prescriptions, although, as a result of the 95% CrI crossed zero for 2 RTx for energy (HS1 and LS1) and three RTx for hypertrophy (HM1, HS2 and HS3), which means these prescriptions may improve, not change or lower muscle energy and measurement. Nevertheless, we posit that that is unlikely to symbolize an ineffectiveness of these specific RTx and that imprecise community estimates confound these findings. These energy (HS1 and LS1) and hypertrophy (HM1, HS2 and HS3) nodes included <60 participants and contributed little direct evidence (figure 2). Within each study testing these prescriptions, strength increased significantly compared with CTRL/baseline in all cases and hypertrophy increased from baseline in most cases. Those prescribing RT can be confident that all RTxs increased strength and hypertrophy compared with no exercise. Network comparisons suggest that most RT prescriptions were comparable for strength and hypertrophy. The 95% CrI contained zero for a striking 91% (101/111) of all between-RTx comparisons (table 2). Nine of the 10 comparisons that did not contain zero were between HM2 or HM3 and a lower-load RTx for strength, suggesting higher-load, multiset programmes caused the largest strength gains. This result remained after sensitivity analyses (online supplemental appendix 11) andaligned with previous meta-analyses that found higher-load RT yields the largest strength gains.17 18 35 A critical point for practitioners is that lower-load RT prescriptions increase strength compared with no exercise. All RT prescriptions may comparably promote muscle hypertrophy, and the influence of load was less apparent. The lack of importance of load for hypertrophy is supported by other analyses,16 17 35 62 but performing RT to momentary muscular failure (fatigue) has been posited as a key component for RT-induced hypertrophy with lower loads.62 Network meta-regression for exercise ‘failure’ (fatigue) did not improve model fit nor substantially alter network estimates, suggesting that lifting to fatigue does not suitably explain the observed hypertrophic response. Our finding in this domain agrees with previous work,63 suggesting that untrained individuals still achieve large gains in skeletal muscle mass without performing RT to failure. Performing RT to momentary muscular failure may, however, be increasingly important for trained individuals.13 For both strength and hypertrophy, though, there was a large credible interval surrounding the non-significant effect estimate for many comparisons between RTxs, so a wide range of different effects are possible for these comparisons. The available evidence does not permit definitive, statistically valid conclusions about the equivalency of each RTx, despite most comparisons between RTxs not being statistically significantly different from each other. Prescriptions for RT with higher loads were more likely to rank in the top three for strength than all lower-load prescriptions, and RT prescriptions with multiple sets per exercise were most likely to rank in the top three for hypertrophy (figure 4). Rankings are sensitive to uncertainties within the network,28 but posterior ranking credible intervals supported higher-load, multiset programmes being the highest-ranked for strength and multiple sets or multiple sessions being the highest-ranked for hypertrophy. Notably, sets and frequency are major components of RT volume, a key factor for hypertrophy.21 64–66 The probability of each condition ranking in the top three was calculated because the top-ranked RTx does not necessarily reflect the best intervention for all individuals.67 Personal preferences, including disliking higher loads or time constraints, including an inability to train more than once weekly, can be observed while still benefiting from RT. In our view, especially given the low participation rates in RT, practitioners should not avoid prescribing, nor should individuals be discouraged from completing non-top-ranked RTx. While all prescriptions increased muscle strength and mass, the top-ranked prescriptions involved higher loads for strength and higher volume for hypertrophy. We do not know how these RTx affect relevant health outcomes. Some data suggest that health benefits exist with low time commitment (30–60 min/week) to RT and greater time commitment with reduced health benefits.4 68 Ours is the first review to assess confidence in RTx recommendations with threshold analysis. Several factors can influence NMA results,55 and the robustness of treatment recommendations should be considered when interpreting results. Previous methods to evaluate the confidence of meta-analytical findings do not consider how potentially influencing factors can change treatment recommendations55 69 70 or are not yet developed for Bayesian NMA.71 Threshold analysis determines how much the available evidence could change before recommendations differ and identifies a new top-ranked treatment.54 55 Sixty-five direct comparisons were identified that could potentially impact the recommendation of HM3 as the top-ranked strength treatment; however, the revised treatment recommendation was HM2 in 60 of these cases and HM1 in the other five cases (online supplemental appendix 10), suggesting that performing RT with higher loads and