Is problematic mobile phone use explained by chronotype and personality?

In this study, the relationships among problematic mobile phone use, age, gender, personality and chronotype of Turkish university students were examined. The study included 902 university students (73% female, 27% male) and their participation in the study was anonymous and voluntary. Data were collected from each participant by assessing a demographic questionnaire, Composite Scale of Morningness (CSM) as a measure of chronotype, the Big Five Inventory (BIG-5) for personality assessment and Mobile Phone Problem Usage Scale (MPPUS). The most important result was that CSM scores were the best predictor for problematic mobile phone usage, and as a consequence, evening-oriented university students scored higher on the MPPUS. This result remained, even when compared with the most influential personality predictor, conscientiousness. In addition, while extraversion positively predicted, emotional stable and chronotype negatively predicted problematic mobile phone use. Lastly, age and gender were not predictors of problematic mobile phone use.     

Reducing overestimation in reported mobile phone use associated with epidemiological studies

Case-control studies of mobile phones are commonly based on retrospective, self-reported exposure information, which are often characterized as involving substantial uncertainty concerning data validity. We assessed the validity of self-reported mobile phone use and developed a statistical model to account for the over-reporting of exposure. We collected information on mobile phone use from 70 volunteers using two sources of data: self-report in an interview and network operator records. We used regression models to obtain bias-corrected estimates of exposure. A correlation coefficient of 0.71 was obtained between the self-reported and the network operators' data on average calling time (log-transformed minutes per month). A simple linear regression model, where the duration of calls acquired from network operators is explained with the self-reported duration fitted the data reasonably well (adjusted R(2) 0.51). The constant term was 2.71 and the regression coefficient 0.49 (logarithmic scale). No significant improvement in the model fit was achieved by including potential predictors of accuracy in self-reported exposure estimates, such as the pattern of mobile phone use, the modality of response to the questionnaire or demographic characteristics. Overestimation in self-reported intensity of mobile phone use can be accounted for by the use of regression calibration. The estimates obtained in our study may not be applicable in other contexts, but similar methods could be used to reduce bias in other studies.     

Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep

BackgroundSmartphones are increasingly integrated into everyday life, but frequency of use has not yet been objectively measured and compared to demographics, health information, and in particular, sleep quality.AimsThe aim of this study was to characterize smartphone use by measuring screen-time directly, determine factors that are associated with increased screen-time, and to test the hypothesis that increased screen-time is associated with poor sleep.MethodsWe performed a cross-sectional analysis in a subset of 653 participants enrolled in the Health eHeart Study, an internet-based longitudinal cohort study open to any interested adult (≥ 18 years). Smartphone screen-time (the number of minutes in each hour the screen was on) was measured continuously via smartphone application. For each participant, total and average screen-time were computed over 30-day windows. Average screen-time specifically during self-reported bedtime hours and sleeping period was also computed. Demographics, medical information, and sleep habits (Pittsburgh Sleep Quality Index–PSQI) were obtained by survey. Linear regression was used to obtain effect estimates.ResultsTotal screen-time over 30 days was a median 38.4 hours (IQR 21.4 to 61.3) and average screen-time over 30 days was a median 3.7 minutes per hour (IQR 2.2 to 5.5). Younger age, self-reported race/ethnicity of Black and "Other" were associated with longer average screen-time after adjustment for potential confounders. Longer average screen-time was associated with shorter sleep duration and worse sleep-efficiency. Longer average screen-times during bedtime and the sleeping period were associated with poor sleep quality, decreased sleep efficiency, and longer sleep onset latency.ConclusionsThese findings on actual smartphone screen-time build upon prior work based on self-report and confirm that adults spend a substantial amount of time using their smartphones. Screen-time differs across age and race, but is similar across socio-economic strata suggesting that cultural factors may drive smartphone use. Screen-time is associated with poor sleep. These findings cannot support conclusions on causation. Effect-cause remains a possibility: poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.     

