Using Technology to Expand and Enhance Applied Behavioral Analysis Programs for Children with Autism in Military Families

Fisher, Wayne W.; Piazza, Cathleen C.; Roane, Henry S.; Luczynski, Kevin C.

doi:10.21236/ada611740

Cited by 7 publications

(9 citation statements)

References 29 publications

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“…With regard to opportunities for positive change, various learning and motivational theories highlight the importance of concepts such as shaping (i.e., training by reinforcing successively improving approximations of a desired behavior: Bouton, 2007; Ferster & Skinner, 1957) and teachable moments (i.e., a time when a person is more likely to internalize information and take action; e.g., Fisher, Piazza, & Roane, 2011). The underlying assumption is that in order to facilitate improvement in some behavioral or cognitive domain, it is important to identify transient oportunities for learning and provide the type of support needed, only when needed in order to gradually move the person’s actions/cognitions in the desired direction.…”

Section: What Is a Jitai?mentioning

confidence: 99%

Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework.

Nahum‐Shani¹,

Hekler²,

2015

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Advances in wireless devices and mobile technology offer many opportunities for delivering just-in-time adaptive interventions (JITAIs)--suites of interventions that adapt over time to an individual’s changing status and circumstances with the goal to address the individual’s need for support, whenever this need arises. A major challenge confronting behavioral scientists aiming to develop a JITAI concerns the selection and integration of existing empirical, theoretical and practical evidence into a scientific model that can inform the construction of a JITAI and help identify scientific gaps. The purpose of this paper is to establish a pragmatic framework that can be used to organize existing evidence into a useful model for JITAI construction. This framework involves clarifying the conceptual purpose of a JITAI, namely the provision of just-in-time support via adaptation, as well as describing the components of a JITAI and articulating a list of concrete questions to guide the establishment of a useful model for JITAI construction. The proposed framework includes an organizing scheme for translating the relatively static scientific models underlying many health behavior interventions into a more dynamic model that better incorporates the element of time. This framework will help to guide the next generation of empirical work to support the creation of effective JITAIs.

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Section: What Is a Jitai?mentioning

confidence: 99%

Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework.

Nahum‐Shani¹,

Hekler²,

2015

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“…Even if this colossally complex issue of obtaining the right data types at sufficient temporal density from a large enough sample based on robust prior knowledge were solved, if the mechanism is known to manifest idiosyncratically (see [13] for many concrete examples), then big data will become not just insufficient but, potentially, problematic as it may wash out or ignore meaningful individual differences. For example, the behavioral science version of reinforcement learning (i.e., increasing future behaviors via giving rewards, like giving a dog food after sitting) is one of the most well understood drivers of behavior across organisms [14, 15]. While the mechanism is universal, it manifests idiosyncratically [14, 15].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the behavioral science version of reinforcement learning (i.e., increasing future behaviors via giving rewards, like giving a dog food after sitting) is one of the most well understood drivers of behavior across organisms [14, 15]. While the mechanism is universal, it manifests idiosyncratically [14, 15]. Think, for example, of the pickiness of children.…”

Section: Introductionmentioning

confidence: 99%

“…Learning histories and individual preferences combine to create tremendous variability in how different people respond [13] to both specific elements in the environment (e.g., strawberries) as well as classes of those elements (e.g., dessert). These concrete details of mechanism manifestation will be averaged out in aggregated analyses, yet it is precisely at that level of concreteness that treatments have to be individualized [14–16]. Because of its focus on advancing goals of an N-of-1 unit and inclusion of that N-of-1 unit in the process, a small data approach has unique capabilities for issues that manifest idiosyncratically and, thus, are important for advancing precision health.…”

Section: Introductionmentioning

confidence: 99%

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Why we need a small data paradigm

Hekler

Klasnja

Chevance

et al. 2019

BMC Med

126

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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.

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“…Perhaps the most common kind of operant intervention is the reinforcement procedure, in which the availability of an incentive is contingent upon the occurrence of a target behavior. Such interventions are effective in the treatment of drug addiction (Lussier et al, 2006; Higgins, Silverman and Heil, 2008), in improving the lives of individuals with developmental disabilities and autism (Fischer, Piazza, & Roane, 2011), and in improving safety and productivity in the workplace (Austin and Carr, 2000), among other applications. These kinds of interventions can also promote medication adherence, though attempts to address medication adherence via patient incentive programs has been relatively uncommon.…”

Section: Introductionmentioning

confidence: 99%

The use of incentives to reinforce medication adherence

DeFulio

Silverman

2012

Preventive Medicine

127

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Objective Poor medication adherence is a longstanding problem, and is especially pertinent for individuals with chronic conditions or diseases. Adherence to medications can improve patient outcomes and greatly reduce the cost of care. The purpose of the present review is to describe the literature on the use of incentives as applied to the problem of medication adherence. Methods We conducted a systematic review of peer-reviewed empirical evaluations of incentives provided to patients contingent upon medication adherence. Results This review suggests that incentive-based medication adherence interventions can be very effective, but there are few controlled studies. The studies on incentive-based medication adherence interventions most commonly feature patients taking medication for drug or alcohol dependence, HIV, or latent tuberculosis. Across studies that reported percent adherence comparisons, incentives increased adherence by a mean of 20 percentage points, but effects varied widely. Cross-study comparisons indicate a positive relationship between the value of the incentive and the impact of the intervention. Post-intervention evaluations were rare, but tended to find that adherence effects diminish after the interventions are discontinued. Conclusions Incentive-based medication adherence interventions are promising but understudied. A significant challenge for research in this area is the development of sustainable and cost-effective long-term interventions.

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Using Technology to Expand and Enhance Applied Behavioral Analysis Programs for Children with Autism in Military Families

Cited by 7 publications

References 29 publications

Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework.

Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework.

Why we need a small data paradigm

The use of incentives to reinforce medication adherence

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