Pub Date : 2023-10-05DOI: 10.1186/s44247-023-00040-1
Valerie Dieter, Peter Martus, Pia Janssen, Inga Krauss
Abstract Background Current health care demonstrates an insufficient provision and utilization of physical exercises despite their recommendation as a first-line treatment in clinical guidelines for patients with knee osteoarthritis (OA). Mobile health (m-health) technologies offer new opportunities to guide and monitor home-based exercise programs by using mobile devices and inertial sensors in combination with a digital application (app). This study will evaluate patient benefits resulting from the use of the specific digital health application re.flex for patients with knee OA. Methods This monocentric, two-arm, randomized controlled parallel-group trial will evaluate the effectiveness of the app- and sensor-guided exercise program re.flex for patients with moderate-to-severe knee OA. We aim to recruit 200 participants via newspapers, newsletters and information events. Participants will be randomly allocated to the intervention group and the control group in a 1:1 ratio. Participants in the control group will not receive any study intervention or instruction for any change to their previous health care utilization. Despite this, they are allowed to make use of usual care provided by their treating physician. The intervention group comprises a 12-week home training program with three sessions per week in addition to usual care. Exercises will be guided and monitored by use of the training app (re.flex) and two accelerometers that are attached proximally and distally to the affected knee joint. Pre- and postmeasurements will take place at baseline (t0) and after 12 weeks (t1). Primary outcomes will be osteoarthritis-specific pain and physical function measured with the Knee Osteoarthritis Outcome Score (KOOS) subscales Pain and Function in daily living (ADL). Second, further self-reported health outcomes, a performance measurement, app logfiles and safety will be assessed. Intervention effects will be calculated by baseline-adjusted analysis of covariance (ANCOVA) using an intention-to-treat approach. Multiple imputation will be applied. Discussion Re.flex can bridge part of the gap between recommendations for strengthening exercises in patients with knee OA and the insufficient actual care situation. This randomized controlled trial is designed to provide conclusions on the effectiveness of the health application re.flex for the population under study and will provide further insight into adherence rates and the safety of its use. Trial registration The trial was registered on 20/01/2023 at www.drks.de (ID: DRKS00030932).
{"title":"Evaluation of a 12-week app-guided exercise intervention in patients with knee osteoarthritis (re.flex): a study protocol for a randomized controlled trial","authors":"Valerie Dieter, Peter Martus, Pia Janssen, Inga Krauss","doi":"10.1186/s44247-023-00040-1","DOIUrl":"https://doi.org/10.1186/s44247-023-00040-1","url":null,"abstract":"Abstract Background Current health care demonstrates an insufficient provision and utilization of physical exercises despite their recommendation as a first-line treatment in clinical guidelines for patients with knee osteoarthritis (OA). Mobile health (m-health) technologies offer new opportunities to guide and monitor home-based exercise programs by using mobile devices and inertial sensors in combination with a digital application (app). This study will evaluate patient benefits resulting from the use of the specific digital health application re.flex for patients with knee OA. Methods This monocentric, two-arm, randomized controlled parallel-group trial will evaluate the effectiveness of the app- and sensor-guided exercise program re.flex for patients with moderate-to-severe knee OA. We aim to recruit 200 participants via newspapers, newsletters and information events. Participants will be randomly allocated to the intervention group and the control group in a 1:1 ratio. Participants in the control group will not receive any study intervention or instruction for any change to their previous health care utilization. Despite this, they are allowed to make use of usual care provided by their treating physician. The intervention group comprises a 12-week home training program with three sessions per week in addition to usual care. Exercises will be guided and monitored by use of the training app (re.flex) and two accelerometers that are attached proximally and distally to the affected knee joint. Pre- and postmeasurements will take place at baseline (t0) and after 12 weeks (t1). Primary outcomes will be osteoarthritis-specific pain and physical function measured with the Knee Osteoarthritis Outcome Score (KOOS) subscales Pain and Function in daily living (ADL). Second, further self-reported health outcomes, a performance measurement, app logfiles and safety will be assessed. Intervention effects will be calculated by baseline-adjusted analysis of covariance (ANCOVA) using an intention-to-treat approach. Multiple imputation will be applied. Discussion Re.flex can bridge part of the gap between recommendations for strengthening exercises in patients with knee OA and the insufficient actual care situation. This randomized controlled trial is designed to provide conclusions on the effectiveness of the health application re.flex for the population under study and will provide further insight into adherence rates and the safety of its use. Trial registration The trial was registered on 20/01/2023 at www.drks.de (ID: DRKS00030932).","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134947206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-03DOI: 10.1186/s44247-023-00038-9
Andrey Mostovov, Damien Jacobs, Leila Farid, Paul Dhellin, Guillaume Baille
Abstract Background The six-minute walk test (6MWT) is widely used to assess functional capacity in patients with various diseases. Use of wearable devices can make this test more accurate and easier to administer, and may even enhance it by providing additional information. The purpose of this study was to evaluate the validity of FeetMe® insoles for assessing the total six-minute walking distance (6MWD) by comparing the FeetMe® estimates and those obtained by a rater to the ground truth measured with a surveyor’s wheel. Results Data were analyzed from healthy volunteers who performed the 6MWT on 10-m and 30-m tracks while wearing FeetMe® insoles ( n = 32), and being simultaneously assessed by a rater ( n = 33) and followed by an investigator with a surveyor’s wheel. The mean average error (MAE) of the estimates was below 13 m on both tracks for FeetMe®, whereas it ranged from 16.24 m to 38.88 m on the 30-m and 10-m tracks for the rater. Conclusion The FeetMe® insoles provided a more accurate estimate and showed greater agreement with the ground truth than the rater, and the accuracy of the FeetMe® estimates did not vary according to the track length. We conclude that the FeetMe insoles are a valid solution for measuring the 6MWD.
