Publications

@article{logacjov2024self,

title = {Self-supervised learning with randomized cross-sensor masked reconstruction for human activity recognition},

author = {Aleksej Logacjov and Kerstin Bach},

year  = {2024},

date = {2024-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {128},

pages = {107478},

publisher = {Pergamon},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{murad2024uncertainty,

title = {Uncertainty-aware autonomous sensing with deep reinforcement learning},

author = {Abdulmajid Murad and Frank Alexander Kraemer and Kerstin Bach and Gavin Taylor},

year  = {2024},

date = {2024-01-01},

journal = {Future Generation Computer Systems},

publisher = {North-Holland},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{logacjov2024selfpab,

title = {SelfPAB: large-scale pre-training on accelerometer data for human activity recognition},

author = {Aleksej Logacjov and Sverre Herland and Astrid Ustad and Kerstin Bach},

year  = {2024},

date = {2024-01-01},

journal = {Applied Intelligence},

pages = {1–19},

publisher = {Springer US},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{granviken2024decision,

title = {Decision Support for Managing Common Musculoskeletal Pain Disorders: Development of a Case-Based Reasoning Application},

author = {Fredrik Granviken and Ottar Vasseljen and Kerstin Bach and Amar Jaiswal and Ingebrigt Meisingset and others},

year  = {2024},

date = {2024-01-01},

journal = {JMIR Formative Research},

volume = {8},

number = {1},

pages = {e44805},

publisher = {JMIR Publications Inc., Toronto, Canada},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hurmuz2024factors,

title = {Factors influencing the use of an artificial intelligence-based app (selfBACK) for tailored self-management support among adults with neck and/or low back pain},

author = {Marian ZM Hurmuz and Stephanie M Jansen-Kosterink and PJ Mork and K Bach and Hermie J Hermens},

year  = {2024},

date = {2024-01-01},

journal = {Disability and Rehabilitation},

pages = {1–10},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bayrak2024evaluation,

title = {Evaluation of Instance-based Explanations: An In-depth Analysis of Counterfactual Evaluation Metrics, Challenges, and the CEval Toolkit},

author = {Betül Bayrak and Kerstin Bach},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Access},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{murad2024uncertaintyb,

title = {Uncertainty-aware autonomous sensing with deep reinforcement learning},

author = {Abdulmajid Murad and Frank Alexander Kraemer and Kerstin Bach and Gavin Taylor},

year  = {2024},

date = {2024-01-01},

journal = {Future Generation Computer Systems},

volume = {156},

pages = {242–253},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{marcuzzi2024artificial,

title = {An Artificial Intelligence–Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trial},

author = {Anna Marcuzzi and Nina Elisabeth Klevanger and Lene Aasdahl and Sigmund Gismervik and Kerstin Bach and Paul Jarle Mork and Anne Lovise Nordstoga},

year  = {2024},

date = {2024-01-01},

journal = {JMIR Human Factors},

volume = {11},

pages = {e55716},

publisher = {JMIR Publications Toronto, Canada},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{logacjov2024machine,

title = {A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information},

author = {Aleksej Logacjov and Eivind Schjelderup Skarpsno and Atle Kongsvold and Kerstin Bach and Paul Jarle Mork},

year  = {2024},

date = {2024-01-01},

journal = {Nature and Science of Sleep},

pages = {699–710},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{verma2023external,

title = {External validation of prediction models for patient-reported outcome measurements collected using the selfBACK mobile app},

author = {Deepika Verma and Kerstin Bach and Paul Jarle Mork},

year  = {2023},

date = {2023-01-01},

journal = {International Journal of Medical Informatics},

volume = {170},

pages = {104936},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{veiga2023towards,

title = {Towards containerized, reuse-oriented AI deployment platforms for cognitive IoT applications},

author = {Tiago Veiga and Hafiz Areeb Asad and Frank Alexander Kraemer and Kerstin Bach},

year  = {2023},

date = {2023-01-01},

journal = {Future Generation Computer Systems},

volume = {142},

pages = {4–13},

publisher = {North-Holland},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ustad2023validation,

