Improving Uptake of a National Web-Based Psychoeducational Workshop for Informal Caregivers of Veterans: Mixed Methods Implementation Evaluation.

BACKGROUND: Although web-based psychoeducational programs may be an efficient, accessible, and scalable option for improving participant well-being, they seldom are sustained beyond trial publication. Implementation evaluations may help optimize program uptake, but few are performed. When the US Department of Veterans Affairs (VA) launched the web-based psychoeducational workshop Building Better Caregivers (BBC) for informal caregivers of veterans nationwide in 2013, the workshop did not enroll as many caregivers as anticipated. OBJECTIVE: This study aims to identify the strengths and weaknesses of initial implementation, strategies likely to improve workshop uptake, whether the VA adopted these strategies, and whether workshop enrollment changed. METHODS: We used mixed methods and the Promoting Action on Research Implementation in Health Services (PARIHS) implementation evaluation framework. In stage 1, we conducted semistructured interviews with caregivers, local staff, and regional and national VA leaders and surveys with caregivers and staff. We collected and analyzed survey and interview data concurrently and integrated the results to identify implementation strengths and weaknesses, and strategies likely to improve workshop uptake. In stage 2, we reinterviewed national leaders to determine whether the VA adopted recommended strategies and used national data to determine whether workshop enrollment changed over time. RESULTS: A total of 54 caregivers (n=32, 59%), staff (n=13, 24%), and regional (n=5, 9%) and national (n=4, 7%) leaders were interviewed. We received survey responses from 72% (23/32) of caregivers and 77% (10/13) of local staff. In stage 1, survey and interview results were consistent across multiple PARIHS constructs. Although participants from low-enrollment centers reported fewer implementation strengths and more weaknesses, qualitative themes were consistent across high- and low-enrollment centers, and across caregiver, staff, and leadership respondent groups. Identified strengths included belief in a positive workshop impact and the use of some successful outreach approaches. Implementation weaknesses included missed opportunities to improve outreach and to better support local staff. From these, we identified and recommended new and enhanced implementation strategies-increased investment in outreach and marketing capabilities; tailoring outreach strategies to multiple stakeholder groups; use of campaigns that are personal, repeated, and detailed, and have diverse delivery options; recurrent training and mentoring for new staff; and comprehensive data management and reporting capabilities. In stage 2, we determined that the VA had adopted several of these strategies in 2016. In the 3 years before and after adoption, cumulative BBC enrollment increased from 2139 (2013-2015) to 4030 (2016-2018) caregivers. CONCLUSIONS: This study expands the limited implementation science literature on best practices to use when implementing web-based psychoeducational programs. We found that robust outreach and marketing strategies and support for local staff were critical to the implementation success of the BBC workshop. Other health systems may want to deploy these strategies when implementing their web-based programs.

Tailored Daily Activity: An Adaptive Physical Activity Smartphone Intervention.

Background:Nontailored and static goals may hinder behavior change. We investigated the feasibility and acceptability of an adaptive proof-of-concept smartphone-delivered intervention by using real-world movement data capture of physical activity (PA) and sedentary behavior (SB) to inform behavior change content delivery.Materials and Methods:A single-group 8-week study with pre- and post-intervention assessments was conducted in Auckland, New Zealand. Participants aged 17-69 years who owned an Android smartphone were recruited and used the application (app). Usage data, self-reported acceptability and PA and SB were assessed. Daily repeated measurement of PA and SB outcomes were analyzed through random-effects mixed models.Results:Participants (n = 69) were predominantly female (78%) with a mean age of 34.5 years (range 18-61). On average, participants opened the app on 11.4 days throughout the 8 weeks. Use decreased over time; 20% of participants opened the app every day. Feedback on behavior (73%), behavior substitution (71%), discrepancy between behavior and goal (58%) and goal setting (54%) were rated as the most useful behavior change techniques by participants. Time spent on light, moderate-to-vigorous intensity and total PA increased post-intervention, whereas time spent on SB decreased.Conclusions:The adaptive proof-of-concept app was considered acceptable, with preliminary support for its positive effects on PA and SB.

Identifying Frameworks for Validation and Monitoring of Consensual Behavioral Intervention Technologies: Narrative Review.

