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

Considerations for Improved Mobile Health Evaluation: Retrospective Qualitative Investigation

BACKGROUND

Mobile phone use and, consequently, mobile health (mHealth) interventions have seen an exponential increase in the last decade. There is an excess of 318,000 health-related apps available free of cost for consumers to download. However, many of these interventions are not evaluated and are lacking appropriate regulations. Randomized controlled trials are often considered the gold standard study design in determining the effectiveness of interventions, but recent literature has identified limitations in the methodology when used to evaluate mHealth.

OBJECTIVE

The objective of this study was to investigate the system developers' experiences of evaluating mHealth interventions in the context of a developing country.

METHODS

We employed a qualitative exploratory approach, conducting semistructured interviews with multidisciplinary members of an mHealth project consortium. A conventional content analysis approach was used to allow codes and themes to be identified directly from the data.

RESULTS

The findings from this study identified the system developers' perceptions of mHealth evaluation, providing an insight into the requirements of an effective mHealth evaluation. This study identified social and technical factors which should be taken into account when evaluating an mHealth intervention.

CONCLUSIONS

Contextual issues represented one of the most recurrent challenges of mHealth evaluation in the context of a developing country, highlighting the importance of a mixed method evaluation. There is a myriad of social, technical, and regulatory variables, which may impact the effectiveness of an mHealth intervention. Failure to account for these variables in an evaluation may limit the ability of the intervention to achieve long-term implementation and scale.

Longer-term effectiveness of systemic family therapy compared with treatment as usual for young people after self-harm: An extended follow up of pragmatic randomised controlled trial

BACKGROUND

Self-harm in adolescents is common and repetition frequent. Evidence for effective interventions to reduce self-harm is limited. Long term follow-up of existing studies is rare.

METHODS

Extended follow up, from 18 to at least 36-months, of the SHIFT trial: a pragmatic, multi-centre, individually-randomised, controlled trial involving young people (11-17) who had self-harmed at least twice and presented to Child & Adolescent Mental Health Services (CAMHS). SHIFT evaluated manualised family therapy (FT) versus treatment as usual (TAU) in reducing repetition of self-harm leading to hospital attendance 18 months post-randomisation.We obtained ONS mortality data, adult mental health data, and further details of hospital attendance from routine Hospital Episode Statistics (HES) data plus researcher follow-up. We assessed longer-term differences in outcome using multivariable Cox Proportional Hazards regression analysis, and assessed all-cause mortality and morbidity relating to hospital attendances for reasons other than self-harm.

STUDY REGISTRATION

ISRCTN 59793150.

OUTCOMES

The original sample of 832 were randomised between April 2010 and December 2013. Extended follow-up continued until February 2017 for a median 55·4 months (range 0-82·5 months), providing post 18-month data for 804 (96·6%) participants, of whom 785 (94·4%) had a minimum of 36-months follow-up.There was no evidence of a between-group difference in the primary outcome during the extended follow-up period (Hazard Ratio (HR) 1·03; 95% CI: 0·83, 1·28; p-value=0·78), consistent with our findings in the original trial with 18 months follow-up (HR 1·14, 95% CI 0·87, 1·49; p-value 0·33). There was a reduced rate of self-harm in older participants aged 15-17 (HR 0·7, 95% CI 0·56, 0·88), as compared with those aged 11-14; and significantly increased rates of self-harm in participants whose index episode combined self-injury and poisoning (HR 1·8, 95% CI 1·2, 2·7). Two deaths were reported during the extended follow up period.

INTERPRETATION

For adolescents referred to CAMHS after self-harm, having self-harmed at least once before, trial FT confers no benefits over TAU in reducing subsequent hospitalisation for self-harm over 18 months or 36 months.

FUNDING

NIHR HTA Reference: 07/33/01.

An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing

Background

Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep.

Purpose

Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices.

Methods

We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2-5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20-100 Hz), and human data (N = 60) from an ActiGraph GT9X.

Results

During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors.

Conclusions

Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.

A design and evaluation framework for digital health interventions

Digital health interventions (DHIs) have the potential to help the growing number of chronic disease patients better manage their everyday lives. However, guidelines for the systematic development of DHIs are still scarce. The current work has, therefore, the objective to propose a framework for the design and evaluation of DHIs (DEDHI). The DEDHI framework is meant to support both researchers and practitioners alike from early conceptual DHI models to large-scale implementations of DHIs in the healthcare market.

Is there a right control condition in mHealth trials? A critical view on pain medicine

When mobile health (mHealth) applications (apps) are investigated, the question of the proper control condition arises. Normally, the randomized controlled trial (RCT) is seen as the gold standard when testing efficacy of clinical interventions. Yet, mHealth apps rarely comprise innovative treatments but rather provide established treatments digitally. The classical RCT utilizing a placebo or waiting group condition may not always be the suitable methodology, since non-treatment is not appropriate if a disease urges treatment and the development of chronic disease needs to be prevented. The present commentary discusses conceivable control conditions in mHealth trials and illustrates their limitations.

Engaging Adolescents With Sexual Health Messaging: A Qualitative Analysis

PURPOSE

Personalized and interactive text messaging interventions may increase participant engagement; yet, how to design messages that retain adolescent attention and positively affect sexual health behaviors remains unclear. The purpose of this study was to identify the characteristics of sexual health text messages perceived as engaging by sexually active adolescent females.

METHODS

We conducted semistructured, open-ended interviews with sexually active females aged 14-19 in one urban emergency department. Participants received automated sexual health information sent via an interactive, two-way texting format. The 343 messages viewed by participants were based on key stakeholder input, relevant theoretical models, and existing evidence-based guidelines. Interviews elicited feedback. Enrollment continued until saturation of themes. Interviews were recorded, transcribed, and coded based on thematic analysis using NVivo 10.

RESULTS

Participants (n = 31) were predominantly Hispanic (28; 90%), insured (29; 94%), and recently sexually active (24; 77%). Themes were as follows: (1) Tone: messages should be direct, factual, entertaining, and respect adolescent autonomy; messages should not be intrusive, presumptive, or preachy. (2) Emotion evoked: participants preferred messages that provoked thought, validated feelings, and empowered. Messages from a reliable source felt comforting, making participants feel cared for and special. (3) Interactivity: participants favored messages that offered choices, such as a mini-conversation. (4) Personalization: messages should look similar to adolescent digital preferences but be individually tailored with relatable characters.

CONCLUSIONS

This study informs the tone, structure, and style of sexual health text messages directed toward adolescent females in the emergency department. Future work should consider these characteristics when designing digital interventions to engage adolescent females.

Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study

Low levels of physical activity (PA) and high levels of sedentary behavior in individuals with spinal cord injury (SCI) have been associated with secondary conditions such as pain, fatigue, weight gain, and deconditioning. One strategy for promoting regular PA is to provide people with an accurate estimate of everyday PA level. The objective of this research was to use a mobile health-based PA measurement system to track PA levels of individuals with SCI in the community and provide them with a behavior-sensitive, just-in-time-adaptive intervention (JITAI) to improve their PA levels. The first, second, and third phases of the study, each with a duration of one month, involved collecting baseline PA levels, providing near-real-time feedback on PA level (PA Feedback), and providing PA Feedback with JITAI, respectively. PA levels in terms of energy expenditure in kilocalories, and minutes of light- and moderate- or vigorous-intensity PA were assessed by an activity monitor during the study. Twenty participants with SCI took part in this research study with a mean (SD) age of 39.4 (12.8) years and 12.4 (12.5) years since injury. Sixteen participants completed the study. Sixteen were male, 16 had paraplegia, and 12 had complete injury. Within-participant comparisons indicated that only two participants had higher energy expenditure (>10%) or lower energy expenditure (<-10%) during PA Feedback with JITAI compared to the baseline. However, eleven participants (69.0%) had higher light- and/or moderate-intensity PA during PA Feedback with JITAI compared to the baseline. To our knowledge, this is the first study to test a PA JITAI for individuals with SCI that responds automatically to monitored PA levels. The results of this pilot study suggest that a sensor-enabled mobile JITAI has potential to improve PA levels of individuals with SCI. Future research should investigate the efficacy of JITAI through a clinical trial.

Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss

This randomized trial demonstrated qualified support for the ability of a machine learning-powered, smartphone-based just-in-time, adaptive intervention to enhance weight loss over and above a commercial weight loss program.

Role of appetitive phenotype trajectory groups on child body weight during a family-based treatment for children with overweight or obesity

OBJECTIVE

Emerging evidence suggests that individual appetitive traits may usefully explain patterns of weight loss in behavioral weight loss treatments for children. The objective of this study was to identify trajectories of child appetitive traits and the impact on child weight changes over time.

METHODS

Secondary data analyses of a randomized noninferiority trial conducted between 2011 and 2015 evaluated children's appetitive traits and weight loss. Children with overweight and obesity (mean age = 10.4; mean BMI z = 2.0; 67% girls; 32% Hispanic) and their parent (mean age = 42.9; mean BMI = 31.9; 87% women; 31% Hispanic) participated in weight loss programs and completed assessments at baseline, 3, 6,12, and 24 months. Repeated assessments of child appetitive traits, including satiety responsiveness, food responsiveness and emotional eating, were used to identify parsimonious grouping of change trajectories. Linear mixed-effects models were used to identify the impact of group trajectory on child BMIz change over time.

RESULTS

One hundred fifty children and their parent enrolled in the study. The three-group trajectory model was the most parsimonious and included a high satiety responsive group (HighSR; 47.4%), a high food responsive group (HighFR; 34.6%), and a high emotional eating group (HighEE; 18.0%). Children in all trajectories lost weight at approximately the same rate during treatment, however, only the HighSR group maintained their weight loss during follow-ups, while the HighFR and HighEE groups regained weight (adjusted p-value < 0.05).

CONCLUSIONS

Distinct trajectories of child appetitive traits were associated with differential weight loss maintenance. Identified high-risk subgroups may suggest opportunities for targeted intervention and maintenance programs.

Moving beyond self-report data collection in the natural environment: A review of the past and future directions for ambulatory assessment in eating disorders

OBJECTIVE

In recent years, ecological momentary assessment (EMA) has been used to repeatedly assess eating disorder (ED) symptoms in naturalistic settings, which has allowed for increased understanding of temporal processes that potentiate ED behaviors. However, there remain notable limitations of self-report EMA, and with the rapid proliferation of technology there are ever-increasing possibilities to improve ambulatory assessment methods to further the understanding and treatment of EDs. Therefore, the purpose of this review was to (a) systematically review the studies in EDs that have utilized ambulatory assessment methods other than self-report, and (b) provide directions for future research and clinical applications.

METHOD

A systematic literature search of electronic databases was conducted, and data regarding study characteristics and methodological quality were extracted.

RESULTS

The search identified 17 studies that used ambulatory assessment methods to gather objective data, and focused primarily on autonomic functioning, physical activity, and cognitive processes in ED and control groups.

DISCUSSION

Together the literature demonstrates the promise of using a range of ecologically valid ambulatory assessment approaches in EDs, though there remains limited research that has utilized methods other than self-report (e.g., wearable sensors), particularly in recent years. Going forward, there are several technology-enhanced momentary assessment methods that have potential to improve the understanding and treatment of EDs.

The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials

Although impressive progress has been made toward developing empirically-supported psychological treatments, the reality remains that a significant proportion of people with mental health problems do not receive these treatments. Finding ways to reduce this treatment gap is crucial. Since app-supported smartphone interventions are touted as a possible solution, access to up-to-date guidance around the evidence base and clinical utility of these interventions is needed. We conducted a meta-analysis of 66 randomized controlled trials of app-supported smartphone interventions for mental health problems. Smartphone interventions significantly outperformed control conditions in improving depressive (g=0.28, n=54) and generalized anxiety (g=0.30, n=39) symptoms, stress levels (g=0.35, n=27), quality of life (g=0.35, n=43), general psychiatric distress (g=0.40, n=12), social anxiety symptoms (g=0.58, n=6), and positive affect (g=0.44, n=6), with most effects being robust even after adjusting for various possible biasing factors (type of control condition, risk of bias rating). Smartphone interventions conferred no significant benefit over control conditions on panic symptoms (g=-0.05, n=3), post-traumatic stress symptoms (g=0.18, n=4), and negative affect (g=-0.08, n=5). Studies that delivered a cognitive behavior therapy (CBT)-based app and offered professional guidance and reminders to engage produced larger effects on multiple outcomes. Smartphone interventions did not differ significantly from active interventions (face-to-face, computerized treatment), although the number of studies was low (n≤13). The efficacy of app-supported smartphone interventions for common mental health problems was thus confirmed. Although mental health apps are not intended to replace professional clinical services, the present findings highlight the potential of apps to serve as a cost-effective, easily accessible, and low intensity intervention for those who cannot receive standard psychological treatment.

