A study was undertaken to examine and test the effectiveness of a self-experimentation model, guided by a mobile app called PACO, in helping college students improve behaviors associated with sleep. Thirteen participants were enrolled in this study and their nightly sleep quality and sleep duration were measured via PACO as they underwent three conditions: a baseline non-intervention phase, an expert-developed intervention phase, in which pre-made intervention examples were provided and used in PACO, and a self-experimentation phase, during which users were invited to develop their own sleep-behavior interventions using PACO. The participants were randomly placed into three groups, and the points of transition between phases was staggered across five weeks according to a multiple baseline design. The goal and hypothesis was to determine if sleep duration and sleep quality (sleep satisfaction) were improved in the final self-experimentation phase compared to the expert-developed experimentation phase and baseline phase, as well as in the expert-developed experimentation phase compared to the baseline phase. The results show little change, and nearly no improvement in the outcome measures between phases, leaving us unable to support the hypothesis. However, the existence of several limitations considered in retrospect, such as the small sample size, the short study time period, and technical difficulties with the PACO application means that no concrete conclusions should be made regarding the effectiveness of the self-experimentation model, nor the usability of PACO. Additional research should be made toward user motivation and modes of teaching the underlying behavioral science principles to casual users to increase effectiveness.

Background: Although current technological advancements have allowed for objective measurements of sedentary behavior via accelerometers, these devices do not provide the contextual information needed to identify targets for behavioral interventions and generate public health guidelines to reduce sedentary behavior. Thus, self-reports still remain an important method of measurement for physical activity and sedentary behaviors.

Objective: This study evaluated the reliability, validity, and sensitivity to change of a smartphone app in assessing sitting, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA).
Methods: Adults (N=28; 49.0 years old, standard deviation [SD] 8.9; 85% men; 73% Caucasian; body mass index=35.0, SD 8.3 kg/m2) reported their sitting, LPA, and MVPA over an 11-week behavioral intervention. During three separate 7-day periods, participants wore the activPAL3c accelerometer/inclinometer as a criterion measure. Intraclass correlation (ICC; 95% CI) and bias estimates (mean difference [δ] and root of mean square error [RMSE]) were used to compare app-based reported behaviors to measured sitting time (lying/seated position), LPA (standing or stepping at <100 steps/minute), and MVPA (stepping at >100 steps/minute).

Results: Test-retest results suggested moderate agreement with the criterion for sedentary time, LPA, and MVPA (ICC=0.65 [0.43-0.82], 0.67 [0.44-0.83] and 0.69 [0.48-0.84], respectively). The agreement between the two measures was poor (ICC=0.05-0.40). The app underestimated sedentary time (δ=-45.9 [-67.6, -24.2] minutes/day, RMSE=201.6) and overestimated LPA and MVPA (δ=18.8 [-1.30 to 38.9] minutes/day, RMSE=183; and δ=29.3 [25.3 to 33.2] minutes/day, RMSE=71.6, respectively). The app underestimated change in time spent during LPA and MVPA but overestimated change in sedentary time. Both measures showed similar directions in changed scores on sedentary time and LPA.

Conclusions: Despite its inaccuracy, the app may be useful as a self-monitoring tool in the context of a behavioral intervention. Future research may help to clarify reasons for under- or over-reporting of behaviors.

Although many studies have looked into the relationship between depression and eating behaviors, most have not looked into the interaction between depressive mood, weight status, and eating behaviors; specifically the consumption of added sugars. This longitudinal study examined the relationship between depressive mood and added sugar consumption among college freshmen, and how weight status play a role in this relationship. A web-based survey assessing depressive mood score and added-sugar foods consumption, and height and weight measurements were obtained. A total of 511 participants (aged 18.5±0.4 years; 70.5% females) were recruited at Arizona State University from August 2015 through January 2016. The main outcomes measured were the relationship between depressive mood score and added sugar consumption (tsp/d) within each participants and between mean weight status groups (underweight & “healthy” weight, overweight, and obese). In the study, the mean added sugar consumption was 19.1±11.87 tsp/d. There were no significant association between depressive mood and added sugar consumption within or between freshman students over time. But overall, there was a slightly positive relationship between depressive mood and added sugar consumption across four time points. No significant interaction was found between BMI, depressive mood, and added sugar consumption within each student, but significant differences in the relationship of depressive mood and added sugar between mean weight status groups (p=0.025). Each individual’s BMI in the previous time points was significantly negatively associated with added sugar consumption in the current time points (beta = -0.70; p=0.010). The results from this study indicates that depressive mood may not affect added sugar intake in this sample. BMI did not have an impact on the relationship within each student, but have an impact between mean weight status groups, so further studies are needed to continue look at how BMI influences the relationship between depressive mood and added sugar consumption.