BACKGROUND: Changing health behaviors, such as smoking, unhealthy eating, inactivity, and alcohol abuse, may have a greater impact on population health than any curative strategy. One of the suggested strategies is the use of behavioral intervention technologies (BITs). They open up new opportunities in the area of prevention and therapy and have begun to show benefits in the durable change of health behaviors in patients or those at risk. A consensual and international paradigm was adopted by health authorities for drugs 50 years ago. It guides their development from research units to their authorization and surveillance. BITs' generalization brings into question their upstream evaluation before being placed on the market and their downstream monitoring once on the market; this is especially the case in view of the marketing information provided by manufacturers and the scarcity and methodological limits of scientific studies on these tools. OBJECTIVE: This study aims to identify and categorize the frameworks for the validation and monitoring of BITs proposed in the literature. METHODS: We conducted a narrative literature review using MEDLINE, PsycINFO, and Web of Science. The review items included the following: name, publication year, name of the creator (ie, first author), country, funding organization, health focus, target group, and design (ie, linear, iterative, evolutive, and/or concurrent). The frameworks were then categorized based on (1) translational research thanks to a continuum of steps and (2) the three paradigms that may have inspired the frameworks: biomedical, engineering, and/or behavioral. RESULTS: We identified 46 frameworks besides the classic US Food and Drug Administration (FDA) five-phase drug development model. A total of 57% (26/46) of frameworks were created in the 2010s and 61% (28/46) involved the final user in an early and systematic way. A total of 4% (2/46) of frameworks had a linear-only sequence of their phases, 37% (17/46) had a linear and iterative structure, 33% (15/46) added an evolutive structure, and 24% (11/46) were associated with a parallel process. Only 12 out of 46 (26%) frameworks covered the continuum of steps and 12 (26%) relied on the three paradigms. CONCLUSIONS: To date, 46 frameworks of BIT validation and surveillance coexist, besides the classic FDA five-phase drug development model, without the predominance of one of them or convergence in a consensual model. Their number has increased exponentially in the last three decades. Three dangerous scenarios are possible: (1) anarchic continuous development of BITs that depend on companies amalgamating health benefits and usability (ie, user experience, data security, and ergonomics) and limiting implementation to several countries; (2) the movement toward the type of framework for drug evaluation centered on establishing its effectiveness before marketing authorization to guarantee its safety for users, which is heavy and costly; and (3) the implementation of a framework reliant on big data analysis based on a posteriori research and an autoregulation of a market, but that does not address the safety risk for the health user, as the market will not regulate safety or efficacy issues. This paper recommends convergence toward an international validation and surveillance framework based on the specificities of BITs, not equivalent to medical devices, to guarantee their effectiveness and safety for users.

Measuring the Implementation of Behavioral Intervention Technologies: Recharacterization of Established Outcomes.

Behavioral intervention technologies (BITs) are websites, software, mobile apps, and sensors designed to help users address or change behaviors, cognitions, and emotional states. BITs have the potential to transform health care delivery, and early research has produced promising findings of efficacy. BITs also favor new models of health care delivery and provide novel data sources for measurement. However, there are few examples of successful BIT implementation and a lack of consensus on as well as inadequate descriptions of BIT implementation measurement. The aim of this viewpoint paper is to provide an overview and characterization of implementation outcomes for the study of BIT use in routine practice settings. Eight outcomes for the evaluation of implementation have been previously described: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability. In a proposed recharacterization of these outcomes with respect to BIT implementation, definitions are clarified, expansions to the level of analysis are identified, and unique measurement characteristics are discussed. Differences between BIT development and implementation, an increased focus on consumer-level outcomes, the expansion of providers who support BIT use, and the blending of BITs with traditional health care services are specifically discussed. BITs have the potential to transform health care delivery. Realizing this potential, however, will hinge on high-quality research that consistently and accurately measures how well such technologies have been integrated into health services. This overview and characterization of implementation outcomes support BIT research by identifying and proposing solutions for key theoretical and practical measurement challenges.

The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions.

