hh.sePublications
Change search
Refine search result
1 - 15 of 15
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Bergdahl, Johan
    et al.
    Centre for Pharmacoepidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
    Jarnbring, Fredrik
    Department of Oral and Maxillofacial Surgery Solna, Karolinska University Hospital, Stockholm, Sweden.
    Ehrenstein, Vera
    Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
    Gammelager, Henrik
    Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
    Granath, Fredrik
    Centre for Pharmacoepidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
    Kieler, Helle
    Centre for Pharmacoepidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
    Svensson, Madeleine
    Centre for Pharmacoepidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
    Tell, Grethe S.
    Department of Public Health and Primary Health Care, University of Bergen, Bergen, Norway.
    Lagerros, Ylva Trolle
    Centre for Pharmacoepidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
    Evaluation of an algorithm ascertaining cases of osteonecrosis of the jaw in the Swedish National Patient Register2013In: Clinical Epidemiology, ISSN 1179-1349, E-ISSN 1179-1349, Vol. 5, no 1, p. 1-7Article in journal (Refereed)
    Abstract [en]

    Background: Osteonecrosis of the jaw (ONJ) is a medical condition associated with antiresorptive drugs, among others, used to treat osteoporosis and bone metastasis. Currently, there is no consensus regarding the definition of ONJ, and no ONJ-specific International Classification of Diseases-10 code exists. Therefore, register-based studies of this condition may be troublesome.

    Purpose: To evaluate an algorithm ascertaining ONJ cases in an attempt to facilitate future assessments of ONJ in clinical and epidemiological studies.

    Methods: By means of the Patient Register and the Prescribed Drug Register, we identified all postmenopausal female residents in Sweden from 2005 through 2009. To identify potential cases of ONJ, we employed an algorithm including the following conditions: periapical abscess with sinus, inflammatory conditions of jaws, alveolitis of jaws, idiopathic aseptic necrosis of bone, osteonecrosis due to drugs, osteonecrosis due to previous trauma, other secondary osteonecrosis, other osteonecrosis, and unspecified osteonecrosis. Women seen at departments of oral and maxillofacial surgery, with at least one of the conditions, were classified as potential cases of ONJ. Conditions in anatomic sites other than the jaw were excluded. Validation was performed through medical record review. Case confirmation was based on the ONJ definition by the American Association of Oral and Maxillofacial Surgeons. The algorithm was evaluated by positive predictive values (PPVs) stratified by diagnosis.

    Results: For the 87 potential cases identified through our algorithm, the medical records were obtained for 83. The overall PPV was 18% (95% confidence interval (CI) 10%–28%). The highest PPV was observed in osteonecrosis due to drugs (83%, 95% CI 36%–100%). Several diagnoses had a PPV of 0 or were not used at all (periapical abscess with sinus, alveolitis of jaws, idiopathic aseptic necrosis of bone, osteonecrosis due to previous trauma, other secondary osteonecrosis, other osteonecrosis, and unspecified osteonecrosis).

    Conclusion: It was possible to ascertain cases of ONJ from the Swedish registers using this algorithm; however, the PPV was low. Thus, further refinements of the algorithm are necessary. © 2013 Bergdahl et al, publisher and licensee Dove Medical Press Ltd.

  • 2.
    Fernström, Maria
    et al.
    Department of Clinical Medicine, School of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Bakkman, Linda
    Department of Medicine, Clinical Epidemiology Unit, Karolinska Institutet, Solna, Stockholm, Sweden.
    Loogna, Peter
    Bariatric Center, Sophiahemmet, Stockholm, Sweden.
    Rooyackers, Olav
    Department of Anaesthesiology and Intensive Care, Karolinska Institutet, Huddinge, Stockholm, Sweden.
    Svensson, Madeleine
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI). Department of Medicine, Clinical Epidemiology Unit, Karolinska Institutet, Solna, Stockholm, Sweden.
    Jakobsson, Towe
    Department of Anaesthesiology and Intensive Care, Karolinska Institutet, Huddinge, Stockholm, Sweden.
    Brandt, Lena
    Department of Clinical Medicine, School of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Lagerros, Ylva Trolle
    Department of Medicine, Clinical Epidemiology Unit, Karolinska Institutet, Solna, Stockholm, Sweden & Department of Endocrinology, Metabolism and Diabetes, Karolinska University Hospital, Huddinge, Stockholm, Sweden.
    Improved Muscle Mitochondrial Capacity Following Gastric Bypass Surgery in Obese Subjects2016In: Obesity Surgery, ISSN 0960-8923, E-ISSN 1708-0428, Vol. 26, no 7, p. 1391-1397Article in journal (Refereed)
    Abstract [en]

    Background

    Weight loss resulting from low-calorie diets is often less than expected. We hypothesized that energy restriction would influence proton leakage and improve mitochondrial efficiency, leading to reduced energy expenditure, partly explaining the difficulties in weight loss maintenance.