It is widely documented and accepted that athletes have difficulty maintaining adequate hydration status and that dehydration is a key risk factor for the heat-related illnesses commonly observed among athletes. Research has also suggested that hydration status can influence cognitive performance. Educational interventions focused on rehydration strategies have had minimal success reducing dehydration rates; hence, alternative interventions promoting adequate hydration status in athletes should be explored. This trial examined the efficacy of a commercial hydration mobile application (app) for reducing dehydration rates in campus athletes. Fifty-eight college students aged 18-40 y, who participated in club-level collegiate athletics were recruited from a large Southwestern university and randomized by team to one of two study arms, the Standard of Care – Education (EDU) or the hydration mobile app (APP), to determine if app technology improved hydration status as compared to traditional education messaging. Twenty-three (79%) in the EDU group and twenty (69%) in the APP group were mildly-dehydrated at baseline based on the three-day averages of hydration assessment (USG 1.010). Moreover, 31% (n=9) and 28% (n=8) of the EDU and APP groups, respectively, were dehydrated (USG 1.020). No significant differences were found between the EDU and APP groups following the intervention. Three-day average post-intervention USG testing showed 76% (n=22) and 72% (n=21) of the EDU and APP groups respectively were at best mildly-dehydrated. Additionally, 28% (n=8) and 17% (n=5) were considered dehydrated. Neither intervention improved hydration status after four weeks of treatment. Further analyses of cognitive measures were conducted by hydration assessment groups at baseline and post-intervention: hydrated (HYD) (USG < 1.020) or dehydrated (DEH) (USG 1.020). No significant differences between hydration status were found between intervention groups. Additionally, no significant improvements were seen for either group, which indicates there is still a need for a novel way to improve hydration status in this population. Multi-dimensional interventions and individualized interventions to improve hydration status in this at-risk population may be more effective. Additional research should be conducted to determine if there is any cognitive performance enhancement associated with dehydration or mild-dehydration by reassessing previous data and conducting future trials.

Mobile devices are a promising channel for delivering just-in-time guidance and support for improving key daily health behaviors. Despite an explosion of mobile phone applications aimed at physical activity and other health behaviors, few have been based on theoretically derived constructs and empirical evidence. Eighty adults ages 45 years and older who were insufficiently physically active, engaged in prolonged daily sitting, and were new to smartphone technology, participated in iterative design development and feasibility testing of three daily activity smartphone applications based on motivational frames drawn from behavioral science theory and evidence. An “analytically” framed custom application focused on personalized goal setting, self-monitoring, and active problem solving around barriers to behavior change. A “socially” framed custom application focused on social comparisons, norms, and support.

An “affectively” framed custom application focused on operant conditioning principles of reinforcement scheduling and emotional transference to an avatar, whose movements and behaviors reflected the physical activity and sedentary levels of the user. To explore the applications' initial efficacy in changing regular physical activity and leisure-time sitting, behavioral changes were assessed across eight weeks in 68 participants using the CHAMPS physical activity questionnaire and the Australian sedentary behavior questionnaire. User acceptability of and satisfaction with the applications was explored via a post-intervention user survey. The results indicated that the three applications were sufficiently robust to significantly improve regular moderate-to-vigorous intensity physical activity and decrease leisure-time sitting during the 8-week behavioral adoption period. Acceptability of the applications was confirmed in the post-intervention surveys for this sample of midlife and older adults new to smartphone technology. Preliminary data exploring sustained use of the applications across a longer time period yielded promising results. The results support further systematic investigation of the efficacy of the applications for changing these key health-promoting behaviors.

Background: Physical activity (PA) interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible.

Objective: To test an adaptive intervention for PA based on Operant and Behavior Economic principles and a percentile-based algorithm. The adaptive intervention was hypothesized to result in greater increases in steps per day than the static intervention.

Methods: Participants (N = 20) were randomized to one of two 6-month treatments: 1) static intervention (SI) or 2) adaptive intervention (AI). Inactive overweight adults (85% women, M = 36.9±9.2 years, 35% non-white) in both groups received a pedometer, email and text message communication, brief health information, and biweekly motivational prompts. The AI group received daily step goals that adjusted up and down based on the percentile-rank algorithm and micro-incentives for goal attainment. This algorithm adjusted goals based on a moving window; an approach that responded to each individual's performance and ensured goals were always challenging but within participants' abilities. The SI group received a static 10,000 steps/day goal with incentives linked to uploading the pedometer's data.

Results: A random-effects repeated-measures model accounted for 180 repeated measures and autocorrelation. After adjusting for covariates, the treatment phase showed greater steps/day relative to the baseline phase (p<.001) and a group by study phase interaction was observed (p = .017). The SI group increased by 1,598 steps/day on average between baseline and treatment while the AI group increased by 2,728 steps/day on average between baseline and treatment; a significant between-group difference of 1,130 steps/day (Cohen's d = .74).

Conclusions: The adaptive intervention outperformed the static intervention for increasing PA. The adaptive goal and feedback algorithm is a “behavior change technology” that could be incorporated into mHealth technologies and scaled to reach large populations.

Background: Little research has explored who responds better to an automated vs. human advisor for health behaviors in general, and for physical activity (PA) promotion in particular. The purpose of this study was to explore baseline factors (i.e., demographics, motivation, interpersonal style, and external resources) that moderate intervention efficacy delivered by either a human or automated advisor.