Digital Health Apps in the Clinical Care of Inflammatory Bowel Disease: Scoping Review

Background

Digital health is poised to transform health care and redefine personalized health. As Internet and mobile phone usage increases, as technology develops new ways to collect data, and as clinical guidelines change, all areas of medicine face new challenges and opportunities. Inflammatory bowel disease (IBD) is one of many chronic diseases that may benefit from these advances in digital health. This review intends to lay a foundation for clinicians and technologists to understand future directions and opportunities together.

Objective

This review covers mobile health apps that have been used in IBD, how they have fit into a clinical care framework, and the challenges that clinicians and technologists face in approaching future opportunities.

Methods

We searched PubMed, Scopus, and ClinicalTrials.gov to identify mobile apps that have been studied and were published in the literature from January 1, 2010, to April 19, 2019. The search terms were (“mobile health” OR “eHealth” OR “digital health” OR “smart phone” OR “mobile app” OR “mobile applications” OR “mHealth” OR “smartphones”) AND (“IBD” OR “Inflammatory bowel disease” OR “Crohn's Disease” (CD) OR “Ulcerative Colitis” (UC) OR “UC” OR “CD”), followed by further analysis of citations from the results. We searched the Apple iTunes app store to identify a limited selection of commercial apps to include for discussion.

Results

A total of 68 articles met the inclusion criteria. A total of 11 digital health apps were identified in the literature and 4 commercial apps were selected to be described in this review. While most apps have some educational component, the majority of apps focus on eliciting patient-reported outcomes related to disease activity, and a few are for treatment management. Significant benefits have been seen in trials relating to education, quality of life, quality of care, treatment adherence, and medication management. No studies have reported a negative impact on any of the above. There are mixed results in terms of effects on office visits and follow-up.

Conclusions

While studies have shown that digital health can fit into, complement, and improve the standard clinical care of patients with IBD, there is a need for further validation and improvement, from both a clinical and patient perspective. Exploring new research methods, like microrandomized trials, may allow for more implementation of technology and rapid advancement of knowledge. New technologies that can objectively and seamlessly capture remote data, as well as complement the clinical shift from symptom-based to inflammation-based care, will help the clinical and health technology communities to understand the full potential of digital health in the care of IBD and other chronic illnesses.

Potential benefits of integrating ecological momentary assessment data into mHealth care systems

The advancement of wearable/ambulatory technologies has brought a huge change to data collection frameworks in recent decades. Mobile health (mHealth) care platforms, which utilize ambulatory devices to collect naturalistic and often intensively sampled data, produce innovative information of potential clinical relevance. For example, such data can inform clinical study design, recruitment approach, data analysis, and delivery of both "traditional" and novel (e.g., mHealth) interventions. We provide a conceptual overview of how data measured continuously or repeatedly via mobile devices (e.g., smartphone and body sensors) in daily life could be fruitfully used within a mHealth care system. We highlight the potential benefits of integrating ecological momentary assessment (EMA) into mHealth platforms for collecting, processing, and modeling data, and delivering and evaluating novel interventions in everyday life. Although the data obtained from EMA and related approaches may hold great potential benefits for mHealth care system, there are also implementation challenges; we briefly discuss the challenges to integrating EMA into mHealth care system.

Adolescent-Sleep-Intervention Research: Current State and Future Directions

Emerging evidence suggests that cognitive-behavioral interventions should be considered as a first-line treatment for adolescent sleep problems and can also address associated mental health concerns. This article’s aim is to prioritize future directions for this research. Studies are needed that (a) leverage innovative technology, which may allow for a more personalized-medicine approach to care; (b) use alternative research designs, such as treatment-optimization strategies; (c) evaluate mechanisms of therapeutic improvements, including mediators and moderators of change, predictors of treatment adherence, dose–response relationships, and specificity of effects of different treatment components; (d) “scale up” the interventions so that a large proportion of adolescents can access them; and (e) develop flexible approaches to delivery (e.g., online platforms) and stepped-care guidelines. National and international standards of secondary school start times of 8:30 a.m. or later are also needed. The field is now mature enough to take these next steps, which will enhance the effectiveness and public health impact of these interventions.

Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials

There is a growing interest in leveraging the prevalence of mobile technology to improve health by delivering momentary, contextualized interventions to individuals' smartphones. A just-in-time adaptive intervention (JITAI) adjusts to an individual's changing state and/or context to provide the right treatment, at the right time, in the right place. Micro-randomized trials (MRTs) allow for the collection of data which aid in the construction of an optimized JITAI by sequentially randomizing participants to different treatment options at each of many decision points throughout the study. Often, this data is collected passively using a mobile phone. To assess the causal effect of treatment on a near-term outcome, care must be taken when designing the data collection system to ensure it is of appropriately high quality. Here, we make several recommendations for collecting and managing data from an MRT. We provide advice on selecting which features to collect and when, choosing between "agents" to implement randomization, identifying sources of missing data, and overcoming other novel challenges. The recommendations are informed by our experience with HeartSteps, an MRT designed to test the effects of an intervention aimed at increasing physical activity in sedentary adults. We also provide a checklist which can be used in designing a data collection system so that scientists can focus more on their questions of interest, and less on cleaning data.

Understanding mechanisms of change: An unpacking study of the evidence-based common-elements treatment approach (CETA) in low and middle income countries

There is a recognized need to better understand "essential ingredients" of psychological treatments, and refine interventions to be more scalable and sustainable. The goal of the present study was to look within a specific modular, flexible, multi-problem transdiagnostic psychological intervention -the Common Elements Treatment Approach (CETA) - and examine questions that would lead to optimizing CETA for scale up and sustainment. Utilizing data from two trials of CETA in two different countries (Thailand and Iraq), this manuscript aims to: 1) determine the "active treatment dose" or how many sessions are needed to achieve clinically meaningful change overall, in CETA); and 2) test how trajectories of client symptom change varied based on client characteristics and/or on delivery of certain elements. Results showed that overall 50% of CETA clients show some improvement after 4-6 sessions (1 SD) and large improvement (2 SD) after 7-10 sessions. Trajectories of change show steady symptom decline over time. Results support gradual exposure as one of the "active ingredients". Findings suggest that modular, flexible transdiagnostic models may allow for more efficient, targeted treatment as we gain more knowledge about key ingredients, their timing within treatment, and client outcomes.