title = {Validation of an activity type recognition model classifying daily physical behavior in older adults: the HAR70+ model},

author = {Astrid Ustad and Aleksej Logacjov and Stine Øverengen Trollebø and Pernille Thingstad and Beatrix Vereijken and Kerstin Bach and Nina Skjæret Maroni},

year  = {2023},

date = {2023-01-01},

journal = {Sensors},

volume = {23},

number = {5},

pages = {2368},

publisher = {MDPI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lervik2023supportprim,

title = {SupportPrim—a computerized clinical decision support system for stratified care for patients with musculoskeletal pain complaints in general practice: study protocol for a randomized controlled trial},

author = {Lars Christian Naterstad Lervik and Ottar Vasseljen and Bjarne Austad and Kerstin Bach and Anita Formo Bones and Fredrik Granviken and Jonathan C Hill and Pål Jørgensen and Torbjørn Øien and Paola Marin Veites and others},

year  = {2023},

date = {2023-01-01},

journal = {Trials},

volume = {24},

number = {1},

pages = {267},

publisher = {BioMed Central London},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kraemer2023online,

title = {Online machine learning for 1-day-ahead prediction of indoor photovoltaic energy},

author = {Frank Alexander Kraemer and Hafiz Areeb Asad and Kerstin Bach and Christian Renner},

year  = {2023},

date = {2023-01-01},

journal = {IEEE access},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{granviken2023acceptance,

title = {Acceptance and use of a clinical decision support system in musculoskeletal pain disorders–the SupportPrim project},

author = {Fredrik Granviken and Ingebrigt Meisingset and Ottar Vasseljen and Kerstin Bach and Anita Formo Bones and Nina Elisabeth Klevanger},

year  = {2023},

date = {2023-01-01},

journal = {BMC Medical Informatics and Decision Making},

volume = {23},

number = {1},

pages = {293},

publisher = {BioMed Central London},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{marcuzzi2023effect,

title = {Effect of an Artificial Intelligence–Based Self-Management App on Musculoskeletal Health in Patients With Neck and/or Low Back Pain Referred to Specialist Care: A Randomized Clinical Trial},

author = {Anna Marcuzzi and Anne Lovise Nordstoga and Kerstin Bach and Lene Aasdahl and Tom Ivar Lund Nilsen and Ellen Marie Bardal and Nora Østbø Boldermo and Gro Falkener Bertheussen and Gunn Hege Marchand and Sigmund Gismervik and others},

year  = {2023},

date = {2023-01-01},

journal = {JAMA Network Open},

volume = {6},

number = {6},

pages = {e2320400–e2320400},

publisher = {American Medical Association},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kongsvold2023can,

title = {Can the bias of self-reported sitting time be corrected? A statistical model validation study based on data from 23 993 adults in the Norwegian HUNT study},

author = {Atle Kongsvold and Mats Flaaten and Aleksej Logacjov and Eivind Schjelderup Skarpsno and Kerstin Bach and Tom Ivar Lund Nilsen and Paul Jarle Mork},

year  = {2023},

date = {2023-01-01},

journal = {International Journal of Behavioral Nutrition and Physical Activity},

volume = {20},

number = {1},

pages = {139},

publisher = {BioMed Central London},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kongsvold2023correction,

title = {Correction: Can the bias of self-reported sitting time be corrected? A statistical model validation study based on data from 23 993 adults in the Norwegian HUNT study},

author = {Atle Kongsvold and Mats Flaaten and Aleksej Logacjov and Eivind Schjelderup Skarpsno and Kerstin Bach and Tom Ivar Lund Nilsen and Paul Jarle Mork},

year  = {2023},

date = {2023-01-01},

journal = {The International Journal of Behavioral Nutrition and Physical Activity},

volume = {20},

publisher = {BMC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{HUNT4Valid2022,

title = {A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living},

author = {Kerstin Bach and Atle Kongsvold and Hilde Bårdstu and Ellen Marie Bardal and Håkon S. Kjærnli and Sverre Herland and Aleksej Logacjov and Paul Jarle Mork},

