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Evidence reviews for the USPSTF use an analytic framework to visually display the key questions that the review will address in order to allow the USPSTF to evaluate the effectiveness and safety of a preventative service. The questions are depicted by linkages that relate interventions and outcomes. A dashed line indicates a health outcome that immediately follows an intermediate outcome. For additional details see the US Preventive Services Task Force Procedure Manual. 13

Reasons for exclusion: Design: Study did not use an included design. Outcomes: Study did not have relevant outcomes or had incomplete outcomes. Comparator: Study used an excluded comparator. Intervention: Study used an excluded intervention/screening approach. Population: Study was not conducted in an average-risk population. Timing: Study only reported first (prevalence) round screening follow-up. Publication type: Study was published in non–English-language or only available in an abstract. Quality: Study did not meet criteria for fair or good quality. Setting: Study was not conducted in a setting relevant to US practice. KQ indicates key question.

DBT indicates digital breast tomosynthesis; DM, digital mammography; and RR, relative risk.

a From random-effects restricted maximum likelihood model.

eMethods. Literature Search Strategies for Primary Literature

eTable 1. Inclusion and Exclusion Criteria

eTable 2. Quality Assessment Criteria

eTable 3. Included Studies and Their Ancillary Publications

eTable 4. Screen-Detected DCIS Diagnosed in Studies Comparing Digital Breast Tomosynthesis and Digital Mammography

eFigure 1. Pooled Analysis of Screen-Detected Invasive Cancers Diagnosed in Trials Comparing Digital Breast Tomosynthesis and Digital Mammography

eFigure 2. Pooled Analysis of Interval Cancers Diagnosed in Trials Comparing Digital Breast Tomosynthesis and Digital Mammography

eFigure 3. Cumulative Probability of False-Positive Biopsy in One NSRI Using BCSC Data Comparing Annual vs Biennial Screening with DBT or DM

eFigure 4. Cumulative Probability of False-Positive Recall in One NSRI Using BCSC Data Comparing Annual vs Biennial Screening with DBT or DM

eFigure 5. Cumulative Probability of False-Positive Recall or Biopsy in One NSRI Using BCSC Data Comparing Annual vs Biennial Screening with DBT or DM, among Women with Extremely Dense Breasts

USPSTF Recommendation: Screening for Breast Cancer JAMA US Preventive Services Task Force April 30, 2024 This 2024 Recommendation Statement from the US Preventive Services Task Force recommends biennial screening mammography for women aged 40 to 74 years (B recommendation) and concludes that evidence is insufficient to assess the balance of benefits and harms of screening mammography in women 75 years or older (I statement) and of screening using ultrasonography or MRI in women with dense breasts on a negative mammogram (I statement). US Preventive Services Task Force; Wanda K. Nicholson, MD, MPH, MBA; Michael Silverstein, MD, MPH; John B. Wong, MD; Michael J. Barry, MD; David Chelmow, MD; Tumaini Rucker Coker, MD, MBA; Esa M. Davis, MD, MPH; Carlos Roberto Jaén, MD, PhD, MS; Marie Krousel-Wood, MD, MSPH; Sei Lee, MD, MAS; Li Li, MD, PhD, MPH; Carol M. Mangione, MD, MSPH; Goutham Rao, MD; John M. Ruiz, PhD; James J. Stevermer, MD, MSPH; Joel Tsevat, MD, MPH; Sandra Millon Underwood, PhD, RN; Sarah Wiehe, MD, MPH
USPSTF Report: Collaborative Modeling to Compare Breast Cancer Screening Strategies JAMA US Preventive Services Task Force April 30, 2024 This modeling study uses Cancer Intervention and Surveillance Modeling Network models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses to estimate outcomes of various mammography screening strategies. Amy Trentham-Dietz, PhD, MS; Christina Hunter Chapman, MD, MS; Jinani Jayasekera, PhD, MS; Kathryn P. Lowry, MD; Brandy M. Heckman-Stoddard, PhD, MPH; John M. Hampton, MS; Jennifer L. Caswell-Jin, MD; Ronald E. Gangnon, PhD; Ying Lu, PhD, MS; Hui Huang, MS; Sarah Stein, PhD; Liyang Sun, MS; Eugenio J. Gil Quessep, MS; Yuanliang Yang, MS; Yifan Lu, BASc; Juhee Song, PhD; Diego F. Muñoz, PhD; Yisheng Li, PhD, MS; Allison W. Kurian, MD, MSc; Karla Kerlikowske, MD; Ellen S. O’Meara, PhD; Brian L. Sprague, PhD; Anna N. A. Tosteson, ScD; Eric J. Feuer, PhD; Donald Berry, PhD; Sylvia K. Plevritis, PhD; Xuelin Huang, PhD; Harry J. de Koning, MD, PhD; Nicolien T. van Ravesteyn, PhD; Sandra J. Lee, ScD; Oguzhan Alagoz, PhD, MS; Clyde B. Schechter, MD, MA; Natasha K. Stout, PhD; Diana L. Miglioretti, PhD, ScM; Jeanne S. Mandelblatt, MD, MPH
Toward More Equitable Breast Cancer Outcomes JAMA Editorial April 30, 2024 Joann G. Elmore, MD, MPH; Christoph I. Lee, MD, MS
Screening for Breast Cancer JAMA JAMA Patient Page April 30, 2024 In this JAMA Patient Page, the US Preventive Services Task Force provides a guide to screening for breast cancer. US Preventive Services Task Force
When Is It Best to Begin Mammograms, and How Often? JAMA Medical News & Perspectives May 3, 2024 This Medical News story discusses new USPSTF recommendations about the timing of screening mammograms. Rita Rubin, MA
New Recommendations for Breast Cancer Screening—In Pursuit of Health Equity JAMA Network Open Editorial April 30, 2024 Lydia E. Pace, MD, MPH; Nancy L. Keating, MD, MPH
USPSTF Breast Cancer Screening Guidelines Do Not Go Far Enough JAMA Oncology Editorial April 30, 2024 Wendie A. Berg, MD, PhD

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Henderson JT , Webber EM , Weyrich MS , Miller M , Melnikow J. Screening for Breast Cancer : Evidence Report and Systematic Review for the US Preventive Services Task Force . JAMA. Published online April 30, 2024. doi:10.1001/jama.2023.25844

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Screening for Breast Cancer : Evidence Report and Systematic Review for the US Preventive Services Task Force

1 Kaiser Permanente Evidence-based Practice Center, Center for Health Research, Portland, Oregon
2 University of California Davis Center for Healthcare Policy and Research, Sacramento
Editorial Toward More Equitable Breast Cancer Outcomes Joann G. Elmore, MD, MPH; Christoph I. Lee, MD, MS JAMA
Editorial New Recommendations for Breast Cancer Screening—In Pursuit of Health Equity Lydia E. Pace, MD, MPH; Nancy L. Keating, MD, MPH JAMA Network Open
Editorial USPSTF Breast Cancer Screening Guidelines Do Not Go Far Enough Wendie A. Berg, MD, PhD JAMA Oncology
US Preventive Services Task Force USPSTF Recommendation: Screening for Breast Cancer US Preventive Services Task Force; Wanda K. Nicholson, MD, MPH, MBA; Michael Silverstein, MD, MPH; John B. Wong, MD; Michael J. Barry, MD; David Chelmow, MD; Tumaini Rucker Coker, MD, MBA; Esa M. Davis, MD, MPH; Carlos Roberto Jaén, MD, PhD, MS; Marie Krousel-Wood, MD, MSPH; Sei Lee, MD, MAS; Li Li, MD, PhD, MPH; Carol M. Mangione, MD, MSPH; Goutham Rao, MD; John M. Ruiz, PhD; James J. Stevermer, MD, MSPH; Joel Tsevat, MD, MPH; Sandra Millon Underwood, PhD, RN; Sarah Wiehe, MD, MPH JAMA
US Preventive Services Task Force USPSTF Report: Collaborative Modeling to Compare Breast Cancer Screening Strategies Amy Trentham-Dietz, PhD, MS; Christina Hunter Chapman, MD, MS; Jinani Jayasekera, PhD, MS; Kathryn P. Lowry, MD; Brandy M. Heckman-Stoddard, PhD, MPH; John M. Hampton, MS; Jennifer L. Caswell-Jin, MD; Ronald E. Gangnon, PhD; Ying Lu, PhD, MS; Hui Huang, MS; Sarah Stein, PhD; Liyang Sun, MS; Eugenio J. Gil Quessep, MS; Yuanliang Yang, MS; Yifan Lu, BASc; Juhee Song, PhD; Diego F. Muñoz, PhD; Yisheng Li, PhD, MS; Allison W. Kurian, MD, MSc; Karla Kerlikowske, MD; Ellen S. O’Meara, PhD; Brian L. Sprague, PhD; Anna N. A. Tosteson, ScD; Eric J. Feuer, PhD; Donald Berry, PhD; Sylvia K. Plevritis, PhD; Xuelin Huang, PhD; Harry J. de Koning, MD, PhD; Nicolien T. van Ravesteyn, PhD; Sandra J. Lee, ScD; Oguzhan Alagoz, PhD, MS; Clyde B. Schechter, MD, MA; Natasha K. Stout, PhD; Diana L. Miglioretti, PhD, ScM; Jeanne S. Mandelblatt, MD, MPH JAMA
JAMA Patient Page Screening for Breast Cancer US Preventive Services Task Force JAMA
Medical News & Perspectives When Is It Best to Begin Mammograms, and How Often? Rita Rubin, MA JAMA

Importance Breast cancer is a leading cause of cancer mortality for US women. Trials have established that screening mammography can reduce mortality risk, but optimal screening ages, intervals, and modalities for population screening guidelines remain unclear.