When All Else Fails, Listen to the Patient: A Viewpoint on the Use of Ecological Momentary Assessment in Clinical Trials

A major problem in mental health clinical trials, such as depression, is low assay sensitivity in primary outcome measures. This has contributed to clinical trial failures, resulting in the exodus of the pharmaceutical industry from the Central Nervous System space. This reduced assay sensitivity in psychiatry outcome measures stems from inappropriately broad measures, recall bias, and poor interrater reliability. Limitations in the ability of traditional measures to differentiate between the trait versus state-like nature of individual depressive symptoms also contributes to measurement error in clinical trials. In this viewpoint, we argue that ecological momentary assessment (EMA)-frequent, real time, in-the-moment assessments of outcomes, delivered via smartphone-can both overcome these psychometric challenges and reduce clinical trial failures by increasing assay sensitivity and minimizing recall and rater bias. Used in this manner, EMA has the potential to further our understanding of treatment response by allowing for the assessment of dynamic interactions between treatment and distinct symptom response.

Text Message Responsivity in a 2-Way Short Message Service Pilot Intervention With Adolescent and Young Adult Survivors of Cancer

Background: Text message interventions hold promise for adolescents and young adults (AYAs) with chronic health conditions, including childhood cancer survivors; however, engagement is often suboptimal. Limited research has studied mobile health intervention outcomes beyond efficacy. Understanding responsivity to different types of text messages (ie, when a participant texts back) can provide practical, actionable information to optimize engagement in future projects.

Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning

The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible health-care for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as sequences of decision rules, one per stage of clinical intervention, that map current patient information to a recommended treatment. However, most existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale. We propose a new reinforcement learning method for estimating an optimal treatment regime that is applicable to data collected using mobile technologies in an out-patient setting. The proposed method accommodates an indefinite time horizon and minute-by-minute decision making that are common in mobile health applications. We show that the proposed estimators are consistent and asymptotically normal under mild conditions. The proposed methods are applied to estimate an optimal dynamic treatment regime for controlling blood glucose levels in patients with type 1 diabetes.

Improving primary care Access in Context and Theory (I-ACT trial): a theory-informed randomised cluster feasibility trial using a realist perspective

Background

Primary care access can be challenging for older, rural, socio-economically disadvantaged populations. Here we report the I-ACT cluster feasibility trial which aims to assess the feasibility of trial design and context-sensitive intervention to improve primary care access for this group and so expand existing theory.

Methods

Four general practices were recruited; three randomised to intervention and one to usual care. Intervention practices received £1500, a support manual and four meetings to develop local, innovative solutions to improve the booking system and transport.

Patients aged over 64 years old and without household car access were recruited to complete questionnaires when booking an appointment or attending the surgery. Outcome measures at 6 months included: self-reported ease of booking an appointment and transport; health care use; patient activation; capability; and quality of life. A process evaluation involved observations and interviews with staff and participants.

Results

Thirty-four patients were recruited (26 female, eight male, mean age 81.6 years for the intervention group and 79.4 for usual care) of 1143 invited (3% response rate). Most were ineligible because of car access. Twenty-nine participants belonged to intervention practices and five to usual care. Practice-level data was available for all participants, but participant self-reported data was unavailable for three. Fifty-six appointment questionnaires were received based on 150 appointments (37.3%).

Practices successfully designed and implemented the following context-sensitive interventions: Practice A: a stacked telephone system and promoting community transport; Practice B: signposting to community transport, appointment flexibility, mobility scooter charging point and promoting the role of receptionists; and Practice C: local taxi firm partnership and training receptionists. Practices found the process acceptable because it gave freedom, time and resource to be innovative or provided an opportunity to implement existing ideas. Data collection methods were acceptable to participants, but some found it difficult remembering to complete booking and appointment questionnaires. Expanded theory highlighted important mechanisms, such as reassurance, confidence, trust and flexibility.

Conclusions

Recruiting older participants without access to a car proved challenging. Retention of participants and practices was good but only about a third of appointment questionnaires were returned. This study design may facilitate a shift from one-size-fits-all interventions to more context-sensitive interventions.

Trial registration

ISRCTN18321951, Registered on 6 March 2017.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3299-2) contains supplementary material, which is available to authorized users.

Breast cancer survivors' preferences for mHealth physical activity interventions: findings from a mixed methods study

PURPOSE

Despite the benefits of physical activity for breast cancer survivors, the majority remain insufficiently active. Mobile health (mHealth) physical activity interventions may be a more scalable strategy to increase activity among survivors. However, little is known about their preferences for mHealth intervention features. This study explored survivors' preferences for these features.

METHODS

Survivors (N = 96; Mage = 55.8 (SD = 10.2)) self-reported demographic and disease characteristics, physical activity. A subset (n = 28) completed a semi-structured phone interview. Transcribed interviews were evaluated using a thematic content analysis approach and consensus review. Following interviews, the full sample self-reported interests and preferences for intervention features via online questionnaires. Quantitative data were analyzed using descriptive statistics.

RESULTS

Five themes emerged from interview data: (1) importance of relevance to breast cancer survivors; (2) easy to use; (3) integration with wearable activity trackers; (4) provide sense of accomplishment; and (5) variability in desired level of structure and personalization. The highest ranked intervention features were: daily and weekly progress feedback (87.5%), newsfeed (86.6%), activity challenges (81.3%), and scheduling tool (79.2%). Survivors were interested in receiving progress feedback (80.2%) and motivational (78.1%) and reminder (75.0%) messages.

CONCLUSIONS

Breast cancer survivors are interested in mHealth physical activity promotion interventions, but preferences varied around themes of relevance, ease of use, and enhancing personal motivation.

IMPLICATIONS FOR CANCER SURVIVORS

Engaging survivors in developing and implementing remotely delivered mHealth activity promotion interventions may enhance their effectiveness.

Taxonomy of approaches to developing interventions to improve health: a systematic methods overview

Background

Interventions need to be developed prior to the feasibility and piloting phase of a study. There are a variety of published approaches to developing interventions, programmes or innovations to improve health. Identifying different types of approach, and synthesising the range of actions taken within this endeavour, can inform future intervention development.

Methods

This study is a systematic methods overview of approaches to intervention development. Approaches were considered for inclusion if they described how to develop or adapt an intervention in a book, website or journal article published after 2007, or were cited in a primary research study reporting the development of a specific intervention published in 2015 or 2016. Approaches were read, a taxonomy of approaches was developed and the range of actions taken across different approaches were synthesised.

Results

Eight categories of approach to intervention development were identified. (1) Partnership, where people who will use the intervention participate equally with the research team in decision-making about the intervention throughout the development process. (2) Target population-centred, where the intervention is based on the views and actions of the people who will use it. (3) Evidence and theory-based, where the intervention is based on published research evidence and existing theories. (4) Implementation-based, where the intervention is developed with attention to ensuring it will be used in the real world. (5) Efficiency-based, where components of an intervention are tested using experimental designs to select components which will optimise efficiency. (6) Stepped or phased, where interventions are developed with an emphasis on following a systematic set of processes. (7) Intervention-specific, where an approach is constructed for a specific type of intervention. (8) Combination, where existing approaches to intervention development are formally combined. The actions from approaches in all eight categories were synthesised to identify 18 actions to consider when developing interventions.