An evaluation of self‐reported mobile phone use compared to billing records among a group of engineers and scientists

Most epidemiologic studies of potential health impacts of mobile phones rely on self‐reported information, which can lead to exposure misclassification. We compared self‐reported questionnaire data among 60 participants, and phone billing records over a 3‐year period (2002–2004). Phone usage information was compared by the calculation of the mean and median number of calls and duration of use, as well as correlation coefficients and associated P‐values. Average call duration from self‐reports was slightly lower than billing records (2.1 min vs. 2.8 min, P = 0.01). Participants reported a higher number of average daily calls than billing records (7.9 vs. 4.1, P = 0.002). Correlation coefficients for average minutes per day of mobile phone use and average number of calls per day were relatively high (R = 0.71 and 0.69, respectively, P < 0.001). Information reported at the monthly level tended to be more accurate than estimates of weekly or daily use. Our findings of modest correlations between self‐reported mobile phone usage and billing records and substantial variability in recall are consistent with previous studies. However, the direction of over‐ and under‐reporting was not consistent with previous research. We did not observe increased variability over longer periods of recall or a pattern of lower accuracy among older age groups compared with younger groups. Study limitations included a relatively small sample size, low participation rates, and potential limited generalizability. The variability within studies and non‐uniformity across studies indicates that estimation of the frequency and duration of phone use by questionnaires should be supplemented with subscriber records whenever practical. Bioelectromagnetics 32:37–48, 2011. © 2010 Wiley‐Liss, Inc.     

An algorithm to construct genetically similar subsets of families with the use of self-reported ethnicity information

We present a simple algorithm that uses self-reported ethnicity information, pedigree structure, and affection status to group families into genetically more homogeneous subsets. This algorithm should prove useful to researchers who wish to perform genetic analyses on more-homogeneous subsets when they suspect that ignoring heterogeneity could lead to false-positive results or loss of power. We applied our algorithm to the self-reported ethnicity information of 159 families from the Veterans Affairs Cooperative Study of schizophrenia. We compared these estimates of population membership with those obtained using the program structure in an analysis of 378 microsatellite markers. We found excellent concordance between family classifications determined using self-reported ethnicity information and our algorithm and those determined using genetic marker data and structure; 158 of the 159 families had concordant classifications. In addition, the degree of admixture estimated using our algorithm and self-reported ethnicity information correlated well with that predicted using the genotype information.     

Impact of random and systematic recall errors and selection bias in case--control studies on mobile phone use and brain tumors in adolescents (CEFALO study)

Whether the use of mobile phones is a risk factor for brain tumors in adolescents is currently being studied. Case--control studies investigating this possible relationship are prone to recall error and selection bias. We assessed the potential impact of random and systematic recall error and selection bias on odds ratios (ORs) by performing simulations based on real data from an ongoing case--control study of mobile phones and brain tumor risk in children and adolescents (CEFALO study). Simulations were conducted for two mobile phone exposure categories: regular and heavy use. Our choice of levels of recall error was guided by a validation study that compared objective network operator data with the self-reported amount of mobile phone use in CEFALO. In our validation study, cases overestimated their number of calls by 9% on average and controls by 34%. Cases also overestimated their duration of calls by 52% on average and controls by 163%. The participation rates in CEFALO were 83% for cases and 71% for controls. In a variety of scenarios, the combined impact of recall error and selection bias on the estimated ORs was complex. These simulations are useful for the interpretation of previous case-control studies on brain tumor and mobile phone use in adults as well as for the interpretation of future studies on adolescents.     