url = {https://journals.humankinetics.com/view/journals/jmpb/5/1/article-p24.xml},

doi = {10.1123/jmpb.2021-0015},

year  = {2022},

date = {2022-12-31},

urldate = {2022-01-01},

journal = {Journal for the Measurement of Physical Behaviour},

volume = {5},

number = {1},

pages = {24 - 31},

publisher = {Human Kinetics},

address = {Champaign IL, USA},

abstract = {Introduction: Accelerometer-based measurements of physical activity types are commonly used to replace self-reports. To advance the field, it is desirable that such measurements allow accurate detection of key daily physical activity types. This study aimed to evaluate the performance of a machine learning classifier for detecting sitting, standing, lying, walking, running, and cycling based on a dual versus single accelerometer setups during free-living. Methods: Twenty-two adults (mean age [SD, range] 38.7 [14.4, 25–68] years) were wearing two Axivity AX3 accelerometers positioned on the low back and thigh along with a GoPro camera positioned on the chest to record lower body movements during free-living. The labeled videos were used as ground truth for training an eXtreme Gradient Boosting classifier using window lengths of 1, 3, and 5 s. Performance of the classifier was evaluated using leave-one-out cross-validation. Results: Total recording time was ∼38 hr. Based on 5-s windowing, the overall accuracy was 96% for the dual accelerometer setup and 93% and 84% for the single thigh and back accelerometer setups, respectively. The decreased accuracy for the single accelerometer setup was due to a poor precision in detecting lying based on the thigh accelerometer recording (77%) and standing based on the back accelerometer recording (64%). Conclusion: Key daily physical activity types can be accurately detected during free-living based on dual accelerometer recording, using an eXtreme Gradient Boosting classifier. The overall accuracy decreases marginally when predictions are based on single thigh accelerometer recording, but detection of lying is poor.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{vonstad2021estimation,

title = {Performance of machine learning models in estimation of ground reaction forces during balance exergaming},

author = {Elise Klæbo Vonstad and Kerstin Bach and Beatrix Vereijken and Xiaomeng Su and Jan Harald Nilsen },

url = {https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-022-00998-5},

doi = {10.1186/s12984-022-00998-5},

issn = {1743-0003},

year  = {2022},

date = {2022-02-13},

urldate = {2021-01-01},

journal = {Journal of Neuro Engineering and Rehabilitation},

volume = {19},

number = {18},

issue = {2022},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{verma2022exploratory,

title = {Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes},

author = {Deepika Verma and Duncan Jansen and Kerstin Bach and Mannes Poel and Paul Jarle Mork and Wendy Oude Nijeweme d’Hollosy},

doi = {https://doi.org/10.1186/s12911-022-01973-9},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {BMC medical informatics and decision making},

volume = {22},

number = {1},

pages = {227},

publisher = {BioMed Central London},

abstract = {Background

Patient-reported outcome measurements (PROMs) are commonly used in clinical practice to support clinical decision making. However, few studies have investigated machine learning methods for predicting PROMs outcomes and thereby support clinical decision making.

Objective

This study investigates to what extent different machine learning methods, applied to two different PROMs datasets, can predict outcomes among patients with non-specific neck and/or low back pain.

Methods

Using two datasets consisting of PROMs from (1) care-seeking low back pain patients in primary care who participated in a randomized controlled trial, and (2) patients with neck and/or low back pain referred to multidisciplinary biopsychosocial rehabilitation, we present data science methods for data prepossessing and evaluate selected regression and classification methods for predicting patient outcomes.

Results

The results show that there is a potential for machine learning to predict and classify PROMs. The prediction models based on baseline measurements perform well, and the number of predictors can be reduced, which is an advantage for implementation in decision support scenarios. The classification task shows that the dataset does not contain all necessary predictors for the care type classification. Overall, the work presents generalizable machine learning pipelines that can be adapted to other PROMs datasets.