Conclusions

This overview of approaches to intervention development can help researchers to understand the variety of existing approaches, and to understand the range of possible actions involved in intervention development, prior to assessing feasibility or piloting the intervention. Findings from this overview will contribute to future guidance on intervention development.

Trial registration

PROSPERO CRD42017080553.

Electronic supplementary material

The online version of this article (10.1186/s40814-019-0425-6) contains supplementary material, which is available to authorized users.

Use of non-face-to-face modalities for emergency department screening, brief intervention, and referral to treatment (ED-SBIRT) for high-risk alcohol use: A scoping review

Background: The purpose of this review was to examine and chart the "scope" of strategies reported in ED-SBIRT (emergency department-based screening, brief intervention and referral to treatment) studies that employ non-face-to-face (nFtF) modalities for high-risk alcohol use (i.e., risk for alcohol-related injury, medical condition, use disorder) and to identify research gaps in the scientific literature. Methods: The scoping review population included study participants with high-risk alcohol use patterns as well as study participants targeted for primary public health prevention (e.g., adolescent ED patients). Core concepts included SBIRT components among intervention studies that incorporated some form of nFtF modality (e.g., computer-assisted brief intervention). The context encompassed ED-based studies or trauma center studies, regardless of geographic location. After screening a total of 1526 unique references, reviewers independently assessed 58 full-text articles for eligibility. Results: A total of 30 full-text articles were included. Articles covered a period of 14 years (2003-2016) and 19 journal titles. Authors reported the use of a wide range of nFtF modalities across all 3 ED-SBIRT components: "screening" (e.g., computer tablet screening), "brief intervention" (e.g., text message-based brief interventions), and "referral to treatment" (e.g., computer-generated feedback with information about alcohol treatment services). The most frequently used nFtF modality was computerized screening and/or baseline assessment. The main results were mixed with respect to showing evidence of ED-SBIRT intervention effects. Conclusions: There is an opportunity for substance use disorder researchers to explore the specific needs of several populations (e.g., ED patients with co-occurring problems such as substance use disorder and violence victimization) and on several methodological issues (e.g., ED-SBIRT theory of change). Substance use disorder researchers should take the lead on establishing guidelines for the reporting of ED-SBIRT studies-including categorization schemes for various nFtF modalities. This would facilitate both secondary research (e.g., meta-analyses) and primary research design.

An expandable approach for design and personalization of digital, just-in-time adaptive interventions

Objective

We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people's individual needs, momentary contexts, and psychosocial variables.

Materials and Methods

We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.

Results

We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.

Conclusion

While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.

OnTrack: development and feasibility of a smartphone app designed to predict and prevent dietary lapses

Given that the overarching goal of weight loss programs is to remain adherent to a dietary prescription, specific moments of nonadherence known as "dietary lapses" can threaten weight control via the excess energy intake they represent and by provoking future lapses. Just-in-time adaptive interventions could be particularly useful in preventing dietary lapses because they use real-time data to generate interventions that are tailored and delivered at a moment computed to be of high risk for a lapse. To this end, we developed a smartphone application (app) called OnTrack that utilizes machine learning to predict dietary lapses and deliver a targeted intervention designed to prevent the lapse from occurring. This study evaluated the feasibility, acceptability, and preliminary effectiveness of OnTrack among weight loss program participants. An open trial was conducted to investigate subjective satisfaction, objective usage, algorithm performance, and changes in lapse frequency and weight loss among individuals (N = 43; 86% female; body mass index = 35.6 kg/m2) attempting to follow a structured online weight management plan for 8 weeks. Participants were adherent with app prompts to submit data, engaged with interventions, and reported high levels of satisfaction. Over the course of the study, participants averaged a 3.13% weight loss and experienced a reduction in unplanned lapses. OnTrack, the first Just-in-time adaptive intervention for dietary lapses was shown to be feasible and acceptable, and OnTrack users experienced weight loss and lapse reduction over the study period. These data provide the basis for further development and evaluation.

Precision Medicine

Precision medicine seeks to maximize the quality of healthcare by individualizing the healthcare process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime which comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, timing of administration, specific diet or exercise recommendation, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes which maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges.

Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods

OBJECTIVES

The conceptual models underlying physical activity interventions have been based largely on differences between more and less active people. Yet physical activity is a dynamic behavior, and such models are not sensitive to factors that regulate behavior at a momentary level or how people respond to individual attempts at intervening. We demonstrate how a control systems engineering approach can be applied to develop personalized models of behavioral responses to an intensive text message-based intervention.

DESIGN & METHOD

To establish proof-of-concept for this approach, 10 adults wore activity monitors for 16 weeks and received five text messages daily at random times. Message content was randomly selected from three types of messages designed to target (1) social-cognitive processes associated with increasing physical activity, (2) social-cognitive processes associated with reducing sedentary behavior, or (3) general facts unrelated to either physical activity or sedentary behavior. A dynamical systems model was estimated for each participant to examine the magnitude and timing of responses to each type of text message.

RESULTS

Models revealed heterogeneous responses to different message types that varied between people and between weekdays and weekends.

CONCLUSIONS

This proof-of-concept demonstration suggests that parameters from this model can be used to develop personalized algorithms for intervention delivery. More generally, these results demonstrate the potential utility of control systems engineering models for optimizing physical activity interventions.

Statistical design and analysis in trials of proportionate interventions: a systematic review

BACKGROUND

In proportionate or adaptive interventions, the dose or intensity can be adjusted based on individual need at predefined decision stages during the delivery of the intervention. The development of such interventions may require an evaluation of the effectiveness of the individual stages in addition to the whole intervention. However, evaluating individual stages of an intervention has various challenges, particularly the statistical design and analysis. This review aimed to identify the use of trials of proportionate interventions and how they are being designed and analysed in current practice.

METHODS

We searched MEDLINE, Web of Science and PsycINFO for articles published between 2010 and 2015 inclusive. We considered trials of proportionate interventions in all fields of research. For each trial, its aims, design and analysis were extracted. The data synthesis was conducted using summary statistics and a narrative format.

