Optimizing behavioral health interventions with single-case designs: from development to dissemination

The effect of lower limb strengthening exercise on orthostatic blood pressure and the skeletal muscle pump in older people with orthostatic hypotension

INTRODUCTION

Activation of muscles during standing is recommended to activate the skeletal muscle pump, increasing venous return and increasing blood pressure (BP) in people with orthostatic hypotension (OH).

AIM

The aim of this study is to determine if increasing the strength of the lower limb muscles can improve the effectiveness of the venous pump and postural BP in older people with OH.

METHODS

Ten older people with OH underwent an 8-week lower limb strengthening intervention. Repeated measurements of orthostatic BP, calf venous ejection fraction (EF) and muscle strength took place before, during and after intervention.

RESULTS

The intervention increased calf muscle strength by 21% (interquartile range: 18-28), p = 0.018, from a median baseline of 38 (34-45) kg. Participants had normal levels of venous EF 64% (51-75) at baseline, with little to no venous reflux. The median ejection volume at baseline was 44 (36-58) mL per calf. Despite increasing muscle strength, venous EF did not increase (percentage change -10% (-16 to 24), p = 0.8) and systolic BP drop did not improve (percentage change 0% (-17 to 16), p = 1.0). Similarly, visual analysis of individual case-series trends revealed increasing muscle strength with no clinically meaningful change in EF or orthostatic BP.

CONCLUSIONS

Muscle strengthening exercise does not increase the effectiveness of the skeletal muscle pump and is not an efficacious intervention for OH. As there is little to no venous pooling in the calf during standing in older people with OH, below knee compression is unlikely to be clinically effective.

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.

Gaming on an Immersive Virtual Reality Platform to Ameliorate the Level of Anxiety in Patients Undergoing Congenital Heart Disease

The double outlet right ventricle (DORV) is an unusual cardiovascular abnormality that is present at birth. The pulmonary artery and the aorta, the cardiovascular system's two major arteries, link to the right ventricle in the DORV. The coronary artery links to the right ventricular, while in an ideal heart, the aorta connects to the left ventricle. DORV is an issue as the right ventricle delivers blood that is low in oxygen, subsequently distributed all around the human being. DORV is always connected with a further cardiac ailment known as a ventricular septal defect (VSD). Behavioral issues are quite prevalent in children. Emotional-obsessive-compulsive disorder, sadness, stress, social fear, and developmental problems are among them. Virtual reality (VR) is a stimulating experience that uses position tracking and three-dimensional (3D) near-eye presentation to give the participant a realistic view of a virtual environment. VR applications involve enjoyment (especially video games). A nine-year-old female complained of a tingling sensation over her right palm. She also had right-side weakness and convulsions, which were restricted to the dominant side. After investigation and evaluation of the condition, it is a known case of congenital heart defects and was planned for surgical correction. The study aims to provide information on an instance of DORV, transposition of great arteries, VSD, and pulmonary stenosis with resolved brain abscess by giving VR video games to reduce anxiety in patients undergoing an operation.

Components of Behavioral Parent Training for Children With Attention-Deficit/Hyperactivity Disorder: A Series of Replicated Single-Case Experiments

Behavioral parent training (BPT) is an evidence-based treatment for children with attention-deficit/hyperactivity disorder (ADHD). Stimulus control techniques (antecedent-based techniques, e.g., clear rules, instructions) and contingency management techniques (consequent-based techniques, e.g., praise, ignore) are the most common ones that are being taught to parents in BPT. However, research into the additive effects of these techniques is scarce. In this replicated single-case experimental ABC phase design, including six children on stable medication for ADHD (8-11 years) and their parents, the added efficacy of consequent-based techniques on top of antecedent-based techniques was evaluated. After a baseline period (phase A), we randomized the commencement time of two sessions parent training in antecedent-based techniques and two sessions parent training in consequent-based techniques for each child. Children's behaviors were assessed by daily parent ratings of selected problem behaviors and an overall behavior rating. Although visual inspection showed that behavior improved for most children in both phases, randomization tests did not demonstrate the added efficacy of the consequent-based techniques on top of the antecedent-based techniques. Limitations of the study and recommendations for future single-case experiments in this population are discussed.

Defining and assessing immediacy in single-case experimental designs

Immediacy is one of six data aspects (alongside level, trend, variability, overlap, and consistency) that has to be accounted for when visually analyzing single-case data. Given that it is one of the aspects that has received considerably less attention than other data aspects, the current text offers a review of the proposed conceptual definitions of immediacy (i.e., what it refers to) and also of the suggested operational definitions (i.e., how exactly is it assessed and/or quantified). Provided that a variety of conceptual and operational definitions is identified, we propose following a sensitivity analysis using a randomization test for assessing immediate effects in single-case experimental designs, by identifying when changes were most clear. In such a sensitivity analysis, the immediate effects are tested for multiple possible intervention points and for different possible operational definitions. Robust immediate effects can be detected if the results for the different operational definitions converge.

The Family of Single-Case Experimental Designs

Single-case experimental designs (SCEDs) represent a family of research designs that use experimental methods to study the effects of treatments on outcomes. The fundamental unit of analysis is the single case-which can be an individual, clinic, or community-ideally with replications of effects within and/or between cases. These designs are flexible and cost-effective and can be used for treatment development, translational research, personalized interventions, and the study of rare diseases and disorders. This article provides a broad overview of the family of single-case experimental designs with corresponding examples, including reversal designs, multiple baseline designs, combined multiple baseline/reversal designs, and integration of single-case designs to identify optimal treatments for individuals into larger randomized controlled trials (RCTs). Personalized N-of-1 trials can be considered a subcategory of SCEDs that overlaps with reversal designs. Relevant issues for each type of design-including comparisons of treatments, design issues such as randomization and blinding, standards for designs, and statistical approaches to complement visual inspection of single-case experimental designs-are also discussed.

