Using impairment and cognitions to predict walking in osteoarthritis: A series of n-of-1 studies with an individually tailored, data-driven intervention

Modeling engagement with a digital behavior change intervention (HeartSteps II): An exploratory system identification approach

OBJECTIVE

Digital behavior change interventions (DBCIs) are feasibly effective tools for addressing physical activity. However, in-depth understanding of participants' long-term engagement with DBCIs remains sparse. Since the effectiveness of DBCIs to impact behavior change depends, in part, upon participant engagement, there is a need to better understand engagement as a dynamic process in response to an individual's ever-changing biological, psychological, social, and environmental context.

METHODS

The year-long micro-randomized trial (MRT) HeartSteps II provides an unprecedented opportunity to investigate DBCI engagement among ethnically diverse participants. We combined data streams from wearable sensors (Fitbit Versa, i.e., walking behavior), the HeartSteps II app (i.e. page views), and ecological momentary assessments (EMAs, i.e. perceived intrinsic and extrinsic motivation) to build the idiographic models. A system identification approach and a fluid analogy model were used to conduct autoregressive with exogenous input (ARX) analyses that tested hypothesized relationships between these variables inspired by Self-Determination Theory (SDT) with DBCI engagement through time.

RESULTS

Data from 11 HeartSteps II participants was used to test aspects of the hypothesized SDT dynamic model. The average age was 46.33 (SD=7.4) years, and the average steps per day at baseline was 5,507 steps (SD=6,239). The hypothesized 5-input SDT-inspired ARX model for app engagement resulted in a 31.75 % weighted RMSEA (31.50 % on validation and 31.91 % on estimation), indicating that the model predicted app page views almost 32 % better relative to the mean of the data. Among Hispanic/Latino participants, the average overall model fit across inventories of the SDT fluid analogy was 34.22 % (SD=10.53) compared to 22.39 % (SD=6.36) among non-Hispanic/Latino Whites, a difference of 11.83 %. Across individuals, the number of daily notification prompts received by the participant was positively associated with increased app page views. The weekend/weekday indicator and perceived daily busyness were also found to be key predictors of the number of daily application page views.

CONCLUSIONS

This novel approach has significant implications for both personalized and adaptive DBCIs by identifying factors that foster or undermine engagement in an individual's respective context. Once identified, these factors can be tailored to promote engagement and support sustained behavior change over time.

Predicting Intention to Participate in Community Physical Activities for Adults with Physical Disabilities

Structural equation modeling was used to derive a relationship predicting the intention to participate in community physical activity among community-dwelling adults with a physical disability in Xiamen, China. The data were collected in a cross-sectional survey. The structural equation modeling combined biomedicine and the theory of planned behavior. It integrated ratings using the rehabilitation set from the international classification of functioning, disability, and health and role-physical scores from the short form 36 health survey questionnaire instrument. The model demonstrated a good ability to predict self-reported participation intentions, explaining 62% of the variance. The standard coefficients showed that activity limitation (27%), role-physical score (21%) and body impairment (14%) were the most influential predictors. ICF-RS ratings and role-physical ratings together can usefully predict physically disabled adults' intention of participating in community physical activities. Suggestions are presented for multidisciplinary intervention and improving this portion of the WHO's classification system.

Methods for Modeling Autocorrelation and Handling Missing Data in Mediation Analysis in Single Case Experimental Designs (SCEDs)

Single-Case Experimental Designs (SCEDs) are increasingly recognized as a valuable alternative to group designs. Mediation analysis is useful in SCEDs contexts because it informs researchers about the underlying mechanism through which an intervention influences the outcome. However, methods for conducting mediation analysis in SCEDs have only recently been proposed. Furthermore, repeated measures of a target behavior present the challenges of autocorrelation and missing data. This paper aims to extend methods for estimating indirect effects in piecewise regression analysis in SCEDs by (1) evaluating three methods for modeling autocorrelation, namely, Newey-West (NW) estimation, feasible generalized least squares (FGLS) estimation, and explicit modeling of an autoregressive structure of order one (AR(1)) in the error terms and (2) evaluating multiple imputation in the presence of data that are missing completely at random. FGLS and AR(1) outperformed NW and OLS estimation in terms of efficiency, Type I error rates, and coverage, while OLS was superior to the methods in terms of power for larger samples. The performance of all methods is consistent across 0% and 20% missing data conditions. 50% missing data led to unsatisfactory power and biased estimates. In light of these findings, we provide recommendations for applied researchers.