Conclusion

This study demonstrates the potential of PROMs in predicting short-term patient outcomes. Our results indicate that machine learning methods can be used to exploit the predictive value of PROMs and thereby support clinical decision making, given that the PROMs hold enough predictive power},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Murad2021,

title = {Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting},

author = {Abdulmajid Murad and Frank Alexander Kraemer and Kerstin Bach and Gavin Taylor},

editor = {Yeh Hsi-Jen James},

url = {https://www.mdpi.com/1424-8220/21/23/8009},

doi = {10.3390/s21238009},

issn = {1424-8220},

year  = {2021},

date = {2021-11-30},

urldate = {2021-01-01},

journal = {Sensors},

volume = {21},

number = {23},

abstract = {Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification in a real-world setting of air quality forecasts. Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability. We also propose improving these models using “free” adversarial training and exploiting temporal and spatial correlation inherent in air quality data. Our experiments demonstrate that the proposed models perform better than previous works in quantifying uncertainty in data-driven air quality forecasts. Overall, Bayesian neural networks provide a more reliable uncertainty estimate but can be challenging to implement and scale. Other scalable methods, such as deep ensemble, Monte Carlo (MC) dropout, and stochastic weight averaging-Gaussian (SWAG), can perform well if applied correctly but with different tradeoffs and slight variations in performance metrics. Finally, our results show the practical impact of uncertainty estimation and demonstrate that, indeed, probabilistic models are more suitable for making informed decisions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LogacjovHARTH21,

title = {HARTH: A Human Activity Recognition Dataset for Machine Learning},

author = {Aleksej Logacjov and Kerstin Bach and Atle Kongsvold and Hilde Bremseth Bårdstu and Paul Jarle Mork},

editor = {Kevin Bell},

url = {https://www.mdpi.com/1424-8220/21/23/7853},

doi = {10.3390/s21237853},

issn = {1424-8220},

year  = {2021},

date = {2021-11-25},

urldate = {2021-01-01},

journal = {Sensors},

volume = {21},

number = {23},

abstract = {Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vonstad21b,

title = {Assessment of Machine Learning Models for Classification of Movement Patterns During a Weight-Shifting Exergame},

author = {Elise Klæbo Vonstad and Beatrix Vereijken and Kerstin Bach and Xiaomeng Su and Jan Harald Nilsen},

url = {https://ieeexplore.ieee.org/abstract/document/9381522},

doi = {10.1109/THMS.2021.3059716},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

journal = {IEEE Transactions on Human-Machine Systems},

volume = {51},

number = {3},

pages = {242 - 252},

abstract = {In exercise gaming (exergaming), reward systems are typically based on rules/templates from joint movement patterns. These rules or templates need broad ranges in definitions of correct movement patterns to accommodate varying body shapes and sizes. This can lead to inaccurate rewards and, thus, inefficient exercise, which can be detrimental to progress. If exergames are to be used in serious settings like rehabilitation, accurate rewards for correctly performed movements are crucial. This article aims to investigate the level of accuracy machine learning/deep learning models can achieve in classification of correct repetitions naturally elicited from a weight-shifting exergame. Twelve healthy elderly (10F, age 70.4 SD 11.4) are recruited. Movements are captured using a marker-based 3-D motion-capture system. Random forest (RF), support vector machine, k-nearest neighbors, and multilayer perceptron (MLP) are the employed models, trained and tested on whole body movement patterns and on subsets of joints. MLP and RF reached the highest recall and F1-score, respectively, when using combined data from joint subsets. MLP recall range are 91% to 94%, and RF F1-score range 79% to 80%. MLP and RF also reached the highest recall and F1-score in each joint subset, respectively. Here, MLP ranged from 93% to 97% recall, while RF ranged from 73% to 80% F1-score. Recall results, show that >9 out of 10 repetitions are classified correctly, indicating that MLP/RF can be used to identify correctly performed repetitions of a weight-shifting exercise when using full-body data and when using joint subset data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{VermaEtAl21a,

