Our findings indicate overestimation of self-reported mobile phone use in comparison with traffic data obtained from network operators. This implies that repeated data downloads from operator databases are required for valid exposure assessment in a prospective cohort study. A second key finding was that the participation rate was unaffected by the length of the questionnaire or the recruitment method.
Self-reported retrospective exposure assessment is subject to substantial error, both systematic and random. Risk estimates can be severely affected by random error in exposure estimates . Non-differential random errors typically deflate risk estimates for dichotomous and continuous exposures (and trend effects for ordered polytomous exposures) towards the null (no effect), reduce their precision, and may therefore mask true effects. Such errors rarely result in spurious associations in the absence of true effects. However, when a categorical (polytomous) exposure indicator is used, random error can result in either under- or overestimation of the effect (downward or upward bias). One possibility to correct for the low precision is regression calibration, which has the potential to improve the control of random error and yield a more realistic dose response. Its anticipated impact on the outcome in an analysis is a steeper exposure-effect gradient, given a true effect. Likewise, systematic error may bias the risk estimates in either direction. Differential error can also bias the results in any direction, depending on the differences between cases and controls in case-control studies and exposure groups in cohort studies.
Our results are in agreement with earlier studies assessing the reliability of self-reported mobile phone use. In the UK , a reasonably good correlation was reported between telephone companies and the self-reported mobile phone use among 90 study subjects over a six-month period. The agreement was slightly better for the call duration (kappa = 0.50, r = 0.60) than the number of calls (kappa = 0.39, r = 0.48), but overestimation was substantial for both of these measures (overestimation by factors of 2.8 and 1.7, respectively). In a study conducted in the US , a reasonably good correlation was reported in call duration between telephone company records and self-reported mobile phone use (r = 0.74). In Germany , a weak correlation (r = 0.34) was found for the average call duration and a moderate correlation for the number of calls (r = 0.62) among 68 subjects during three months. The self-reported cumulative use agreed quite well with the operator records (3.2 versus 3.1 hours). In another German study , information on the duration of calls was obtained from software-modified phones for 45 subjects. The self-reported cumulative duration of calls showed a moderate correlation with the recorded time (r = 0.48), with some overestimation (mean call times 61.6 versus 53.8 minutes). In a large international study with 672 subjects from 11 countries , self-reported mobile phone use was evaluated in relation to operator records and software-modified phones. The overall correlation between the reported and recorded use was 0.69, both in terms of the number and duration of calls (kappa values 0.5 and 0.49, respectively). The duration of calls was overestimated by a factor of 1.42, while the number of calls was slightly underestimated. Retrospective exposure assessment based on interviews or questionnaires has major inherent uncertainties and constitutes a major source of error in case-control studies of mobile phone use.
This study confirms earlier findings that people tend to overestimate their mobile phone use [7–10, 25, 26]. However, some overestimation may be due to the formulation of the questions. The number and duration of calls were reported as ranges and cumulative exposure was estimated from the mid-points of the two intervals. This adds uncertainty, as the distribution within the reported range may be skewed, with the highest frequencies and durations occurring only rarely. However, this problem is difficult to avoid, as mobile phone use typically varies from day to day. Our findings also rely on the assumption of stability in mobile phone use over time, as the actual use records were obtained for a three-month period at the beginning of the year, which was not always the period for which the study subjects estimated their mobile phone use at recruitment (see the lower part of Figure 1). The sensitivity analysis restricted to the subjects with the best match for the periods of self-reported and operator-derived data showed only a slightly smaller mean difference between the two data sources when compared to the difference for the entire data set. This suggests that the discrepancy in the time period explains only a minor proportion of the observed differences between self-reported use and operator records. In the full study, the first three-month period of mobile phone data that is collected should be matched to the subjects' date of recruitment.
The full cohort study will collect exposure information both prospectively and retrospectively. Since historical operator records (typically more than 12 months) are not available, retrospective exposure information must be based on self-reported use only. However, the exposure assessment between self-reported and operator-derived data can be validated for this study. Retrospective exposure assessment can thus be based on self-reported mobile phone use corrected for subject-specific factors and will therefore probably be more accurate than unadjusted self-reported estimates.
As mobile phone use is not constant over time, the call duration during the six-month period before recruitment may not be representative of mobile phone use during the follow-up phase. For the full study, however, call data will be obtained for a three-month period each year. Therefore, misclassification due to seasonal variation is likely to be the major concern. This problem could be alleviated to some extent by acquiring anonymous or summary data on mobile phone use from the operators for the entire year and using it to validate the prediction based on three-month data.