    Methods

    Eleven women with a median BMI of 38.5 kg/m2 (q-range 37–40), and referred to gastric bypass surgery participated. Before surgery, and at 6 months of follow-up, muscle biopsies were collected from the vastus lateralis muscle. Mitochondria were isolated and analyzed for coupled (state 3) and uncoupled (state 4) respiration and mitochondrial capacity (P/O ratio).

    Results

    At follow-up, the participants had a median BMI of 29.6 kg/m2 (28.3–32.0). State 3 increased from 20.6 (17.9–28.9) to 34.9 nmol O2/min/U citrate synthase (CS) (27.0–49.0), p = 0.01, while state 4 increased from 2.8 (1.8–4.2) to 4.2 nmol O2/min/U CS (3.1–6.1), although not statistically significant. The P/O ratio increased from 2.7 (2.5–2.8) to 3.2 (3.0–3.4), p = 0.02, indicating improved mitochondrial efficiency.

    Conclusions

    Six months after gastric bypass surgery, the mitochondrial capacity for coupled, i.e., ATP-generating, respiration increased, and the P/O ratio improved. Uncoupled respiration was not enhanced to the same extent. This could partly explain the decreased basal metabolism and the reduced inclination for weight loss during energy restriction. © Springer Science+Business Media New York 2015

  • 3.
    Maddison, Ralph
    et al.
    Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Burwood, Australia & National Institute for Health Innovation, School of Population Health, University of Auckland, Auckland, New Zealand.
    Gemming, Luke
    National Institute for Health Innovation, School of Population Health, University of Auckland, Auckland, New Zealand.
    Monedero, Javier
    School of Health & Human Performance, Dublin City University, Dublin, Ireland.
    Bolger, Linda
    School of Health & Human Performance, Dublin City University, Dublin, Ireland.
    Belton, Sarahjane
    School of Health & Human Performance, Dublin City University, Dublin, Ireland.
    Issartel, Johann
    School of Health & Human Performance, Dublin City University, Dublin, Ireland.
    Marsh, Samantha
    National Institute for Health Innovation, School of Population Health, University of Auckland, Auckland, New Zealand.
    Direito, Artur
    National Institute for Health Innovation, School of Population Health, University of Auckland, Auckland, New Zealand.
    Solenhill, Madeleine
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Zhao, Jinfeng
    Department of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand.
    Exeter, Daniel John
    Department of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand.
    Vathsangam, Harshvardhan
    Robotic Embedded Systems Laboratory, Robotics and Autonomous Systems Center, University of Southern California, Los Angeles, CA, United States.
    Rawstorn, Jonathan Charles
    Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Burwood, Australia & National Institute for Health Innovation, School of Population Health, University of Auckland, Auckland, New Zealand.
    Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study2017In: JMIR mhealth and uhealth, E-ISSN 2291-5222, Vol. 5, no 8, article id e122Article in journal (Refereed)
    Abstract [en]

    Background: The use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data-which includes billions of digital traces-offers scientists a new lens to examine PA in fine-grained detail and allows us to track people's geocoded movement patterns to determine their interaction with the environment. Objective: The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data. Methods: The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement. Results: A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [-0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t(6123)=101.49, P<.001), and biases were larger during high-intensity activities (F-3,F-6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps. Conclusions: The Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics.

