Time spent in physical activity, sedentary behavior (SB), and sleep (i.e., movement behaviors) importantly affect health and well-being of older adults (2018 Physical Activity Guidelines Advisory Committee, 2018; Chaput et al., 2020; Saunders et al., 2020). Studies have shown that more time spent in physical activity, less time spent in SB, and moderate amounts of sleep are associated with a lower prevalence of chronic diseases, better cognitive functions, better health-related quality of life, lower risk of falls, and longevity (2018 Physical Activity Guidelines Advisory Committee, 2018; Chaput et al., 2020; Saunders et al., 2020).
Epidemiological studies traditionally explored the relationships between time spent in specific movement behavior (e.g., physical activity) and health outcomes. Recently, there has been a change in research paradigm toward exploring the relationships between a combination of movement behaviors (e.g., combination of physical activity, SB, and sleep) and health outcomes (Dumuid et al., 2017; Pedišić et al., 2017). This change in paradigm was initiated by the realization that physical activity, SB, and sleep are exhaustive and mutually exclusive components of a 24-hr day (they are commonly referred to as “24-hr movement behaviors”). Each moment of the day, an individual is either physically active, sedentary, or sleeping. Furthermore, if the individual changes the amount of time spent in one movement behavior (e.g., decrease in SB), this inevitably leads to changes in the amount of time spent in the others (e.g., increase in physical activity and/or sleep). Movement behaviors are integral parts of a 24-hr day, and for this reason, it is methodologically sound that we consider them in combination (Dumuid et al., 2017, 2020).
Assessment of movement behaviors among older adults is important for research, policy, and practice. Assessment is commonly performed using the self-reported questionnaires (2018 Physical Activity Guidelines Advisory Committee, 2018; Chaput et al., 2020; Saunders et al., 2020). There are numerous questionnaires available that could be used to assess moderate to vigorous physical activity (MVPA), SB, or sleep among older adults (Bakker et al., 2020; Ibáñez et al., 2018; Rodrigues et al., 2022; Sattler et al., 2020). However, most of the existing questionnaires were developed to assess only one or two movement behaviors. A recent systematic review reported that questionnaires for assessing 24-hr movement behaviors among adults and older adults are lacking (Rodrigues et al., 2022). More recently, a novel questionnaire that enables the assessment of time spent in sleep, SB, light physical activity (LPA), and MVPA has been developed, and showed satisfactory validity among working adult population (Kastelic et al., 2022).
However, the findings on the validity of the novel questionnaire (Daily Activity Behaviours Questionnaire [DABQ]) among working adults could not be generalizable to the population of older adults. Self-reported questionnaires rely on cognitive functioning (e.g., understanding of the items, recall ability), which declines when people age (Knäuper et al., 2016). Moreover, the German version of the DABQ, the “Schlaf- und Aktivitätsfragebogen (SAF),” has never been validated before. Therefore, the aim of this study was to explore the validity of the German version of DABQ for the assessment of time spent in sleep, SB, LPA, and MVPA among older adults. We hypothesized that DABQ would show satisfactory validity for all estimates of 24-hr movement behaviors.
Methods
Study Design and Participants
A convenience sample of healthy older adults that enrolled in a senior physical activity training group at the Center of Active Aging (Sankt Pölten, Austria) were recruited for this study. The inclusion criteria were an age of 60 years or above, ability to walk (with or without mobility aids), read, speak, and understand German language. The required sample size of 77 participants was calculated using Bonett’s formula (Bonett & Wright, 2000) for a Spearman’s correlation coefficient of .38 (which equals to the lowest validity correlation observed for the DABQ; Kastelic et al., 2022) using a 95% confidence interval of ±.20 width. All 77 participants who were randomly recruited from the senior physical activity training group accepted the invitation to participate in the study and successfully completed the protocol. Participants were asked to wear the activity monitor for a period of 8 days and to complete the movement behaviors questionnaire on the day when they returned the activity monitor. The questionnaire was self-administered and completed in a paper-and-pencil format. However, the research staff was available for any assistance when the questionnaire was administered.
