Exploring the Discriminant Validity of the Modified Arm Care Screen (MACS), Designed for Overhead Athletes, in Detecting Musculoskeletal Risk Factors in the General Population

Abstract

Background: Shoulder pain is the third most common musculoskeletal issue in primary care, affecting up to 50% of patients six months post-consultation, leading to significant functional impairments and societal costs, especially due to sick leave. Shoulder injuries are particularly prevalent among ‘overhead athletes’ in sports like swimming, volleyball, and handball, with high injury rates reported annually. Screening tools like the Arm Care Screen (ACS) have been used to effectively identify athletes at risk of shoulder injuries. However, their applicability to the general population is less understood. This study aimed to assess the discriminant validity of a modified ACS (MACS) in detecting musculoskeletal risk factors among non-athletes. Methods: A prospective cross-sectional study was conducted with 30 asymptomatic subjects over 18, excluding those with a history of shoulder injuries or surgeries. The MACS, comprising four tests, was administered, and its diagnostic performance was evaluated through sensitivity, specificity, predictive values, and likelihood ratios. Results: The results indicated low sensitivity (0–47.62%) and variable specificity (55.56–100%), suggesting that the MACS may not effectively identify risk factors in the general population. Positive and negative predictive values were inconsistent (ranging from 0 to 100), as well as positive and negative likelihood ratios (ranging from 0 to 3.47), highlighting the need for non-athlete-specific screening tools. Conclusion: While the MACS shows promise in athletes, its application in the general population requires further refinement. This study underscores the necessity for tailored screening methods to enhance the early detection and prevention of musculoskeletal issues in diverse populations.

1. Introduction

Shoulder pain ranks as the third most prevalent musculoskeletal issue in primary care, following low back pain and knee pain. The prognosis for individuals experiencing shoulder pain varies significantly, with about 50% still reporting symptoms six months after seeking medical attention [1]. Beyond the pain, many suffer from functional impairments that can disrupt their work, hobbies, social interactions, and sports activities. This often leads to psychological distress and a diminished quality of life [2]. The societal costs of shoulder pain are substantial. A cost-estimation study conducted in Sweden revealed that the financial burden per patient is approximately EUR 4.139, with over 80% of these expenses attributed to sick leave [3].

Shoulder injuries are especially common in sports that require repetitive overhead movements at high speeds or in extreme positions, such as swimming, volleyball, and handball. Athletes participating in these sports, often referred to as ‘overhead athletes’, are at a significant risk of shoulder injuries [4]. Numerous studies have attempted to identify risk factors and prevention strategies for these athletes [5]. The risk of shoulder injuries varies across different sports and depends on the definition used, such as time lost from sports, the need for medical attention, or the severity of the injury [5].

In swimming, between 23% and 38% of athletes experience shoulder injuries within a year [4]. Similarly, 23% of volleyball players report dominant shoulder pain during the ongoing season [6]. In a large study of elite handball players, 44% to 75% had experienced shoulder pain previously, with 20% to 52% reporting current shoulder pain [7]. The prevalence of weekly shoulder pain and substantial shoulder injuries in these athletes is 28% and 12%, respectively [4]. High rates of shoulder injuries are also observed in other overhead sports, such as baseball and water polo, with variations depending on factors like age, sex, and competition level [4].

Screening tools have been used in the past to identify athletes at risk of developing shoulder pain [8]. These tools are either related to sporting level, workload ratio, muscle strength and endurance [5,9], or special tests such as the Arm Care Screen Test (ACS) [10] or the Closed Kinetic Chain Upper Extremity Stability Test (CKCUEST) [11]. Although these tests have shown promising results in overhead athletes, less is known about the usefulness of similar tools in the general population.

