Background Electronic health (eHealth) is the use of information and communication technology in the context of health care and health research. Recently, there has been a rise in the number of eHealth modalities and the frequency with which they are used to deliver technology-assisted self-management interventions for people living with chronic pain. However, there has been little or no research directly comparing these eHealth modalities. Objective The aim of this systematic review with a network meta-analysis (NMA) is to compare the effectiveness of eHealth modalities in the context of chronic pain. Methods Randomized controlled trials (N>20 per arm) that investigated interventions for adults with chronic pain, delivered via an eHealth modality, were included. Included studies were categorized into their primary node of delivery. Data were extracted on the primary outcome, pain interference, and secondary outcomes, pain severity, psychological distress, and health-related quality of life. Pairwise meta-analyses were undertaken where possible, and an NMA was conducted to generate indirect comparisons and rankings of modalities for reducing pain interference. Results The search returned 18,470 studies with 18,349 being excluded (duplicates=2310; title and abstract=16,039). Of the remaining papers, 30 studies with 5394 randomized participants were included in the review. Rankings tentatively indicated that modern eHealth modalities are the most effective, with a 43% chance that mobile apps delivered the most effective interventions, followed by a 34% chance that interventions delivered via virtual reality were the most effective. Conclusions This systematic review with an NMA generated comparisons between eHealth modalities previously not compared to determine which delivered the most effective interventions for the reduction of pain interference in chronic pain patients. There are limitations with this review, in particular, the underrepresented nature of some eHealth modalities included in the analysis. However, in the event that the review is regularly updated, a clear ranking of eHealth modalities for the reduction of pain interference will emerge.

The purpose of this study was to model hierarchical classification as contextually controlled, generalized relational responding or relational framing. In Experiment 1, a training procedure involving nonarbitrarily related multidimensional stimuli was used to establish two arbitrary shapes as contextual cues for 'member of' and 'includes' relational responding, respectively. Subsequently those cues were used to establish a network of arbitrary stimuli in particular hierarchical relations with each other, and then test for derivation of further untrained hierarchical relations as well as for transformation of functions. Resultant patterns of relational framing showed properties of transitive class containment, asymmetrical class containment, and unilateral property induction, consistent with conceptions of hierarchical classification as described within the cognitive developmental literature. Experiment 2 extended the basic model by using "fuzzy category" stimuli and providing a better controlled test of transformation of functions. Limitations and future research directions are discussed.

BackgroundAs eHealth interventions prove both efficacious and practical, and as they arguably overcome certain barriers encountered by traditional face-to-face treatment for chronic pain, their number has increased dramatically in recent times. However, there is a dearth of research that focuses on evaluating and comparing the different types of technology-assisted interventions. This is a protocol for a systematic review that aims to evaluate the eHealth modalities in the context of psychological and non-psychological (other than non-drug) interventions for chronic pain.Methods/designWe will search the Cochrane Central Register of Controlled Trials (CENTRAL: The Cochrane Library), MEDLINE, Embase and PsycINFO. Randomised controlled trials (RCTs) with more than 20 participants per trial arm that have evaluated non-drug psychological or non-psychological interventions delivered via an eHealth modality and have pain as an outcome measure will be included. Two review authors will independently extract data and assess the study suitability in accordance with the Cochrane Collaboration Risk of Bias Tool. Studies will be included if they measure at least one outcome variable in accordance with the IMMPACT guidelines (i.e. pain severity, pain interference, physical functioning, symptoms, emotional functioning, global improvement and disposition). Secondary outcomes will be measures of depression and health-related quality of life (HRQoL). A network meta-analysis will be conducted based on direct comparisons to generate indirect comparisons of modalities across treatment trials, which will return rankings for the eHealth modalities in terms of their effectiveness.DiscussionMost trials that use an eHealth intervention to manage chronic pain typically use one modality. As a result, little evidence exists to support which modality type is the most effective. The current review will address this gap in the literature and compare the different eHealth modalities used for technology-assisted interventions for chronic pain. With the growing reliance and use of technology as a medium for delivering treatment for chronic conditions more generally, it is imperative that research identify the most efficacious eHealth modalities and systematically identify the most important features of such treatment types, so they may be replicated and used for research and in the provision of care.Trial registrationPROSPERO, CRD42016035595 Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-017-0414-x) contains supplementary material, which is available to authorized users.

Three experiments investigated responding consistent with transitive class containment, a feature of hierarchical classification. Experiment 1 replicated key components of a preliminary attempt to model hierarchical classification (Griffee & Dougher, 2002) and tested for responding consistent with transitive class containment. Only 2 out of 5 participants showed the expected pattern. Experiment 2 tested whether repeated exposures to the Experiment 1 protocol would give rise to the expected pattern more reliably. None of 3 novel participants demonstrated the pattern. In Experiment 3, physically similar stimuli used in Experiments 1 and 2 were replaced across testing cycles by arbitrary stimuli. Transitive‐class‐containment‐consistent responding was observed in all 3 novel participants. Implications, limitations and future research are discussed.

Systems‐based approaches to societal problem‐solving entail a capacity to synthesise our knowledge and skills such that we can resolve shared problems. However, the increasing range of knowledge specialisms, scientific and engineering methods, and skill profiles at the population‐level challenges solidarity. It is also difficult to identify unifying goals and establish sustainable educational infrastructures that support transdisciplinary teamwork. Drawing upon the collective intelligence of a multidisciplinary group, this paper highlights challenges to integrating content expertise and methodological expertise in team‐based efforts to address complex social issues. Challenges identified include the following: stakeholder participation, heterogeneity and conflict, supporting effective group dynamics, defining goals, planning and resourcing, resistance and fears amongst group members, and the absence of effective teamwork methodologies. A systems model developed by the group helped to clarify interdependencies between challenges. We conclude by highlighting the need to develop societal infrastructures supporting our capacity for teamwork into the future. © 2018 John Wiley & Sons, Ltd.