{"title":"Validation of the six-minute walking distance measured by FeetMe® insoles","authors":"Andrey Mostovov, Damien Jacobs, Leila Farid, Paul Dhellin, Guillaume Baille","doi":"10.1186/s44247-023-00038-9","DOIUrl":"https://doi.org/10.1186/s44247-023-00038-9","url":null,"abstract":"Abstract Background The six-minute walk test (6MWT) is widely used to assess functional capacity in patients with various diseases. Use of wearable devices can make this test more accurate and easier to administer, and may even enhance it by providing additional information. The purpose of this study was to evaluate the validity of FeetMe® insoles for assessing the total six-minute walking distance (6MWD) by comparing the FeetMe® estimates and those obtained by a rater to the ground truth measured with a surveyor’s wheel. Results Data were analyzed from healthy volunteers who performed the 6MWT on 10-m and 30-m tracks while wearing FeetMe® insoles ( n = 32), and being simultaneously assessed by a rater ( n = 33) and followed by an investigator with a surveyor’s wheel. The mean average error (MAE) of the estimates was below 13 m on both tracks for FeetMe®, whereas it ranged from 16.24 m to 38.88 m on the 30-m and 10-m tracks for the rater. Conclusion The FeetMe® insoles provided a more accurate estimate and showed greater agreement with the ground truth than the rater, and the accuracy of the FeetMe® estimates did not vary according to the track length. We conclude that the FeetMe insoles are a valid solution for measuring the 6MWD.","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135738747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Background In this study, we aimed to describe patient characteristics and medication adherence among medication access mobile application users and nonusers. Methods This was a cross-sectional study of a randomly selected sample of patients who refilled their medications either through the mobile application ‘MNG-HA Care’ or by phone call to a government-funded multispecialty hospital in Riyadh, Saudi Arabia. Data were collected through an online survey and filed either via WhatsApp or by phone call. Medication adherence was assessed using the five-item Medication Adherence Report Scale (MARS-5). Results A total of 280 respondents were recruited, and their mean age was 48.8 years (standard deviation (SD): 17.8). More than 75% of application users and nonusers were younger (18–64 years) and lived in urban areas, 58% were male, 37.5% held a bachelor’s degree, and 40% were unemployed. The number of respondents who accessed the mobile application (mobile application users) was 212, and 64.2% of them were adherent to their medications. Sixty-eight of the respondents used a phone call for refills (mobile application nonusers), and 77.9% of them were adherent to their medications. The most common self-reported reasons for using the application were to book an appointment and to request a medication refill. The most common self-reported reasons for not using the application were respondents’ lack of knowledge about the availability of the application and preference for speaking directly to the health care provider. Adjusted multivariate logistic regression analysis revealed that medication adherence was not associated with application use (Odds Ratio (OR): 0.65; 95% CI: 0.33–1.29). However, male patients had significantly higher adherence than females (OR 2.68, 95% CI 1.31 to 5.51), and employed patients had significantly lower adherence than unemployed patients (OR 0.37, 95% CI 0.17 to 0.81). Conclusions Providing patients with access to their medication list through a mobile application alone did not significantly impact medication adherence. Further research is needed to explore the potential benefits of incorporating additional features, such as medication instructions and reminders within mobile applications, to improve medication adherence.