Valuing the Diversity of Research Methods to Advance Nutrition Science

The ASN Board of Directors appointed the Nutrition Research Task Force to develop a report on scientific methods used in nutrition science to advance discovery, interpretation, and application of knowledge in the field. The genesis of this report was growing concern about the tone of discourse among nutrition professionals and the implications of acrimony on the productive study and translation of nutrition science. Too often, honest differences of opinion are cast as conflicts instead of areas of needed collaboration. Recognition of the value (and limitations) of contributions from well-executed nutrition science derived from the various approaches used in the discipline, as well as appreciation of how their layering will yield the strongest evidence base, will provide a basis for greater productivity and impact. Greater collaborative efforts within the field of nutrition science will require an understanding that each method or approach has a place and function that should be valued and used together to create the nutrition evidence base. Precision nutrition was identified as an important emerging nutrition topic by the preponderance of task force members, and this theme was adopted for the report because it lent itself to integration of many approaches in nutrition science. Although the primary audience for this report is nutrition researchers and other nutrition professionals, a secondary aim is to develop a document useful for the various audiences that translate nutrition research, including journalists, clinicians, and policymakers. The intent is to promote accurate, transparent, verifiable evidence-based communication about nutrition science. This will facilitate reasoned interpretation and application of emerging findings and, thereby, improve understanding and trust in nutrition science and appropriate characterization, development, and adoption of recommendations.

An Internet-delivered Cognitive-Behavioral Therapy (iCBT) for Prolonged Grief Disorder (PGD) in adults: A multiple-baseline single-case experimental design study

The death of a loved one has physical, psychological, and social consequences. Between 9.8 and 21.5 % of people who lose a loved one develop Prolonged Grief Disorder (PGD). Internet- and computer-based interventions (i.e., Internet-delivered Cognitive-Behavioral Therapy, iCBT) are cost-effective and scalable alternatives that make it possible to reach more people with PGD. The main goal of the present investigation was to examine the effect and feasibility (usability and satisfaction) of an iCBT (GROw program) for adults with PGD. A secondary objective was to detect adherence to the app (Emotional Monitor) used to measure daily grief symptoms. The study had a single-case multiple-baseline AB design with six participants. The GROw program is organized sequentially in eight modules, and it is based on the dual-process model of coping with bereavement. Evaluations included a pre-to-post treatment assessment of depression, grief symptoms, and typical grief beliefs, along with daily measures of symptom frequency and intensity on the Emotional Monitor App. Treatment opinions and adherence to the App were also collected. Efficacy data were calculated using a Nonoverlap of All Pairs (NAP) analysis and Reliable Change Index (RCI). The mean age of the sample was 29.5 years (SD = 8.19). Two participants dropped out of the study. Adherence to the App varied across patients (4.8 % -77.8 %). Most participants (75 %) showed a clinically significant change (recovered) in depression, and 50 % obtained a clinically significant improvement (recovered) in symptoms of loss and typical beliefs in complicated grief. The participants reported high usability and satisfaction with the treatment content and format. In sum, the GROw program was very well accepted and generally feasible, and it has strong potential for treating PGD. The results support scaling up the treatment by using more complex designs with larger samples (i.e., randomized controlled trials comparing GROw with active conditions).

Testing a community developed training protocol for an evidence-based treatment

The implementation of evidence-based treatments (EBTs) in community behavioral health settings is a recommended practice, yet training experienced by community-based clinicians may require novel and creative training methods. The current study focused on creating a training protocol for Alternative for Families: a Cognitive-Behavioral Therapy from both evidence-based foundations and community-based agency feedback to promote better EBT integration into agencies. Twenty-four clinicians from three agencies were trained using a community-informed training protocol. Outcomes for clinician-reports of organizational functioning, self-reports of skill and knowledge, and observational single-subject data of clinician skills were assessed. Minimal improvements were found for clinician skills across self-report and observations. More research on tailoring trainings to meet needs of community agencies and clinicians should be explored to determine best practice in wide-scale implementation efforts.

Case Report: ASI intervention on a child with autism in Saudi Arabia

Background: Ayres Sensory Integration (ASI) is widely employed by occupational therapists working with clients who experience challenges in sensory integration, including those with autism spectrum disorder (ASD). However, there is a dearth of research examining the feasibility of ASI outside of Western nations. This study documented the barriers associated with ASI in Saudi Arabia and assessed whether the intervention could improve process and participation skills.

Methods: Single-Subject experimental design was used. The participant was a 4-year-old girl with ASD from Saudi Arabia. Data were gathered on sensory processing, motor skills, and participation in activities of daily living. The study used semi-structured interviews and assessments (Sensory Integration and Praxis Tests, the Sensory Processing Measure-Preschool, and the Peabody Developmental Motor Scale-2) to develop goals, identify outcome measures, and plan an ASI intervention.

Results: Despite the limited availability of resources (e.g., toys, treatment spaces) and Arabic measures, improvements were observed on motor and sensory tasks and in occupational performance.

Conclusion: ASI that adheres to the ASI fidelity tool can be of value for Saudi Arabian children with ASD. Additionally, the study provides a stepping-stone to further research for occupational therapists in Saudi Arabia working with children with ASD.

Using Health Concept Surveying to Elicit Usable Evidence: Case Studies of a Novel Evaluation Methodology

BACKGROUND

Developers, designers, and researchers use rapid prototyping methods to project the adoption and acceptability of their health intervention technology (HIT) before the technology becomes mature enough to be deployed. Although these methods are useful for gathering feedback that advances the development of HITs, they rarely provide usable evidence that can contribute to our broader understanding of HITs.

OBJECTIVE

In this research, we aim to develop and demonstrate a variation of vignette testing that supports developers and designers in evaluating early-stage HIT designs while generating usable evidence for the broader research community.

METHODS

We proposed a method called health concept surveying for untangling the causal relationships that people develop around conceptual HITs. In health concept surveying, investigators gather reactions to design concepts through a scenario-based survey instrument. As the investigator manipulates characteristics related to their HIT, the survey instrument also measures proximal cognitive factors according to a health behavior change model to project how HIT design decisions may affect the adoption and acceptability of an HIT. Responses to the survey instrument were analyzed using path analysis to untangle the causal effects of these factors on the outcome variables.

