Nurse-led Care Program with Patients with Heart Failure Using Johnson's Behavioral System Model: A Randomized Controlled Trial.

Patients with heart failure experience system imbalance and have multiple symptoms. A nurse-led care program based on Johnson's behavioral system model was used to improve the balance of the behavioral system of heart failure patients. One hundred and fifty patients were randomly assigned into two groups. In the experimental group, the patient's status was evaluated by a behavioral subsystem assessment tool related to the level of imbalance. The patients in the intervention group received care individually based on their worst subsystem scores over a period of 2 weeks. The results showed significant improvement in restorative, ingestive, eliminative, aggressive/protective, dependency, and achievement (p < .05) subsystems in the experimental group. However, no significant difference was seen in sexual and affiliative (p > .05) subsystems.

Using Nonparticipant Observation as a Method to Understand Implementation Context in Evidence-Based Practice.

BACKGROUND: The uptake of evidence-based knowledge in practice is influenced by context. Observations are suggested as a valuable but under-used approach in implementation research for gaining a holistic understanding of contexts. AIM: The aim of this paper is to demonstrate how data from observations can provide insights about context and evidence use in implementation research. METHODS: Data were collected over 24 months in a randomised trial with an embedded realist evaluation in 24 nursing homes across four European countries; notes from 183 observations (representing 335 hours) were triangulated with interview transcripts and context survey data (from 357 staff interviews and 725 questionnaire responses, respectively). RESULTS: Although there were similarities in several elements of context within survey, interview and observation data, the observations provided additional features of the implementation context. In particular, observations demonstrated if and how the resources (staffing and supplies) and leadership (formal and informal, teamwork, and professional autonomy) affected knowledge use and implementation. Further, the observations illuminated the influence of standards and the physical nursing environment on evidence-based practice, and the dynamic interaction between different aspects of context. LINKING EVIDENCE TO ACTION: Although qualitative observations are resource-intensive, they add value when used with other data collection methods, further enlightening the understanding of the implementation context and how evidence use and sharing are influenced by context elements. Observations can enhance an understanding of the context, evidence use and knowledge-sharing triad in implementation research.

Clinical application of the Newborn Behavioral Observation (NBO) System to characterize the behavioral pattern of newborns at biological and social risk / Aplicação clínica do Newborn Behavioral Observation (NBO) System para caracterizar o padrão comportamental dos recém-nascidos de risco biológico e social

Abstract Objective To compare the behavior of preterm newborns and full-term newborns using the Newborn Behavioral Observation and to evaluate the mothers' experience when participating in this observation. Method This was a cross-sectional study performed at a referral hospital for high-risk births, involving mothers and neonates before hospital discharge. The mothers answered the sociodemographic questionnaire, participated in the Newborn Behavioral Observation session, and evaluated the experience by answering the parents' questionnaire at the end. The characteristics of the preterm newborn and full-term newborn groups and the autonomic, motor, organization of states, and responsiveness scores were compared. Linear regression was performed to test the association of the characteristics of mothers and neonates with the scores in the autonomic, motor, organization of states, and responsiveness domains. Results The Newborn Behavioral Observation was performed with 170 newborns (eight twins and 77% preterm newborns). Approximately 15% of the mothers were adolescents and had nine years of schooling, on average. The groups differed regarding weight for gestational age, age at observation, APGAR score, feeding, and primiparity. The linear regression adjusted for these variables showed that only prematurity remained associated with differences in the scores of the motor (p = 0.002) and responsiveness (p = 0.02) domains. No statistical difference was observed between the groups in the score attributed to one's own knowledge prior to the session (p = 0.10). After the session, these means increased in both groups. This increase was significantly higher in the preterm newborn group (p = 0.02). Conclusions The Newborn Behavioral Observation increased the mothers' knowledge about the behavior of their children, especially in mothers of preterm newborns, and identified differences in the behavior of preterm newborns and full-term newborns regarding the motor and responsiveness domains.

