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The increasing percentage of elderly in our society is challenging the health care system. To meet the challenge, we have implemented an experience-based master's programme in digital health care. The 3-yrs 90 ECTS programme consists of physical sessions of three days duration and weekly 2-3-hour digital lectures and bi-weekly supervisions. A main goal of the program has been to involve the students in relevant local and regional health problems as well as inviting health care personnel to participate in the planning of the study program, present relevant health problems and challenges and follow our open digital health workshops. In this way we have managed to create a stimulating learning environment for both students on further education and local and regional health care personnel.

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This paper explores the significance of physical activity for individuals with intellectual disabilities and proposes an innovative approach using augmented reality exergames to promote adherence. An augmented reality-based app for sorting elements while walking was specifically designed and implemented. Exergames hold promise as effective interventions for promoting physical activity and improving the overall well-being of individuals with intellectual disabilities.

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This paper presents the design, implementation and early tests of an app that collects a comprehensive set of health-related data, as part of the EU-project WARIFA. To achieve the main aim of the project - using AI to prevent chronic conditions - a wide range of data needs to be collected and stored at a backend server for processing. The methods and elements for creating this system are presented, as well as results from the co-creation process and early user-tests. Challenges regarding complexity, security, and privacy are discussed, as well as the needs and prospectives for easier ways of collecting health-related data.

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Epilepsy is a prevalent neurological disorder characterized by unpredictable seizures despite some known risk factors. Using an mHealth system and Empatica EmbracePlus, we aim to examine if biometric sensor data can detect and predict epileptic seizures. Five participants with epilepsy will be followed for a minimum of 15 seizures. Expected results and possible implications are presented. This study may provide new insights into biological indicators for seizure forecasting.

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Migraine is a chronic headache disorder. A prototype mobile app-based system was implemented to test data collection and improve data coverage for the Empatica E4 biometric sensor device. Results from the prototype testing are reported. Future iteration of the app will be tested with patients with migraine to predict events and potentially reduce event duration and therefore disease burden.

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BACKGROUND: Identifying barriers that can be modified to promote physical activity is important for informing health interventions for adults with intellectual disabilities. OBJECTIVES: Exploring participation in physical activity considering age, sex, living conditions, and health conditions. Further, identifying barriers significantly associated with sedentary activity after adjustment for physical activity correlates. METHODS: A cross-sectional study including physical activity and barrier questions from the POMONA-15 health indicators. Multivariate logistic regression analysis with sedentary activity level as dependent variable. RESULTS: Among 213 participants with intellectual disabilities, 36% reported predominately sedentary activities, 53% light and 11% moderate/vigorous physical activity. Barriers related to sedentary activity after adjustment were transportation, health conditions, mobility impairment, and lack of activities at the day activity centre. CONCLUSIONS: The findings highlight the need to enhance physical activity opportunities at day activity centres, tailor programmes for wheelchair users, and improve access to physical activity facilities for adults with intellectual disabilities.

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We created and carried out a cross-sectional anonymous structured questionnaire on what motivates users of mobile health applications and wearables to share their collected health related data. The questionnaire was distributed online in English, French, and Norwegian. In addition, a flyer with information of where to locate the online questionnaire was distributed during a Swiss health conference. We used snowball sampling and encouraged participants to forward the questionnaires to friends, family, and others. Data were collected between October 2018 and March 2020. 58.1 % (n = 473) responded to the English survey, 34.3 % (n = 279) responded to the French survey, and 7.6 % (n = 62) responded to the Norwegian survey. The questionnaire contained 38 questions divided into seven themes: Background and health goals, Wearables and sensors, Mobile applications, Logging of health data, Data sharing- and integration, Social media and entertainment, and Demographics (age, gender, country of origin, chronic disease status, and chronic disease caretaker status). Answer options were single answer, multiple-choice, open-ended, or on a 4-point Likert scale. Questions were defined based on 16 in-person interviews with people without any chronic disorder, people with diabetes, and people with sickle cell disease. All questions were optional. Data were collected from 814 participants. All answers to the open-ended questions have been translated into English. This dataset is especially interesting for researchers interesting in what motivates people with and without chronic disease across countries to use mHealth tools and share their collected health data. Only a subset of variables has been analyzed so far and new research questions on motivation can potentially be answered using this dataset.