Objective To review studies comparing different breast cancer screening strategies for the US Preventive Services Task Force.

Data Sources MEDLINE, Cochrane Library through August 22, 2022; literature surveillance through March 2024.

Study Selection English-language publications; randomized clinical trials and nonrandomized studies comparing screening strategies; expanded criteria for screening harms.

Data Extraction and Synthesis Two reviewers independently assessed study eligibility and quality; data extracted from fair- and good-quality studies.

Main Outcomes and Measures Mortality, morbidity, progression to advanced cancer, interval cancers, screening harms.

Results Seven randomized clinical trials and 13 nonrandomized studies were included; 2 nonrandomized studies reported mortality outcomes. A nonrandomized trial emulation study estimated no mortality difference for screening beyond age 74 years (adjusted hazard ratio, 1.00 [95% CI, 0.83 to 1.19]). Advanced cancer detection did not differ following annual or biennial screening intervals in a nonrandomized study. Three trials compared digital breast tomosynthesis (DBT) mammography screening with digital mammography alone. With DBT, more invasive cancers were detected at the first screening round than with digital mammography, but there were no statistically significant differences in interval cancers (pooled relative risk, 0.87 [95% CI, 0.64-1.17]; 3 studies [n = 130 196]; I 2 = 0%). Risk of advanced cancer (stage II or higher) at the subsequent screening round was not statistically significant for DBT vs digital mammography in the individual trials. Limited evidence from trials and nonrandomized studies suggested lower recall rates with DBT. An RCT randomizing individuals with dense breasts to invitations for supplemental screening with magnetic resonance imaging reported reduced interval cancer risk (relative risk, 0.47 [95% CI, 0.29-0.77]) and additional false-positive recalls and biopsy results with the intervention; no longer-term advanced breast cancer incidence or morbidity and mortality outcomes were available. One RCT and 1 nonrandomized study of supplemental ultrasound screening reported additional false-positives and no differences in interval cancers.

Conclusions and Relevance Evidence comparing the effectiveness of different breast cancer screening strategies is inconclusive because key studies have not yet been completed and few studies have reported the stage shift or mortality outcomes necessary to assess relative benefits.

Breast cancer is the second leading cause of cancer mortality for US women, despite a steady overall decline in breast-cancer mortality rates over the past 20 years. 1 The average age-adjusted rate for the years 2016-2020 was 19.6 per 100 000, with an estimated 43 170 deaths in 2023. 1 , 2 The majority of cases occur between the ages of 55 and 74 years, 1 and incidence is highest among women ages 70 to 74 (468.2 per 100 000). 3 Non-Hispanic White women have the highest breast cancer incidence, 4 but mortality is 40% higher for non-Hispanic Black women (27.6 per 100 000) compared with White women (19.7 per 100 000); non-Hispanic Black women experience lower 5-year survival regardless of the cancer subtype or stage at the time of detection. 1 , 5 - 7

Previous reviews of breast cancer screening effectiveness established the benefits and harms of mammography based primarily on large, long-term trials. 8 , 9 In 2016, the US Preventive Services Task Force (USPSTF) recommended screening for breast cancer in women starting at age 50 years every 2 years continuing through age 74 years (B recommendation) and that screening from ages 40 to 49 years should be based on clinical discussions of patient preferences and individual breast cancer risk (C recommendation). 10 This comparative effectiveness systematic review of breast cancer screening strategies was conducted concurrently with a separate decision modeling study. 11 Both informed the USPSTF updated breast cancer screening recommendations. 12

This review addressed 3 key questions (KQs) on the comparative effectiveness and harms of different screening strategies ( Figure 1 ). Methodological details including study selection, a list of excluded studies, detailed study-level results for all outcomes and for specific subpopulations, and contextual observations are available in the full evidence report. 14

Studies included in the 2016 USPSTF reviews 8 , 9 , 15 , 16 were evaluated for inclusion with eligibility criteria for the current review. In addition, database searches for relevant studies published between January 2014 and August 22, 2022, were conducted in MEDLINE, the Cochrane Central Register of Controlled Clinical Trials, and the Cochrane Database of Systematic Reviews (eMethods in the Supplement ). Reference lists of other systematic reviews were searched to identify additional relevant studies. ClinicalTrials.gov was searched for relevant ongoing trials. Ongoing surveillance to identify newly published studies was conducted through March 2024 to identify major studies published in the interim. Two new nonrandomized studies were identified 17 , 18 and are not further discussed, as they would not change interpretation of the review findings or conclusions.

Two independent reviewers screened titles, abstracts, and relevant full-text articles to ensure consistency with a priori inclusion and exclusion criteria (eTable 1 in the Supplement ). We included English-language studies of asymptomatic screening populations not at high risk for breast cancer. The eligible population for this review is adult females (sex assigned at birth). For consistency with the underlying evidence, the term “women” is used throughout this report; however, cancer registries and studies of breast cancer generally infer gender based on physiology and medical history rather than measuring self-reported gender. Included studies compared mammography screening modalities (mammography with or without digital breast tomosynthesis [DBT]), different screening strategies with respect to interval, age to start, age to stop, or supplemental screening strategies using ultrasound or magnetic resonance imaging (MRI) with mammography.

For KQ1, randomized clinical trials (RCTs) or nonrandomized studies of interventions with contemporaneous comparison groups that reported breast cancer morbidity, mortality, all-cause mortality, or quality of life were included. For KQ2, the primary outcome of interest was progression to advanced breast cancer, defined for this review as stage IIB or higher, which encompasses tumors with local lymph node involvement or distant metastases. 19 Study-defined advanced breast cancer outcomes were used when this outcome was not reported (eg, stage II or higher). Invasive breast cancer detection outcomes from multiple screening rounds can indicate whether a screening modality or strategy reduces the risk of advanced cancer by detecting early cancers that would otherwise have progressed (stage shift), thereby potentially reducing breast cancer morbidity and mortality. 20 - 23

For KQ3, RCTs and nonrandomized studies of interventions reporting adverse events, including psychological harms, radiation exposure, and interval invasive cancers (incident or missed due to false-negative screening) were included, regardless of the number of screening rounds reported. False-positive recall, false-positive biopsy recommendation, and false-positive biopsy rates (individuals who underwent a biopsy for a benign lesion) were obtained from included RCTs and from nonrandomized studies reporting cumulative rates of these potential harms of screening.

Two reviewers evaluated all articles that met inclusion criteria using prespecified quality criteria (eTable 2 in the Supplement ). Discordant quality ratings were resolved through discussion and input from a third reviewer. Risk-of-bias assessment was conducted using the USPSTF-specific criteria for randomized trials 13 and an adapted tool from the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I). 24 Studies determined to be at high risk of bias were excluded. One reviewer extracted key elements of included studies into standardized evidence tables in DistillerSR (Evidence Partners) and a second reviewer checked the data for accuracy. Limited evidence on sub-KQs is available in the full report. 14 When available, reported relative risks were provided in the tables, but we calculated and reported crude effect estimates and confidence intervals when studies did not provide them. For KQ2 intermediate detection outcomes, the definition of advanced cancer reported in the studies was used for synthesis; commonly this was stage II or later. Comparisons of prognostic characteristics or markers (eg, grade, tumor size, nodal involvement, receptor status) were included for comparisons as data allowed.

All quantitative analyses were conducted in Stata version 16 (StataCorp). The presence of statistical heterogeneity was assessed among pooled studies using the I 2 statistic. Where effects were sufficiently consistent and clinical and statistical heterogeneity low, random-effects meta-analyses were conducted using the restricted maximum likelihood; all tests were 2-sided, with P < .05 indicating statistical significance.

Aggregate strength of evidence (ie, high, moderate, or low) was assessed for each KQ and comparison using the approach described in the Methods Guide for the Effectiveness and Comparative Effectiveness Reviews, 25 based on consistency, precision, publication bias, and study quality.

Investigators reviewed 10 378 unique citations and 419 full-text articles for all KQs ( Figure 2 ). Twenty studies reported in 45 publications were included. 26 - 45 A full list of included studies by KQ is located in eTable 3 in the Supplement .

Key Question 1. What is the comparative effectiveness of different mammography-based breast cancer screening strategies (eg, by modality, interval, initiation and stopping age, use of supplemental imaging, or personalization based on risk factors) on breast cancer morbidity and mortality?