title = {Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review},

author = {Deepika Verma and Kerstin Bach and Paul Jarle Mork},

editor = {Kamran Sedig},

url = {https://www.mdpi.com/2227-9709/8/3/56},

doi = {10.3390/informatics8030056},

year  = {2021},

date = {2021-08-25},

urldate = {2021-08-25},

journal = {Informatics},

volume = {8},

number = {3},

pages = {56},

abstract = {The field of patient-centred healthcare has, during recent years, adopted machine learning and data science techniques to support clinical decision making and improve patient outcomes. We conduct a literature review with the aim of summarising the existing methodologies that apply machine learning methods on patient-reported outcome measures datasets for predicting clinical outcomes to support further research and development within the field. We identify 15 articles published within the last decade that employ machine learning methods at various stages of exploiting datasets consisting of patient-reported outcome measures for predicting clinical outcomes, presenting promising research and demonstrating the utility of patient-reported outcome measures data for developmental research, personalised treatment and precision medicine with the help of machine learning-based decision-support systems. Furthermore, we identify and discuss the gaps and challenges, such as inconsistency in reporting the results across different articles, use of different evaluation metrics, legal aspects of using the data, and data unavailability, among others, which can potentially be addressed in future studies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{selfback21,

title = {Effectiveness of App-Delivered, Tailored Self-management Support for Adults With Lower Back Pain–Related Disability A selfBACK Randomized Clinical Trial},

author = {Louise Fleng Sandal and Kerstin Bach and Cecilie K. Øverås and Malene Jagd Svendsen and Tina Dalager and Jesper Stejnicher Drongstrup Jensen and Atle Kongsvold and Anne Lovise Nordstoga and Ellen Marie Bardal and Ilya Ashikhmin and Karen Wood and Charlotte Diana Nørregaard Rasmussen and Mette Jensen Stochkendahl and Barbara I. Nicholl and Nirmalie Wiratunga and Kay Cooper and Jan Hartvigsen and Per Kjær and Gisela Sjøgaard and Tom I. L. Nilsen and Frances S. Mair and Karen Søgaard and Paul Jarle Mork},

url = {https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2782459?utm_source=twitter&utm_campaign=content-shareicons&utm_content=article_engagement&utm_medium=social&utm_term=080221#.YQgKH8dDh },

doi = {10.1001/jamainternmed.2021.4097},

year  = {2021},

date = {2021-08-02},

urldate = {2021-08-02},

journal = {JAMA Internal Medicine},

abstract = {Importance  Lower back pain (LBP) is a prevalent and challenging condition in primary care. The effectiveness of an individually tailored self-management support tool delivered via a smartphone app has not been rigorously tested.



Objective  To investigate the effectiveness of selfBACK, an evidence-based, individually tailored self-management support system delivered through an app as an adjunct to usual care for adults with LBP-related disability.



Design, Setting, and Participants  This randomized clinical trial with an intention-to-treat data analysis enrolled eligible individuals who sought care for LBP in a primary care or an outpatient spine clinic in Denmark and Norway from March 8 to December 14, 2019. Participants were 18 years or older, had nonspecific LBP, scored 6 points or higher on the Roland-Morris Disability Questionnaire (RMDQ), and had a smartphone and access to email.



Interventions  The selfBACK app provided weekly recommendations for physical activity, strength and flexibility exercises, and daily educational messages. Self-management recommendations were tailored to participant characteristics and symptoms. Usual care included advice or treatment offered to participants by their clinician.



Main Outcomes and Measures  Primary outcome was the mean difference in RMDQ scores between the intervention group and control group at 3 months. Secondary outcomes included average and worst LBP intensity levels in the preceding week as measured on the numerical rating scale, ability to cope as assessed with the Pain Self-Efficacy Questionnaire, fear-avoidance belief as assessed by the Fear-Avoidance Beliefs Questionnaire, cognitive and emotional representations of illness as assessed by the Brief Illness Perception Questionnaire, health-related quality of life as assessed by the EuroQol-5 Dimension questionnaire, physical activity level as assessed by the Saltin-Grimby Physical Activity Level Scale, and overall improvement as assessed by the Global Perceived Effect scale. Outcomes were measured at baseline, 6 weeks, 3 months, 6 months, and 9 months.



