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3.
Contemp Clin Trials ; 100: 106217, 2020 Nov 14.
Artigo em Inglês | MEDLINE | ID: mdl-33197609

RESUMO

BACKGROUND: Behavioral lifestyle intervention (BLI) is recommended as a first-line treatment for obesity. While BLI has been adapted for online delivery to improve potential for dissemination while reducing costs and barriers to access, weight losses are typically inferior to gold standard treatment delivered in-person. It is therefore important to refine and optimize online BLI in order to improve the proportion of individuals who achieve a minimum clinically significant weight loss and mean weight loss. STUDY DESIGN: Five experimental intervention components will be tested as adjuncts to an established 12-month online BLI: virtual reality for BLI skills training, interactive video feedback, tailored intervention to promote physical activity, skills for dysregulated eating, and social support combined with friendly competition. Following the Multiphase Optimization Strategy (MOST) framework, the components will first be refined and finalized during Preparation Phase pilot testing and then evaluated in a factorial experiment with 384 adults with overweight or obesity. A priori optimization criteria that balance efficacy and efficiency will be used to create a finalized treatment package that produces the best weight loss outcomes with the fewest intervention components. Mediation analysis will be conducted to test hypothesized mechanisms of action and a moderator analysis will be conducted to understand for whom and under what circumstances the interventions are effective. CONCLUSION: This study will provide important information about intervention strategies that are useful for improving outcomes of online BLI. The finalized treatment package will be suitable for testing in a future randomized trial in the MOST Evaluation Phase.

4.
Health Psychol ; 2020 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-33252961

RESUMO

OBJECTIVE: Despite evidence that goal setting is valuable for physical activity promotion, recent studies highlighted a potential oversimplification in the application of this behavior change technique. While more difficult performance goals might trigger higher physical activity levels, higher performance goals might concurrently be more difficult to achieve, which could reduce long-term motivation. This study examined (a) the association between performance goal difficulty and physical activity and (b) the association between performance goal difficulty and goal achievement. METHOD: This study used data from an e-Health intervention among inactive overweight adults (n = 20). The study duration included a 2-week baseline period and an intervention phase of 80 days. During the intervention, participants received a daily step goal experimentally manipulated by taking participants' baseline physical activity median (i.e., number of steps) multiplied by a pseudorandom factor ranging from 1 to 2.6. A continuous measure of goal achievement was inferred for each day by dividing the daily number of steps by the goal prescribed that day. Linear and generalized additive models were fit for each participant. RESULTS: The results confirm that, for a majority of the participants involved in the study, performance goal difficulty was positively and significantly associated with physical activity (n = 14), but, concurrently, negatively and significantly associated with goal achievement (n = 19). These associations were mainly linear. CONCLUSION: At the daily level, setting a higher physical activity goal leads to engaging in higher physical activity levels, but concurrently lower goal achievement. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

5.
Health Psychol ; 39(9): 841-845, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32833485

RESUMO

The Science of Behavior Change Network (SOBC) offers a pragmatic "experimental medicine" approach for advancing mechanisms of change regarding behavior. The key promise of the SOBC is to facilitate more effective knowledge accumulation about not only whether behavior change occurs in response to an intervention, but also how and why behavior change occurs. This work is being advanced during a time of rapid evolution on scientific best practices, particularly "open science" practices, which at their core, seek to increase the trustworthiness of science. The purpose of this commentary is to facilitate a broader discussion on opportunities and challenges involved with conducting mechanistic science related to behavior change (i.e., SOBC) via open science practices. The 10 studies published in this special issue highlight the considerable complexity involved in a mechanistic science of behavior change. Conducting this type of science will require a rich, multifaceted "team science" approach that can match that level of complexity, while constantly striving toward being as straightforward or as simple as possible, no simpler. Effective open science practices, which involve the sharing of resources whenever possible, can facilitate this type of team science. Moving to this new future would benefit from careful shifts in our scientific culture and financial models toward better supporting team and open science. In addition, there is also need for continued advancements in methods and infrastructure that can support the inherent complexities involved in advancing a mechanistic science of behavior change. (PsycInfo Database Record (c) 2020 APA, all rights reserved).


