The tendency for clients to regain the weight they have lost in therapy represents an important problem in the behavioral treatment of obesity. This experiment evaluated two strategies for enhancing maintenance: relapse prevention training and posttreatment client-therapist contact by mail and telephone. Moderately obese volunteers (n = 129) who averaged 57% over ideal weight were randomly assigned to one of six experimental conditions in a 3 X 2 factorial design. Three treatment conditions (nonbehavioral therapy, behavior therapy, or behavior therapy plus relapse prevention training) were crossed with two posttreatment conditions (posttreatment client-therapist contact by telephone and mail, or no posttreatment contact). All treatments produced substantial initial weight losses (M = 18.66 Ib, or 8.5 kg), but subjects tended to regain weight during the follow-up period. Posttreatment client-therapist contact by mail and telephone significantly enhanced the maintenance of weight loss for groups that received nonbehavioral treatment or behavior therapy plus relapse prevention training, but it did not improve maintenance for groups that received behavior therapy only. At the 12-month follow-up session, the only condition that maintained its mean posttreatment weight loss was the one that received behavior therapy plus relapse prevention training and posttreatment contact.In recent years, the limitations of behavior time of long-term follow-up sessions, many therapy for obesity have become increasingly have regained much of the weight they lost in apparent (cf.

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Business Process Management systems (BPMS) are a rich source of events that document the execution of processes and activities within these systems. Business Process Management analytics is the family of methods and tools that can be applied to these event streams in order to support decision making in organizations. The analysis of process events can focus on the behavior of completed processes, evaluate currently running process instances, or focus on predicting the behavior of process instances in the future. This chapter provides an overview of the different methods and technologies that can be employed in each of these three areas of process analytics. We discuss the underlying format and types of process events as the common source of analytics information, present techniques for the aggregation and composition of these events, and outline methods that support backward-and forward-looking process analytics.