Exercise and Auricular Acupuncture for Chronic Low Back Pain: A Feasibility Randomised Controlled Trial

Objectives: To evaluate the feasibility of a randomized-controlled trial (RCT) investigating the effects of adding auricular acupuncture (AA) to exercise for participants with chronic low-back pain (CLBP). Methods: Participants with CLBP were recruited from primary care and a university population and were randomly allocated (n=51) to 1 of 2 groups: (1) "Exercise Alone (E)"-12-week program consisting of 6 weeks of supervised exercise followed by 6 weeks unsupervised exercise (n=27); or (2) "Exercise and AA (EAA)"-12-week exercise program and AA (n=24). Outcome measures were recorded at baseline, week 8, week 13, and 6 months. The primary outcome measure was the Oswestry Disability Questionnaire. Results: Participants in the EAA group demonstrated a greater mean improvement of 10.7% points (95% confidence interval, -15.3,-5.7) (effect size=1.20) in the Oswestry Disability Questionnaire at 6 months compared with 6.7% points (95% confidence interval, -11.4,-1.9) in the E group (effect size=0.58). There was also a trend towards a greater mean improvement in quality of life, LBP intensity and bothersomeness, and fear-avoidance beliefs in the EAA group. The dropout rate for this trial was lower than anticipated (15% at 6 mo), adherence with exercise was similar (72% E; 65% EAA). Adverse effects for AA ranged from 1% to 14% of participants. Discussion: Findings of this study showed that a main RCT is feasible and that 56 participants per group would need to be recruited, using multiple recruitment approaches. AA was safe and demonstrated additional benefits when combined with exercise for people with CLBP, which requires confirmation in a fully powered RCT.

Pedometer-driven walking for chronic low back pain:A feasibility randomized controlled trial

OBJECTIVES: To evaluate the feasibility of an RCT of a pedometer-driven walking program and education/advice to remain active compared with education/advice only for treatment of chronic low back pain (CLBP). METHODS: Fifty-seven participants with CLBP recruited from primary care were randomly allocated to either: (1) education/advice (E, n=17) or (2) education/advice plus an 8-week pedometer-driven walking program (EWP, n=40). Step targets, actual daily step counts, and adverse events were recorded in a walking diary over the 8 weeks of intervention for the EWP group only. All other outcomes (eg, functional disability using the Oswestry Disability Questionnaire (ODQ), pain scores, physical activity (PA) measurement etc.) were recorded at baseline, week 9 (immediately post-intervention), and 6 months in both groups. RESULTS: The recruitment rate was 22% and the dropout rate was lower than anticipated (13% to 18% at 6 mo). Adherence with the EWP was high, 93% (n=37/40) walked for =6 weeks, and increased their steps/day [mean absolute increase in steps/d, 2776, 95% confidence interval (CI), 1996-3557] by 59% (95% CI, 40.73%-76.25%) from baseline. Mean percentage adherence with weekly step targets was 70% (95% CI, 62%-77%). Eight (20%) minor-related adverse events were observed in 13% (5/40) of the participants. The EWP group participants demonstrated an 8.2% point improvement [95% CI, -13 to -3.4] on the ODQ at 6 months compared with 1.6% points [95% CI, -9.3 to 6.1) for the E group (between group d=0.44). There was also a larger mean improvement in pain (d=0.4) and a larger increase in PA (d=0.59) at 6 months in EWP. DISCUSSION: This preliminary study demonstrated that a main RCT is feasible. EWP was safe and produced a real increase in walking; CLBP function and pain improved, and participants perceived a greater improvement in their PA levels. These improvements require confirmation in a fully powered RCT.

Jigsaw Java: Intelligent, Automated Marking

The popularity of Computing degrees in the UK has been increasing significantly over the past number of years. In Northern Ireland, from 2007 to 2015, there has been a 40% increase in acceptances to Computer Science degrees with England seeing a 60% increase over the same period (UCAS, 2016). However, this is tainted as Computer Science degrees also continue to maintain the highest dropout rates.
In Queen’s University Belfast we currently have a Level 1 intake of over 400 students across a number of computing pathways. Our drive as staff is to empower and motivate the students to fully engage with the course content. All students take a Java programming module the aim of which is to provide an understanding of the basic principles of object-oriented design. In order to assess these skills, we have developed Jigsaw Java as an innovative assessment tool offering intelligent, semi-supervised automated marking of code.
Jigsaw Java allows students to answer programming questions using a drag-and-drop interface to place code fragments into position. Their answer is compared to the sample solution and if it matches, marks are allocated accordingly. However, if a match is not found then the corresponding code is executed using sample data to determine if its logic is acceptable. If it is, the solution is flagged to be checked by staff and if satisfactory is saved as an alternative solution. This means that appropriate marks can be allocated and should another student have submitted the same placement of code fragments this does not need to be executed or checked again. Rather the system now knows how to assess it.
Jigsaw Java is also able to consider partial marks dependent on code placement and will “learn” over time. Given the number of students, Jigsaw Java will improve the consistency and timeliness of marking.