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BackgroundThe therapeutic alliance between patients and physical therapists has been shown to influence clinical outcomes in patients with chronic low back pain when consulting in-person. However, no studies have examined whether the therapeutic alliance developed between patients with knee osteoarthritis and physical therapists during telephonic consultations influences clinical outcomes. ObjectiveThis study aims to investigate whether the therapeutic alliance between patients with knee osteoarthritis and physical therapists measured after the second consultation is associated with outcomes following telephone-delivered exercise and advice. MethodsSecondary analysis of 87 patients in the intervention arm of a randomized controlled trial allocated to receive 5 to 10 telephone consultations with one of 8 physical therapists over a period of 6 months, involving education and prescription of a strengthening and physical activity program. Separate regression models investigated the association between patient and therapist ratings of therapeutic alliance (measured after the second consultation using the Working Alliance Inventory Short Form) and outcomes (pain, function, self-efficacy, quality of life, global change, adherence to prescribed exercise, physical activity) at 6 and 12 months, with relevant covariates included. ResultsThere was some evidence of a weak association between patient ratings of the alliance and some outcomes at 6 months (improvements in average knee pain: regression coefficient −0.10, 95% CI −0.16 to −0.03; self-efficacy: 0.16, 0.04-0.28; global improvement in function: odds ratio 1.26, 95% CI 1.04-1.39, and overall improvement: odds ratio 1.26, 95% CI 1.06-1.51; but also with worsening in fear of movement: regression coefficient −0.13, 95% CI −0.23 to −0.04). In addition, there was some evidence of a weak association between patient ratings of the alliance and some outcomes at 12 months (improvements in self-efficacy: regression coefficient 0.15, 95% CI 0.03-0.27; global improvement in both function, odds ratio 1.19, 95% CI 0.03-1.37; and pain, odds ratio 1.14, 95% CI 1.01-1.30; and overall improvement: odds ratio 1.21, 95% CI 1.02-1.42). The data suggest that associations between therapist ratings of therapeutic alliance and outcomes were not strong, except for improved quality of life at 12 months (regression coefficient 0.01, 95% CI 0.0003-0.01). ConclusionsHigher patient ratings, but not higher therapist ratings, of the therapeutic alliance were weakly associated with improvements in some clinical outcomes and with worsening in one outcome. Although the findings suggest that patients who perceive a stronger alliance with their therapist may achieve better clinical outcomes, the observed relationships were generally weak and unlikely to be clinically significant. The limitations include the fact that measures of therapeutic alliance have not been validated for use in musculoskeletal physical therapy settings. There was a risk of type 1 error; however, findings were interpreted on the basis of clinical significance rather than statistical significance alone. Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12616000054415; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=369204

BackgroundExercise is a core recommended treatment for knee osteoarthritis (OA), yet adherence declines, particularly following cessation of clinician supervision. ObjectiveThis study aims to evaluate whether a 24-week SMS intervention improves adherence to unsupervised home exercise in people with knee OA and obesity compared with no SMS. MethodsA two-group superiority randomized controlled trial was performed in a community setting. Participants were people aged 50 years with knee OA and BMI ≥30 kg/m2 who had undertaken a 12-week physiotherapist-supervised exercise program as part of a preceding clinical trial. Both groups were asked to continue their home exercise program unsupervised three times per week for 24 weeks and were randomly allocated to a behavior change theory–informed, automated, semi-interactive SMS intervention addressing exercise barriers and facilitators or to control (no SMS). Primary outcomes were self-reported home exercise adherence at 24 weeks measured by the Exercise Adherence Rating Scale (EARS) Section B (0-24, higher number indicating greater adherence) and the number of days exercised in the past week (0-3). Secondary outcomes included self-rated adherence (numeric rating scale), knee pain, physical function, quality of life, global change, physical activity, self-efficacy, pain catastrophizing, and kinesiophobia. ResultsA total of 110 participants (56 SMS group and 54 no SMS) were enrolled and 99 (90.0%) completed both primary outcomes (48/56, 86% SMS group and 51/54, 94% no SMS). At 24 weeks, the SMS group reported higher EARS scores (mean 16.5, SD 6.5 vs mean 13.3, SD 7.0; mean difference 3.1, 95% CI 0.8-5.5; P=.01) and more days exercised in the past week (mean 1.8, SD 1.2 vs mean 1.3, SD 1.2; mean difference 0.6, 95% CI 0.2-1.0; P=.01) than the control group. There was no evidence of between-group differences in secondary outcomes. ConclusionsAn SMS program increased self-reported adherence to unsupervised home exercise in people with knee OA and obesity, although this did not translate into improved clinical outcomes. Trial RegistrationAustralian New Zealand Clinical Trials Registry 12617001243303; https://tinyurl.com/y2ud7on5 International Registered Report Identifier (IRRID)RR2-10.1186/s12891-019-2801-z

