The Family of Single-Case Experimental Designs

Assessing the Effectiveness of STAPP@Work, a Self-Management Mobile App, in Reducing Work Stress and Preventing Burnout: Single-Case Experimental Design Study

BACKGROUND

Work-related stress and burnout remain common problems among employees, leading to impaired health and higher absenteeism. The use of mobile health apps to promote well-being has grown substantially; however, the impact of such apps on reducing stress and preventing burnout is limited.

OBJECTIVE

This study aims to assess the effectiveness of STAPP@Work, a mobile-based stress management intervention, on perceived stress, coping self-efficacy, and the level of burnout among mental health employees.

METHODS

The study used a single-case experimental design to examine the use of STAPP@Work among mental health employees without a known diagnosis of burnout (N=63). Participants used the app for 1 week per month repeatedly for a period of 6 months. Using a reversal design, the participants used the app 6 times to assess replicated immediate (1 week after use) and lasting (3 weeks after use) effects. The Perceived Stress Scale, the Coping Self-Efficacy Scale, and the Burnout Assessment Tool were used to measure the outcomes. Linear mixed models were used to analyze the data.

RESULTS

After 6 months of app use for 1 week per month, the participants showed a statistically significant decrease in perceived stress (b=-0.38, 95% CI -0.67 to -0.09; P=.01; Cohen d=0.50) and burnout symptoms (b=-0.31, 95% CI -0.51 to -0.12; P=.002; Cohen d=0.63) as well as a statistically significant improvement in problem-focused coping self-efficacy (b=0.42, 95% CI 0-0.85; P=.049; Cohen d=0.42). Long-term use of the app provided consistent reductions in burnout symptoms over time, including in the level of exhaustion and emotional impairment.

CONCLUSIONS

The use of an app-based stress management intervention has been shown to reduce burnout symptoms and enhance coping self-efficacy among mental health workers. Prevention of burnout and minimization of work-related stress are of utmost importance to protect employee health and reduce absenteeism.

A randomized n-of-1 study comparing blood pressure measured on a clothed arm and on an arm with a rolled-up sleeve

BACKGROUND

Blood pressure [BP] should be measured using a bare upper arm with an appropriately sized cuff. In practice, it is more convenient to measure BP on a bare arm below a rolled-up sleeve or on a sleeved arm.

AIM

A n-of-1 randomized controlled trial was performed to assess the difference between BP measurements over a sleeve or below a rolled-up sleeve.

METHODS

The study subject was male, white, 72 years old, BMI 26 kg/m2, arm circumference 29 cm, and under stable antihypertensive treatment. The mean of three BP measurements over a thin sleeve was compared with measurements below a rolled-up sleeve. Additional measurements on a completely bare arm, with thicker sleeves and up to three layers were performed. The order of measurements was determined by chance and two oscillometric devices were used. Descriptive statistics, Bland-Altman test and 2-side T test were used for comparisons.

RESULTS

504 measurements were performed: 50 % over the sleeve and 50 % below the rolled-up sleeve. The mean systolic blood pressure (SBP) was respectively 116.9 ± 9. 2 [95% CI 115.7-118.0, range 96-135] and 122.8 ± 9.2 [95% CI 121.7-124.0, range 103-139, p = 0.001] mm Hg. The mean diastolic blood pressure [DBP] was respectively 67.6 ± 6.8 [95% CI 66.8-68.4, range 52-84] and 71.8 ± 6.8 [95% CI 71.0-72.7, range 55-85, p = 0.001] mm Hg. There was no significant difference between the measurements over the sleeve and on the completely bare arm [n = 94, p = 0.97]. Sleeve thickness with 2 layers up to 3 mm thick did not affect the results.

CONCLUSIONS

Blood pressure measurements over a thin sleeve were significantly lower than measurements below a rolled-up sleeve and match measurements on a completely bare arm.

Testing Behavior Change Techniques to Increase Physical Activity in Middle-Aged and Older Adults: Protocol for a Randomized Personalized Trial Series

BACKGROUND

Being physically active is critical to successful aging, but most middle-aged and older adults do not move enough. Research has shown that even small increases in activity can have a significant impact on risk reduction and improve quality of life. Some behavior change techniques (BCTs) can increase activity, but prior studies on their effectiveness have primarily tested them in between-subjects trials and in aggregate. These design approaches, while robust, fail to identify those BCTs most influential for a given individual. In contrast, a personalized, or N-of-1, trial design can assess a person's response to each specific intervention.

OBJECTIVE

This study is designed to test the feasibility, acceptability, and preliminary effectiveness of a remotely delivered personalized behavioral intervention to increase low-intensity physical activity (ie, walking) in adults aged 45 to 75 years.