  • 4.
    Solenhill, Madeleine
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI). National Institute for Health Innovation, University of Auckland, Auckland, New Zeland.
    "More of the same is not enough" - dags att tänka om kring framtidens hälsointerventioner2015In: Dietistaktuellt, ISSN 1102-9285, Vol. XXIV, no 4, p. 44-48Article in journal (Other (popular science, discussion, etc.))
  • 5.
    Solenhill, Madeleine
    et al.
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Grotta, Alessandra
    Karolinska Institutet, Stockholm, Sweden.
    Pasquali, Elena
    Karolinska Institutet, Huddinge, Sweden.
    Bakkman, Linda
    Karolinska Institutet, Stockholm, Sweden.
    Bellocco, Rino
    Karolinska Institutet, Stockholm, Sweden & University of Milano-Bicocca, Milan, Italy.
    Lagerros, Ylva Trolle
    Karolinska Institutet, Stockholm, Sweden & Karolinska University Hospital, Stockholm, Sweden.
    The Effect of Tailored Web-Based Feedback and Optional Telephone Coaching on Health Improvements: A Randomized Intervention Among Employees in the Transport Service Industry2016In: Journal of Medical Internet Research, ISSN 1438-8871, E-ISSN 1438-8871, Vol. 18, no 8, article id e158Article in journal (Refereed)
    Abstract [en]

    Background: Lifestyle-related health problems is an important health concern in the transport service industry. Web- and telephone-based interventions could be suitable for this target group requiring tailored approaches.

    Objective: To evaluate the effect of tailored web-based health feedback and optional telephone coaching with respect to improved lifestyle factors (Body Mass Index [BMI], dietary intake, physical activity, stress, sleep, tobacco- and alcohol consumption, disease history, self-perceived health, and motivation to change health habits), in comparison to no health feedback or telephone coaching.

    Methods: 3,876 employees in the Swedish transport services were e-mailed a web-based questionnaire. They were randomized to either: A) control group (498 out of 1,238 answered, 40.2%) or B) intervention web (482 out of 1,305 answered, 36.9%), or C) intervention web+telephone (493 out of 1,333 answered, 37.0%). All groups received an identical questionnaire, only the interventions differed. Group B received tailored web-based health feedback and group C received tailored web-based health feedback + optional telephone coaching if the participants’ reported health habits did not meet the national guidelines, or if they expressed motivation to change health habits. The web-based feedback was fully automated. Telephone coaching was performed by trained health counsellors. Nine months later, all participants received a follow-up questionnaire and intervention web+telephone. Descriptive statistics, Chi-square test, analysis of variance, and generalized estimation equations (GEE) models were employed.

    Results: 981 out of 1,473 (66.6%) employees participated at baseline (men: 66.7%, mean age: 44 years, mean BMI: 26.4 kg/m2) and at follow-up. No significant differences were found in reported health habits between the three groups over time. However, significant changes were found for motivation to change. The intervention groups reported higher motivation to improve dietary habits (n=144 out of 301 participants [47.8%] and n=165 out of 324 participants [50.9%] for B and C, respectively) and physical activity habits (n=181 out of 301 participants [60.1%] and n=207 out of 324 participants [63.9%] for B and C, respectively) compared to the control group A (n=122 out of 356 participants [34.3%] for diet and n=177 out of 356 participants [49.7%] for physical activity). At follow-up, the intervention groups had significantly decreased their motivation (group B: P<.001 for change in diet; P<.001 for change in physical activity; group C: P=.007 for change in diet; P<.001 for change in physical activity), whereas the control group reported significantly increased motivation to change diet and physical activity (P<.001 for change in diet; P<.001 for change in physical activity). © Madeleine Solenhill, Alessandra Grotta, Elena Pasquali, Linda Bakkman, Rino Bellocco, Ylva Trolle Lagerros.

    Conclusions: Tailored web-based health feedback and the offering of optional telephone coaching did not have a positive health effect on employees in the transport services. However, our findings suggest an increased short-term motivation to change health behaviors related to diet and physical activity among those receiving tailored web-based health feedback.

  • 6.
    Svensson, Madeleine
    Karolinska Institutet, Stockholm, Sweden.
    Motivational technologies to promote weight loss – from Internet to gadgets2011Conference paper (Refereed)
  • 7.
    Svensson, Madeleine
    et al.
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Bellocco, Rino
    Department of Statistics, University of Milano-Bicocca, Milan, Italy.
    Bakkman, Linda
    Unit of Clinical Epidemiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
    Trolle Lagerros, Ylva
    Unit of Clinical Epidemiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
    An Interactive Internet-Based Plate for Assessing Lunchtime Food Intake: A Validation Study on Male Employees2013In: Journal of Medical Internet Research, ISSN 1438-8871, E-ISSN 1438-8871, Vol. 15, no 1, article id e13Article in journal (Refereed)
    Abstract [en]

    Background: Misreporting food intake is common because most health screenings rely on self-reports. The more accurate methods (eg, weighing food) are costly, time consuming, and impractical.