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Background: Many individuals with intellectual disability (ID) have a sedentary lifestyle. Few interventions aimed at increasing their level of physical activity (PA) have shown lasting effects. Aim: To assess the feasibility and acceptability of a pilot intervention study using innovative mobile health (mHealth) support systems to encourage PA in individuals with ID. Methods: Nine individuals with ID and a low level of PA, aged 16-36 years, were included in the present convergent triangulation mixed method design. Two mHealth support systems (apps) were developed and tested. PA was measured with a Fitbit smartwatch, accelerometer, the International Physical Activity Questionnaire-Short Form (IPAQ-S), and Goal attainment scaling. Data were collected through online pre-, mid- (4 weeks), and post-intervention (12 weeks) questionnaires and activity trackers. Semi-structured qualitative interviews with participants and/or a family or staff member were held after the 12-week follow-up. Data were analyzed using conventional nonparametric statistics and thematic analyses. Results: The response rate and retention to the trial were 16% and 100%, respectively. Data quality was high, except for missing data from Fitbit activity trackers of approximately 30% from the 4- and 12-week follow-up stages. The feasibility challenges with activity trackers include rashes, size, non-acceptance, and loss of motivation. Participants and family members/staff reported interest in the study theme and were pleased with the data collection method. All but one participant achieved their PA goals. Most participants reported being satisfied with the apps as they were enjoyable or provided a reminder for performing physical and other activities. Social support for PA among family members also increased. However, app support from staff and family members was needed, and apps were not used regularly. Two of nine participants (22%) had increased their PA measured as steps per day with Fitbit at the 12-week follow-up. Conclusions: The acceptability and feasibility of using tailored mobile applications in natural settings to increase PA among adults with ID are promising. This study aligns with previous studies in showing the challenges to increasing PA, which requires the inclusion of family members, staff, and stakeholders. The intervention requires modifications before a randomized controlled trial can be conducted.

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Data from consumer-based devices for collecting personal health-related data could be useful in diagnostics and treatment. This requires a flexible and scalable software and system architecture to handle the data. This study examines the existing mSpider platform, addresses shortcomings in security and development, and suggests a full risk analysis, a more loosely coupled component- based system for long term stability, better scalability, and maintainability. The goal is to create a human digital twin platform for an operational production environment.

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New digital technologies like activity trackers, nudge concepts, and approaches can inspire and improve personal health. There is increasing interest in employing such devices to monitor people's health and well-being. These devices can continually gather and examine health-related information from people and groups in their familiar surroundings. Context-aware nudges can assist people in self-managing and enhancing their health. In this protocol paper, we describe how we plan to investigate what motivates people to engage in physical activity (PA), what influences them to accept nudges, and how participant motivation for PA may be impacted by technology use.

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BACKGROUND: Serious public-health concerns such as overweight and obesity are in many cases caused by excess intake of food combined with decreases in physical activity. Smart scales with wireless data transfer can, together with smart watches and trackers, observe changes in the population's health. They can present us with a picture of our metabolism, body health, and disease risks. Combining body composition data with physical activity measurements from devices such as smart watches could contribute to building a human digital twin. OBJECTIVE: The objectives of this study were to (1) investigate the evolution of smart scales in the last decade, (2) map status and supported sensors of smart scales, (3) get an overview of how smart scales have been used in research, and (4) identify smart scales for current and future research. METHOD: We searched for devices through web shops and smart scale tests/reviews, extracting data from the manufacturer's official website, user manuals when available, and data from web shops. We also searched scientific literature databases for smart scale usage in scientific papers. RESULT: We identified 165 smart scales with a wireless connection from 72 different manufacturers, released between 2009 and end of 2021. Of these devices, 49 (28%) had been discontinued by end of 2021. We found that the use of major variables such as fat and muscle mass have been as good as constant over the years, and that minor variables such as visceral fat and protein mass have increased since 2015. The main contribution is a representative overview of consumer grade smart scales between 2009 and 2021. CONCLUSION: The last six years have seen a distinct increase of these devices in the marketplace, measuring body composition with bone mass, muscle mass, fat mass, and water mass, in addition to weight. Still, the number of research projects featuring connected smart scales are few. One reason could be the lack of professionally accurate measurements, though trend analysis might be a more feasible usage scenario.