A growing number of investigators have commented on the lack of models to inform the design of behavioral intervention technologies (BITs). BITs, which include a subset of mHealth and eHealth interventions, employ a broad range of technologies, such as mobile phones, the Web, and sensors, to support users in changing behaviors and cognitions related to health, mental health, and wellness. We propose a model that conceptually defines BITs, from the clinical aim to the technological delivery framework. The BIT model defines both the conceptual and technological architecture of a BIT. Conceptually, a BIT model should answer the questions why, what, how (conceptual and technical), and when. While BITs generally have a larger treatment goal, such goals generally consist of smaller intervention aims (the "why") such as promotion or reduction of specific behaviors, and behavior change strategies (the conceptual "how"), such as education, goal setting, and monitoring. Behavior change strategies are instantiated with specific intervention components or "elements" (the "what"). The characteristics of intervention elements may be further defined or modified (the technical "how") to meet the needs, capabilities, and preferences of a user. Finally, many BITs require specification of a workflow that defines when an intervention component will be delivered. The BIT model includes a technological framework (BIT-Tech) that can integrate and implement the intervention elements, characteristics, and workflow to deliver the entire BIT to users over time. This implementation may be either predefined or include adaptive systems that can tailor the intervention based on data from the user and the user's environment. The BIT model provides a step towards formalizing the translation of developer aims into intervention components, larger treatments, and methods of delivery in a manner that supports research and communication between investigators on how to design, develop, and deploy BITs.

System design of a text messaging program to support the mental health needs of non-treatment seeking young adults.

Young adults (ages 18-25) experience the highest levels of mental health problems of any adult age group, but have the lowest mental health treatment rates. Text messages are the most used feature on the mobile phone and provide an opportunity to reach non-treatment engaged users throughout the day in a conversational manner. We present the design of an automated text message-based intervention for symptom self-management. The intervention comprises: (1) psychological strategies (i.e., types of evidence-based techniques leveraged to achieve symptom reduction) and (2) interaction types or the form that intervention content takes as it is delivered to and elicited from users.

Impact of Technology-Based Intervention for Improving Self-Management Behaviors in Black Adults with Poor Cardiovascular Health: A Randomized Control Trial.

Cardiovascular disease (CVD) is the number one killer of adults in the U.S., with marked ethnic/racial disparities in prevalence, risk factors, associated health behaviors, and death rates. In this study, we recruited and randomized Blacks with poor cardiovascular health in the Atlanta Metro area to receive an intervention comparing two approaches to engagement with a behavioral intervention technology for CVD. Generalized Linear Mixed Models results from a 6-month intervention indicate that 53% of all participants experienced a statistical improvement in Life's Simple 7 (LS7), 54% in BMI, 61% in blood glucose, and 53% in systolic blood pressure. Females demonstrated a statistically significant improvement in BMI and diastolic blood pressure and a reduction in self-reported physical activity. We found no significant differences in changes in LS7 or their constituent parts but found strong evidence that health coaches can help improve overall LS7 in participants living in at-risk neighborhoods. In terms of clinical significance, our result indicates that improvements in LS7 correspond to a 7% lifetime reduction of incident CVD. Our findings suggest that technology-enabled self-management can be effective for managing selected CVD risk factors among Blacks.

Engaging Parents and Health Care Stakeholders to Inform Development of a Behavioral Intervention Technology to Promote Pediatric Behavioral Health: Mixed Methods Study.