    Objectives: We developed a new instrument for reporting food intake—an Internet-based interactive virtual food plate. The objective of this study was to validate this instrument’s ability to assess lunch intake.

    Methods: Participants were asked to compose an ordinary lunch meal using both a virtual and a real lunch plate (with real food on a real plate). The participants ate their real lunch meals on-site. Before and after pictures of the composed lunch meals were taken. Both meals included identical food items. Participants were randomized to start with either instrument. The 2 instruments were compared using correlation and concordance measures (total energy intake, nutritional components, quantity of food, and participant characteristics).

    Results: A total of 55 men (median age: 45 years, median body mass index [BMI]: 25.8 kg/m2) participated. We found an overall overestimation of reported median energy intake using the computer plate (3044 kJ, interquartile range [IQR] 1202 kJ) compared with the real lunch plate (2734 kJ, IQR 1051 kJ, P<.001). Spearman rank correlations and concordance correlations for energy intake and nutritional components ranged between 0.58 to 0.79 and 0.65 to 0.81, respectively.

    Conclusion: Although it slightly overestimated, our computer plate provides promising results in assessing lunch intake. © Filippo Castiglione.

  • 8.
    Svensson, Madeleine
    et al.
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI), Sport Health and Physical activity. Karolinska Institutet, Department of Medicine, Unit of Clinical Epidemiology, Stockholm, Sweden.
    Hult, Mari
    Karolinska Institutet, Department of Medicine, Unit of Clinical Epidemiology, Stockholm, Sweden.
    van der Mark, Marianne
    Karolinska Institutet, Department of Medicine, Unit of Clinical Epidemiology, Stockholm, Sweden.
    Grotta, Alessandra
    Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden.
    Jonasson, Josefine
    Obesity Center Norrtull, Stockholm, Sweden .
    von Hausswolff-Juhlin, Yvonne
    Karolinska Institutet, Department of Neuroscience, Stockholm, Sweden.
    Rössner, Stephan
    Karolinska University Hospital, Department of Medicine, Stockholm, Sweden & Apple Bay Obesity Research Center, Stockholm, Sweden.
    Trolle Lagerros, Ylva
    Karolinska Institutet, Department of Medicine, Unit of Clinical Epidemiology, Stockholm, Sweden.
    The Change in Eating Behaviors in a Web-Based Weight Loss Program: A Longitudinal Analysis of Study Completers2014In: Journal of Medical Internet Research, ISSN 1438-8871, E-ISSN 1438-8871, Vol. 16, no 11, article id e234Article in journal (Refereed)
    Abstract [en]

    Background: Eating behaviors are essential components in weight loss programs, but limited research has explored eating behaviors in Web-based weight loss programs.

    Objectives: The aim was to evaluate an interactive Web-based weight loss program on eating behaviors using the 18-item Three-Factor Eating Questionnaire Revised (TFEQ-R18) which measures uncontrolled eating, emotional eating, and cognitive restrained eating. Our Web-based weight loss program is comprised of information about healthy lifestyle choices, weekly chats with experts, social networking features, databases for recipe searches, and features allowing members to self-report and track their weight, physical activity, and dietary intake on the website.

    Methods: On registering for the weight loss program, 23,333 members agreed to take part in the research study. The participants were then asked to complete the TFEQ-R18 questionnaire at baseline and after 3 and 6 months of participation. All data collection was conducted online, with no face-to-face contact. To study changes in TFEQ-R18 eating behaviors we restricted our study to those members who completed all 3 TFEQ-R18 questionnaires. These participants were defined as "completers" and the remaining as "noncompleters." The relationships between sex, change in eating behaviors, and total weight loss were studied using repeated measures ANOVA and Pearson correlation coefficient.