Methods: Data were from the CHAT Trial, a 12-month randomized controlled trial to increase PA among underactive older adults (full trial N = 218) via a human advisor or automated interactive voice response advisor. Trial results indicated significant increases in PA in both interventions by 12 months that were maintained at 18-months. Regression was used to explore moderation of the two interventions.

Results: Results indicated amotivation (i.e., lack of intent in PA) moderated 12-month PA (d = 0.55, p < 0.01) and private self-consciousness (i.e., tendency to attune to one’s own inner thoughts and emotions) moderated 18-month PA (d = 0.34, p < 0.05) but a variety of other factors (e.g., demographics) did not (p > 0.12).

Conclusions: Results provide preliminary evidence for generating hypotheses about pathways for supporting later clinical decision-making with regard to the use of either human- vs. computer-delivered interventions for PA promotion.

Monitoring vital physiological signals, such as heart rate, blood pressure and breathing pattern, are basic requirements in the diagnosis and management of various diseases. Traditionally, these signals are measured only in hospital and clinical settings. An important recent trend is the development of portable devices for tracking these physiological signals non-invasively by using optical methods. These portable devices, when combined with cell phones, tablets or other mobile devices, provide a new opportunity for everyone to monitor one’s vital signs out of clinic.

This thesis work develops camera-based systems and algorithms to monitor several physiological waveforms and parameters, without having to bring the sensors in contact with a subject. Based on skin color change, photoplethysmogram (PPG) waveform is recorded, from which heart rate and pulse transit time are obtained. Using a dual-wavelength illumination and triggered camera control system, blood oxygen saturation level is captured. By monitoring shoulder movement using differential imaging processing method, respiratory information is acquired, including breathing rate and breathing volume. Ballistocardiogram (BCG) is obtained based on facial feature detection and motion tracking. Blood pressure is further calculated from simultaneously recorded PPG and BCG, based on the time difference between these two waveforms.

The developed methods have been validated by comparisons against reference devices and through pilot studies. All of the aforementioned measurements are conducted without any physical contact between sensors and subjects. The work presented herein provides alternative solutions to track one’s health and wellness under normal living condition.

PURPOSE: This study aimed to identify whether increased Pokémon GO use resulted in increased daily steps, compared to days when an individual did not play. In addition, this study examined Pokémon GO as a use case for for the study of gamification, particularly whether traditionally identified game mechanics in gamification literature were successfully identified as elements players enjoy when playing Pokémon GO. METHODS: A mixed methods approach, with 17 participants taking part in a daily physical activity tracking study and 14 participants participating in semi-structured interviews. In the use study, participant steps were tracked for one week using the Apple Health Kit application, and participants were also asked to provide daily answers to a variety of questions assessing game preferences and daily use of Pokémon GO - using the application called PACO. The semi-structured interviews examined self-reported physical activity, and asked questions pertaining to use of Pokémon GO, such as motivation to play. RESULTS: Results assessed by t-test indicate a small but non-significant trend towards increased steps taken on days when a participant played vs. did not play (t(72)=- .56, p=.57, mplay=5,0153220, mnonplay=4,5152,959). This was confirmed with a mixed model test showing that when controlling for time and participant’s baseline level of steps, there was no significant effect on steps/day. Results from the daily surveys and also the semi-structured interviews, indicated that nostalgia (i.e., catching ones’ favorite childhood Pokémon), was a strong motivator for many to play the game, which was counter to theoretical expectations. In line with previous theory, results suggested that operant conditioning principles appeared to be at work in terms of fostering game play use. DISCUSSION: Results of this study, which was a primarily hypothesis generating endeavor, indicated possible trends toward increased steps on days when a person plays Pokémon G), but - with such a small sample, and short-term length of study - no firm conclusions can be drawn. Further, results indicate the particular value of nostalgia as a driver towards game play for Pokémon GO.

This study aimed to identify the emotional/affective sources of discrepancies between physical activity behavior and a widely used self-perception measure of physical activity motivation. Overweight women (body mass index [BMI] ≥ 25 kg/m2, 18-64 years of age; N=37) were recruited from Arizona State University community through flyers and online newsletters. Participants wore a SenseWear accelerometer for 6 nights and 7 days and followed their normal patterns of daily living. Participants then completed a single lab visit and verbally responded to questions from the Behavorial Regulation Exercise Questionnaire (BREQ-2) while being video and audio recorded. Captured emotional responses were evaluated with facial recognition software (Noldus FaceReader). Discrepancies between BREQ-2 responses and physical activity behavior were associated with happiness and sadness emotional responses extracted from the facial recognition software using regression-based analyses. Results indicated an association between monitored physical activities and captured emotional response - specifically sadness - and that as intensity in physical activity increases, motivation increases. Associations between happiness/sadness and physical activity were not observed for all intensities of physical activity. A marginally significant association was observed for amotivation and sedentary, light-intensity physical activity, and moderate-vigorous physical activity in the sample. This study demonstrates a proof-of-concept for the integration of an empirical evaluation of happiness and sadness emotional states into the relationship between physical activity motivation and behavior.