To improve people’s lives, human-computer interaction researchers are increasingly designing technological solutions based on behavior change theory, such as social comparison theory (SCT). However, how researchers operationalize such a theory as a design remains largely unclear. One way to clarify this methodological step is to clearly state which functional elements of a design are aimed at operationalizing a specific behavior change theory construct to evaluate if such aims were successful. In this article, we investigate how the operationalization of functional elements of theories and designs can be more easily conveyed. First, we present a scoping review of the literature to determine the state of operationalizations of SCT as behavior change designs. Second, we introduce a new tool to facilitate the operationalization process. We term the tool blueprints. A blueprint explicates essential functional elements of a behavior change theory by describing it in relation to necessary and sufficient building blocks incorporated in a design. We describe the process of developing a blueprint for SCT. Last, we illustrate how the blueprint can be used during the design refinement and reflection process.
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Behavior change is a topic that is of great interest to many people. People can use apps to exercise more, eat healthier, or learn a new skill, but and digital interventions and games are also used by policy makers and companies to create a safe environment for the general public or to increase sales. Given this interest in behavior change, it is not surprising that this topic has seen a lot of interest from the scientific community. This has resulted in a wide range of theories and techniques to bring about behavior change. However, maintaining behavior change is rarely addressed, and as a result poorly understood. In this paper, we take a first step in the design of digital interventions for long-term behavior change by placing a range of behavior change techniques on a long-term behavior change timeline.
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Background Movement behaviors (i.e., physical activity levels, sedentary behavior) in people with stroke are not self-contained but cluster in patterns. Recent research identified three commonly distinct movement behavior patterns in people with stroke. However, it remains unknown if movement behavior patterns remain stable and if individuals change in movement behavior pattern over time. Objectives 1) To investigate the stability of the composition of movement behavior patterns over time, and 2) determine if individuals change their movement behavior resulting in allocation to another movement behavior pattern within the first two years after discharge to home in people with a first-ever stroke. Methods Accelerometer data of 200 people with stroke of the RISE-cohort study were analyzed. Ten movement behavior variables were compressed using Principal Componence Analysis and K-means clustering was used to identify movement behavior patterns at three weeks, six months, one year, and two years after home discharge. The stability of the components within movement behavior patterns was investigated. Frequencies of individuals’ movement behavior pattern and changes in movement behavior pattern allocation were objectified. Results The composition of the movement behavior patterns at discharge did not change over time. At baseline, there were 22% sedentary exercisers (active/sedentary), 45% sedentary movers (inactive/sedentary) and 33% sedentary prolongers (inactive/highly sedentary). Thirty-five percent of the stroke survivors allocated to another movement behavior pattern within the first two years, of whom 63% deteriorated to a movement behavior pattern with higher health risks. After two years there were, 19% sedentary exercisers, 42% sedentary movers, and 39% sedentary prolongers. Conclusions The composition of movement behavior patterns remains stable over time. However, individuals change their movement behavior. Significantly more people allocated to a movement behavior pattern with higher health risks. The increase of people allocated to sedentary movers and sedentary prolongers is of great concern. It underlines the importance of improving or maintaining healthy movement behavior to prevent future health risks after stroke.
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Regular physical activity is considered to be an important component of a healthy lifestyle that decreases the risk of coronary heart disease, diabetes mellitus type 2, hypertension, colon and breast cancer, obesity and other debilitating conditions. Physical activity can also improve functional capacity and therefore also the quality of life in older adults. Despite all these favorable aspects, a substantial part of the Dutch older adult population is still underactive or even sedentary. To change this for the better, the Groningen Active Living Model (GALM) was developed.Aim of GALM is to stimulate recreational sports activities in sedentary and underactive older adults in the 55-65 age band. After a door-to-door visit as part of an intensive recruitment phase, a fitness test was conducted followed by the GALM recreational sports program. This program was based on principles from evolutionary-biological play theory and insights fromsocial cognitive theory. The program was versatile in nature (e.g. softball, dance, self-defense, swimming, athletics, etc.) in two main ways: a) to improve compliance with the program different sports were offered, which was reported to be more appealing for older adults; b) by aiming at more components of motor fitness (e.g. strength, flexibility, speed, endurance and coordination). Between 1997 and 2005 more than 552,000 persons were visited door-to-door, over 55,700 were tested, and 41,310 participated in the GALM recreational sports program. The aim of the present thesis is to determine the effects of participation in the GALM recreational sports program on physical activity, health and fitness outcomes.Chapter 2 describes the effectiveness of the GALM recruitment in selecting and recruiting sedentary and underactive older adults. Three municipalities in the Netherlands were selected, and in every municipality four neighborhoods were included. Two of each of the four neighborhoods were randomly assigned as intervention and the others as control neighborhoods. In total, 8,504 persons were mailed and received a home visit. During this home visit the GALM recruitment questionnaire was collected on which the selection between sedentary/underactive and physically active older adults was based. Ultimately we succeeded inincluding 12.3% (315 of the 2,551 qualifying) of the older adults, 79.4% of whom could be indeed considered sedentary or underactive. The cost of successfully recruiting an older adult was estimated at $84.To assess the effects of a physical activity intervention on health and fitness and explain the results, it is necessary to know program characteristics regarding frequency, intensity, time and content of the activities. With respect to the GALM recreational sports activity program, the only unknown characteristic was intensity. Chapter 3 describes the intensity of this program systematically. Using heart rate monitors, data of 97 persons (mean age 60.1 yr) were