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BACKGROUND: Several studies have shown that individuals with intellectual disabilities (IDs) have low levels of physical activity (PA), and intervention studies on PA suggest inconsistent evidence. The use of technology as a means of motivation for PA has yet to be extensively explored and needs to be further investigated. OBJECTIVE: We aim to assess the feasibility and acceptability of procedures for an intervention arm in a future trial on mobile health (mHealth) to support PA for individuals with IDs. In addition, we aim to examine how the use of technology can influence motivation for PA among participants, their caregivers, and staff members. METHODS: A mixed methods pilot study of an intervention arm will be carried out in a planned randomized controlled trial (RCT). Ten participants with ID and their caregivers or a staff member will be included. Information will always be provided by a caregiver or a staff member, or participants with ID if possible. Assessments will be carried out at baseline, follow-up after 4 weeks, and 12 weeks, and include questionnaires on PA, social support, self-efficacy, and challenging behavior. PA will be measured with 2 different activity trackers (Fitbit and Axivity) for 1 week at all assessments. Feasibility will be assessed as recruitment and adherence rate, missing data, usability of the motivational mHealth tool, and estimates of effectiveness. Acceptability of study procedures, activity measures, and motivation for participation in PA will be additionally assessed with qualitative methods at the end of the intervention. RESULTS: Enrollment commenced in May 2021. Data collection was completed in March 2022. CONCLUSIONS: This pilot study will evaluate the feasibility and acceptability of study procedures of the intervention arm of a planned RCT to address feasibility issues, improve study procedures, and estimate effectiveness of the study measures. How the use of technology can influence motivation for PA will also be examined, which can help guide and improve future PA interventions involving the use of technology. TRIAL REGISTRATION: ClinicalTrials.gov NCT04929106; https://clinicaltrials.gov/ct2/show/NCT04929106. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37849.

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OBJECTIVES: Accelerometer-based wrist-worn fitness trackers and smartwatches (wearables) appeared on the consumer market in 2011. Many wearable devices have been released since. The objective of this data paper is to describe a dataset of 423 wearables released before July 2017. DATA DESCRIPTION: We identified wearables and extracted information from six online and offline databases. We also visited websites for all identified companies/brands to identify additional wearables, as well as obtained additional information for each identified device. Twelve attributes were collected: wearable name, company/brand name, release year, country of origin, whether the wearable was crowd funded, form factor (fitness tracker or smartwatch), and sensors supported. Support for the following sensors were mapped: accelerometer, magnetometer, gyroscope, altimeter or barometer, global-positioning-system, and optical pulse sensor (i.e., photoplethysmograph). The search was conducted between May 15th and July 1st, 2017. The included data gives an overview of most in-scope wearables released before July 2017 and allows researchers to conduct additional analysis not performed in the related article. Further insights can be achieved by complementing this list with wearable models released after July 2017.

