Explore the vast range of titles available.

Psychologists typically rely on self-report data when quantifying mobile phone usage, despite little evidence of its validity. In this paper we explore the accuracy of using self-reported estimates when compared with actual smartphone use. We also include source code to process and visualise these data. We compared 23 participants' actual smartphone use over a two-week period with self-reported estimates and the Mobile Phone Problem Use Scale. Our results indicate that estimated time spent using a smartphone may be an adequate measure of use, unless a greater resolution of data are required. Estimates concerning the number of times an individual used their phone across a typical day did not correlate with actual smartphone use. Neither estimated duration nor number of uses correlated with the Mobile Phone Problem Use Scale. We conclude that estimated smartphone use should be interpreted with caution in psychological research.

Smartphone apps claim to help conditions from addiction to schizophrenia, but few have been thoroughly tested.

Data from controlled trials indicate that Web-based interventions generally suffer from low engagement and high attrition. This is important because the level of exposure to intervention content is linked to intervention effectiveness. However, data from real-life Web-based behavior change interventions are scarce, especially when looking at physical activity promotion. The aims of this study were to (1) examine the engagement with the freely available physical activity promotion program 10,000 Steps, (2) examine how the use of a smartphone app may be helpful in increasing engagement with the intervention and in decreasing nonusage attrition, and (3) identify sociodemographic- and engagement-related determinants of nonusage attrition. Users (N=16,948) were grouped based on which platform (website, app) they logged their physical activity: Web only, app only, or Web and app. Groups were compared on sociodemographics and engagement parameters (duration of usage, number of individual and workplace challenges started, and number of physical activity log days) using ANOVA and chi-square tests. For a subsample of users that had been members for at least 3 months (n=11,651), Kaplan-Meier survival curves were estimated to plot attrition over the first 3 months after registration. A Cox regression model was used to determine predictors of nonusage attrition. In the overall sample, user groups differed significantly in all sociodemographics and engagement parameters. Engagement with the program was highest for Web-and-app users. In the subsample, 50.00% (5826/11,651) of users stopped logging physical activity through the program after 30 days. Cox regression showed that user group predicted nonusage attrition: Web-and-app users (hazard ratio=0.86, 95% CI 0.81-0.93, P<.001) and app-only users (hazard ratio=0.63, 95% CI 0.58-0.68, P<.001) showed a reduced attrition risk compared to Web-only users. Further, having a higher number of individual challenges (hazard ratio=0.62, 95% CI 0.59-0.66, P<.001), workplace challenges (hazard ratio=0.94, 95% CI 0.90-0.97, P<.001), physical activity logging days (hazard ratio=0.921, 95% CI 0.919-0.922, P<.001), and steps logged per day (hazard ratio=0.99999, 95% CI 0.99998-0.99999, P<.001) were associated with reduced nonusage attrition risk as well as older age (hazard ratio=0.992, 95% CI 0.991-0.994, P<.001), being male (hazard ratio=0.85, 95% CI 0.82-0.89, P<.001), and being non-Australian (hazard ratio=0.87, 95% CI 0.82-0.91, P<.001). Compared to other freely accessible Web-based health behavior interventions, the 10,000 Steps program showed high engagement. The use of an app alone or in addition to the website can enhance program engagement and reduce risk of attrition. Better understanding of participant reasons for reducing engagement can assist in clarifying how to best address this issue to maximize behavior change.