Status of N-of-1 Trials in Chronic Pain Management: A Narrative Review

N-of-1 trials are randomized controlled clinical trials conducted exclusively on a single patient. The ultimate aim of N-of-1 trials is to optimize a strategy in a particular individual. Chronic pain is a common but refractory clinical problem. Its diverse etiologies and broad variations among patients often lead to the requirement of individualizing medicine. Thus, chronic pain represents a classical condition for N-of-1 clinical trials. Studies have indicated that N-of-1 benefits patients with chronic pain, multiple comorbidities, and uncertain variations during therapies; however, this approach it is not yet adopted as the first choice in pain clinics. To dissect the current status of N-of-1 in chronic pain management, as well as the limitations for its implementation, we herein studied all N-of-1 studies related to chronic pain by searching three major databases (PubMed, ClinicalTrial.gov, Cochrane Library) for publications between 1985 and 2020. Of 35 eligibility papers, 19 were selected for analysis. Results confirmed that N-of-1 trials have solved the refractory cases including osteoarthritis, chronic musculoskeletal pain, and neuropathic pain; however, none of the trials dealt with cancer pain. Longer time and more efforts are needed from investigators when carrying out N-of-1 trials, which inevitably result in implementation difficulties. Of note, all recruited trials were conducted in developed countries. As mobile devices have been introduced and protocols improve, renewed interest in the implementation of N-of-1 trials will occur. Collectively, a previously underestimated conflict between "precision medicine" and "poor implementation" has put N-of-1 in a challenging position for chronic pain management.

Exploring Symptom Fluctuations and Triggers in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Novel Patient-Centred N-of-1 Observational Designs: A Protocol for a Feasibility and Acceptability Study

BACKGROUND

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic condition of unknown aetiology associated with a range of disabling symptoms, including post-exertional malaise, chronic fatigue, musculoskeletal pain, orthostatic intolerance, unrefreshing sleep, and cognitive dysfunction. ME/CFS is a heterogeneous disorder, with significant variation in symptom type and severity between individuals, as well as within individuals over time. The diversity of ME/CFS symptom presentation makes management challenging; treatments supported by data from randomised controlled trials may not work for all individuals due to the variability in experienced symptoms. Studies using quantitative N-of-1 observational designs involve repeated outcome measurements in an individual over time and can generate rigorous individual-specific conclusions about symptom patterns and triggers in individuals with ME/CFS. This study aims to explore the feasibility and acceptability of using novel patient-centred N-of-1 observational designs to explore symptom fluctuations and triggers in ME/CFS at the individual level.

METHODS AND ANALYSIS

Individuals with a medical diagnosis of ME/CFS will be recruited through ME/CFS patient organisations to participate in a series of patient-centred N-of-1 observational studies. Using a wrist-worn electronic diary, participants will complete ecological momentary assessments of fatigue, stress, mood, and cognitive demand, three times per day for a period of 6-12 weeks. Personally relevant symptoms and triggers will also be incorporated into the questionnaire design. Physical activity will be objectively measured via an integrated accelerometer. Feasibility and acceptability outcomes will be assessed including the percentage of diary entries completed, as well as recruitment and retention rate, feasibility of analysing and interpreting the data collected, and participant views about participation elicited via a post-study semi-structured interview.

DISCUSSION

This study will assess the feasibility and acceptability of patient-centred N-of-1 observational studies to assess diseases with complex presentations such as ME/CFS, as well as provide individual-level evidence about fluctuations and triggers of ME/CFS symptoms that may aid self-management.