RESULTS

Our review identified 44 proportionate intervention trials, comprising 28 trial results, 13 protocols and three secondary analyses. These were mostly described as stepped care (n=37) and mainly focussed on mental health research (n=30). The other studies were aimed at finding an optimal adaptive treatment strategy (n=7) in a variety of therapeutic areas. Further terminology used included adaptive intervention, staged intervention, sequentially multiple assignment trial or a two-phase design. The median number of decision stages in the interventions was two and only one study explicitly evaluated the effect of the individual stages.

CONCLUSIONS

Trials of proportionate staged interventions are being used predominantly within the mental health field. However, few studies consider the different stages of the interventions, either at the design or the analysis phase, and how they may interact with one another. There is a need for further guidance on the design, analyses and reporting across trials of proportionate interventions.

TRIAL REGISTRATION

Prospero, CRD42016033781. Registered on 2 February 2016.

Learning the Dynamic Treatment Regimes from Medical Registry Data through Deep Q-network

This paper presents the deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexity in heterogeneous disease progression and treatment choices, with the goal of providing doctors and patients the data-driven personalized decision recommendations. The proposed DRL framework comprises (i) a supervised learning step to predict expert actions, and (ii) a deep reinforcement learning step to estimate the long-term value function of Dynamic Treatment Regimes. Both steps depend on deep neural networks. As a key motivational example, we have implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease after transplantation. In the experimental results, we have demonstrated promising accuracy in predicting human experts' decisions, as well as the high expected reward function in the DRL-based dynamic treatment regimes.

Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial

Background

Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user’s context from smartphone sensor data is a promising approach to further enhance tailoring.

Objective

The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants’ states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data.

Methods

In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up.

Results

Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants.

Conclusions

This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost.

Trial Registration

ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d)

International Registered Report Identifier (IRRID)

DERR1-10.2196/11540

Personalized medicine: motivation, challenges, and progress

There is a great deal of hype surrounding the concept of personalized medicine. Personalized medicine is rooted in the belief that since individuals possess nuanced and unique characteristics at the molecular, physiological, environmental exposure, and behavioral levels, they may need to have interventions provided to them for diseases they possess that are tailored to these nuanced and unique characteristics. This belief has been verified to some degree through the application of emerging technologies such as DNA sequencing, proteomics, imaging protocols, and wireless health monitoring devices, which have revealed great inter-individual variation in disease processes. In this review, we consider the motivation for personalized medicine, its historical precedents, the emerging technologies that are enabling it, some recent experiences including successes and setbacks, ways of vetting and deploying personalized medicines, and future directions, including potential ways of treating individuals with fertility and sterility issues. We also consider current limitations of personalized medicine. We ultimately argue that since aspects of personalized medicine are rooted in biological realities, personalized medicine practices in certain contexts are likely to be inevitable, especially as relevant assays and deployment strategies become more efficient and cost-effective.

An Analytics Platform to Evaluate Effective Engagement With Pediatric Mobile Health Apps: Design, Development, and Formative Evaluation

BACKGROUND

Mobile health (mHealth) apps for pediatric chronic conditions are growing in availability and challenge investigators to conduct rigorous evaluations that keep pace with mHealth innovation. Traditional research methods are poorly suited to operationalize the agile, iterative trials required to evidence and optimize these digitally mediated interventions.

OBJECTIVE

We sought to contribute a resource to support the quantification, analysis, and visualization of analytic indicators of effective engagement with mHealth apps for chronic conditions.

METHODS

We applied user-centered design methods to design and develop an Analytics Platform to Evaluate Effective Engagement (APEEE) with consumer mHealth apps for chronic conditions and implemented the platform to analyze both retrospective and prospective data generated from a smartphone-based pain self-management app called iCanCope for young people with chronic pain.

RESULTS

Through APEEE, we were able to automate the process of defining, operationalizing, and evaluating effective engagement with iCanCope. Configuring the platform to integrate with the app was feasible and provided investigators with a resource to consolidate, analyze, and visualize engagement data generated by participants in real time. Preliminary efforts to evaluate APEEE showed that investigators perceived the platform to be an acceptable evaluative resource and were satisfied with its design, functionality, and performance. Investigators saw potential in APEEE to accelerate and augment evidence generation and expressed enthusiasm for adopting the platform to support their evaluative practice once fully implemented.

CONCLUSIONS

Dynamic, real-time analytic platforms may provide investigators with a powerful means to characterize the breadth and depth of mHealth app engagement required to achieve intended health outcomes. Successful implementation of APEEE into evaluative practice may contribute to the realization of effective and evidence-based mHealth care.

App-Based Intervention Combining Evidence-Based Behavior Change Techniques With a Model-Based Reasoning System to Promote Physical Activity Among Young Adults (Active2Gether): Descriptive Study of the Development and Content

BACKGROUND

The Active2Gether intervention is an app-based intervention designed to help and encourage young adults to become and remain physically active by means of personalized, real-time activity tracking and context-specific feedback.

OBJECTIVE

The objective of our study was to describe the development and content of the Active2Gether intervention for physical activity promotion.

METHODS

A systematic and stepwise approach was used to develop the Active2Gether intervention. This included formulating objectives and a theoretical framework, selecting behavior change techniques, specifying the tailoring, pilot testing, and describing an evaluation protocol.

RESULTS

The development of the Active2Gether intervention comprised seven steps: analyzing the (health) problem, developing a program framework, writing (tailored) messages, developing tailoring assessments, developing the Active2Gether intervention, pilot testing, and testing and evaluating the intervention. The primary objective of the intervention was to increase the total time spent in moderate-vigorous physical activity for those who do not meet the Dutch guideline, maintain physical activity levels of those who meet the guideline, or further increase physical activity levels if they so indicated. The theoretical framework is informed by the social cognitive theory, and insights from other theories and evidence were added for specific topics. Development of the intervention content and communication channel resulted in the development of an app that provides highly tailored coaching messages that are framed in an autonomy-supportive style. These coaching messages include behavior change techniques aiming to address relevant behavioral determinants (eg, self-efficacy and outcome expectations) and are partly context specific. A model-based reasoning engine has been developed to tailor the intervention with respect to the type of support provided by the app, send relevant and context-specific messages to the user, and tailor the graphs displayed in the app. For the input of the tailoring, different instruments and sensors are used, such as an activity monitor (Fitbit One), Web-based and mobile questionnaires, and the location services on the user's mobile phone.

CONCLUSIONS

The systematic and stepwise approach resulted in an intervention that is based on theory and input from end users. The use of a model-based reasoning system to provide context-specific coaching messages goes beyond many existing eHealth and mHealth interventions.