Results  A total of 461 participants were included in the analysis; the population had a mean [SD] age of 47.5 [14.7] years and included 255 women (55%). Of these participants, 232 were randomized to the intervention group and 229 to the control group. By the 3-month follow-up, 399 participants (87%) had completed the trial. The adjusted mean difference in RMDQ score between the 2 groups at 3 months was 0.79 (95% CI, 0.06-1.51; P = .03), favoring the selfBACK intervention. The percentage of participants who reported a score improvement of at least 4 points on the RMDQ was 52% in the intervention group vs 39% in the control group (adjusted odds ratio, 1.76; 95% CI, 1.15-2.70; P = .01).



Conclusions and Relevance  Among adults who sought care for LBP in a primary care or an outpatient spine clinic, those who used the selfBACK system as an adjunct to usual care had reduced pain-related disability at 3 months. The improvement in pain-related disability was small and of uncertain clinical significance. Process evaluation may provide insights into refining the selfBACK app to increase its effectiveness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{VeigaEtAl21a,

title = {From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development},

author = {Tiago Veiga, Arne Munch-Ellingsen, Christoforos Papastergiopoulos, Dimitrios Tzovaras, Ilias Kalamaras, Kerstin Bach, Konstantinos Votis, Sigmund Akselsen},

editor = {Hsi-Jen James Yeh},

url = {https://www.mdpi.com/1424-8220/21/9/3190},

doi = {10.3390/s21093190},

year  = {2021},

date = {2021-05-05},

journal = {Sensors},

volume = {21},

number = {9},

pages = {3190},

abstract = {Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, the development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MathisenEtAl21,

title = {Using extended siamese networks to provide decision support in aquaculture operations},

author = {Bjørn Magnus Mathisen, Kerstin Bach, Agnar Aamodt},

url = {https://link.springer.com/article/10.1007/s10489-021-02251-3#article-info},

doi = {10.1007/s10489-021-02251-3},

year  = {2021},

date = {2021-03-26},

journal = {Applied Intelligence},

pages = {1-12},

abstract = {Aquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Marcuzzi2021,

title = {Individually tailored self-management app-based intervention (selfBACK) versus a self-management web-based intervention (e-Help) or usual care in people with low back and neck pain referred to secondary care: protocol for a multiarm randomised clinical trial},

author = {Anna Marcuzzi and Kerstin Bach and Anne Lovise Nordstoga and Gro Falkener Bertheussen and Ilya Ashikhmin and Nora Østbø Boldermo and Else-Norun Kvarner and Tom Ivar Lund Nilsen and Gunn Hege Marchand and Solveig Osborg Ose and Lene Aasdahl and Silje Lill Kaspersen and Ellen Marie Bardal and Janne-Birgitte Børke and Paul Jarle Mork and Sigmund Gismervik},

url = {https://bmjopen.bmj.com/content/11/9/e047921},

doi = {10.1136/bmjopen-2020-047921},

issn = {2044-6055},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {BMJ Open},

volume = {11},

number = {9},

publisher = {British Medical Journal Publishing Group},

abstract = {Introduction Low back pain (LBP) and neck pain (NP) are common and costly conditions. Self-management is a key element in the care of persistent LBP and NP. Artificial intelligence can be used to support and tailor self-management interventions, but their effectiveness needs to be ascertained. The aims of this trial are (1) to evaluate the effectiveness of an individually tailored app-based self-management intervention (selfBACK) adjunct to usual care in people with LBP and/or NP in secondary care compared with usual care only, and (2) to compare the effectiveness of selfBACK with a web-based self-management intervention without individual tailoring (e-Help).Methods and analysis This is a randomised, assessor-blind clinical trial with three parallel arms: (1) selfBACK app adjunct to usual care; (2) e-Help website adjunct to usual care and (3) usual care only. Patients referred to St Olavs Hospital, Trondheim (Norway) with LBP and/or NP and accepted for assessment/treatment at the multidisciplinary outpatient clinic for back or neck rehabilitation are invited to the study. Eligible and consenting participants are randomised to one of the three arms with equal allocation ratio. We aim to include 279 participants (93 in each arm). Outcome variables are assessed at baseline (before randomisation) and at 6-week, 3-month and 6-month follow-up. The primary outcome is musculoskeletal health measured by the Musculoskeletal Health Questionnaire at 3 months. A mixed-methods process evaluation will document patients’ and clinicians’ experiences with the interventions. A health economic evaluation will estimate the cost-effectiveness of both interventions’ adjunct to usual care. Ethics and dissemination The trial is approved by the Regional Committee for Medical and Health Research Ethics in Central Norway (Ref. 2019/64084). The results of the trial will be published in peer-review journals and presentations at national and international conferences relevant to this topic. Trial registration number NCT04463043.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vonstad21a,