The data for this study were collected between November 2021 and March 2022. All participants signed an informed consent form before enrolling to the study. The study was performed in accordance with the Declaration of Helsinki, and was approved by the ethics committee (Ethical Committee of the University Hospital Bratislava–Hospital of Ladislav Dérer, the Academician, approval number: 31/2020).
Measures
Self-Reported Movement Behaviors: DABQ
The DABQ asks about the time spent in sleep, SB, LPA, and MVPA in the past 7 days. Sleep time is assessed using questions about time in bed, sleep latency, wake after sleep onset, and daytime napping. Responses to questions about time in bed (e.g., “In the past 7 days, at what time did you on average wake up?”) are provided as time (hr:min), while responses to questions on other sleep indicators (e.g., “In the past 7 days, how long did it take you on average to fall asleep?”) are provided as numerical entries (number of days and/or minutes). When DABQ is completed by retired individuals (as in our study), SB is assessed using a single question, while MVPA is assessed using questions on time spent walking, engaging in sports activities, and engaging in physically more demanding nonsports activities. Response to question about SB (i.e., “In the past seven days, what proportion of your wake time did you spend sitting or lying?”) is provided using a visual analog scale (ranging from none of the time to all of the time). Responses to questions on MVPA are provided as numerical entries (e.g., “In the past seven days, how much time (on average per day) did you spend walking to and from places or for recreational purposes?”) and using a visual analog scale (e.g., “What proportion of your time walking was spent walking quickly?”). LPA is assessed during DABQ data processing as the remaining time of a 24-hr day.
DABQ showed satisfactory reliability (ICC = .59–.65) and validity (ρ = .38–.66) among working adult population (Kastelic et al., 2022). The questionnaire is available in four European languages (Croatian, English, German, and Slovenian languages), and it can be found at https://healthytimeuse.com/de/pages/7 (accessed on September 28, 2022). In this study, we used the paper-based German version of DABQ (i.e., SAF). The German version of DABQ was developed by following the translation process that included forward translation of the DABQ (i.e., from English to German language) by three bilingual researchers, reconciliation on the best forward translated version, back translation (i.e., from German to English language) by professional translator, back translation review, and harmonization, and proofreading of the final forward translation of the DABQ (Wild et al., 2005).
Device-Measured Movement Behaviors: Accelerometer activPAL
Accelerometer activPAL4 micro (PAL Technologies Ltd., Glasgow, Scotland) is a thigh-worn activity monitor that enables assessment of postural allocation, including the assessment of time spent in sleep, SB, LPA, and MVPA (Carlson et al., 2021; Lyden et al., 2017). For this study, participants were asked to wear activPAL (attached with adhesive dressing on the thigh, midway the anterior superior iliac spine, and the knee) for a period of 8 days, 24 hr per day (except for swimming and sauna). Participants were also asked to complete the sleep diary for the period of activPAL data collection. The activPALs were initialized, and the data downloaded after the data collection period, using the proprietary software (PALconnect, version 8.11.4.89, PAL Technologies Ltd.).
Data on Participant Characteristics
The following data on participant characteristics were collected on the day when participants started to wear the activity monitor: sex (male/female), age (in years), body weight (in kilograms), and body height (in centimeters). Sex and age were self-reported, while body weight and body height were measured by a researcher using a scale (Omron, BF-511) and stadiometer (seca 206, Seca GmbH), respectively. Body mass index was calculated as body weight (in kilograms) divided by squared body height (in meters). Participants were categorized by body mass index into the following groups: underweight (<18.5 kg/m2), “normal” weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30 kg/m2) (World Health Organization, 2023).
Data Processing
Data collected using the DABQ were entered into the proprietary Microsoft Excel tool (DABQanalyser, version 3.0, University of Primorska) that is available at https://healthytimeuse.com/de/pages/7 (Kastelic & Šarabon, 2022). This tool can identify (in)valid entries and compute the DABQ output variables. Sleep time was calculated by deducting sleep latency and wake after sleep onset from time in bed, while adding daytime napping. SB was calculated by multiplying the reported proportion of wake time spent in SB with wake time (wake time was calculated as: 24 hr − sleep). MVPA was calculated by summing time spent walking quickly, engaging in sports activities, and engaging in physically more demanding nonsports activities that increase breathing rate. LPA was calculated as the remaining time to 24 hr (i.e., LPA = 24 hr − sleep − SB − MVPA). If reported sleep time, SB, and MVPA exceeds 24 hr, the proprietary tool (DABQanalyser) proportionally rescales reported behaviors to fit 24 hr. However, none of our participants reported movement behaviors that exceeded 24 hr.