Background/Aims: The standard approach to designing stepped wedge trials that recruit participants in a continuous stream is to divide time into periods of equal length. But the choice of design in such cases is infinitely more flexible: each cluster could cross from the control to the intervention at any point on the continuous time-scale. We consider the case of a stepped wedge design with clusters randomised to just three sequences (designs with small numbers of sequences may be preferred for their simplicity and practicality) and investigate the choice of design that minimises the variance of the treatment effect estimator under different assumptions about the intra-cluster correlation. Methods: We make some simplifying assumptions in order to calculate the variance: in particular that we recruit the same number of participants, m , from each cluster over the course of the trial, and that participants present at regularly spaced intervals. We consider an intra-cluster correlation that decays exponentially with separation in time between the presentation of two individuals from the same cluster, from a value of ρ for two individuals who present at the same time, to a value of ρ τ for individuals presenting at the start and end of the trial recruitment interval. We restrict attention to three-sequence designs with centrosymmetry – the property that if we reverse time and swap the intervention and control conditions then the design looks the same. We obtain an expression for the variance of the treatment effect estimator adjusted for effects of time, using methods for generalised least squares estimation, and we evaluate this expression numerically for different designs, and for different parameter values. Results: There is a two-dimensional space of possible three-sequence, centrosymmetric stepped wedge designs with continuous recruitment. The variance of the treatment effect estimator for given ρ and τ can be plotted as a contour map over this space. The shape of this variance surface depends on τ and on the parameter m ρ / (1 − ρ) , but typically indicates a broad, flat region of close-to-optimal designs. The 'standard' design with equally spaced periods and 1:1:1 allocation rarely performs well, however. Conclusions: In many different settings, a relatively simple design can be found (e.g. one based on simple fractions) that offers close-to-optimal efficiency in that setting. There may also be designs that are robustly efficient over a wide range of settings. Contour maps of the kind we illustrate can help guide this choice. If efficiency is offered as one of the justifications for using a stepped wedge design, then it is worth designing with optimal efficiency in mind. [ABSTRACT FROM AUTHOR]

Many clinical trials involve partially clustered data, where some observations belong to a cluster and others can be considered independent. For example, neonatal trials may include infants from single or multiple births. Sample size and analysis methods for these trials have received limited attention. A simulation study was conducted to (1) assess whether existing power formulas based on generalized estimating equations (GEEs) provide an adequate approximation to the power achieved by mixed effects models, and (2) compare the performance of mixed models vs GEEs in estimating the effect of treatment on a continuous outcome. We considered clusters that exist prior to randomization with a maximum cluster size of 2, three methods of randomizing the clustered observations, and simulated datasets with uninformative cluster size and the sample size required to achieve 80% power according to GEE‐based formulas with an independence or exchangeable working correlation structure. The empirical power of the mixed model approach was close to the nominal level when sample size was calculated using the exchangeable GEE formula, but was often too high when the sample size was based on the independence GEE formula. The independence GEE always converged and performed well in all scenarios. Performance of the exchangeable GEE and mixed model was also acceptable under cluster randomization, though under‐coverage and inflated type I error rates could occur with other methods of randomization. Analysis of partially clustered trials using GEEs with an independence working correlation structure may be preferred to avoid the limitations of mixed models and exchangeable GEEs. [ABSTRACT FROM AUTHOR]

* OBJECTIVE: To determine if adding lumbar neuromuscular control retraining exercises to a 12-week program of strengthening exercises had greater effect for improving disability than 12 weeks of strengthening exercises alone in people with chronic low back pain (LBP). * DESIGN: Single-center, participant- and assessor-blinded, comparative effectiveness randomized controlled trial. tMETHODS: Sixty-nine participants (31 females; 29 males; mean age: 46.5 years) with nonspecific chronic LBP were recruited for a 12-week program involving lumbar extension neuromuscular retraining in addition to resistance exercises (intervention) or 12 weeks of resistance exercises alone (control). The primary outcome measure was the Oswestry Disability Index. Secondary outcome measures included the Numeric Rating Scale, Tampa Scale for Kinesiophobia, Pain Self-Efficacy Questionnaire, and the International Physical Activity Questionnaire. Outcomes were measured at baseline, 6 weeks, and 12 weeks. * RESULTS: Forty-three participants (22 control, 21 intervention) completed all outcome measures at 6 and 12 weeks. Fourteen participants were lost to follow-up, and 12 participants discontinued due to COVID-19 restrictions. Both groups demonstrated clinically important changes in disability, pain intensity, and kinesiophobia. The difference between groups with respect to disability was imprecise and not clinically meaningful (mean difference, -4.4; 95% CI: -10.2, 1.4) at 12 weeks. Differences in secondary outcomes at 6 or 12 weeks were also small with wide confidence intervals. * CONCLUSIONS: Adding lumbar neuromuscular control retraining to a series of resistance exercises offered no additional benefit over resistance exercises alone over a 12-week period. [ABSTRACT FROM AUTHOR]