RESULTS

We demonstrated health concept surveying in 3 case studies of sensor-based health-screening apps. Our first study (N=54) showed that a wait time incentive could influence more people to go see a dermatologist after a positive test for skin cancer. Our second study (N=54), evaluating a similar application design, showed that although visual explanations of algorithmic decisions could increase participant trust in negative test results, the trust would not have been enough to affect people's decision-making. Our third study (N=263) showed that people might prioritize test specificity or sensitivity depending on the nature of the medical condition.

CONCLUSIONS

Beyond the findings from our 3 case studies, our research uses the framing of the Health Belief Model to elicit and understand the intrinsic and extrinsic factors that may affect the adoption and acceptability of an HIT without having to build a working prototype. We have made our survey instrument publicly available so that others can leverage it for their own investigations.

Single case designs for early phase behavioral translational research in health psychology

OBJECTIVE

The biomedical research community has long recognized that much of the basic research being conducted, whether in the biological, behavioral or social sciences, is not readily translated into clinical and public health applications. This translational gap is due in part to challenges inherent in moving research findings from basic or discovery research to applied research that addresses clinical or public health problems. In the behavioral and social sciences, research designs typically used in the early phases of translational research are small, underpowered "pilot" studies that may lack sufficient statistical power to test the research question of interest. While this approach is discouraged, these studies are often employed to estimate effect sizes before embarking on a larger trial with adequate statistical power to test the research hypothesis. The goal of this paper is to provide an alternative approach to early phase studies using single case designs (SCDs).

METHOD

Review basic principles of SCDs; provide a series of hypothetical SCD replication experiments to illustrate (1) how data from SCDs can be analyzed to test the effects of an intervention on behavioral and biological outcomes and (2) how sample sizes can be derived for larger randomized controlled trials (RCTs) based on clinically meaningful effects from SCDs; and review feedback between SCDs and RCTs.

RESULTS

The paper illustrates the use of SCD reversal and multiple baseline designs for early phase translational research.

CONCLUSION

SCDs provide a flexible and efficient platform for the use of experimental methods in early phase translational research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Optimizing Implementation in Cancer Control (OPTICC): protocol for an implementation science center

BACKGROUND

Evidence-based interventions (EBIs) could reduce cervical cancer deaths by 90%, colorectal cancer deaths by 70%, and lung cancer deaths by 95% if widely and effectively implemented in the USA. Yet, EBI implementation, when it occurs, is often suboptimal. This manuscript outlines the protocol for Optimizing Implementation in Cancer Control (OPTICC), a new implementation science center funded as part of the National Cancer Institute Implementation Science Consortium. OPTICC is designed to address three aims. Aim 1 is to develop a research program that supports developing, testing, and refining of innovative, efficient methods for optimizing EBI implementation in cancer control. Aim 2 is to support a diverse implementation laboratory of clinical and community partners to conduct rapid, implementation studies anywhere along the cancer care continuum for a wide range of cancers. Aim 3 is to build implementation science capacity in cancer control by training new investigators, engaging established investigators in cancer-focused implementation science, and contributing to the Implementation Science Consortium in Cancer.

METHODS

Three cores serve as OPTICC's foundation. The Administrative Core plans coordinates and evaluates the Center's activities and leads its capacity-building efforts. The Implementation Laboratory Core (I-Lab) coordinates a network of diverse clinical and community sites, wherein studies are conducted to optimize EBI implementation, implement cancer control EBIs, and shape the Center's agenda. The Research Program Core conducts innovative implementation studies, measurement and methods studies, and pilot studies that advance the Center's theme. A three-stage approach to optimizing EBI implementation is taken-(I) identify and prioritize determinants, (II) match strategies, and (III) optimize strategies-that is informed by a transdisciplinary team of experts leveraging multiphase optimization strategies and criteria, user-centered design, and agile science.

DISCUSSION

OPTICC will develop, test, and refine efficient and economical methods for optimizing EBI implementation by building implementation science capacity in cancer researchers through applications with our I-Lab partners. Once refined, OPTICC will disseminate its methods as toolkits accompanied by massive open online courses, and an interactive website, the latter of which seeks to simultaneously accumulate knowledge across OPTICC studies.

Leveraging applied behavior analysis research and practice in the service of public health

Human behavior plays a central role in all domains of public health. Applied behavior analysis (ABA) research and practice can contribute to public health solutions that directly address human behavior. In this paper, we describe the field of public health, identify points of interaction between public health and ABA, summarize what ABA research has already contributed, and provide several recommendations for how ABA research and practice could continue to promote public health outcomes. A clearer focus on behavior and widespread adoption of research designs and interventions informed by the ABA literature could lead to better public health outcomes. Reciprocally, better integration of public health goals and strategies into ABA research, harnessing of technology, and more collaboration would help diversify and disseminate our applied science and could yield more effective and scalable interventions to prevent and treat public health problems.

Precision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision

BACKGROUND

In 2015, Collins and Varmus articulated a vision for precision medicine emphasizing molecular characterization of illness to identify actionable biomarkers to support individualized treatment. Researchers have argued for a broader conceptualization, precision health. Precision health is an ambitious conceptualization of health, which includes dynamic linkages between research and practice as well as medicine, population health, and public health. The goal is a unified approach to match a full range of promotion, prevention, diagnostic, and treatment interventions to fundamental and actionable determinants of health; to not just address symptoms, but to directly target genetic, biological, environmental, and social and behavioral determinants of health.

PURPOSE

The purpose of this paper is to elucidate the role of social and behavioral sciences within precision health.

MAIN BODY

Recent technologies, research frameworks, and methods are enabling new approaches to measure, intervene, and conduct social and behavioral science research. These approaches support three opportunities in precision health that the social and behavioral sciences could colead including: (a) developing interventions that continuously "tune" to each person's evolving needs; (b) enhancing and accelerating links between research and practice; and (c) studying mechanisms of change in real-world contexts. There are three challenges for precision health: (a) methods of knowledge organization and curation; (b) ethical conduct of research; and (c) equitable implementation of precision health.

CONCLUSIONS

Precision health requires active coleadership from social and behavioral scientists. Prior work and evidence firmly demonstrate why the social and behavioral sciences should colead with regard to three opportunity and three challenge areas.