    Results: In total, 22,800 individuals participated (females: 19,065/22,800, 83.62%; mean age 39.6, SD 11.4 years; BMI 29.0 kg/m2; males: 3735/22,800, 16.38%; mean age 43.2, SD 11.7 years; BMI 30.8 kg/m2). Noncompleters (n=22,180) were younger and reported a lower score of uncontrolled eating and a higher score of cognitive restrained eating. Over time, completers (n=620) decreased their uncontrolled eating score (from 56.3 to 32.0; P<.001) and increased their cognitive restrained eating (from 50.6 to 62.9; P<.001). Males decreased their emotional eating (from 57.2 to 35.9; P<.001), but no significant change was found among females. The baseline cognitive restrained eating score was significantly and positively associated with weight loss for completers in both men (P=.02) and women (P=.002).

    Conclusions: To our knowledge, this is the largest TFEQ sample that has been documented. This Web-based weight loss intervention suggests that eating behaviors (cognitive restrained eating, uncontrolled eating, and emotional eating) measured by TFEQ-R18 were significantly changed during 6 months of participation. Our findings indicate differences in eating behaviors with respect to sex, but should be interpreted with caution because attrition was high.

  • 9.
    Svensson, Madeleine
    et al.
    Karolinska Institutet, Unit of Clinical Epidemiology, Stockholm, Sweden.
    Lagerros, Ylva Trolle
    Karolinska Institutet, Unit of Clinical Epidemiology, Stockholm, Sweden.
    Motivational technologies to promote weight loss—From Internet to gadgets2010In: Patient Education and Counseling, ISSN 0738-3991, E-ISSN 1873-5134, Vol. 79, no 3, p. 356-360Article, review/survey (Refereed)
  • 10.
    Svensson, Madeleine
    et al.
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Pasquali, Elena
    Division of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy.
    Bellocco, Rino
    Unit of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Petersson, Lena
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Bakkman, Linda
    Unit of Clinical Epidemiology, Karolinska Institutet, Stockholm, Sweden.
    Trolle Lagerros, Ylva
    Unit of Clinical Epidemiology, Stockholm, Sweden.
    The Effects of a Randomized Workplace Lifestyle Intervention - Using Web-Based Feedback with Health Behavior Theories for Self-Empowered Health and Health Literacy2013Conference paper (Refereed)
  • 11.
    Svensson, Madeleine
    et al.
    Unit of Clinical Epidemiology, T2, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
    Svensson, Tobias
    Unit of Clinical Epidemiology, T2, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
    Hansen, Andreas Wolff
    National Institute of Public Health, University of Southern Denmark, Odense, Denmark.
    Lagerros, Ylva Trolle
    Unit of Clinical Epidemiology, T2, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
    The effect of reminders in a web-based intervention study2012In: European Journal of Epidemiology, ISSN 0393-2990, E-ISSN 1573-7284, Vol. 27, no 5, p. 333-340Article in journal (Refereed)
    Abstract [en]

    Knowledge on effective strategies to encourage participation in epidemiological web-based research is scant. We studied the effects of reminders on overall participation. 3,876 employees were e-mailed a baseline web-based lifestyle questionnaire. Nine months later, a follow-up questionnaire was sent. To encourage study participation, 4–5 and 11 e-mail reminders were sent at baseline and follow-up, respectively. Additional reminders (media articles, flyers, SMS etc) were also administered. Reminders (e-mails + additional) were given in low (≤6 reminders), medium (7–9 reminders) or high amounts (>9 reminders). Participation was examined with respect to participant characteristics (i.e. age, sex, Body Mass Index, occupation), type/number of reminders, and time of participation. Most participants were males, 35–49 years, and field workers (non-office based). About 29 % responded before any e-mail reminder, following 26 and 45 % after 1 respective ≥ 2 e-mail reminders. Participant characteristics were not related to when the participants responded. The 4–5 e-mail reminders increased total response rate by 15 %, the eleven by 21 % (greatest increases in September). Those receiving medium amounts of reminders (reference) had the highest response rate (75 %), likewise office workers (54 %) compared to field workers (33 %). High amounts of reminders were particularly effective on office workers. The participants’ characteristics were not related to when they responded in this web-based study. Frequent reminders were effective on response rates, especially for those with high Internet availability. The highest increases in response rates were found in September. © The Author(s) 2012.