A screening tool known as the Arm Care Screen (ACS), based on Functional Movement System (FMS) principles, has been implemented to quickly and easily identify baseball players at a higher risk of shoulder injury [10]. The ACS has demonstrated excellent reliability among high school baseball coaches, with intra-rater agreement (k = 0.76; 95% CI 0.54–0.95) and inter-rater agreement (k = 0.89; 95% CI 0.77–0.99) [12]. Previous studies have shown that the ACS can effectively differentiate between baseball players who have musculoskeletal risk factors for shoulder injuries and those who do not [10]. Additionally, recent examinations of a modified version of the ACS for its discriminant validity have yielded promising results in other overhead athletes, including those in volleyball, tennis, and basketball [8].

Although preliminary findings indicate that the modified ACS can effectively screen overhead athletes both on the field and during comprehensive clinical assessments, its discriminant validity in the general population remains less understood. No relevant studies have investigated the necessity of screening tools in evaluating musculoskeletal risk factors in the general population, particularly in those engaging in daily or occupational overhead activities. The prevalence of pathologies within these populations, such as shoulder impingement, rotator cuff tears, and tendonitis, is significant [1]. Recent data suggest that the percentage of individuals experiencing such conditions remains high [1], indicating a substantial need for effective screening tools to mitigate these risks.

Consequently, the objective of this study was to assess the discriminant validity of the Modified Arm Care Screen (MACS) in identifying common musculoskeletal risk factors in the general population. We hypothesized that suboptimal performance on the MACS subtests would exhibit high sensitivity in detecting the presence of at least one associated musculoskeletal risk factor among individuals in the general population who participate in daily overhead activities. Our research hypothesis was that the MACS would demonstrate high diagnostic accuracy in detecting at least one associated musculoskeletal risk factor among individuals in the general population who engaged in daily overhead activities. Our null hypothesis was that the MACS would not demonstrate high diagnostic accuracy in detecting at least one associated musculoskeletal risk factor among individuals in the general population who engaged in daily overhead activities.

2. Materials and Methods

A prospective cross-sectional method was employed to evaluate the MACS’s effectiveness in differentiating between the presence and absence of musculoskeletal risk factors among individuals in the general population. This research adhered to the Standards for Reporting Diagnostic Accuracy Studies (STARD) guidelines, ensuring the standardized and comprehensive reporting of the study’s diagnostic accuracy design. Prior to data collection, ethical approval was granted by the University of West Attica (approval number: 14679/14). All participants provided informed consent after being fully informed of the study’s procedures and objectives. This research builds on a previously published study that examined the discriminant validity of the MACS in overhead athletes [8]. The current study shifts the focus to the general population.

2.1. Sample

To determine the appropriate sample size, our calculations were based on a prevalence rate of 50% for the risk factor of participant injury, as suggested by previous research studies [1,8,10]. To achieve a sensitivity of 0.95 and a specificity of 0.85 for the Arm Care Screen (ACS) with a precision of 0.20, a confidence level of 95%, and an expected dropout rate of 10%, a minimum sample size of 28 participants was required [8,13,14].

The eligibility criteria for participation included being an active individual over the age of 18, being asymptomatic, and having no history of shoulder surgery or shoulder-related treatments. Asymptomatic status was defined according to our previous research [14]. Additionally, participants were required to regularly perform overhead activities in their daily lives. The exclusion criteria included any prior occurrence of shoulder pain that led to abstention from activities of daily living for more than three consecutive days, as well as a history of shoulder injury or physical therapy related to the chest, shoulder, or neck. A total of 30 subjects met the inclusion criteria and participated in the study.

2.2. Modified ACS

Data collection was carried out on weekends to reduce the likelihood of work-related injuries or symptoms of delayed onset muscle soreness from daily activities. Over a period of two months, two independent assessors, trained in administering the MACS, conducted the assessments. Prior to testing, demographic details of the participants were documented.

The MACS consists of four subtests (Figure 1):

Reciprocal Shoulder Mobility: The participant stands with feet together and reaches one hand behind the head while extending the other behind and up the back, attempting to touch both fingertips. A positive result is recorded if the participant cannot perform this on one or both sides while maintaining an upright posture.

Test for 90/90 Total Body Rotation: Standing with feet together and arms positioned at 90 degrees, the participant rotates their entire body—including hips, shoulders, and head—while keeping their feet straight ahead. A positive result is noted if the opposite shoulder becomes obscured while maintaining proper posture.