title = {Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training},

author = {Elise Klæbo Vonstad and Xiaomeng Su and Beatrix Vereijken and Kerstin Bach and Jan Harald Nilsen},

url = {https://www.mdpi.com/1424-8220/20/23/6940},

doi = {10.3390/s20236940},

year  = {2020},

date = {2020-12-04},

urldate = {2020-12-04},

journal = {Sensors},

volume = {20},

number = {23},

pages = {6940},

abstract = {Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (ie, variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N= 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon’s signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Rasmussen2021,

title = {App-Delivered Self-Management Intervention Trial selfBACK for People With Low Back Pain: Protocol for Implementation and Process Evaluation},

author = {Charlotte Diana Nørregaard Rasmussen and Malene Jagd Svendsen and Karen Wood and Barbara I Nicholl and Frances S Mair and Louise Fleng Sandal and Paul Jarle Mork and Karen Søgaard and Kerstin Bach and Mette Jensen Stochkendahl},

url = {https://pubmed.ncbi.nlm.nih.gov/33118959/},

doi = {10.2196/20308},

year  = {2020},

date = {2020-09-29},

urldate = {2020-09-29},

journal = {JMIR Research Protocols},

volume = {9},

number = {10},

pages = {e20308},

abstract = {Background: Implementation and process evaluation is vital for understanding how interventions function in different settings, including if and why interventions have different effects or do not work at all.



Objective: This paper presents the protocol for an implementation and process evaluation embedded in a multicenter randomized controlled trial conducted in Denmark and Norway (the selfBACK project). selfBACK is a data-driven decision support system that provides participants with weekly self-management plans for low back pain. These plans are delivered through a smartphone app and tailored to individual participants by using case-based reasoning methodology. In the trial, we compare selfBACK in addition to usual care with usual care alone.



Methods: The aim of this study is to conduct a convergent mixed-methods implementation and process evaluation of the selfBACK app by following the reach, effectiveness, adoption, implementation, and maintenance framework. We will evaluate the process of implementing selfBACK and investigate how participants use the intervention in daily life. The evaluation will also cover the reach of the intervention, health care provider willingness to adopt it, and participant satisfaction with the intervention. We will gather quantitative measures by questionnaires and measures of data analytics on app use and perform a qualitative exploration of the implementation using semistructured interviews theoretically informed by normalization process theory. Data collection will be conducted between March 2019 and October 2020.



Results: The trial opened for recruitment in February 2019. This mixed-methods implementation and evaluation study is embedded in the randomized controlled trial and will be collecting data from March 2019 to October 2020; dissemination of trial results is planned thereafter. The results from the process evaluation are expected 2021-2022.