  • 12.
    Svensson, Madeleine
    et al.
    Karolinska Institutet, Stockholm, Sweden.
    Svensson, Tobias
    Karolinska Institutet, Stockholm, Sweden.
    Lagerros, Ylva Trolle
    Karolinska Institutet, Stockholm, Sweden.
    Strategies to Encourage E-health: The Effects of Using Different Reminders to Various Extents on Overall Response Patterns in a Large Randomized Internet-based Intervention Study2011Conference paper (Refereed)
    Abstract [en]

    Background

    The use of the Internet as a research tool has dramatically increased in the past several years. Yet, the current literature favors the response rate achieved from paper-based studies. Knowledge of successful methods to increase participation in Internet-based research is scarce. The objective of this study is to examine the effects of different reminders to encourage study participation on overall response patterns in an Internet-based intervention study.

    Methods

    In 2008, 3,876 employees at four companies in the railway sector in Sweden were randomly e-mailed an Internet-based lifestyle questionnaire consisting of: A) questions, B) questions + interactive personalized automated feedback, or C) questions + interactive personalized automated feedback + telephone counseling. The questionnaire assessed health aspects including diet, physical activity, sleep, stress, alcohol/tobacco consumption, and motivation to change health. Interactive feedback was provided for all health sections; telephone counseling was offered for diet, physical activity, alcohol and smoking habits. Nine months later, a follow-up questionnaire (C) was e-mailed to examine health improvements. 4-5 and 11 e-mail reminders were sent at baseline and follow-up, respectively. Additional reminders (flyers, texts in internal media/bulletin board, information talks, SMS, visit by the research group etc) were also administered at the four companies, to various extents. The number of additional reminders was summarized and analyses were based on the total number of received additional reminders (low, moderate or high). Response patterns were examined in relation to basic characteristics, company, work type (office/field worker), received e-mail reminders, and total number of received additional reminders. As a result of the study, the companies received recommendations for future health implementations.

    Results

    38% and 36% completed the baseline and follow-up questionnaire, respectively. The majority of the participants was male, non-smokers, employed as field workers, and had a BMI ≥25. The 4-5 e-mail reminders increased the total response rate by 15%; the 11 e-mail reminders by 21%. Additional reminders had a marginal effect on total response rate, yet generated a positive effective on the response rate among office workers (71%). Since the planning process of the study, the company involved had the highest overall response rate (61%: P<0.001), despite receiving a moderate number of additional reminders. The employees at this company were almost 1.80 (CI: 1.55-2.08) times more likely to participate in the baseline questionnaire, compared to the company which entered the study just prior to the start and had the lowest overall response rate. Participant characteristics including sex, age, BMI, smoking, motivation to change health habits, and version of the completed questionnaire (A, B or C) were not associated with time of response. The highest participation at follow-up, however, was found for those who completed baseline questionnaire A, consisting solely of questions.

    Conclusions

    A well-established collaboration with the participants prior to study start and to send out e-mail reminders on a continuous basis are two effective strategies to increase the response rate in Internet-based studies. Additional reminders conducted in the work setting may only be effective among office workers participating in Internet-based studies.

  • 13.
    Svensson, Madeleine
    et al.
    Karolinska Institutet, Stockholm, Sweden.
    Van der Mark, Marianne
    Karolinska Institutet, Stockholm, Sweden.
    Trolle Lagerros, Ylva
    Karolinska Institutet, Stockholm, Sweden.
    TFEQ-R18 on the Internet – results from a Swedish cohort of 22,800 participants2010Conference paper (Refereed)
  • 14.
    van der Mark, Marianne
    et al.
    Unit of Clinical Epidemiology, Karolinska Institutet, Stockholm, Sweden.
    Jonasson, Josefine
    Obesity Unit, Karolinska Hospital, Huddinge, Stockholm, Sweden.
    Svensson, Madeleine
    Unit of Clinical Epidemiology, Karolinska Institutet, Stockholm, Sweden & Obesity Unit, Karolinska Hospital, Huddinge, Stockholm, Sweden.
    Linné, Yvonne
    Obesity Unit, Karolinska Hospital, Huddinge, Stockholm, Sweden.
    Rössner, Stephan
    Obesity Unit, Karolinska Hospital, Huddinge, Stockholm, Sweden.
    Lagerros, Ylva Trolle
    Unit of Clinical Epidemiology, Karolinska Institutet, Stockholm, Sweden & Obesity Unit, Karolinska Hospital, Huddinge, Stockholm, Sweden.
    Older Members Perform Better in an Internet-Based Behavioral Weight Loss Program Compared to Younger Members2009In: Obesity Facts, ISSN 1662-4025, E-ISSN 1662-4033, Vol. 2, no 2, p. 74-79Article in journal (Refereed)
    Abstract [en]