Clinician's Guide to Evaluating and Developing eHealth Interventions for Mental Health

Objective

This review aimed to examine key information regarding technology‐delivered interventions for patients with mental health and/or substance use disorders and to provide support for efforts by psychiatrists and other mental health professionals in recommending applications or helping to develop new technology‐delivered interventions.

Methods

The authors reviewed existing information about the appraisal, development, and evaluation of technology‐delivered interventions (eHealth interventions).

Results

High‐level guidance is available for clinicians who want to evaluate eHealth applications for their patients. Clinicians should be familiar with existing models of eHealth intervention development and with traditional as well as unique elements in the evaluation of efficacy for these approaches. However, existing intervention development models have not been empirically validated, and only one includes empirical optimization as an inherent part of its process.

Conclusions

Because of the proliferation of eHealth interventions, mental health professionals should bring to this area the same level of content knowledge, understanding of development and evaluation processes, and rigorous skepticism as they do for pharmacotherapy and therapist‐delivered behavioral interventions.

eHealth interventions are rapidly transforming mental health treatment.

Clinicians should apply the same rigor to evaluating eHealth interventions that they apply to pharmacological treatments or other behavioral therapies.

High‐level guidance and recommendations regarding ideal eHealth intervention development are available to aid in such evaluation, but empirical evidence in support of these guidelines is lacking.

Formative, multimethod case studies of learn to quit, an acceptance and commitment therapy smoking cessation app designed for people with serious mental illness

Despite public health efforts, individuals with serious mental illness (SMI) still have very high rates of tobacco smoking. Innovative approaches to reach this population are needed. These series of case studies aimed to descriptively evaluate the usability, user experience (UX), and user engagement (UE) of Learn to Quit (LTQ), an acceptance and commitment therapy smoking cessation app designed for people with SMI, and to compare it with an app designed for the general population, NCI (National Cancer Institute) QuitGuide (QG). Both apps were combined with nicotine replacement therapy and technical coaching. Inspired by the ORBIT model, we implemented two case studies with crossover AB interventions, two B-phase training designs, and three bi-phasic AB single-case designs with Start-Point and Order randomization (A = QG, B = LTQ). Study outcomes were measured using the System Usability Scale, UX interviews, and background analytics. LTQ's usability levels were above the standard cutoff and on average higher than QG. UX outcomes suggested the relative benefits of LTQ's visual design, gamification and simple design structure. LTQ's overall UE was high; the app was opened for an average of 14 min per day (vs. QG: 7 min). However, users showed low levels of UE with each of the app's tracking feature. Measures of psychiatric functioning suggested the safety of LTQ in people with SMI. LTQ appears to be a usable and engaging smoking cessation app in people with SMI. An optimized version of LTQ should be tested in a Phase II study.

Explicit Grammar Intervention in Young School-Aged Children With Developmental Language Disorder: An Efficacy Study Using Single-Case Experimental Design

Purpose This study evaluated the efficacy of an explicit, combined metalinguistic training and grammar facilitation intervention aimed at improving regular past tense marking for nine children aged 5;10-6;8 (years;months) with developmental language disorder. Method This study used an ABA across-participant multiple-baseline single-case experimental design. Participants were seen one-on-one twice a week for 20- to 30-min sessions for 10 weeks and received explicit grammar intervention combining metalinguistic training using the SHAPE CODING system with grammar facilitation techniques (a systematic cueing hierarchy). In each session, 50 trials to produce the target form were completed, resulting in a total of 1,000 trials over 20 individual therapy sessions. Repeated measures of morphosyntax were collected using probes, including trained past tense verbs, untrained past tense verbs, third-person singular verbs as an extension probe, and possessive 's as a control probe. Probing contexts included expressive morphosyntax and grammaticality judgment. Outcome measures also included pre-poststandard measures of expressive and receptive grammar. Results Analyses of repeated measures demonstrated significant improvement in past tense production on trained verbs (eight of nine children) and untrained verbs (seven of nine children), indicating efficacy of the treatment. These gains were maintained for 5 weeks. The majority of children made significant improvement on standardized measures of expressive grammar (eight of nine children). Only five of nine children improved on grammaticality judgment or receptive measures. Conclusion Results continue to support the efficacy of explicit grammar interventions to improve past tense marking in early school-aged children. Future research should aim to evaluate the efficacy of similar interventions with group comparison studies and determine whether explicit grammar interventions can improve other aspects of grammatical difficulty for early school-aged children with developmental language disorder. Supplemental Material https://doi.org/10.23641/asha.11958771.

Efficacy of a Virtual Reality Biofeedback Game (DEEP) to Reduce Anxiety and Disruptive Classroom Behavior: Single-Case Study

BACKGROUND

Many adolescents in special education are affected by anxiety in addition to their behavioral problems. Anxiety leads to substantial long-term problems and may underlie disruptive behaviors in the classroom as a result of the individual's inability to tolerate anxiety-provoking situations. Thus, interventions in special needs schools that help adolescents cope with anxiety and, in turn, diminish disruptive classroom behaviors are needed.

OBJECTIVE

This study aimed to evaluate the effect of a virtual reality biofeedback game, DEEP, on daily levels of state-anxiety and disruptive classroom behavior in a clinical sample. In addition, the study also aimed to examine the duration of the calm or relaxed state after playing DEEP.

METHODS

A total of 8 adolescents attending a special secondary school for students with behavioral and psychiatric problems participated in a single-case experimental ABAB study. Over a 4-week period, participants completed 6 DEEP sessions. In addition, momentary assessments (ie, 3 times a day) of self-reported state-anxiety and teacher-reported classroom behavior were collected throughout all A and B phases.

RESULTS

From analyzing the individual profiles, it was found that 6 participants showed reductions in anxiety, and 5 participants showed reductions in disruptive classroom behaviors after the introduction of DEEP. On a group level, results showed a small but significant reduction of anxiety (d=-0.29) and a small, nonsignificant reduction of disruptive classroom behavior (d=-0.16) on days when participants played DEEP. Moreover, it was found that the calm or relaxed state of participants after playing DEEP lasted for about 2 hours on average.

