Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Valid, reliable, and direct measures of physical activity (PA) are critical to assessing the impact of lifestyle PA interventions. However, little is known about the extent to which objective measures have been used to assess the outcomes of lifestyle PA interventions. This systematic review had two aims: 1) evaluate the extent to which PA is measured objectively in lifestyle PA interventions targeting adults and 2) explore and summarize what objective measures have been used and what PA dimensions and metrics have been reported. Pubmed, Cochrane Central Register, and PsychInfo were searched for lifestyle PA interventions conducted between 2006 and 2016. Of the 342 articles that met the inclusion criteria, 239 studies measured PA via subjective measures and 103 studies measured PA via objective measures. The proportion of studies using objective measures increased from 4.4% to 70.6% from 2006 to 2016. All studies measuring PA objectively utilized wearable devices; half (50.5%) used pedometers only and 40.8% used accelerometers only. A majority of the 103 studies reported steps (73.8%) as their PA metric. Incorporating objective measures of PA should continue to be a priority in PA research. More work is needed to address the challenges of comprehensive and consistent collecting, reporting, and analyzing of PA metrics.

U.S. Latinos experience high rates of cardio-metabolic diseases and have high rates of physical inactivity and sedentary behavior. Understanding the environmental factors associated with physical activity and sedentary behaviors among Latinos could inform future interventions. The purpose of this study is to explore the neighborhood environment correlates of physical activity and sedentary behavior in a sample of U.S. Latino adults. Cross-sectional study of 602 Latino adults in Lawrence, MA. Survey assessments of physical activity, sedentary behavior, and neighborhood environment were verbally administered. The neighborhood environment scale assessed violence, safety, aesthetic quality, walkability, availability of healthy foods, social cohesion, and activities with neighbors. After controlling forage, gender, education, body mass index (BMI), and smoking status, two variables were associated with the outcomes of interest. Living in more walkable neighborhoods was associated with an increased likelihood of engaging in adequate levels of physical activity (>150 min per week, as recommended by the American College of Sports Medicine (ACSM)) (OR = 1.403, p = .018); and greater frequency of activities with neighbors was associated with greater sedentary behavior (β = .072, p = .05). There were different neighborhood environment correlates of physical activity and sedentary behavior in this Latino community. Focusing on a greater understanding of the distinct social and physical environmental correlates of physical activity and sedentary behavior may provide important insights for reducing CVD risk and health disparities among Latinos.

Background: Despite the growing evidence indicating the efficacy of digital cognitive behavioral interventions (dCBIs) for behavioral health (BH) treatment, broad and consistent use of such interventions has been limited by knowledge obtained in real-world settings, including factors that impact provider uptake/referral. Engaging providers early in the implementation process offers an opportunity to explore their needs and behaviors, integrate interventions into workflows, and better understand provider setting capabilities. Objective: This study assessed providers’ views on the feasibility and acceptability of delivering a cognitive behavioral therapy (CBT)-based mobile app in multiple care settings. Methods: Participating providers included BH and physical health (PH) providers from a women’s health center, an outpatient BH clinic, and both rural/urban primary care settings. All participating providers cocreated workflows through facilitated workshops, including establishing feedback loops between the project team and providers and identifying clinical champions at each site. Over a 12-week period, the providers referred adult patients experiencing anxiety or depression to a mobile app-based dCBI, RxWell, and provided other indicated treatments as part of usual care. Referrals were completed by the providers through the electronic medical record. To better understand facilitators of and challenges in integrating RxWell into routine practice and perceptions of sustainability, a series of qualitative interviews was conducted. Interview data were analyzed to identify major themes using an inductive content analysis approach. Results: A total of 19 provider interviews were conducted to discover motivators and barriers for referring RxWell. The providers benefited from a focused discussion on how to incorporate the referral process into their workflow, and knowing the app content was rooted in evidence. Although the providers believed engaging in experiential learning was important, they indicated that more education on the digital health coach role and how to monitor patient progress is needed. The providers thought patient engagement may be impacted by motivation, a lack of comfort using a smartphone, or preference for in-person therapy. The providers also expressed enthusiasm in continuing to refer the app. They liked the ability to provide patients with support between sessions, to have an extra treatment option that teaches BH exercises, and to have a CBT treatment option that overcomes barriers (eg, wait times, copays, travel) to traditional therapy modalities. Conclusions: Digital intervention success in health care settings relies heavily on engagement of key stakeholders, such as providers, in both design and implementation of the intervention and focused evaluation within intended care setting(s). Scaling digital interventions to meet the mental health needs of patients in usual care settings leans on thoughtfully constructed and streamlined workflows to enable seamless referral of patients by providers. Our findings strongly suggest that providers are supportive of digital tool integration to support the mental health of patients and endorse its use within their routine workflow.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Efforts to translate evidence-based weight loss interventions, such as the Diabetes Prevention Program (DPP), to low-income, postpartum women are limited by poor intervention attendance and high attrition rates. Strategies that improve engagement and retention in this population are needed to maximize the reach of evidence-based weight loss interventions Objective: To adapt a DPP-based weight loss intervention (Fresh Start) for Facebook delivery and to evaluate its feasibility among low-income postpartum women. Methods: This study comprised of three single group pilot studies where feasibility outcomes iteratively informed changes from one pilot to the next. We paralleled the in-person program to Facebook delivery by translating the protocol to a content library to be delivered in a Facebook feed with additional input from lifestyle coaches. Low-income postpartum women were recruited from Women, Infants, and Children (WIC) clinics in Worcester, MA. Participants were enrolled into a 16-week weight loss intervention delivered via Facebook. During the first eight weeks, Facebook intervention posts were delivered two times per day with additional posts from coaches. For the following 8 weeks, posts were delivered once per day without additional coaching. Outcome measures were collected at baseline and 16-week follow-up and included changes in weight as well as feasibility outcomes including retention, engagement (defined by likes, comments, and posts), and acceptability. Results: Pilot 1 (n = 27) had a retention rate of 89% and 62% engaged in the group during the 8-week coached phase. Mean weight loss was 2.6 pounds (SD = 8.64) and 75% reported being satisfied with the program. Pilot 2 (n = 24) had a retention rate of 83% and 55.2% engaged in the group during the 8-week coached phase. Mean weight loss was 2.5 pounds (SD = 9.23) and 80% were satisfied with the program. Pilot 3 (n = 16) had a retention rate of 88% and 67% engaged in the group during the 8-week coached phased. Mean weight loss was 7.0 pounds (SD = 11.6) and 64.3% were satisfied with the program. Conclusions: Findings demonstrated that a Facebook-delivered intervention was acceptable and could be feasibly delivered to the low-income, postpartum women. Future research is needed to evaluate the efficacy of a Facebook-delivered weight loss intervention.

Background. Sedentary behavior has been associated with increased risk for cardiovascular disease (CVD) among primarily White samples, while studies among Latinos have shown mixed results. Purpose. To explore relationships between sedentary behavior and CVD risk factors among a sample of Latino adults. Methods. A cross-sectional study of 602 Latino adults. Surveys of sedentary behavior and physical activity were orally administered. Anthropometric measurements included weight, height, waist circumference, and blood pressure. Medical record data for diabetes and dyslipidemia were obtained. Results. Sedentary behavior was associated with BMI (β = .164, p < .001) and waist circumference (β = .162, p < .001). Sedentary behavior was not associated with blood pressure, high cholesterol, diabetes, or physical activity. Conclusions. The consequences of sedentary behavior may differ across groups. Evaluating the relationship between sedentary behavior and CVD risk is critical in identifying behaviors contributing to CVD.