摘要背景在本研究中，我们旨在描述药物获取移动应用程序用户和非用户的患者特征和药物依从性。这是一项横断面研究，随机选择患者样本，通过移动应用程序“MNG-HA Care”或通过电话向沙特阿拉伯利雅得政府资助的多专科医院补充药物。数据通过在线调查收集，并通过WhatsApp或电话提交。用药依从性采用5项用药依从性报告量表(MARS-5)进行评估。结果共纳入调查对象280人，平均年龄48.8岁(标准差:17.8)。超过75%的应用程序用户和非用户是年轻人(18-64岁)，居住在城市地区，58%是男性，37.5%拥有学士学位，40%失业。访问移动应用程序(移动应用程序用户)的受访者人数为212人，其中64.2%的人坚持服药。68名受访者使用电话补药(不使用移动应用程序)，77.9%的人坚持服药。使用该应用程序的最常见的自我报告原因是预约和请求药物补充。自我报告的不使用该应用程序的最常见原因是受访者对该应用程序的可用性缺乏了解，并且倾向于直接与卫生保健提供者交谈。调整后的多因素logistic回归分析显示，药物依从性与应用程序使用无关(优势比(OR): 0.65;95% ci: 0.33-1.29)。然而，男性患者的依从性明显高于女性(OR 2.68, 95% CI 1.31至5.51)，就职患者的依从性明显低于未就职患者(OR 0.37, 95% CI 0.17至0.81)。结论仅通过移动应用程序向患者提供药物清单对药物依从性没有显著影响。需要进一步的研究来探索在移动应用程序中加入额外功能的潜在好处，例如药物说明和提醒，以提高药物依从性。
{"title":"Description of patient characteristics and medication adherence among medication access mobile application users and nonusers: a single-center questionnaire-based cross-sectional study","authors":"Ghadah Assiri, Dalal Alabdulkarim, Asrar Alanazi, Sarah Altamimi, Nadin Lafi Alanazi, Wael Khawagi","doi":"10.1186/s44247-023-00039-8","DOIUrl":"https://doi.org/10.1186/s44247-023-00039-8","url":null,"abstract":"Abstract Background In this study, we aimed to describe patient characteristics and medication adherence among medication access mobile application users and nonusers. Methods This was a cross-sectional study of a randomly selected sample of patients who refilled their medications either through the mobile application ‘MNG-HA Care’ or by phone call to a government-funded multispecialty hospital in Riyadh, Saudi Arabia. Data were collected through an online survey and filed either via WhatsApp or by phone call. Medication adherence was assessed using the five-item Medication Adherence Report Scale (MARS-5). Results A total of 280 respondents were recruited, and their mean age was 48.8 years (standard deviation (SD): 17.8). More than 75% of application users and nonusers were younger (18–64 years) and lived in urban areas, 58% were male, 37.5% held a bachelor’s degree, and 40% were unemployed. The number of respondents who accessed the mobile application (mobile application users) was 212, and 64.2% of them were adherent to their medications. Sixty-eight of the respondents used a phone call for refills (mobile application nonusers), and 77.9% of them were adherent to their medications. The most common self-reported reasons for using the application were to book an appointment and to request a medication refill. The most common self-reported reasons for not using the application were respondents’ lack of knowledge about the availability of the application and preference for speaking directly to the health care provider. Adjusted multivariate logistic regression analysis revealed that medication adherence was not associated with application use (Odds Ratio (OR): 0.65; 95% CI: 0.33–1.29). However, male patients had significantly higher adherence than females (OR 2.68, 95% CI 1.31 to 5.51), and employed patients had significantly lower adherence than unemployed patients (OR 0.37, 95% CI 0.17 to 0.81). Conclusions Providing patients with access to their medication list through a mobile application alone did not significantly impact medication adherence. Further research is needed to explore the potential benefits of incorporating additional features, such as medication instructions and reminders within mobile applications, to improve medication adherence.","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135791439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-18DOI: 10.1186/s44247-023-00034-z
Summer Mengelkoch, Matthew Espinosa, Stephen A. Butler, Laura Joigneau Prieto, Emma Russell, Chris Ramshaw, Shardi Nahavandi, Sarah E. Hill
Abstract Background Digital decision aids are becoming increasingly common in many areas of healthcare. These aids are designed to involve patients in medical decision making, with the aim of improving patient outcomes while decreasing healthcare burden. Previously developed contraceptive-based decision aids have been found to be effective at increasing women’s knowledge about reproductive health and contraception. Here, we sought to evaluate the effectiveness of a novel contraceptive-based decision aid at increasing women’s self-efficacy and knowledge about their reproductive health and contraceptive options, as well as their perceptions of their learning. This study was registered as a clinic trial at ClinicalTrials.gov (Contraception Decision Aid Use and Patient Outcomes, ID# NCT05177783) on 05/01/2022. Methods The Tuune® contraceptive decision aid’s effectiveness was evaluated by conducting an experiment in which 324 women were assigned to use the Tuune® decision aid or a control decision aid. Primary outcomes included reproductive health self-efficacy, reproductive health and contraceptive knowledge, and perceptions of learning. Secondary analyses examined whether prior experience using hormonal contraceptives moderated the relationship between decision aid and each outcome measure. Results Women assigned to use the Tuune® decision aid exhibited greater reproductive health self-efficacy, greater knowledge about reproductive health and contraception, and perceived having learned more than women assigned to use the control decision aid ( p s ≤ .029). This pattern was also observed in women with previous contraceptive use experience, where women using Tuune® reported better outcomes than women using the control aid, regardless of their history of hormonal contraceptive use experience, although this interaction was not significant ( p = .089). Conclusions Use of the Tuune® contraceptive-based decision aid improved each of the predicted outcomes relative to a control decision aid. This suggests that use of the Tuune® contraceptive-based decision aid is well poised to increase women’s confidence and knowledge about contraceptive use and may also reduce burden on healthcare systems.