Lower-Body Diagonal Reach: The participant stands two shoe lengths away from a wall, balancing on one leg, and reaches the opposite leg behind and across to touch the wall three times without touching the floor. The test is then repeated, aiming to touch the wall five times consecutively without losing balance. Failing to maintain balance for all five touches is considered a positive outcome. This test was adapted from core elements of the Y Balance test [13].

Rotary Stability: The participant begins on their hands and knees, with arms and thighs vertically aligned. The feet should also remain vertical, with toes resting on the floor. The participant maintains ground contact with their thumbs, knees, and toes. During the test, they extend their right arm forward and right leg backward, assuming a one-sided superman position, then retract the elbow and knee until they touch above the ground.

2.3. Risk Factors

To accurately identify musculoskeletal risk factors using the MACS, specific cut-off thresholds for each risk factor were established based on previous methods. These thresholds, which determined whether values fell below predefined points for physical impairments, were derived from prior research and the existing literature, as shown in Table 1. Matsel et al. [14] used these same thresholds in their study. Two physical therapists independently assessed ten musculoskeletal risk factors apart from the ACS scoring process.

Nine of these risk factors were outlined in the study by Matsel et al. [10], with the addition of the Closed Kinetic Chain Upper Extremity Stability (CKCUES) test [15]. The procedures for CKCUES have been previously described [16]. A subject scoring below the reported threshold values (18.5 touches for males and 20.5 touches for females from a modified position) indicated a risk factor. Although a recent study suggested that scoring fewer than 21 touches made individuals 18 times more likely to sustain an injury [17], we chose to use the earlier threshold values [18], which sets different cut-offs for men (18.5 touches) and women (20.5 touches). All risk factors, testing procedures, and ACS comparison data are outlined in Table 1.

To validate the ACS, results from the four ACS assessments (pass/fail) were compared to corresponding risk factor measurements (pass, above cut-off; fail, below cut-off). The diagnostic performance of the ACS was evaluated using the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratios (LRs).

The classification for each result was as follows:

True positive (TP): Recorded when a participant had at least one shoulder or thoracic mobility risk factor, correctly identified by a positive result on the ACS reciprocal shoulder mobility test on either side.

True negative (TN): Noted when the participant passed all goniometric shoulder and thoracic range of motion (ROM) tests and received a negative result on both sides of the reciprocal shoulder mobility test.

False positive (FP): Occurred when no shoulder or thoracic risk factors were present, but the participant tested positive on either side of the reciprocal shoulder mobility test.

False negative (FN): Observed when a participant had at least one shoulder or thoracic risk factor but tested negative on both sides of the reciprocal shoulder mobility test.

The same process was applied to evaluate the remaining three tests within the ACS: Total Body Rotation, Lower Body Diagonal Reach, and Rotary Stability, as detailed in Table 1.

Table 1. ACS tests and corresponding comparisons with musculoskeletal risk factors.