Resumo Objetivo Comparar o comportamento de recém-nascidos pré-termo e a termo utilizando a Newborn Behavioral Observation e avaliar a experiência das mães em participar dessa observação. Método Estudo transversal realizado em hospital de referência para partos de risco, envolvendo mães e neonatos antes da alta hospitalar. As mães responderam ao questionário sociodemográfico, participaram da sessão de Newborn Behavioral Observation e ao final avaliaram a experiência e responderam ao questionário de pais. As características dos grupos de recém-nascidos pré-termo e recém-nascidos termo e os escores dos domínios autonômico, motor, organização dos estados e responsividade foram comparados. Realizou-se regressão linear para testar a associação de características das mães e neonatos com os escores nos domínios autonômico, motor, organização dos estados e responsividade. Resultados A Newborn Behavioral Observation foi realizada com 170 recém-nascidos (oito gemelares e 77% pré-termo). Cerca de 15% das mães eram adolescentes e estudaram em média por 9 anos. Os grupos diferiram quanto ao peso para idade gestacional, idade na observação, APGAR, alimentação e primiparidade. A regressão linear ajustada para essas variáveis mostrou que apenas a prematuridade manteve-se associada a diferenças nos escores dos domínios motor (p = 0,002) e responsividade (p = 0,02). Não houve diferença estatística entre os grupos na pontuação atribuída ao próprio conhecimento antes da sessão (p = 0,10). Após a sessão estas médias subiram em ambos os grupos. Esse aumento foi significativamente maior no grupo de recém-nascidos pré-termo (p = 0,02). Conclusões A Newborn Behavioral Observation aumentou o conhecimento das mães sobre o comportamento dos filhos, principalmente para as mães de recém-nascidos pré-termo, e identificou diferenças no comportamento de recém-nascidos pré-termo e recém-nascidos termo nos domínios motor e responsividade.

Systematic review of smartphone-based passive sensing for health and wellbeing.

OBJECTIVE: To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing. MATERIAL AND METHODS: A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction. RESULTS: Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n = 15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used the Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges. DISCUSSION: Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to developing passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges. CONCLUSION: As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts.

"Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health": Correction to Ben-Zeev et al. (2015).

UNLABELLED: Reports an error in "Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health" by Dror Ben-Zeev, Emily A. Scherer, Rui Wang, Haiyi Xie and Andrew T. Campbell (Psychiatric Rehabilitation Journal, 2015[Sep], Vol 38[3], 218-226). Model fit statistics in Table 1 are reported as a row for Model 2, but not for Model 1, due to a production error. Model 1 fit statistics should appear as a row with the following information: 2LL 1490.0, AIC 1498.0 & BIC 1505.3. (The following abstract of the original article appeared in record 2015-14736-001.) OBJECTIVE: Optimal mental health care is dependent upon sensitive and early detection of mental health problems. We have introduced a state-of-the-art method for the current study for remote behavioral monitoring that transports assessment out of the clinic and into the environments in which individuals negotiate their daily lives. The objective of this study was to examine whether the information captured with multimodal smartphone sensors can serve as behavioral markers for one's mental health. We hypothesized that (a) unobtrusively collected smartphone sensor data would be associated with individuals' daily levels of stress, and (b) sensor data would be associated with changes in depression, stress, and subjective loneliness over time. METHOD: A total of 47 young adults (age range: 19-30 years) were recruited for the study. Individuals were enrolled as a single cohort and participated in the study over a 10-week period. Participants were provided with smartphones embedded with a range of sensors and software that enabled continuous tracking of their geospatial activity (using the Global Positioning System and wireless fidelity), kinesthetic activity (using multiaxial accelerometers), sleep duration (modeled using device-usage data, accelerometer inferences, ambient sound features, and ambient light levels), and time spent proximal to human speech (i.e., speech duration using microphone and speech detection algorithms). Participants completed daily ratings of stress, as well as pre- and postmeasures of depression (Patient Health Questionnaire-9; Spitzer, Kroenke, & Williams, 1999), stress (Perceived Stress Scale; Cohen et al., 1983), and loneliness (Revised UCLA Loneliness Scale; Russell, Peplau, & Cutrona, 1980). RESULTS: Mixed-effects linear modeling showed that sensor-derived geospatial activity (p < .05), sleep duration (p < .05), and variability in geospatial activity (p < .05), were associated with daily stress levels. Penalized functional regression showed associations between changes in depression and sensor-derived speech duration (p < .05), geospatial activity (p < .05), and sleep duration (p < .05). Changes in loneliness were associated with sensor-derived kinesthetic activity (p < .01). CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Smartphones can be harnessed as instruments for unobtrusive monitoring of several behavioral indicators of mental health. Creative leveraging of smartphone sensing could provide novel opportunities for close-to-invisible psychiatric assessment at a scale and efficiency that far exceeds what is currently feasible with existing assessment technologies. (PsycINFO Database Record