Mobile phone-based data collection encompasses the richness of social network research. Both individual-level and network-level measures can be recorded. For example, health-related behaviors can be reported via mobile assessment. Social interactions can be assessed by phone-log data. Yet the potential of mobile phone data collection has largely been untapped. This is especially true of egocentric studies in public health settings where mobile phones can enhance both data collection and intervention delivery, e.g. mobile users can video chat with counselors. This is due in part to privacy issues and other barriers that are more difficult to address outside of academic settings where most mobile research to date has taken place. In this article, we aim to inform a broader discussion on mobile research. In particular, benefits and challenges to mobile phone-based data collection are highlighted through our mobile phone-based pilot study that was conducted on egocentric networks of 12 gay men (n = 44 total participants). HIV-transmission and general health behaviors were reported through a mobile phone-based daily assessment that was administered through study participants' own mobile phones. Phone log information was collected from gay men with Android phones. Benefits and challenges to mobile implementation are discussed, along with the application of multi-level models to the type of longitudinal egocentric data that we collected.

Stata's mi commands provide powerful tools to conduct multiple imputation in the presence of ignorable missing data. In this article, I present Stata code to extend the capabilities of the mi commands to address two areas of statistical inference where results are not easily aggregated across imputed datasets. First, mi commands are restricted to covariate selection. I show how to address model fit to correctly specify a model. Second, the mi commands readily aggregate model-based standard errors. I show how standard errors can be bootstrapped for situations where model assumptions may not be met. I illustrate model specification and bootstrapping on frequency counts for the number of times that alcohol was consumed in data with missing observations from a behavioral intervention.

Background

Relationships between alcohol, marijuana and other drug (AOD) use and contextual factors have mostly been established through retrospective self-report. Given the embeddedness of cell phones in adolescents' daily activities, cell phone-based ecological momentary assessment (CEMA) provides an opportunity to better understand AOD use in youth and how cell phones can be used to self-monitor and deliver interventions. We use CEMA to examine AOD use in Latino youth who have been especially understudied.

Methods

Twenty-eight mostly Latino youth (ages 13-18) in outpatient substance abuse treatment recorded AOD use, contextual factors, cravings, and affect through once-daily CEMA over one month periods. Random-effects logistic regression was used to compare contextual factors between periods of AOD use and non-use.

Results

The most frequent contextual factors reported during AOD use were being with close friends and "hanging out" as the primary activity. During AOD use compared to non-use, youth were more likely to be with close friends (OR=4.76; p<0.01), around users (OR=17.69; p<0.01), and at a friend's house (OR=5.97; p<0.01). Alcohol use was more frequently reported at night (63% vs 34%) and on weekends relative to other substances (64% vs 49%). Strong cravings were more frequently reported on AOD-use days (OR=7.34; p<0.01). Types of positive and negative affect were reported with similar frequencies, regardless of AOD use.

Conclusions

Reporting on social context, location, day and time of day, and cravings all show promise in developing cell phone-based interventions triggered by contextual data.

BACKGROUND

Health behavior patterns reported through daily diary data are important to understand and intervene upon at the individual level in N-of-1 trials and related study designs. There is often interest in relationships between multiple outcomes, such as stress and health behavior. However, analyses often utilize regressions that evaluate aggregate effects across individuals, and standard analyses target single outcomes.

OBJECTIVE

This paper aims to illustrate how individuals’ daily reports of stress and health behavior (time series) can be explored using visualization tools.

METHODS

Secondary analysis was conducted on 6 months of daily diary reports of stress and health behavior (physical activity and diet quality) from mostly ethnic minority mothers who pilot-tested a self-monitoring mobile health app. Time series with minimal missing data from 14 of the 44 mothers were analyzed. Correlations between stress and health behavior within each time series were reported as a preliminary step. Stress and health behavior time series patterns were visualized by plotting moving averages and time points where mean shifts in the data occurred (changepoints).

RESULTS

Median correlation was small and negative for associations of stress with physical activity (r=−.14) and diet quality (r=−.08). Moving averages and changepoints for stress and health behavior were aligned for some participants but not for others. A third subset of participants exhibited little variation in stress and health behavior reports.

CONCLUSIONS

Median correlations in this study corroborate prior findings. In addition, time series visualizations highlighted variations in stress and health behavior across individuals and time points, which are difficult to capture through correlations and regression-based summary measures.

Behavioral interventions are increasingly based on holistic approaches to health with an understanding that health-related behaviors are linked. A motivating example is provided by the Philani study, an intervention trial conducted to improve the health of South African mothers and their children. Inter-related health problems around maternal alcohol use, malnutrition, and HIV were addressed; multiple endpoints were targeted. The traditional hypothesis testing paradigm that tests significance on one primary outcome did not suffice. Past multiple endpoint studies have utilized a sign test on the number of estimated differences between treatment and control that favor the intervention. However, in order to preserve type 1 error, one must account for correlations among the outcomes. We propose an alternative approach that counts the number of significant treatment-control differences. Monte Carlo simulation is used to adjust for correlation, providing updated critical values and p values. Our method is implemented through an R package and applied to the Philani data to test the intervention's overall effect.