ACS	Risk Factors
Reciprocal Shoulder Mobility	Dominant Shoulder Internal Rotation < 45° at 90° Abduction: The passive range of motion for internal rotation in the dominant shoulder, measured at 90° of abduction, was less than 45° [19]. Glenohumeral Internal Rotation Deficit (GIRD) ≥ 20°: The internal rotation difference between the non-dominant and dominant shoulder was 20° or more [20,21]. Shoulder Total Range of Motion Deficit (TROM) ≥ 10°: The total range of motion difference between the dominant and non-dominant shoulder was 10° or more [19,22]. Shoulder Flexion Deficit ≥ 5°: The difference in shoulder flexion passive range of motion (PROM) between the dominant and non-dominant shoulder was 5° or greater [23]. Thoracic Spine Rotation PROM < 50°: The passive range of motion for thoracic spine rotation, measured in a quadruped position, was less than 50° for either the dominant or non-dominant side [24,25].
Total Body Rotation	Limited Hip Internal Rotation (IR) Passive Range of Motion (PROM) ≤ 36°: Either the stance or stride hip demonstrated an internal rotation PROM of 36° or less while the participant was in the prone position [26,27]. Restricted Hip External Rotation (ER) Passive Range of Motion (PROM) ≤ 40°: Either the stance or stride hip showed an external rotation PROM of 40° or less with the participant in the prone position [28].
Lower Body Diagonal Reach	Normalized Y Balance Test–Posterior Lateral (YBT-PL) Reach Distance: The YBT-PL reach distance was measured for both the stance and stride legs using the Y Balance Test. To account for the effect of player height on reach distance, the YBT-PL reach was normalized by dividing it by the length of the participant’s dominant lower limb and then multiplying by 100. The average normalized YBT-PL reach distances were computed for each age group. Reach distances below the lower third quartile for the respective age categories—youth (<92 cm), high school (<95 cm), and college (<98 cm)—were considered risk factors [29]. YBT-PL Reach Asymmetry: An absolute difference of 5.5 cm or more between the YBT-PL reach distances of the stance leg and the stride leg was identified as a risk factor [30].
Rotary Stability	Closed Kinetic Chain Upper Extremity Stability Test (CKCUES): Subjects who scored below the reference values (18.5 touches for males and 20.5 touches for females from a modified position) were deemed to have an increased risk factor [17,18].

(Retrieved from Paraskevopoulos et al. [8]).

Table 1. ACS tests and corresponding comparisons with musculoskeletal risk factors.

ACS	Risk Factors
Reciprocal Shoulder Mobility	Dominant Shoulder Internal Rotation < 45° at 90° Abduction: The passive range of motion for internal rotation in the dominant shoulder, measured at 90° of abduction, was less than 45° [19]. Glenohumeral Internal Rotation Deficit (GIRD) ≥ 20°: The internal rotation difference between the non-dominant and dominant shoulder was 20° or more [20,21]. Shoulder Total Range of Motion Deficit (TROM) ≥ 10°: The total range of motion difference between the dominant and non-dominant shoulder was 10° or more [19,22]. Shoulder Flexion Deficit ≥ 5°: The difference in shoulder flexion passive range of motion (PROM) between the dominant and non-dominant shoulder was 5° or greater [23]. Thoracic Spine Rotation PROM < 50°: The passive range of motion for thoracic spine rotation, measured in a quadruped position, was less than 50° for either the dominant or non-dominant side [24,25].
Total Body Rotation	Limited Hip Internal Rotation (IR) Passive Range of Motion (PROM) ≤ 36°: Either the stance or stride hip demonstrated an internal rotation PROM of 36° or less while the participant was in the prone position [26,27]. Restricted Hip External Rotation (ER) Passive Range of Motion (PROM) ≤ 40°: Either the stance or stride hip showed an external rotation PROM of 40° or less with the participant in the prone position [28].
Lower Body Diagonal Reach	Normalized Y Balance Test–Posterior Lateral (YBT-PL) Reach Distance: The YBT-PL reach distance was measured for both the stance and stride legs using the Y Balance Test. To account for the effect of player height on reach distance, the YBT-PL reach was normalized by dividing it by the length of the participant’s dominant lower limb and then multiplying by 100. The average normalized YBT-PL reach distances were computed for each age group. Reach distances below the lower third quartile for the respective age categories—youth (<92 cm), high school (<95 cm), and college (<98 cm)—were considered risk factors [29]. YBT-PL Reach Asymmetry: An absolute difference of 5.5 cm or more between the YBT-PL reach distances of the stance leg and the stride leg was identified as a risk factor [30].
Rotary Stability	Closed Kinetic Chain Upper Extremity Stability Test (CKCUES): Subjects who scored below the reference values (18.5 touches for males and 20.5 touches for females from a modified position) were deemed to have an increased risk factor [17,18].

(Retrieved from Paraskevopoulos et al. [8]).