METHODS

The intervention will be administered over 10 weeks, starting with a 2-week baseline period followed by 4 BCTs (goal-setting, self-monitoring, feedback, and action planning) delivered one at a time, each for 2 weeks. In total, 60 participants will be randomized post baseline to 1 of 24 intervention sequences. Physical activity will be continuously measured by a wearable activity tracker, and intervention components and outcome measures will be delivered and collected by email, SMS text messages, and surveys. The effect of the overall intervention on step counts relative to baseline will be examined using generalized linear mixed models with an autoregressive model that accounts for possible autocorrelation and linear trends for daily steps across time. Participant satisfaction with the study components and attitudes and opinions toward personalized trials will be measured at the intervention's conclusion.

RESULTS

Pooled change in daily step count will be reported between baseline and individual BCTs and baseline versus overall intervention. Self-efficacy scores will be compared between baseline and individual BCTs and between baseline and the overall intervention. Mean and SD will be reported for survey measures (participant satisfaction with study components and attitudes and opinions toward personalized trials).

CONCLUSIONS

Assessing the feasibility and acceptability of delivering a personalized, remote physical activity intervention for middle-aged and older adults will inform what steps will be needed to scale up to a fully powered and within-subjects experimental design remotely. Examining the effect of each BCT in isolation will allow for their unique impact to be assessed and support design of future behavioral interventions. In using a personalized trial design, the heterogeneity of individual responses for each BCT can be quantified and inform later National Institutes of Health stages of intervention development trials.

TRIAL REGISTRATION

clinicaltrials.gov NCT04967313; https://clinicaltrials.gov/ct2/show/NCT04967313.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/43418.

N-of-1 Trials, Their Reporting Guidelines, and the Advancement of Open Science Principles

N-of-1 trials are multiple crossover trials done over time within a single person; they can also be done with a series of individuals. Their focus on the individual as the unit of analysis maintains statistical power while accommodating greater differences between patients than most standard clinical trials. This makes them particularly useful in rare diseases, while also being applicable across many health conditions and populations. Best practices recommend the use of reporting guidelines to publish research in a standardized and transparent fashion. N-of-1 trials have the SPIRIT extension for N-of-1 protocols (SPENT) and the CONSORT extension for N-of-1 trials (CENT). Open science is a recent movement focused on making scientific knowledge fully available to anyone, increasing collaboration, and sharing of scientific efforts. Open science goals increase research transparency, rigor, and reproducibility, and reduce research waste. Many organizations and articles focus on specific aspects of open science, for example, open access publishing. Throughout the trajectory of research (idea, development, running a trial, analysis, publication, dissemination, knowledge translation/reflection), many open science ideals are addressed by the individual-focused nature of N-of-1 trials, including issues such as patient perspectives in research development, personalization, and publications, enhanced equity from the broader inclusion criteria possible, and easier remote trials options. However, N-of-1 trials also help us understand areas of caution, such as monitoring of post hoc analyses and the nuances of confidentiality for rare diseases in open data sharing. The N-of-1 reporting guidelines encourage rigor and transparency of N-of-1 considerations for key aspects of the research trajectory.

Personalized (N-of-1) Trials for Patient-Centered Treatments of Multimorbidity

Treatment of patients who suffer from concurrent health conditions is not well served by (1) evidence-based clinical guidelines that mainly specify treatment of single conditions and (2) conventional randomized controlled trials (RCTs) that identify treatments as safe and effective on average. Clinical decision-making based on the average patient effect may be inappropriate for treatment of those with multimorbidity who experience burdens and obstacles that may be unique to their personal situation. We describe how the personalized (N-of-1) trials can be integrated with an automatic platform and virtual/remote technologies to improve patient-centered care for those living with multimorbidity. To illustrate, we present a hypothetical clinical scenario-survivors of both coronavirus disease 2019 (COVID-19) and cancer who chronically suffer from sleeplessness and fatigue. Then, we will describe how the four standard phases of conventional RCT development can be modified for personalized trials and applied to the multimorbidity clinical scenario, outline how personalized trials can be adapted and extended to compare the benefits of personalized trials versus between-subject trial design, and explain how personalized trials can address special problems associated with multimorbidity for which conventional trials are poorly suited.

Introducing Data Sciences to N-of-1 Designs, Statistics, Use-Cases, the Future, and the Moniker 'N-of-1' Trial

This article, an introduction to HDSR's "Personalized (N-of-1) Trials: Methods, Applications, and Impact" special issue, describes the rationale for a primer of the methods, data types and management, designs, and use cases for personalized (N-of-1) trials. It explains that the design and implementation of personalized (N-of-1) trials is only useful if patients volunteer for research involving them and actively participate in clinical services that use them. However, 'N-of-1 trials' may be an inadequate name to enact such patient engagement. The authors briefly review what patients have reported about the 'N-of-1' label and propose a more consumer-friendly moniker for this type of research and clinical approach to improve evidence-based science.