Data collected using the activPAL were processed using the proprietary software (PALanalysis, version 8.11.4.61, PAL Technologies Ltd.). We used CREA algorithm with 24-hr protocol to identify a valid day that was defined as ≥20 hr of wear time within a certain calendar day. The CREA algorithm allows the user to manually enter the sleep onset (hr:min) and sleep offset (hr:min) for each day. We combined the information from sleep diary and CREA algorithm to determine sleep onset and sleep offset, as proposed previously (Carlson et al., 2021). Data were then exported as.csv file. Diary reported daytime napping duration (if any) was added to sleep time to obtain total sleep time, and deducted from sedentary time to obtain total time spent in SB. For time spent in MVPA, we used the METs method (Lyden et al., 2017), which was shown to provide most accurate MVPA estimates from the activPAL data (Ortega et al., 2023). ActivPAL data were processed using the R package activpalProcessing (Lyden, 2016) to obtain MVPA estimates. Time spent in LPA was calculated as: 24 hr − sleep − SB − MVPA.
Statistical Analysis
Data were imported and analyzed in R (version 4.0.5; R Core Team, 2020) and R Studio (version 1.4.1106; R Studio Team, 2020) using the packages BlandAltmanLeh (Lehnert, 2020), DescTools (Signorell et al., 2020), summarytools (Comtois, 2022), and tidyverse (Wickham et al., 2019). Data on participant characteristics and data on movement behaviors obtained by DABQ and activPAL were presented as mean and SD. Mean difference and 95% confidence intervals (CIs) between each of the n-pairs of measurements (DABQ − activPAL) was calculated to explore the systematic difference between the DABQ and activPAL estimates. SD of difference was also calculated to explore the random difference between the DABQ and activPAL estimates. To explore the correlation between DABQ and activPAL estimates, Spearman correlation coefficient (Spearman’s ρ) and its 95% CI were calculated.
To further visualize the agreement between DABQ and activPAL estimates, Bland–Altman plots for time spent in sleep, SB, LPA, and MVPA were constructed (Giavarina, 2015). Mean difference and its 95% CI were added to the plots to visualize the systematic differences, and limits of agreement (i.e., 1.96 × SD of the difference) and its 95% CI were added to the plots to visualize the random differences between DABQ and activPAL estimates.
Results
Seventy-seven participants completed DABQ and provided at least 7 days of valid activPAL data. The sample consisted of 45 females (58%) and 32 (42%) males (Table 1). Their mean (±SD) age was 68 (±5) years, ranging from 60 to 81 years. Most participants were overweight (38%) or had obesity (34%). No significant differences (p > .05) in age and body mass index were observed between female and male participants. All participants were retired.
Participant Characteristics
| Participants, n (%) | Age (SD), years | Body weight (SD), cm | Body height (SD), kg | Body mass index (SD), kg/m2 | |
|---|---|---|---|---|---|
| Female | 45 (58.4) | 67.8 (5.0) | 75 (14) | 162 (6) | 28.4 (5.3) |
| Male | 32 (41.6) | 68.5 (3.9) | 86 (13) | 177 (7) | 27.5 (3.4) |
| All | 77 (100) | 68.1 (4.6) | 79 (15) | 168 (9) | 28.0 (4.6) |
The mean difference between DABQ and activPAL for estimating sleep time did not differ significantly (mean difference = 8 min/day, 95% CI [−6, 22]; Table 2). When compared with activPAL, DABQ estimates for time spent in SB (mean difference = −135 min/day, 95% CI [−174, −97]) and MVPA (mean difference = −13 min/day, 95% CI [−25, −2]) were statistically significantly lower, and for time spent in LPA (mean difference = 141 min/day, 95% CI [104, 179]) was statistically significantly higher. Spearman’s correlation coefficients between DABQ and activPAL estimates for time spent in sleep, SB, LPA, and MVPA were .69, .35, .24, and .52, respectively.