Mobile telephone use is associated with changes in cognitive function in young adolescents

As part of the Mobile Radiofrequency Phone Exposed Users' Study (MoRPhEUS), a cross‐sectional epidemiological study examined cognitive function in secondary school students. We recruited 317, 7th grade students (144 boys, 173 girls, median age 13 years) from 20 schools around Melbourne, Australia. Participants completed an exposure questionnaire based on the Interphone study, a computerised cognitive test battery, and the Stroop colour‐word test. The principal exposure metric was the total number of reported mobile phone voice calls per week. Linear regression models were fitted to cognitive test response times and accuracies. Age, gender, ethnicity, socio‐economic status and handedness were fitted as covariates and standard errors were adjusted for clustering by school. The accuracy of working memory was poorer, reaction time for a simple learning task shorter, associative learning response time shorter and accuracy poorer in children reporting more mobile phone voice calls. There were no significant relationships between exposure and signal detection, movement monitoring or estimation. The completion time for Stroop word naming tasks was longer for those reporting more mobile phone voice calls. The findings were similar for total short message service (SMS, also known as text) messages per week, suggesting these cognitive changes were unlikely due to radiofrequency (RF) exposure. Overall, mobile phone use was associated with faster and less accurate responding to higher level cognitive tasks. These behaviours may have been learned through frequent use of a mobile phone. Bioelectromagnetics 30:678–686, 2009. © 2009 Wiley‐Liss, Inc.     

SAR measurement due to mobile phone exposure in a simulated biological media

The specific absorption rate (SAR) measurements are carried out for compliance testing of personal 3G Mobile phone. The accuracy of this experimental setup has been checked by comparing the SAR in 10?gm of simulated tissue and an arbitrary shaped box. This has been carried out using a 3G mobile Phone at 1718.5?MHz, in a medium simulating brain and muscle phantom. The SAR measurement system consists of a stepper motor to move a monopole E-field probe in two dimensions inside an arbitrary shaped box. The phantom is filled with appropriate frequency-specific fluids with measured electrical properties (dielectric constant and conductivity). That is close to the average for gray and white matters of the brain at the frequencies of interest (1718.5?MHz). Induced fields are measured using a specially designed monopole probe in its close vicinity. The probe is immersed in the phantom material. The measured data for induced fields are used to compute SAR values at various locations with respect to the mobile phone location. It is concluded that these SAR values are position dependent and well below the safety criteria prescribed for human exposure.     

SAR measurement due to mobile phone exposure in a simulated biological media

The specific absorption rate (SAR) measurements are carried out for compliance testing of personal 3G Mobile phone. The accuracy of this experimental setup has been checked by comparing the SAR in 10 gm of simulated tissue and an arbitrary shaped box. This has been carried out using a 3G mobile Phone at 1718.5 MHz, in a medium simulating brain and muscle phantom. The SAR measurement system consists of a stepper motor to move a monopole E-field probe in two dimensions inside an arbitrary shaped box. The phantom is filled with appropriate frequency-specific fluids with measured electrical properties (dielectric constant and conductivity). That is close to the average for gray and white matters of the brain at the frequencies of interest (1718.5 MHz). Induced fields are measured using a specially designed monopole probe in its close vicinity. The probe is immersed in the phantom material. The measured data for induced fields are used to compute SAR values at various locations with respect to the mobile phone location. It is concluded that these SAR values are position dependent and well below the safety criteria prescribed for human exposure.     

Deriving fine-scale models of human mobility from aggregated origin-destination flow data

The spatial dynamics of epidemics are fundamentally affected by patterns of human mobility. Mobile phone call detail records (CDRs) are a rich source of mobility data, and allow semi-mechanistic models of movement to be parameterised even for resource-poor settings. While the gravity model typically reproduces human movement reasonably well at the administrative level spatial scale, past studies suggest that parameter estimates vary with the level of spatial discretisation at which models are fitted. Given that privacy concerns usually preclude public release of very fine-scale movement data, such variation would be problematic for individual-based simulations of epidemic spread parametrised at a fine spatial scale. We therefore present new methods to fit fine-scale mathematical mobility models (here we implement variants of the gravity and radiation models) to spatially aggregated movement data and investigate how model parameter estimates vary with spatial resolution. We use gridded population data at 1km resolution to derive population counts at different spatial scales (down to ∼ 5km grids) and implement mobility models at each scale. Parameters are estimated from administrative-level flow data between overnight locations in Kenya and Namibia derived from CDRs: where the model spatial resolution exceeds that of the mobility data, we compare the flow data between a particular origin and destination with the sum of all model flows between cells that lie within those particular origin and destination administrative units. Clear evidence of over-dispersion supports the use of negative binomial instead of Poisson likelihood for count data with high values. Radiation models use fewer parameters than the gravity model and better predict trips between overnight locations for both considered countries. Results show that estimates for some parameters change between countries and with spatial resolution and highlight how imperfect flow data and spatial population distribution can influence model fit.     