Assuntos
Transtornos Mentais/terapia , Pesquisa Biomédica/métodos , Humanos
6.
Front Public Health ; 8: 260, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32695740

RESUMO

Although group-level evidence supports the use of behavioral interventions to enhance cognitive and emotional well-being, different interventions may be more acceptable or effective for different people. N-of-1 trials are single-patient crossover trials designed to estimate treatment effectiveness in a single patient. We designed a mobile health (mHealth) supported N-of-1 trial platform permitting US adult volunteers to conduct their own 30-day self-experiments testing a behavioral intervention of their choice (deep breathing/meditation, gratitude journaling, physical activity, or helpful acts) on daily measurements of stress, focus, and happiness. We assessed uptake of the study, perceived usability of the N-of-1 trial system, and influence of results (both reported and perceived) on enthusiasm for the chosen intervention (defined as perceived helpfulness of the chosen intervention and intent to continue performing the intervention in the future). Following a social media and public radio campaign, 447 adults enrolled in the study and 259 completed the post-study survey. Most were highly educated. Perceived system usability was high (mean scale score 4.35/5.0, SD 0.57). Enthusiasm for the chosen intervention was greater among those with higher pre-study expectations that the activity would be beneficial for them (p < 0.001), those who obtained more positive N-of-1 results (as directly reported to participants) (p < 0.001), and those who interpreted their N-of-1 study results more positively (p < 0.001). However, reported results did not significantly influence enthusiasm after controlling for participants' interpretations. The interaction between pre-study expectation of benefit and N-of-1 results interpretation was significant (p < 0.001), such that those with the lowest starting pre-study expectations reported greater intervention enthusiasm when provided with results they interpreted as positive. We conclude that N-of-1 behavioral trials can be appealing to a broad albeit highly educated and mostly female audience, that usability was acceptable, and that N-of-1 behavioral trials may have the greatest utility among those most skeptical of the intervention to begin with.

7.
Exerc Sport Sci Rev ; 48(4): 170-179, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32658043

RESUMO

Physical activity is dynamic, complex, and often regulated idiosyncratically. In this article, we review how techniques used in control systems engineering are being applied to refine physical activity theory and interventions. We hypothesize that person-specific adaptive behavioral interventions grounded in system identification and model predictive control will lead to greater physical activity than more generic, conventional intervention approaches.


Assuntos
Metodologias Computacionais , Exercício Físico/psicologia , Comportamentos Relacionados com a Saúde , Promoção da Saúde/métodos , Terapia Comportamental , Técnicas de Apoio para a Decisão , Humanos
8.
JAMIA Open ; 3(1): 2-8, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32607481

RESUMO

The active involvement of citizen scientists in setting research agendas, partnering with academic investigators to conduct research, analyzing and disseminating results, and implementing learnings from research can improve both processes and outcomes. Adopting a citizen science approach to the practice of precision medicine in clinical care and research will require healthcare providers, researchers, and institutions to address a number of technical, organizational, and citizen scientist collaboration issues. Some changes can be made with relative ease, while others will necessitate cultural shifts, redistribution of power, recommitment to shared goals, and improved communication. This perspective, based on a workshop held at the 2018 AMIA Annual Symposium, identifies current barriers and needed changes to facilitate broad adoption of a citizen science-based approach in healthcare.

9.
Transl Behav Med ; 2020 May 18.
Artigo em Inglês | MEDLINE | ID: mdl-32421196

RESUMO

Precision health initiatives aim to progressively move from traditional, group-level approaches to health diagnostics and treatments toward ones that are individualized, contextualized, and timely. This article aims to provide an overview of key methods and approaches that can help facilitate this transition in the health behavior change domain. This article is a narrative review of the methods used to observe and change complex health behaviors. On the basis of the available literature, we argue that health behavior change researchers should progressively transition from (i) low- to high-resolution behavioral assessments, (ii) group-only to group- and individual-level statistical inference, (iii) narrative theoretical models to dynamic computational models, and (iv) static to adaptive and continuous tuning interventions. Rather than providing an exhaustive and technical presentation of each method and approach, this article articulates why and how researchers interested in health behavior change can apply these innovative methods. Practical examples contributing to these efforts are presented. If successfully adopted and implemented, the four propositions in this article have the potential to greatly improve our public health and behavior change practices in the near future.