Validity of the German Version of DABQ Among Older Adults When Compared With activPAL
| Movement behavior | DABQ mean (SD), min/day | activPAL mean (SD), min/day | Mean difference [95% CI], min/day | SD of difference, min/day | Spearman’s ρ [95% CI] |
|---|---|---|---|---|---|
| Sleep | 461 (84) | 453 (51) | 8 [−6, 22] | 61 | .69 [.55, .79]*** |
| SB | 452 (174) | 587 (106) | −135 [−174, −97] | 169 | .35 [.14, .53]** |
| LPA | 467 (159) | 326 (94) | 141 [104, 178] | 162 | .24 [.02, .44]* |
| MVPA | 60 (60) | 74 (24) | −13 [−25, −2] | 51 | .52 [.33, .66]*** |
Note. DABQ = Daily Activity Behaviours Questionnaire; CI = confidence interval; SB = sedentary behavior; LPA = light physical activity; MVPA = moderate to vigorous physical activity.
*p ≤ .05. **p ≤ .01. ***p ≤ .001.
Figure 1 shows the Bland–Altman plots, where the difference between each n-pair of the measurements (DABQ − activPAL for each participant) is plotted on the vertical axis, and the average value of each n-pair ([DABQ + activPAL]/2) is plotted on the horizontal axis. The observed limits of agreement for sleep estimate ranged from −111 to 127 min/day, for SB estimate from −467 to 196 min/day, for LPA estimate from −177 to 459, and for MVPA estimate from −112 to 86 min/day.
—Bland–Altman plots comparing DABQ and activPAL estimates on time spent in (a) sleep, (b) SB, (c) LPA, and (d) MVPA. Black lines show the mean difference and limits of agreement, and gray areas show their 95% confidence intervals. DABQ = Daily Activity Behaviours Questionnaire; SB = sedentary behavior; LPA = light physical activity; MVPA = moderate to vigorous physical activity.
Citation: Journal of Aging and Physical Activity 31, 6; 10.1123/japa.2022-0417
Discussion
This study explored the validity of the German version of DABQ—one of the first questionnaires that enabled the assessment of movement behaviors across the whole 24-hr day. Our findings revealed that validity correlation coefficients of DABQ estimates of time spent in sleep, SB, LPA, and MVPA ranged between .24 and .69. No significant differences between DABQ and activPAL for estimating sleep time were observed, while DABQ underreported SB for 2.3 hr/day and MVPA for 13 min/day, and overreported LPA for 2.4 hr/day.
Comparison With Previous Studies
Our results showed comparable validity for estimating sleep time as it was reported for the Slovenian version of the DABQ (Spearman’s ρ = .66; Kastelic et al., 2022). The comparison with most other existing sleep questionnaires is not possible, as only validity of the multidimensional sleep-index score is usually reported (Fabbri et al., 2021; Ibáñez et al., 2018). However, some studies explored the validity of single-item sleep duration questionnaires and reported substantially lower validity (with correlation coefficients ranging from .14 to .39; Lee, 2021; van Hees et al., 2015) that the one found for the DABQ. It might be more difficult for older adults to directly report the total duration of sleep time by using a single-item question, as this requires some calculations. In DABQ, the total sleep duration is calculated by the researcher, while the responder is asked about the time when they fell asleep and woke up, and about the duration of sleep onset latency, awakenings during sleep time, and napping time.
For the SB estimate, we found a somewhat lower validity than the one reported for the Slovenian version of DABQ among the working adult population (Spearman’s ρ = .42; Kastelic et al., 2022). Total duration of SB is assessed using a single item when DABQ is completed by the retired/nonemployed responder, but it is assessed using several items (separately for occupational SB, commuting SB, and other nonoccupational SB) when completed by the employed responder. It was proposed previously that it might be easier to recall behaviors if asked within a certain context (e.g., domain-specific durations) compared with no context (e.g., daily durations) (Troiano et al., 2020), which might have been the case in our study. We also found that DABQ underreported time spent in SB, which should be taken into account when interpreting data in future studies. However, underestimation of SB was similar to many other single-item SB questionnaires (underestimation ranging from 1.9 to 4.2 hr/day), and validity of the SB estimate was also comparable to many other single-item SB questionnaires that were completed by older adults (with correlation coefficients ranging from .20 to .36; Chastin et al., 2018).