Adjusting body mass for measurement error with invalid validation data

We propose a new method for using validation data to correct self-reported weight and height in surveys that do not measure respondents. The standard correction in prior research regresses actual measures on reported values using an external validation dataset, and then uses the estimated coefficients to predict actual measures in the primary dataset. This approach requires the strong assumption that the expectations of measured weight and height conditional on the reported values are the same in both datasets. In contrast, we use percentile ranks rather than levels of reported weight and height. Our approach requires the weaker assumption that the conditional expectations of actual measures are increasing in reported values in both samples. This makes our correction more robust to differences in measurement error across surveys as long as both surveys represent the same population. We examine three nationally representative datasets and find that misreporting appears to be sensitive to differences in survey context. When we compare predicted BMI distributions using the two validation approaches, we find that the standard correction is affected by differences in misreporting while our correction is not. Finally, we present several examples that demonstrate the potential importance of our correction for future econometric analyses and estimates of obesity rates.     

Poor-to-moderate agreement between self and proxy interviews of mobile phone use

In epidemiological studies, cases cannot always be interviewed due to them being too ill or already deceased. Under these circumstances, proxy interviews are often conducted; however, the veridicality of information about mobile phone use gained by proxy interviews has been doubted. The issue is undecided due to the lack of empirical data. We conducted a study of 119 heterosexual couples. Both partners answered two questionnaires about mobile phone use, one about their own use and one about their partner's use. Overall agreement assessed using Cohen's kappa, Passing and Bablok regression, and concordance coefficients between self and proxy data was poor to moderate (e.g., concordance coefficients of 0.55 for duration of use). The only item with good agreement was whether or not a prepaid phone was used (Cohen's kappa 0.78 and 0.63 for male and female estimates, respectively), and to a lesser degree, the onset of mobile phone use (concordance coefficients of 0.66 and 0.61). Poorest agreement was obtained for the side of the head the mobile phone was held during calls (kappa coefficients of 0.20 and 0.24 for female and male estimates, respectively). We conclude that the assessment of mobile phone use by proxy data cannot be relied on except for information about onset of mobile phone use, use of prepaid or contract phones, and, to a lesser degree, duration of daily use. Agreement concerning the important information about side of the head the mobile phone is held during calls was poorest and only slightly better than chance. Bioelectromagnetics 33:561–567, 2012. © 2012 Wiley Periodicals, Inc.     

How smartphones are changing the face of mobile and participatory healthcare: an overview,with example from eCAALYX

The latest generation of smartphones are increasingly viewed as handheld computers rather than as phones, due to their powerful on-board computing capability, capacious memories, large screens and open operating systems that encourage application development. This paper provides a brief state-of-the-art overview of health and healthcare smartphone apps (applications) on the market today, including emerging trends and market uptake. Platforms available today include Android, Apple iOS, RIM BlackBerry, Symbian, and Windows (Windows Mobile 6.x and the emerging Windows Phone 7 platform). The paper covers apps targeting both laypersons/patients and healthcare professionals in various scenarios, e.g., health, fitness and lifestyle education and management apps; ambient assisted living apps; continuing professional education tools; and apps for public health surveillance. Among the surveyed apps are those assisting in chronic disease management, whether as standalone apps or part of a BAN (Body Area Network) and remote server configuration. We describe in detail the development of a smartphone app within eCAALYX (Enhanced Complete Ambient Assisted Living Experiment, 2009-2012), an EU-funded project for older people with multiple chronic conditions. The eCAALYX Android smartphone app receives input from a BAN (a patient-wearable smart garment with wireless health sensors) and the GPS (Global Positioning System) location sensor in the smartphone, and communicates over the Internet with a remote server accessible by healthcare professionals who are in charge of the remote monitoring and management of the older patient with multiple chronic conditions. Finally, we briefly discuss barriers to adoption of health and healthcare smartphone apps (e.g., cost, network bandwidth and battery power efficiency, usability, privacy issues, etc.), as well as some workarounds to mitigate those barriers.     