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BACKGROUND: Consumer-based activity trackers are increasingly used in research, as they have the potential to promote increased physical activity and can be used for estimating physical activity among participants. However, the accuracy of newer consumer-based devices is mostly unknown, and validation studies are needed. OBJECTIVE: The objective of this study was to compare the Polar Vantage watch (Polar Electro Oy) and Oura ring (generation 2; Oura Health Oy) activity trackers to research-based instruments for measuring physical activity, total energy expenditure, resting heart rate, and sleep duration in free-living adults. METHODS: A total of 21 participants wore 2 consumer-based activity trackers (Polar watch and Oura ring), an ActiGraph accelerometer (ActiGraph LLC), and an Actiheart accelerometer and heart rate monitor (CamNtech Ltd) and completed a sleep diary for up to 7 days. We assessed Polar watch and Oura ring validity and comparability for measuring physical activity, total energy expenditure, resting heart rate (Oura), and sleep duration. We analyzed repeated measures correlations, Bland-Altman plots, and mean absolute percentage errors. RESULTS: The Polar watch and Oura ring values strongly correlated (P<.001) with the ActiGraph values for steps (Polar: r=0.75, 95% CI 0.54-0.92; Oura: r=0.77, 95% CI 0.62-0.87), moderate-to-vigorous physical activity (Polar: r=0.76, 95% CI 0.62-0.88; Oura: r=0.70, 95% CI 0.49-0.82), and total energy expenditure (Polar: r=0.69, 95% CI 0.48-0.88; Oura: r=0.70, 95% CI 0.51-0.83) and strongly or very strongly correlated (P<.001) with the sleep diary-derived sleep durations (Polar: r=0.74, 95% CI 0.56-0.88; Oura: r=0.82, 95% CI 0.68-0.91). Oura ring-derived resting heart rates had a very strong correlation (P<.001) with the Actiheart-derived resting heart rates (r=0.9, 95% CI 0.85-0.96). However, the mean absolute percentage error was high for all variables except Oura ring-derived sleep duration (10%) and resting heart rate (3%), which the Oura ring underreported on average by 1 beat per minute. CONCLUSIONS: The Oura ring can potentially be used as an alternative to the Actiheart to measure resting heart rate. As for sleep duration, the Polar watch and Oura ring can potentially be used as replacements for a manual sleep diary, depending on the acceptable error. Neither the Polar watch nor the Oura ring can replace the ActiGraph when it comes to measuring steps, moderate-to-vigorous physical activity, and total energy expenditure, but they may be used as additional sources of physical activity measures in some settings. On average, the Polar Vantage watch reported higher outputs compared to those reported by the Oura ring for steps, moderate-to-vigorous physical activity, and total energy expenditure.

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Physical activity (PA) data were downloaded from 113 participants who owned a Garmin or Fitbit activity tracker in 2019 and 2020. Upon participant authorization, data were automatically downloaded from the Garmin and Fitbit cloud storages. The mSpider tool, a solution for automatic and continuous data extraction from activity tracker providers, were used to download participant data. Available data are daily averages by year, as well as monthly averages between 2019 and 2020, for steps, activity energy expenditure (AEE), total energy expenditure (TEE), moderate-to-vigorous physical activity (MVPA), light PA (LPA), moderate PA (MPA), vigorous PA (VPA), and sedentary time. In addition, March 2020 was divided in two, giving the daily average before and after the Norwegian COVID-19 lockdown date. Raw daily values for these variables are also included in a separate file. In addition, daily values for non-wear time are also include as raw data. In a previous study, differences between months, i.e., comparing 2019 with 2020 for months between March to December, were analysed for steps, MVPA, and AEE [1]. Further insights may be achieved by exploring other variables. This includes: (1) monthly averages for TEE, LPA, MPA, VPA, and sedentary time, (2) yearly averages (2019 and 2020) for steps, MVPA, TEE, AEE, LPA, MPA, VPA, and sedentary time (3) monthly average for steps, MVPA, TEE, AEE, LPA, MPA, VPA, and sedentary time for January, February, and March 2019, as well as March 2020. Additional analysis can also be conducted on the raw data.