10.
Transl Behav Med ; 2020 Apr 22.
Artigo em Inglês | MEDLINE | ID: mdl-32320039

RESUMO

Digital health promises to increase intervention reach and effectiveness for a range of behavioral health outcomes. Behavioral scientists have a unique opportunity to infuse their expertise in all phases of a digital health intervention, from design to implementation. The aim of this study was to assess behavioral scientists' interests and needs with respect to digital health endeavors, as well as gather expert insight into the role of behavioral science in the evolution of digital health. The study used a two-phased approach: (a) a survey of behavioral scientists' current needs and interests with respect to digital health endeavors (n = 346); (b) a series of interviews with digital health stakeholders for their expert insight on the evolution of the health field (n = 15). In terms of current needs and interests, the large majority of surveyed behavioral scientists (77%) already participate in digital health projects, and from those who have not done so yet, the majority (65%) reported intending to do so in the future. In terms of the expected evolution of the digital health field, interviewed stakeholders anticipated a number of changes, from overall landscape changes through evolving models of reimbursement to more significant oversight and regulations. These findings provide a timely insight into behavioral scientists' current needs, barriers, and attitudes toward the use of technology in health care and public health. Results might also highlight the areas where behavioral scientists can leverage their expertise to both enhance digital health's potential to improve health, as well as to prevent the potential unintended consequences that can emerge from scaling the use of technology in health care.

11.
Ann Behav Med ; 54(11): 805-826, 2020 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-32338719

RESUMO

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.

12.
J Behav Med ; 43(2): 254-261, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-31997127

RESUMO

This study examined the between-person associations of seven health behaviors in adults with obesity participating in a weight loss intervention, as well as the covariations between these behaviors within-individuals across the intervention. The present study included data from a 12-month weight loss trial (N = 278). Seven health behaviors (physical activity, sedentary behavior, sleep duration, and consumption of fruits, vegetables, total fat and added sugar) were measured at baseline, 6- and 12-months. Between- and within-participants network analyses were conducted to examine how these behaviors were associated through the 12-month intervention and covaried across months. At the between-participants level, associations were found within the different diet behaviors and between total fat and sedentary behaviors. At the within-participants level, covariations were found between sedentary and diet behaviors, and within diet behaviors. Findings suggest that successful multiple health behaviors change interventions among adults with obesity will need to (1) simultaneously target sedentary and diet behaviors; and (2) prevent potential compensatory behaviors in the diet domain.

13.
Digit Health ; 5: 2055207619872077, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31467683

RESUMO

Objective: This pilot study tested a course-based intervention to help people with multiple sclerosis (MS) match their daily activity to symptom severity ("sweet spot") using wearable activity trackers. Methods: This two-phase study recruited online research network members reporting MS and who were utilizing Fitbit One™ activity trackers. In the first phase, participant interviews assessed demand based on physical activity and the use of behavior-change techniques. The second phase assessed the demand, limited efficacy, acceptability, and practicality of a "Wearables 101" course that integrated behavior change and self-experimentation principles. Tracker data were used to determine the percent of matches between daily symptom-based step goals and step counts. Results: Participants expressed demand in the form of interest in gaining insights about a possible "sweet spot" behavioral target, if a system could be produced to support that. Limited efficacy results were mixed, with approximately one-third of participants dropping out and only half matching their daily target goals for at least 50% of days. In terms of practicality, participants commented on the burden of daily measurement and the need for a longer baseline period. Participants noted that tracking helped support an understanding of the link between activities and symptom severity, suggesting acceptability. Conclusions: Results suggested that the intervention demand and acceptability criteria were demonstrated more strongly than limited efficacy and practicality. The matching intervention tested in this study will require refinement in baseline measurement, goal definition, and reduced data-gathering burden. Such changes may improve efficacy and practicality requirements and, by extension, later impact of the intervention on MS outcomes. Overall, these results provide justification for additional work on refining the intervention to increase practicality and efficacy.