Two nonrandomized studies reported on the association of different screening programs with breast cancer morbidity and mortality. One study was designed to compare different ages to stop screening 30 and another compared annual and triennial screening intervals. 41

A fair-quality observational study (n = 1 058 013) on age to stop screening used an emulated trial methodology to analyze a random sample of US Medicare A and B claims data for enrollees aged 70 to 84 years (1999 to 2008), eligible for breast cancer screening, and with at least a 10-year estimated life expectancy. The study estimated the effect of stopping screening at ages 70, 75, and 80 years compared with continued annual screening. 30 , 46 Continuation of screening between the ages of 70 and 74 years was associated with reduced mortality risk based on survival analysis (hazard ratio, 0.78 [95% CI, 0.63 to 0.95]), but the absolute difference in the risk of death for the age group was small and the confidence interval included null (1.0 fewer deaths per 1000 screened [95% CI, −2.3 to 0.1]). These results indicate a difference in the cumulative incidence curves that approached a difference in the mortality risk for the age group. Conversely, continued screening vs no screening from ages 75 to 84 years did not result in statistically significant differences in the absolute risk of breast cancer mortality (0.07 fewer deaths per 1000 [95% CI, –0.93 to 1.3]) or the cumulative mortality incidence (hazard ratio, 1.00 [95% CI, 0.83 to 1.19]).

A fair-quality nonrandomized clinical study (n = 14 765) conducted in Finland during the years 1985 to 1995 assigned participants aged 40 to 49 years to annual or triennial screening invitations by alternating birth year. 41 The study reported no difference in breast cancer mortality: 20.3 deaths per 100 000 person-years with annual screening invitations and 17.9 deaths per 100 000 person-years with triennial screening invitations (relative risk [RR], 1.14 [95% CI, 0.59-1.27]).

Key Question 2. What is the comparative effectiveness of different mammography-based breast cancer screening strategies (eg, by modality, interval, initiation and stopping age, use of supplemental imaging, or personalization based on risk factors) on the incidence of and progression to advanced breast cancer?

No eligible studies of age to start or stop screening, supplemental screening, or personalized screening were included, because no RCTs or nonrandomized studies reported more than a single round of screening comparing screening strategies. For screening interval, 1 RCT 26 and 1 nonrandomized study, 41 and for comparisons of different screening modalities (DBT vs digital mammography) 3 RCTs 27 , 33 , 42 and 2 nonrandomized studies, 34 , 44 met eligibility criteria.

Two fair-quality studies addressed the effect of screening interval on the characteristics of detected cancers. A fair-quality United Kingdom Co-ordinating Committee on Cancer Research (UKCCCR) RCT comparing screening intervals was conducted as part of the UK National Breast Screening Program. The study randomized participants aged 50 to 62 years to annual (n = 37 530) or triennial (n = 38 492) breast cancer screening during the years 1989 to 1996. 26 After 3 years of screening (1 incidence screen in the triennial screening group), a similar number of cancers (screen-detected and interval) had been diagnosed in the annual and triennial screening groups (6.26 and 5.40 per 1000 screened, respectively; RR, 1.16 [95% CI, 0.96 to 1.40]). No statistically significant differences were found in the cancer characteristics (tumor size, nodal status, histological grade) between groups over the course of the study.

A fair-quality nonrandomized study using Breast Cancer Surveillance Consortium (BCSC) registry data (1996 to 2012) 39 found the relative risk of being diagnosed with a breast cancer with less favorable prognostic characteristics (stage IIB or higher, tumor size >15 mm, or node-positive) was not statistically different for women screened biennially compared with those screened annually for any age category (40-49, 50-59, 60-69, 70-85 years).

Three fair-quality RCTs 27 , 33 , 42 reported cancer detection over 2 rounds of screening, comparing the effects of screening with DBT and digital mammography on the presence of advanced cancer at subsequent screening rounds ( Table 1 ). Participants were randomized to the DBT intervention group or the digital mammography control group at a first round of screening, followed in 2 trials by a second round of screening with digital mammography for all second-round participants (Proteus Donna, 27 RETomo 42 ) and in 1 trial with DBT for all second-round participants (To-Be 33 ). The trials used an identical screening modality for both study groups at the second round because using the same instrument is a stronger design for detection of stage shift.

The RCTs reported increased detection of invasive cancer with DBT at the first round of screening (pooled RR, 1.41 [95% CI, 1.20 to 1.64]; 3 RCTs [n = 129 492]; I 2 = 7.6%) and no statistical difference in invasive cancer at the subsequent screening (pooled RR, 0.87 [95% CI, 0.73 to 1.05]; 3 RCTs [n = 105 064]; I 2 = 0%) (eFigure 1 in the Supplement ). 27 , 33 , 42 There was no statistically significant difference in the incidence of advanced cancers at the subsequent screening round (progression of cancers not found at prior screening that would indicate stage shift) in the individual trials ( Figure 3 ). Results were inconsistent and thus not pooled for the advanced cancer, larger tumor (>20 mm), and node-positive cancer outcomes. The results for histologic grade 3 cancer at the second screening were consistent (pooled RR, 0.97 [95% CI, 0.61-1.55]; 3 RCTs [n = 105 244]; I 2 = 0%) ( Figure 3 ). Due to the small number of cases, it was not possible to assess differences in the detection of cancers lacking hormone or growth factor receptors (ie, triple-negative cancers) that have the worst prognosis among breast cancer subtypes.

Two fair-quality nonrandomized studies of interventions (NRSIs), including a US study using BCSC data, compared breast cancer detection outcomes from screening over multiple rounds (≥2) with either DBT-based mammography or digital mammography alone. 34 , 44 The findings were generally consistent with the trial results for cancer detection and stage shift.

Key Question 3. What are the comparative harms of different mammography-based breast cancer screening strategies (modality, interval, initiation age, use of supplemental imaging, or personalization based on risk factors)?

No eligible studies of age to start screening or personalized screening were identified. For age to stop screening, 1 fair-quality nonrandomized study met eligibility criteria. 30 For comparisons of potential harms associated with different screening intervals, a fair-quality RCT 26 and 2 fair-quality nonrandomized studies 39 , 41 were included. For comparisons of different screening modalities (DBT vs digital mammography), 4 RCTs (3 good- and 1 fair-quality) 27 , 31 , 33 , 42 and 7 fair-quality nonrandomized studies were included. 28 , 32 , 34 - 36 , 43 , 44

In the NRSI using an emulated trial methodology to evaluate the age to stop screening, 30 the 8-year cumulative proportion of participants with a breast cancer diagnosis was higher among those who continued annual screening from ages 70 to 84 years (5.5%) compared with those who discontinued screening (3.9%) at age 70 years. Because fewer cancers were diagnosed among those who discontinued screening, there was a lower risk of undergoing cancer treatment and experiencing related morbidity. Notably, for participants aged 75 to 84 years, screening (and treatment) were not associated with lower breast cancer mortality (see KQ1 results).

The UKCCCR trial included for KQ2 26 reported fewer interval cancers (false-negative and incident cancers) diagnosed in the annual invitation group compared with triennial screening (1.84 vs 2.70 per 1000 women screened, respectively; RR, 0.68 [95% CI, 0.50 to 0.92]). The nonrandomized clinical trial conducted in Finland included for KQ1 41 also reported interval cancers diagnosed with annual vs triennial screening and found no statistical difference in incidence ( P = .22, data not reported). Data from 2 studies from the BCSC registry reported higher probabilities of false-positive recalls and biopsy recommendations with annual screening compared with biennial screening and no statistical difference in interval cancers in adjusted analyses. 32 , 39 , 44

Four RCTs (3 good-quality, 1 fair-quality) 27 , 31 , 33 , 42 and 7 fair-quality nonrandomized studies 28 , 32 , 34 - 36 , 43 , 44 reported outcomes related to potential screening harms associated with DBT-based screening compared with digital mammography–only screening, including interval cancer rates, round-specific and cumulative false-positive recalls and biopsies, and radiation exposure. Meta-analysis of 3 large trials did not show a statistically significant difference in rates of interval cancer after screening with DBT compared with digital mammography (pooled RR, 0.87 [95% CI, 0.64 to 1.17]; 3 RCTs [n = 130 196]; I 2 = 0%) (eFigure 2 in the Supplement ). 27 , 33 , 42

Data on interval cancers were also obtained from 7 nonrandomized studies. 28 , 32 , 34 - 36 , 43 , 44 The most recent BCSC analysis, reporting interval cancer rates across multiple screening rounds with either DBT or digital mammography, did not identify statistically significant differences in invasive or advanced interval cancers. 44

The effects of DBT screening on false-positive recall and false-positive biopsy rates varied across studies 27 , 33 , 42 and by screening round, with small or no statistical differences between study groups, not consistently favoring DBT-based mammography or digital mammography.