BACKGROUND: Despite effective psychosocial interventions, gaps in access to care persist for youth and families in need. Behavioral intervention technologies (BITs) that apply psychosocial intervention strategies using technological features represent a modality for targeted prevention that is promising for the transformation of primary care behavioral health by empowering parents to take charge of the behavioral health care of their children. To realize the potential of BITs for parents, research is needed to understand the status quo of parental self-help and parent-provider collaboration to address behavioral health challenges and unmet parental needs that could be addressed by BITs. OBJECTIVE: The aim of this study is to conduct foundational research with parents and health care stakeholders (HCS) to discover current practices and unmet needs related to common behavioral health challenges to inform the design, build, and testing of BITs to address these care gaps within a predominantly rural health system. METHODS: We conducted a convergent mixed-parallel study within a large, predominantly rural health system in which the BITs will be developed and implemented. We analyzed data from parent surveys (N=385) on current practices and preferences related to behavioral health topics to be addressed in BITs along with focus group data of 48 HCS in 9 clinics regarding internal and external contextual factors contributing to unmet parental needs and current practices. By comparing and relating the findings, we formed interpretations that will inform subsequent BIT development activities. RESULTS: Parents frequently endorsed several behavioral health topics, and several topics were relatively more or less frequently endorsed based on the child's age. The HCS suggested that BITs may connect families with evidence-based guidance sooner and indicated that a web-based platform aligns with how parents already seek behavioral health guidance. Areas of divergence between parents and HCS were related to internalizing problems and cross-cutting issues such as parenting stress, which may be more difficult for health care HCS to detect or address because of the time constraints of routine medical visits. CONCLUSIONS: These findings provide a rich understanding of the complexity involved in meeting parents' needs for behavioral health guidance in a primary care setting using BITs. User testing studies for BIT prototypes are needed to successfully design, build, and test effective BITs to empower parents to take charge of promoting the behavioral health of their children.

The Supportive Accountability Inventory: Psychometric properties of a measure of supportive accountability in coached digital interventions.

BACKGROUND: One of the most widely used coaching models is Supportive Accountability (SA) which aims to provide intervention users with clear expectations for intervention use, regular monitoring, and a sense that coaches are trustworthy, benevolent, and have domain expertise. However, few measures exist to study the role of the SA model on coached digital interventions. We developed the Supportive Accountability Inventory (SAI) and evaluated the underlying factor structure and psychometric properties of this brief self-report measure. METHOD: Using data from a two-arm randomized trial of a remote intervention for major depressive disorder (telephone CBT [tCBT] or a stepped care model of web-based CBT [iCBT] and tCBT), we conducted an Exploratory Factor Analysis on the SAI item pool and explored the final SAI's relationship to iCBT engagement as well as to depression outcomes. Participants in our analyses (n = 52) included those randomized to a receive iCBT, but were not stepped up to tCBT due to insufficient response to iCBT, had not remitted prior to the 10-week assessment point, and completed the pool of 8 potential SAI items. RESULTS: The best fitting EFA model included only 6 items from the original pool of 8 and contained two factors: Monitoring and Expectation. Final model fit was mixed, but acceptable (χ 2 (4) = 5.24, p = 0.26; RMSR = 0.03; RMSEA = 0.091; TLI = 0.967). Internal consistency was acceptable at α = 0.68. The SAI demonstrated good convergent and divergent validity. The SAI at the 10-week/mid-treatment mark was significantly associated with the number of days of iCBT use (r = 0.29, p = .037), but, contrary to expectations, was not predictive of either PHQ-9 scores (F(2,46) = 0.14, p = .89) or QIDS-C scores (F(2,46) = 0.84, p = .44) at post-treatment. CONCLUSION: The SAI is a brief measure of the SA framework constructs. Continued development to improve the SAI and expand the constructs it assesses is necessary, but the SAI represents the first step towards a measure of a coaching protocol that can support both coached digital mental health intervention adherence and improved outcomes.

Developing a mHealth Routine Outcome Monitoring and Feedback App ("SMART Track") to Support Self-Management of Addictive Behaviours.

Background: Routine outcome monitoring (ROM) has been implemented across a range of addiction treatment services, settings and organisations. Mutual support groups are a notable exception. Innovative solutions are needed. SMART Track is a purpose built smartphone app designed to capture ROM data and provide tailored feedback to adults attending Australian SMART Recovery groups for addictive behaviour(s). Objective: Details regarding the formative stage of app development is essential, but often neglected. Improved consideration of the end-user is vital for curtailing app attrition and enhancing engagement. This paper provides a pragmatic example of how principles embedded in published frameworks can be operationalised to address these priorities during the design and development of the SMART Track app. Methods: Three published frameworks for creating digital health technologies ("Person-Based Approach," "BIT" Model and IDEAS framework) were integrated and applied across two stages of research to inform the development, design and content of SMART Track. These frameworks were chosen to ensure that SMART Track was informed by the needs and preferences of the end-user ("Person-Based"); best practise recommendations for mHealth development ("BIT" Model) and a collaborative, iterative development process between the multi-disciplinary research team, app developers and end-users (IDEAS framework). Results: Stage one of the research process generated in-depth knowledge to inform app development, including a comprehensive set of aims (clinical, research/organisation, and usage); clear articulation of the target behaviour (self-monitoring of recovery related behaviours and experiences); relevant theory (self-determination and social control); appropriate behavioural strategies (e.g., behaviour change taxonomy and process motivators) and key factors that may influence engagement (e.g., transparency, relevance and trust). These findings were synthesised into guiding principles that were applied during stage two in an iterative approach to app design, content and development. Conclusions: This paper contributes new knowledge on important person-centred and theoretical considerations that underpin a novel ROM and feedback app for people with addictive behaviour(s). Although person-centred design and best-practise recommendations were employed, further research is needed to determine whether this leads to improved usage outcomes. Clinical Trial Registration: Pilot Trial: http://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377336.