Study Objectives: Several inexpensive, readily available smartphone apps that claim to monitor sleep are popular among patients. However, their accuracy is unknown, which limits their widespread clinical use. We therefore conducted this study to evaluate the validity of parameters reported by one such app, the Sleep Time app (Azumio, Inc., Palo Alto, CA, USA) for iPhones. Methods: Twenty volunteers with no previously diagnosed sleep disorders underwent in-laboratory polysomnography (PSG) while simultaneously using the app. Parameters reported by the app were then compared to those obtained by PSG. In addition, an epoch-by-epoch analysis was performed by dividing the PSG and app graph into 15-min epochs. Results: There was no correlation between PSG and app sleep efficiency (r = −0.127, p = 0.592), light sleep percentage (r = 0.024, p = 0.921), deep sleep percentage (r = 0.181, p = 0.444) or sleep latency (rs = 0.384, p = 0.094). The app slightly and nonsignificantly overestimated sleep efficiency by 0.12% (95% confidence interval [CI] −4.9 to 5.1%, p = 0.962), significantly underestimated light sleep by 27.9% (95% CI 19.4–36.4%, p < 0.0001), significantly overestimated deep sleep by 11.1% (CI 4.7–17.4%, p = 0.008) and significantly overestimated sleep latency by 15.6 min (CI 9.7–21.6, p < 0.0001). Epochwise comparison showed low overall accuracy (45.9%) due to poor interstage discrimination, but high accuracy in sleep-wake detection (85.9%). The app had high sensitivity but poor specificity in detecting sleep (89.9% and 50%, respectively). Conclusions: Our study shows that the absolute parameters and sleep staging reported by the Sleep Time app (Azumio, Inc.) for iPhones correlate poorly with PSG. Further studies comparing app sleep-wake detection to actigraphy may help elucidate its potential clinical utility. Commentary: A commentary on this article appears in this issue on page 695. Citation: Bhat S, Ferraris A, Gupta D, Mozafarian M, DeBari VA, Gushway-Henry N, Gowda SP, Polos PG, Rubinstein M, Seidu H, Chokroverty S. Is there a clinical role for smartphone sleep apps? Comparison of sleep cycle detection by a smartphone application to polysomnography. J Clin Sleep Med 2015;11(7):709–715.

This article presents a review of the research on the use of mobile touch-screen devices such as PDAs, iPod Touches, iPads and smart phones by people with developmental disabilities. Most of the research has been on very basic use of the devices as speech generating devices, as a means of providing video, pictorial and/or audio self-prompting and for leisure activities such as listening to music and watching videos. Most research studies were small-n designs that provided a preponderant level of research evidence. There is a clear need for more research with younger participants and with a much wider range of apps, including educational apps.

This study investigated the mediating effects of loneliness and depression on the relationship between adult attachment and smartphone addiction in university students. Methods A total of 200 university students participated in this study. The data was analysed using descriptive statistics, correlation analysis, and structural equation modeling. Results There were significant positive relationships between attachment anxiety, loneliness, depression, and smartphone addiction. However, attachment anxiety was not significantly correlated with smartphone addiction. The results also showed that loneliness did not directly mediate between attachment anxiety and smartphone addiction. In addition, loneliness and depression serially mediated between attachment anxiety and smartphone addiction. Conclusion The results suggest there are mediating effects of loneliness and depression in the relationship between attachment anxiety and smartphone addiction. The hypothesized model was found to be a suitable model for predicting smartphone addiction among university students. Future study is required to find a causal path to prevent smartphone addiction among university students.

Despite the substantial increase in the number of adolescent smartphone users, few studies have investigated the behavioural effects of smartphone use on adolescent students as it relates to musculoskeletal discomfort. The purpose of this study was to explore the association between smartphone use and musculoskeletal discomfort in students at a Taiwanese junior college. We hypothesised that the duration of smartphone use would be associated with increased instances of musculoskeletal discomfort in these students. This cross-sectional study employed a convenience sampling method to recruit students from a junior college in southern Taiwan. All the students (n = 315) were asked to answer questionnaires on smartphone use. A descriptive analysis, stepwise regression, and logistic regression were used to examine specific components of smartphone use and their relationship to musculoskeletal discomfort. Nearly half of the participants experienced neck and shoulder discomfort. The stepwise regression results indicated that the number of body parts with discomfort (F = 6.009, p < 0.05) increased with hours spent using ancillary smartphone functions. The logistic regression analysis showed that the students who talked on the phone > 3 h/day had a higher risk of upper back discomfort than did those who talked on the phone < 1 h/day [odds ratio (OR) = 4.23, p < 0.05]. This study revealed that the relationship between smartphone use and musculoskeletal discomfort is related to the duration of smartphone ancillary function use. Moreover, hours spent talking on the phone was a predictor of upper back discomfort.