Evidence from 2 nonrandomized BCSC studies provided false-positive results across several screening rounds. 32 , 44 In 1 study, rates of false-positive recall and false-positive biopsy rates were lower with DBT in initial screening rounds, but differences were attenuated and not statistically significant compared with digital mammography only after additional rounds of screening ( Table 2 ). 44 The other study reported no statistical difference in 10-year cumulative false-positive biopsy recommendation rates between biennial DBT and digital mammography screening, but false-positive recall was slightly lower with DBT (eFigures 3 and 4 in the Supplement ); no differences by modality were identified for individuals with extremely dense breasts in stratified analyses (eFigure 5 in the Supplement ). 32

Four RCTs 27 , 31 , 33 , 42 and 1 NRSI 35 reported the mean, median, or relative radiation dose received in each study group at a single screening round. The 3 studies using DBT/digital mammography screening reported radiation exposure approximately 2 times higher in the intervention group compared with the digital mammography–only group. 27 , 35 , 42 Differences between study groups in radiation exposure were smaller in studies using DBT with synthetic digital mammography. 33 , 47

The Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial, a good-quality RCT conducted in the Netherlands, randomized (1:4) participants aged 50 to 75 years with extremely dense breasts and negative mammography findings (2011-2015) (n = 40 373) to an invitation or no invitation for supplemental MRI screening. 45 (The RCT was not included for KQ2 because second round results in the control group were unavailable). Fifty-nine percent of those randomized to the invitation underwent an MRI examination (n = 4783). In intention-to-treat analysis, 2.2 per 1000 experienced interval breast cancer diagnoses in the supplemental screening invitation group, compared with 4.7 per 1000 screened in the digital mammography control group (RR, 0.47 [95% CI, 0.29 to 0.77]). Adverse events related to the supplemental MRI screening reported in the trial included 5 classified as serious adverse events (2 vasovagal reactions and 3 allergic reactions to the contrast agent) and 2 reports of extravasation (leaking) of the contrast agents and 1 shoulder subluxation. Twenty-seven participants (0.6% of the MRI group) reported a serious adverse event within 30 days of the MRI. Those who underwent supplemental MRI screening also experienced additional recalls (94.9 per 1000 screened), false-positive recalls (80.0 per 1000 screened), and false-positive biopsies (62.7 per 1000 screened).

A fair-quality nonrandomized study used claims data from commercially insured women (MarketScan database) aged 40 to 64 years who had received at least 1 bilateral screening breast MRI (n = 9208) or mammogram (n = 9208) between January 2017 and June 2018. 29 Following propensity score matching, those undergoing screening with MRI were more likely to have additional health care cascade events such as office visits and follow-up tests unrelated to breast conditions (adjusted difference between groups, 19.6 per 100 screened [95% CI, 8.6 to 30.7]) in the subsequent 6 months.

A fair-quality RCT, the Japan Strategic Anti-cancer Randomized Trial, randomly assigned asymptomatic women aged 40 to 49 years (2007-2011) to breast cancer screening with mammography plus handheld ultrasound (digital mammography/ultrasound) (n = 36 859) or mammography only (digital mammography) (n = 36 139). 40 The relative risk of invasive interval cancer was not statistically significantly different for digital mammography/ultrasound vs digital mammography only (RR, 0.58 [95% CI, 0.31 to 1.08]). This result differs from the statistically significant population-average effect reported in the study ( P = .03), which included interval ductal carcinoma in situ (proportion difference, −0.05% [95% CI, −0.09 to 0]). Those undergoing ultrasound in addition to digital mammography experienced 48.0 per 1000 additional false-positive recall results compared with those assigned to digital mammography screening only.

A fair-quality nonrandomized study using data from 2 BCSC registry sites compared screening outcomes for participants receiving ultrasonography on the same day as a screening mammogram (digital mammography/ultrasound) (n = 3386, contributing 6081 screens) compared with those that received only a mammogram (digital mammography) (n = 15 176, contributing 30 062 screens). 37 However, 31% of participants had a first-degree family history of breast cancer or previous breast biopsy. There was no statistical difference in interval cancer risk (adjusted RR, 0.67 [95% CI, 0.33 to 1.37]), and rates of false-positive biopsy were twice as high for the mammography/ultrasound group (adjusted RR, 2.23 [95% CI, 1.03 to 2.58]).

Prior screening effectiveness reviews based on large trials initiated in previous decades established a statistically significant mortality benefit for mammography screening of women aged 50 to 69 years. 8 , 9 , 15 The current review considered comparative effectiveness questions on the relative benefits and harms of different screening start and stop ages, intervals, and modalities for women at average breast cancer risk. Findings are summarized in Table 3 .

The evidence was insufficient for addressing the age to start or end screening. No eligible studies comparing different ages to start screening were identified. Limited evidence from 1 nonrandomized study, using an emulated trial study design, suggested that screening beyond age 74 years may not reduce breast cancer mortality. 30

Evidence was also insufficient for evaluating the effect of screening intervals on breast cancer morbidity and mortality. Two nonrandomized studies found no difference in breast cancer outcomes. 26 , 39 Moderate evidence supported longer screening intervals (eg, biennial) to reduce the cumulative risk of false-positive recall and biopsy. The observational studies of different screening intervals compared individuals who self-selected or were referred for different screening intervals, contributing to risk of bias in the results.

Results from 3 RCTs 27 , 33 , 42 and 2 nonrandomized studies 34 , 44 provided moderate evidence that DBT-based mammography does not reduce the risk of invasive interval cancer or advanced cancer at subsequent screening rounds. Additional rounds of screening and longer follow-up are needed to fully evaluate whether DBT reduces breast cancer morbidity and mortality. Consistent with trial findings, a nonrandomized BCSC study did not find reduced risks of advanced or interval cancers with DBT. 44 Limited evidence from trials on harms of screening with DBT 27 , 33 , 42 indicated similar false-positive recall and biopsy rates. An observational BCSC study did not show differences in the 10-year cumulative false-positive biopsy rates 32 ; lower false-positive recall and biopsy with DBT screening were attenuated after several screening rounds. 44 Additional research is needed to ascertain whether DBT-based screening would reduce false-positives over a lifetime of screening.

The evidence was not adequate to evaluate the benefits and harms of supplemental MRI screening for people with dense breasts. No eligible studies were identified that provide evidence on breast cancer morbidity or mortality outcomes with supplemental MRI screening compared with mammography alone among individuals with dense breasts. The DENSE trial 45 reported fewer interval cancers with 1 round of supplemental MRI screening, but results from a second screening round are not yet published. Evidence of higher advanced cancer incidence in the mammography-only group relative to the MRI group would be needed to anticipate effects on morbidity or mortality. Supplemental MRI led to additional false-positive recalls and biopsies, and uncommon but serious adverse events were observed. 45 Two recent systematic reviews of the test performance literature reported higher cancer detection with supplemental MRI screening along with substantially increased recall and biopsy rates among individuals without cancer. 48 , 49

Lack of a standardized and reliable assessment tool for measuring breast density and density variation across the lifespan pose challenges for research into the optimal screening strategy for persons with dense breasts. 16 Research is also needed to evaluate personalized risk-based screening, based on breast cancer risk factors and personal screening preferences. The ongoing WISDOM trial and My Personalized Breast Screening study (expected completion in 2025) may help to address these research gaps. 50 , 51

Breast cancer is an active area of research, yet few longitudinal RCTs comparing different screening strategies have been conducted following completion of the major trials that established the effectiveness of mammography for reducing breast cancer mortality for women aged 50 to 69 years. This review included 6 new randomized trials, 27 , 31 , 33 , 40 , 42 , 45 4 comparing DBT with digital mammography screening 27 , 31 , 33 , 42 and 2 on supplemental screening compared with mammography alone. 40 , 45 Three of these trials are ongoing 31 , 40 , 45 and have reported preliminary results only. Observational studies were also included, but few studies were available that followed up a screening population over time to compare the health outcomes associated with different screening approaches. These studies, while potentially more representative of a screening population, have higher risk of biased results due to confounding and selection.

Changes in population health, imaging technologies, and available treatments may limit the applicability of previous studies. Recent trials included in this review were conducted outside of the US and enrolled mostly White European populations. No studies evaluated screening outcomes for racial or ethnic groups in the US that experience health inequities and higher rates of breast cancer mortality. Black women are at highest risk of breast cancer mortality, 52 with lower 5-year survival than all other race and ethnicity groups. 7 Breast cancer mortality risk also increases at younger ages for Black women compared with White women. 53 This review did not address additional factors beyond screening that contribute to breast cancer mortality inequities. 54 Rigorous research is essential to understand and identify improvements needed along the pathway from screening to treatment 55 and to address inequities in follow-up time after a positive screening result, time to diagnosis, 56 - 60 and receipt of high-quality treatment and support services. 59 , 61 , 62

Evidence comparing outcomes for different screening intervals and ages to start and stop screening was limited or absent. Trials of personalized screening based on risk and patient preferences are in progress and may address evidence gaps related to optimal screening start ages and intervals. Research is needed to better characterize potential harms of screening, including patient perspectives on experiencing false-positive screening results. Women with false-positive screening results may be less likely to return for their next scheduled mammogram, as reported in a large US health system study. 55 , 63 Rigorous studies that enroll screening populations and report advanced cancer detection, morbidity, and mortality outcomes from multiple rounds of screening are needed to overcome persistent limitations in the evidence on breast cancer screening. Multiple screening rounds are essential to determine whether a screening modality or strategy reduces the risk of advanced cancer by detecting early cancers that would otherwise have progressed (stage shift), potentially reducing breast cancer morbidity and mortality. 20 - 23 , 64

The potential benefits of risk-stratified screening strategies, including the use of supplemental screening with ultrasound or MRI, have not been fully evaluated, although some harms are evident. Longer term follow-up on existing comparative effectiveness trials, complete results from ongoing RCTs of personalized screening programs, 65 , 66 and rigorous new studies are needed to further strengthen the evidence and optimize breast cancer screening strategies.