Regarding physical activity estimates, our results showed higher validity for MVPA compared with LPA. This might be explained by the fact that DABQ is asking about the time spent in MVPA, while LPA is calculated as the remaining time to 24-hr (i.e., LPA = 24 hr − sleep − SB − MVPA). As such, LPA might be more prone to measurement errors. For the MVPA estimate, we found a somewhat higher validity than the one reported for the Slovenian version of DABQ among the working adult population (Spearman’s ρ = .38; Kastelic et al., 2022). Moreover, MVPA was underreported to a lesser extent compared with reports in the Slovenian study (mean difference = 39 min/day). All our participants engaged in a senior physical activity training group, and it might be they were better aware of their physical activity level. Also, it was proposed previously that structured activities (e.g., engaging in sport activities, exercise) are easier to recall accurately, than unstructured, and more fragmented activities (e.g., physical activities as part of daily living) (Troiano et al., 2020), which might have also happened in our study. Studies on the validity of physical activity questionnaires among older adults often reported an overestimation of MVPA (Forsén et al., 2010; Sattler et al., 2020), which contrasts with findings from the DABQ. It was proposed previously that DABQ might not capture the entire lower intensity spectrum of moderate physical activity, and that activPALs METs method might even overestimate MVPA (Kastelic et al., 2022). Nevertheless, the validity of the physical activity estimates in our study is comparable to many other physical activity questionnaires, when these are completed by older adults (with correlation coefficients ranging from .21 to .56; Forsén et al., 2010; Sattler et al., 2020).
Taken together, the German version of DABQ showed similar validity as was previously reported for existing questionnaires for the assessment of time spent in physical activity, SB, or sleep. These findings are indicating satisfactory validity of the German version of DABQ to be used for epidemiological research and population surveillance among older adults. However, DABQ has an important advantage of enabling assessment of movement behaviors across the whole 24-hr day. It was recently shown that such questionnaires are lacking (Rodrigues et al., 2022), indicating that DABQ is among the first validated questionnaires that resonate with the emerging 24-hr movement paradigm.
Limitations of the Study
Some limitations of our study need to be acknowledged. First, our study included only generally healthy older adults, thus the results might not be generalizable to older adult populations that suffer from substantial health problems. Second, we assessed the validity of the German version of DABQ, thus the results might also not be generalizable to other versions of the DABQ (e.g., English version). Third, we used activPAL activity monitor as a reference measure; while activPAL is considered a gold standard for the assessment of free-living SB (Chastin et al., 2018), it is not viewed as such for the assessment of sleep, LPA, and MVPA. This indicates that we explored criterion validity for SB estimate, while we examined convergent validity for estimates on sleep, LPA, and MVPA. However, activPAL provides valid estimates of all movement behaviors (Lyden et al., 2017), and it is one of the preferred activity monitors for the assessment of movement behaviors across the whole 24-hr day (Stevens et al., 2020).
Conclusions
German version of the DABQ showed satisfactory validity for the use in epidemiological research and population surveillance among older adults. DABQ is among the first validated questionnaires that resonates with the emerging 24-hr movement paradigm. It enables researchers to use a single self-reported questionnaire (i.e., low burden and low-cost method) for the assessment of 24-hr movement behaviors among older adults. Further studies should also explore other measurement properties of the DABQ (e.g., reliability, responsiveness).
Acknowledgments
The authors gratefully acknowledge the European Commission for funding the InnoRenew CoE project (Grant Agreement #739574) under the Horizon2020 Widespread-Teaming program and the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund). The authors also acknowledge European Regional Development Fund and Physiko- and Rheumatherapie Institute through the Centre of Active Ageing project in the Interreg Slovakia–Austria cross-border cooperation program (partners: Faculty for Physical Education and Sports, Comenius University in Bratislava: Institute for Physical Medicine and Rehabilitation, Physiko- and Rheumatherapie GmbH).
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