Conclusions: This study will provide a detailed understanding of how self-management of low back pain can be improved and how a digital health intervention can be used as an add-on to usual care to support patients to self-manage their low back pain. We will provide knowledge that can be used to explore the possibilities of extending the generic components of the selfBACK system and key drivers that could be of use in other conditions and diseases where self-management is an essential prevention or treatment strategy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nordstoga2020,

title = {Usability and acceptability of an app (SELFBACK) to support self-management of low back pain: a mixed methods study.},

author = {Anne Lovise Nordstoga and Kerstin Bach and Sadiq Sani and Nirmalie Wiratunga and Paul Jarle Mork and Morten Willumsen and Kay Cooper},

url = {https://preprints.jmir.org/preprint/18729/accepted},

doi = {10.2196/18729},

year  = {2020},

date = {2020-09-15},

journal = { JMIR Rehabilitation and Assistive Technologies},

volume = {7},

number = {2},

pages = {e18729},

publisher = {JMIR Publications},

abstract = {Self-management is the key recommendation for managing non-specific low back pain (LBP). However, there are well-documented barriers to self-management, therefore methods of facilitating adherence are required. Smartphone apps are increasingly being used to provide feedback and reinforcement to support self-management of long-term conditions such as LBP. The aim of this study was to assess the usability and acceptability of the selfBACK smartphone app, designed to support and facilitate self-management of non-specific LBP. The app provides weekly self-management plans, comprising physical activity, strength/flexibility exercises, and patient education. The plans are tailored to the patient's characteristics and symptom progress by using case-based reasoning methodology. The study was carried out in two stages, using a mixed-methods approach. All participants undertook surveys and semi-structured telephone interviews were conducted with a subgroup of participants. Stage 1 assessed an app version with only the physical activity component and a web-questionnaire that collects information necessary for tailoring the self-management plans. The physical activity component included monitoring of steps recorded by a wristband, goal-setting, and a scheme for sending personalised, timely and motivational notifications to the user's smartphone. Findings from stage 1 were used to refine the app and inform further development. Stage 2 investigated an app version that incorporated three self-management components (physical activity, exercises and education). A total of sixteen participants (age range 23-71 years) with ongoing or chronic non-specific LBP were included in stage 1, and eleven participants (age range 32-56) were included in stage 2. In stage 1, 94% of participants reported that the baseline questionnaire was easy to answer and 84% found completion time to be acceptable. Overall, participants were positive about the usability of the physical activity component but only 31% found the app functions to be well integrated. 90% of the participants were satisfied with the notifications and 80% perceived the notifications to be personalised. In stage 2, all participants reported that the web-questionnaire was easy to answer and the completion time acceptable. The physical activity and exercise components were rated useful by 80%, while 60% rated the educational component useful. Overall, participants were satisfied with the usability of the app; however, only 50% found the functions to be well integrated and 20% found them to be inconsistent. Overall, 80% of participants reported it to be useful for self-management. The interviews largely reinforced the survey findings in both stages. This study has demonstrated that participants considered the selfBACK app to be acceptable and usable, and that they thought it would be useful for supporting self-management of LBP. However, we identified some limitations and suggestions, which will be useful in guiding further development of the selfBACK app and other mHealth interventions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SandalEtAl2020,

title = {A digital decision support system (selfBACK) for improved self-management of low back pain: a pilot study with 6-week follow-up},

author = {Louise Fleng Sandal and Cecilie K Øverås and Anne Lovise Nordstoga and Karen Wood and Kerstin Bach and Jan Hartvigsen and Karen Søgaard and Paul Jarle Mork},

editor = {Gillian Lancaster and Lehana Thabane},

url = {https://pilotfeasibilitystudies.biomedcentral.com/articles/10.1186/s40814-020-00604-2#citeas},

doi = {https://doi.org/10.1186/s40814-020-00604-2},

issn = {2055-5784},

year  = {2020},

date = {2020-05-23},

journal = {Pilot and Feasibility Studies volume},

volume = {6},

number = {72},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/aim/AlthoffBBM20,

title = {The 27th International Conference on Case-Based Reasoning},

author = {Klaus-Dieter Althoff and Kerstin Bach and Ralph Bergmann and Cindy Marling},

url = {https://doi.org/10.1609/aimag.v41i1.5288},

doi = {10.1609/aimag.v41i1.5288},

year  = {2020},

date = {2020-01-01},

journal = {AI Mag.},

volume = {41},

number = {1},

pages = {101--102},

keywords = {},

pubstate = {published},

tppubtype = {article}

}