collected in three municipalities. The mean intensity of all 15 GALM sessions was 73.7% of the predicted maximal heart rate. Six percent of the monitored heart rate time could be classified as light, 33% as moderate and 61% as hard. In summary, the GALM recreational sports program meets the 1998 ACSM recommendations for intensity necessary to improve cardiorespiratory fitness.Chapters 4 and 5 describe the effects of 6 and 12 months of participation in the GALM recreational sports program, and 181 persons were followed over time. Results after 6 months revealed only few significant between-group differences favoring the intervention group (i.e. sleep, diastolic blood pressure, perceived fitness score and grip strength). Changes in energyexpenditure for leisure-time physical activities (EELTPA) showed an increase in both study groups. From 6 to 12 months a decrease in EELTPA occurred in the intervention group and an increase in the control group. The significant positive time effects for the health outcomes (diastolic blood pressure, BMI, percentage of body fat) that were found after 6 months were diminishedfrom 6 to 12 months. However, the energy expenditure for recreational sports activities (EERECSPORT) demonstrated a continuous increase over 12 months. Parallel to this, significant main effects for time were found in performance-based fitness outcomes (i.e. simple reaction time, leg strength, flexibility of hamstrings and lower back, and aerobic endurance). After 12 months only a significant between-group difference for flexibility of the hamstrings andlower back was found, favoring the control group. In conclusion, a short-term increase in EELTPA was found with accompanying improvements in health outcomes that more or less disappeared in 6 to 12 months. In the long term, results showed a continuous increase in EERECSPORT and performance-based fitness. This latter increase is probably a reflection of the significantimprovement over time in EERECSPORT and the fact that recreational sports activities are of a higher intensity.Aerobic endurance is regarded as the most important component of motor fitness that is relevant for older adults to function independently. In Chapter 6, the development in aerobic endurance after 18 months of participation in the GALM recreational sports program was assessed by means of changes in heart rate during fixed submaximal exercise. Since both groups were comparable regarding changes in energy expenditure for physical activity after 6 months and testing confirmed this, both groups were combined and considered as one group. Multilevel analyses were conducted and models for change were developed. A significant decrease in heart rate over time was found at all walking speeds (4, 5, 6 and 7 km/h). The average decrease in heart rate was 5.5, 6.0, 10.0 and 9.0 beats/min for the 4, 5, 6 and 7 km/h walking speeds, respectively. The relative decrease varied from 5.1 to 7.4% relative to average heart rates at baseline. These results illustrate that participation in the GALM recreational sports program has a positive significant effect on aerobic endurance, and that the participants are able to perform at submaximal intensity more easily.Based on the overall results it can be concluded that this study contributes to the field in how to effectively recruit sedentary and underactive older adults and stimulate them to become and stay active in recreational sports activities. As far as we know, this recruitment in combination with the recreational sport program is not only unique but also effective toward increasing performance-based fitness in the long term. Short-term effects were found in other leisure-time activities and health outcomes. To further stimulate other leisure-time and probably health outcomes besides the favorable effects that were already seen, additional interventions that pay more attention to behavioral change in terms of how to integrate other activities besides sports activities are recommended.
Performance feedback is an important mechanism of adaptation in learning theories, as it provides one of the motivations for organizations to learn (Pettit, Crossan, and Vera 2017). Embedded in the behavioral theory of the firm, organizational learning from performance feedback predicts the probability for organizations to change with an emphasis on organizational aspirations, which serve as a threshold against which absolute performance is evaluated (Cyert and March 1963; Greve 2003). It postulates that performance becomes a ‘problem’, or the trigger to search for alternative procedures, strategies, products and behaviors, when performance is below that threshold. This search is known as problemistic search. Missing from this body of research, is empirically grounded understanding if the characteristics of performance feedback over time matter for the triggering function of the feedback. I explore this gap. This investigation adds temporality as a dimension of the performance feedback concept guided by a worldview of ongoing change and flux where conditions and choices are not given, but made relevant by actors and enacted upon (Tsoukas and Chia 2002). The general aim of the study is to complement the current knowledge of performance feedback as a trigger for problemistic search with an explicit process temporal approach. The main question guiding this project is how temporal patterns of performance feedback influence organizational change, which I answer in four chapters, each zooming into one sub-question.First, I focus on the temporal order of performance feedback by examining performance feedback and change sequences organizations go through. In this section time is under study and the goal is to explore how feedback patterns have evolved over time, just as the change states organizations pass through. Second, I focus on the plurality of performance feedback by investigating performance feedback from multiple aspiration levels (i.e. multiple qualitatively different metrics and multiple reference points) and how over time clusters of performance feedback sequences have evolved. Next, I look into the rate and scope of change relative to performance feedback sequences and add an element of signal strength to the feedback. In the last chapter, time is a predictor (in the sequences), and, it is under study (in the timing of responses). I focus on the timing of organizational responses in relation to performance feedback sequences of multiple metrics and reference points.In sum, all chapters are guided by the timing problem of performance feedback, meaning that performance feedback does not come ‘available’ at a single point in time. Similarly to stones with unequal weight dropped in the river, performance feedback with different strength comes available at multiple points in time and it is plausible that sometimes it is considered by decision-makers as problematic and sometimes it is not, because of the sequence it is part of. Overall, the investigation is grounded in the general principles of organizational learning from performance feedback, and the concept of time as duration, sequences and timing, with a focus on specification of when things happen. The context of the study is universities of applied sciences and hotels in The Netherlands. Project partner: Tilburg University, School of Social and Behavioral Sciences, Department of Organization Studies