Evidence comparing the effectiveness of different breast cancer screening strategies is inconclusive because key studies have not yet been completed and few studies have reported the stage shift or mortality outcomes necessary to assess relative benefits.

Accepted for Publication: November 23, 2023.

Published Online: April 30, 2024. doi:10.1001/jama.2023.25844

Corresponding Author: Jillian T. Henderson, PhD, MPH, Kaiser Permanente Evidence-based Practice Center, Center for Health Research, Kaiser Permanente Northwest, 3800 N Interstate Ave, Portland, OR 97227 ( [email protected] ).

Author Contributions: Dr Henderson had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical review of the manuscript for important intellectual content: Henderson, Weyrich, Miller.

Statistical analysis: Henderson.

Administrative, technical, or material support: Webber, Melnikow.

Supervision: Henderson.

Conflict of Interest Disclosures: None reported.

Funding/Support: This research was funded under contract number 75Q80120D00004, Task Order 75Q80121F32004, from the Agency for Healthcare Research and Quality (AHRQ), US Department of Health and Human Services.

Role of the Funder/Sponsor: Investigators worked with USPSTF members and AHRQ staff to develop the scope, analytic framework, and key questions for this review. AHRQ had no role in study selection, quality assessment, or synthesis. AHRQ staff provided project oversight, reviewed the report to ensure that the analysis met methodological standards, and distributed the draft for peer review. Otherwise, AHRQ had no role in the conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript findings.

Disclaimer: The opinions expressed in this document are those of the authors and do not reflect the official position of AHRQ or the US Department of Health and Human Services.

Additional Contributions: The authors gratefully acknowledge the following individuals for their contributions to this project: Howard Tracer, MD (AHRQ); Heidi D. Nelson, MD, MPH, MACP (Kaiser Permanente Bernard J. Tyson School of Medicine); current and former members of the USPSTF who contributed to topic deliberations; and Evidence-based Practice Center staff members Melinda Davies, MA, Jill Pope, and Leslie A. Purdue, MPH, for technical and editorial assistance at the Kaiser Permanente Center for Health Research. USPSTF members, peer reviewers, and federal partner reviewers did not receive financial compensation for their contributions.

Additional Information: A draft version of this evidence report underwent external peer review from 5 content and methods experts (Nehmat Houssami, MBBS, MPH, Med, PhD [University of Sydney-Australia]; Patricia Ganz, MD [UCLA]; Gerald Gartlehner, MD, MPH [Cochrane Austria]; Karla Kerlikowske, MD [UC San Francisco]; Lisa Newman, MD, MPH [New York Presbyterian/Weill Cornell Medical Center]) and 4 scientific representatives from 3 federal partner organizations (Centers for Disease Control and Prevention; Office of Research on Women’s Health; National Institute on Minority Health and Health Disparities). Comments were presented to the USPSTF during its deliberation of the evidence and were considered in preparing the final evidence review.

Editorial Disclaimer: This evidence report is presented as a document in support of the accompanying USPSTF Recommendation Statement. It did not undergo additional peer review after submission to JAMA .

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Published on 30.4.2024 in Vol 26 (2024)

Digital Food Frequency Questionnaire Assessing Adherence to the Norwegian Food–Based Dietary Guidelines and Other National Lifestyle Recommendations: Instrument Validation Study

Authors of this article:

Original Paper

Hege Berg Henriksen 1 , PhD ;
Markus Dines Knudsen 1, 2, 3 , PhD ;
Anette Hjartåker 1 , PhD ;
Rune Blomhoff 1, 4 , PhD ;
Monica Hauger Carlsen 1 , PhD

1 Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway

2 Section for Colorectal Cancer Screening, Cancer Registry of Norway, Oslo, Norway

3 Department of Transplantation Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital, Oslo, Norway

4 Department of Clinical Service, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway

Corresponding Author:

Hege Berg Henriksen, PhD

Department of Nutrition

Institute of Basic Medical Sciences

University of Oslo

Sognsvannsveien 9

PO Box 1046, Blindern

Phone: 47 99459673

Email: [email protected]

Background: Valid assessment tools are needed when investigating adherence to national dietary and lifestyle guidelines.

Objective: The relative validity of the new digital food frequency questionnaire, the DIGIKOST-FFQ, against 7-day weighed food records and activity sensors was investigated.

Methods: In total, 77 participants were included in the validation study and completed the DIGIKOST-FFQ and the weighed food record, and of these, 56 (73%) also used the activity sensors. The DIGIKOST-FFQ estimates the intake of foods according to the Norwegian food–based dietary guidelines (FBDGs) in addition to lifestyle factors.

Results: At the group level, the DIGIKOST-FFQ showed good validity in estimating intakes according to the Norwegian FBDG. The median differences were small and well below portion sizes for all foods except “water” (median difference 230 g/day). The DIGIKOST-FFQ was able to rank individual intakes for all foods ( r =0.2-0.7). However, ranking estimates of vegetable intakes should be interpreted with caution. Between 69% and 88% of the participants were classified into the same or adjacent quartile for foods and between 71% and 82% for different activity intensities. The Bland-Altman plots showed acceptable agreements between DIGIKOST-FFQ and the reference methods. The absolute amount of time in “moderate to vigorous intensity” was underestimated with the DIGIKOST-FFQ. However, estimated time in “moderate to vigorous intensity,” “vigorous intensity,” and “sedentary time” showed acceptable correlations and good agreement between the methods. The DIGIKOST-FFQ was able to identify adherence to the Norwegian FBDG and physical activity recommendations.

Conclusions: The DIGIKOST-FFQ gave valid estimates of dietary intakes and was able to identify individuals with different degrees of adherence to the Norwegian FBDG and physical activity recommendations. Moderate physical activity was underreported, water was overreported, and vegetables showed poor correlation, which are important to consider when interpreting the data. Good agreement was observed between the methods in estimating dietary intakes and time in “moderate to vigorous physical activity,” “sedentary time,” and “sleep.”

Introduction

Monitoring dietary intake and physical activity with valid and feasible methods in a population is of great importance, as there is a growing body of evidence on the strong association between lifestyle and risk of several chronic diseases [ 1 - 5 ].

Even though self-administered web-based 24-hour dietary recalls and food are increasingly used in epidemiological studies [ 6 , 7 ], the most common type of dietary assessment tool in observational and intervention studies is still the food frequency questionnaire (FFQ) [ 8 , 9 ]. Generally, the FFQs assess the frequency of food intakes and portion size of the food consumed, are typically self-administrated, and vary with regard to which food items or nutrients are included, portion sizes, frequency categories, length of the questionnaire, and duration of the registration period (ie, weeks, months, or years) [ 6 , 10 - 12 ]. The FFQs may be used to estimate habitual dietary intakes over a longer period of time and to rank individuals according to dietary intakes and may generate data on dietary intakes on a group level. Thus, FFQs are valuable in assessing the degree of adherence to dietary recommendations in a population [ 8 , 9 , 13 - 15 ].

As with all dietary assessment methods, FFQs are prone to reporting errors, particularly systematic errors, often related to memory, knowledge of portion sizes, and understanding of the questions [ 11 , 12 , 16 , 17 ]. This can result in over- or underestimation of dietary intakes [ 18 , 19 ], which may attenuate the observed association between diet and health outcomes [ 20 , 21 ]. Systematic error may be reduced with clear instruction and the use of relevant examples, such as pictures of portion sizes and informative text.

The emerging use of digital applications has resulted in modifications of traditional printed assessment tools into web-based tools in both research and the clinic [ 22 , 23 ]. Web-based dietary assessment tools have demonstrated good validity and give comparable data with printed versions [ 6 , 23 - 25 ].

We have developed a short digital semiquantitative food and lifestyle frequency questionnaire (the DIGIKOST-FFQ) based on a validated printed questionnaire called NORDIET-FFQ [ 26 ] developed at the Department of Nutrition, University of Oslo, Norway. The DIGIKOST-FFQ is designed to assess adherence to the Norwegian food–based dietary guidelines (FBDGs) and national lifestyle guidelines [ 3 , 27 ] and has been revised according to the results from a qualitative evaluation study (ie, focus group interviews and usability testing) [ 27 ].

Generally, the DIGIKOST-FFQ delivers similar data on the main dietary and physical activity recommendations as the NORDIET-FFQ. However, some adjustments have been made based on the results from the validation study of the NORDIET-FFQ ( Multimedia Appendix 1 [ 3 , 26 - 38 ]). In addition, the DIGIKOST-FFQ is based on a digital platform constituting several technical functions to improve the feasibility and the understandability of the questions [ 27 ].