TRIAL REGISTRATION

Australian and New Zealand Clinical Trials Registry: ACTRN12618001898246. Registered on 22 November 2018.

Self-Regulatory Processes, Motivation to Conserve Resources and Activity Levels in People With Chronic Pain: A Series of Digital N-of-1 Observational Studies

Objectives

Motivational and self-regulatory processes during goal pursuit may account for activity patterns in people with chronic pain. This article describes a series of N-of-1 observational studies designed to investigate the influence of goal-related factors on fluctuations in motivation to conserve resources and objectively measured activity levels.

Methods

Four participants with chronic pain who attended a formal pain management program (PMP; 41–59 years old; three female) were recruited and completed digital daily diaries for 11–12 weeks. The daily dairies, delivered via text message, measured self-regulatory fatigue, goal self-efficacy, goal striving, perceived demands, pain, and motivation to conserve resources. Continuously worn accelerometers measured physical activity and sedentary time. Analyses were conducted individually for each participant. The effects of self-regulatory fatigue, goal self-efficacy, goal striving, perceived demands, and pain on motivation to conserve resources, physical activity and sedentary time were assessed with dynamic regression modeling.

Results

Different patterns of associations between the predictors and outcomes were observed across participants. Most associations occurred concurrently (e.g., on the same day). Perceived demand was the only variable to predict motivation to conserve resources, physical activity, and sedentary time. Motivation to conserve resources and sedentary time were most frequently predicted by goal striving and perceived demand. Self-regulatory fatigue and pain intensity both predicted motivation to conserve resources in two participants and sedentary time in one participant. Motivation to conserve resources predicted sedentary time in two participants.

Conclusion

This study was the first to examine the impact of fluctuations in self-regulatory processes on motivation to conserve resources and objective activity levels within individuals with chronic pain. The results generally supported recent affective-motivational views of goal pursuit in chronic pain. This study demonstrated that N-of-1 observational studies can be conducted with patients during a PMP using digital technologies. The use of these approaches may facilitate the application of personalized medicine.

Analysing N-of-1 observational data in health psychology and behavioural medicine: a 10-step SPSS tutorial for beginners

Background: N-of-1 observational studies can be used to describe natural intra-individual changes in health-related behaviours or symptoms over time, to test behavioural theories and to develop highly personalised health interventions. To date, N-of-1 observational methods have been under-used in health psychology and behavioural medicine. One reason for this may be the perceived complexity of statistical analysis of N-of-1 data.

Objective: This tutorial paper describes a 10-step procedure for the analysis of N-of-1 observational data using dynamic regression modelling in SPSS for researchers, students and clinicians who are new to this area. The 10-step procedure is illustrated using real data from an N-of-1 observational study exploring the relationship between pain and physical activity.

Conclusion: The availability of a user-friendly and robust statistical technique for the analysis of N-of-1 data using SPSS may foster increased awareness, knowledge and skills and establish N-of-1 designs as a useful methodological tool in health psychology and behavioural medicine.

Behavior Change Techniques Used in Theory of Planned Behavior Physical Activity Interventions Among Older Adults: A Systematic Review

Physical activity interventions among older adults vary widely in the techniques used to elicit behavior change. The purpose of this systematic review was to determine what behavior change techniques (BCTs) are used in interventions to increase physical activity among older adults using the theory of planned behavior and to make suggestions for BCTs that appear to be more effective. A database search identified peer-reviewed articles documenting interventions based on the theory of planned behavior. Seven articles (three randomized controlled trial, three quasi-experimental, and one n-of-1) from four countries (the United States, the United Kingdom, Australia, and the Netherlands) were included for review. Researchers independently coded BCTs using a hierarchical taxonomy of 93 BCTs. The most frequently coded BCTs included Goal Setting (n = 5 studies), Action Planning (n = 5 studies), and Credible Source (n = 5 studies). Of the 93 BCTs in the taxonomy, only 26 were used, indicating potential opportunities to implement and evaluate less commonly used techniques in future studies.