Stressful life events and daily hassles affect people living with HIV (PLWH). However, capturing stress-related events and cognitive impairment is often plagued with recall biases. Incorporating reliable information technology, such as mobile phones, can be a resourceful method for measuring health behaviors (MHB). We report findings from an MHB pilot study with 32 African American, Latino/Hispanic, and White PLWH from Los Angeles. Participants reported perceived stressors in their daily routines using a smartphone Ecological Momentary Assessment (EMA) application. Participants self-initiated in-the-moment stressful events reports for up to 6 weeks. Stressful event EMAs queried perceived stress levels (1-10 scale) and open-ended text descriptions. Qualitative analysis of participant text responses was completed using grounded thematic coding. Participants reported multiple stressors in their daily routines, impacting activities of daily living or daily functioning. Eliciting input from PLWH via EMA in real time is a novel approach for assessing and identifying sources of stress.

Stressful life events and daily hassles affect people living with HIV (PLWH). However, capturing stress-related events and cognitive impairment is often plagued with recall biases. Incorporating reliable information technology, such as mobile phones, can be a resourceful method for measuring health behaviors (MHB). We report findings from an MHB pilot study with 32 African American, Latino/Hispanic, and White PLWH from Los Angeles. Participants reported perceived stressors in their daily routines using a smartphone Ecological Momentary Assessment (EMA) application. Participants self-initiated in-the-moment stressful events reports for up to 6 weeks. Stressful event EMAs queried perceived stress levels (1-10 scale) and open-ended text descriptions. Qualitative analysis of participant text responses was completed using grounded thematic coding. Participants reported multiple stressors in their daily routines, impacting activities of daily living or daily functioning. Eliciting input from PLWH via EMA in real time is a novel approach for assessing and identifying sources of stress.

Background

Health behavior patterns reported through daily diary data are important to understand and intervene upon at the individual level in N-of-1 trials and related study designs. There is often interest in relationships between multiple outcomes, such as stress and health behavior. However, analyses often utilize regressions that evaluate aggregate effects across individuals, and standard analyses target single outcomes.

Objective

This paper aims to illustrate how individuals' daily reports of stress and health behavior (time series) can be explored using visualization tools.

Methods

Secondary analysis was conducted on 6 months of daily diary reports of stress and health behavior (physical activity and diet quality) from mostly ethnic minority mothers who pilot-tested a self-monitoring mobile health app. Time series with minimal missing data from 14 of the 44 mothers were analyzed. Correlations between stress and health behavior within each time series were reported as a preliminary step. Stress and health behavior time series patterns were visualized by plotting moving averages and time points where mean shifts in the data occurred (changepoints).

Results

Median correlation was small and negative for associations of stress with physical activity (r=-.14) and diet quality (r=-.08). Moving averages and changepoints for stress and health behavior were aligned for some participants but not for others. A third subset of participants exhibited little variation in stress and health behavior reports.

Conclusions

Median correlations in this study corroborate prior findings. In addition, time series visualizations highlighted variations in stress and health behavior across individuals and time points, which are difficult to capture through correlations and regression-based summary measures.

Background

This study examined clients' treatment satisfaction with the services provided by methadone maintenance treatment (MMT) in China and explored relevant factors that are directly or indirectly associated with treatment satisfaction.

Methods

The study used baseline data from a randomized controlled trial conducted among 2,448 clients from 68 MMT clinics in five provinces of China. The participants reported their demographic characteristics, treatment-related factors, depressive symptoms, treatment progression, counseling rapport, and treatment satisfaction. Structural equation modeling (SEM) was used to test the direct and indirect relationships among various factors and treatment satisfaction.

Results

Clients' demographic characteristics, such as older age, had both a direct effect on treatment satisfaction and an indirect effect mediated by counseling rapport. Depressive symptoms and a lack of social support had a direct negative impact on treatment satisfaction and an indirect effect mediated by treatment progression and counseling rapport. Both mediators: treatment progression (estimate=0.227, p<0.01) and counseling rapport (estimate=0.229, p<0.01), showed positive associations with treatment satisfaction.

Conclusion

The findings reiterate the complex nature of MMT clients' treatment satisfaction and its interrelationship with multidimensional factors. The study has implications for evaluating the quality of care provided by MMT programs and suggests several strategies that can potentially improve MMT clients' level of treatment satisfaction.

Purpose

Mammography, the standard method of breast cancer screening, misses many cancers, especially in dense-breasted women. We compared the performance and diagnostic yield of mammography alone versus an automated whole breast ultrasound (AWBU) plus mammography in women with dense breasts and/or at elevated risk of breast cancer.

Methods

AWBU screening was tested in 4,419 women having routine mammography (

Trial registration

ClinicalTrials.gov Identifier: NCT00649337). Cancers occurring during the study and subsequent 1-year follow-up were evaluated. Sensitivity, specificity and positive predictive value (PPV) of biopsy recommendation for mammography alone, AWBU and mammography with AWBU were calculated.

Results

Breast cancer detection doubled from 23 to 46 in 6,425 studies using AWBU with mammography, resulting in an increase in diagnostic yield from 3.6 per 1,000 with mammography alone to 7.2 per 1,000 by adding AWBU. PPV for biopsy based on mammography findings was 39.0% and for AWBU 38.4%. The number of detected invasive cancers 10 mm or less in size tripled from 7 to 21 when AWBU findings were added to mammography.

Conclusion

AWBU resulted in significant cancer detection improvement compared with mammography alone. Additional detection and the smaller size of invasive cancers may justify this technology's expense for women with dense breasts and/or at high risk for breast cancer.