CONCLUSIONS

This study demonstrates the potential of the game, DEEP, as an intervention for anxiety and disruptive classroom behavior in a special school setting. Future research is needed to fully optimize and personalize DEEP as an intervention for the heterogeneous special school population.

Workshop on the Development and Evaluation of Digital Therapeutics for Health Behavior Change: Science, Methods, and Projects

The health care field has integrated advances into digital technology at an accelerating pace to improve health behavior, health care delivery, and cost-effectiveness of care. The realm of behavioral science has embraced this evolution of digital health, allowing for an exciting roadmap for advancing care by addressing the many challenges to the field via technological innovations. Digital therapeutics offer the potential to extend the reach of effective interventions at reduced cost and patient burden and to increase the potency of existing interventions. Intervention models have included the use of digital tools as supplements to standard care models, as tools that can replace a portion of treatment as usual, or as stand-alone tools accessed outside of care settings or direct to the consumer. To advance the potential public health impact of this promising line of research, multiple areas warrant further development and investigation. The Center for Technology and Behavioral Health (CTBH), a P30 Center of Excellence supported by the National Institute on Drug Abuse at the National Institutes of Health, is an interdisciplinary research center at Dartmouth College focused on the goal of harnessing existing and emerging technologies to effectively develop and deliver evidence-based interventions for substance use and co-occurring disorders. The CTBH launched a series of workshops to encourage and expand multidisciplinary collaborations among Dartmouth scientists and international CTBH affiliates engaged in research related to digital technology and behavioral health (eg, addiction science, behavioral health intervention, technology development, computer science and engineering, digital security, health economics, and implementation science). This paper summarizes a workshop conducted on the Development and Evaluation of Digital Therapeutics for Behavior Change, which addressed (1) principles of behavior change, (2) methods of identifying and testing the underlying mechanisms of behavior change, (3) conceptual frameworks for optimizing applications for mental health and addictive behavior, and (4) the diversity of experimental methods and designs that are essential to the successful development and testing of digital therapeutics. Examples were presented of ongoing CTBH projects focused on identifying and improving the measurement of health behavior change mechanisms and the development and evaluation of digital therapeutics. In summary, the workshop showcased the myriad research targets that will be instrumental in promoting and accelerating progress in the field of digital health and health behavior change and illustrated how the CTBH provides a model of multidisciplinary leadership and collaboration that can facilitate innovative, science-based efforts to address the health behavior challenges afflicting our communities.

Technology-based contingency management and e-cigarettes during the initial weeks of a smoking quit attempt

Contingency management (CM) interventions are among the most effective behavioral interventions for smoking. This study assessed the effects of CM and electronic cigarettes (ECs) on smoking reductions and abstinence for durations of 30-36 days. Twelve participants were exposed to Baseline, EC alone, and EC + CM conditions. An internet-based platform was used to monitor smoking via breath carbon monoxide (CO) and deliver CM for smoking abstinence (CO ≤4 ppm). A Bluetooth-enabled EC monitored daily EC puffs. Abstinence rates were equivalent between EC (34.4%) and EC + CM (30.4%) conditions. Both conditions promoted smoking reductions. We observed an inverse correlation between smoking and EC puffs (r = -.62, p < .05). Results suggest the use of electronic cigarettes can promote smoking reductions and abstinence, and CM did not improve these outcomes. Larger magnitude consequences or tailoring EC characteristics (e.g., flavor) may have improved outcomes. Technology-based methods to collect intensive, longitudinal measures of smoking and electronic cigarette use may be useful to characterize their environmental determinants.

The Effect of Systematic Prompting on the Acquisition of Five Muscle-Strengthening Exercises by Adults With Mild Intellectual Disabilities

Adults with intellectual disability have significantly lower musculoskeletal fitness than their peers without a disability. Appropriate instructional strategies are needed to facilitate their acquisition and maintenance of musculoskeletal fitness. In this multiple-baseline across-participants single-subject study, the authors evaluated the effects of a multicomponent package that included a video-enhanced system of least-to-most prompts on the acquisition of 5 muscle-strengthening exercises in 3 women with mild intellectual disability, age 24-37 yr. Results show substantive gains in correct and independent performance of steps in the 5 exercises during the treatment condition. The improved performance was maintained 2 wk after the last treatment session and in a large YMCA gym. The study suggests that use of the video-enhanced system of least-to-most prompts can lead to improved acquisition and maintenance of muscle-strengthening exercises by adults with mild intellectual disability.

Why we need a small data paradigm

BACKGROUND

There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various 'big data' efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary 'small data' paradigm that can function both autonomously from and in collaboration with big data is also needed. By 'small data' we build on Estrin's formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit.

MAIN BODY

The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality.

CONCLUSION

Small data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.

A multicomponent behavioral intervention for smoking cessation during pregnancy: a nonconcurrent multiple-baseline design

Given serious consequences of maternal smoking, we aimed to develop and test a multicomponent behavioral intervention to enhance smoking cessation during pregnancy. In this nonconcurrent, multiple-baseline intervention pilot study, 48 daily smoking pregnant women (mean 13.7 weeks of gestation) were recruited from Buffalo, NY, USA. Upon completion of the repeated baseline smoking monitoring (up to 3 weeks), 30 continuous smokers received a contingent financial incentive-based intervention with three additional components (education and counseling, monitoring and feedback, and family support). After the quit date, participants met with counselors (~1 hr/visit) daily for 2 weeks and twice a week for another 6 weeks. Twenty-one out of 30 participants quit smoking completely (verified by urine cotinine) after receiving the intervention, and the other nine nonquitters decreased smoking substantially. The estimated smoking cessation rate was 70.0% (21/30) at the second week of the intervention, and 63.3% (19/30) at the conclusion of the 8-week intervention assuming the dropouts as smoking. In interrupted time series analysis, the mean daily number of cigarettes smoked among quitters decreased by 6.52, 5.34, and 4.67 among early, delayed, and late intervention groups, respectively. Quitters' mean urine cotinine level maintained stably high before the intervention but decreased rapidly to the nonsmoking range once the intervention was initiated. Most participants (85.7%) reported meeting or exceeding expectations, and 100% would recommend the program to others. This pilot multicomponent intervention was feasible and acceptable to most participants, resulting in a high smoking cessation rate among pregnant smokers who were unlikely to quit spontaneously.