multiple sets/exercise are robust recommendation for optimising RT-induced strength gains. The top-ranked RTx for hypertrophy—HM2—was sensitive to the uncertainty of only two comparisons, and HM1 was the revised recommendation because both comparisons were from the same multi-arm study.72 Furthermore, 127 of the 161 direct comparisons would need to change by more than four SDs to alter HM2 as the top recommendation for hypertrophy. The optimised recommendations of higher load, multiple-set programmes for strength and HM2 for hypertrophy were extremely robust. Current guidelines collectively advise healthy adults to complete RT at least twice weekly.10–12 73 The results herein support these recommendations and should not deter practitioners from promoting existing guidelines to improve strength and hypertrophy, nor do these results contradict the effectiveness of guidelines incorporating additional RTx variables, such as rest intervals and contraction type and velocity.10 12 However, our results support RT at less than recommended often-cited levels for enhancing strength and hypertrophy. Most individuals do not meet current guidelines, and RTx complexities may impede the adoption of RT. Minimal-dose approaches have been proposed to reduce barriers to RT,74 and our results strongly support the WHO’s claim, ‘Doing some activity is better than none’.73 While others attempt to optimise RTx,75 we propose that, for most adults, regularly engaging in any RTx is more important than training to optimise strength and hypertrophy outcomes. Our analysis found multiple RTx comparable for healthy adults to increase muscle strength and mass. Thus, adults should engage in RT, even if they cannot meet existing recommendations. Limitations Risk of bias was frequently introduced by protocol deviations, randomisation procedures and selection of the reported result for both outcomes (online supplemental appendix 7). All three domains were regularly rated “Some concerns” because participants were aware of the intervention, appropriate analyses to estimate the effect of assignment were not performed and randomisation, concealment and prespecified analysis procedures were rarely reported. Double-blinding RT is unfeasible, but the remaining issues are prevalent and reoccurring in RT research.76 Researchers should preregister analysis plans and report randomisation procedures to reduce bias. Several limitations require acknowledgement and consideration when interpreting the findings of this review. Well-trained elite athletes/military persons and individuals with chronic disease were excluded, so the results should be translated to these populations with caution and additional insights.13 77–79 Mobility, gait speed and balance/flexibility findings should also be interpreted with caution due to the limited evidence available, which could be attributed to including only healthy older (>55 12 months) adults (eg, not frail). The coding framework for RT prescriptions prevented the inclusion of periodized RT programmes overlapping situations (eg, masses starting from 60–90% 1RM) from being captured within the community. Initially, our goal was to additional divide the load and set prescriptions; nonetheless, this yielded sparse, disconnected networks, violating a important assumption of NMA.49 The continual RTx variables investigated herein (load, units, frequency) had been categorized categorically, so future work may use dose-response/model-based NMA strategies to discover these RTx variables as steady predictors.80 81 A number of acute RT variables weren’t factored into the included RT prescriptions (eg, inter-set relaxation, time below pressure, repetition velocity, volitional fatigue, tempo); the place attainable, NMR was used to discover if these elements improved mannequin match and altered results. Outcomes from NMR are correlative, nonetheless, and needs to be interpreted cautiously.82 Nonetheless, many variables (inter-set relaxation, tempo, time below pressure) had been reported too sometimes for inclusion as covariates. Calculating the relative weekly quantity load (ie, load × repetitions/set × variety of units × variety of workouts × weekly frequency), which ought to influence outcomes,21 additionally required approximations that hindered mannequin match. The precept of specificity17 (ie, the similarity between coaching and testing motion) and approximations of muscle mass (,83 eg, lean mass) may infringe on transitivity assumptions37 when integrating outcomes from a number of research and NMR with the covariates measurement software and area had been imperfect options. Together with one measurement per end result for every research might restrict the totality of proof captured by this assessment, so future methodological work may discover the combination of a number of correlated impact sizes in NMA, as in latest pairwise meta-analyses.63 84 More and more, within-subject fashions are used resulting from their elevated statistical energy.85 To our data, nonetheless, no strategies can be found to account for the extra correlation when together with within-subject and between-subject comparisons in NMA. With consideration for these limitations, guideline builders and practitioners can acquire significant insights from this evaluation.