Just-in-Time but Not Too Much: Determining Treatment Timing in Mobile Health

There is a growing scientific interest in the use and development of just-in-time adaptive interventions in mobile health. These mobile interventions typically involve treatments, such as reminders, activity suggestions and motivational messages, delivered via notifications on a smartphone or a wearable to help users make healthy decisions in the moment. To be effective in influencing health, the combination of the right treatment and right delivery time is likely critical. A variety of prediction/detection algorithms have been developed with the goal of pinpointing the best delivery times. The best delivery times might be times of greatest risk and/or times at which the user might be most receptive to the treatment notifications. In addition, to avoid over burdening users, there is often a constraint on the number of treatments that should be provided per time interval (e.g., day or week). Yet there may be many more times at which the user is predicted or detected to be at risk and/or receptive. The goal then is to spread treatment uniformly across all of these times. In this paper, we introduce a method that spreads the treatment uniformly across the delivery times. This method can also be used to provide data for learning whether the treatments are effective at the delivery times. This work is motivated by our work on two mobile health studies, a smoking cessation study and a physical activity study.

To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App

Background

Mobile health (mHealth) apps provide an opportunity for easy, just-in-time access to health promotion and self-management support. However, poor user engagement with these apps remains a significant unresolved challenge.

Objective

This study aimed to assess the effect of sending versus not sending a push notification containing a contextually tailored health message on proximal engagement, measured here as self-monitoring via the app. Secondary aims were to examine whether this effect varies by the number of weeks enrolled in the program or by weekday versus weekend. An exploratory aim was to describe how the effect on proximal engagement differs between weekday versus weekend by the time of day.

Methods

The study analyzes the causal effects of push notifications on proximal engagement in 1255 users of a commercial workplace well-being intervention app over 89 days. The app employs a microrandomized trial (MRT) design to send push notifications. At 1 of 6 times per day (8:30 am, 12:30 pm, 5:30 pm, 6:30 pm, 7:30 pm, and 8:30 pm; selected randomly), available users were randomized with equal probability to be sent or not sent a push notification containing a tailored health message. The primary outcome of interest was whether the user self-monitored behaviors and feelings at some time during the next 24 hours via the app. A generalization of log-linear regression analysis, adapted for use with data arising from an MRT, was used to examine the effect of sending a push notification versus not sending a push notification on the probability of engagement over the next 24 hours.

Results

Users were estimated to be 3.9% more likely to engage with the app in the next 24 hours when a tailored health message was sent versus when it was not sent (risk ratio 1.039; 95% CI 1.01 to 1.08; P<.05). The effect of sending the message attenuated over the course of the study, but this effect was not statistically significant (P=.84). The effect of sending the message was greater on weekends than on weekdays, but the difference between these effects was not statistically significant (P=.18). When sent a tailored health message on weekends, the users were 8.7% more likely to engage with the app (95% CI 1.01 to 1.17), whereas on weekdays, the users were 2.5% more likely to engage with the app (95% CI 0.98 to 1.07). The effect of sending a tailored health message was greatest at 12:30 pm on weekends, when the users were 11.8% more likely to engage (90% CI 1.02 to 1.13).

Conclusions

Sending a push notification containing a tailored health message was associated with greater engagement in an mHealth app. Results suggested that users are more likely to engage with the app within 24 hours when push notifications are sent at mid-day on weekends.

SMARTer Approach to Personalizing Intervention for Children With Autism Spectrum Disorder

PURPOSE

This review article introduces research methods for personalization of intervention. Our goals are to review evidence-based practices for improving social communication impairment in children with autism spectrum disorder generally and then how these practices can be systematized in ways that personalize intervention, especially for children who respond slowly to an initial evidence-based practice.

METHOD

The narrative reflects on the current status of modular and targeted interventions on social communication outcomes in the field of autism research. Questions are introduced regarding personalization of interventions that can be addressed through research methods. These research methods include adaptive treatment designs and the Sequential Multiple Assignment Randomized Trial. Examples of empirical studies using research designs are presented to answer questions of personalization.

CONCLUSION

Bridging the gap between research studies and clinical practice can be advanced by research that attempts to answer questions pertinent to the broad heterogeneity in children with autism spectrum disorder, their response to interventions, and the fact that a single intervention is not effective for all children.

PRESENTATION VIDEO

https://doi.org/10.23641/asha.7298021.

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent-but mostly unresolved-issues in the area of dynamic multilevel modeling.

Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults

BACKGROUND

Chronic pain is a globally prevalent condition. It is closely linked with psychological well-being, and it is often concomitant with anxiety, negative affect, and in some cases even depressive disorders. In the case of musculoskeletal chronic pain, frequent physical activity is beneficial. However, reluctance to engage in physical activity is common due to negative psychological associations (eg, fear) between movement and pain. It is known that encouragement, self-efficacy, and positive beliefs are effective to bolster physical activity. However, given that the majority of time is spent away from personnel who can give such encouragement, there is a great need for an automated ubiquitous solution.

OBJECTIVE

MyBehaviorCBP is a mobile phone app that uses machine learning on sensor-based and self-reported physical activity data to find routine behaviors and automatically generate physical activity recommendations that are similar to existing behaviors. Since the recommendations are based on routine behavior, they are likely to be perceived as familiar and therefore likely to be actualized even in the presence of negative beliefs. In this paper, we report the preliminary efficacy of MyBehaviorCBP based on a pilot trial on individuals with chronic back pain.

METHODS

A 5-week pilot study was conducted on people with chronic back pain (N=10). After a week long baseline period with no recommendations, participants received generic recommendations from an expert for 2 weeks, which served as the control condition. Then, in the next 2 weeks, MyBehaviorCBP recommendations were issued. An exit survey was conducted to compare acceptance toward the different forms of recommendations and map out future improvement opportunities.

RESULTS

In all, 90% (9/10) of participants felt positive about trying the MyBehaviorCBP recommendations, and no participant found the recommendations unhelpful. Several significant differences were observed in other outcome measures. Participants found MyBehaviorCBP recommendations easier to adopt compared to the control (β_int=0.42, P<.001) on a 5-point Likert scale. The MyBehaviorCBP recommendations were actualized more (β_int=0.46, P<.001) with an increase in approximately 5 minutes of further walking per day (β_int=4.9 minutes, P=.02) compared to the control. For future improvement opportunities, participants wanted push notifications and adaptation for weather, pain level, or weekend/weekday.

CONCLUSIONS

In the pilot study, MyBehaviorCBP's automated approach was found to have positive effects. Specifically, the recommendations were actualized more, and perceived to be easier to follow. To the best of our knowledge, this is the first time an automated approach has achieved preliminary success to promote physical activity in a chronic pain context. Further studies are needed to examine MyBehaviorCBP's efficacy on a larger cohort and over a longer period of time.