2.4. Statistical Analysis

Descriptive statistics were used to analyze the demographic data. Musculoskeletal risk factors were classified according to prior methods and documented in separate 2 × 2 tables for each of the four elements of the modified ACS. To examine the relationships between each ACS component and the corresponding risk factors, Chi-square tests were conducted. If any cell frequencies were found to be less than five, Fisher’s exact test was applied instead [31]. The Phi (Φ) coefficient was used to measure the strength of these associations, as recommended [32]. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratios (LRs), and odds ratios were calculated following the methods outlined in earlier research [33,34]. All statistical analyses were conducted with SPSS (IBM, version 25). A significance level of p < 0.05 was set for all tests to determine statistical significance.

3. Results

The demographic data for all 30 subjects are detailed in

Table 2. Demographics of the included sample (N = 30).

Mean Age	22.7 ± 3.1
Gender (Male/Female)	14–16
Height (cm)	172.7 ± 10.4
Weight (kg)	68.4 ± 12.8
BMI	23
Limb dominance (L, Left; R, Right)	2 L–28 R

. Chi-square tests showed that there were no significant associations between the individual components of the modified ACS and their related risk factors. The strength of these associations, as measured by Phi (Φ) values, ranged from 0.089 to 0.905, indicating a small-to-medium level of association (

Table 3. Detecting accuracy and association in each component of the modified ACS compared to the risk factors.

	Reciprocal Shoulder Mobility				90/90 Total Body Rotation			Lower Body Diagonal Reach			Core Stability
		≥1 Risk Factor				≥1 Risk Factor			≥1 Risk Factor			≥1 Risk Factor
	Shoulder Mobility	Yes	No		Total Body Rotation	Yes	No	Diagonal Reach	Yes	No	Rotary Stability	Yes	No
	Fail	10	2		Fail	0	3	Fail	6	0	Fail	4	0
	Pass	12	5		Pass	13	14	Pass	14	9	Pass	24	1
Chi-Square for Association		p = 0.481, Phi = 0.12				p = 0.626, Phi = 0.089			p = 0.053, Phi = 0.354			p = 0.464, Phi = 0.134

). Additionally, Table 3 illustrates the precision of each modified ACS component in identifying individuals with musculoskeletal risk factors.

Sensitivity across the four components of the modified ACS varied from 47 to 0, reflecting a relatively low true positive rate. In contrast, specificity values exhibited a broader range, from 100 to 55, indicating a moderate-to-strong positive rate. Positive likelihood ratios (LRs), which represent the fold increase in the odds of having a condition given a positive test result, ranged from 1.07 to 3.47 [35]. Negative LRs, representing the fold decrease in the odds of having a condition given a negative test result, ranged from 0.75 to 1.21 [35]. The positive predictive value (PPV), representing the likelihood of pathology in individuals who tested positive, varied between 0% and 100%, while the negative predictive value (NPV), which reflects the probability of no pathology in those who tested negative, ranged from 65% to 75% (

Table 4. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratios (LRs), and accuracy of the components of the modified ACS.

ACS Component	Reciprocal Shoulder Mobility		Total Body Rotation		Lower Body Diagonal Reach		Rotary Stability
Statistic	Value	95% CI	Value	95% CI	Value	95% CI	Value	95% CI
Sensitivity	47.62%	25.71–70.22%	0.00%	0.00–24.71%	31.58%	12.58–56.55%	14.29%	4.03–32.67%
Specificity	55.56%	21.20–86.30%	82.35%	56.57–96.20%	90.91%	58.72–99.77%	100.00%	15.81–100.00%
Positive Likelihood Ratio	1.07	0.45–2.52	0.00		3.47	0.48–25.22
Negative Likelihood Ratio	0.94	0.46–1.92	1.21	0.97–1.51	0.75	0.53–1.08	0.86	0.74–1.00
Disease Prevalence	30%		30%		30%		30%
Positive Predictive Value	31.47%	16.31–51.97%	0.00%		59.82%	17.01–91.53%	100.00%	39.76–100.00%
Negative Predictive Value	71.22%	54.82–83.46%	65.77%	60.66–70.54%	75.61%	68.42–81.60%	73.13%	70.06–76.00%
Accuracy	53.17%	34.18–71.52%	57.65%	38.35–75.37%	73.11%	53.87–87.56%	74.29%	55.13–88.40%
Odds Ratio	2.08	0.33 to 13.14	0.15	0.007 to 3.254	8.51	0.428 to 169.46	0.55	0.019 to 15.78

).

