The Validity and Reliability of Two Commercially Available Load Sensors for Clinical Strength Assessment.

OBJECTIVE: Handheld dynamometers are common tools for assessing/monitoring muscular strength and endurance. Health/fitness Bluetooth load sensors may provide a cost-effective alternative; however, research is needed to evaluate the validity and reliability of such devices. This study assessed the validity and reliability of two commercially available Bluetooth load sensors (Activ5 by Activbody and Progressor by Tindeq). METHODS: Four tests were conducted on each device: stepped loading, stress relaxation, simulated exercise, and hysteresis. Each test type was repeated three times using the Instron ElectroPuls mechanical testing device (a gold-standard system). Test-retest reliability was assessed through intraclass correlations. Agreement with the gold standard was assessed with Pearson's correlation, interclass correlation, and Lin's concordance correlation. RESULTS: The Activ5 and Progressor had excellent test-retest reliability across all four tests (ICC(3,1) ≥ 0.999, all p ≤ 0.001). Agreement with the gold standard was excellent for both the Activ5 (ρ ≥ 0.998, ICC(3,1) ≥ 0.971, ρc ≥ 0.971, all p's ≤ 0.001) and Progressor (ρ ≥ 0.999, ICC(3,1) ≥ 0.999, ρc ≥ 0.999, all p's ≤ 0.001). Measurement error increased for both devices as applied load increased. CONCLUSION: Excellent test-retest reliability was found, suggesting that both devices can be used in a clinical setting to measure patient progress over time; however, the Activ5 consistently had poorer agreement with the gold standard (particularly at higher loads).

Wearable Technology to Increase Self-Awareness of Low Back Pain: A Survey of Technology Needs among Health Care Workers.

Low back pain (LBP) is a leading contributor to musculoskeletal injury worldwide and carries a high economic cost. The healthcare industry is the most burdened, with nurses, in particular, being highly prone to LBP. Wearable technologies have the potential to address the challenges of monitoring postures that contribute to LBP and increase self-awareness of workplace postures and movements. We aimed to gain insight into workers' perceptions of LBP and whether they would consider using wearable monitoring technologies to reduce injury risks. We conducted a cross-sectional survey to gather information from a selected population of nurses. Sixty-four participants completed the survey, and data were analyzed with the support of Machine Learning techniques. Findings from this study indicate that the surveyed population (64 nurses) is interested in these new approaches to monitor movement and posture in the workplace. This technology can potentially change the way ergonomic guidelines are implemented in this population.