A smartphone-based health care chatbot to promote self-management of chronic pain (SELMA) : pilot randomized controlled trial

Background: Ongoing pain is one of the most common diseases and has major physical, psychological, social, and economic impacts. A mobile health intervention utilizing a fully automated text-based health care chatbot (TBHC) may offer an innovative way not only to deliver coping strategies and psychoeducation for pain management but also to build a working alliance between a participant and the TBHC. Objective: The objectives of this study are twofold: (1) to describe the design and implementation to promote the chatbot painSELfMAnagement (SELMA), a 2-month smartphone-based cognitive behavior therapy (CBT) TBHC intervention for pain self-management in patients with ongoing or cyclic pain, and (2) to present findings from a pilot randomized controlled trial, in which effectiveness, influence of intention to change behavior, pain duration, working alliance, acceptance, and adherence were evaluated. Methods: Participants were recruited online and in collaboration with pain experts, and were randomized to interact with SELMA for 8 weeks either every day or every other day concerning CBT-based pain management (n=59), or weekly concerning content not related to pain management (n=43). Pain-related impairment (primary outcome), general well-being, pain intensity, and the bond scale of working alliance were measured at baseline and postintervention. Intention to change behavior and pain duration were measured at baseline only, and acceptance postintervention was assessed via self-reporting instruments. Adherence was assessed via usage data. Results: From May 2018 to August 2018, 311 adults downloaded the SELMA app, 102 of whom consented to participate and met the inclusion criteria. The average age of the women (88/102, 86.4%) and men (14/102, 13.6%) participating was 43.7 (SD 12.7) years. Baseline group comparison did not differ with respect to any demographic or clinical variable. The intervention group reported no significant change in pain-related impairment (P=.68) compared to the control group postintervention. The intention to change behavior was positively related to pain-related impairment (P=.01) and pain intensity (P=.01). Working alliance with the TBHC SELMA was comparable to that obtained in guided internet therapies with human coaches. Participants enjoyed using the app, perceiving it as useful and easy to use. Participants of the intervention group replied with an average answer ratio of 0.71 (SD 0.20) to 200 (SD 58.45) conversations initiated by SELMA. Participants’ comments revealed an appreciation of the empathic and responsible interaction with the TBHC SELMA. A main criticism was that there was no option to enter free text for the patients’ own comments. Conclusions: SELMA is feasible, as revealed mainly by positive feedback and valuable suggestions for future revisions. For example, the participants’ intention to change behavior or a more homogenous sample (eg, with a specific type of chronic pain) should be considered in further tailoring of SELMA

Online vs. In-Store Shopping: How Problem Solving Strategies of Decision Support Systems influence Confidence in Purchase Decisions

Several studies have investigated the relevance of Decision Support Systems (DSS) on purchasebehaviour. Even though these studies show several aspects of the utility of DSS, they are limited toonline purchase situations, the use of one decision making strategy and one DSS technology. In thispaper, we therefore develop a theoretical model that measures the impact of DSS strategies relative toa given purchase problem and an adequate use of DSS technology on consumers’ perceivedconfidence in purchase decisions for both online and in-store purchase situations. Further, threemediating decision process variables are considered: perceived personalization of a DSS, perceivedrelevance of recommendations, and cognitive trust in DSS competence. As this paper represents awork in progress, the theoretical model has still to be tested empirically with regard to the proposedmethod. But as a result, we expect that the model not only allows evaluating different kinds ofpurchase-directed DSS but let researchers also draw conclusions on the appropriate use of technologyand decision strategies of one individual DSS

Towards the Design of Evidence-based Mental Health Information Systems: A Preliminary Literature Review

Mental disorders belong to a significant and serious disease pattern with an increasing prevalence worldwide. Due to limited health personnel and financial resources, scalability of mental health services and tailored care of individuals are two key barriers for a successful treatment. Mental health information systems (MHIS) are expected to address these barriers and thus, it is of utmost importance to understand the underlying technological rules that guide the design of evidence-based MHIS interventions. However, up till now, there is no systematic literature review on the anatomy of MHIS that quantitatively tests the effects of MHIS service configurations on treatment success. We therefore conducted, as a very first step a preliminary review on MHIS in this research-in-progress. This review has not only the objective to present state-of-the-art on MHIS but also to propose a set of fine-grained evaluation criteria relevant for future work on the design of evidence-based MHIS interventions

Quantitative Longitudinal Research: A Review of IS Literature, and a Set of Methodological Guidelines

Data captured at different points in time provides the basis for longitudinal research. It is unquestioned that several IS phenomena deal with change over time such as post-adoption behavior with respect to IT artifacts. However, cross-sectional research designs are predominantly applied in the IS field up till now. This paper is therefore written not only to motivate the IS community to apply longitudinal research to time-variant IS phenomena but also to discuss common pitfalls. For this purpose, we outline various longitudinal studies and provide four guidelines that should be considered during their planning. In particular, common methodological issues like space and amount of repeated observations or attrition are discussed. Finally, an overview of common longitudinal research questions and corresponding methods of longitudinal analyses is provided

Mobile Decision Support vs. Interpersonal Sales Communication: Predictors of Buying Intentions for Price Bundles

Mobile recommendation agents (MRA) are a new class of decision support systems that provide consumers with product information during purchase situations in stores. They allow merging of local information with global information provided by online content sources. Currently design criteria for MRA are missing. Studies on purchase decision support systems indicate the importance of price, single product quality, and attitude on buying intentions. It is assumed that purchase decision tasks on price bundles increase utility effects of MRA. We present an empirical study that investigates the impact of cues on price, bundle quality, and discount provided by MRA on consumer’s buying intentions in comparison with interpersonal sales communication between consumer and sales personnel. Our results show that MRA can be used to inform consumers about bundle qualities under best-value strategy conditions, which will be used for future MRA designs

Enhancing Asthma Control through IT: Design, Implementation and Planned Evaluation of the Mobile Asthma Companion

The personal and financial burden of asthma highly depends on a patient’s disease self-management skill. Scalable mHealth apps, designed to empower patients, have the potential to play a crucial role in asthma disease management. However, the actual clinical efficacy of mHealth asthma apps is poorly understood due to the lack of both methodologically sound research and accessible evidence-based apps. We therefore apply design science with the goal to design, implement and evaluate a mHealth app for people with asthma, the Mobile Asthma Companion (MAC). The current prototype of MAC delivers health literacy knowledge triggered by nocturnal cough rates. We conclude by proposing a randomized controlled trial to test the efficacy of our prototype

Design And Lab Experiment Of A Stress Detection Service Based On Mouse Movements

Workplace stress can negatively affect the health condition of employees and with it, the performance of organizations. Although there exist approaches to measure work-related stress, two major limitations are the low resolution of stress data and its obtrusive measurement. The current work applies design science research with the goal to design, implement and evaluate a Stress Detection Service (SDS) that senses the degree of work-related stress solely based on mouse movements of knowledge workers. Using van Gemmert and van Galen’s stress theory and Bakker and Demerouti’s Job Demands-Resource model as justificatory knowledge, we implemented a first SDS prototype that senses mouse movements and perceived stress levels. Experimental results indicate that two feature sets of mouse movements, i.e. average deviation from an optimal mouse trajectory and average mouse speed, can classify high versus low stress with an overall accuracy of 78%. Future work regarding a second build-and-evaluate loop of a SDS, then tailored to the field setting, is discussed