{"title":"Tuuned in: use of an online contraceptive decision aid for women increases reproductive self-efficacy and knowledge; results of an experimental clinical trial","authors":"Summer Mengelkoch, Matthew Espinosa, Stephen A. Butler, Laura Joigneau Prieto, Emma Russell, Chris Ramshaw, Shardi Nahavandi, Sarah E. Hill","doi":"10.1186/s44247-023-00034-z","DOIUrl":"https://doi.org/10.1186/s44247-023-00034-z","url":null,"abstract":"Abstract Background Digital decision aids are becoming increasingly common in many areas of healthcare. These aids are designed to involve patients in medical decision making, with the aim of improving patient outcomes while decreasing healthcare burden. Previously developed contraceptive-based decision aids have been found to be effective at increasing women’s knowledge about reproductive health and contraception. Here, we sought to evaluate the effectiveness of a novel contraceptive-based decision aid at increasing women’s self-efficacy and knowledge about their reproductive health and contraceptive options, as well as their perceptions of their learning. This study was registered as a clinic trial at ClinicalTrials.gov (Contraception Decision Aid Use and Patient Outcomes, ID# NCT05177783) on 05/01/2022. Methods The Tuune® contraceptive decision aid’s effectiveness was evaluated by conducting an experiment in which 324 women were assigned to use the Tuune® decision aid or a control decision aid. Primary outcomes included reproductive health self-efficacy, reproductive health and contraceptive knowledge, and perceptions of learning. Secondary analyses examined whether prior experience using hormonal contraceptives moderated the relationship between decision aid and each outcome measure. Results Women assigned to use the Tuune® decision aid exhibited greater reproductive health self-efficacy, greater knowledge about reproductive health and contraception, and perceived having learned more than women assigned to use the control decision aid ( p s ≤ .029). This pattern was also observed in women with previous contraceptive use experience, where women using Tuune® reported better outcomes than women using the control aid, regardless of their history of hormonal contraceptive use experience, although this interaction was not significant ( p = .089). Conclusions Use of the Tuune® contraceptive-based decision aid improved each of the predicted outcomes relative to a control decision aid. This suggests that use of the Tuune® contraceptive-based decision aid is well poised to increase women’s confidence and knowledge about contraceptive use and may also reduce burden on healthcare systems.","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135110872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-12DOI: 10.1186/s44247-023-00045-w
Thomas Wetere Tulu, Tsz Kin Wan, Ching Long Chan, Chun Hei Wu, Peter Yat Ming Woo, Cee Zhung Steven Tseng, Asmir Vodencarevic, Cristina Menni, Kei Hang Katie Chan
{"title":"Correction: Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers","authors":"Thomas Wetere Tulu, Tsz Kin Wan, Ching Long Chan, Chun Hei Wu, Peter Yat Ming Woo, Cee Zhung Steven Tseng, Asmir Vodencarevic, Cristina Menni, Kei Hang Katie Chan","doi":"10.1186/s44247-023-00045-w","DOIUrl":"https://doi.org/10.1186/s44247-023-00045-w","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135878150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1186/s44247-023-00035-y
Shaina Raza, Elham Dolatabadi, Nancy Ondrusek, Laura Rosella, Brian Schwartz
Abstract Background Social determinants of health are non-medical factors that influence health outcomes (SDOH). There is a wealth of SDOH information available in electronic health records, clinical reports, and social media data, usually in free text format. Extracting key information from free text poses a significant challenge and necessitates the use of natural language processing (NLP) techniques to extract key information. Objective The objective of this research is to advance the automatic extraction of SDOH from clinical texts. Setting and data The case reports of COVID-19 patients from the published literature are curated to create a corpus. A portion of the data is annotated by experts to create ground truth labels, and semi-supervised learning method is used for corpus re-annotation. Methods An NLP framework is developed and tested to extract SDOH from the free texts. A two-way evaluation method is used to assess the quantity and quality of the methods. Results The proposed NER implementation achieves an accuracy (F1-score) of 92.98% on our test set and generalizes well on benchmark data. A careful analysis of case examples demonstrates the superiority of the proposed approach in correctly classifying the named entities. Conclusions NLP can be used to extract key information, such as SDOH factors from free texts. A more accurate understanding of SDOH is needed to further improve healthcare outcomes.
