The individual sensitivity for ones internal bodily signals (“interoceptive awareness”) has been shown to be of relevance for a broad range of cognitive and affective functions. Interoceptive awareness has been primarily assessed via measuring the sensitivity for ones cardiac signals (“cardiac awareness”) which can be non-invasively measured by heartbeat perception tasks. It is an open question whether cardiac awareness is related to the sensitivity for other bodily, visceral functions. This study investigated the relationship between cardiac awareness and the sensitivity for gastric functions in healthy female persons by using non-invasive methods. Heartbeat perception as a measure for cardiac awareness was assessed by a heartbeat tracking task and gastric sensitivity was assessed by a water load test. Gastric myoelectrical activity was measured by electrogastrography (EGG) and subjective feelings of fullness, valence, arousal and nausea were assessed. The results show that cardiac awareness was inversely correlated with ingested water volume and with normogastric activity after water load. However, persons with good and poor cardiac awareness did not differ in their subjective ratings of fullness, nausea and affective feelings after drinking. This suggests that good heartbeat perceivers ingested less water because they subjectively felt more intense signals of fullness during this lower amount of water intake compared to poor heartbeat perceivers who ingested more water until feeling the same signs of fullness. These findings demonstrate that cardiac awareness is related to greater sensitivity for gastric functions, suggesting that there is a general sensitivity for interoceptive processes across the gastric and cardiac modality.

Measuring the energy intake (kcal) of a person in day-to-day life is difficult. The best laboratory tool achieves 95 % accuracy on average, while tools used in daily living typically achieve 60–80 % accuracy. This paper describes a new method for measuring intake via automated tracking of wrist motion. Our method uses a watch-like device with a micro-electro-mechanical gyroscope to detect and record when an individual has taken a bite of food. Two tests of the accuracy of our device in counting bites found that our method has 94 % sensitivity in a controlled meal setting and 86 % sensitivity in an uncontrolled meal setting, with one false positive per every 5 bites in both settings. Preliminary data from daily living indicates that bites measured by the device are positively related to caloric intake illustrating the potential of the device to monitor energy intake. Future research should seek to further explore the relationship between bites taken and kilocalories consumed to validate the device as an automated measure of energy intake.

This paper is motivated by the growing prevalence of obesity, a health problem affecting over 500 million people. Measurements of energy intake are commonly used for the study and treatment of obesity. However, the most widely used tools rely upon self-report and require a considerable manual effort, leading to underreporting of consumption, noncompliance, and discontinued use over the long term. The purpose of this paper is to describe a new method that uses a watch-like configuration of sensors to continuously track wrist motion throughout the day and automatically detect periods of eating. Our method uses the novel idea that meals tend to be preceded and succeeded by the periods of vigorous wrist motion. We describe an algorithm that segments and classifies such periods as eating or noneating activities. We also evaluate our method on a large dataset (43 subjects, 449 total h of data, containing 116 periods of eating) collected during free-living. Our results show an accuracy of 81% for detecting eating at 1-s resolution in comparison to manually marked event logs of periods eating. These results indicate that vigorous wrist motion is a useful indicator for identifying the boundaries of eating activities, and that our method should prove useful in the continued development of body-worn sensor tools for monitoring energy intake.

Existing research on workplace incivility has demonstrated an association with a host of negative outcomes, including increased burnout, turnover intentions, and physical symptoms. With the rise in Internet communication over the last decade, interpersonal mistreatment has spilled over to the Internet, but little is known about the impact of incivility communicated via e-mail on employee psychological and performance outcomes. The current study presents a within-subjects experiment wherein incivility and support were manipulated in a laboratory-based simulated workplace setting. Eighty-four participants completed a series of math tasks while interacting with either an uncivil or a supportive supervisor via e-mail. Data were collected on energy, cardiac activity, mood, task performance, and engagement. Findings indicate that participants reported higher levels of negative affect and lower levels of energy after working with the uncivil supervisor than with the supportive supervisor. Additionally, participants performed significantly worse on the math tasks and had lower engagement in the uncivil condition than the supportive condition, and these relationships were mediated by energy. No differences were found in cardiac activity between the two conditions. Results are discussed in terms of their implications for the 21st century world of work.

To reduce the occurrence of SS, a degree of peripheral vision of the external world should be provided. Furthermore, users and designers should be aware that head movement behavior may be affected by HMD characteristics.

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Objective: Predicting who responds to placebo treatment-and under which circumstances-has been a question of interest and investigation for generations. However, the literature is disparate and inconclusive. This review aims to identify publications that provide high quality data on the topic of placebo response (PR) prediction. Methods:To identify studies concerned with PR prediction, independent searches were performed in an expert database (for all symptom modalities) and in PubMed (for pain only). Articles were selected when (a) they assessed putative predictors prior to placebo treatment and (b) an adequate control group was included when the associations of predictors and PRs were analyzed.Results: Twenty studies were identified, most with pain as dependent variable. Most predictors of PRs were psychological constructs related to actions, expected outcomes and the emotional valence attached to these events (goal-seeking, self-efficacy/-esteem, locus of control, optimism). Other predictors involved behavioral control (desire for control, eating restraint), personality variables (fun seeking, sensation seeking, neuroticism), or biological markers (sex, a single nucleotide polymorphism related to dopamine metabolism). Finally, suggestibility and beliefs in expectation biases, body consciousness, and baseline symptom severity were found to be predictive. Conclusions:While results are heterogeneous, some congruence of predictors can be identified. PRs mainly appear to be moderated by expectations of how the symptom might change after treatment, or expectations of how symptom repetition can be coped with. It is suggested to include the listed constructs in future research. Furthermore, a closer look at variables moderating symptom change in control groups seems warranted.

Objective To examine the use of two different mobile diet self-monitoring methods for weight loss. Methods Overweight adults (n=81; mean BMI 34.7±5.6 kg/m2) were randomized to self-monitor their diet with a mobile app (App, n=42) or wearable Bite Counter device (Bite, n=39). Both groups received the same behavioral weight loss information via twice weekly podcasts. Weight, physical activity (International Physical Activity Questionnaire), and energy intake (2 dietary recalls) were assessed at 0, 3, and 6 months. Results At six months, 75% of participants completed the trial. The App group lost significantly more weight (-6.8±0.8 kg) than the Bite group (-3.0±0.8 kg; group×time interaction: P<0.001). Changes in energy intake (-621±157 App, -456±167 Bite; P=0.47) or number of days diet was tracked (90.7±59.2 App, 68.4±61.2 Bite; P=0.09) did not differ between groups, but the Bite group had significant increases in physical activity METs min/wk (+2015.4±684.6; P=0.02) compared to little change in the App group (-136.5±630.6; P=0.02). Total weight loss was significantly correlated with number of podcasts downloaded (r=-0.33, P<0.01) and number of days diet was tracked (r=-0.33, P<0.01). Conclusions While frequency of diet tracking was similar between the App and Bite groups, there was greater weight loss observed in the App group.