14.
BMC Med ; 17(1): 133, 2019 07 17.
Artigo em Inglês | MEDLINE | ID: mdl-31311528

RESUMO

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.


Assuntos
Interpretação Estatística de Dados , Conjuntos de Dados como Assunto/provisão & distribução , Medicina de Precisão , Comportamento Cooperativo , Ciência de Dados/métodos , Ciência de Dados/tendências , Conjuntos de Dados como Assunto/normas , Conjuntos de Dados como Assunto/estatística & dados numéricos , Assistência à Saúde/métodos , Assistência à Saúde/estatística & dados numéricos , Ensaios de Triagem em Larga Escala/métodos , Ensaios de Triagem em Larga Escala/estatística & dados numéricos , Humanos , Aprendizagem , Medicina de Precisão/métodos , Medicina de Precisão/estatística & dados numéricos , Análise de Pequenas Áreas
15.
J Behav Med ; 42(1): 67-83, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30825090

RESUMO

Since its earliest days, the field of behavioral medicine has leveraged technology to increase the reach and effectiveness of its interventions. Here, we highlight key areas of opportunity and recommend next steps to further advance intervention development, evaluation, and commercialization with a focus on three technologies: mobile applications (apps), social media, and wearable devices. Ultimately, we argue that future of digital health behavioral science research lies in finding ways to advance more robust academic-industry partnerships. These include academics consciously working towards preparing and training the work force of the twenty first century for digital health, actively working towards advancing methods that can balance the needs for efficiency in industry with the desire for rigor and reproducibility in academia, and the need to advance common practices and procedures that support more ethical practices for promoting healthy behavior.


Assuntos
Terapia Comportamental , Medicina do Comportamento/tendências , Aplicativos Móveis/tendências , Dispositivos Eletrônicos Vestíveis/tendências , Humanos , Reprodutibilidade dos Testes , Mídias Sociais
16.
Ann Behav Med ; 53(6): 573-582, 2019 05 03.
Artigo em Inglês | MEDLINE | ID: mdl-30192907

RESUMO

BACKGROUND: HeartSteps is an mHealth intervention that encourages regular walking via activity suggestions tailored to the individuals' current context. PURPOSE: We conducted a micro-randomized trial (MRT) to evaluate the efficacy of HeartSteps' activity suggestions to optimize the intervention. METHODS: We conducted a 6-week MRT with 44 adults. Contextually tailored suggestions could be delivered up to five times per day at user-selected times. At each of these five times, for each participant on each day of the study, HeartSteps randomized whether to provide an activity suggestion, and, if so, whether to provide a walking or an antisedentary suggestion. We used a centered and weighted least squares method to analyze the effect of suggestions on the 30-min step count following suggestion randomization. RESULTS: Averaging over study days and types of activity suggestions, delivering a suggestion versus no suggestion increased the 30-min step count by 14% (p = .06), 35 additional steps over the 253-step average. The effect was not evenly distributed in time. Providing any type of suggestion versus no suggestion initially increased the step count by 66% (167 steps; p < .01), but this effect diminished over time. Averaging over study days, delivering a walking suggestion versus no suggestion increased the average step count by 24% (59 steps; p = .02). This increase was greater at the start of study (107% or 271 additional steps; p < .01), but decreased over time. Antisedentary suggestions had no detectable effect on the 30-min step count. CONCLUSION: Contextually tailored walking suggestions are a promising way of initiating bouts of walking throughout the day. CLINICAL TRIAL INFORMATION: This study was registered on ClinicalTrials.gov number NCT03225521.