Therefore, investigating the relative validity of this new digital assessment tool is of great importance to current and future studies applying the assessment method for dietary data collection. Thus, in this project, we aimed to investigate the relative validity of the DIGIKOST-FFQ in assessing dietary intake, physical activity, and adherence to the Norwegian FBDG in an adult and healthy population in Norway, with weighed food record (WR) and activity sensor as the reference methods.

Study Design and Participants

Participants were recruited between April and September 2021 and randomly extracted by the National Registry of Norway and by advertisements on Facebook. Adult (18 years and older) men and women living in the Oslo region in Norway were eligible for the study. The study design was cross-sectional. All participants completed the DIGIKOST-FFQ, and after 1-2 months, they filled in a 7-day WR and used an activity sensor (SenseWear Armband Mini [SWA]; BodyMedia). In addition to the WR, the participants received a digital scale and an instruction on how to weigh and record all foods and beverages consumed during a period of 7 consecutive days. Due to the COVID-19 pandemic, the instructions were given in a video meeting (Zoom; Zoom Video Communications) by the responsible researchers in the study. Then, the participants were invited to meet outside the study center at the Department of Nutrition, University of Oslo at a pickup point to receive the WR, a digital scale, SWA, and the prepaid postal envelopes for returning the equipment after use.

During the WR registration period, the participants also wore the SWA to record all physical activity, sedentary time, and sleep. By the end of the registration period, the participants returned the completed WRs and the SWA to the study center by postal mail. At the end of the study, all participants were offered to voluntarily fill out the DIGIKOST-FFQ once more in order to receive an individual feedback report benchmarking their dietary intakes and physical activity against the Norwegian FBDG. In addition, they could be randomly selected to win a gift certificate of NOK 500 (US $56.91). The digital scale was also given to the participants as a gift.

The first version of the DIGIKOST-FFQ underwent evaluation in focus group interviews and usability testing [ 27 ]. The final version of the DIGIKOST-FFQ is described in Multimedia Appendix 1 . In brief, the DIGIKOST is a digital diet and lifestyle assessment tool designed to assess dietary intake and other lifestyle factors and evaluate these according to the Norwegian FBDG. DIGIKOST also has a report function, the DIGIKOST report, which is an individual feedback report on respondents’ adherence to the Norwegian FBDG, with specific and personalized advice on how to fulfill the recommendations . The DIGIKOST-FFQ is based on a software platform called Nettskjema, developed and administered by the University Information Technology Center at the University of Oslo, Norway [ 28 ]. The main login option in DIGIKOST is the ID-port (e-ID used by the Norwegian Agency for Public Management and eGovernment [Difi]) [ 29 , 30 ]. The DIGIKOST-FFQ takes approximately 20 minutes to complete [ 27 ] and includes 103 food and lifestyle items, of which 78 questions about food items (grams per day), 7 questions about physical activity (minutes per week), sedentary time, and sleep (hours per day), 8 questions about tobacco use, and 10 questions about body weight and demographic data. The main food groups cover foods rich in fiber (ie, fruits and berries, vegetables, and whole grain products), fish, dairy products, meat, oils, margarine, and beverages (Table S1 in Multimedia Appendix 1 ). The responses from the DIGIKOST-FFQ are directly transferred into a secure server called services for sensitive data, and the crude variables are automatically transformed by unique algorithms into food groups, activities, and lifestyle indices according to the national recommendations [ 26 ]. The DIGIKOST data set also contains 2 indices, the Norwegian diet index and the Norwegian lifestyle index [ 32 ] ( Multimedia Appendix 1 ). The Norwegian diet index consists of 12 components corresponding to the Norwegian FBDG with a 3-level scoring approach including 3 categories representing low, intermediate, and high adherence, giving a composite diet index ranging in scores from 0 (lowest adherence) to 20 (highest adherence) points. The Norwegian lifestyle index consists of 5 components (ie, diet, physical activity, normal weight, alcoholic drinks, and tobacco use) with a 3-level equal scoring approach, and a composite lifestyle index ranging from 0 to 5 points.

Seven-Day WR

Dietary data were retrieved from the WR and manually coded and imported into the food and nutrient calculation system, KBS (version 7.3, 2018, AE-10 database), developed at the Department of Nutrition, University of Oslo [ 39 ].

Physical Activity Sensor

We used the activity sensors called SWA to objectively measure different activity intensities. The SWA was placed on the nondominant arm at the upper part around the triceps muscle. The participants were instructed to use the SWA all day and night and to only remove it during water-based activities like showers or swimming. All activity data generated from the SWA were exported to a computer, and all calculations on activities were conducted in the SenseWear Professional software (version 7.0; BodyMedia Inc).

Sample Size

An adequate number of participants in validation studies are shown to be 80-100 [ 10 , 11 , 40 , 41 ]. This ensured statistical power of 80% to detect differences of 1 portion of “fruit” or “vegetable” per day (1 portion=100 g) between test and reference methods, assuming an SD of 1.6 portions (or 160 g) and a significance level of 5% [ 42 , 43 ]. Moreover, in order to detect a Pearson correlation coefficient of 0.5 or higher between the DIGIKOST-FFQ and the reference methods, a sample size of 38 men and 38 women is required to achieve a significance level of 5% and a power of 90% [ 44 ].

Statistical Analyses

All analyses were performed using SPSS software (version 29; IBM Corp). Normal distribution for continuous variables was checked by visual inspection of histograms and quantile-quantile plots. Dietary and activity variables were presented in median and IQR. Comparisons between the methods were presented as median differences, and categorical variables were presented in frequencies and percentages. Paired 2-tailed t tests were used to compare differences in normal distributed variables, and Wilcoxon signed rank test in nonnormal distributed variables. P values <.05 were considered statistically significant. Ranking of individual dietary intakes and activity levels were tested by Spearman ρ for nonparametric variables and the 95% CI for the correlation estimate. A correlation below 0.3 was defined as poor, whereas a correlation between 0.3 and 0.49 was fair, and above 0.5 was satisfactory, according to Hankin et al [ 45 ].

Cross-classification of individual intakes between the methods was estimated by ranking participants’ intakes, dividing them into quartiles, and calculating how many were classified in the same and same plus adjacent, misclassified with 2 quartiles, or classified in the opposite quartile (grossly misclassified, 3 quartiles apart).

Bland-Altman plots were used to explore and visualize bias, such as under- or overreporting (mean differences), limits of agreement (mean difference and 1.96 SD), and the presence of outliers in the data. Sensitivity and specificity were calculated to evaluate the participants’ adherence to the Norwegian FBDGs identified by both methods. The sensitivity was defined as the proportion of participants who were classified as not fulfilling the recommendations both by the DIGIKOST-FFQ and the WR or SWA divided by the number of participants not fulfilling the recommendations according to the WR or SWA only. Specificity was defined as the proportion of participants who were classified as fulfilling the recommendations both by the DIGIKOST-FFQ and WR or SWA divided by the number of participants fulfilling the recommendations according to the WR or SWA only. Values above 60% were defined as good for both measures. Adherence to the Norwegian diet index and physical activity recommendations were examined on a continuous scale. For all the different diet and physical activity recommendations, participants were assigned points of adherence, defined as high (1 point), intermediate (0.5 point), and low (0 point) to each specific recommendation.

Ethical Considerations

This study was carried out in accordance with the Helsinki Declaration. The Norwegian Data Protection Services (Sikt, project registration 277679) has approved the DIGIKOST protocol and the informed consent. The participant signed the informed consent electronically prior to participating in the study. They were also informed about the ability to opt out of the study as well as information regarding the safe storage of all personal data. All data were analyzed in the safe and approved server, and with restricted access, called services for sensitive data at the University of Oslo [ 28 ]. Only tables and figures without individual sensitive data were exported. No data were collected about the invited participants who did not participate.

Participant Characteristics

In total, 77 (male: n=16 and female: n=61) participants, corresponding to 6% (n=77) of the 1249 who were invited and 59% (n=77) of the 131 who consented, participated in this study. All of these 77 participants fulfilled the inclusion criterion by the completion of both the DIGIKOST-FFQ and the WR. Of these, 56 (73%) also used the SWA ( Figure 1 and Table 1 ). The mean age was 45 (SD 14.6) years, and the mean BMI was 24.5 (SD 3.9) kg/m 2 . Most of the participants were highly educated (college or higher: n=70, 91%) and currently employed (n=53, 69%). Total daily energy intake estimated from the WR was on mean 8057 (SD 2157) kJ, and energy expenditure measured from the SWA was on mean 10,365 (SD 1827) kJ/day ( Table 1 ).

Dietary Intakes From the DIGIKOST-FFQ and WR

Estimates of median dietary intakes from DIGIKOST-FFQ and WR, median differences, correlation coefficients, and cross-classifications are presented in Table 2 . All estimated intakes of food groups from the DIGIKOST-FFQ were significantly different from the WR estimated intakes, except for processed meat, sugar-rich beverages, and dietary supplements.

a FFQ: food frequency questionnaire.

b WR: weighed food record.

c For dietary supplements, n=40 (52%) and n=14 (18%) for DIGIKOST-FFQ and WR, respectively, and Fischer exact test was performed ( P <.39).

d Wilcoxon signed rank test.