The accuracy, measuring the overall success in correctly classifying participants, ranged from 53% to 74% across the four components of the modified ACS. Additionally, athletes with pre-existing risk factors were significantly more likely to fail multiple tests. Specifically, they were 3.88 times more likely (95% CI 1.186 to 12.74) to fail the reciprocal shoulder mobility screen, 130.3 times more likely (95% CI 20.171 to 842.128) to fail the total body rotation screen, 48.87 times more likely (95% CI 11.086 to 215.461) to fail the lower body diagonal reach, and 35.53 times more likely (95% CI 7.45 to 169.33) to fail the rotary stability test compared to athletes without risk factors. These findings are detailed in Table 4.

The findings highlight that the Modified Arm Care Screen (MACS) components demonstrated varying levels of sensitivity and specificity in detecting musculoskeletal risk factors. Sensitivity values, which reflect the test’s ability to identify true positives, were generally low, ranging from 0% to 47.6%. This indicates that the MACS components were not particularly effective in consistently detecting individuals with risk factors. However, specificity values, which measure the ability to correctly identify those without risk factors, were higher, ranging from 55.6% to 100%. The high specificity in certain components suggests that the MACS was more reliable in ruling out individuals without risk factors than in identifying those with them. Notably, the total body rotation and rotary stability tests had the highest specificity, making them more effective in excluding false positives. These findings suggest that while the MACS shows promise in identifying individuals without risk factors, further refinement is necessary to improve its sensitivity and overall diagnostic accuracy in detecting those with musculoskeletal vulnerabilities.

4. Discussion

The primary objective of this study was to assess the discriminant validity of the modified Arm Care Screen (MACS) in identifying musculoskeletal risk factors among individuals in the general population who participate in daily overhead activities. Through comprehensive statistical analysis, we derived key diagnostic values such as the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratios (LRs), and odds ratios. Contrary to our initial hypothesis, the findings indicated that the modified ACS did not effectively identify musculoskeletal risk factors among individuals in the general population.

4.1. Key Findings

This study demonstrated that the modified ACS, while effective in identifying risk factors in overhead athletes, did not translate well to the general population. This discrepancy may be attributed to several factors, including differences in physical demands, movement patterns, and baseline musculoskeletal health between athletes and non-athletes [36]. These variations necessitate the development of screening tools that are specifically tailored to the unique characteristics and needs of subjects in the general population [36].

4.2. Sensitivity and Specificity

The sensitivity values for the modified ACS components ranged from 0% to 47.62%, indicating a low true positive rate in detecting musculoskeletal risk factors among individuals in the general population. Conversely, the specificity values ranged from 55.56% to 100%, suggesting a moderate-to-high true negative rate. This disparity underscores the need for more targeted screening tools for individuals in the general population, as the MACS may not adequately capture the nuances of their musculoskeletal health [35,36]. The low sensitivity suggests that many individuals with risk factors were not flagged by the MACS, potentially missing early intervention opportunities [10].

4.3. Positive and Negative Predictive Values

The PPV and NPV values further highlighted the limitations of the modified ACS in the general population. With PPV values ranging from 0% to 100% and NPV values from 65% to 75%, the modified ACS showed inconsistent performance. The high PPV for certain components, such as the Rotary Stability test, indicates a strong probability of risk factors in those who test positive. However, the low NPV suggests a significant number of false negatives, potentially overlooking individuals with actual risk factors [35]. This inconsistency in predictive values indicates the need for enhanced diagnostic criteria and screening protocols tailored to the general population [8].