Extending the Passive-Sensing Toolbox: Using Smart-Home Technology in Psychological Science

New smart-home devices provide the opportunity to advance psychological science and theory through novel research opportunities in home environments. These technologies extend the in vivo research and intervention capabilities afforded by other assessment techniques such as Ecological Momentary Assessment methods as well as mobile and wearable devices. Smart-home devices contain a multitude of sensors capable of continuously and unobtrusively collecting multimodal data within home contexts. These devices have some complementary strengths and limitations compared with other assessment methods. This article (a) briefly reviews data collection methods in home environments, (b) discusses the unique advantages of smart-home devices, (c) describes the extant smart-home literature, (d) explores how these devices may advance evaluation and refinement of psychological theories, (e) describes examples of psychological processes that are potential targets for smart-home assessment and intervention, (f) considers methodological challenges and barriers, (g) discusses ethical considerations, and (h) concludes with a discussion of future directions for research and the merging of passive-sensing technologies with active self-report methods. This article aims to highlight the potential utility of smart-home devices within psychological research to evaluate psychological theories related to behavior within the home context.

Quantile Coarsening Analysis of High-Volume Wearable Activity Data in a Longitudinal Observational Study

Owing to advances in sensor technologies on wearable devices, it is feasible to measure physical activity of an individual continuously over a long period. These devices afford opportunities to understand individual behaviors, which may then provide a basis for tailored behavior interventions. The large volume of data however poses challenges in data management and analysis. We propose a novel quantile coarsening analysis (QCA) of daily physical activity data, with a goal to reduce the volume of data while preserving key information. We applied QCA to a longitudinal study of 79 healthy participants whose step counts were monitored for up to 1 year by a Fitbit device, performed cluster analysis of daily activity, and identified individual activity signature or pattern in terms of the clusters identified. Using 21,393 time series of daily physical activity, we identified eight clusters. Employment and partner status were each associated with 5 of the 8 clusters. Using less than 2% of the original data, QCA provides accurate approximation of the mean physical activity, forms meaningful activity patterns associated with individual characteristics, and is a versatile tool for dimension reduction of densely sampled data.

Rethinking Evaluations Of mHealth Systems For Behavior Change

Much of the recent research in mobile health (mHealth) has focused on the development of apps and wearables for promoting healthy behavior changes, such as losing weight, increasing physical activity, or adhering to a medication regimen. These interactive systems help users make changes in their behavior by, for instance, tracking healthrelated activities and states, providing feedback, helping users set and track goals, and facilitating supportive social interactions. We refer to the features that implement such functionality as the system's "intervention components," as they are designed to actuate psychosocial mechanisms (e.g., modeling, selfefficacy, positive reinforcement, etc.) thought to mediate the behavior change process. As with any other type of behavioral intervention, mHealth systems are only effective for some users and some of the time, but insofar as they do work, they do so mainly through the mechanisms of change that are activated via users' interactions with the system's intervention components.

A Church-Based Weight Loss Intervention in African American Adults using Text Messages (LEAN Study): Cluster Randomized Controlled Trial

Background

African American adults experience a high prevalence of obesity and its associated comorbidities, including diabetes. Church-based interventions have been shown to be effective in decreasing weight in this population. mHealth interventions can address two needs for obesity treatment in this community, including enhancing weight loss and providing wide dissemination.

Objective

This study aimed to assess the feasibility and efficacy of a church-based weight loss intervention that incorporates mHealth technology.

Methods

In this study, 8 churches (n=97) were randomly assigned to the intervention or delayed intervention condition (control group). We recruited participants through their respective church. Volunteer church members were trained by study staff to deliver the 10-session, 6-month intervention. Participants in the intervention group attended group sessions and received automated short message service (SMS) text messages designed to reinforce behavioral strategies. Conversely, participants in the delayed intervention condition received SMS text messages related to health conditions relevant for African American adults. We obtained measures of body composition, blood pressure, blood glucose, and cholesterol.

Results

We successfully recruited 97 African American adults, with a mean age of 56.0 (SE 10.3) years and a mean body mass index of 38.6 (SE 6.4) kg/m2 (89/97, 91.8% females), who attended the churches that were randomized to the intervention (n=68) or control (n=29) condition. Of these, 74.2% (72/97) of the participants (47/68, 69.1% intervention; 25/29, 86.2% delayed intervention) completed the 6-month assessment. The average intervention group attendance was 55%. There was a significant difference in weight loss (P=.04) between participants in the intervention (–1.5 (SE 0.5) kg) and control (0.11 (SE 0.6) kg) groups. Among participants in the intervention group, the correlation between the number of SMS text messages sent and the percent body fat loss was r=.3 with P=.04. The participants reported high satisfaction with the automated SMS text messages.

Conclusions

Automated SMS text messages were well-received by participants, suggesting that more enhanced mHealth technologies are a viable option for interventions targeting African American adults.

Trial Registration

ClinicalTrials.gov NCT02863887; https://clinicaltrials.gov/ct2/show/NCT02863887 (Archived by WebCite at http://www.webcitation.org/71JiYzizO)

Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research

Whereas substance-use researchers have long combined self-report with objective measures of behavior and physiology inside the laboratory, developments in mobile/wearable electronic technology are increasingly allowing for the collection of both subjective and objective information in participants' daily lives. For self-report, ecological momentary assessment (EMA), as implemented on contemporary smartphones or personal digital assistants, can provide researchers with near-real-time information on participants' behavior and mood in their natural environments. Data from portable/wearable electronic sensors measuring participants' internal and external environments can be combined with EMA (e.g., by timestamps recorded on questionnaires) to provide objective information useful in determining the momentary context of behavior and mood and/or validating participants' self-reports. Here, we review three objective ambulatory monitoring techniques that have been combined with EMA, with a focus on detecting drug use and/or measuring the behavioral or physiological correlates of mental events (i.e., emotions, cognitions): (1) collection and processing of biological samples in the field to measure drug use or participants' physiological activity (e.g., hypothalamic-pituitary-adrenal axis activity); (2) global positioning system (GPS) location information to link environmental characteristics (disorder/disadvantage, retail drug outlets) to drug use and affect; (3) ambulatory electronic physiological monitoring (e.g., electrocardiography) to detect drug use and mental events, as advances in machine learning algorithms make it possible to distinguish target changes from confounds (e.g., physical activity). Finally, we consider several other mobile/wearable technologies that hold promise to be combined with EMA, as well as potential challenges faced by researchers working with multiple mobile/wearable technologies simultaneously in the field.