The assessment of mobile phone use through operator databases is crucial, but these data were not received for 17% of the study participants, for whom prospective and retrospective exposure assessment could only be based on self-reported data. Operator data were probably missing due to a change of network operator or errors in the mobile phone numbers. Some subjects may have changed their network operator between the time the samples were gathered and the recruitment letters were sent. Operator data were unavailable from one of the three major network operators. Mobile traffic data were identified and obtained based on the mobile phone numbers only. Study participants gave their phone numbers both with the informed consent and in the questionnaire, and these numbers were entered into the database manually without double entry. Possible errors were found by comparing the numbers between these two sources and by checking the logic of the numbers (the length, the possible digits). However, study subjects often indicated their numbers on one form only or gave different numbers in different sources. In the full cohort study, there should be double entry for mobile phone numbers. By restricting the study to subjects with a maximum of two mobile phone numbers in use, estimation of the annual mobile phone use could be more accurate than in this pilot study.
Operator data may not completely represent the personal use of mobile phones. For example, study participants may use other people's phones (or vice versa), they may forget their phone numbers thus preventing operator data from being retrieved, or operator data may be wrongly linked. The questionnaire tried to address the problem of using other people's phones with the following alternatives for each phone: "Less than half of my calls have been made with this phone" and "More than half of my calls have been made with this phone." In the full study, the range of alternatives will be wider. The problem of forgetting mobile phone numbers is likely to be less relevant for prospective data collection, as mobile phone numbers are checked in the repeated questionnaires.
The study covered only private subscribers because the actual user cannot always be identified for corporate subscriptions. Neither can a private person give permission for a corporate subscription. This excludes a group of potentially heavy users with company mobile phones and may give rise to exposure misclassification if only their private secondary subscription data are obtained. However, the study questionnaire elicits information separately for each mobile phone. In this way, exposure due to company phones can be taken into account as self-reported data.
The radiofrequency exposure of mobile phones can be estimated from the exposure time and intensity of exposure. The exposure time can be calculated from operator-derived data adjusted for the mode of use (excluding use with hands-free devices). Studies of the determinants of power output (as a proxy for field strength) have given inconsistent results. In Sweden, substantial urban-rural differences were shown [28, 29], while in Italy larger differences were observed between outdoor and indoor use , and regional differences dominated in the United States, [30–32]. In a German study, no strong and systematic determinants of output power were identified . These factors will be covered in the study questionnaire, but their use in exposure assessment is challenging. It appears that the output power determinants depend on the network characteristics (design features and technical characteristics), and are therefore non-uniform in various settings. Furthermore, mobile phone use changes with new technologies and the study questionnaire should be revised and repeated regularly with new modes of use. If determination of the intensity of exposure could be improved with operator-derived data on output power and the network used (GSM versus UMTS), the exposure assessment would be more valid.
Ensuring a balanced distribution of the subjects with respect to age and sex was complicated by the fact that the owner of the subscription is not always the actual phone user (who is the target person and potential study participant). Operator records only include the subscription owner, but as minors are not allowed to open subscriptions, their phones are typically listed with the names of their parents. Since the study population was successfully established with a similar age and sex distribution across exposure strata, this is unlikely to be a problem in a prospective cohort study either. Moreover, if we have some overlap in demographic characteristics across exposure groups, incomplete balance can be adjusted for in the analyses.
Some 20% of the study subjects used at least two mobile phone numbers, usually with different network operators. Due to competition for market shares, operators use aggressive marketing tactics to attract customers and people change their network operators quite often. The regulation guaranteeing the possibility to maintain their old phone numbers has further increased switching between operators. The current network operator of each mobile phone number can be retrieved from a service provider maintained by the Finnish network operators. Thus, collaboration with all the major network operators is crucial to ensure comprehensive coverage of exposure. Information on changes in mobile phone numbers can be acquired from repeated questionnaires.
Based on the pilot study, the major challenges for a cohort study will be maximizing the participation rate in order to control the costs. The timing of the recruitment and ensuring media attention are likely to be important. The collection of information through web-based questionnaires may provide a possibility to increase participation. The Finnish regulation of research ethics does not allow the use of financial incentives. Other methods shown to increase response rates include personalized letters and questionnaires, colored ink, stamped return envelopes, reminders and questionnaires sent from universities rather than commercial organizations [33, 34].
A prospective cohort study of mobile phone users and health appears to be feasible in Finland based on the pilot study. The recruitment of subjects from operators' databases was successful with a balanced distribution of exposures achieved in relation to age and sex. Furthermore, the retrieval of data on mobile phone use from operators' records was successful for the first year for both operators and all study years for one operator. The response rate was relatively low, but as an eventual full study would be based on internal comparisons, the low participation would affect the cost, but not the validity of the results. The low response rate may reflect the fact that Finns do not perceive a hazard from mobile phones or that mobile phones are such an integral part of everyday life that even a small risk would be acceptable (a situation comparable, for instance, to traffic-related health risks). Alternatively, a long follow-up with health information collected from multiple sources may affect the willingness to join the study. Finally, providing access to call records may be considered a sensitive issue due to privacy reasons. As there were no differences in the response rates between the recruitment methods, the less expensive two-phase recruitment seems preferable.