1857/5506 (33.7%) men reported using computers and 347 (6.3%) received a diagnosis of dementia during an average follow up of 6.0 years (range: 6 months to 8.5 years). The hazard ratio (HR) of dementia was lower among computer users than non-users (HR = 0.62, 95%CI = 0.47–0.81, after adjustment for age, educational attainment, size of social network, and presence of depression or of significant clinical morbidity). The HR of dementia appeared to decrease with increasing frequency of computer use: 0.68 (95%CI = 0.41–1.13), 0.61 (95%CI = 0.39–0.94) and 0.59 (95%CI = 0.40–0.87) for less than weekly, at least weekly and daily. The HR of dementia was 0.66 (95%CI = 0.50–0.86) after the analysis was further adjusted for baseline cognitive function, as measured by the Mini-Mental State Examination.

Cohort study of 5506 community-dwelling men aged 69 to 87 years followed for up to 8.5 years. Use of computers measured as daily, weekly, less than weekly and never. Participants also reported their use of email, internet, word processors, games or other computer activities. The primary outcome was the incidence of ICD-10 diagnosis of dementia as recorded by the Western Australian Data Linkage System.

As the use of computers has previously been associated with improved cognitive function in adulthood and old age [10] and participation in cognitively stimulating activities reduces the long-term risk of dementia [6] , [11] , we hypothesized that older computer users would have lower risk of developing dementia than non-users over a follow up period of up to 8 years. We conducted this study to test this hypothesis.

In this context, the increasing ease of access to personal computers that has occurred over the past 20 years offers hope that the growing exposure of older adults to this technology will enhance their participation in mentally stimulating activities and contribute to maintain cognitive function and reduce the prevalence of dementia in the community. In Australia, 47% of the population over the age of 60 years used computers in 2009, compared with only 29% in 2003 [9] . In Western Australia, 66% of older adults reported having a computer at home in 2009, and 58% had access to the internet [9] . Over 80% of those with access to the internet used email and chat sites or conducted general browsing. About 50% used the internet to pay bills, manage their finances or to access government services. Over 1/3 purchased goods and about 10% used the internet to manage their shares [9] . However, the health effects of computer use and internet access remains uncertain.

As the World's population ages, the number of people experiencing cognitive decline and dementia will continue to increase. Currently available estimates suggest that over 24 million people worldwide had dementia in 2005, with this number expected to reach 50 million by 2025 [1] . The direct and indirect costs associated with dementia will also continue to rise, and conditions such as Alzheimer's disease are expected to become leading causes of health expenditure in developed and developing countries [2] . Such considerations have stimulated the search for factors that might delay or prevent the progression of cognitive decline in older adults at risk, with promising results being reported for physical activity [3] , adequate management of diabetes and hypertension [4] , [5] , and participation in cognitively stimulating activities [6] . Data from the Bronx Aging Study showed that the hazard of dementia over 5 years was decreased amongst older adults involved in cognitively stimulating activities, with the lowest risk observed for the most active participants [6] . A subsequent randomized trial of cognitive training for adults aged 65–94 years (ACTIVE trial) found that the 10-week intervention was associated with specific cognitive gains over 2 years [7] , whereas reasoning training led to less pronounced decline in self-reported instrumental activities of daily living over 5 years [8] . Although the ACTIVE trial was unable to establish if the intervention decreased the onset of dementia amongst participants, its results are consistent with the hypothesis that regular involvement in mentally demanding activities improves function and may reduce the risk of dementia.

Methods

Ethics statement The study was conducted according to the principles expressed in the Declaration of Helsinki, and the Human Research Ethics Committee of the University of Western Australia approved the study protocol and all men provided written informed consent to participate.

Study design and participants This study used a longitudinal population-based sample of older men living in the Perth metropolitan area, the Health In Men Study (HIMS). Details regarding the recruitment of participants have been described elsewhere [12]. Briefly, we recruited a community-representative sample of 19352 Australian men aged 65 to 85 years living in the Perth metropolitan area between 1996 and 1998, of whom 12203 completed the first assessment of HIMS. Five years later 5583 of the 10940 surviving men agreed to participate in a follow up assessment that included questions about the use of computers (HIMS wave 2, 2001 to 2004). Of these, 30 men were excluded from further participation in this study because they had a recorded diagnosis of dementia in the Western Australian Data Linkage System (WADLS) prior to the date of their assessment (prevalent cases – please see details about the diagnosis of dementia below). Another 47 men did not answer the questions regarding the use of computers and were also excluded, leaving a study sample of 5506 participants.