A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Protocol Development for an Expert System to Provide Adaptive User Feedback.

BACKGROUND: Bipolar disorder is a severe mental illness that results in significant morbidity and mortality. While pharmacotherapy is the primary treatment, adjunctive psychotherapy can improve outcomes. However, access to therapy is limited. Smartphones and other technologies can increase access to therapeutic strategies that enhance self-management while simultaneously augmenting care by providing adaptive delivery of content to users as well as alerts to providers to facilitate clinical care communication. Unfortunately, while adaptive interventions are being developed and tested to improve care, information describing the components of adaptive interventions is often not published in sufficient detail to facilitate replication and improvement of these interventions. OBJECTIVE: To contribute to and support the improvement and dissemination of technology-based mental health interventions, we provide a detailed description of the expert system for adaptively delivering content and facilitating clinical care communication for LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder. METHODS: Information from empirically supported psychotherapies for bipolar disorder, health psychology behavior change theories, and chronic disease self-management models was combined with user-centered design data and psychiatrist feedback to guide the development of the expert system. RESULTS: Decision points determining the timing of intervention option adaptation were selected to occur daily and weekly based on self-report data for medication adherence, sleep duration, routine, and wellness levels. These data were selected for use as the tailoring variables determining which intervention options to deliver when and to whom. Decision rules linking delivery of options and tailoring variable thresholds were developed based on existing literature regarding bipolar disorder clinical status and psychiatrist feedback. To address the need for treatment adaptation with varying clinical statuses, decision rules for a clinical status state machine were developed using self-reported wellness rating data. Clinical status from this state machine was incorporated into hierarchal decision tables that select content for delivery to users and alerts to providers. The majority of the adaptive content addresses sleep duration, medication adherence, managing signs and symptoms, building and utilizing support, and keeping a regular routine, as well as determinants underlying engagement in these target behaviors as follows: attitudes and perceptions, knowledge, support, evaluation, and planning. However, when problems with early warning signs, symptoms, and transitions to more acute clinical states are detected, the decision rules shift the adaptive content to focus on managing signs and symptoms, and engaging with psychiatric providers. CONCLUSIONS: Adaptive mental health technologies have the potential to enhance the self-management of mental health disorders. The need for individuals with bipolar disorder to engage in the management of multiple target behaviors and to address changes in clinical status highlights the importance of detailed reporting of adaptive intervention components to allow replication and improvement of adaptive mental health technologies for complex mental health problems.

A Smartphone-Based Self-management Intervention for Bipolar Disorder (LiveWell): User-Centered Development Approach.