The impact of trait mindfulness on relational outcomes in novice yoga practitioners participating in an academic yoga course

OBJECTIVE

This study examined the role of dispositional mindfulness in impacting the relational benefits of yoga in novice practitioners in an undergraduate academic yoga course.

PARTICIPANTS

21 college students enrolled in a yoga course throughout a 15-week academic semester (January-April 2016).

METHODS

Participants attended lab assessments at the beginning and end of the semester and were asked to complete eight consecutive Internet-based daily surveys across six separate bursts, yielding 48 data points for each participant.

RESULTS

Multivariate time-series analyses revealed within-person subgroup differences such that in general, those in the high trait mindfulness group gleaned benefits from yoga practice with respect to relational outcomes.

CONCLUSIONS

This study demonstrated trait mindfulness impacts relational outcomes in novice yoga practitioners, suggesting those low in trait mindfulness may not garner immediate relational benefits from yoga and could benefit from additional strategies to bolster against any negative influences of initiating yoga practice.

Development and Validation of a Functional Behavioural Assessment Ontology to Support Behavioural Health Interventions

Background

In the cognitive-behavioral approach, Functional Behavioural Assessment is one of the most effective methods to identify the variables that determine a problem behavior. In this context, the use of modern technologies can encourage the collection and sharing of behavioral patterns, effective intervention strategies, and statistical evidence about antecedents and consequences of clusters of problem behaviors, encouraging the designing of function-based interventions.

Objective

The paper describes the development and validation process used to design a specific Functional Behavioural Assessment Ontology (FBA-Ontology). The FBA-Ontology is a semantic representation of the variables that intervene in a behavioral observation process, facilitating the systematic collection of behavioral data, the consequential planning of treatment strategies and, indirectly, the scientific advancement in this field of study.

Methods

The ontology has been developed deducing concepts and relationships of the ontology from a gold standard and then performing a machine-based validation and a human-based assessment to validate the Functional Behavioural Assessment Ontology. These validation and verification processes were aimed to verify how much the ontology is conceptually well founded and semantically and syntactically correct.

Results

The Pellet reasoner checked the logical consistency and the integrity of classes and properties defined in the ontology, not detecting any violation of constraints in the ontology definition. To assess whether the ontology definition is coherent with the knowledge domain, human evaluation of the ontology was performed asking 84 people to fill in a questionnaire composed by 13 questions assessing concepts, relations between concepts, and concepts’ attributes. The response rate for the survey was 29/84 (34.52%). The domain experts confirmed that the concepts, the attributes, and the relationships between concepts defined in the FBA-Ontology are valid and well represent the Functional Behavioural Assessment process.

Conclusions

The new ontology developed could be a useful tool to design new evidence-based systems in the Behavioral Interventions practices, encouraging the link with other Linked Open Data datasets and repositories to provide users with new models of eHealth focused on the management of problem behaviors. Therefore, new research is needed to develop and implement innovative strategies to improve the poor reproducibility and translatability of basic research findings in the field of behavioral assessment.

Use of the experience sampling method in the context of clinical trials

Objective

The experience sampling method (ESM) is a structured diary technique to appraise subjective experiences in daily life. It is applied in psychiatric patients, as well as in patients with somatic illness. Despite the potential of ESM assessment, the improved logistics and its increased administration in research, its use in clinical trials remains limited. This paper introduces ESM for clinical trials in psychiatry and beyond.

Methods

ESM is an ecologically valid method that yields a comprehensive view of an individual's daily life. It allows the assessment of various constructs (eg, quality of life, psychopathology) and psychological mechanisms (eg, stress-sensitivity, coping). These constructs are difficult to assess using cross-sectional questionnaires. ESM can be applied in treatment monitoring, as an ecological momentary intervention, in clinical trials, or in single case clinical trials. Technological advances (eg, smartphone applications) make its implementation easier.

Results

Advantages of ESM are highlighted and disadvantages are discussed. Furthermore, the ecological nature of ESM data and its consequences are explored, including the potential pitfalls of ambiguously formulated research questions and the specificities of ESM in statistical analyses. The last section focuses on ESM in relation to clinical trials and discusses its future use in optimising clinical decision-making.

Conclusions

ESM can be a valuable asset in clinical trial research and should be used more often to study the benefits of treatment in psychiatry and somatic health.

Agile science: creating useful products for behavior change in the real world

Evidence-based practice is important for behavioral interventions but there is debate on how best to support real-world behavior change. The purpose of this paper is to define products and a preliminary process for efficiently and adaptively creating and curating a knowledge base for behavior change for real-world implementation. We look to evidence-based practice suggestions and draw parallels to software development. We argue to target three products: (1) the smallest, meaningful, self-contained, and repurposable behavior change modules of an intervention; (2) “computational models” that define the interaction between modules, individuals, and context; and (3) “personalization” algorithms, which are decision rules for intervention adaptation. The “agile science” process includes a generation phase whereby contender operational definitions and constructs of the three products are created and assessed for feasibility and an evaluation phase, whereby effect size estimates/casual inferences are created. The process emphasizes early-and-often sharing. If correct, agile science could enable a more robust knowledge base for behavior change.

Application of N-of-1 Experiments to Test the Efficacy of Inactivity Alert Features in Fitness Trackers to Increase Breaks from Sitting in Older Adults

BACKGROUND

Frequent breaks from sitting could improve health. Many commercially available fitness trackers deliver vibration alerts that could be used to cue sitting breaks. As a potentially pragmatic approach to promote frequent breaks from sitting, we tested the efficacy of inactivity alerts among obese older adults, a highly sedentary population.