The median differences were however generally small and below a regular portion size for the different food groups. The ranking of individual intakes was fair and satisfactory for all food groups and poor for “vegetables.” Based on the Bland-Altman plot evaluations, the agreement between the 2 methods was good, with most of the differences within the 95% limit of agreement for each food group and evenly distributed above and below the mean difference ( Figures 2 and 3 ). In total, 69% or more of the participants were classified into the same or adjacent quartile for all food groups, and gross misclassification ranged from 0 for legumes, fatty fish, and alcohol to 13% for unsalted nuts ( Table 2 ). The sensitivity and specificity of the DIGIKOST-FFQ are presented in Table 3 . The sensitivity for different food groups ranged from 32% for red meat to 90% for low-fat dairy products. For specificity, the food groups ranged from 40% for unsalted nuts to 100% for sugar- and fat-rich foods. Most of the participants achieved intermediate adherence to the diet score recommendation with both methods ( Table 4 ) [ 32 ]. More detailed results are presented in the sections below.

a MVPA: moderate to vigorous physical activity.

b Fischer exact test ( P <.4).

b No adherence.

c Intermediate adherence.

d High adherence.

e FFQ: food frequency questionnaire.

f SWA: SenseWear Armband Mini.

g WR: weighed food record.

Fruits, Berries, Vegetables, Whole Grains, and Unsalted Nuts

The DIGIKOST-FFQ slightly overestimated intakes of “fruits and berries” with a median difference of 31 g/day ( Table 2 ). The correlation was satisfactory ( r =0.64), and 87% of the individual intakes were classified in the correct or adjacent quartile, and only 1% was grossly misclassified. For intakes up to 200 g/day, the limits of agreement were 200 g above and below the mean difference, which increased with higher intakes ( Figure 2 A). “Vegetables” were overestimated with 45 g/day and with a similar distribution of limits of agreement as for “fruits and berries” ( Figure 2 B). In total, 69% of the individual intakes of “vegetables” were classified in the correct or adjacent quartile, and 10% were grossly misclassified. The ranking of individual intakes of “vegetables” was poor ( r =0.24). The median difference for intakes of “whole grains” was 43 g/day, the correlation was satisfactory ( r =0.51), and 81% of the participants were classified in the same or adjacent quartile, whereas 1% was grossly misclassified. Limits of agreements ranged from –48 to 133 g/day, and the plot indicated a trend for overestimation with increased intakes ( Figure 2 D). “Unsalted nuts” were estimated with a median difference of 2.3 g/day and with a borderline satisfactory correlation ( r =0.48). In total, 74% of the participants were classified in the same or adjacent quartile, and 13% were grossly misclassified ( Table 2 ). The distribution of the differences increased with higher intakes, and the limits of agreement were wide ( Figure 2 C).

Fish, Dairy Products, and Meat

Median differences for “fish” and “fatty fish” intakes were 22 and 10 g/day, respectively ( Table 2 ). Ranking of individual intakes was satisfactory with correlations of r =0.52 and r =0.61 for “fish” and “fatty fish,” respectively. Limits of agreements ranged from –68 to 117 g/day, and the distribution of the differences increased with higher mean intakes up to about 100 g/day of “fish.” For “fatty fish,” the agreement was good with evenly distributed differences above and below the mean difference and narrow limits of agreement (–49 to 71 g/day; Figure 2 E and F). Classification of individual intakes was good for both “fish” and “fatty fish” with low gross misclassification ( Table 2 ).

The DIGIKOST-FFQ showed a minor underestimation of dairy products on the group level, acceptable ranking ( r =0.65), low gross misclassification of individual intakes, and good agreement between the methods ( Table 2 and Figure 3 A and B). The DIGIKOST-FFQ was able to estimate intakes of “red meat” and “processed meat” on the group level and to rank individual intakes of both food groups ( Table 2 and Figure 2 G and H). More than 82% of the participants were classified in the same or adjacent quartile, and few were grossly misclassified.

Sugar- and Fat-Rich Foods and Beverages, Other Beverages, and Dietary Supplements

There were good agreements between the methods in estimating intakes of “sugar- and fat-rich foods” and “sugar-rich beverages” on the group level, fair ranking of individual intakes, and good quartile classifications ( Table 2 and Figure 3 G and H). Intakes of “water” were overestimated by the DIGIKOST-FFQ, and the ranking of individual intake was fair ( Table 2 and Figure 3 E). The DIGIKOST-FFQ was able to estimate the intake of “ethanol” both on group and individual levels ( Table 2 ). More than 79% of the participants were classified in the same or adjacent quartile for intakes of “sugar- and fat-rich foods,” “sugar-rich beverages,” “water,” and “ethanol” ( Table 2 ).

Physical Activity, Sleep, and Sedentary Time

The DIGIKOST-FFQ underestimated time in moderate intensity (median –468 min/week), which increased with higher amounts of time in moderate physical intensity ( Table 5 and Figure 4 A). The correlation between the methods was borderline fair ( r =0.29; Table 4 ). In total, 73% of the participants were classified in the correct or adjacent quartile, and 5% were grossly misclassified. On the group level, vigorous intensity was overestimated by 18 min/week, and the individual differences increased with increased time in “vigorous physical activity” ( Table 5 and Figure 4 B). There was a fair correlation between the methods for vigorous intensity ( P =.40), and no gross misclassification. The DIGIKOST-FFQ was able to rank individual time in “moderate to vigorous intensity” ( P =.64), and 79% were classified in the correct or adjacent quartile, and few were grossly misclassified ( Table 5 ). On the group level, time in “moderate to vigorous intensity” was underestimated by DIGIKOST-FFQ, which also increased with higher amounts of time in this intensity ( Table 5 and Figure 4 C). The underreporting of “moderate to vigorous intensity” was particularly seen among individuals with BMI <25 kg/m 2 ( Figure 4 D).

b SWA: SenseWear Armband Mini.

c Wilcoxon signed rank test.

d MPA: moderate physical activity.

e VPA: vigorous physical activity.

f MVPA: moderate to vigorous physical activity.

The DIGIKOST-FFQ was able to estimate time in “sedentary” activities on the group level, with a trend for overreporting with higher amounts of time in this activity, and the correlation between the methods was poor ( r =0.27; Table 5 and Figure 4 E). A high number of participants (75%) were classified in the correct or adjacent quartile, and 9% were grossly misclassified. Median time in “sleep” was estimated to be 7 hours per day with both methods, with a trend for underreporting by the DIGIKOST-FFQ with increased time in “sleep” ( Table 5 and Figure 4 F). The correlation was poor ( r =0.19), but the classification of individual time in quartiles was good (71%; Table 5 ).

Adherence to Dietary Guidelines and Physical Activity Recommendations

The sensitivity was above 60% for all dietary guidelines except for “whole grains,” “fish,” “red meat,” and “drinks with added sugar” ( Table 3 ). The specificity was above 60% for all dietary guidelines except for “unsalted nuts,” “processed meat,” “low-fat dairy products,” and “dietary supplements” ( Table 3 ). The total mean diet score of the Norwegian diet index (ranging from 0 to 20 points) was estimated to be 11.2 (SD 3.2) and 10.4 (SD 3.1) points with the DIGIKOST-FFQ and WR, respectively ( Multimedia Appendix 2 ) [ 32 ]. Adherences to each component in the Norwegian diet index measured by DIGIKOST-FFQ, WR, and SWA are shown in Tables 4 and 6 . Intermediate adherences were the most frequent for the composite “diet score,” “fruits and berries,” “vegetables,” and “margarine, butter, and oils” estimated with both DIGIKOST-FFQ and WR. Low adherences were shown by both methods for “unsalted nuts,” “processed meat,” and “sugar- and fat-rich foods,” whereas high adherences were shown for “fish,” “sugar-rich drinks,” and “dietary supplements.” Approximately, evenly distributed adherences (ie, low, intermediate, and high) with both methods were shown for “red meat” ( Table 6 ). High adherence to the recommendation of physical activity was estimated as the most frequent by both methods ( Table 4 ).

a No adherence.

b Intermediate adherence.

c High adherence.

d FFQ: food frequency questionnaire.

e WR: weighed food record.

f Not available.

Principal Findings

We investigated the relative validity of the new digital assessment tool, DIGIKOST-FFQ, with 7-day WR and activity sensors as reference methods. Generally, the DIGIKOST-FFQ was able to rank individual intakes of foods and time spent in physical activities adequately and also able to evaluate dietary intakes and adherences to the Norwegian FBDG and physical activity recommendation.