Assuntos
Promoção da Saúde/métodos , Avaliação de Processos e Resultados em Cuidados de Saúde , Telemedicina/métodos , Caminhada , Adulto , Feminino , Humanos , Masculino , Pessoa de Meia-Idade
17.
Lancet Digit Health ; 1(7): e344-e352, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-33323209

RESUMO

BACKGROUND: Smartphone apps might enable interventions to increase physical activity, but few randomised trials testing this hypothesis have been done. The MyHeart Counts Cardiovascular Health Study is a longitudinal smartphone-based study with the aim of elucidating the determinants of cardiovascular health. We aimed to investigate the effect of four different physical activity coaching interventions on daily step count in a substudy of the MyHeart Counts Study. METHODS: In this randomised, controlled crossover trial, we recruited adults (aged ≥18 years) in the USA with access to an iPhone smartphone (Apple, Cupertino, CA, USA; version 5S or newer) who had downloaded the MyHeart Counts app (version 2.0). After completion of a 1 week baseline period of interaction with the MyHeart Counts app, participants were randomly assigned to receive one of 24 permutations (four combinations of four 7 day interventions) in a crossover design using a random number generator built into the app. Interventions consisted of either daily prompts to complete 10 000 steps, hourly prompts to stand following 1 h of sitting, instructions to read the guidelines from the American Heart Association website, or e-coaching based upon the individual's personal activity patterns from the baseline week of data collection. Participants completed the trial in a free-living setting. Due to the nature of the interventions, participants could not be masked from the intervention. Investigators were not masked to intervention allocation. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in the modified intention-to-treat analysis set, which included all participants who had completed 7 days of baseline monitoring and at least 1 day of one of the four interventions. This trial is registered with ClinicalTrials.gov, NCT03090321. FINDINGS: Between Dec 12, 2016, and June 6, 2018, 2783 participants consented to enrol in the coaching study, of whom 1075 completed 7 days of baseline monitoring and at least 1 day of one of the four interventions and thus were included in the modified intention-to-treat analysis set. 493 individuals completed the full set of assigned interventions. All four interventions significantly increased mean daily step count from baseline (mean daily step count 2914 [SE 74]): mean step count increased by 319 steps (75) for participants in the American Heart Association website prompt group (p<0·0001), 267 steps (74) for participants in the hourly stand prompt group (p=0·0003), 254 steps (74) for participants in the cluster-specific prompts group (p=0·0006), and by 226 steps (75) for participants in the 10 000 daily step prompt group (p=0·0026 vs baseline). INTERPRETATION: Four smartphone-based physical activity coaching interventions significantly increased daily physical activity. These findings suggests that digital interventions delivered via a mobile app have the ability to increase short-term physical activity levels in a free-living cohort. FUNDING: Stanford Data Science Initiative.


Assuntos
Doenças Cardiovasculares/prevenção & controle , Exercício Físico/fisiologia , Promoção da Saúde , Aplicativos Móveis/estatística & dados numéricos , Adulto , Estudos Cross-Over , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Smartphone , Estados Unidos
19.
J Med Internet Res ; 20(6): e214, 2018 06 28.
Artigo em Inglês | MEDLINE | ID: mdl-29954725

RESUMO

BACKGROUND: Adaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions. OBJECTIVE: The purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions. OVERVIEW: We start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step. IMPLICATIONS: Control engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities.


Assuntos
Terapia Comportamental/métodos , Engenharia Biomédica/métodos , Telemedicina/métodos , Humanos
20.
J Biomed Inform ; 79: 82-97, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-29409750

RESUMO

BACKGROUND: Control systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: (1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and (2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach. METHOD: A personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1-2 was used to inform personalized daily step goals delivered in weeks 3-14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5× baseline median steps/day, and points ranging from 100 to 500 (i.e., $0.20-$1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively. RESULTS: Participants (N = 20, mean age = 47.25 ±â€¯6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ±â€¯6.82 kg/m2) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample. CONCLUSION: The idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions.


Assuntos
Exercício Físico , Comportamentos Relacionados com a Saúde , Monitorização Ambulatorial/instrumentação , Caminhada , Adulto , Idoso , Telefone Celular , Cognição , Feminino , Humanos , Modelos Lineares , Masculino , Pessoa de Meia-Idade , Aplicativos Móveis , Monitorização Ambulatorial/métodos , Motivação , Distribuição Normal , Cooperação do Paciente , Reprodutibilidade dos Testes , Software
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