Outcome of interest: dementia The primary outcome of interest of the study was a recorded diagnosis of a dementia syndrome in WADLS for the first time after the HIMS wave 2 assessment. WADLS brings together name-identified records for all in-patient hospital admissions as well as public sector mental health services (in-patient, out-patient and community mental health services), and includes all morbidity and mortality data of Western Australia coded according to the International Classification of diseases tenth revision (ICD-10) and, for events that occurred before 1996, the ICD-9. The validity of these data linkage is well established [13], [14]. The diagnosis of dementia was the primary endpoint of interest of the study, and was defined according to the following ICD-9 and ICD-10 codes: all listed diagnoses (primary and secondary) for Alzheimer's dementia 331.0, F00, G30; Vascular dementia 290.4, F01; fronto-temporal dementia 331.1, F02.0, G31.0; Huntington's disease 333.4, G10, F02.2; Parkinson's dementia or dementia with Lewy bodies F02.3, 331.82; and non-specific dementia 290.0, 290.1, 290.2, 290.3, 290.8, 290.9, 294.1, 294.8, 331.2, F02.8, F03, F05.1, G31.1, G31.8, G31.9. To improve case ascertainment, the text terms of the above conditions were also searched in the morbidity and mortality data systems, along with the following alternative medical terms: multi-infarct dementia, arteriosclerotic dementia, fronto-temporal lobe dementia, primary progressive dementia, corticobasal dementia, and Pick's dementia. As the accuracy of specific diagnostic causes of dementia in WADLS is uncertain, we opted to group all entries under the general heading of ‘dementia’. In addition, we retrieved from WADLS information about the dates of all contacts associated with a diagnosis of dementia and considered the date of onset to be the same as the date of the first recorded event.

Explanatory variables During HIMS wave 2, we asked participants: ‘How often do you use a personal computer?’ Possible answers were ‘never’, ‘every day’, ‘at least every week’, ‘less than every week’. We classified participants who answered ‘never’ as ‘computer no-users’ and those who offered any of the other three answers as ‘computer users’. Computer users were also asked: ‘What do you use a personal computer for?’ Participants could choose one or more of the following answers: word processing, internet, email, games, other. We calculated the age of participants as the difference in days between the date of the assessment for HIMS wave 2 and their date of birth divided by 365.25. In addition, men reported the highest level of education achieved (completed or not high school) and their country of birth. Participants completed the rating of the Duke Social Support Index, which is a valid measure of network support [15]. For the purposes of this study, we assessed three aspects of network support: number of times in the past week spent with somebody who does not live in the same house, number of times in the past week that participant talked to somebody on the phone, and number of times in the past week that participant went out for meetings or social gatherings. Possible answers ranged from zero to 7 (i.e., every day), yielding a maximum total score of 21. We also used the Index of Relative Socio-Economic Disadvantage (IRSED), which is a component of the Socio-Economic Indexes for Areas (SEIFA) of Australia [16]. Lower IRSED rankings indicate greater socio-economic disadvantage [16]. Participants also reported whether they had trouble seeing newspaper print even with glasses (yes/no) and hearing a conversation even with a hearing aid (yes/no). Those who answered ‘yes’ to any of these two questions were considered to show evidence of sensory impairment. They completed the 15-item Geriatric Depression Scale (GDS-15) during HIMS wave 2 and, a priori, those with a total score of 7 or more were considered to display clinically significant depressive symptoms at the time of assessment. This relatively high cut-point was chosen to ensure high specificity for the diagnosis of depression in this sample [17]. We used administrative medical information from WADLS during the 10 years prior to assessment at HIMS wave 2 to calculate the Charlson index and determine the presence of significant medical comorbidity in our sample [18]. The index takes into account 17 common medical conditions that predict 1-year mortality: myocardial infarction, congestive heart failure, peripheral arterial disease, cerebrovascular disease, dementia, chronic pulmonary disease, connective tissue disease, ulcer disease, liver disease, diabetes (including diabetes with end organ damage), hemiplegia, renal disease, leukemia, lymphoma, other tumours, metastatic tumours and AIDS. Charlson and colleagues used adjusted relative risks to assign integer weights to these conditions within a composite index score that ranges from 0 to 37. Coding algorithms to define comorbidities followed the procedures described by Quan et al. [19] and scores were calculated using Stagg's Charlson's index Stata 9.2 routine (StataCorp, College Station, Texas). We stratified scores to reflect the increasing severity of comorbidity associated with the index, and considered that men with a score of 3 or more showed evidence of significant medical morbidity [20]. During the second wave of the study, a subsample of 3888 men agreed to complete the Mini-Mental State Examination (MMSE), which is a brief neuropsychological battery that assesses cognitive function [21]. Total scores range from 0 to 30, with higher scores indicating better cognitive function [22].