Fecal calprotectin is a reliable noninvasive marker for intestinal inflammation usable for monitoring patients with inflammatory bowel disease. Tests are usually performed by enzyme-linked immunosorbent assay (ELISA), which is time consuming and delays results, thus limiting its use in clinical practice. Our aim was to evaluate CalproSmart, a new rapid test for fecal calprotectin performed by patients themselves at home, and compare it to gold standard ELISA.MethodsA total of 221 patients with inflammatory bowel disease (115 ulcerative colitis and 106 Crohn's disease) were included. The CalproSmart test involves extraction of feces, application to the lateral flow device, and taking a picture with a smartphone after 10 minutes of incubation. Results appear on the screen within seconds. Patients were instructed at inclusion and had a video guide of the procedure as support. When using CalproSmart at home, patients also sent in 2 fecal samples to be analyzed by ELISA.ResultsTotally, 894 fecal calprotectin results were obtained by ELISA, and 632 of them from CalproSmart. The correlation coefficient was 0.685, higher for academics than nonacademics (0.768 versus 0.637; P = 0.0037). The intra-assay and interassay coefficients of variation of the CalproSmart test were 4.42% and 12.49%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value were 82%, 85%, 47%, and 97%, respectively, with an optimal cutoff at 150 μg/g.ConclusionsThe CalproSmart test performed by patients with inflammatory bowel disease for fast assessment of gut inflammation seems a reliable alternative to ELISA and presents a new way of monitoring patients by eHealth.

Objective. South Korea has the highest rate of smartphone ownership worldwide, which is a potential concern given that smartphone dependency may have deleterious effects on health. We investigated the relationship between smartphone dependency and anxiety. Methods. Participants included 1,236 smartphone-using students (725 men and 511 women) from six universities in Suwon, South Korea. Participants completed measures of smartphone use, smartphone dependency, anxiety, and general characteristics (i.e., demographic, health-related, and socioeconomic characteristics). To measure smartphone dependency and anxiety, we used questionnaires of Yang's test developed from Young's Internet Addiction Test and Zung's Self-Rating Anxiety Scale. We used multiple logistic regression to determine the association between smartphone dependency and anxiety after adjusting for relevant factors. Results. On a scale from 25 to 100, with higher scores on the smartphone dependency test indicating greater dependency, women were significantly more dependent on smartphones than were men (mean smartphone dependency score: 50.7 vs. 56.0 for men and women, respectively, p<0.001). However, the amount of time spent using smartphones and the purpose of smartphone use affected smartphone dependency in both men and women. Particularly, when daily use time increased, smartphone dependency showed an increasing trend. Compared with times of use <2 hours vs. ≥6 hours, men scored 46.2 and 56.0 on the smartphone dependency test, while women scored 48.0 and 60.4, respectively (p<0.001). Finally, for both men and women, increases in smartphone dependency were associated with increased anxiety scores. With each one-point increase in smartphone dependency score, the risk of abnormal anxiety in men and women increased by 10.1% and 9.2%, respectively (p<0.001). Conclusion. Among this group of university students in South Korea, smartphone dependency appeared to be associated with increased anxiety. Standards for smartphone use might help prevent deleterious health effects.

We examined the types of technology used by medical students in clinical clerkships, and the perception of technology implementation into the curriculum. An online survey about technology use was completed prior to general surgery clinical clerkship. Types of devices and frequency/comfort of use were recorded. Perceptions of the benefits and barriers to technology use in clerkship learning were elicited. 125/131 (95.4%) students responded. Most students owned a smart phone (95.2%), tablet (52.8%), or both (50%); 61.6% spent > 11 h/week learning on a device at the Johns Hopkins School of Medicine for educational purposes. Technology use was seen as beneficial by 97.6% of students. Classes that used technology extensively were preferred by 54% of students, although 47.2% perceived decreased faculty/classmate interaction. Students use mobile technology to improve how they learn new material, and prefer taking classes that incorporate information technology. However, in-person/blended curricula are preferable to completely online courses.