{"title":"Discovering social determinants of health from case reports using natural language processing: algorithmic development and validation","authors":"Shaina Raza, Elham Dolatabadi, Nancy Ondrusek, Laura Rosella, Brian Schwartz","doi":"10.1186/s44247-023-00035-y","DOIUrl":"https://doi.org/10.1186/s44247-023-00035-y","url":null,"abstract":"Abstract Background Social determinants of health are non-medical factors that influence health outcomes (SDOH). There is a wealth of SDOH information available in electronic health records, clinical reports, and social media data, usually in free text format. Extracting key information from free text poses a significant challenge and necessitates the use of natural language processing (NLP) techniques to extract key information. Objective The objective of this research is to advance the automatic extraction of SDOH from clinical texts. Setting and data The case reports of COVID-19 patients from the published literature are curated to create a corpus. A portion of the data is annotated by experts to create ground truth labels, and semi-supervised learning method is used for corpus re-annotation. Methods An NLP framework is developed and tested to extract SDOH from the free texts. A two-way evaluation method is used to assess the quantity and quality of the methods. Results The proposed NER implementation achieves an accuracy (F1-score) of 92.98% on our test set and generalizes well on benchmark data. A careful analysis of case examples demonstrates the superiority of the proposed approach in correctly classifying the named entities. Conclusions NLP can be used to extract key information, such as SDOH factors from free texts. A more accurate understanding of SDOH is needed to further improve healthcare outcomes.","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135938654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-07DOI: 10.1186/s44247-023-00036-x
M. Matre, T. Johansen, A. Olsen, S. Tornås, AC Martinsen, A. Lund, F. Becker, C. Brunborg, J. Spikman, J. Ponsford, D. Neumann, S. McDonald, M. Løvstad
{"title":"A protocol for the development and validation of a virtual reality-based clinical test of social cognition","authors":"M. Matre, T. Johansen, A. Olsen, S. Tornås, AC Martinsen, A. Lund, F. Becker, C. Brunborg, J. Spikman, J. Ponsford, D. Neumann, S. McDonald, M. Løvstad","doi":"10.1186/s44247-023-00036-x","DOIUrl":"https://doi.org/10.1186/s44247-023-00036-x","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48323806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-07DOI: 10.1186/s44247-023-00037-w
Lauri Lukka, Antti Salonen, M. Vesterinen, Veli-Matti Karhulahti, S. Palva, J. Palva
{"title":"The qualities of patients interested in using a game-based digital mental health intervention for depression: a sequential mixed methods study","authors":"Lauri Lukka, Antti Salonen, M. Vesterinen, Veli-Matti Karhulahti, S. Palva, J. Palva","doi":"10.1186/s44247-023-00037-w","DOIUrl":"https://doi.org/10.1186/s44247-023-00037-w","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42471605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-05DOI: 10.1186/s44247-023-00033-0
Megan M. MacPherson, Shabana Kapadia
{"title":"Barriers and facilitators to patient-to-provider messaging using the COM-B model and theoretical domains framework: a rapid umbrella review","authors":"Megan M. MacPherson, Shabana Kapadia","doi":"10.1186/s44247-023-00033-0","DOIUrl":"https://doi.org/10.1186/s44247-023-00033-0","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44938484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-04DOI: 10.1186/s44247-023-00032-1
M. Geldof, Nina Gerlach, A. Portela
{"title":"Digitalization of home-based records for maternal, newborn, and child health: a scoping review","authors":"M. Geldof, Nina Gerlach, A. Portela","doi":"10.1186/s44247-023-00032-1","DOIUrl":"https://doi.org/10.1186/s44247-023-00032-1","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42413801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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