METHODS

We conducted 10 sequential N-of-1 (single-case) experimental ABA trials. Participants (mean age = 68, mean BMI = 35) were monitored for a baseline phase ("A1") followed by an intervention phase ("B"). The intervention was then removed and participants were monitored to test an experimental effect (reversal "A2" phase). Total time in the study was limited to 25 days. During the intervention phase ("B"), participants used fitness trackers to stand up or move every time they received an alert (every 15 or 20 minutes of inactivity). Participants wore activPAL devices to measure breaks from sitting each day. Randomization tests were used to determine whether the number of breaks was significantly higher during the "B" phase than the two "A" phases.

RESULTS

Breaks were higher by 7.2 breaks per day during the "B" phase compared to the mean of the "A" phases. Seven out of 10 participants had more sitting breaks during the intervention phase which subsequently decreased during the reversal "A2" phase (combined p-value < .05).

CONCLUSION

Inactivity alert features within commercially available devices are efficacious for promoting modest improvements in breaks from sitting among older adults with obesity and could be a simple health-promoting strategy in this population.

Toward Usable Evidence: Optimizing Knowledge Accumulation in HCI Research on Health Behavior Change

Over the last ten years, HCI researchers have introduced a range of novel ways to support health behavior change, from glanceable displays to sophisticated game dynamics. Yet, this research has not had as much impact as its originality warrants. A key reason for this is that common forms of evaluation used in HCI make it difficult to effectively accumulate-and use-knowledge across research projects. This paper proposes a strategy for HCI research on behavior change that retains the field's focus on novel technical contributions while enabling accumulation of evidence that can increase impact of individual research projects both in HCI and the broader behavior-change science. The core of this strategy is an emphasis on the discovery of causal effects of individual components of behavior-change technologies and the precise ways in which those effects vary with individual differences, design choices, and contexts in which those technologies are used.

Research on Skin Cancer-Related Behaviors and Outcomes in the NIH Grant Portfolio, 2000-2014: Skin Cancer Intervention Across the Cancer Control Continuum (SCI-3C)

Importance

The Surgeon General's Call to Action to Prevent Skin Cancer broadly identified research gaps, but specific objectives are needed to further behavioral intervention research.

Objective

To review National Institute of Health (NIH) grants targeting skin cancer-related behaviors and relevant outcomes.

Design, Setting, and Participants

A portfolio analysis of the title, abstract, specific aims, and research plans of identified grant applications from 2000 to 2014 targeting skin cancer-related behaviors or testing behavioral intervention effects on cancer-relevant outcomes along the cancer continuum.

Main Outcomes and Measures

Funding trends were compared along the cancer control continuum, with respect to investigator demographics and use of theory, technology, policy, and changes to environmental surroundings (built environment).

Results

A total of 112 submitted applications met inclusion criteria; of these, 40 (35.7%) were funded, and 31 of the 40 were interventions. Comparing the 40 funded grants with the 72 unfunded grants, the overall success rates did not differ significantly between male (33.3%) and female (37.3%) investigators, nor did the frequency of R01 awards (36.7% and 28.1%, respectively). Among intervention awards, most (24 of 31) addressed prevention. Fewer awards targeted detection alone or in conjunction with prevention (3) or cancer survivorship (4), and no grant addressed emotional sequelae or adherence behavior related to diagnosis or treatment. Fewer than half of funded grants aimed for clinically related targets (eg, sunburn reduction). Use of theory and technology occurred in more than 75% of grants. However, the full capability of proposed technology was infrequently used, and rarely did constructs of the proposed behavior change theory clearly and comprehensively drive the intervention approach. Policy or environmental manipulation was present in all dissemination grants but was rarely used elsewhere, and 19.4% included policy implementation and 25.8% proposed changes in built environment.

Conclusions and Relevance

Grant success rate in skin cancer-related behavioral science compares favorably to the overall NIH grant success rate (approximately 18%), and the success rate of male and female investigators was not statistically different. However, gaps exist in behavioral research addressing all points of the skin cancer control continuum, measuring interventions that hit clinically related targets, and leveraging technology, theory, and environmental manipulation to optimize intervention approach.

Sample size calculations for micro-randomized trials in mHealth

The use and development of mobile interventions are experiencing rapid growth. In "just-in-time" mobile interventions, treatments are provided via a mobile device, and they are intended to help an individual make healthy decisions 'in the moment,' and thus have a proximal, near future impact. Currently, the development of mobile interventions is proceeding at a much faster pace than that of associated data science methods. A first step toward developing data-based methods is to provide an experimental design for testing the proximal effects of these just-in-time treatments. In this paper, we propose a 'micro-randomized' trial design for this purpose. In a micro-randomized trial, treatments are sequentially randomized throughout the conduct of the study, with the result that each participant may be randomized at the 100s or 1000s of occasions at which a treatment might be provided. Further, we develop a test statistic for assessing the proximal effect of a treatment as well as an associated sample size calculator. We conduct simulation evaluations of the sample size calculator in various settings. Rules of thumb that might be used in designing a micro-randomized trial are discussed. This work is motivated by our collaboration on the HeartSteps mobile application designed to increase physical activity. Copyright © 2015 John Wiley & Sons, Ltd.

Microrandomized trials: An experimental design for developing just-in-time adaptive interventions

OBJECTIVE

This article presents an experimental design, the microrandomized trial, developed to support optimization of just-in-time adaptive interventions (JITAIs). JITAIs are mHealth technologies that aim to deliver the right intervention components at the right times and locations to optimally support individuals' health behaviors. Microrandomized trials offer a way to optimize such interventions by enabling modeling of causal effects and time-varying effect moderation for individual intervention components within a JITAI.

METHOD

The article describes the microrandomized trial design, enumerates research questions that this experimental design can help answer, and provides an overview of the data analyses that can be used to assess the causal effects of studied intervention components and investigate time-varying moderation of those effects.

RESULTS

Microrandomized trials enable causal modeling of proximal effects of the randomized intervention components and assessment of time-varying moderation of those effects.

CONCLUSION

Microrandomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions' effects, enabling creation of more effective JITAIs.