BACKGROUND: Bipolar disorder is a serious mental illness that results in significant morbidity and mortality. Pharmacotherapy is the primary treatment for bipolar disorder; however, adjunctive psychotherapy can help individuals use self-management strategies to improve outcomes. Yet access to this therapy is limited. Smartphones and other technologies have the potential to increase access to therapeutic strategies that enhance self-management while simultaneously providing real-time user feedback and provider alerts to augment care. OBJECTIVE: This paper describes the user-centered development of LiveWell, a smartphone-based self-management intervention for bipolar disorder, to contribute to and support the ongoing improvement and dissemination of technology-based mental health interventions. METHODS: Individuals with bipolar disorder first participated in a field trial of a simple smartphone app for self-monitoring of behavioral targets. To develop a complete technology-based intervention for bipolar disorder, this field trial was followed by design sessions, usability testing, and a pilot study of a smartphone-based self-management intervention for bipolar disorder. Throughout all phases of development, intervention revisions were made based on user feedback. RESULTS: The core of the LiveWell intervention consists of a daily self-monitoring tool, the Daily Check-in. This self-monitoring tool underwent multiple revisions during the user-centered development process. Daily Check-in mood and thought rating scales were collapsed into a single wellness rating scale to accommodate user development of personalized scale anchors. These anchors are meant to assist users in identifying early warning signs and symptoms of impending episodes to take action based on personalized plans. When users identified personal anchors for the wellness scale, the anchors most commonly reflected behavioral signs and symptoms (40%), followed by cognitive (25%), mood (15%), physical (10%), and motivational (7%) signs and symptoms. Changes to the Daily Check-in were also made to help users distinguish between getting adequate sleep and keeping a regular routine. At the end of the pilot study, users reported that the Daily Check-in made them more aware of early warning signs and symptoms and how much they were sleeping. Users also reported that they liked personalizing their anchors and plans and felt this process was useful. Users experienced some difficulties with developing, tracking, and achieving target goals. Users also did not consistently follow up with app recommendations to contact providers when Daily Check-in data suggested they needed additional assistance. As a result, the human support roles for the technology were expanded beyond app use support to include support for self-management and clinical care communication. The development of these human support roles was aided by feedback on the technology's usability from the users and the coaches who provided the human support. CONCLUSIONS: User input guided the development of intervention content, technology, and coaching support for LiveWell. Users valued the provision of monitoring tools and the ability to personalize plans for staying well, supporting the role of monitoring and personalization as important features of digital mental health technologies. Users also valued human support of the technology in the form of a coach, and user difficulties with aspects of self-management and care-provider communication led to an expansion of the coach's support roles. Obtaining feedback from both users and coaches played an important role in the development of both the LiveWell technology and human support. Attention to all stakeholders involved in the use of mental health technologies is essential for optimizing intervention development.

A Smartphone-Based Self-Management Intervention for Individuals with Bipolar Disorder (LiveWell): Qualitative Study on User Experiences of the Behavior Change Process.

BACKGROUND: Bipolar disorder is a severe mental illness characterized by recurrent episodes of depressed, elevated, and mixed mood states. The addition of psychotherapy to pharmacological management can decrease symptoms, lower relapse rates, and improve quality of life; however, access to psychotherapy is limited. Mental health technologies such as smartphone apps are being studied as a means to increase access to and enhance the effectiveness of adjunctive psychotherapies for bipolar disorder. Individuals with bipolar disorder find this intervention format acceptable, but our understanding of how people utilize and integrate these tools into their behavior change and maintenance processes remains limited. OBJECTIVE: The objective of this study was to explore how individuals with bipolar disorder perceive and utilize a smartphone intervention for health behavior change and maintenance. METHODS: Individuals with bipolar disorder were recruited via flyers placed at university-affiliated and private outpatient mental health practices to participate in a pilot study of LiveWell, a smartphone-based self-management intervention. At the end of the study, all participants completed in-depth qualitative exit interviews. The behavior change framework developed to organize the intervention design was used to deductively code behavioral targets and determinants involved in target engagement. Inductive coding was used to identify themes not captured by this framework. RESULTS: In terms of behavioral targets, participants emphasized the importance of managing mood episode-related signs and symptoms. They also discussed the importance of maintaining regular routines, sleep duration, and medication adherence. Participants emphasized that receiving support from a coach as well as seeking and receiving assistance from family, friends, and providers were important for managing behavioral targets and staying well. In terms of determinants, participants stressed the important role of monitoring for their behavior change and maintenance efforts. Monitoring facilitated self-awareness and reflection, which was considered valuable for staying well. Some participants also felt that the intervention facilitated learning information necessary for managing bipolar disorder but others felt that the information provided was too basic. CONCLUSIONS: In addition to addressing acceptability, satisfaction, and engagement, a person-based design of mental health technologies can be used to understand how people experience the impact of these technologies on their behavior change and maintenance efforts. This understanding may then be used to guide ongoing intervention development. The participants' perceptions aligned with the intervention's primary behavioral targets and use of a monitoring tool as a core intervention feature. Participant feedback further indicates that developing additional content and tools to address building and engaging social support may be an important avenue for improving LiveWell. A comprehensive behavior change framework to understand participant perceptions of their behavior change and maintenance efforts may help facilitate ongoing intervention development.

