Sleep deprivation increases pain sensitivity following acute muscle soreness

OBJECTIVE

The aim of this cross-sectional group comparison study was to investigate whether sleep disturbance facilitates pain sensitivity caused by an acute muscle injury.

METHODS

Thirty-six healthy individuals were included and randomly assigned to one of three groups in a non-balanced manner: a control group (n = 11) and two groups who performed eccentric exercise for quadriceps to cause delayed onset of muscle soreness (DOMS). The difference between the DOMS groups was that one followed their habitual sleep pattern (Sleep group, n = 12) and the other had their sleep withdrawn for one night (No-Sleep group, n = 13). The level of DOMS was indicated using a 6-point Likert Scale and pain sensitivity was assessed using Pressure Pain Thresholds (PPT) at the lower legs and shoulder at baseline (Day-1) and after 48 h (Day-3). Additionally, pain distribution following suprathreshold pressure stimulation (STPS) on the quadriceps muscle was assessed on the same days.

RESULTS

PPTs were significantly reduced at Day-3 compared with Day-1 in both DOMS groups. The relative change between days was larger in the No-Sleep group compared with controls (P<0.05) whilst no significant change was seen in the Sleep group compared with controls. Furthermore, no significant differences were found between groups nor days for the subjective perception of DOMS (Likert Scale) and the size of the area of STPS.

CONCLUSIONS

The loss of sleep further increases pain sensitivity following an acute soft tissue injury, demonstrating a potential causative role of the lack of sleep on complex pain states following musculoskeletal injuries.

Assessment of range and quality of neck movement using a smartphone-based application

This study had the objective of measuring the validity of using a smartphone-based application to measure range of motion (ROM) and quality of movement (QOM) of neck motion by comparing it with 3D-motion capture analysis.

METHODS

Thirty healthy volunteers participated in this cross-sectional study. A helmet fitted with markers for motion capture analysis and a smartphone were fastened to the head of the participants. The smartphone recorded data using a beta version of Balancy (MEDEI, Denmark). Assessments of full active movement in transverse and sagittal planes were performed. Recordings were made simultaneously with the camera system and the smartphone. ROM and jerkiness were compared with a repeated measures ANOVA and a Pearson product moment was calculated to compare the outcomes from the different applications. Bland-Altman plots were generated to determine the levels of agreement.

RESULTS

No difference was found between modalities when comparing measurements of jerkiness or ROM. An excellent Pearson product moment was found for the outcomes of the two modalities for ROM (Pearson's r: 0.83 - 0.96) and jerkiness (Pearson's r: 0.86 - 0.95). The Bland-Altman plot revealed a systemic offset where the phone consistently measured higher values for ROM and lower values for jerkiness.

CONCLUSIONS

This study demonstrated that a smartphone-based application can be used to accurately measure ROM and jerkiness during neck movements. These results indicate the utility of using a smartphone-based application to assess neck movement in humans. The findings have implications for assessment of neck movement in research and clinical practice.