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BACKGROUND: A healthy diet throughout the life course improves health and reduces the risk of disease. There is a need for new knowledge of the relation between diet and health, but existing methods to collect information on food and nutrient intake have their limitations. Evaluations of new tools to assess dietary intake are needed, especially in old people, where the introduction of new technology might impose challenges. OBJECTIVE: We aimed to examine the usability of a new web-based dietary assessment tool in older adult women and men. METHODS: A total of 60 women and men (participation 83%, 57% women) aged 60-74 years recruited by convenience and snowball sampling completed a 24-hour web-based dietary recall using the newly developed Norwegian version of Measure Your Food On One Day (myfood24). Total energy and nutrient intakes were calculated in myfood24, primarily on the basis of the Norwegian Food Composition Table. No guidance or support was provided to complete the recall. Usability was assessed using the system usability scale (SUS), where an SUS score of ≥68 was considered satisfactory. We examined the responses to single SUS items and the mean (SD) SUS score in groups stratified by sex, age, educational level, and device used to complete the recall (smartphone, tablet device, or computer). RESULTS: The mean total energy intake was 5815 (SD 3093) kJ. A total of 14% of participants had an energy intake of <2100 kJ (ie, 500 kilocalories) and none had an intake of >16,800 kJ (ie, 4000 kilocalories). Mean energy proportions from carbohydrates, fat, protein, alcohol, and fiber was within the national recommendations. The mean SUS score was 55.5 (SD 18.6), and 27% of participants had SUS scores above the satisfactory product cut-off. Higher SUS scores were associated with younger age and lower education, but not with the type of device used. CONCLUSIONS: We found the overall usability of a new web-based dietary assessment tool to be less than satisfactory in accordance with standard usability criteria in a sample of 60-74-year-old Norwegians. The observed total energy intakes suggest that several of the participants underreported their intake during the completion of the dietary recall. Implementing web-based dietary assessment tools in older adults is feasible, but guidance and support might be needed to ensure valid completion.

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BACKGROUND: Physical inactivity and obesity are global public health challenges. Older adults are important to target for prevention and management of disease and chronic conditions. However, many individuals struggle with maintaining increased physical activity (PA) and improved diet. This feasibility study provides the foundation for the RESTART trial, a randomized controlled trial (RCT) to test a complex intervention to facilitate favourable lifestyle changes older adults can sustain. The primary objective of this study was to investigate study feasibility (recruitment, adherence, side-effects, and logistics) using an interdisciplinary approach. METHODS: This 1-year prospective mixed-method single-arm feasibility study was conducted in Tromsø, Norway, from September 2017. We invited by mail randomly selected participants from the seventh survey of the Tromsø Study (2015-2016) aged 55-75 years with sedentary lifestyle, obesity, and elevated cardiovascular risk. Participants attended a 6-month complex lifestyle intervention program, comprising instructor-led high-intensive exercise and nutritionist- and psychologist-led counselling, followed by a 6-month follow-up. All participants used a Polar activity tracker for daily activity monitoring during the intervention. Participants were interviewed three times throughout the study. Primary outcome was study feasibility measures. RESULTS: We invited potential participants (n=75) by mail of which 27 % (n=20) agreed to participate. Telephone screening excluded four participants, and altogether 16 participants completed baseline screening. The intervention and test procedures of primary and secondary outcomes were feasible and acceptable for the participants. There were no exercise-induced injuries, indicating that the intervention program is safe. Participants experienced that the dietary and psychological counselling were delivered too early in the intervention and in too close proximity to the start of the exercise program. Minor logistic improvements were implemented throughout the intervention period. CONCLUSION: This study indicates that it is feasible to conduct a full-scale RCT of a multi-component randomized intervention trial, based on the model of the present study. No dropouts due to exercise-induced injury indicates that the exercises were safe. While minor improvements in logistics were implemented during the intervention, we will improve recruitment and adherence strategies, rearrange schedule of intervention contents (exercise, diet, and psychology), as well as improve the content of the dietary and behavioural counselling to maximize outcome effects in the RESTART protocol. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03807323 Registered 16 January 2019 - retrospectively registered.

