This co-creation study aimed to develop a smartphone self-test application for balance and leg strength in collaboration between older adults and the research team. The paper describes older participants’ preferences for, and their contribution to, the application design. Technology to assess movements is available in smartphones with built-in sensors, and one of the challenges is to develop a valuable self-test for older adults. The participants contributed to the design of the application’s instructions and user interface. Multiple data collection methods were used: user-test with Think aloud method, mock-ups, homework assignment as co-researcher, audio and video recordings. Qualitative content analysis with a deductive-inductive approach was used, guided by the Optimized Honeycomb model for user experience (UX) as a categorization matrix. The analysis resulted in 17 subcategories within the seven facets of the UX Honeycomb model (findable, accessible, usable, desirable, credible, useful, and valuable), and describes the older participants’ preferences and experiences. The main results were participants’ desire to know why, to get clear and appropriate information, and expectations of the self-test to be useful. It was feasible and valuable to develop the self-test application in co-creation with the intended user-group, in order to get direct feedback and suggestions for the development.

Motion analysis using inertial measurement units (IMU) has emerged as an alternative to optical motion capture. However, the validity and reliability of upper limb measurements varies significantly between studies. The objective of this study was to determine how sensor placement affects kinematic output in the assessment of motion of the arm, shoulder, and scapula. IMUs were placed proximally/distally on arms, and medially/laterally on the scapula, in a group of eleven healthy participants, while performing nine different motion tasks. Linear regressions and mixed models analysed how these different sensor placements affected the estimated joint motion by establishing the linear relationship between sensors placed on the same body segment. The placement of sensors affected the measured kinematic output considerably, most prominent affect was seen for sensor placement on scapula during flexion and abduction, and on forearm during pronation/supination. The slope of the linear regression lines was 2.5 during flexion, 2.7 during abduction, and 1.8 for forearm pronation/supination. The results of this study suggest that the forearm sensor should be placed on the dorsal side of the forearm, at the distal end; the upper arm sensor should be placed laterally, on the distal part of the arm; and the sensor on the scapula should be placed cranially, along the spine of scapula.

Treadmill workstations that enable office workers to walk on a treadmill while working at their computers might increase physical activity in offices, but long-term effects are unknown. We therefore investigated whether treadmill workstations in offices increased daily walking time. We did a randomised controlled trial of healthy office workers who were either overweight or obese. We recruited participants from 13 different companies, which comprised 17 offices, in Umeå, Sweden. We included people who were aged 40-67 years, had sedentary work tasks, and had a body-mass index (BMI) between 25 kg/m2 and 40 kg/m2. After the baseline measurement, we stratified participants by their BMI (25-30 kg/m2 and >30 to 40 kg/m2); subsequently, an external statistician randomly assigned these participants (1:1) to either the intervention group (who received treadmill workstations for optional use) or the control group (who continued to work at their sit-stand desks as usual). Participants in the intervention group received reminders in boosting emails sent out to them at four occasions during the study period. Researchers were masked to group assignment until after analysis of the primary outcome. After the baseline measurement, participants were not masked to group belongings. The primary outcome was total daily walking time at weekdays and weekends, measured at baseline, 2 months, 6 months, 10 months, and 13 months with the accelerometer activPAL (PAL Technologies, Glasgow, UK), which was worn on the thigh of participants for 24 h a day for 7 consecutive days. We used an intention-to-treat approach for our analyses. This trial is registered with ClinicalTrials.gov, number NCT01997970, and is closed to new participants. Between Nov 1, 2013, and June 30, 2014, a total of 80 participants were recruited and enrolled (n=40 in both the intervention and control groups). Daily walking time during total time awake at weekdays increased between baseline and 13 months by 18 min (95% CI 9 to 26) in the intervention group and 1 min (-7 to 9) in the control group (difference 22 min [95% CI 7 to 37], pinteraction=0·00045); for weekend walking, the change from baseline to 13 months was 5 min (-8 to 18) in the intervention group and 8 min (-5 to 21) in the control group (difference -1 min [-19 to 17]; pinteraction=0·00045). Neither measure met our predetermined primary outcome of 30 min difference in total walking time between the intervention and control group, so the primary outcome of the trial was not met. One adverse event was reported in a participant who accidently stepped on their Achilles tendon. In a sedentary work environment, treadmill workstations result in a statistically significant but smaller-than-expected increase in daily walking time. Future studies need to investigate how increasing physical activity at work might have potentially compensatory effects on non-work activity. Umeå University, the Västerbotten County Council, and the Mayo Clinic Foundation for Research.

This paper presents an assessment tool for objective neck movement analysis of subjects suffering from chronic whiplash-associated disorders (WAD). Three-dimensional (3-D) motion data is collected by a commercially available motion analysis system. Head rotation, defined in this paper as the rotation angle around the instantaneous helical axis (IHA), is used for extracting a number of variables (e.g., angular velocity and range, symmetry of motion). Statistically significant differences were found between controls and subjects with chronic WAD in a number of variables.