From ideas to efficacy: The ORBIT model for developing behavioral treatments for chronic diseases

OBJECTIVE

Given the critical role of behavior in preventing and treating chronic diseases, it is important to accelerate the development of behavioral treatments that can improve chronic disease prevention and outcomes. Findings from basic behavioral and social sciences research hold great promise for addressing behaviorally based clinical health problems, yet there is currently no established pathway for translating fundamental behavioral science discoveries into health-related treatments ready for Phase III efficacy testing. This article provides a systematic framework for developing behavioral treatments for preventing and treating chronic diseases.

METHOD

The Obesity-Related Behavioral Intervention Trials (ORBIT) model for behavioral treatment development features a flexible and progressive process, prespecified clinically significant milestones for forward movement, and return to earlier stages for refinement and optimization.

RESULTS

This article presents the background and rationale for the ORBIT model, a summary of key questions for each phase, a selection of study designs and methodologies well-suited to answering these questions, and prespecified milestones for forward or backward movement across phases.

CONCLUSIONS

The ORBIT model provides a progressive, clinically relevant approach to increasing the number of evidence-based behavioral treatments available to prevent and treat chronic diseases. (PsycINFO Database Record

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

OBJECTIVES

First, this study compares the ability of an integrated model of activity and activity limitations, the International Classification of Functioning, Disability and Health (ICF), and the Theory of Planned Behaviour (TPB) to predict walking within individuals with osteoarthritis. Second, the effectiveness of a walking intervention in these individuals is determined.

DESIGN

A series of n-of-1 studies with an AB intervention design was used.

METHODS

Diary methods were used to study four community-dwelling individuals with lower-limb osteoarthritis. Data on impairment symptoms (pain, pain on movement, and joint stiffness), cognitions (intention, self-efficacy, and perceived controllability), and walking (step count) were collected twice daily for 12 weeks. At 6 weeks, an individually tailored, data-driven walking intervention using action planning or a control cognition manipulation was delivered. Simulation modelling analysis examined cross-correlations and differences in baseline and intervention phase means. Post-hoc mediation analyses examined theoretical relationships and multiple regression analyses compared theoretical models.

RESULTS

Cognitions, intention in particular, were better and more consistent within individual predictors of walking than impairment. The walking intervention did not increase walking in any of the three participants receiving it. The integrated model and the TPB, which recognize a predictive role for cognitions, were significant predictors of walking variance in all participants, whilst the biomedical ICF model was only predictive for one participant.

CONCLUSION

Despite the lack of evidence for an individually tailored walking intervention, predictive data suggest that interventions for people with osteoarthritis that address cognitions are likely to be more effective than those that address impairment only. Further within-individual investigation, including testing mediational relationships, is warranted.

STATEMENT OF CONTRIBUTION

What is already known on this subject? N-of-1 methods have been used to study within-individual predictors of walking in healthy and chronic pain populations An integrated biomedical and behavioural model of activity and activity limitations recognizes the roles of impairment and psychology (cognitions) Interventions modifying cognitions can increase physical activity in people with mobility limitations What does this study add? N-of-1 methods are suitable to study within-individual predictors of walking and interventions in osteoarthritis An integrated and a psychological model are better predictors of walking in osteoarthritis than a biomedical model There was no support for an individually tailored, data-driven walking intervention.

Mechanisms of change associated with technology-based interventions for substance use

BACKGROUND

Technology-based interventions (TBIs) for substance use disorders have been increasing steadily. The mechanisms by which TBIs produce change in substance use outcomes have not been reviewed. This article is the first review of the conceptual and empirical underpinnings of the mechanisms associated with TBIs for substance use disorders.

METHODS

We review the literature on potential mechanisms associated with TBIs targeting tobacco, alcohol, and poly-substance use. We did not identify TBIs targeting other drug classes and that assessed mechanisms.

RESULTS

Research suggests that TBIs impact outcomes via similar potential mechanisms as in non-TBIs (e.g., in-person treatment), with the exception of substance use outcomes being associated with changes in the quality of coping skills. The most frequent potential mechanisms detected were self-efficacy for tobacco abstinence and perceived peer drinking for alcohol abstinence.

CONCLUSIONS

Research on mechanisms associated with TBIs is still in a nascent stage. We provide several recommendations for future work, including broadening the range of mechanisms assessed and increasing the frequency of assessment to detect temporal relations between mechanisms and outcomes. We also discuss unique challenges and opportunities afforded by technology that can advance theory, method, and clinical practice.

INTEGRATING TECHNOLOGICAL ADVANCEMENTS IN BEHAVIORAL INTERVENTIONS TO PROMOTE HEALTH: UNPRECEDENTED OPORTUNITIES FOR BEHAVIOR ANALYSTS

The use of mobile devices is growing worldwide in both industrialized and developing nations. Alongside the worldwide penetration of web-enabled devices, the leading causes of morbidity and mortality are increasingly modifiable lifestyle factors (e.g., improving one's diet and exercising more). Behavior analysts have the opportunity to promote health by combining effective behavioral methods with technological advancements. The objectives of this paper are (1) to highlight the public health gains that may be achieved by integrating technology with a behavior analytic approach to developing interventions, and (2) to review some of the currently, under-examined issues related to merging technology and behavior analysis (enhancing sustainability, obtaining frequent measures of behavior, conducting component analyses, evaluating cost-effectiveness, incorporating behavior analysis in the creation of consumer-based applications, and reducing health disparities). Thorough consideration of these issues may inspire the development, implementation, and dissemination of innovative, efficacious interventions that substantially improve global public health.

A New Frontier: Integrating Behavioral and Digital Technology to Promote Health Behavior

Modifiable behavioral risk factors such as cigarette smoking, physical inactivity, and obesity contribute to over 40 % of premature deaths in the USA. Advances in digital and information technology are creating unprecedented opportunities for behavior analysts to assess and modify these risk factors. Technological advances include mobile devices, wearable sensors, biomarker detectors, and real-time access to therapeutic support via information technology. Integrating these advances with behavioral technology in the form of conceptually systematic principles and procedures could usher in a new generation of effective and scalable behavioral interventions targeting health behavior. In this selective review of the literature, we discuss how technological tools can assess and modify a range of antecedents and consequences of healthy and unhealthy behavior. We also describe practical, methodological, and conceptual advantages for behavior analysts that stem from the use of technology to assess and treat health behavior.