Although the last decade has witnessed mounting research on the development and evaluation of positive interventions, investigators still know little about the target population of such interventions: happiness seekers. The present research asked three questions about happiness seekers: (1) What are their general characteristics?, (2) What do they purposefully do to become happier?, and (3) How do they make use of self-help resources? In Study 1, we identified two distinct clusters of online happiness seekers. In Study 2, we asked happiness seekers to report on their use of 14 types of happiness-seeking behaviors. In Study 3, we tracked happiness seekers' usage of an iPhone application that offered access to eight different happiness-increasing activities, and assessed their resulting happiness and mood improvements. Together, these studies provide a preliminary portrait of happiness seekers' characteristics and naturalistic behaviors.

People spend considerable amounts of time and money listening to music, watching TV and movies, and reading books and magazines, yet almost no attention in psychology has been devoted to understanding individual differences in preferences for such entertainment. The present research was designed to examine the structure and correlates of entertainment genre preferences. Analyses of the genre preferences of over 3,000 individuals revealed a remarkably clear factor structure. Using multiple samples, methods, and geographic regions, data converged to reveal five entertainment-preference dimensions: Communal, Aesthetic, Dark, Thrilling, and Cerebral. Preferences for these entertainment dimensions were uniquely related to demographics and personality traits. Results also indicated that personality accounted for significant proportions of variance in entertainment preferences over and above demographics. The results provide a foundation for developing and testing hypotheses about the psychology of entertainment preferences. Keywords Personality; Media; Entertainment; PreferencesIn an average week, the typical American spends approximately 38 hours watching television shows and movies, 8 hours reading books, magazines, and newspapers, and 18 hours listening to recorded music and radio (Motion Picture Association of America, 2007). Assuming the average person sleeps eight hours a night, people spend roughly 55% of their waking hours attending to entertainment media. Americans spend almost as much of their annual incomes on entertainment as they do on health care, and more money is spent on entertainment than on education, personal care, and charitable donations (United States Bureau of Labor, 2008). Of the money spent on entertainment, cable and satellite TV account for 39%, books, magazines, and newspapers for 23%, movie consumption for 19%, recorded music for 6%, and Internet, video games, mobile content, and satellite radio combined for 13% (Motion Picture Association of America, 2007). NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptConsidering that entertainment media are nearly ubiquitous, it is astonishing that they have received so little attention in personality and social psychology. Indeed, of the nearly 15,000 articles published between 1932 and 2008 in the Journal of Personality and Social Psychology, Journal of Personality, and Personality and Social Psychology Bulletin, "television," "movie," "film," "music," "book," "magazine," or "media" were listed as subject headings in only 90 of them-a mere 0.6%. Many psychologists have argued that researchers need to broaden their research foci and pay more attention to ordinary aspects of people's daily lives so that we can develop an understanding of social behavior that is more ecologically sensitive (e.g., Funder, 2001;Rozin, 2001). Entertainment is undoubtedly important to people and permeates many aspects of social life, yet we know little about it. The present work was designed to explore the landscape of this undeveloped te...

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BackgroundAssessing the efficacy of Internet interventions that are already in the market introduces both challenges and opportunities. While vast, often unprecedented amounts of data may be available (hundreds of thousands, and sometimes millions of participants with high dimensions of assessed variables), the data are observational in nature, are partly unstructured (eg, free text, images, sensor data), do not include a natural control group to be used for comparison, and typically exhibit high attrition rates. New approaches are therefore needed to use these existing data and derive new insights that can augment traditional smaller-group randomized controlled trials.ObjectiveOur objective was to demonstrate how emerging big data approaches can help explore questions about the effectiveness and process of an Internet well-being intervention.MethodsWe drew data from the user base of a well-being website and app called Happify. To explore effectiveness, multilevel models focusing on within-person variation explored whether greater usage predicted higher well-being in a sample of 152,747 users. In addition, to explore the underlying processes that accompany improvement, we analyzed language for 10,818 users who had a sufficient volume of free-text response and timespan of platform usage. A topic model constructed from this free text provided language-based correlates of individual user improvement in outcome measures, providing insights into the beneficial underlying processes experienced by users.ResultsOn a measure of positive emotion, the average user improved 1.38 points per week (SE 0.01, t122,455=113.60, P<.001, 95% CI 1.36–1.41), about a 27% increase over 8 weeks. Within a given individual user, more usage predicted more positive emotion and less usage predicted less positive emotion (estimate 0.09, SE 0.01, t6047=9.15, P=.001, 95% CI .07–.12). This estimate predicted that a given user would report positive emotion 1.26 points higher after a 2-week period when they used Happify daily than during a week when they didn’t use it at all. Among highly engaged users, 200 automatically clustered topics showed a significant (corrected P<.001) effect on change in well-being over time, illustrating which topics may be more beneficial than others when engaging with the interventions. In particular, topics that are related to addressing negative thoughts and feelings were correlated with improvement over time.ConclusionsUsing observational analyses on naturalistic big data, we can explore the relationship between usage and well-being among people using an Internet well-being intervention and provide new insights into the underlying mechanisms that accompany it. By leveraging big data to power these new types of analyses, we can explore the workings of an intervention from new angles, and harness the insights that surface to feed back into the intervention and improve it further in the future.

Psychological interventions targeting wellbeing can reliably increase wellbeing and decrease depressive symptoms. However, only a handful of studies have implemented wellbeing interventions online, and those studies have largely done so in a way that prioritizes experimental control over realism and scalability. We sought to take existing wellbeing interventions with established efficacy and to evaluate their impact when translated into a format that is publicly accessible, scalable, and designed with the goal of engaging users. Participants in this fully online trial were first-time registrants of the Happify platform, a fully automated web and mobile wellbeing intervention grounded in positive psychology, cognitivebehavioral therapy, and mindfulness-based stress reduction, which has offered wellbeing programs to over 3 million registrants to date. Consenting participants were randomly assigned to access the full Happify platform or a psychoeducation comparison condition and further categorized by their usage during the study: recommended usage (a minimum of 2-3 activities per week) or low usage (usage less than the recommended level). Participants were assessed on depressive symptoms, anxiety symptoms, and a composite measure of resilience at baseline and 8 weeks later. Participants who used Happify at the recommended level reported fewer depressive and anxiety symptoms and greater resilience after 8 weeks than participants who used Happify at a low level or participants who used the psychoeducation condition at any level. The Happify group also experienced greater rates of reduction in depression and anxiety symptom severity category, and had a greater net benefit (% users who improved minus % users who deteriorated), compared to the other groups. The results of this study suggest a successful first attempt at implementing and scaling a comprehensive package of lab-tested wellbeing interventions without losing efficacy.