A discussion on disease severity index values. Part I: warning on inherent errors and suggestions to maximise accuracy

A special type of ordinal scale comprising a number of intervals of known numeric ranges can be used when estimating severity of a plant disease. The interval ranges are most often based on the percent area with symptoms [e.g. the Horsfall–Barratt (H–B) scale]. Studies in plant pathology and plant breeding often use this type of ordinal scale. The disease severity is estimated by a rater as a value on the scale and has been used to determine a disease severity index (DSI) on a percentage basis, where DSI (%) = [sum (class frequency × score of rating class)]/[(total number of plants) × (maximal disease index)] × 100. However, very few studies have investigated the effects of different scales on accuracy of the DSI. Therefore, the objectives of this study were to investigate the process of calculating a DSI on a percentage basis from ordinal scale data, and to use simulation approaches to explore the effect of using different methods for calculation of the interval range and the nature of the ordinal scales used on the DSI estimates (%). We found that the DSI is particularly prone to overestimation when using the above formula if the midpoint values of the rating class are not considered. Moreover, the results of the simulation studies show that, if rater estimates are unbiased, compared with other methods tested in this study, the most accurate method for estimation of a DSI is to use the midpoint of the severity range for each class with an amended 10% ordinal scale (an ordinal scale based on a 10% linear scale emphasising severities ≤50% disease, with additional grades at low severities). As for biased conditions, the accuracy for calculating DSI estimates (%) will depend mainly on the degree and direction of the rater bias relative to the actual mean value.     

Remote Sensing of Atmospheric Optical Depth Using a Smartphone Sun Photometer

In recent years, smart phones have been explored for making a variety of mobile measurements. Smart phones feature many advanced sensors such as cameras, GPS capability, and accelerometers within a handheld device that is portable, inexpensive, and consistently located with an end user. In this work, a smartphone was used as a sun photometer for the remote sensing of atmospheric optical depth. The top-of-the-atmosphere (TOA) irradiance was estimated through the construction of Langley plots on days when the sky was cloudless and clear. Changes in optical depth were monitored on a different day when clouds intermittently blocked the sun. The device demonstrated a measurement precision of 1.2% relative standard deviation for replicate photograph measurements (38 trials, 134 datum). However, when the accuracy of the method was assessed through using optical filters of known transmittance, a more substantial uncertainty was apparent in the data. Roughly 95% of replicate smart phone measured transmittances are expected to lie within ±11.6% of the true transmittance value. This uncertainty in transmission corresponds to an optical depth of approx. ±0.12–0.13 suggesting the smartphone sun photometer would be useful only in polluted areas that experience significant optical depths. The device can be used as a tool in the classroom to present how aerosols and gases effect atmospheric transmission. If improvements in measurement precision can be achieved, future work may allow monitoring networks to be developed in which citizen scientists submit acquired data from a variety of locations.     

Rapid assessment of forest canopy and light regime using smartphone hemispherical photography