User Retention and Engagement With a Mobile App Intervention to Support Self-Management in Australians With Type 1 or Type 2 Diabetes (My Care Hub): Mixed Methods Study.

BACKGROUND: Mobile health apps are commonly used to support diabetes self-management (DSM). However, there is limited research assessing whether such apps are able to meet the basic requirements of retaining and engaging users. OBJECTIVE: This study aimed to evaluate participants' retention and engagement with My Care Hub, a mobile app for DSM. METHODS: The study employed an explanatory mixed methods design. Participants were people with type 1 or type 2 diabetes who used the health app intervention for 3 weeks. Retention was measured by completion of the postintervention survey. Engagement was measured using system log indices and interviews. Retention and system log indices were presented using descriptive statistics. Transcripts were analyzed using content analysis to develop themes interpreted according to the behavioral intervention technology theory. RESULTS: Of the 50 individuals enrolled, 42 (84%) adhered to the study protocol. System usage data showed multiple and frequent interactions with the app by most of the enrolled participants (42/50, 84%). Two-thirds of participants who inputted data during the first week returned to use the app after week 1 (36/42, 85%) and week 2 (30/42, 71%) of installation. Most daily used features were tracking of blood glucose (BG; 28/42, 68%) and accessing educational information (6/42, 13%). The interview results revealed the app's potential as a behavior change intervention tool, particularly because it eased participants' self-care efforts and improved their engagement with DSM activities such as BG monitoring, physical exercise, and healthy eating. Participants suggested additional functionalities such as extended access to historical analytic data, automated data transmission from the BG meter, and periodic update of meals and corresponding nutrients to further enhance engagement with the app. CONCLUSIONS: The findings of this short-term intervention study suggested acceptable levels of participant retention and engagement with My Care Hub, indicating that it may be a promising tool for extending DSM support and education beyond the confines of a physical clinic.

Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change.

Few interventions to promote physical activity (PA) adapt dynamically to changes in individuals' behavior. Interventions targeting determinants of behavior are linked with increased effectiveness and should reflect changes in behavior over time. This article describes the application of two frameworks to assist the development of an adaptive evidence-based smartphone-delivered intervention aimed at influencing PA and sedentary behaviors (SB). Intervention mapping was used to identify the determinants influencing uptake of PA and optimal behavior change techniques (BCTs). Behavioral intervention technology was used to translate and operationalize the BCTs and its modes of delivery. The intervention was based on the integrated behavior change model, focused on nine determinants, consisted of 33 BCTs, and included three main components: (1) automated capture of daily PA and SB via an existing smartphone application, (2) classification of the individual into an activity profile according to their PA and SB, and (3) behavior change content delivery in a dynamic fashion via a proof-of-concept application. This article illustrates how two complementary frameworks can be used to guide the development of a mobile health behavior change program. This approach can guide the development of future mHealth programs.

Implementing Internet-Based Self-Care Programs in Primary Care: Qualitative Analysis of Determinants of Practice for Patients and Providers.

BACKGROUND: Access to evidence-based interventions for common mental health conditions is limited due to geographic distance, scheduling, stigma, and provider availability. Internet-based self-care programs may mitigate these barriers. However, little is known about internet-based self-care program implementation in US health care systems. OBJECTIVE: The objective of this study was to identify determinants of practice for internet-based self-care program use in primary care by eliciting provider and administrator perspectives on internet-based self-care program implementation. METHODS: The objective was explored through qualitative analysis of semistructured interviews with primary care providers and administrators from the Veterans Health Administration. Participants were identified using a reputation-based snowball design. Interviews focused on identifying determinants of practice for the use of internet-based self-care programs at the point of care in Veterans Health Administration primary care. Qualitative analysis of transcripts was performed using thematic coding. RESULTS: A total of 20 physicians, psychologists, social workers, and nurses participated in interviews. Among this group, internet-based self-care program use was relatively low, but support for the platform was assessed as relatively high. Themes were organized into determinants active at patient and provider levels. Perceived patient-level determinants included literacy, age, internet access, patient expectations, internet-based self-care program fit with patient experiences, interest and motivation, and face-to-face human contact. Perceived provider-level determinants included familiarity with internet-based self-care programs, changes to traditional care delivery, face-to-face human contact, competing demands, and age. CONCLUSIONS: This exploration of perspectives on internet-based self-care program implementation among Veterans Health Administration providers and administrators revealed key determinants of practice, which can be used to develop comprehensive strategies for the implementation of internet-based self-care programs in primary care settings.