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A key challenge in lifestyle interventions is long-term maintenance of favorable lifestyle changes. Middle-aged and older adults are important target groups. The purpose of this analysis was to investigate changes in adiposity, physical activity, cardiometabolic risk factors, diet, physical capacity, and well-being, in inactive middle-aged and older women and men with obesity and elevated cardiovascular disease risk, participating in an interdisciplinary single-arm complex lifestyle intervention pilot study. Participants were recruited from the population-based Tromsø Study 2015-2016 with inclusion criteria age 55-74 years, body mass index (BMI) ≥30kg/m2, sedentary lifestyle, no prior myocardial infarction and elevated cardiovascular risk. Participants (11 men and 5 women aged 57-74 years) underwent a 6-month intervention of two 1-hour group-sessions per week with instructor-led gradually intensified exercise (endurance and strength), one individual and three 2-hour group counselling sessions with nutritionist (Nordic Nutrition Recommendations) and psychologist (Implementation intention strategies). We investigated changes in adiposity (weight, BMI, body composition, waist circumference), physical activity (self-reported and via physical activity trackers), cardiometabolic risk factors (blood pressure, HbA1c, blood lipids), diet (intake of energy, nutrients, foods), physical capacity (aerobic capacity, muscle strength), and psychological well-being, measured at baseline and end-of-intervention, using mean-comparison paired t-tests. Further, we investigated self-reported healthy lifestyle maintenance six months after end-of-intervention, and monthly changes in daily step count, moderate-to-vigorous physical activity (MVPA) and total energy expenditure. From baseline to end-of-intervention, there was a mean decrease in weight, BMI, fat mass, waist circumference, intake of total- and saturated fat, and increase in lean mass, lateral pulldown and leg press. We detected no changes in mean levels of physical activity, cardiometabolic risk factors or well-being. Six months after end-of-intervention, 25% responded healthy lifestyle achievement and maintenance, while objectively measured physical activity remained unchanged. The results are useful for development of a protocol for a full-scale trial. Trial registration: The study was registered at www.ClinicalTrials.gov registry (NCT03807323).

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The health and well-being of informal caregivers often take a backseat to those that they care for. While systems, technologies, and services that provide care and support for those with chronic illnesses are established and continuously improved, those that support informal caregivers are less explored. An international survey about motivations to use mHealth technologies was posted to online platforms related to chronic illnesses. We focused on responses regarding the facilitators and challenges of achieving health goals, including the use of mHealth technologies, for the subgroup who identified as "Caregivers". Findings indicate that mHealth technology is not yet the most important motivational factor for achieving health goals in this group, but greater future potential is suggested.

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BACKGROUND: Consumer-based physical activity trackers have increased in popularity. The widespread use of these devices and the long-term nature of the recorded data provides a valuable source of physical activity data for epidemiological research. The challenges include the large heterogeneity between activity tracker models in terms of available data types, the accuracy of recorded data, and how this data can be shared between different providers and third-party systems. OBJECTIVE: The aim of this study is to develop a system to record data on physical activity from different providers of consumer-based activity trackers and to examine its usability as a tool for physical activity monitoring in epidemiological research. The longitudinal nature of the data and the concurrent pandemic outbreak allowed us to show how the system can be used for surveillance of physical activity levels before, during, and after a COVID-19 lockdown. METHODS: We developed a system (mSpider) for automatic recording of data on physical activity from participants wearing activity trackers from Apple, Fitbit, Garmin, Oura, Polar, Samsung, and Withings, as well as trackers storing data in Google Fit and Apple Health. To test the system throughout development, we recruited 35 volunteers to wear a provided activity tracker from early 2019 and onward. In addition, we recruited 113 participants with privately owned activity trackers worn before, during, and after the COVID-19 lockdown in Norway. We examined monthly changes in the number of steps, minutes of moderate-to-vigorous physical activity, and activity energy expenditure between 2019 and 2020 using bar plots and two-sided paired sample t tests and Wilcoxon signed-rank tests. RESULTS: Compared to March 2019, there was a significant reduction in mean step count and mean activity energy expenditure during the March 2020 lockdown period. The reduction in steps and activity energy expenditure was temporary, and the following monthly comparisons showed no significant change between 2019 and 2020. A small significant increase in moderate-to-vigorous physical activity was observed for several monthly comparisons after the lockdown period and when comparing March-December 2019 with March-December 2020. CONCLUSIONS: mSpider is a working prototype currently able to record physical activity data from providers of consumer-based activity trackers. The system was successfully used to examine changes in physical activity levels during the COVID-19 period.

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