4.4. Likelihood Ratios and Odds Ratios

The likelihood ratios (LRs) and odds ratios provided additional insights into the diagnostic accuracy of the modified ACS. Positive LRs ranged from 1.07 to 3.47, while negative LRs ranged from 0.75 to 1.21. These values reflect a limited increase in the probability of having a risk factor given a positive test result, and a slight decrease in the probability given a negative test result. The odds ratios for failing the ACS components were significantly higher in subjects with pre-existing risk factors, particularly for the Total Body Rotation and Lower Body Diagonal Reach tests. This indicates that individuals with musculoskeletal risk factors are more likely to fail these specific tests, but the overall effectiveness of the ACS in the general population remains questionable [37]. This reinforces the importance of developing diagnostic tools that offer higher sensitivity and specificity for the general population [36,38,39,40].

4.5. Implications for Screening and Prevention

The findings suggest that while the MACS may serve as a useful tool for the screening and prevention of shoulder injuries in overhead athletes, its application in the general population is limited. The discrepancies in diagnostic accuracy between athletes and individuals in the general population highlight the need for population-specific screening tools [37]. These individuals may require assessments that account for their unique physical activities and musculoskeletal profiles, rather than relying on tools developed for athletic populations [5]. This points to a broader implication for public health initiatives aimed at injury prevention, emphasizing the customization of screening methods to fit various lifestyle and physical activity levels [5].

4.6. Limitations and Future Research

This study had several limitations, including the exclusion of individuals with a history of shoulder pain or surgery. Furthermore, the reliance on subjective assessments, such as pass/fail criteria in the MACS subtests, may have introduced observer bias, despite the training of assessors. Another limitation is the lack of standardized thresholds for certain risk factors in non-athletic populations, which may have impacted the generalizability of the results. Finally, the study’s focus on short-term outcomes limits the ability to evaluate the long-term predictive value of the MACS in preventing injuries or functional impairments. To enhance the usefulness of the MACS, it must be conducted regularly and with a large sample size to assess whether the identified risk factors actually lead to injuries in this population. Furthermore, the assessment primarily focuses on mobility, without accounting for force production (i.e., strength), despite low strength levels being stronger predictors of musculoskeletal (MSK) injuries than mobility. Future screening programs should aim to consider this limitation when designing tests to evaluate the risk of injury.

Future research should aim to include a larger, more diverse sample and explore the development of screening tools tailored to non-athletes. Additionally, longitudinal studies could provide valuable insights into the effectiveness of such tools in preventing musculoskeletal injuries over time. There is a clear need for the refinement of MACS components and the exploration of alternative risk factors that may be more relevant to non-athletic populations [36]. Future research should focus on refining the Modified Arm Care Screen (MACS) to improve its applicability and validity for the general population. One key possible direction is to modify the MACS components to better reflect common musculoskeletal risk factors in non-athletic populations, such as a sedentary lifestyle, poor posture, and age-related mobility limitations. Incorporating additional functional movement tests, like sit-to-stand or overhead reach tasks, may provide a more comprehensive assessment of upper body mobility in everyday settings.

4.7. Main Key Points

• The MACS showed poor performance in identifying musculoskeletal risk factors in the general population, with low sensitivity (0–47.62%) and high specificity (55.56–100%), indicating a need for better-targeted screening tools for non-athletes.
• Predictive values and likelihood ratios highlighted the limitations of the MACS, with inconsistent positive predictive values (PPV 0–100%) and negative predictive values (NPV 65–75%), suggesting a high risk of false negatives and missed early interventions in the general population.
• This study emphasizes the need for population-specific screening tools, as the MACS is more effective for athletes but lacks diagnostic accuracy for the general population, particularly due to differences in physical demands and movement patterns.

5. Conclusions

In conclusion, while the MACS is effective for overhead athletes, it does not adequately identify musculoskeletal risk factors among individuals in the general population. These findings underscore the importance of developing and validating population-specific screening tools to enhance the accuracy of musculoskeletal risk assessment and injury prevention strategies. Further research is necessary to refine these tools and improve their applicability across different populations. By advancing our understanding and methods of screening, we can better address the diverse needs of individuals and enhance preventative care in musculoskeletal health.