Smartphone-Based Psychotherapeutic Micro-Interventions to Improve Mood in a Real-World Setting.

BACKGROUND: Using mobile communication technology as new personalized approach to treat mental disorders or to more generally improve quality of life is highly promising. Knowledge about intervention components that target key psychopathological processes in terms of transdiagnostic psychotherapy approaches is urgently needed. We explored the use of smartphone-based micro-interventions based on psychotherapeutic techniques, guided by short video-clips, to elicit mood changes. METHOD: As part of a larger neurofeedback study, all subjects-after being randomly assigned to an experimental or control neurofeedback condition-underwent daily smartphone-based micro-interventions for 13 consecutive days. They were free to choose out of provided techniques, including viscerosensory attention, emotional imagery, facial expression, and contemplative repetition. Changes in mood were assessed in real world using the Multidimensional Mood State Questionnaire (scales: good-bad, GB; awake-tired, AT; and calm-nervous, CN). RESULTS: Twenty-seven men participated on at least 11 days and were thus included in the analyses. Altogether, they underwent 335, generally well-tolerated, micro-intervention sessions, with viscerosensory attention (178 sessions, 53.13%) and contemplative repetition (68 sessions, 20.30%) being the most frequently applied techniques. Mixed models indicated that subjects showed better mood [GB: b = 0.464, 95%confidence interval (CI) [0.068, 0.860], t (613.3) = 2.298, p = 0.022] and became more awake [AT: b = 0.514, 95%CI [0.103, 0.925], t (612.4) = 2.456, p = 0.014] and calmer [CN: b = 0.685, 95%CI [0.360, 1.010], t (612.3) = 4.137, p < 0.001] from pre- to post-micro-intervention. These mood improvements from pre- to post-micro-intervention were associated with changes in mood from the 1st day until the last day with regard to GB mood (r = 0.614, 95%CI [0.297, 0.809], p < 0.001), but not AT mood (r = 0.279, 95%CI [-0.122, 0.602], p = 0.167) and CN mood (r = 0.277, 95%CI [0.124, 0.601], p = 0.170). DISCUSSION: Our findings provide evidence for the applicability of smartphone-based micro-interventions eliciting short-term mood changes, based on techniques used in psychotherapeutic approaches, such as mindfulness-based psychotherapy, transcendental meditation, and other contemplative therapies. The results encourage exploring these techniques' capability to improve mood in randomized controlled studies and patients. Smartphone-based micro-interventions are promising to modify mood in real-world settings, complementing other psychotherapeutic interventions, in line with the precision medicine approach. The here presented data were collected within a randomized trial, registered at ClinicalTrials.gov (Identifier: NCT01921088) https://clinicaltrials.gov/ct2/show/NCT01921088.

How are previous physical activity and self-efficacy related to future physical activity and self-efficacy?

Self-efficacy (SE) has been found to be a robust predictor of success in achieving physical activity (PA) goals. While much of the current research has focused on SE as a trait, SE as a state has received less attention. Using day-to-day measurements obtained over 84 days, we examined the relationship between state SE and PA. Postmenopausal women (n = 71) participated in a 12-week PA intervention administered via cell phone and monitored their daily PA using a pedometer. At the end of each day, they reported their state SE and number of steps. Using a longitudinal model, state SE was found to be a robust predictor of PA even after accounting for trait SE and other covariates. The findings offer insights about the temporal relationship between SE and PA over the course of an intervention, which can be of interest to researchers and intervention designers.