Assessing feasibility and acceptability of web-based enhanced relapse prevention for bipolar disorder (ERPonline): a randomized controlled trial

Background: Interventions that teach people with Bipolar Disorder (BD) to recognise and respond to early warning signs of relapse are NICE recommended but implementation in clinical practice is poor. Objective: This study tests the feasibility and acceptability of a randomised controlled trial to evaluate an online enhanced relapse prevention intervention (ERPonline), and reports preliminary evidence of effectiveness. Methods: Single blind, parallel primarily online randomised controlled trial (n=96) over 48 weeks comparing ERPonline plus usual treatment to waitlist (WL) control plus usual treatment for people with BD recruited through National Health Services, voluntary organisations, and media. Randomisation was independent, minimised on number of previous episodes (<8,8-20,21+). Primary outcomes were feasibility and acceptability assessed by rates of study recruitment and retention, levels of intervention use, adverse events and participant feedback. Process and clinical outcomes were assessed by telephone and online and compared using linear models with intention-to-treat analysis. Results: Two hundred and eighty people registered interest online, from which ninety-six met inclusion criteria, consented and were randomised (49 to WL, 47 to ERPonline) over seventeen months, with 80% retention in telephone and online follow up, except week 48 online (76%). Acceptability was high for both ERPonline and trial methods. ERPonline cost approximately £19,340 to create, and £2176 per year to host and maintain the site. Qualitative data highlighted the importance of the relationship users have with online interventions and how this is created as an extension of the relationship with the humans perceived as offering and supporting its use. Differences between the group means suggested that access to ERPonline was associated with: a more positive model of bipolar disorder at 24 (10.70 (0.90-20.5 95%CIs)) and 48 weeks (13.1 (2.44-23.93 95%CIs)); increased monitoring of early warning signs of depression at 48 weeks (-1.39 (-2.61, -.163 95%CIs)) and of (hypo)mania at 24 (-1.72 (-2.98, -0.47 95%CIs)) and 48 weeks (-1.61 (-2.92, -0.30 95%CIs)), compared to WL. There was no evidence of impact of ERPonline on clinical outcomes or medication adherence, but relapse rates across both arms were very low (15%) and the sample remained high functioning throughout. One person died by suicide prior to randomisation. Five people in ERPonline and six in WL control reported ideas of suicide or self-harm during the study. None were deemed study related by an independent Trial Steering Committee. Conclusions: ERPonline offers a cheap accessible option for people seeking ongoing support following successful treatment. However, given high functioning and low relapse rates in this study, testing clinical effectiveness for this population would require very large sample sizes. Building in human support to use ERPonline should be considere

A motor unit-based model of muscle fatigue

Muscle fatigue is a temporary decline in the force and power capacity of skeletal muscle resulting from muscle activity. Because control of muscle is realized at the level of the motor unit (MU), it seems important to consider the physiological properties of motor units when attempting to understand and predict muscle fatigue. Therefore, we developed a phenomenological model of motor unit fatigue as a tractable means to predict muscle fatigue for a variety of tasks and to illustrate the individual contractile responses of MUs whose collective action determines the trajectory of changes in muscle force capacity during prolonged activity. An existing MU population model was used to simulate MU firing rates and isometric muscle forces and, to that model, we added fatigue-related changes in MU force, contraction time, and firing rate associated with sustained voluntary contractions. The model accurately estimated endurance times for sustained isometric contractions across a wide range of target levels. In addition, simulations were run for situations that have little experimental precedent to demonstrate the potential utility of the model to predict motor unit fatigue for more complicated, real-world applications. Moreover, the model provided insight into the complex orchestration of MU force contributions during fatigue, that would be unattainable with current experimental approaches