Unravelling the impact of the sex of the anaesthesia provider on the outcomes of patients requires careful statistical analysis and the validity of many assumptions. A recent study in the British Journal of Anaesthesia investigates the effect of anaesthesia provider sex on patient outcomes, using data from two academic healthcare networks in the USA. The authors show that female provider sex was associated with a lower risk of intraoperative complications. They also show that there was no meaningful difference between male and female providers with respect to postoperative outcomes. There have been several recent studies considering the effect of healthcare provider sex on outcomes. We will discuss the interpretation of these results and the validity of the underlying assumptions. [ABSTRACT FROM AUTHOR]

This article introduces the 'staircase' design, derived from the zigzag pattern of steps along the diagonal of a stepped wedge design schematic where clusters switch from control to intervention conditions. Unlike a complete stepped wedge design where all participating clusters must collect and provide data for the entire trial duration, clusters in a staircase design are only required to be involved and collect data for a limited number of pre- and post-switch periods. This could alleviate some of the burden on participating clusters, encouraging involvement in the trial and reducing the likelihood of attrition. Staircase designs are already being implemented, although in the absence of a dedicated methodology, approaches to sample size and power calculations have been inconsistent. We provide expressions for the variance of the treatment effect estimator when a linear mixed model for an outcome is assumed for the analysis of staircase designs in order to enable appropriate sample size and power calculations. These include explicit variance expressions for basic staircase designs with one pre- and one post-switch measurement period. We show how the variance of the treatment effect estimator is related to key design parameters and demonstrate power calculations for examples based on a real trial. [ABSTRACT FROM AUTHOR]

A frequently applied assumption in the analysis of data from cluster randomised trials is that the outcomes from all participants within a cluster are equally correlated. That is, the intracluster correlation, which describes the degree of dependence between outcomes from participants in the same cluster, is the same for each pair of participants in a cluster. However, recent work has discussed the importance of allowing for this correlation to decay as the time between the measurement of participants in a cluster increases. Incorrect omission of such a decay can lead to under-powered studies, and confidence intervals for estimated treatment effects can be too narrow or too wide, depending on the characteristics of the design. When planning studies, researchers often rely on previously reported analyses of trials to inform their choice of intracluster correlation. However, most reported analyses of clustered data do not incorporate a correlation decay. Thus, often all that is available are estimates of intracluster correlations obtained under the potentially incorrect assumption of no decay. In this article, we show that it is possible to use intracluster correlation values obtained from models that incorrectly omit a decay to inform plausible choices of decaying correlations. Our focus is on intracluster correlation estimates for continuous outcomes obtained by fitting linear mixed models with exchangeable or block-exchangeable correlation structures. We describe how plausible values for decaying correlations may be obtained given these estimated intracluster correlations. An online app is presented that allows users to obtain plausible values of the decay, which can be used at the trial planning stage to assess the sensitivity of sample size and power calculations to decaying correlation structures. [ABSTRACT FROM AUTHOR]

Stepped wedge trials are increasingly adopted because practical constraints necessitate staggered roll‐out. While a complete design requires clusters to collect data in all periods, resource and patient‐centered considerations may call for an incomplete stepped wedge design to minimize data collection burden. To study incomplete designs, we expand the metric of information content to discrete outcomes. We operate under a marginal model with general link and variance functions, and derive information content expressions when data elements (cells, sequences, periods) are omitted. We show that the centrosymmetric patterns of information content can hold for discrete outcomes with the variance‐stabilizing link function. We perform numerical studies under the canonical link function, and find that while the patterns of information content for cells are approximately centrosymmetric for all examined underlying secular trends, the patterns of information content for sequences or periods are more sensitive to the secular trend, and may be far from centrosymmetric. [ABSTRACT FROM AUTHOR]

In a randomized controlled trial, outcomes of different subjects may be independent at baseline, but correlated at a follow‐up measurement due to treatment. This treatment‐related clustering at follow‐up can arise for instance because the treatment is given in a group or because subjects are treated individually but by the same therapist (therapist effect). There is substantial literature on the design and analysis of such trials when estimation of the intervention effect is based on a follow‐up measurement (eg, directly after treatment or at a later time point). However, often the baseline measurement of the outcome is highly correlated with the follow‐up measurement, and this information can be used in the analysis. For a randomized design with a baseline and a follow‐up measurement, we compare sample size requirements for analyses with and without adjustment for this baseline measure. We show that adjusting for baseline reduces required sample size. This reduction depends on the variance of the difference between arms at baseline, the variance of this difference at follow‐up, and the correlation between the two. From this, we derive sample size formulas for partially or fully nested designs, and cluster randomized trials with treatment as a partially or fully cross‐classified factor. Also, we discuss situations where clusters are already present at baseline or where treatment by cluster interaction is present. For the partially nested design, we work out practical design considerations (eg, use of content‐matter input, design factors and optimal allocation ratio) and investigate small sample properties of the sample size formula. [ABSTRACT FROM AUTHOR]