The DIGIKOST-FFQ is based on the paper form NORDIET-FFQ, and improvements were made to enhance the quality of the DIGIKOST-FFQ estimates. For example, in the DIGIKOST-FFQ, questions about fruits and berries were presented as single food items as opposed to aggregated questions in the paper FFQ, which underreported the fruit, berry, and vegetable intakes [ 26 ]. In this validation study, improved correlations and agreement between the test and reference methods for estimated fruit, berry, and vegetable intakes were observed. Moreover, WR has been documented to underreport energy and food intake [ 46 , 47 ]. Thus, the overreporting of fruit and vegetable intakes in the DIGIKOST-FFQ along with the improved correlations and agreement between the methods in this validation study indicate a higher accuracy of the intake estimates from the DIGIKOST-FFQ for these food groups as compared to the NORDIET-FFQ. In particular, the DIGIKOST-FFQ showed a borderline fair correlation in ranking individual intakes of vegetables. However, the median difference between the methods was well below 1 portion of vegetable (ie, 100 g), the agreement between the methods was good, and the DIGIKOST-FFQ was able to identify adherence to the recommendation of vegetables.

Few participants reported intakes of unsalted nuts, and low amounts were reported for those with intakes. This may explain the high share grossly misclassification of nut intakes in this study.

Adding single-item questions regarding whole grain products along with automatic counting of slices of bread consumed per week to help the responder to report intakes of bread in the DIGIKOST-FFQ ( Multimedia Appendix 1 ) may have successfully contributed to the improvement of the reported intakes of whole grains. In addition, legumes were added as a new item in the DIGIKOST-FFQ, and the good agreement and correlation observed for intakes of legumes contributed to the overall good ability of DIGIKOST-FFQ to identify individuals with different adherences to the recommendations for all fiber-rich foods.

Reported intakes of fish, meat, dairy products, sugar- and fat-rich foods, sugar-rich beverages, ethanol, water, and dietary supplements were good with the DIGIKOST-FFQ. We hypothesize that the overreporting of water, corresponding to 1 glass of water per day, might be explained by the warmer climate in June 2021, during which most of the participants completed the DIGIKOST-FFQ, as compared to the colder climate in August 2021 when they completed the WR, thus resulting in higher intakes of water in June.

Underreporting of physical activity by questionnaires is a common issue when compared to objective activity sensors, which record all activities within 24 hours a day, whereas questionnaires rely only on a few questions supported by others [ 48 , 49 ]. In particular, sex and BMI have been shown to alter the association between self-reported and objectively measured physical activity [ 49 ]. Additionally, questionnaires are subjected to memory and conceptualization of different degree of intensities, such as the difference between moderate and vigorous intensities. In this study, there was a trend for normal-weighed individuals to underreport moderate physical activity more than overweighed or obese individuals. Overall, on the group level, the DIGIKOST-FFQ underreported moderate to vigorous physical activity by 450 minutes per week when compared to SWA. In particular, low and moderate intensities of physical activity, such as transport to work (eg, biking), gardening, and housework, have been challenging to report in questionnaires [ 50 , 51 ], which was also observed in this study. Moreover, the aim of the DIGIKOST tool is to estimate adherence to the recommendations and to give advice in the individual report in order to spend more time in physical activity. Therefore, ranking individual time in different activities and classifying the individuals according to adherence are important abilities of the assessment tool.

Adding illustrations of different intensities with images and text for the participants to better differentiate between the performance of physical activity during the day and week may increase the quality of the estimated time in moderate physical activity. Generally, the results from this study showed that the DIGIKOST-FFQ was able to rank individual dietary intakes and identify adherences to most of the dietary guidelines and physical activity recommendations. This conforms to previous studies showing the ability of FFQs to classify individuals according to different intakes of foods and time in activities [ 8 , 9 ].

Strengths and Limitations

We included 77 participants in the study, which is an adequate number in validation studies. The announcement by email lists from the National Registry of Norway was accompanied by an advertisement in Facebook. Recruitment through Facebook has shown to be the most successful strategy. Generally, the use of social media for recruitment in health research has shown to be effective in hard-to-reach populations [ 52 , 53 ]. In this study, most of the participants were women with higher education than the general Norwegian population [ 54 ]. This might compromise the external validity of this study.

In this study, the DIGIKOST-FFQ (test method) was administered prior to the reference methods in order to avoid the learning effect from the reference method [ 11 ]. Moreover, the DIGIKOST-FFQ asks for dietary intakes and activities the last 2 months, whereas the reference methods (WR and SWA) prospectively recorded dietary intake and activities for 7 consecutive days and completed within 1-2 months after fulfilling the DIGIKOST-FFQ. The average range of days between completion of DIGIKOST-FFQ and reference methods was 19 (SD 16.8) days. The expectancy of meaningful changes in dietary intakes or activities during this period is low.

The measurement errors usually associated with printed questionnaires have also been documented in digital questionnaires such as underreporting and recall errors [ 6 , 10 , 12 , 26 , 31 , 41 , 55 , 56 ]. Closed questions, predefined categories of frequency and amounts, and foods not included in the questionnaire may result in misclassification and underreporting. In DIGIKOST-FFQ, underreporting was documented for some food groups and activities, but there were few gross misclassifications, which indicates the good ability to identify individuals with different adherence to the recommendations. Ideally, in validation studies, the error in the reference method should be independent from the test method [ 16 ]. This study used a self-reported instrument, that is, the WR, as the reference method against the DIGIKOST-FFQ. Since both methods are self-reported, the errors may not be independent, thus inflating the validity correlation coefficient and an optimistic evaluation of the validity of the FFQ. Even though the WR is commonly used as a reference method in the validation of FFQs [ 11 , 12 ] and does not rely on the memory of food intakes, no interpretation of portion size is needed due to the weighing. Moreover, the WR provides data on foods that are really consumed, which are not provided by using biomarkers as a reference method. The DIGIKOST-FFQ does not cover a whole diet, since the aim of the tool is to estimate adherence to the recommendations, thus biomarkers for energy intake or nutrients are not used in this study. In the future, revisions of the DIGIKOST tool developments may be conducted to enable estimations of energy and food intakes; however, that is not the design and aim of this version.

Conclusions

The DIGIKOST-FFQ was able to assess dietary intake and physical activity and identify individuals with different adherences to the Norwegian FBDG and physical activity recommendation. However, moderate physical activity was underreported, water was overreported, and vegetables showed a low correlation between the methods, which is important to take into account when interpreting the data in future studies. The new digital questionnaire contributes with valuable data for research projects focusing on improved lifestyle, prevention of diseases, and increased quality of life and survival, as well as a screening tool in the clinic.

Acknowledgments

The authors would like to acknowledge project coordinator Nina Nordberg Gjerrestad and the participants in the study. The authors would also like to thank Professor Sveinung Berntsen for generating the physical activity data from the SenseWear Armband. The Throne Holst Foundation for Nutrition Research and Aktieselskabet Freia Chocolade Fabriks Medisinske Fond funded this study.

Authors' Contributions

HBH had the main responsibility for writing the paper. HBH, MDK, MHC, AH, and RB contributed to the conception and the design of the study and drafting of the paper. MDK and HBH contributed to the acquisition of data. HBH, MDK, and MHC contributed to analyses. All authors contributed to the interpretation of the data and the writing and approval of the final paper.

Conflicts of Interest

None declared.

DIGIKOST: a digital application for diet and lifestyle assessment and benchmarking against national guidelines.

Adherence to total diet score and each single component measured with both assessment tools.

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Abbreviations

Edited by G Eysenbach, T de Azevedo Cardoso; submitted 07.10.23; peer-reviewed by D Brassard; comments to author 19.12.23; revised version received 12.02.24; accepted 09.03.24; published 30.04.24.

©Hege Berg Henriksen, Markus Dines Knudsen, Anette Hjartåker, Rune Blomhoff, Monica Hauger Carlsen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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New study shows NHS digital weight management program is effective at achieving clinically meaningful weight loss

by Samantha Rey, Imperial College London

The success of a national weight management program has been highlighted in a study in the journal Obesity . The 12-week NHS Digital Weight Management Program gives diet, exercise and lifestyle advice via a phone app or online, to patients referred by their GP with a high BMI plus hypertension, diabetes or both.

The 32,000 people who finished the program in the first year saw an average weight loss of 2.2 kilos (5lbs); with those who attended more (at least 60%) of the program losing an average of 3.9 kilos (8.5lbs).

Senior author of the study, Imperial College London's Professor Jonathan Valabhji, also established the program in his former role as NHS England's National Clinical Director for Diabetes and Obesity.

He said, "The key is that it was made more accessible to patients in their daily lives. It's rewarding and encouraging to see the difference it is making."

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When conducting research, evidence-based practice is a method for framing clinical questions that will help yield optimal search results. PubMed.gov is a free research tool from the National Library of Medicine®. This course will show you how to use evidence-based practice when searching clinical questions using PubMed®.
New study shows NHS digital weight management program is effective at
More information: Katharine Taylor et al, Early outcomes of referrals to the English National Health Service Digital Weight Management Programme, Obesity (2024). DOI: 10.1002/oby.24024 Journal ...

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Digital Food Frequency Questionnaire Assessing Adherence to the Norwegian Food–Based Dietary Guidelines and Other National Lifestyle Recommendations: Instrument Validation Study