    Background: New technology offers increased opportunities for weight control. However, it is not clear whether older people with less computer training can make use of this tool. Our objective was to examine how members above the age of 65 years performed in an internet-based behavioral weight loss program, compared to younger members. Methods: Data from members (n = 23,233) of an internet-based behavioral weight loss program were analyzed. We restricted our study to active participants accessing the weight club, during a 6-month period (n = 4,440). The number of logins, food intake, and weight records were examined. Participants were divided into age tertiles separately for men and women. The oldest tertile was further subdivided into two groups: above and below the age of 65 years. Results: Participants aged 65 or older were more likely to remain active in the weight club for at least 6 months compared to younger age groups. They had the highest frequency of recordings of food intake and current weight. Among women, those older than 65 years had on average the highest percentage of weight loss (5.6 kg, 6.8%). Men above 65 years of age had the highest number of logins, on average 161 times during the 6-month period. Conclusion: Older participants are performing equally well or even better in an internet-based behavioral weight loss program than younger participants. Internet-based programs could be a promising and attractive option for older adults requiring assistance in losing weight. © 2009 S. Karger AG, Basel.

  • 15.
    Westerlund, Anna
    et al.
    Karolinska Institutet, Stockholm, Sweden.
    Bellocco, Rino
    Karolinska Institutet, Stockholm, Sweden.
    Svensson, Madeleine
    Karolinska Institutet, Stockholm, Sweden.
    Sundström, Johan
    Uppsala University, Uppsala, Sweden.
    Åkerstedt, Torbjörn
    Stockholm University, Stockholm, Sweden.
    Lagerros, Ylva Trolle
    Karolinska Institutet, Stockholm, Sweden.
    Sleep duration does not predict major adverse cardiac events in the Swedish National March cohort study2011In: Sleep Medicine, ISSN 1389-9457, E-ISSN 1878-5506, Vol. 12, no Supplement 1, p. S22-S22Article in journal (Refereed)
    Abstract [en]

    Introduction and Objectives: Experimental research suggests that sleep deprivation may alter physiological factors associated with an increased risk for cardiovascular diseases (CVDs). Prior observational studies examining the effects of sleep duration have focused on narrowly defined CVD outcomes, such as myocardial infarction or stroke only. A more comprehensive measure of CVDs is lacking. Therefore, we examined the relationship between sleep duration and Major Adverse Cardiac Events (MACE).

    Materials and Methods: In 1997, 39,047 Swedish residents (women: 64%, age: 18-94 years) were enrolled in the National March cohort study and asked to self-report their habitual sleep duration in a questionnaire. They were followed-up over approximately 7 years to study incidents of MACE. Events were defined as death from all CVDs, nonfatal myocardial infarction, stroke, or heart failure. The relationship between sleep duration and MACE was analyzed using Cox proportional hazards models.

    Results: A total of 1,730 events were observed during a median follow-up period of 7.25 years. We found 665 nonfatal myocardial infarctions, 641 nonfatal strokes, 212 nonfatal heart failures, and 198 deaths from all CVDs. Age- and sex-adjusted hazard ratios (95% confidence intervals) of MACE (with 7 hours of sleep/day as the reference group) for individuals reporting ≤5, 6, and ≥ 8 hours of sleep were 1.24 (1.05-1.47), 1.03 (0.91-1.16), and 1.09 (0.97-1.23), respectively. Adjusting for BMI and physical activity did not change the hazard ratios. When adjusting for additional confounders, e.g., depressive symptoms, sleep apnea, and smoking, the association between ≤5h of sleep and MACE was attenuated (HR: 1.22, 95% CI: 0.98-1.52).

    Conclusion: Sleep duration was not associated with the risk of Major Adverse Cardiac Events. Sleep duration, however, may not in itself explain the effects of inadequate sleep on cardiovascular diseases. Yet, it may serve as an essential component in the understanding of cardiovascular diseases. Copyright © 2011 Elsevier B.V. All rights reserved.

1 - 15 of 15
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf