Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior can be developed utilizing longitudinal data from mobile devices and control systems engineering models.

Objective-To review eHealth intervention studies for adults and children that targeted behavior change for physical activity, healthy eating, or both behaviors.Data Sources-Systematic literature searches were performed using five databases: Medline, PsychInfo, CINAHL, ERIC, and the Cochrane Library to retrieve articles. Study Inclusion and ExclusionCriteria-Articles published in scientific journals were included if they evaluated an intervention for physical activity and/or dietary behaviors, or focused on weight loss; used randomized or quasi-experimental designs; measured outcomes at baseline and a follow-up period; and included an intervention where participants interacted with some type of electronic technology either as the main intervention or an adjunct component. All studies were published between 2000 and 2005.Results-Eighty-six publications were initially identified, of which 49 met the inclusion criteria (13 physical activity publications, 16 dietary behaviors publications, and 20 weight loss or both physical activity and diet publications), and represented 47 different studies. Studies were described on multiple dimensions, including sample characteristics, design, intervention, measures, and results. eHealth interventions were superior to comparison groups for 21/41 (51%) studies (3 physical activity, 7 diet, 11 weight loss/physical activity and diet). Twenty-four studies had indeterminate results, and in four studies the comparison conditions outperformed eHealth interventions.Conclusions-Published studies of eHealth interventions for physical activity and dietary behavior change are in their infancy. Results indicated mixed findings related to the effectiveness of eHealth interventions. Interventions that feature interactive technologies need to be refined and more rigorously evaluated to fully determine their potential as tools to facilitate health behavior change.

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and, (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, future possibilities and set a grand goal for the emerging field of mHealth research.

A great deal of clinical cancer care is delivered in the home by informal caregivers (e.g. family, friends), who are often untrained. Caregivers' context varies widely, with many providing care despite low levels of resources and high levels of additional demands. Background Changes in health care have shifted much cancer care to the home, with limited data to inform this transition. We studied the characteristics, care tasks, and needs of informal caregivers of cancer patients. Methods Caregivers of seven geographically and institutionally defined cohorts of newly diagnosed colorectal and lung cancer patients completed self-administered questionnaires (n = 677). We combined this information with patient survey and chart abstraction data and focused on caregivers who reported providing, unpaid, at least 50% of the patient's informal cancer care. Results Over half of caregivers (55%) cared for a patient with metastatic disease, severe comorbidity, or undergoing current treatment. Besides assisting with activities of daily living, caregivers provided cancer-specific care such as watching for treatment side effects (68%), helping manage pain, nausea or fatigue (47%), administering medicine (34%), deciding whether to call a doctor (30%), deciding whether medicine was needed (29%), and changing bandages (19%). However, half of caregivers reported not getting training perceived as necessary. In addition, 49% of caregivers worked for pay, 21% reported poor or fair health, and 21% provided unpaid care for other individuals. One in four reported low confidence in the quality of the care they provided. Conclusions Much assistance for cancer patients is delivered in the home by informal caregivers, often without desired training, with a significant minority having limited resources and high additional demands. Future research should explore the potentially high yield of addressing caregiver needs in improving quality of cancer care and both survivors' and caregivers' outcomes.

No abstract

Time-varying multidimensional individual processes predict within daily physical activity levels.

Background. Informal care provides many benefits to cancer patients, but can be costly to caregivers. This study quantified the economic burden for informal caregivers of lung cancer (LC) and colorectal cancer (CRC) patients, examining differences by cancer type, phase of disease, stage at diagnosis, patient age, and relationship.Methods. A cross-sectional survey of caregivers of LC and CRC patients participating in the Share Thoughts on Care survey was conducted. Economic burden was calculated using the opportunity cost of caregiver time, the value of work hours lost, and out-of-pocket expenditures. Factors associated with economic burden to caregivers were modeled using fixed-effects generalized least squares estimation.Results. Informal caregivers (1,629) completed mailed surveys. Of these, 663, 822, and 144 were surveyed dur-

Objectively measured MVPA displayed stronger associations with physiological and anthropometric biomarkers than self-reported MVPA. However, self-reported and objectively measured MVPA appear to capture distinct aspects of PA that are independently associated with certain biomarkers. Further understanding of the distinct contributions of self-reported and objectively measured PA to health outcomes could help to better identify optimal activity level and pattern.