Hemispherical photography (HP), implemented with cameras equipped with “fisheye” lenses, is a widely used method for describing forest canopies and light regimes. A promising technological advance is the availability of low‐cost fisheye lenses for smartphone cameras. However, smartphone camera sensors cannot record a full hemisphere. We investigate whether smartphone HP is a cheaper and faster but still adequate operational alternative to traditional cameras for describing forest canopies and light regimes. We collected hemispherical pictures with both smartphone and traditional cameras in 223 forest sample points, across different overstory species and canopy densities. The smartphone image acquisition followed a faster and simpler protocol than that for the traditional camera. We automatically thresholded all images. We processed the traditional camera images for Canopy Openness (CO) and Site Factor estimation. For smartphone images, we took two pictures with different orientations per point and used two processing protocols: (i) we estimated and averaged total canopy gap from the two single pictures, and (ii) merging the two pictures together, we formed images closer to full hemispheres and estimated from them CO and Site Factors. We compared the same parameters obtained from different cameras and estimated generalized linear mixed models (GLMMs) between them. Total canopy gap estimated from the first processing protocol for smartphone pictures was on average significantly higher than CO estimated from traditional camera images, although with a consistent bias. Canopy Openness and Site Factors estimated from merged smartphone pictures of the second processing protocol were on average significantly higher than those from traditional cameras images, although with relatively little absolute differences and scatter. Smartphone HP is an acceptable alternative to HP using traditional cameras, providing similar results with a faster and cheaper methodology. Smartphone outputs can be directly used as they are for ecological studies, or converted with specific models for a better comparison to traditional cameras.     

Redrawing the US Obesity Landscape: Bias-Corrected Estimates of State-Specific Adult Obesity Prevalence

Background

State-level estimates from the Centers for Disease Control and Prevention (CDC) underestimate the obesity epidemic because they use self-reported height and weight. We describe a novel bias-correction method and produce corrected state-level estimates of obesity and severe obesity.

Methods

Using non-parametric statistical matching, we adjusted self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) 2013 (n = 386,795) using measured data from the National Health and Nutrition Examination Survey (NHANES) (n = 16,924). We validated our national estimates against NHANES and estimated bias-corrected state-specific prevalence of obesity (BMI≥30) and severe obesity (BMI≥35). We compared these results with previous adjustment methods.

Results

Compared to NHANES, self-reported BRFSS data underestimated national prevalence of obesity by 16% (28.67% vs 34.01%), and severe obesity by 23% (11.03% vs 14.26%). Our method was not significantly different from NHANES for obesity or severe obesity, while previous methods underestimated both. Only four states had a corrected obesity prevalence below 30%, with four exceeding 40%–in contrast, most states were below 30% in CDC maps.

Conclusions

Twelve million adults with obesity (including 6.7 million with severe obesity) were misclassified by CDC state-level estimates. Previous bias-correction methods also resulted in underestimates. Accurate state-level estimates are necessary to plan for resources to address the obesity epidemic.     

A Smartphone App and Cloud-Based Consultation System for Burn Injury Emergency Care

Background

Each year more than 10 million people worldwide are burned severely enough to require medical attention, with clinical outcomes noticeably worse in resource poor settings. Expert clinical advice on acute injuries can play a determinant role and there is a need for novel approaches that allow for timely access to advice. We developed an interactive mobile phone application that enables transfer of both patient data and pictures of a wound from the point-of-care to a remote burns expert who, in turn, provides advice back.

Methods and Results

The application is an integrated clinical decision support system that includes a mobile phone application and server software running in a cloud environment. The client application is installed on a smartphone and structured patient data and photographs can be captured in a protocol driven manner. The user can indicate the specific injured body surface(s) through a touchscreen interface and an integrated calculator estimates the total body surface area that the burn injury affects. Predefined standardised care advice including total fluid requirement is provided immediately by the software and the case data are relayed to a cloud server. A text message is automatically sent to a burn expert on call who then can access the cloud server with the smartphone app or a web browser, review the case and pictures, and respond with both structured and personalized advice to the health care professional at the point-of-care.

Conclusions

In this article, we present the design of the smartphone and the server application alongside the type of structured patient data collected together with the pictures taken at point-of-care. We report on how the application will be introduced at point-of-care and how its clinical impact will be evaluated prior to roll out. Challenges, strengths and limitations of the system are identified that may help materialising or hinder the expected outcome to provide a solution for remote consultation on burns that can be integrated into routine acute clinical care and thereby promote equity in injury emergency care, a growing public health burden.     

Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine,Physical Activity,Emotion, and Food Environment

IntroductionSmartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person’s eating behavior, and the value of such approach compared to typical regression models.MethodUsing a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject’s activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments.ResultsAmple compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately.ConclusionA typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement.